EasyCV DOCUMENTATION¶

EasyCV is an all-in-one computer vision toolbox based on PyTorch, mainly focus on self-supervised learning, image classification, metric-learning, object detection and so on.

Prepare Datasets¶

[Prepare Cifar](#Prepare Cifar)
[Prepare Imagenet](#Prepare Imagenet)
[Prepare Imagenet-TFrecords](#Prepare Imagenet-TFrecords)
[Prepare COCO](#Prepare COCO)
[Prepare PAI-Itag detection](#Prepare PAI-Itag detection)

Prepare Cifar¶

Download dataset cifar10 and uncompress files to data/cifar, directory structure is as follows:

data/cifar
└── cifar-10-batches-py
    ├── batches.meta
    ├── data_batch_1
    ├── data_batch_2
    ├── data_batch_3
    ├── data_batch_4
    ├── data_batch_5
    ├── readme.html
    ├── read.py
    └── test_batch

Prepare Imagenet¶

Go to the download-url, Register an account and log in .
Download the following files：
- Training images (Task 1 & 2). 138GB.
- Validation images (all tasks). 6.3GB.
Unzip the downloaded file.
Using this scrip to get data meta.

Prepare Imagenet-TFrecords¶

Go to the download-url, Register an account and log in .
The dataset is divided into two parts, part0 (79GB) and part1 (75GB), you need download all of them.

Prepare COCO¶

Download COCO2017 dataset to data/coco, directory structure is as follows

data/coco
├── annotations
├── train2017
└── val2017

Prepare PAI-Itag detection¶

Download SmallCOCO dataset to data/coco, directory structure is as follows:

data/coco/
├── train2017
├── train2017_20_local.manifest
├── val2017
└── val2017_20_local.manifest

replace train_data and val_data path in config file

sed -i 's#train2017.manifest#train2017_20_local.manifest#g' configs/detection/yolox_coco_pai.py
sed -i 's#val2017.manifest#val2017_20_local.manifest#g' configs/detection/yolox_coco_pai.py

Quick Start¶

Prerequisites¶

python >= 3.6
Pytorch >= 1.5
mmcv >= 1.2.0
nvidia-dali == 0.25.0

Installation¶

Prepare environment¶

Create a conda virtual environment and activate it.

conda create -n ev python=3.6 -y
conda activate ev

Install PyTorch and torchvision

The master branch works with PyTorch 1.5.1 or higher.
```
conda install pytorch==1.7.0 torchvision==0.8.0 -c pytorch
```

Install some python dependencies

replace {cu_version} and {torch_version} to the version used in your environment

# install mmcv
pip install mmcv-full==1.4.4 -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
# for example, install mmcv-full for cuda10.1 and pytorch 1.7.0
pip install mmcv-full==1.4.4 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.7.0/index.html

# install nvidia-dali
pip install http://pai-vision-data-hz.cn-hangzhou.oss-cdn.aliyun-inc.com/third_party/nvidia_dali_cuda100-0.25.0-1535750-py3-none-manylinux2014_x86_64.whl

# install common_io for MaxCompute table read (optional)
pip install https://tfsmoke1.oss-cn-zhangjiakou.aliyuncs.com/tunnel_paiio/common_io/py3/common_io-0.1.0-cp36-cp36m-linux_x86_64.whl

Install EasyCV

You can simply install easycv with the following command:

pip install pai-easycv

or clone the repository and then install it:

git clone https://github.com/Alibaba/EasyCV.git
cd easycv
pip install -r requirements.txt
pip install -v -e .  # or "python setup.py develop"

Install pai_nni and blade_compressin

When you use model quantize and prune, you need to install pai_nni and blade_compressin with the following command:

# install torch >= 1.8.0
pip install torch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0

# install mmcv >= 1.3.0 (torch version >= 1.8.0 does not support mmcv version < 1.3.0)
pip install mmcv-full==1.4.4 -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html

# install onnx and pai_nni
pip install onnx
pip install https://pai-nni.oss-cn-zhangjiakou.aliyuncs.com/release/2.5/pai_nni-2.5-py3-none-manylinux1_x86_64.whl

# install blade_compression
pip install http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/third_party/blade_compression-0.0.1-py3-none-any.whl

Verification¶

Simple verification

```python
from easycv.apis import *
```

You can also verify your installation using follwing quick-start examples

Examples¶

Self-supervised Learning Model Zoo¶

Pretrained models¶

MAE¶

Pretrained on ImageNet dataset.

Config	Epochs	Download
mae_vit_base_patch16_8xb64_400e	400	model
mae_vit_base_patch16_8xb64_1600e	1600	model
mae_vit_large_patch16_8xb32_1600e	1600	model

DINO¶

Pretrained on ImageNet dataset.

Config	Epochs	Download
dino_deit_small_p16_8xb32_100e	100	model - log

MoBY¶

Pretrained on ImageNet dataset.

Config	Epochs	Download
moby_deit_small_p16_4xb128_300e	300	model - log

MoCo V2¶

Pretrained on ImageNet dataset.

Config	Epochs	Download
mocov2_resnet50_8xb32_200e	200	model - log

SwAV¶

Pretrained on ImageNet dataset.

Config	Epochs	Download
swav_resnet50_8xb32_200e	200	model - log

Benchmarks¶

For detailed usage of benchmark tools, please refer to benchmark README.md.

ImageNet Linear Evaluation¶

Algorithm	Linear Eval Config	Pretrained Config	Top-1 (%)	Download
SwAV	swav_resnet50_8xb2048_20e_feature	swav_resnet50_8xb32_200e	73.618	log
DINO	dino_deit_small_p16_8xb2048_20e_feature	dino_deit_small_p16_8xb32_100e	71.248	log
MoBY	moby_deit_small_p16_8xb2048_30e_feature	moby_deit_small_p16_4xb128_300e	72.214	log
MoCo-v2	mocov2_resnet50_8xb2048_40e_feature	mocov2_resnet50_8xb32_200e	66.8	log

ImageNet Finetuning¶

Algorithm	Fintune Config	Pretrained Config	Top-1 (%)	Download
MAE	mae_vit_base_patch16_8xb64_100e_lrdecay075_fintune	mae_vit_base_patch16_8xb64_400e	83.13	fintune model - log
	mae_vit_base_patch16_8xb64_100e_lrdecay065_fintune	mae_vit_base_patch16_8xb64_1600e	83.49	fintune model - log
	mae_vit_large_patch16_8xb16_50e_lrdecay075_fintune	mae_vit_large_patch16_8xb32_1600e	85.49	fintune model - log

Detection Model Zoo¶

YOLOX¶

Pretrained on COCO2017 dataset.

Algorithm	Config	mAP^{val _0.5:0.95}	AP^val ₅₀	Download
YOLOX-s	yolox_s_8xb16_300e_coco	40.0	58.9	model - log
YOLOX-m	yolox_m_8xb16_300e_coco	46.3	64.9	model - log
YOLOX-l	yolox_l_8xb8_300e_coco	48.9	67.5	model - log
YOLOX-x	yolox_x_8xb8_300e_coco	50.9	69.2	model - log
YOLOX-tiny	yolox_tiny_8xb16_300e_coco	31.5	49.2	model - log
YOLOX-nano	yolox_nano_8xb16_300e_coco	26.5	42.6	model - log

Develop¶

1. Code Style¶

We adopt PEP8 as the preferred code style.

We use the following toolsseed isortseed isortseed isort for linting and formatting:

flake8: linter
yapf: formatter
isort: sort imports

Style configurations of yapf and isort can be found in setup.cfg. We use pre-commit hook that checks and formats for flake8, yapf, seed-isort-config, isort, trailing whitespaces, fixes end-of-files, sorts requirments.txt automatically on every commit. The config for a pre-commit hook is stored in .pre-commit-config. After you clone the repository, you will need to install initialize pre-commit hook.

pip install -r requirements/tests.txt

From the repository folder

pre-commit install

After this on every commit check code linters and formatter will be enforced.

If you want to use pre-commit to check all the files, you can run

pre-commit run --all-files

If you only want to format and lint your code, you can run

sh scripts/linter.sh

2. Test¶

2.1 Unit test¶

bash scripts/ci_test.sh

2.2 Test data¶

if you add new data, please do the following to commit it to git-lfs before “git commit”:

python git-lfs/git_lfs.py add data/test/new_data
python git-lfs/git_lfs.py push

3. Build pip package¶

python setup.py sdist bdist_wheel

self-supervised learning tutorial¶

Data Preparation¶

To download the dataset, please refer to prepare_data.md.

Self-supervised learning support imagenet(raw and tfrecord) format data.

Imagenet format¶

You can download Imagenet data or use your own unlabeld image data. You should provide a directory which contains images for self-supervised training and a filelist which contains image path to the root directory. For example, the image directory is as follows

images/
├── 0001.jpg
├── 0002.jpg
├── 0003.jpg
|...
└── 9999.jpg

the content of filelist is

jpg
jpg
jpg
...
jpg

Local & PAI-DSW¶

We use configs/selfsup/mocov2/mocov2_rn50_8xb32_200e_jpg.py as an example config in which two config variable should be modified

data_train_list = 'filelist.txt'
data_train_root = 'images'

Training¶

Single gpu:

python tools/train.py \
		${CONFIG_PATH} \
		--work_dir ${WORK_DIR}

Multi gpus:

bash tools/dist_train.sh \
		${NUM_GPUS} \
		${CONFIG_PATH} \
		--work_dir ${WORK_DIR}

Arguments

NUM_GPUS: number of gpus
CONFIG_PATH: the config file path of a selfsup method
WORK_DIR: your path to save models and logs

Examples:

Edit data_rootpath in the ${CONFIG_PATH} to your own data path.

GPUS=8
bash tools/dist_train.sh configs/selfsup/mocov2/mocov2_rn50_8xb32_200e_jpg.py $GPUS

Export model¶

python tools/export.py \
		${CONFIG_PATH} \
		${CHECKPOINT} \
		${EXPORT_PATH}

Arguments

CONFIG_PATH: the config file path of a selfsup method
CHECKPOINT:your checkpoint file of a selfsup method named as epoch_*.pth
EXPORT_PATH: your path to save export model

Examples:

python tools/export.py configs/selfsup/mocov2/mocov2_rn50_8xb32_200e_jpg.py \
    work_dirs/selfsup/mocov2/epoch_200.pth \
    work_dirs/selfsup/mocov2/epoch_200_export.pth

Feature extract¶

Download test_image

import cv2
from easycv.predictors.feature_extractor import TorchFeatureExtractor

output_ckpt = 'work_dirs/selfsup/mocov2/epoch_200_export.pth'
fe = TorchFeatureExtractor(output_ckpt)

img = cv2.imread('248347732153_1040.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
feature = fe.predict([img])
print(feature[0]['feature'].shape)

yolox tutorial¶

Data preparation¶

To download the dataset, please refer to prepare_data.md.

Yolox support both coco format and PAI-Itag detection format,

COCO format¶

To use coco data to train detection, you can refer to configs/detection/yolox/yolox_s_8xb16_300e_coco.py for more configuration details.

PAI-Itag detection format¶

To use pai-itag detection format data to train detection, you can refer to configs/detection/yolox/yolox_s_8xb16_300e_coco_pai.py for more configuration details.

Local & PAI-DSW¶

To use COCO format data, use config file configs/detection/yolox/yolox_s_8xb16_300e_coco.py

To use PAI-Itag format data, use config file configs/detection/yolox/yolox_s_8xb16_300e_coco_pai.py

Train¶

Single gpu:

python tools/train.py \
		${CONFIG_PATH} \
		--work_dir ${WORK_DIR}

Multi gpus:

bash tools/dist_train.sh \
		${NUM_GPUS} \
		${CONFIG_PATH} \
		--work_dir ${WORK_DIR}

Arguments

NUM_GPUS: number of gpus
CONFIG_PATH: the config file path of a detection method
WORK_DIR: your path to save models and logs

Examples:

Edit data_rootpath in the ${CONFIG_PATH} to your own data path.

GPUS=8
bash tools/dist_train.sh configs/detection/yolox/yolox_s_8xb16_300e_coco.py $GPUS

Evaluation¶

Single gpu:

python tools/eval.py \
		${CONFIG_PATH} \
		${CHECKPOINT} \
		--eval

Multi gpus:

bash tools/dist_test.sh \
		${CONFIG_PATH} \
		${NUM_GPUS} \
		${CHECKPOINT} \
		--eval

Arguments

CONFIG_PATH: the config file path of a detection method
NUM_GPUS: number of gpus
CHECKPOINT: the checkpoint file named as epoch_*.pth.

Examples:

GPUS=8
bash tools/dist_test.sh configs/detection/yolox/yolox_s_8xb16_300e_coco.py $GPUS work_dirs/detection/yolox/epoch_300.pth --eval

Export model¶

python tools/export.py \
		${CONFIG_PATH} \
		${CHECKPOINT} \
		${EXPORT_PATH}

Arguments

CONFIG_PATH: the config file path of a detection method
CHECKPOINT:your checkpoint file of a detection method named as epoch_*.pth.
EXPORT_PATH: your path to save export model

Examples:

python tools/export.py configs/detection/yolox/yolox_s_8xb16_300e_coco.py \
        work_dirs/detection/yolox/epoch_300.pth \
        work_dirs/detection/yolox/epoch_300_export.pth

Inference¶

Download test_image

import cv2
from easycv.predictors import TorchYoloXPredictor

output_ckpt = 'work_dirs/detection/yolox/epoch_300.pth'
detector = TorchYoloXPredictor(output_ckpt)

img = cv2.imread('000000017627.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
output = detector.predict([img])
print(output)

# visualize image
from matplotlib import pyplot as plt
image = img.copy()
for box, cls_name in zip(output[0]['detection_boxes'], output[0]['detection_class_names']):
    # box is [x1,y1,x2,y2]
    box = [int(b) for b in box]
    image = cv2.rectangle(image, tuple(box[:2]), tuple(box[2:4]), (0,255,0), 2)
    cv2.putText(image, cls_name, (box[0], box[1]-5), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,255), 2)
plt.imshow(image)
plt.show()

image classification tutorial¶

Data Preparation¶

To download the dataset, please refer to prepare_data.md.

Image classification support cifar and imagenet(raw and tfrecord) format data.

Cifar¶

To use Cifar data to train classification, you can refer to configs/classification/cifar10/swintiny_b64_5e_jpg.py for more configuration details.

Imagenet format¶

You can also use your self-defined data which follows imagenet format, you should provide a root directory which condatains images for classification training and a filelist which contains image path to the root directory. For example, the image root directory is as follows

images/
├── 0001.jpg
├── 0002.jpg
├── 0003.jpg
|...
└── 9999.jpg

each line of the filelist consists of two parts, subpath to the image files starting from the image root directory, class label string for the corresponding image, which are seperated by space

jpg label1
jpg label2
jpg label3
...
jpg label9999

To use Imagenet format data to train classification, you can refer to configs/classification/imagenet/imagenet_rn50_jpg.py for more configuration details.

Local & PAI-DSW¶

Training¶

Single gpu:

python tools/train.py \
		${CONFIG_PATH} \
		--work_dir ${WORK_DIR}

Multi gpus:

bash tools/dist_train.sh \
		${NUM_GPUS} \
		${CONFIG_PATH} \
		--work_dir ${WORK_DIR}

Arguments

NUM_GPUS: number of gpus
CONFIG_PATH: the config file path of a image classification method
WORK_DIR: your path to save models and logs

Examples:

Edit data_rootpath in the ${CONFIG_PATH} to your own data path.

single gpu training:
```shell
python tools/train.py  configs/classification/cifar10/swintiny_b64_5e_jpg.py --work_dir work_dirs/classification/cifar10/swintiny  --fp16
```

multi gpu training
```shell
GPUS=8
bash tools/dist_train.sh configs/classification/cifar10/swintiny_b64_5e_jpg.py $GPUS --fp16
```

training using python api
```python
import easycv.tools

import os
# config_path can be a local file or http url
config_path = 'configs/classification/cifar10/swintiny_b64_5e_jpg.py'
easycv.tools.train(config_path, gpus=8, fp16=False, master_port=29527)
```

Evaluation¶

Single gpu:

python tools/eval.py \
		${CONFIG_PATH} \
		${CHECKPOINT} \
		--eval

Multi gpus:

bash tools/dist_test.sh \
		${CONFIG_PATH} \
		${NUM_GPUS} \
		${CHECKPOINT} \
		--eval

Arguments

CONFIG_PATH: the config file path of a image classification method
NUM_GPUS: number of gpus
CHECKPOINT: the checkpoint file named as epoch_*.pth

Examples:

single gpu evaluation
```shell
python tools/eval.py configs/classification/cifar10/swintiny_b64_5e_jpg.py work_dirs/classification/cifar10/swintiny/epoch_350.pth --eval --fp16
```

multi-gpu evaluation

```shell
GPUS=8
bash tools/dist_test.sh configs/classification/cifar10/swintiny_b64_5e_jpg.py $GPUS work_dirs/classification/cifar10/swintiny/epoch_350.pth --eval --fp16
```

evaluation using python api
```python
import easycv.tools

import os
os.environ['CUDA_VISIBLE_DEVICES']='3,4,5,6'
config_path = 'configs/classification/cifar10/swintiny_b64_5e_jpg.py'
checkpoint_path = 'work_dirs/classification/cifar10/swintiny/epoch_350.pth'
easycv.tools.eval(config_path, checkpoint_path, gpus=8)
```

Export model for inference¶

If SyncBN is configured, we should replace it with BN in config file
```python
# imagenet_rn50.py
model = dict(
    ...
    backbone=dict(
        ...
        norm_cfg=dict(type='BN')), # SyncBN --> BN
    ...)
```

```shell
python tools/export.py configs/classification/cifar10/swintiny_b64_5e_jpg.py \
    work_dirs/classification/cifar10/swintiny/epoch_350.pth \
    work_dirs/classification/cifar10/swintiny/epoch_350_export.pth
```

or using python api
```python
import easycv.tools

config_path = './imagenet_rn50.py'
checkpoint_path = 'oss://pai-vision-data-hz/pretrained_models/easycv/resnet/resnet50.pth'
export_path = './resnet50_export.pt'
easycv.tools.export(config_path, checkpoint_path, export_path)
```

Inference¶

Download [test_image](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/cifar10/qince_data/predict/aeroplane_s_000004.png)


```python
import cv2
from easycv.predictors.classifier import TorchClassifier

output_ckpt = 'work_dirs/classification/cifar10/swintiny/epoch_350_export.pth'
tcls = TorchClassifier(output_ckpt)

img = cv2.imread('aeroplane_s_000004.png')
# input image should be RGB order
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
output = tcls.predict([img])
print(output)
```

file tutorial¶

The file module of easycv supports operations both on local and oss files, oss introduction please refer to: https://www.aliyun.com/product/oss .

If you operate oss files, you need refer to [access_oss](# access_oss) to authorize oss first.

Support operations¶

access_oss¶

Authorize oss.

Method1:

from easycv.file import io
io.access_oss(
ak_id='your_accesskey_id',
ak_secret='your_accesskey_secret',
hosts='your endpoint' or ['your endpoint1', 'your endpoint2'],
buckets='your bucket' or ['your bucket1', 'your bucket2'])

Method2:

Add oss config to your local file ~/.ossutilconfig, as follows: More oss config information, please refer to: https://help.aliyun.com/document_detail/120072.html

[Credentials]
language = CH
endpoint = your endpoint
accessKeyID = your_accesskey_id
accessKeySecret = your_accesskey_secret
[Bucket-Endpoint]
bucket1 = endpoint1
bucket2 = endpoint2

Then run the following command, the config file will be read by default to authorize oss.

from easycv.file import io
io.access_oss()

open¶

Support w,wb, a, r, rb modes on oss path. Local path is the same usage as the python build-in open.

Example for oss:

io.access_oss please refer to [access_oss](# access_oss).

from easycv.file import io

io.access_oss('your oss config')

# Write something to a oss file.
with io.open('oss://bucket_name/demo.txt', 'w') as f:
	f.write("test")

# Read from a oss file.
with io.open('oss://bucket_name/demo.txt', 'r') as f:
  print(f.read())

Example for local:

from easycv.file import io

# Write something to a oss file.
with io.open('/your/local/path/demo.txt', 'w') as f:
	f.write("test")

# Read from a oss file.
with io.open('/your/local/path/demo.txt', 'r') as f:
  print(f.read())

exists¶

Whether the file exists, same usage as os.path.exists. Support local path and oss path.

Example for oss:

io.access_oss please refer to [access_oss](# access_oss).

from easycv.file import io

io.access_oss('your oss config')

ret = io.exists('oss://bucket_name/dir')
print(ret)

Example for Local:

from easycv.file import io

ret = io.exists('oss://bucket_name/dir')
print(ret)

move¶

Move src to dst, same usage as shutil.move. Support local path and oss path.

Example for oss:

io.access_oss please refer to [access_oss](# access_oss).

from easycv.file import io

io.access_oss('your oss config')

# move oss file to local
io.move('oss://bucket_name/file.txt', '/your/local/path/file.txt')
# move oss file to oss
io.move('oss://bucket_name/dir1/file.txt', 'oss://bucket_name/dir2/file.txt')
# move local file to oss
io.move('/your/local/file.txt', 'oss://bucket_name/file.txt')
# move directory
io.move('oss://bucket_name/dir1/', 'oss://bucket_name/dir2/')

Example for local:

from easycv.file import io

# move local file to local
io.move('/your/local/path1/file.txt', '/your/local/path2/file.txt')
# move local dir to local
io.move('/your/local/dir1', '/your/local/dir2')

copy¶

Copy a file from src to dst. Same usage as shutil.copyfile.If you want to copy a directory, please refert to [copytree](# copytree).

Example for oss:

io.access_oss please refer to [access_oss](# access_oss).

from easycv.file import io

io.access_oss('your oss config')

# Copy a file from local to oss:
io.copy('/your/local/file.txt', 'oss://bucket/dir/file.txt')
# Copy a oss file to local:
io.copy('oss://bucket/dir/file.txt', '/your/local/file.txt')
# Copy a file from oss to oss::
io.copy('oss://bucket/dir/file.txt', 'oss://bucket/dir/file2.txt')

Example for local:

from easycv.file import io

# Copy a file from local to local:
io.copy('/your/local/path1/file.txt', '/your/local/path2/file.txt'')

copytree¶

Copy files recursively from src to dst. Same usage as shutil.copytree.

If you want to copy a file, please use [copy](# copy).

Example for oss:

io.access_oss please refer to [access_oss](# access_oss).

from easycv.file import io

io.access_oss('your oss config')

# copy files from local to oss
io.copytree(src='/your/local/dir1', dst='oss://bucket_name/dir2')
# copy files from oss to local
io.copytree(src='oss://bucket_name/dir2', dst='/your/local/dir1')
# copy files from oss to oss
io.copytree(src='oss://bucket_name/dir1', dst='oss://bucket_name/dir2')

Example for local:

from easycv.file import io

# copy files from local to local
io.copytree(src='/your/local/dir1', dst='/your/local/dir2')

listdir¶

List all objects in path. Same usage as os.listdir.

Example for oss:

io.access_oss please refer to [access_oss](# access_oss).

from easycv.file import io

io.access_oss('your oss config')

ret = io.listdir('oss://bucket/dir', recursive=True)
print(ret)

Example for local:

from easycv.file import io

ret = io.listdir('oss://bucket/dir', recursive=True)
print(ret)

remove¶

Remove a file or a directory recursively. Same usage as os.remove or shutil.rmtree.

Example for oss:

io.access_oss please refer to [access_oss](# access_oss).

from easycv.file import io

io.access_oss('your oss config')

# Remove a oss file
io.remove('oss://bucket_name/file.txt')
# Remove a oss directory
io.remove('oss://bucket_name/dir/')

Example for local:

from easycv.file import io

# Remove a local file
io.remove('/your/local/path/file.txt')
# Remove a local directory
io.remove('/your/local/dir/')

rmtree¶

Remove directory recursively, same usage as shutil.rmtree.

Example for oss:

io.access_oss please refer to [access_oss](# access_oss).

from easycv.file import io

io.access_oss('your oss config')

io.remove('oss://bucket_name/dir_name/')

Example for local:

from easycv.file import io

io.remove('/your/local/dir/')

makedirs¶

Create directories recursively, same usage as os.makedirs.

Example for oss:

io.access_oss please refer to [access_oss](# access_oss).

from easycv.file import io

io.access_oss('your oss config')

io.makedirs('oss://bucket/new_dir/')

Example for local:

from easycv.file import io

io.makedirs('/your/local/new_dir/')

isdir¶

Return whether a path is directory, same usage as os.path.isdir.

Example for oss:

io.access_oss please refer to [access_oss](# access_oss).

from easycv.file import io

io.access_oss('your oss config') # only oss file need, refer to `IO.access_oss`
ret = io.isdir('oss://bucket/dir/')
print(ret)

Example for local:

from easycv.file import io

ret = io.isdir('your/local/dir/')
print(ret)

isfile¶

Return whether a path is file object, same usage as os.path.isfile.

Example for oss:

io.access_oss please refer to [access_oss](# access_oss).

from easycv.file import io

io.access_oss('your oss config')
ret = io.isfile('oss://bucket/file.txt')
print(ret)

Example for local:

from easycv.file import io

ret = io.isfile('/your/local/path/file.txt')
print(ret)

glob¶

Return a list of paths matching a pathname pattern.

Example for oss:

io.access_oss please refer to [access_oss](# access_oss).

from easycv.file import io

io.access_oss('your oss config')

ret = io.glob('oss://bucket/dir/*.txt')
print(ret)

Example for local:

from easycv.file import io

ret = io.glob('/your/local/dir/*.txt')
print(ret)

size¶

Get the size of file path, same usage as os.path.getsize.

Example for oss:

io.access_oss please refer to [access_oss](# access_oss).

from easycv.file import io

io.access_oss('your oss config')

size = io.size('oss://bucket/file.txt')
print(size)

Example for local:

from easycv.file import io

size = io.size('/your/local/path/file.txt')
print(size)

v 0.2.2 (07/04/2022)¶

initial commit & first release

SOTA SSL Algorithms

EasyCV provides state-of-the-art algorithms in self-supervised learning based on contrastive learning such as SimCLR, MoCO V2, Swav, DINO and also MAE based on masked image modeling. We also provides standard benchmark tools for ssl model evaluation.

Vision Transformers

EasyCV aims to provide plenty vision transformer models trained either using supervised learning or self-supervised learning, such as ViT, Swin-Transformer and XCit. More models will be added in the future.

Functionality & Extensibility

In addition to SSL, EasyCV also support image classification, object detection, metric learning, and more area will be supported in the future. Although convering different area, EasyCV decompose the framework into different componets such as dataset, model, running hook, making it easy to add new compoenets and combining it with existing modules. EasyCV provide simple and comprehensive interface for inference. Additionaly, all models are supported on PAI-EAS, which can be easily deployed as online service and support automatic scaling and service moniting.

Efficiency

EasyCV support multi-gpu and multi worker training. EasyCV use DALI to accelerate data io and preprocessing process, and use fp16 to accelerate training process. For inference optimization, EasyCV export model using jit script, which can be optimized by PAI-Blade.

easycv.apis package¶

Submodules¶

easycv.apis.export module¶

easycv.apis.export.export(cfg, ckpt_path, filename)[source]¶

export model for inference

Parameters

cfg – Config object
ckpt_path (str) – path to checkpoint file
filename (str) – filename to save exported models

easycv.apis.test module¶

easycv.apis.test.single_cpu_test(model, data_loader, mode='test', show=False, out_dir=None, show_score_thr=0.3, **kwargs)[source]¶

easycv.apis.test.single_gpu_test(model, data_loader, mode='test', use_fp16=False, **kwargs)[source]¶

Test model with single.

This method tests model with single

Parameters

model (str) – Model to be tested.
data_loader (nn.Dataloader) – Pytorch data loader.
model – mode for model to forward
use_fp16 – Use fp16 inference

Returns

The prediction results.

Return type

list

easycv.apis.test.multi_gpu_test(model, data_loader, mode='test', tmpdir=None, gpu_collect=False, use_fp16=False, **kwargs)[source]¶

Test model with multiple gpus.

This method tests model with multiple gpus and collects the results under two different modes: gpu and cpu modes. By setting ‘gpu_collect=True’ it encodes results to gpu tensors and use gpu communication for results collection. On cpu mode it saves the results on different gpus to ‘tmpdir’ and collects them by the rank 0 worker.

Parameters

model (str) – Model to be tested.
data_loader (nn.Dataloader) – Pytorch data loader.
model – mode for model to forward
tmpdir (str) – Path of directory to save the temporary results from different gpus under cpu mode.
gpu_collect (bool) – Option to use either gpu or cpu to collect results.
use_fp16 – Use fp16 inference

Returns

The prediction results.

Return type

list

easycv.apis.test.collect_results_cpu(result_part, size, tmpdir=None)[source]¶

easycv.apis.test.serialize_tensor(tensor_collection)[source]¶

easycv.apis.test.collect_results_gpu(result_part, size)[source]¶

easycv.apis.train module¶

easycv.apis.train.set_random_seed(seed, deterministic=False)[source]¶

Set random seed.

Parameters

seed (int) – Seed to be used.
deterministic (bool) – Whether to set the deterministic option for CUDNN backend, i.e., set torch.backends.cudnn.deterministic to True and torch.backends.cudnn.benchmark to False. Default: False.

easycv.apis.train.train_model(model, data_loaders, cfg, distributed=False, timestamp=None, meta=None, use_fp16=False, validate=True, gpu_collect=True)[source]¶

Training API.

Parameters

model (nn.Module) – user defined model
data_loaders – a list of dataloader for training data
cfg – config object
distributed – distributed training or not
timestamp – time str formated as ‘%Y%m%d_%H%M%S’
meta – a dict containing meta data info, such as env_info, seed, iter, epoch
use_fp16 – use fp16 training or not
validate – do evaluation while training
gpu_collect – use gpu collect or cpu collect for tensor gathering

easycv.apis.train.get_skip_list_keywords(model)[source]¶

easycv.apis.train.build_optimizer(model, optimizer_cfg)[source]¶

Build optimizer from configs.

Parameters

model (nn.Module) – The model with parameters to be optimized.
optimizer_cfg (dict) –
The config dict of the optimizer.
Positional fields are:
- type: class name of the optimizer.
- lr: base learning rate.
Optional fields are:
- any arguments of the corresponding optimizer type, e.g., weight_decay, momentum, etc.
- paramwise_options: a dict with regular expression as keys to match parameter names and a dict containing options as values. Options include 6 fields: lr, lr_mult, momentum, momentum_mult, weight_decay, weight_decay_mult.

Returns

The initialized optimizer.

Return type

torch.optim.Optimizer

Example

>>> model = torch.nn.modules.Conv1d(1, 1, 1)
>>> paramwise_options = {
>>>     '(bn|gn)(\d+)?.(weight|bias)': dict(weight_decay_mult=0.1),
>>>     '\Ahead.': dict(lr_mult=10, momentum=0)}
>>> optimizer_cfg = dict(type='SGD', lr=0.01, momentum=0.9,
>>>                      weight_decay=0.0001,
>>>                      paramwise_options=paramwise_options)
>>> optimizer = build_optimizer(model, optimizer_cfg)

easycv.apis.train_misc module¶

easycv.apis.train_misc.build_yolo_optimizer(model, optimizer_cfg)[source]¶: build optimizer for yolo.

easycv.datasets package¶

Subpackages¶

easycv.datasets.classification package¶

class easycv.datasets.classification.ClsDataset(data_source, pipeline)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

Dataset for classification

Parameters

data_source – data source to parse input data
pipeline – transforms list

__init__(data_source, pipeline)[source]¶: Initialize self. See help(type(self)) for accurate signature.

evaluate(results, evaluators, logger=None, topk=(1, 5))[source]¶

evaluate classification task

Parameters

results – a dict of list of tensor, including prediction and groundtruth info, where prediction tensor is NxC，and the same with groundtruth labels.
evaluators – a list of evaluator

Returns

a dict of float, different metric values

Return type

eval_result

visualize(results, vis_num=10, **kwargs)[source]¶

Visulaize the model output on validation data. :param results: A dictionary containing

class: List of length number of test images. img_metas: List of length number of test images,

dict of image meta info, containing filename, img_shape, origin_img_shape and so on.

Parameters: vis_num – number of images visualized

Returns: A dictionary containing: images: Visulaized images, list of np.ndarray. img_metas: List of length number of test images,

dict of image meta info, containing filename, img_shape, origin_img_shape and so on.

class easycv.datasets.classification.ClsOdpsDataset(data_source, pipeline, image_key='url_image', label_key='label', **kwargs)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

Dataset for rotation prediction

__init__(data_source, pipeline, image_key='url_image', label_key='label', **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

evaluate(results, evaluators, logger=None)[source]¶

Subpackages¶

easycv.datasets.classification.data_sources package¶

class easycv.datasets.classification.data_sources.ClsSourceCifar10(root, split)[source]¶

Bases: object

CLASSES = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']¶

__init__(root, split)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_length()[source]¶

get_sample(idx)[source]¶

class easycv.datasets.classification.data_sources.ClsSourceCifar100(root, split)[source]¶

Bases: object

CLASSES = None¶

__init__(root, split)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_length()[source]¶

get_sample(idx)[source]¶

class easycv.datasets.classification.data_sources.ClsSourceImageListByClass(root, list_file, m_per_class=2, delimeter=' ', split_huge_listfile_byrank=False, cache_path='data/', max_try=20)[source]¶

Bases: object

Get the same m_per_class samples by the label idx.

Parameters

list_file – str / list(str), str means a input image list file path, this file contains records as image_path label in list_file list(str) means multi image list, each one contains some records as image_path label
root – str / list(str), root path for image_path, each list_file will need a root.
m_per_class – num of samples for each class.
delimeter – str, delimeter of each line in the list_file
split_huge_listfile_byrank – Adapt to the situation that the memory cannot fully load a huge amount of data list. If split, data list will be split to each rank.
cache_path – if split_huge_listfile_byrank is true, cache list_file will be saved to cache_path.
max_try – int, max try numbers of reading image

__init__(root, list_file, m_per_class=2, delimeter=' ', split_huge_listfile_byrank=False, cache_path='data/', max_try=20)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_length()[source]¶

get_sample(idx)[source]¶

class easycv.datasets.classification.data_sources.ClsSourceImageList(list_file, root='', delimeter=' ', split_huge_listfile_byrank=False, split_label_balance=False, cache_path='data/', max_try=20)[source]¶

Bases: object

data source for classification

Parameters

list_file – str / list(str), str means a input image list file path, this file contains records as image_path label in list_file list(str) means multi image list, each one contains some records as image_path label
root – str / list(str), root path for image_path, each list_file will need a root, if len(root) < len(list_file), we will use root[-1] to fill root list.
delimeter – str, delimeter of each line in the list_file
split_huge_listfile_byrank – Adapt to the situation that the memory cannot fully load a huge amount of data list. If split, data list will be split to each rank.
split_label_balance – if split_huge_listfile_byrank is true, whether split with label balance
cache_path – if split_huge_listfile_byrank is true, cache list_file will be saved to cache_path.
max_try – int, max try numbers of reading image

__init__(list_file, root='', delimeter=' ', split_huge_listfile_byrank=False, split_label_balance=False, cache_path='data/', max_try=20)[source]¶: Initialize self. See help(type(self)) for accurate signature.

static parse_list_file(list_file, root, delimeter)[source]¶

get_length()[source]¶

get_sample(idx)[source]¶

class easycv.datasets.classification.data_sources.ClsSourceImageNetTFRecord(list_file='', root='', file_pattern=None, cache_path='data/cache/', max_try=10)[source]¶

Bases: object

data source for imagenet tfrecord.

__init__(list_file='', root='', file_pattern=None, cache_path='data/cache/', max_try=10)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Submodules¶

easycv.datasets.classification.data_sources.cifar module¶

class easycv.datasets.classification.data_sources.cifar.ClsSourceCifar10(root, split)[source]¶

Bases: object

CLASSES = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']¶

__init__(root, split)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_length()[source]¶

get_sample(idx)[source]¶

class easycv.datasets.classification.data_sources.cifar.ClsSourceCifar100(root, split)[source]¶

Bases: object

CLASSES = None¶

__init__(root, split)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_length()[source]¶

get_sample(idx)[source]¶

easycv.datasets.classification.data_sources.class_list module¶

class easycv.datasets.classification.data_sources.class_list.ClsSourceImageListByClass(root, list_file, m_per_class=2, delimeter=' ', split_huge_listfile_byrank=False, cache_path='data/', max_try=20)[source]¶

Bases: object

Get the same m_per_class samples by the label idx.

Parameters

list_file – str / list(str), str means a input image list file path, this file contains records as image_path label in list_file list(str) means multi image list, each one contains some records as image_path label
root – str / list(str), root path for image_path, each list_file will need a root.
m_per_class – num of samples for each class.
delimeter – str, delimeter of each line in the list_file
split_huge_listfile_byrank – Adapt to the situation that the memory cannot fully load a huge amount of data list. If split, data list will be split to each rank.
cache_path – if split_huge_listfile_byrank is true, cache list_file will be saved to cache_path.
max_try – int, max try numbers of reading image

__init__(root, list_file, m_per_class=2, delimeter=' ', split_huge_listfile_byrank=False, cache_path='data/', max_try=20)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_length()[source]¶

get_sample(idx)[source]¶

easycv.datasets.classification.data_sources.fashiongen_h5 module¶

class easycv.datasets.classification.data_sources.fashiongen_h5.FashionGenH5(h5file_path, return_label=True, cache_path='data/fashionGenH5')[source]¶

Bases: object

__init__(h5file_path, return_label=True, cache_path='data/fashionGenH5')[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_length()[source]¶

get_sample(idx)[source]¶

easycv.datasets.classification.data_sources.image_list module¶

class easycv.datasets.classification.data_sources.image_list.ClsSourceImageList(list_file, root='', delimeter=' ', split_huge_listfile_byrank=False, split_label_balance=False, cache_path='data/', max_try=20)[source]¶

Bases: object

data source for classification

Parameters

list_file – str / list(str), str means a input image list file path, this file contains records as image_path label in list_file list(str) means multi image list, each one contains some records as image_path label
root – str / list(str), root path for image_path, each list_file will need a root, if len(root) < len(list_file), we will use root[-1] to fill root list.
delimeter – str, delimeter of each line in the list_file
split_huge_listfile_byrank – Adapt to the situation that the memory cannot fully load a huge amount of data list. If split, data list will be split to each rank.
split_label_balance – if split_huge_listfile_byrank is true, whether split with label balance
cache_path – if split_huge_listfile_byrank is true, cache list_file will be saved to cache_path.
max_try – int, max try numbers of reading image

__init__(list_file, root='', delimeter=' ', split_huge_listfile_byrank=False, split_label_balance=False, cache_path='data/', max_try=20)[source]¶: Initialize self. See help(type(self)) for accurate signature.

static parse_list_file(list_file, root, delimeter)[source]¶

get_length()[source]¶

get_sample(idx)[source]¶

easycv.datasets.classification.data_sources.imagenet_tfrecord module¶

class easycv.datasets.classification.data_sources.imagenet_tfrecord.ClsSourceImageNetTFRecord(list_file='', root='', file_pattern=None, cache_path='data/cache/', max_try=10)[source]¶

Bases: object

data source for imagenet tfrecord.

__init__(list_file='', root='', file_pattern=None, cache_path='data/cache/', max_try=10)[source]¶: Initialize self. See help(type(self)) for accurate signature.

easycv.datasets.classification.data_sources.utils module¶

easycv.datasets.classification.data_sources.utils.split_listfile_byrank(list_file, label_balance, save_path='data/', delimeter=' ')[source]¶

easycv.datasets.classification.pipelines package¶

class easycv.datasets.classification.pipelines.MMAutoAugment(policies=[[{'type': 'Posterize', 'bits': 4, 'prob': 0.4}, {'type': 'Rotate', 'angle': 30.0, 'prob': 0.6}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 5, 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}], [{'type': 'Solarize', 'thr': 170.66666666666666, 'prob': 0.6}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 6, 'prob': 0.8}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'Rotate', 'angle': 10.0, 'prob': 0.2}, {'type': 'Solarize', 'thr': 28.444444444444443, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 30.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.0}, {'type': 'Equalize', 'prob': 0.8}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.2, 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.8, 'prob': 0.8}, {'type': 'Solarize', 'thr': 56.888888888888886, 'prob': 0.8}], [{'type': 'Sharpness', 'magnitude': 0.7, 'prob': 0.4}, {'type': 'Invert', 'prob': 0.6}], [{'type': 'Shear', 'magnitude': 0.16666666666666666, 'prob': 0.6, 'direction': 'horizontal'}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}]], hparams={'pad_val': 128})[source]¶

Bases: object

Auto augmentation. This data augmentation is proposed in AutoAugment: Learning Augmentation Policies from Data. :param policies: The policies of auto augmentation. Each

policy in policies is a specific augmentation policy, and is composed by several augmentations (dict). When AutoAugment is called, a random policy in policies will be selected to augment images.

Parameters: hparams (dict) – Configs of hyperparameters. Hyperparameters will be used in policies that require these arguments if these arguments are not set in policy dicts. Defaults to use _HPARAMS_DEFAULT.

__init__(policies=[[{'type': 'Posterize', 'bits': 4, 'prob': 0.4}, {'type': 'Rotate', 'angle': 30.0, 'prob': 0.6}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 5, 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}], [{'type': 'Solarize', 'thr': 170.66666666666666, 'prob': 0.6}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 6, 'prob': 0.8}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'Rotate', 'angle': 10.0, 'prob': 0.2}, {'type': 'Solarize', 'thr': 28.444444444444443, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 30.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.0}, {'type': 'Equalize', 'prob': 0.8}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.2, 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.8, 'prob': 0.8}, {'type': 'Solarize', 'thr': 56.888888888888886, 'prob': 0.8}], [{'type': 'Sharpness', 'magnitude': 0.7, 'prob': 0.4}, {'type': 'Invert', 'prob': 0.6}], [{'type': 'Shear', 'magnitude': 0.16666666666666666, 'prob': 0.6, 'direction': 'horizontal'}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}]], hparams={'pad_val': 128})[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.MMRandAugment(num_policies, magnitude_level, magnitude_std=0.0, total_level=30, policies=[{'type': 'AutoContrast'}, {'type': 'Equalize'}, {'type': 'Invert'}, {'type': 'Rotate', 'magnitude_key': 'angle', 'magnitude_range': (0, 30)}, {'type': 'Posterize', 'magnitude_key': 'bits', 'magnitude_range': (4, 0)}, {'type': 'Solarize', 'magnitude_key': 'thr', 'magnitude_range': (256, 0)}, {'type': 'SolarizeAdd', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 110)}, {'type': 'ColorTransform', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Contrast', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Brightness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Sharpness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'horizontal'}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'vertical'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'horizontal'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'vertical'}], hparams={'pad_val': 128})[source]¶

Bases: object

Random augmentation. This data augmentation is proposed in RandAugment: Practical automated data augmentation with a reduced search space. :param policies: The policies of random augmentation. Each

policy in policies is one specific augmentation policy (dict). The policy shall at least have key type, indicating the type of augmentation. For those which have magnitude, (given to the fact they are named differently in different augmentation, ) magnitude_key and magnitude_range shall be the magnitude argument (str) and the range of magnitude (tuple in the format of (val1, val2)), respectively. Note that val1 is not necessarily less than val2.

Parameters

num_policies (int) – Number of policies to select from policies each time.
magnitude_level (int | float) – Magnitude level for all the augmentation selected.
total_level (int | float) – Total level for the magnitude. Defaults to 30.
magnitude_std (Number | str) –
Deviation of magnitude noise applied. - If positive number, magnitude is sampled from normal distribution

(mean=magnitude, std=magnitude_std).
- If 0 or negative number, magnitude remains unchanged.
- If str “inf”, magnitude is sampled from uniform distribution (range=[min, magnitude]).
hparams (dict) – Configs of hyperparameters. Hyperparameters will be used in policies that require these arguments if these arguments are not set in policy dicts. Defaults to use _HPARAMS_DEFAULT.

Note

magnitude_std will introduce some randomness to policy, modified by https://github.com/rwightman/pytorch-image-models. When magnitude_std=0, we calculate the magnitude as follows: .. math:

\text{magnitude} = \frac{\text{magnitude\_level}}
{\text{total\_level}} \times (\text{val2} - \text{val1})
+ \text{val1}

__init__(num_policies, magnitude_level, magnitude_std=0.0, total_level=30, policies=[{'type': 'AutoContrast'}, {'type': 'Equalize'}, {'type': 'Invert'}, {'type': 'Rotate', 'magnitude_key': 'angle', 'magnitude_range': (0, 30)}, {'type': 'Posterize', 'magnitude_key': 'bits', 'magnitude_range': (4, 0)}, {'type': 'Solarize', 'magnitude_key': 'thr', 'magnitude_range': (256, 0)}, {'type': 'SolarizeAdd', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 110)}, {'type': 'ColorTransform', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Contrast', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Brightness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Sharpness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'horizontal'}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'vertical'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'horizontal'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'vertical'}], hparams={'pad_val': 128})[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.MMRandomErasing(erase_prob=0.5, min_area_ratio=0.02, max_area_ratio=0.4, aspect_range=(0.3, 3.3333333333333335), mode='const', fill_color=(128, 128, 128), fill_std=None)[source]¶

Bases: object

Randomly selects a rectangle region in an image and erase pixels. :param erase_prob: Probability that image will be randomly erased.

Default: 0.5

Parameters

min_area_ratio (float) – Minimum erased area / input image area Default: 0.02
max_area_ratio (float) – Maximum erased area / input image area Default: 0.4
aspect_range (sequence | float) – Aspect ratio range of erased area. if float, it will be converted to (aspect_ratio, 1/aspect_ratio) Default: (3/10, 10/3)
mode (str) – Fill method in erased area, can be: - const (default): All pixels are assign with the same value. - rand: each pixel is assigned with a random value in [0, 255]
fill_color (sequence | Number) – Base color filled in erased area. Defaults to (128, 128, 128).
fill_std (sequence | Number, optional) – If set and mode is ‘rand’, fill erased area with random color from normal distribution (mean=fill_color, std=fill_std); If not set, fill erased area with random color from uniform distribution (0~255). Defaults to None.

Note

See Random Erasing Data Augmentation This paper provided 4 modes: RE-R, RE-M, RE-0, RE-255, and use RE-M as default. The config of these 4 modes are: - RE-R: RandomErasing(mode=’rand’) - RE-M: RandomErasing(mode=’const’, fill_color=(123.67, 116.3, 103.5)) - RE-0: RandomErasing(mode=’const’, fill_color=0) - RE-255: RandomErasing(mode=’const’, fill_color=255)

__init__(erase_prob=0.5, min_area_ratio=0.02, max_area_ratio=0.4, aspect_range=(0.3, 3.3333333333333335), mode='const', fill_color=(128, 128, 128), fill_std=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Submodules¶

easycv.datasets.classification.pipelines.auto_augment module¶

easycv.datasets.classification.pipelines.auto_augment.random_negative(value, random_negative_prob)[source]¶: Randomly negate value based on random_negative_prob.

easycv.datasets.classification.pipelines.auto_augment.merge_hparams(policy: dict, hparams: dict)[source]¶

Merge hyperparameters into policy config. Only merge partial hyperparameters required of the policy. :param policy: Original policy config dict. :type policy: dict :param hparams: Hyperparameters need to be merged. :type hparams: dict

Returns: Policy config dict after adding hparams.
Return type: dict

class easycv.datasets.classification.pipelines.auto_augment.MMAutoAugment(policies=[[{'type': 'Posterize', 'bits': 4, 'prob': 0.4}, {'type': 'Rotate', 'angle': 30.0, 'prob': 0.6}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 5, 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}], [{'type': 'Solarize', 'thr': 170.66666666666666, 'prob': 0.6}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 6, 'prob': 0.8}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'Rotate', 'angle': 10.0, 'prob': 0.2}, {'type': 'Solarize', 'thr': 28.444444444444443, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 30.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.0}, {'type': 'Equalize', 'prob': 0.8}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.2, 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.8, 'prob': 0.8}, {'type': 'Solarize', 'thr': 56.888888888888886, 'prob': 0.8}], [{'type': 'Sharpness', 'magnitude': 0.7, 'prob': 0.4}, {'type': 'Invert', 'prob': 0.6}], [{'type': 'Shear', 'magnitude': 0.16666666666666666, 'prob': 0.6, 'direction': 'horizontal'}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}]], hparams={'pad_val': 128})[source]¶

Bases: object

Auto augmentation. This data augmentation is proposed in AutoAugment: Learning Augmentation Policies from Data. :param policies: The policies of auto augmentation. Each

policy in policies is a specific augmentation policy, and is composed by several augmentations (dict). When AutoAugment is called, a random policy in policies will be selected to augment images.

Parameters: hparams (dict) – Configs of hyperparameters. Hyperparameters will be used in policies that require these arguments if these arguments are not set in policy dicts. Defaults to use _HPARAMS_DEFAULT.

__init__(policies=[[{'type': 'Posterize', 'bits': 4, 'prob': 0.4}, {'type': 'Rotate', 'angle': 30.0, 'prob': 0.6}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 5, 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}], [{'type': 'Solarize', 'thr': 170.66666666666666, 'prob': 0.6}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 6, 'prob': 0.8}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'Rotate', 'angle': 10.0, 'prob': 0.2}, {'type': 'Solarize', 'thr': 28.444444444444443, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 30.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.0}, {'type': 'Equalize', 'prob': 0.8}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.2, 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.8, 'prob': 0.8}, {'type': 'Solarize', 'thr': 56.888888888888886, 'prob': 0.8}], [{'type': 'Sharpness', 'magnitude': 0.7, 'prob': 0.4}, {'type': 'Invert', 'prob': 0.6}], [{'type': 'Shear', 'magnitude': 0.16666666666666666, 'prob': 0.6, 'direction': 'horizontal'}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}]], hparams={'pad_val': 128})[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.MMRandAugment(num_policies, magnitude_level, magnitude_std=0.0, total_level=30, policies=[{'type': 'AutoContrast'}, {'type': 'Equalize'}, {'type': 'Invert'}, {'type': 'Rotate', 'magnitude_key': 'angle', 'magnitude_range': (0, 30)}, {'type': 'Posterize', 'magnitude_key': 'bits', 'magnitude_range': (4, 0)}, {'type': 'Solarize', 'magnitude_key': 'thr', 'magnitude_range': (256, 0)}, {'type': 'SolarizeAdd', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 110)}, {'type': 'ColorTransform', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Contrast', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Brightness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Sharpness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'horizontal'}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'vertical'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'horizontal'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'vertical'}], hparams={'pad_val': 128})[source]¶

Bases: object

Random augmentation. This data augmentation is proposed in RandAugment: Practical automated data augmentation with a reduced search space. :param policies: The policies of random augmentation. Each

policy in policies is one specific augmentation policy (dict). The policy shall at least have key type, indicating the type of augmentation. For those which have magnitude, (given to the fact they are named differently in different augmentation, ) magnitude_key and magnitude_range shall be the magnitude argument (str) and the range of magnitude (tuple in the format of (val1, val2)), respectively. Note that val1 is not necessarily less than val2.

Parameters

num_policies (int) – Number of policies to select from policies each time.
magnitude_level (int | float) – Magnitude level for all the augmentation selected.
total_level (int | float) – Total level for the magnitude. Defaults to 30.
magnitude_std (Number | str) –
Deviation of magnitude noise applied. - If positive number, magnitude is sampled from normal distribution

(mean=magnitude, std=magnitude_std).
- If 0 or negative number, magnitude remains unchanged.
- If str “inf”, magnitude is sampled from uniform distribution (range=[min, magnitude]).
hparams (dict) – Configs of hyperparameters. Hyperparameters will be used in policies that require these arguments if these arguments are not set in policy dicts. Defaults to use _HPARAMS_DEFAULT.

Note

magnitude_std will introduce some randomness to policy, modified by https://github.com/rwightman/pytorch-image-models. When magnitude_std=0, we calculate the magnitude as follows: .. math:

\text{magnitude} = \frac{\text{magnitude\_level}}
{\text{total\_level}} \times (\text{val2} - \text{val1})
+ \text{val1}

__init__(num_policies, magnitude_level, magnitude_std=0.0, total_level=30, policies=[{'type': 'AutoContrast'}, {'type': 'Equalize'}, {'type': 'Invert'}, {'type': 'Rotate', 'magnitude_key': 'angle', 'magnitude_range': (0, 30)}, {'type': 'Posterize', 'magnitude_key': 'bits', 'magnitude_range': (4, 0)}, {'type': 'Solarize', 'magnitude_key': 'thr', 'magnitude_range': (256, 0)}, {'type': 'SolarizeAdd', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 110)}, {'type': 'ColorTransform', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Contrast', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Brightness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Sharpness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'horizontal'}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'vertical'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'horizontal'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'vertical'}], hparams={'pad_val': 128})[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Shear(magnitude, pad_val=128, prob=0.5, direction='horizontal', random_negative_prob=0.5, interpolation='bicubic')[source]¶

Bases: object

Shear images. :param magnitude: The magnitude used for shear. :type magnitude: int | float :param pad_val: Pixel pad_val value for constant fill.

If a sequence of length 3, it is used to pad_val R, G, B channels respectively. Defaults to 128.

Parameters

prob (float) – The probability for performing Shear therefore should be in range [0, 1]. Defaults to 0.5.
direction (str) – The shearing direction. Options are ‘horizontal’ and ‘vertical’. Defaults to ‘horizontal’.
random_negative_prob (float) – The probability that turns the magnitude negative, which should be in range [0,1]. Defaults to 0.5.
interpolation (str) – Interpolation method. Options are ‘nearest’, ‘bilinear’, ‘bicubic’, ‘area’, ‘lanczos’. Defaults to ‘bicubic’.

__init__(magnitude, pad_val=128, prob=0.5, direction='horizontal', random_negative_prob=0.5, interpolation='bicubic')[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Translate(magnitude, pad_val=128, prob=0.5, direction='horizontal', random_negative_prob=0.5, interpolation='nearest')[source]¶

Bases: object

Translate images. :param magnitude: The magnitude used for translate. Note that

the offset is calculated by magnitude * size in the corresponding direction. With a magnitude of 1, the whole image will be moved out of the range.

Parameters

pad_val (int, Sequence[int]) – Pixel pad_val value for constant fill. If a sequence of length 3, it is used to pad_val R, G, B channels respectively. Defaults to 128.
prob (float) – The probability for performing translate therefore should be in range [0, 1]. Defaults to 0.5.
direction (str) – The translating direction. Options are ‘horizontal’ and ‘vertical’. Defaults to ‘horizontal’.
random_negative_prob (float) – The probability that turns the magnitude negative, which should be in range [0,1]. Defaults to 0.5.
interpolation (str) – Interpolation method. Options are ‘nearest’, ‘bilinear’, ‘bicubic’, ‘area’, ‘lanczos’. Defaults to ‘nearest’.

__init__(magnitude, pad_val=128, prob=0.5, direction='horizontal', random_negative_prob=0.5, interpolation='nearest')[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Rotate(angle, center=None, scale=1.0, pad_val=128, prob=0.5, random_negative_prob=0.5, interpolation='nearest')[source]¶

Bases: object

Rotate images. :param angle: The angle used for rotate. Positive values stand for

clockwise rotation.

Parameters

center (tuple[float], optional) – Center point (w, h) of the rotation in the source image. If None, the center of the image will be used. Defaults to None.
scale (float) – Isotropic scale factor. Defaults to 1.0.
pad_val (int, Sequence[int]) – Pixel pad_val value for constant fill. If a sequence of length 3, it is used to pad_val R, G, B channels respectively. Defaults to 128.
prob (float) – The probability for performing Rotate therefore should be in range [0, 1]. Defaults to 0.5.
random_negative_prob (float) – The probability that turns the angle negative, which should be in range [0,1]. Defaults to 0.5.
interpolation (str) – Interpolation method. Options are ‘nearest’, ‘bilinear’, ‘bicubic’, ‘area’, ‘lanczos’. Defaults to ‘nearest’.

__init__(angle, center=None, scale=1.0, pad_val=128, prob=0.5, random_negative_prob=0.5, interpolation='nearest')[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.AutoContrast(prob=0.5)[source]¶

Bases: object

Auto adjust image contrast. :param prob: The probability for performing invert therefore should

be in range [0, 1]. Defaults to 0.5.

__init__(prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Invert(prob=0.5)[source]¶

Bases: object

Invert images. :param prob: The probability for performing invert therefore should

be in range [0, 1]. Defaults to 0.5.

__init__(prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Equalize(prob=0.5)[source]¶

Bases: object

Equalize the image histogram. :param prob: The probability for performing invert therefore should

be in range [0, 1]. Defaults to 0.5.

__init__(prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Solarize(thr, prob=0.5)[source]¶

Bases: object

Solarize images (invert all pixel values above a threshold). :param thr: The threshold above which the pixels value will be

inverted.

Parameters: prob (float) – The probability for solarizing therefore should be in range [0, 1]. Defaults to 0.5.

__init__(thr, prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.SolarizeAdd(magnitude, thr=128, prob=0.5)[source]¶

Bases: object

SolarizeAdd images (add a certain value to pixels below a threshold). :param magnitude: The value to be added to pixels below the thr. :type magnitude: int | float :param thr: The threshold below which the pixels value will be

adjusted.

Parameters: prob (float) – The probability for solarizing therefore should be in range [0, 1]. Defaults to 0.5.

__init__(magnitude, thr=128, prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Posterize(bits, prob=0.5)[source]¶

Bases: object

Posterize images (reduce the number of bits for each color channel). :param bits: Number of bits for each pixel in the output img,

which should be less or equal to 8.

Parameters: prob (float) – The probability for posterizing therefore should be in range [0, 1]. Defaults to 0.5.

__init__(bits, prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Contrast(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶

Bases: object

Adjust images contrast. :param magnitude: The magnitude used for adjusting contrast. A

positive magnitude would enhance the contrast and a negative magnitude would make the image grayer. A magnitude=0 gives the origin img.

Parameters

prob (float) – The probability for performing contrast adjusting therefore should be in range [0, 1]. Defaults to 0.5.
random_negative_prob (float) – The probability that turns the magnitude negative, which should be in range [0,1]. Defaults to 0.5.

__init__(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.ColorTransform(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶

Bases: object

Adjust images color balance. :param magnitude: The magnitude used for color transform. A

positive magnitude would enhance the color and a negative magnitude would make the image grayer. A magnitude=0 gives the origin img.

Parameters

prob (float) – The probability for performing ColorTransform therefore should be in range [0, 1]. Defaults to 0.5.
random_negative_prob (float) – The probability that turns the magnitude negative, which should be in range [0,1]. Defaults to 0.5.

__init__(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Brightness(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶

Bases: object

Adjust images brightness. :param magnitude: The magnitude used for adjusting brightness. A

positive magnitude would enhance the brightness and a negative magnitude would make the image darker. A magnitude=0 gives the origin img.

Parameters

prob (float) – The probability for performing contrast adjusting therefore should be in range [0, 1]. Defaults to 0.5.
random_negative_prob (float) – The probability that turns the magnitude negative, which should be in range [0,1]. Defaults to 0.5.

__init__(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Sharpness(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶

Bases: object

Adjust images sharpness. :param magnitude: The magnitude used for adjusting sharpness. A

positive magnitude would enhance the sharpness and a negative magnitude would make the image bulr. A magnitude=0 gives the origin img.

Parameters

prob (float) – The probability for performing contrast adjusting therefore should be in range [0, 1]. Defaults to 0.5.
random_negative_prob (float) – The probability that turns the magnitude negative, which should be in range [0,1]. Defaults to 0.5.

__init__(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Cutout(shape, pad_val=128, prob=0.5)[source]¶

Bases: object

Cutout images. :param shape: Expected cutout shape (h, w).

If given as a single value, the value will be used for both h and w.

Parameters

pad_val (int, Sequence[int]) – Pixel pad_val value for constant fill. If it is a sequence, it must have the same length with the image channels. Defaults to 128.
prob (float) – The probability for performing cutout therefore should be in range [0, 1]. Defaults to 0.5.

__init__(shape, pad_val=128, prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

easycv.datasets.classification.pipelines.transform module¶

class easycv.datasets.classification.pipelines.transform.MMRandomErasing(erase_prob=0.5, min_area_ratio=0.02, max_area_ratio=0.4, aspect_range=(0.3, 3.3333333333333335), mode='const', fill_color=(128, 128, 128), fill_std=None)[source]¶

Bases: object

Randomly selects a rectangle region in an image and erase pixels. :param erase_prob: Probability that image will be randomly erased.

Default: 0.5

Parameters

min_area_ratio (float) – Minimum erased area / input image area Default: 0.02
max_area_ratio (float) – Maximum erased area / input image area Default: 0.4
aspect_range (sequence | float) – Aspect ratio range of erased area. if float, it will be converted to (aspect_ratio, 1/aspect_ratio) Default: (3/10, 10/3)
mode (str) – Fill method in erased area, can be: - const (default): All pixels are assign with the same value. - rand: each pixel is assigned with a random value in [0, 255]
fill_color (sequence | Number) – Base color filled in erased area. Defaults to (128, 128, 128).
fill_std (sequence | Number, optional) – If set and mode is ‘rand’, fill erased area with random color from normal distribution (mean=fill_color, std=fill_std); If not set, fill erased area with random color from uniform distribution (0~255). Defaults to None.

Note

See Random Erasing Data Augmentation This paper provided 4 modes: RE-R, RE-M, RE-0, RE-255, and use RE-M as default. The config of these 4 modes are: - RE-R: RandomErasing(mode=’rand’) - RE-M: RandomErasing(mode=’const’, fill_color=(123.67, 116.3, 103.5)) - RE-0: RandomErasing(mode=’const’, fill_color=0) - RE-255: RandomErasing(mode=’const’, fill_color=255)

__init__(erase_prob=0.5, min_area_ratio=0.02, max_area_ratio=0.4, aspect_range=(0.3, 3.3333333333333335), mode='const', fill_color=(128, 128, 128), fill_std=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Submodules¶

easycv.datasets.classification.odps module¶

class easycv.datasets.classification.odps.ClsOdpsDataset(data_source, pipeline, image_key='url_image', label_key='label', **kwargs)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

Dataset for rotation prediction

__init__(data_source, pipeline, image_key='url_image', label_key='label', **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

evaluate(results, evaluators, logger=None)[source]¶

easycv.datasets.classification.raw module¶

class easycv.datasets.classification.raw.ClsDataset(data_source, pipeline)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

Dataset for classification

Parameters

data_source – data source to parse input data
pipeline – transforms list

__init__(data_source, pipeline)[source]¶: Initialize self. See help(type(self)) for accurate signature.

evaluate(results, evaluators, logger=None, topk=(1, 5))[source]¶

evaluate classification task

Parameters

results – a dict of list of tensor, including prediction and groundtruth info, where prediction tensor is NxC，and the same with groundtruth labels.
evaluators – a list of evaluator

Returns

a dict of float, different metric values

Return type

eval_result

visualize(results, vis_num=10, **kwargs)[source]¶

Visulaize the model output on validation data. :param results: A dictionary containing

class: List of length number of test images. img_metas: List of length number of test images,

dict of image meta info, containing filename, img_shape, origin_img_shape and so on.

Parameters: vis_num – number of images visualized

Returns: A dictionary containing: images: Visulaized images, list of np.ndarray. img_metas: List of length number of test images,

dict of image meta info, containing filename, img_shape, origin_img_shape and so on.

easycv.datasets.detection package¶

class easycv.datasets.detection.DetDataset(data_source, pipeline, profiling=False, classes=None)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

Dataset for Detection

__init__(data_source, pipeline, profiling=False, classes=None)[source]¶

Parameters

data_source – Data_source config dict
pipeline – Pipeline config list
profiling – If set True, will print pipeline time
classes – A list of class names, used in evaluation for result and groundtruth visualization

evaluate(results, evaluators=None, logger=None)[source]¶

Evaluates the detection boxes. :param results: A dictionary containing

detection_boxes: List of length number of test images.
Float32 numpy array of shape [num_boxes, 4] and format [ymin, xmin, ymax, xmax] in absolute image coordinates.

detection_scores: List of length number of test images,
detection scores for the boxes, float32 numpy array of shape [num_boxes].

detection_classes: List of length number of test images,
integer numpy array of shape [num_boxes] containing 1-indexed detection classes for the boxes.

img_metas: List of length number of test images,
dict of image meta info, containing filename, img_shape, origin_img_shape, scale_factor and so on.

Parameters: evaluators – evaluators to calculate metric with results and groundtruth_dict

visualize(results, vis_num=10, score_thr=0.3, **kwargs)[source]¶

Visulaize the model output on validation data. :param results: A dictionary containing

detection_boxes: List of length number of test images.
Float32 numpy array of shape [num_boxes, 4] and format [ymin, xmin, ymax, xmax] in absolute image coordinates.

detection_scores: List of length number of test images,
detection scores for the boxes, float32 numpy array of shape [num_boxes].

detection_classes: List of length number of test images,
integer numpy array of shape [num_boxes] containing 1-indexed detection classes for the boxes.

img_metas: List of length number of test images,
dict of image meta info, containing filename, img_shape, origin_img_shape, scale_factor and so on.

Parameters

vis_num – number of images visualized
score_thr – The threshold to filter box, boxes with scores greater than score_thr will be kept.

Returns: A dictionary containing: images: Visulaized images. img_metas: List of length number of test images,

dict of image meta info, containing filename, img_shape, origin_img_shape, scale_factor and so on.

class easycv.datasets.detection.DetImagesMixDataset(data_source, pipeline, dynamic_scale=None, skip_type_keys=None, profiling=False, classes=None, yolo_format=True, label_padding=True)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

A wrapper of multiple images mixed dataset.

Suitable for training on multiple images mixed data augmentation like mosaic and mixup. For the augmentation pipeline of mixed image data, the get_indexes method needs to be provided to obtain the image indexes, and you can set skip_flags to change the pipeline running process. At the same time, we provide the dynamic_scale parameter to dynamically change the output image size.

output boxes format: cx, cy, w, h

Parameters

data_source (DetSourceCoco) – The dataset to be mixed.
pipeline (Sequence[dict]) – Sequence of transform object or config dict to be composed.
dynamic_scale (tuple[int], optional) – The image scale can be changed dynamically. Default to None.
skip_type_keys (list[str], optional) – Sequence of type string to be skip pipeline. Default to None.
label_padding – out labeling padding [N, 120, 5]

__init__(data_source, pipeline, dynamic_scale=None, skip_type_keys=None, profiling=False, classes=None, yolo_format=True, label_padding=True)[source]¶: Args: data_source: Data_source config dict pipeline: Pipeline config list profiling: If set True, will print pipeline time classes: A list of class names, used in evaluation for result and groundtruth visualization

update_skip_type_keys(skip_type_keys)[source]¶

Update skip_type_keys. It is called by an external hook.

Parameters: skip_type_keys (list[str], optional) – Sequence of type string to be skip pipeline.

update_dynamic_scale(dynamic_scale)[source]¶

Update dynamic_scale. It is called by an external hook.

Parameters: dynamic_scale (tuple[int]) – The image scale can be changed dynamically.

results2json(results, outfile_prefix)[source]¶

Dump the detection results to a COCO style json file.

There are 3 types of results: proposals, bbox predictions, mask predictions, and they have different data types. This method will automatically recognize the type, and dump them to json files.

Parameters

results (list[list | tuple | ndarray]) – Testing results of the dataset.
outfile_prefix (str) – The filename prefix of the json files. If the prefix is “somepath/xxx”, the json files will be named “somepath/xxx.bbox.json”, “somepath/xxx.segm.json”, “somepath/xxx.proposal.json”.

Returns

str]: Possible keys are “bbox”, “segm”, “proposal”, and values are corresponding filenames.

Return type

dict[str

format_results(results, jsonfile_prefix=None, **kwargs)[source]¶

Format the results to json (standard format for COCO evaluation).

Parameters

results (list[tuple | numpy.ndarray]) – Testing results of the dataset.
jsonfile_prefix (str | None) – The prefix of json files. It includes the file path and the prefix of filename, e.g., “a/b/prefix”. If not specified, a temp file will be created. Default: None.

Returns

(result_files, tmp_dir), result_files is a dict containing the json filepaths, tmp_dir is the temporal directory created for saving json files when jsonfile_prefix is not specified.

Return type

tuple

Subpackages¶

easycv.datasets.detection.data_sources package¶

class easycv.datasets.detection.data_sources.DetSourceCoco(ann_file, img_prefix, pipeline, filter_empty_gt=False, classes=None, iscrowd=False)[source]¶

Bases: object

coco data source

__init__(ann_file, img_prefix, pipeline, filter_empty_gt=False, classes=None, iscrowd=False)[source]¶

Parameters

ann_file – Path of annotation file.
img_prefix – coco path prefix
filter_empty_gt – bool, if filter empty gt
iscrowd – when traing setted as False, when val setted as Tre

get_length()[source]¶: Total number of samples of data.

load_annotations(ann_file)[source]¶

Load annotation from COCO style annotation file.

Parameters: ann_file (str) – Path of annotation file.
Returns: Annotation info from COCO api.
Return type: list[dict]

get_ann_info(idx)[source]¶

Get COCO annotation by index.

Parameters: idx (int) – Index of data.
Returns: Annotation info of specified index.
Return type: dict

get_cat_ids(idx)[source]¶

Get COCO category ids by index.

Parameters: idx (int) – Index of data.
Returns: All categories in the image of specified index.
Return type: list[int]

xyxy2xywh(bbox)[source]¶

Convert xyxy style bounding boxes to xywh style for COCO evaluation.

Parameters: bbox (numpy.ndarray) – The bounding boxes, shape (4, ), in xyxy order.
Returns: The converted bounding boxes, in xywh order.
Return type: list[float]

pre_pipeline(results)[source]¶: Prepare results dict for pipeline.

prepare_train_img(idx)[source]¶

Get training data and annotations after pipeline.

Parameters: idx (int) – Index of data.
Returns: Training data and annotation after pipeline with new keys introduced by pipeline.
Return type: dict

get_sample(idx)[source]¶

class easycv.datasets.detection.data_sources.DetSourcePAI(path, classes=[], cache_at_init=False, cache_on_the_fly=False, **kwargs)[source]¶

Bases: easycv.datasets.detection.data_sources.voc.DetSourceVOC

data format please refer to: https://help.aliyun.com/document_detail/311173.html

__init__(path, classes=[], cache_at_init=False, cache_on_the_fly=False, **kwargs)[source]¶

Parameters

path – Path of manifest path with pai label format
classes – classes list
cache_at_init – if set True, will cache in memory in __init__ for faster training
cache_on_the_fly – if set True, will cache in memroy during training

get_source_info(row_str)[source]¶

class easycv.datasets.detection.data_sources.DetSourceRaw(img_root_path, label_root_path, cache_at_init=False, cache_on_the_fly=False, delimeter=' ', **kwargs)[source]¶

Bases: easycv.datasets.detection.data_sources.voc.DetSourceVOC

data dir is as follows: ``` |- data_dir

|-images
|-1.jpg |-…

|-labels
|-1.txt |-…

` Label txt file is as follows: The first column is the label id, and columns 2 to 5 are coordinates relative to the image width and height [x_center, y_center, bbox_w, bbox_h]. ` 15 0.519398 0.544087 0.476359 0.572061 2 0.501859 0.820726 0.996281 0.332178 … ``` .. rubric:: Example

data_source = DetSourceRaw(: img_root_path=’/your/data_dir/images’, label_root_path=’/your/data_dir/labels’,

)

__init__(img_root_path, label_root_path, cache_at_init=False, cache_on_the_fly=False, delimeter=' ', **kwargs)[source]¶

Parameters

img_root_path – images dir path
label_root_path – labels dir path
cache_at_init – if set True, will cache in memory in __init__ for faster training
cache_on_the_fly – if set True, will cache in memroy during training

get_source_info(img_and_label)[source]¶

class easycv.datasets.detection.data_sources.DetSourceVOC(path, classes=[], img_root_path=None, label_root_path=None, cache_at_init=False, cache_on_the_fly=False, img_suffix='.jpg', label_suffix='.xml', **kwargs)[source]¶

Bases: object

data dir is as follows: ``` |- voc_data

|-ImageSets

|-Main
|-train.txt |-…

|-JPEGImages
|-00001.jpg |-…

|-Annotations
|-00001.xml |-…

``` Example1:

data_source = DetSourceVOC(
path=’/your/voc_data/ImageSets/Main/train.txt’, classes=${VOC_CLASSES},

)

Example1:

data_source = DetSourceVOC(: path=’/your/voc_data/train.txt’, classes=${VOC_CLASSES}, img_root_path=’/your/voc_data/images’, img_root_path=’/your/voc_data/annotations’

)

__init__(path, classes=[], img_root_path=None, label_root_path=None, cache_at_init=False, cache_on_the_fly=False, img_suffix='.jpg', label_suffix='.xml', **kwargs)[source]¶

Parameters

path – path of img id list file in ImageSets/Main/
classes – classes list
img_root_path – image dir path, if None, default to detect the image dir by the relative path of the path according to the VOC data format.
label_root_path – label dir path, if None, default to detect the label dir by the relative path of the path according to the VOC data format.
cache_at_init – if set True, will cache in memory in __init__ for faster training
cache_on_the_fly – if set True, will cache in memroy during training
img_suffix – suffix of image file
label_suffix – suffix of label file

get_source_info(img_and_label)[source]¶

build_sample(data)[source]¶

build_samples(iterable)[source]¶

load_image(img_path)[source]¶

get_length()[source]¶

get_ann_info(idx)[source]¶: Get raw annotation info, include bounding boxes, labels and so on. bboxes format is as [x1, y1, x2, y2] without normalization.

get_sample(idx)[source]¶

Submodules¶

easycv.datasets.detection.data_sources.coco module¶

class easycv.datasets.detection.data_sources.coco.DetSourceCoco(ann_file, img_prefix, pipeline, filter_empty_gt=False, classes=None, iscrowd=False)[source]¶

Bases: object

coco data source

__init__(ann_file, img_prefix, pipeline, filter_empty_gt=False, classes=None, iscrowd=False)[source]¶

Parameters

ann_file – Path of annotation file.
img_prefix – coco path prefix
filter_empty_gt – bool, if filter empty gt
iscrowd – when traing setted as False, when val setted as Tre

get_length()[source]¶: Total number of samples of data.

load_annotations(ann_file)[source]¶

Load annotation from COCO style annotation file.

Parameters: ann_file (str) – Path of annotation file.
Returns: Annotation info from COCO api.
Return type: list[dict]

get_ann_info(idx)[source]¶

Get COCO annotation by index.

Parameters: idx (int) – Index of data.
Returns: Annotation info of specified index.
Return type: dict

get_cat_ids(idx)[source]¶

Get COCO category ids by index.

Parameters: idx (int) – Index of data.
Returns: All categories in the image of specified index.
Return type: list[int]

xyxy2xywh(bbox)[source]¶

Convert xyxy style bounding boxes to xywh style for COCO evaluation.

Parameters: bbox (numpy.ndarray) – The bounding boxes, shape (4, ), in xyxy order.
Returns: The converted bounding boxes, in xywh order.
Return type: list[float]

pre_pipeline(results)[source]¶: Prepare results dict for pipeline.

prepare_train_img(idx)[source]¶

Get training data and annotations after pipeline.

Parameters: idx (int) – Index of data.
Returns: Training data and annotation after pipeline with new keys introduced by pipeline.
Return type: dict

get_sample(idx)[source]¶

easycv.datasets.detection.data_sources.pai_format module¶

easycv.datasets.detection.data_sources.pai_format.get_prior_task_id(keys)[source]¶: “The task id ends with check is the highest priority.

easycv.datasets.detection.data_sources.pai_format.is_itag_v2(row)[source]¶: The keyword of the data source is picUrl in v1, but is source in v2

easycv.datasets.detection.data_sources.pai_format.parser_manifest_row_str(row_str)[source]¶

class easycv.datasets.detection.data_sources.pai_format.DetSourcePAI(path, classes=[], cache_at_init=False, cache_on_the_fly=False, **kwargs)[source]¶

Bases: easycv.datasets.detection.data_sources.voc.DetSourceVOC

data format please refer to: https://help.aliyun.com/document_detail/311173.html

__init__(path, classes=[], cache_at_init=False, cache_on_the_fly=False, **kwargs)[source]¶

Parameters

path – Path of manifest path with pai label format
classes – classes list
cache_at_init – if set True, will cache in memory in __init__ for faster training
cache_on_the_fly – if set True, will cache in memroy during training

get_source_info(row_str)[source]¶

easycv.datasets.detection.data_sources.raw module¶

class easycv.datasets.detection.data_sources.raw.DetSourceRaw(img_root_path, label_root_path, cache_at_init=False, cache_on_the_fly=False, delimeter=' ', **kwargs)[source]¶

Bases: easycv.datasets.detection.data_sources.voc.DetSourceVOC

data dir is as follows: ``` |- data_dir

|-images
|-1.jpg |-…

|-labels
|-1.txt |-…

` Label txt file is as follows: The first column is the label id, and columns 2 to 5 are coordinates relative to the image width and height [x_center, y_center, bbox_w, bbox_h]. ` 15 0.519398 0.544087 0.476359 0.572061 2 0.501859 0.820726 0.996281 0.332178 … ``` .. rubric:: Example

data_source = DetSourceRaw(: img_root_path=’/your/data_dir/images’, label_root_path=’/your/data_dir/labels’,

)

__init__(img_root_path, label_root_path, cache_at_init=False, cache_on_the_fly=False, delimeter=' ', **kwargs)[source]¶

Parameters

img_root_path – images dir path
label_root_path – labels dir path
cache_at_init – if set True, will cache in memory in __init__ for faster training
cache_on_the_fly – if set True, will cache in memroy during training

get_source_info(img_and_label)[source]¶

easycv.datasets.detection.data_sources.utils module¶

easycv.datasets.detection.data_sources.utils.exif_size(img)[source]¶

easycv.datasets.detection.data_sources.voc module¶

easycv.datasets.detection.data_sources.voc.parse_xml(xml_path, classes)[source]¶

class easycv.datasets.detection.data_sources.voc.DetSourceVOC(path, classes=[], img_root_path=None, label_root_path=None, cache_at_init=False, cache_on_the_fly=False, img_suffix='.jpg', label_suffix='.xml', **kwargs)[source]¶

Bases: object

data dir is as follows: ``` |- voc_data

|-ImageSets

|-Main
|-train.txt |-…

|-JPEGImages
|-00001.jpg |-…

|-Annotations
|-00001.xml |-…

``` Example1:

data_source = DetSourceVOC(
path=’/your/voc_data/ImageSets/Main/train.txt’, classes=${VOC_CLASSES},

)

Example1:

data_source = DetSourceVOC(: path=’/your/voc_data/train.txt’, classes=${VOC_CLASSES}, img_root_path=’/your/voc_data/images’, img_root_path=’/your/voc_data/annotations’

)

__init__(path, classes=[], img_root_path=None, label_root_path=None, cache_at_init=False, cache_on_the_fly=False, img_suffix='.jpg', label_suffix='.xml', **kwargs)[source]¶

Parameters

path – path of img id list file in ImageSets/Main/
classes – classes list
img_root_path – image dir path, if None, default to detect the image dir by the relative path of the path according to the VOC data format.
label_root_path – label dir path, if None, default to detect the label dir by the relative path of the path according to the VOC data format.
cache_at_init – if set True, will cache in memory in __init__ for faster training
cache_on_the_fly – if set True, will cache in memroy during training
img_suffix – suffix of image file
label_suffix – suffix of label file

get_source_info(img_and_label)[source]¶

build_sample(data)[source]¶

build_samples(iterable)[source]¶

load_image(img_path)[source]¶

get_length()[source]¶

get_ann_info(idx)[source]¶: Get raw annotation info, include bounding boxes, labels and so on. bboxes format is as [x1, y1, x2, y2] without normalization.

get_sample(idx)[source]¶

easycv.datasets.detection.pipelines package¶

class easycv.datasets.detection.pipelines.MMToTensor[source]¶

Bases: object

Transform image to Tensor.

Required key: ‘img’. Modifies key: ‘img’.

Parameters: results (dict) – contain all information about training.

class easycv.datasets.detection.pipelines.NormalizeTensor(mean, std)[source]¶

Bases: object

Normalize the Tensor image (CxHxW), with mean and std.

Required key: ‘img’. Modifies key: ‘img’.

Parameters

mean (list[float]) – Mean values of 3 channels.
std (list[float]) – Std values of 3 channels.

__init__(mean, std)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.detection.pipelines.MMMosaic(img_scale=(640, 640), center_ratio_range=(0.5, 1.5), pad_val=114)[source]¶

Bases: object

Mosaic augmentation.

Given 4 images, mosaic transform combines them into one output image. The output image is composed of the parts from each sub- image.

                   mosaic transform
                      center_x
           +------------------------------+
           |       pad        |  pad      |
           |      +-----------+           |
           |      |           |           |
           |      |  image1   |--------+  |
           |      |           |        |  |
           |      |           | image2 |  |
center_y   |----+-------------+-----------|
           |    |   cropped   |           |
           |pad |   image3    |  image4   |
           |    |             |           |
           +----|-------------+-----------+
                |             |
                +-------------+

The mosaic transform steps are as follows:

    1. Choose the mosaic center as the intersections of 4 images
    2. Get the left top image according to the index, and randomly
       sample another 3 images from the custom dataset.
    3. Sub image will be cropped if image is larger than mosaic patch

Parameters

img_scale (Sequence[int]) – Image size after mosaic pipeline of single image. Default to (640, 640).
center_ratio_range (Sequence[float]) – Center ratio range of mosaic output. Default to (0.5, 1.5).
pad_val (int) – Pad value. Default to 114.

__init__(img_scale=(640, 640), center_ratio_range=(0.5, 1.5), pad_val=114)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_indexes(dataset)[source]¶

Call function to collect indexes.

Parameters: dataset (DetImagesMixDataset) – The dataset.
Returns: indexes.
Return type: list

class easycv.datasets.detection.pipelines.MMMixUp(img_scale=(640, 640), ratio_range=(0.5, 1.5), flip_ratio=0.5, pad_val=114, max_iters=15, min_bbox_size=5, min_area_ratio=0.2, max_aspect_ratio=20)[source]¶

Bases: object

MixUp data augmentation.

                    mixup transform
           +------------------------------+
           | mixup image   |              |
           |      +--------|--------+     |
           |      |        |        |     |
           |---------------+        |     |
           |      |                 |     |
           |      |      image      |     |
           |      |                 |     |
           |      |                 |     |
           |      |-----------------+     |
           |             pad              |
           +------------------------------+

The mixup transform steps are as follows::

   1. Another random image is picked by dataset and embedded in
      the top left patch(after padding and resizing)
   2. The target of mixup transform is the weighted average of mixup
      image and origin image.

Parameters

img_scale (Sequence[int]) – Image output size after mixup pipeline. Default: (640, 640).
ratio_range (Sequence[float]) – Scale ratio of mixup image. Default: (0.5, 1.5).
flip_ratio (float) – Horizontal flip ratio of mixup image. Default: 0.5.
pad_val (int) – Pad value. Default: 114.
max_iters (int) – The maximum number of iterations. If the number of iterations is greater than max_iters, but gt_bbox is still empty, then the iteration is terminated. Default: 15.
min_bbox_size (float) – Width and height threshold to filter bboxes. If the height or width of a box is smaller than this value, it will be removed. Default: 5.
min_area_ratio (float) – Threshold of area ratio between original bboxes and wrapped bboxes. If smaller than this value, the box will be removed. Default: 0.2.
max_aspect_ratio (float) – Aspect ratio of width and height threshold to filter bboxes. If max(h/w, w/h) larger than this value, the box will be removed. Default: 20.

__init__(img_scale=(640, 640), ratio_range=(0.5, 1.5), flip_ratio=0.5, pad_val=114, max_iters=15, min_bbox_size=5, min_area_ratio=0.2, max_aspect_ratio=20)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_indexes(dataset)[source]¶

Call function to collect indexes.

Parameters: dataset (DetImagesMixDataset) – The dataset.
Returns: indexes.
Return type: list

class easycv.datasets.detection.pipelines.MMRandomAffine(max_rotate_degree=10.0, max_translate_ratio=0.1, scaling_ratio_range=(0.5, 1.5), max_shear_degree=2.0, border=(0, 0), border_val=(114, 114, 114), min_bbox_size=2, min_area_ratio=0.2, max_aspect_ratio=20)[source]¶

Bases: object

Random affine transform data augmentation. for yolox

This operation randomly generates affine transform matrix which including rotation, translation, shear and scaling transforms.

Parameters

max_rotate_degree (float) – Maximum degrees of rotation transform. Default: 10.
max_translate_ratio (float) – Maximum ratio of translation. Default: 0.1.
scaling_ratio_range (tuple[float]) – Min and max ratio of scaling transform. Default: (0.5, 1.5).
max_shear_degree (float) – Maximum degrees of shear transform. Default: 2.
border (tuple[int]) – Distance from height and width sides of input image to adjust output shape. Only used in mosaic dataset. Default: (0, 0).
border_val (tuple[int]) – Border padding values of 3 channels. Default: (114, 114, 114).
min_bbox_size (float) – Width and height threshold to filter bboxes. If the height or width of a box is smaller than this value, it will be removed. Default: 2.
min_area_ratio (float) – Threshold of area ratio between original bboxes and wrapped bboxes. If smaller than this value, the box will be removed. Default: 0.2.
max_aspect_ratio (float) – Aspect ratio of width and height threshold to filter bboxes. If max(h/w, w/h) larger than this value, the box will be removed.

__init__(max_rotate_degree=10.0, max_translate_ratio=0.1, scaling_ratio_range=(0.5, 1.5), max_shear_degree=2.0, border=(0, 0), border_val=(114, 114, 114), min_bbox_size=2, min_area_ratio=0.2, max_aspect_ratio=20)[source]¶: Initialize self. See help(type(self)) for accurate signature.

filter_gt_bboxes(origin_bboxes, wrapped_bboxes)[source]¶

class easycv.datasets.detection.pipelines.MMPhotoMetricDistortion(brightness_delta=32, contrast_range=(0.5, 1.5), saturation_range=(0.5, 1.5), hue_delta=18)[source]¶

Bases: object

Apply photometric distortion to image sequentially, every transformation is applied with a probability of 0.5. The position of random contrast is in second or second to last.

random brightness
random contrast (mode 0)
convert color from BGR to HSV
random saturation
random hue
convert color from HSV to BGR
random contrast (mode 1)
randomly swap channels

Parameters

brightness_delta (int) – delta of brightness.
contrast_range (tuple) – range of contrast.
saturation_range (tuple) – range of saturation.
hue_delta (int) – delta of hue.

__init__(brightness_delta=32, contrast_range=(0.5, 1.5), saturation_range=(0.5, 1.5), hue_delta=18)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.detection.pipelines.MMResize(img_scale=None, multiscale_mode='range', ratio_range=None, keep_ratio=True, bbox_clip_border=True, backend='cv2', override=False)[source]¶

Bases: object

Resize images & bbox & mask.

This transform resizes the input image to some scale. Bboxes and masks are then resized with the same scale factor. If the input dict contains the key “scale”, then the scale in the input dict is used, otherwise the specified scale in the init method is used. If the input dict contains the key “scale_factor” (if MultiScaleFlipAug does not give img_scale but scale_factor), the actual scale will be computed by image shape and scale_factor.

img_scale can either be a tuple (single-scale) or a list of tuple (multi-scale). There are 3 multiscale modes:

ratio_range is not None: randomly sample a ratio from the ratio range and multiply it with the image scale.
ratio_range is None and multiscale_mode == "range": randomly sample a scale from the multiscale range.
ratio_range is None and multiscale_mode == "value": randomly sample a scale from multiple scales.

Parameters

img_scale (tuple or list[tuple]) – Images scales for resizing.
multiscale_mode (str) – Either “range” or “value”.
ratio_range (tuple[float]) – (min_ratio, max_ratio)
keep_ratio (bool) – Whether to keep the aspect ratio when resizing the image.
bbox_clip_border (bool, optional) – Whether clip the objects outside the border of the image. Defaults to True.
backend (str) – Image resize backend, choices are ‘cv2’ and ‘pillow’. These two backends generates slightly different results. Defaults to ‘cv2’.
override (bool, optional) – Whether to override scale and scale_factor so as to call resize twice. Default False. If True, after the first resizing, the existed scale and scale_factor will be ignored so the second resizing can be allowed. This option is a work-around for multiple times of resize in DETR. Defaults to False.

__init__(img_scale=None, multiscale_mode='range', ratio_range=None, keep_ratio=True, bbox_clip_border=True, backend='cv2', override=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

static random_select(img_scales)[source]¶

Randomly select an img_scale from given candidates.

Parameters: img_scales (list[tuple]) – Images scales for selection.
Returns: Returns a tuple (img_scale, scale_dix), where img_scale is the selected image scale and scale_idx is the selected index in the given candidates.
Return type: (tuple, int)

static random_sample(img_scales)[source]¶

Randomly sample an img_scale when multiscale_mode=='range'.

Parameters: img_scales (list[tuple]) – Images scale range for sampling. There must be two tuples in img_scales, which specify the lower and upper bound of image scales.
Returns: Returns a tuple (img_scale, None), where img_scale is sampled scale and None is just a placeholder to be consistent with random_select().
Return type: (tuple, None)

static random_sample_ratio(img_scale, ratio_range)[source]¶

Randomly sample an img_scale when ratio_range is specified.

A ratio will be randomly sampled from the range specified by ratio_range. Then it would be multiplied with img_scale to generate sampled scale.

Parameters

img_scale (tuple) – Images scale base to multiply with ratio.
ratio_range (tuple[float]) – The minimum and maximum ratio to scale the img_scale.

Returns

Returns a tuple (scale, None), where scale is sampled ratio multiplied with img_scale and None is just a placeholder to be consistent with random_select().

Return type

(tuple, None)

class easycv.datasets.detection.pipelines.MMRandomFlip(flip_ratio=None, direction='horizontal')[source]¶

Bases: object

Flip the image & bbox & mask.

If the input dict contains the key “flip”, then the flag will be used, otherwise it will be randomly decided by a ratio specified in the init method.

When random flip is enabled, flip_ratio/direction can either be a float/string or tuple of float/string. There are 3 flip modes:

flip_ratio is float, direction is string: the image will be
direction``ly flipped with probability of ``flip_ratio . E.g., flip_ratio=0.5, direction='horizontal', then image will be horizontally flipped with probability of 0.5.
flip_ratio is float, direction is list of string: the image wil
be direction[i]``ly flipped with probability of ``flip_ratio/len(direction). E.g., flip_ratio=0.5, direction=['horizontal', 'vertical'], then image will be horizontally flipped with probability of 0.25, vertically with probability of 0.25.
flip_ratio is list of float, direction is list of string:
given len(flip_ratio) == len(direction), the image wil be direction[i]``ly flipped with probability of ``flip_ratio[i]. E.g., flip_ratio=[0.3, 0.5], direction=['horizontal', 'vertical'], then image will be horizontally flipped with probability of 0.3, vertically with probability of 0.5.

Parameters

flip_ratio (float | list[float], optional) – The flipping probability. Default: None.
direction (str | list[str], optional) – The flipping direction. Options are ‘horizontal’, ‘vertical’, ‘diagonal’. Default: ‘horizontal’. If input is a list, the length must equal flip_ratio. Each element in flip_ratio indicates the flip probability of corresponding direction.

__init__(flip_ratio=None, direction='horizontal')[source]¶: Initialize self. See help(type(self)) for accurate signature.

bbox_flip(bboxes, img_shape, direction)[source]¶

Flip bboxes horizontally.

Parameters

bboxes (numpy.ndarray) – Bounding boxes, shape (…, 4*k)
img_shape (tuple[int]) – Image shape (height, width)
direction (str) – Flip direction. Options are ‘horizontal’, ‘vertical’.

Returns

Flipped bounding boxes.

Return type

numpy.ndarray

class easycv.datasets.detection.pipelines.MMPad(size=None, size_divisor=None, pad_to_square=False, pad_val=0)[source]¶

Bases: object

Pad the image & mask.

There are two padding modes: (1) pad to a fixed size and (2) pad to the minimum size that is divisible by some number. Added keys are “pad_shape”, “pad_fixed_size”, “pad_size_divisor”,

Parameters

size (tuple, optional) – Fixed padding size.
size_divisor (int, optional) – The divisor of padded size.
pad_to_square (bool) – Whether to pad the image into a square. Currently only used for YOLOX. Default: False.
pad_val (float, optional) – Padding value, 0 by default.

__init__(size=None, size_divisor=None, pad_to_square=False, pad_val=0)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.detection.pipelines.MMNormalize(mean, std, to_rgb=True)[source]¶

Bases: object

Normalize the image.

Added key is “img_norm_cfg”.

Parameters

mean (sequence) – Mean values of 3 channels.
std (sequence) – Std values of 3 channels.
to_rgb (bool) – Whether to convert the image from BGR to RGB, default is true.

__init__(mean, std, to_rgb=True)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.detection.pipelines.LoadImageFromFile(to_float32=False, color_type='color', file_client_args={'backend': 'disk'})[source]¶

Bases: object

Load an image from file.

Required keys are “img_prefix” and “img_info” (a dict that must contain the key “filename”). Added or updated keys are “filename”, “img”, “img_shape”, “ori_shape” (same as img_shape), “pad_shape” (same as img_shape), “scale_factor” (1.0) and “img_norm_cfg” (means=0 and stds=1).

Parameters

to_float32 (bool) – Whether to convert the loaded image to a float32 numpy array. If set to False, the loaded image is an uint8 array. Defaults to False.
color_type (str) – The flag argument for mmcv.imfrombytes(). Defaults to ‘color’.
file_client_args (dict) – Arguments to instantiate a FileClient. See mmcv.fileio.FileClient for details. Defaults to dict(backend='disk').

__init__(to_float32=False, color_type='color', file_client_args={'backend': 'disk'})[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.detection.pipelines.LoadMultiChannelImageFromFiles(to_float32=False, color_type='unchanged', file_client_args={'backend': 'disk'})[source]¶

Bases: object

Load multi-channel images from a list of separate channel files.

Required keys are “img_prefix” and “img_info” (a dict that must contain the key “filename”, which is expected to be a list of filenames). Added or updated keys are “filename”, “img”, “img_shape”, “ori_shape” (same as img_shape), “pad_shape” (same as img_shape), “scale_factor” (1.0) and “img_norm_cfg” (means=0 and stds=1).

Parameters

to_float32 (bool) – Whether to convert the loaded image to a float32 numpy array. If set to False, the loaded image is an uint8 array. Defaults to False.
color_type (str) – The flag argument for mmcv.imfrombytes(). Defaults to ‘color’.
file_client_args (dict) – Arguments to instantiate a FileClient. See mmcv.fileio.FileClient for details. Defaults to dict(backend='disk').

__init__(to_float32=False, color_type='unchanged', file_client_args={'backend': 'disk'})[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.detection.pipelines.LoadAnnotations(with_bbox=True, with_label=True, with_mask=False, with_seg=False, poly2mask=True, file_client_args={'backend': 'disk'})[source]¶

Bases: object

Load multiple types of annotations.

Parameters

with_bbox (bool) – Whether to parse and load the bbox annotation. Default: True.
with_label (bool) – Whether to parse and load the label annotation. Default: True.
with_mask (bool) – Whether to parse and load the mask annotation. Default: False.
with_seg (bool) – Whether to parse and load the semantic segmentation annotation. Default: False.
poly2mask (bool) – Whether to convert the instance masks from polygons to bitmaps. Default: True.
file_client_args (dict) – Arguments to instantiate a FileClient. See mmcv.fileio.FileClient for details. Defaults to dict(backend='disk').

__init__(with_bbox=True, with_label=True, with_mask=False, with_seg=False, poly2mask=True, file_client_args={'backend': 'disk'})[source]¶: Initialize self. See help(type(self)) for accurate signature.

process_polygons(polygons)[source]¶

Convert polygons to list of ndarray and filter invalid polygons.

Parameters: polygons (list[list]) – Polygons of one instance.
Returns: Processed polygons.
Return type: list[numpy.ndarray]

class easycv.datasets.detection.pipelines.MMMultiScaleFlipAug(transforms, img_scale=None, scale_factor=None, flip=False, flip_direction='horizontal')[source]¶

Bases: object

Test-time augmentation with multiple scales and flipping.

An example configuration is as followed:

img_scale=[(1333, 400), (1333, 800)],
flip=True,
transforms=[
    dict(type='Resize', keep_ratio=True),
    dict(type='RandomFlip'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='Collect', keys=['img']),
]

After MultiScaleFLipAug with above configuration, the results are wrapped into lists of the same length as followed:

dict(
    img=[...],
    img_shape=[...],
    scale=[(1333, 400), (1333, 400), (1333, 800), (1333, 800)]
    flip=[False, True, False, True]
    ...
)

Parameters

transforms (list[dict]) – Transforms to apply in each augmentation.
img_scale (tuple | list[tuple] | None) – Images scales for resizing.
scale_factor (float | list[float] | None) – Scale factors for resizing.
flip (bool) – Whether apply flip augmentation. Default: False.
flip_direction (str | list[str]) – Flip augmentation directions, options are “horizontal”, “vertical” and “diagonal”. If flip_direction is a list, multiple flip augmentations will be applied. It has no effect when flip == False. Default: “horizontal”.

__init__(transforms, img_scale=None, scale_factor=None, flip=False, flip_direction='horizontal')[source]¶: Initialize self. See help(type(self)) for accurate signature.

Submodules¶

easycv.datasets.detection.pipelines.mm_transforms module¶

class easycv.datasets.detection.pipelines.mm_transforms.MMToTensor[source]¶

Bases: object

Transform image to Tensor.

Required key: ‘img’. Modifies key: ‘img’.

Parameters: results (dict) – contain all information about training.

class easycv.datasets.detection.pipelines.mm_transforms.NormalizeTensor(mean, std)[source]¶

Bases: object

Normalize the Tensor image (CxHxW), with mean and std.

Required key: ‘img’. Modifies key: ‘img’.

Parameters

mean (list[float]) – Mean values of 3 channels.
std (list[float]) – Std values of 3 channels.

__init__(mean, std)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.detection.pipelines.mm_transforms.MMMosaic(img_scale=(640, 640), center_ratio_range=(0.5, 1.5), pad_val=114)[source]¶

Bases: object

Mosaic augmentation.

Given 4 images, mosaic transform combines them into one output image. The output image is composed of the parts from each sub- image.

                   mosaic transform
                      center_x
           +------------------------------+
           |       pad        |  pad      |
           |      +-----------+           |
           |      |           |           |
           |      |  image1   |--------+  |
           |      |           |        |  |
           |      |           | image2 |  |
center_y   |----+-------------+-----------|
           |    |   cropped   |           |
           |pad |   image3    |  image4   |
           |    |             |           |
           +----|-------------+-----------+
                |             |
                +-------------+

The mosaic transform steps are as follows:

    1. Choose the mosaic center as the intersections of 4 images
    2. Get the left top image according to the index, and randomly
       sample another 3 images from the custom dataset.
    3. Sub image will be cropped if image is larger than mosaic patch

Parameters

img_scale (Sequence[int]) – Image size after mosaic pipeline of single image. Default to (640, 640).
center_ratio_range (Sequence[float]) – Center ratio range of mosaic output. Default to (0.5, 1.5).
pad_val (int) – Pad value. Default to 114.

__init__(img_scale=(640, 640), center_ratio_range=(0.5, 1.5), pad_val=114)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_indexes(dataset)[source]¶

Call function to collect indexes.

Parameters: dataset (DetImagesMixDataset) – The dataset.
Returns: indexes.
Return type: list

class easycv.datasets.detection.pipelines.mm_transforms.MMMixUp(img_scale=(640, 640), ratio_range=(0.5, 1.5), flip_ratio=0.5, pad_val=114, max_iters=15, min_bbox_size=5, min_area_ratio=0.2, max_aspect_ratio=20)[source]¶

Bases: object

MixUp data augmentation.

                    mixup transform
           +------------------------------+
           | mixup image   |              |
           |      +--------|--------+     |
           |      |        |        |     |
           |---------------+        |     |
           |      |                 |     |
           |      |      image      |     |
           |      |                 |     |
           |      |                 |     |
           |      |-----------------+     |
           |             pad              |
           +------------------------------+

The mixup transform steps are as follows::

   1. Another random image is picked by dataset and embedded in
      the top left patch(after padding and resizing)
   2. The target of mixup transform is the weighted average of mixup
      image and origin image.

Parameters

img_scale (Sequence[int]) – Image output size after mixup pipeline. Default: (640, 640).
ratio_range (Sequence[float]) – Scale ratio of mixup image. Default: (0.5, 1.5).
flip_ratio (float) – Horizontal flip ratio of mixup image. Default: 0.5.
pad_val (int) – Pad value. Default: 114.
max_iters (int) – The maximum number of iterations. If the number of iterations is greater than max_iters, but gt_bbox is still empty, then the iteration is terminated. Default: 15.
min_bbox_size (float) – Width and height threshold to filter bboxes. If the height or width of a box is smaller than this value, it will be removed. Default: 5.
min_area_ratio (float) – Threshold of area ratio between original bboxes and wrapped bboxes. If smaller than this value, the box will be removed. Default: 0.2.
max_aspect_ratio (float) – Aspect ratio of width and height threshold to filter bboxes. If max(h/w, w/h) larger than this value, the box will be removed. Default: 20.

__init__(img_scale=(640, 640), ratio_range=(0.5, 1.5), flip_ratio=0.5, pad_val=114, max_iters=15, min_bbox_size=5, min_area_ratio=0.2, max_aspect_ratio=20)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_indexes(dataset)[source]¶

Call function to collect indexes.

Parameters: dataset (DetImagesMixDataset) – The dataset.
Returns: indexes.
Return type: list

class easycv.datasets.detection.pipelines.mm_transforms.MMRandomAffine(max_rotate_degree=10.0, max_translate_ratio=0.1, scaling_ratio_range=(0.5, 1.5), max_shear_degree=2.0, border=(0, 0), border_val=(114, 114, 114), min_bbox_size=2, min_area_ratio=0.2, max_aspect_ratio=20)[source]¶

Bases: object

Random affine transform data augmentation. for yolox

This operation randomly generates affine transform matrix which including rotation, translation, shear and scaling transforms.

Parameters

max_rotate_degree (float) – Maximum degrees of rotation transform. Default: 10.
max_translate_ratio (float) – Maximum ratio of translation. Default: 0.1.
scaling_ratio_range (tuple[float]) – Min and max ratio of scaling transform. Default: (0.5, 1.5).
max_shear_degree (float) – Maximum degrees of shear transform. Default: 2.
border (tuple[int]) – Distance from height and width sides of input image to adjust output shape. Only used in mosaic dataset. Default: (0, 0).
border_val (tuple[int]) – Border padding values of 3 channels. Default: (114, 114, 114).
min_bbox_size (float) – Width and height threshold to filter bboxes. If the height or width of a box is smaller than this value, it will be removed. Default: 2.
min_area_ratio (float) – Threshold of area ratio between original bboxes and wrapped bboxes. If smaller than this value, the box will be removed. Default: 0.2.
max_aspect_ratio (float) – Aspect ratio of width and height threshold to filter bboxes. If max(h/w, w/h) larger than this value, the box will be removed.

__init__(max_rotate_degree=10.0, max_translate_ratio=0.1, scaling_ratio_range=(0.5, 1.5), max_shear_degree=2.0, border=(0, 0), border_val=(114, 114, 114), min_bbox_size=2, min_area_ratio=0.2, max_aspect_ratio=20)[source]¶: Initialize self. See help(type(self)) for accurate signature.

filter_gt_bboxes(origin_bboxes, wrapped_bboxes)[source]¶

class easycv.datasets.detection.pipelines.mm_transforms.MMPhotoMetricDistortion(brightness_delta=32, contrast_range=(0.5, 1.5), saturation_range=(0.5, 1.5), hue_delta=18)[source]¶

Bases: object

Apply photometric distortion to image sequentially, every transformation is applied with a probability of 0.5. The position of random contrast is in second or second to last.

random brightness
random contrast (mode 0)
convert color from BGR to HSV
random saturation
random hue
convert color from HSV to BGR
random contrast (mode 1)
randomly swap channels

Parameters

brightness_delta (int) – delta of brightness.
contrast_range (tuple) – range of contrast.
saturation_range (tuple) – range of saturation.
hue_delta (int) – delta of hue.

__init__(brightness_delta=32, contrast_range=(0.5, 1.5), saturation_range=(0.5, 1.5), hue_delta=18)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.detection.pipelines.mm_transforms.MMResize(img_scale=None, multiscale_mode='range', ratio_range=None, keep_ratio=True, bbox_clip_border=True, backend='cv2', override=False)[source]¶

Bases: object

Resize images & bbox & mask.

This transform resizes the input image to some scale. Bboxes and masks are then resized with the same scale factor. If the input dict contains the key “scale”, then the scale in the input dict is used, otherwise the specified scale in the init method is used. If the input dict contains the key “scale_factor” (if MultiScaleFlipAug does not give img_scale but scale_factor), the actual scale will be computed by image shape and scale_factor.

img_scale can either be a tuple (single-scale) or a list of tuple (multi-scale). There are 3 multiscale modes:

ratio_range is not None: randomly sample a ratio from the ratio range and multiply it with the image scale.
ratio_range is None and multiscale_mode == "range": randomly sample a scale from the multiscale range.
ratio_range is None and multiscale_mode == "value": randomly sample a scale from multiple scales.

Parameters

img_scale (tuple or list[tuple]) – Images scales for resizing.
multiscale_mode (str) – Either “range” or “value”.
ratio_range (tuple[float]) – (min_ratio, max_ratio)
keep_ratio (bool) – Whether to keep the aspect ratio when resizing the image.
bbox_clip_border (bool, optional) – Whether clip the objects outside the border of the image. Defaults to True.
backend (str) – Image resize backend, choices are ‘cv2’ and ‘pillow’. These two backends generates slightly different results. Defaults to ‘cv2’.
override (bool, optional) – Whether to override scale and scale_factor so as to call resize twice. Default False. If True, after the first resizing, the existed scale and scale_factor will be ignored so the second resizing can be allowed. This option is a work-around for multiple times of resize in DETR. Defaults to False.

__init__(img_scale=None, multiscale_mode='range', ratio_range=None, keep_ratio=True, bbox_clip_border=True, backend='cv2', override=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

static random_select(img_scales)[source]¶

Randomly select an img_scale from given candidates.

Parameters: img_scales (list[tuple]) – Images scales for selection.
Returns: Returns a tuple (img_scale, scale_dix), where img_scale is the selected image scale and scale_idx is the selected index in the given candidates.
Return type: (tuple, int)

static random_sample(img_scales)[source]¶

Randomly sample an img_scale when multiscale_mode=='range'.

Parameters: img_scales (list[tuple]) – Images scale range for sampling. There must be two tuples in img_scales, which specify the lower and upper bound of image scales.
Returns: Returns a tuple (img_scale, None), where img_scale is sampled scale and None is just a placeholder to be consistent with random_select().
Return type: (tuple, None)

static random_sample_ratio(img_scale, ratio_range)[source]¶

Randomly sample an img_scale when ratio_range is specified.

A ratio will be randomly sampled from the range specified by ratio_range. Then it would be multiplied with img_scale to generate sampled scale.

Parameters

img_scale (tuple) – Images scale base to multiply with ratio.
ratio_range (tuple[float]) – The minimum and maximum ratio to scale the img_scale.

Returns

Returns a tuple (scale, None), where scale is sampled ratio multiplied with img_scale and None is just a placeholder to be consistent with random_select().

Return type

(tuple, None)

class easycv.datasets.detection.pipelines.mm_transforms.MMRandomFlip(flip_ratio=None, direction='horizontal')[source]¶

Bases: object

Flip the image & bbox & mask.

If the input dict contains the key “flip”, then the flag will be used, otherwise it will be randomly decided by a ratio specified in the init method.

When random flip is enabled, flip_ratio/direction can either be a float/string or tuple of float/string. There are 3 flip modes:

flip_ratio is float, direction is string: the image will be
direction``ly flipped with probability of ``flip_ratio . E.g., flip_ratio=0.5, direction='horizontal', then image will be horizontally flipped with probability of 0.5.
flip_ratio is float, direction is list of string: the image wil
be direction[i]``ly flipped with probability of ``flip_ratio/len(direction). E.g., flip_ratio=0.5, direction=['horizontal', 'vertical'], then image will be horizontally flipped with probability of 0.25, vertically with probability of 0.25.
flip_ratio is list of float, direction is list of string:
given len(flip_ratio) == len(direction), the image wil be direction[i]``ly flipped with probability of ``flip_ratio[i]. E.g., flip_ratio=[0.3, 0.5], direction=['horizontal', 'vertical'], then image will be horizontally flipped with probability of 0.3, vertically with probability of 0.5.

Parameters

flip_ratio (float | list[float], optional) – The flipping probability. Default: None.
direction (str | list[str], optional) – The flipping direction. Options are ‘horizontal’, ‘vertical’, ‘diagonal’. Default: ‘horizontal’. If input is a list, the length must equal flip_ratio. Each element in flip_ratio indicates the flip probability of corresponding direction.

__init__(flip_ratio=None, direction='horizontal')[source]¶: Initialize self. See help(type(self)) for accurate signature.

bbox_flip(bboxes, img_shape, direction)[source]¶

Flip bboxes horizontally.

Parameters

bboxes (numpy.ndarray) – Bounding boxes, shape (…, 4*k)
img_shape (tuple[int]) – Image shape (height, width)
direction (str) – Flip direction. Options are ‘horizontal’, ‘vertical’.

Returns

Flipped bounding boxes.

Return type

numpy.ndarray

class easycv.datasets.detection.pipelines.mm_transforms.MMPad(size=None, size_divisor=None, pad_to_square=False, pad_val=0)[source]¶

Bases: object

Pad the image & mask.

There are two padding modes: (1) pad to a fixed size and (2) pad to the minimum size that is divisible by some number. Added keys are “pad_shape”, “pad_fixed_size”, “pad_size_divisor”,

Parameters

size (tuple, optional) – Fixed padding size.
size_divisor (int, optional) – The divisor of padded size.
pad_to_square (bool) – Whether to pad the image into a square. Currently only used for YOLOX. Default: False.
pad_val (float, optional) – Padding value, 0 by default.

__init__(size=None, size_divisor=None, pad_to_square=False, pad_val=0)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.detection.pipelines.mm_transforms.MMNormalize(mean, std, to_rgb=True)[source]¶

Bases: object

Normalize the image.

Added key is “img_norm_cfg”.

Parameters

mean (sequence) – Mean values of 3 channels.
std (sequence) – Std values of 3 channels.
to_rgb (bool) – Whether to convert the image from BGR to RGB, default is true.

__init__(mean, std, to_rgb=True)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.detection.pipelines.mm_transforms.LoadImageFromFile(to_float32=False, color_type='color', file_client_args={'backend': 'disk'})[source]¶

Bases: object

Load an image from file.

Required keys are “img_prefix” and “img_info” (a dict that must contain the key “filename”). Added or updated keys are “filename”, “img”, “img_shape”, “ori_shape” (same as img_shape), “pad_shape” (same as img_shape), “scale_factor” (1.0) and “img_norm_cfg” (means=0 and stds=1).

Parameters

to_float32 (bool) – Whether to convert the loaded image to a float32 numpy array. If set to False, the loaded image is an uint8 array. Defaults to False.
color_type (str) – The flag argument for mmcv.imfrombytes(). Defaults to ‘color’.
file_client_args (dict) – Arguments to instantiate a FileClient. See mmcv.fileio.FileClient for details. Defaults to dict(backend='disk').

__init__(to_float32=False, color_type='color', file_client_args={'backend': 'disk'})[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.detection.pipelines.mm_transforms.LoadMultiChannelImageFromFiles(to_float32=False, color_type='unchanged', file_client_args={'backend': 'disk'})[source]¶

Bases: object

Load multi-channel images from a list of separate channel files.

Required keys are “img_prefix” and “img_info” (a dict that must contain the key “filename”, which is expected to be a list of filenames). Added or updated keys are “filename”, “img”, “img_shape”, “ori_shape” (same as img_shape), “pad_shape” (same as img_shape), “scale_factor” (1.0) and “img_norm_cfg” (means=0 and stds=1).

Parameters

to_float32 (bool) – Whether to convert the loaded image to a float32 numpy array. If set to False, the loaded image is an uint8 array. Defaults to False.
color_type (str) – The flag argument for mmcv.imfrombytes(). Defaults to ‘color’.
file_client_args (dict) – Arguments to instantiate a FileClient. See mmcv.fileio.FileClient for details. Defaults to dict(backend='disk').

__init__(to_float32=False, color_type='unchanged', file_client_args={'backend': 'disk'})[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.detection.pipelines.mm_transforms.LoadAnnotations(with_bbox=True, with_label=True, with_mask=False, with_seg=False, poly2mask=True, file_client_args={'backend': 'disk'})[source]¶

Bases: object

Load multiple types of annotations.

Parameters

with_bbox (bool) – Whether to parse and load the bbox annotation. Default: True.
with_label (bool) – Whether to parse and load the label annotation. Default: True.
with_mask (bool) – Whether to parse and load the mask annotation. Default: False.
with_seg (bool) – Whether to parse and load the semantic segmentation annotation. Default: False.
poly2mask (bool) – Whether to convert the instance masks from polygons to bitmaps. Default: True.
file_client_args (dict) – Arguments to instantiate a FileClient. See mmcv.fileio.FileClient for details. Defaults to dict(backend='disk').

__init__(with_bbox=True, with_label=True, with_mask=False, with_seg=False, poly2mask=True, file_client_args={'backend': 'disk'})[source]¶: Initialize self. See help(type(self)) for accurate signature.

process_polygons(polygons)[source]¶

Convert polygons to list of ndarray and filter invalid polygons.

Parameters: polygons (list[list]) – Polygons of one instance.
Returns: Processed polygons.
Return type: list[numpy.ndarray]

class easycv.datasets.detection.pipelines.mm_transforms.MMMultiScaleFlipAug(transforms, img_scale=None, scale_factor=None, flip=False, flip_direction='horizontal')[source]¶

Bases: object

Test-time augmentation with multiple scales and flipping.

An example configuration is as followed:

img_scale=[(1333, 400), (1333, 800)],
flip=True,
transforms=[
    dict(type='Resize', keep_ratio=True),
    dict(type='RandomFlip'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='Collect', keys=['img']),
]

After MultiScaleFLipAug with above configuration, the results are wrapped into lists of the same length as followed:

dict(
    img=[...],
    img_shape=[...],
    scale=[(1333, 400), (1333, 400), (1333, 800), (1333, 800)]
    flip=[False, True, False, True]
    ...
)

Parameters

transforms (list[dict]) – Transforms to apply in each augmentation.
img_scale (tuple | list[tuple] | None) – Images scales for resizing.
scale_factor (float | list[float] | None) – Scale factors for resizing.
flip (bool) – Whether apply flip augmentation. Default: False.
flip_direction (str | list[str]) – Flip augmentation directions, options are “horizontal”, “vertical” and “diagonal”. If flip_direction is a list, multiple flip augmentations will be applied. It has no effect when flip == False. Default: “horizontal”.

__init__(transforms, img_scale=None, scale_factor=None, flip=False, flip_direction='horizontal')[source]¶: Initialize self. See help(type(self)) for accurate signature.

Submodules¶

easycv.datasets.detection.mix module¶

class easycv.datasets.detection.mix.DetImagesMixDataset(data_source, pipeline, dynamic_scale=None, skip_type_keys=None, profiling=False, classes=None, yolo_format=True, label_padding=True)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

A wrapper of multiple images mixed dataset.

Suitable for training on multiple images mixed data augmentation like mosaic and mixup. For the augmentation pipeline of mixed image data, the get_indexes method needs to be provided to obtain the image indexes, and you can set skip_flags to change the pipeline running process. At the same time, we provide the dynamic_scale parameter to dynamically change the output image size.

output boxes format: cx, cy, w, h

Parameters

data_source (DetSourceCoco) – The dataset to be mixed.
pipeline (Sequence[dict]) – Sequence of transform object or config dict to be composed.
dynamic_scale (tuple[int], optional) – The image scale can be changed dynamically. Default to None.
skip_type_keys (list[str], optional) – Sequence of type string to be skip pipeline. Default to None.
label_padding – out labeling padding [N, 120, 5]

__init__(data_source, pipeline, dynamic_scale=None, skip_type_keys=None, profiling=False, classes=None, yolo_format=True, label_padding=True)[source]¶: Args: data_source: Data_source config dict pipeline: Pipeline config list profiling: If set True, will print pipeline time classes: A list of class names, used in evaluation for result and groundtruth visualization

update_skip_type_keys(skip_type_keys)[source]¶

Update skip_type_keys. It is called by an external hook.

Parameters: skip_type_keys (list[str], optional) – Sequence of type string to be skip pipeline.

update_dynamic_scale(dynamic_scale)[source]¶

Update dynamic_scale. It is called by an external hook.

Parameters: dynamic_scale (tuple[int]) – The image scale can be changed dynamically.

results2json(results, outfile_prefix)[source]¶

Dump the detection results to a COCO style json file.

There are 3 types of results: proposals, bbox predictions, mask predictions, and they have different data types. This method will automatically recognize the type, and dump them to json files.

Parameters

results (list[list | tuple | ndarray]) – Testing results of the dataset.
outfile_prefix (str) – The filename prefix of the json files. If the prefix is “somepath/xxx”, the json files will be named “somepath/xxx.bbox.json”, “somepath/xxx.segm.json”, “somepath/xxx.proposal.json”.

Returns

str]: Possible keys are “bbox”, “segm”, “proposal”, and values are corresponding filenames.

Return type

dict[str

format_results(results, jsonfile_prefix=None, **kwargs)[source]¶

Format the results to json (standard format for COCO evaluation).

Parameters

results (list[tuple | numpy.ndarray]) – Testing results of the dataset.
jsonfile_prefix (str | None) – The prefix of json files. It includes the file path and the prefix of filename, e.g., “a/b/prefix”. If not specified, a temp file will be created. Default: None.

Returns

(result_files, tmp_dir), result_files is a dict containing the json filepaths, tmp_dir is the temporal directory created for saving json files when jsonfile_prefix is not specified.

Return type

tuple

easycv.datasets.detection.raw module¶

class easycv.datasets.detection.raw.DetDataset(data_source, pipeline, profiling=False, classes=None)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

Dataset for Detection

__init__(data_source, pipeline, profiling=False, classes=None)[source]¶

Parameters

data_source – Data_source config dict
pipeline – Pipeline config list
profiling – If set True, will print pipeline time
classes – A list of class names, used in evaluation for result and groundtruth visualization

evaluate(results, evaluators=None, logger=None)[source]¶

Evaluates the detection boxes. :param results: A dictionary containing

detection_boxes: List of length number of test images.
Float32 numpy array of shape [num_boxes, 4] and format [ymin, xmin, ymax, xmax] in absolute image coordinates.

detection_scores: List of length number of test images,
detection scores for the boxes, float32 numpy array of shape [num_boxes].

detection_classes: List of length number of test images,
integer numpy array of shape [num_boxes] containing 1-indexed detection classes for the boxes.

img_metas: List of length number of test images,
dict of image meta info, containing filename, img_shape, origin_img_shape, scale_factor and so on.

Parameters: evaluators – evaluators to calculate metric with results and groundtruth_dict

visualize(results, vis_num=10, score_thr=0.3, **kwargs)[source]¶

Visulaize the model output on validation data. :param results: A dictionary containing

detection_boxes: List of length number of test images.
Float32 numpy array of shape [num_boxes, 4] and format [ymin, xmin, ymax, xmax] in absolute image coordinates.

detection_scores: List of length number of test images,
detection scores for the boxes, float32 numpy array of shape [num_boxes].

detection_classes: List of length number of test images,
integer numpy array of shape [num_boxes] containing 1-indexed detection classes for the boxes.

img_metas: List of length number of test images,
dict of image meta info, containing filename, img_shape, origin_img_shape, scale_factor and so on.

Parameters

vis_num – number of images visualized
score_thr – The threshold to filter box, boxes with scores greater than score_thr will be kept.

Returns: A dictionary containing: images: Visulaized images. img_metas: List of length number of test images,

dict of image meta info, containing filename, img_shape, origin_img_shape, scale_factor and so on.

easycv.datasets.loader package¶

class easycv.datasets.loader.GroupSampler(dataset, samples_per_gpu=1)[source]¶

Bases: Generic[torch.utils.data.sampler.T_co]

__init__(dataset, samples_per_gpu=1)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.loader.DistributedGroupSampler(dataset, samples_per_gpu=1, num_replicas=None, rank=None)[source]¶

Bases: Generic[torch.utils.data.sampler.T_co]

Sampler that restricts data loading to a subset of the dataset.

It is especially useful in conjunction with torch.nn.parallel.DistributedDataParallel. In such case, each process can pass a DistributedSampler instance as a DataLoader sampler, and load a subset of the original dataset that is exclusive to it. .. note:

Dataset is assumed to be of constant size.

Parameters

dataset – Dataset used for sampling.
num_replicas (optional) – Number of processes participating in distributed training.
rank (optional) – Rank of the current process within num_replicas.

__init__(dataset, samples_per_gpu=1, num_replicas=None, rank=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

set_epoch(epoch)[source]¶

easycv.datasets.loader.build_dataloader(dataset, imgs_per_gpu, workers_per_gpu, num_gpus=1, dist=True, shuffle=True, replace=False, seed=None, reuse_worker_cache=False, odps_config=None, persistent_workers=False, **kwargs)[source]¶

Build PyTorch DataLoader.

In distributed training, each GPU/process has a dataloader. In non-distributed training, there is only one dataloader for all GPUs.

Parameters

dataset (Dataset) – A PyTorch dataset.
imgs_per_gpu (int) – Number of images on each GPU, i.e., batch size of each GPU.
workers_per_gpu (int) – How many subprocesses to use for data loading for each GPU.
num_gpus (int) – Number of GPUs. Only used in non-distributed training.
dist (bool) – Distributed training/test or not. Default: True.
shuffle (bool) – Whether to shuffle the data at every epoch. Default: True.
replace (bool) – Replace or not in random shuffle. It works on when shuffle is True.
reuse_worker_cache (bool) – If set true, will reuse worker process so that cached data in worker process can be reused.
persistent_workers (bool) – After pytorch1.7, could use persistent_workers=True to avoid reconstruct dataworker before each epoch, speed up before epoch
kwargs – any keyword argument to be used to initialize DataLoader

Returns

A PyTorch dataloader.

Return type

DataLoader

class easycv.datasets.loader.DistributedGivenIterationSampler(dataset, total_iter, batch_size, num_replicas=None, rank=None, last_iter=- 1)[source]¶

Bases: Generic[torch.utils.data.sampler.T_co]

__init__(dataset, total_iter, batch_size, num_replicas=None, rank=None, last_iter=- 1)[source]¶: Initialize self. See help(type(self)) for accurate signature.

set_uniform_indices(labels, num_classes)[source]¶

gen_new_list()[source]¶

set_epoch(epoch)[source]¶

Submodules¶

easycv.datasets.loader.build_loader module¶

easycv.datasets.loader.build_loader.build_dataloader(dataset, imgs_per_gpu, workers_per_gpu, num_gpus=1, dist=True, shuffle=True, replace=False, seed=None, reuse_worker_cache=False, odps_config=None, persistent_workers=False, **kwargs)[source]¶

Build PyTorch DataLoader.

In distributed training, each GPU/process has a dataloader. In non-distributed training, there is only one dataloader for all GPUs.

Parameters

dataset (Dataset) – A PyTorch dataset.
imgs_per_gpu (int) – Number of images on each GPU, i.e., batch size of each GPU.
workers_per_gpu (int) – How many subprocesses to use for data loading for each GPU.
num_gpus (int) – Number of GPUs. Only used in non-distributed training.
dist (bool) – Distributed training/test or not. Default: True.
shuffle (bool) – Whether to shuffle the data at every epoch. Default: True.
replace (bool) – Replace or not in random shuffle. It works on when shuffle is True.
reuse_worker_cache (bool) – If set true, will reuse worker process so that cached data in worker process can be reused.
persistent_workers (bool) – After pytorch1.7, could use persistent_workers=True to avoid reconstruct dataworker before each epoch, speed up before epoch
kwargs – any keyword argument to be used to initialize DataLoader

Returns

A PyTorch dataloader.

Return type

DataLoader

easycv.datasets.loader.build_loader.worker_init_fn(worker_id, seed=None, odps_config=None)[source]¶

class easycv.datasets.loader.build_loader.InfiniteDataLoader(*args, **kwargs)[source]¶

Bases: Generic[torch.utils.data.dataloader.T_co]

Dataloader that reuses workers. https://github.com/pytorch/pytorch/issues/15849

Uses same syntax as vanilla DataLoader.

__init__(*args, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

dataset: torch.utils.data.dataset.Dataset[torch.utils.data.dataloader.T_co]¶

batch_size: Optional[int]¶

num_workers: int¶

pin_memory: bool¶

drop_last: bool¶

timeout: float¶

sampler: Union[torch.utils.data.sampler.Sampler, Iterable]¶

prefetch_factor: int¶

easycv.datasets.loader.sampler module¶

class easycv.datasets.loader.sampler.DistributedMPSampler(dataset, num_replicas=None, rank=None, shuffle=True, split_huge_listfile_byrank=False)[source]¶

Bases: torch.utils.data.sampler.Sampler[torch.utils.data.distributed.T_co]

__init__(dataset, num_replicas=None, rank=None, shuffle=True, split_huge_listfile_byrank=False)[source]¶

A Distribute sampler which support sample m instance from one class once for classification dataset

dataset: pytorch dataset object num_replicas (optional): Number of processes participating in

distributed training.

rank (optional): Rank of the current process within num_replicas. shuffle (optional): If true (default), sampler will shuffle the indices split_huge_listfile_byrank: if split, return all indice for each rank, because list for each rank has been

split before build dataset in dist training

generate_indice()[source]¶

get_label_dict()[source]¶

calculate_this_label_list()[source]¶

class easycv.datasets.loader.sampler.DistributedSampler(dataset, num_replicas=None, rank=None, shuffle=True, replace=False, split_huge_listfile_byrank=False)[source]¶

Bases: torch.utils.data.sampler.Sampler[torch.utils.data.distributed.T_co]

__init__(dataset, num_replicas=None, rank=None, shuffle=True, replace=False, split_huge_listfile_byrank=False)[source]¶

A Distribute sampler which support sample m instance from one class once for classification dataset

Parameters

dataset – pytorch dataset object
num_replicas (optional) – Number of processes participating in distributed training.
rank (optional) – Rank of the current process within num_replicas.
shuffle (optional) – If true (default), sampler will shuffle the indices
split_huge_listfile_byrank – if split, return all indice for each rank, because list for each rank has been split before build dataset in dist training

generate_new_list()[source]¶

set_uniform_indices(labels, num_classes)[source]¶

class easycv.datasets.loader.sampler.GroupSampler(dataset, samples_per_gpu=1)[source]¶

Bases: Generic[torch.utils.data.sampler.T_co]

__init__(dataset, samples_per_gpu=1)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.loader.sampler.DistributedGroupSampler(dataset, samples_per_gpu=1, num_replicas=None, rank=None)[source]¶

Bases: Generic[torch.utils.data.sampler.T_co]

Sampler that restricts data loading to a subset of the dataset.

It is especially useful in conjunction with torch.nn.parallel.DistributedDataParallel. In such case, each process can pass a DistributedSampler instance as a DataLoader sampler, and load a subset of the original dataset that is exclusive to it. .. note:

Dataset is assumed to be of constant size.

Parameters

dataset – Dataset used for sampling.
num_replicas (optional) – Number of processes participating in distributed training.
rank (optional) – Rank of the current process within num_replicas.

__init__(dataset, samples_per_gpu=1, num_replicas=None, rank=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

set_epoch(epoch)[source]¶

class easycv.datasets.loader.sampler.DistributedGivenIterationSampler(dataset, total_iter, batch_size, num_replicas=None, rank=None, last_iter=- 1)[source]¶

Bases: Generic[torch.utils.data.sampler.T_co]

__init__(dataset, total_iter, batch_size, num_replicas=None, rank=None, last_iter=- 1)[source]¶: Initialize self. See help(type(self)) for accurate signature.

set_uniform_indices(labels, num_classes)[source]¶

gen_new_list()[source]¶

set_epoch(epoch)[source]¶

easycv.datasets.pose package¶

class easycv.datasets.pose.PoseTopDownDataset(data_source, pipeline, profiling=False)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

PoseTopDownDataset dataset for top-down pose estimation. The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.

Parameters

data_source – Data_source config dict
pipeline – Pipeline config list
profiling – If set True, will print pipeline time

__init__(data_source, pipeline, profiling=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

evaluate(outputs, evaluators, **kwargs)[source]¶

Subpackages¶

easycv.datasets.pose.data_sources package¶

class easycv.datasets.pose.data_sources.PoseTopDownSourceCoco(ann_file, img_prefix, data_cfg, dataset_info=None, test_mode=False)[source]¶

Bases: easycv.datasets.pose.data_sources.top_down.PoseTopDownSource

CocoSource for top-down pose estimation.

Microsoft COCO: Common Objects in Context’ ECCV’2014 More details can be found in the `paper .

The source loads raw features to build a data meta object containing the image info, annotation info and others.

COCO keypoint indexes:

'nose',
'left_eye',
'right_eye',
'left_ear',
'right_ear',
'left_shoulder',
'right_shoulder',
'left_elbow',
'right_elbow',
'left_wrist',
'right_wrist',
'left_hip',
'right_hip',
'left_knee',
'right_knee',
'left_ankle',
'right_ankle'

Parameters

ann_file (str) – Path to the annotation file.
img_prefix (str) – Path to a directory where images are held. Default: None.
data_cfg (dict) – config
dataset_info (DatasetInfo) – A class containing all dataset info.
test_mode (bool) – Store True when building test or
dataset. Default (validation) – False.

__init__(ann_file, img_prefix, data_cfg, dataset_info=None, test_mode=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.pose.data_sources.PoseTopDownSource(ann_file, img_prefix, data_cfg, dataset_info, coco_style=True, test_mode=False)[source]¶

Bases: object

Class for keypoint 2D top-down pose estimation with single-view RGB image as the data source.

Parameters

ann_file (str) – Path to the annotation file.
img_prefix (str) – Path to a directory where images are held. Default: None.
data_cfg (dict) – config
dataset_info (DatasetInfo) – A class containing all dataset info.
coco_style (bool) – Whether the annotation json is coco-style. Default: True
test_mode (bool) – Store True when building test or validation dataset. Default: False.

__init__(ann_file, img_prefix, data_cfg, dataset_info, coco_style=True, test_mode=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

load_image(image_file)[source]¶

get_length()[source]¶: Get the size of the dataset.

get_sample(idx)[source]¶

Submodules¶

easycv.datasets.pose.data_sources.coco module¶

class easycv.datasets.pose.data_sources.coco.PoseTopDownSourceCoco(ann_file, img_prefix, data_cfg, dataset_info=None, test_mode=False)[source]¶

Bases: easycv.datasets.pose.data_sources.top_down.PoseTopDownSource

CocoSource for top-down pose estimation.

Microsoft COCO: Common Objects in Context’ ECCV’2014 More details can be found in the `paper .

The source loads raw features to build a data meta object containing the image info, annotation info and others.

COCO keypoint indexes:

'nose',
'left_eye',
'right_eye',
'left_ear',
'right_ear',
'left_shoulder',
'right_shoulder',
'left_elbow',
'right_elbow',
'left_wrist',
'right_wrist',
'left_hip',
'right_hip',
'left_knee',
'right_knee',
'left_ankle',
'right_ankle'

Parameters

ann_file (str) – Path to the annotation file.
img_prefix (str) – Path to a directory where images are held. Default: None.
data_cfg (dict) – config
dataset_info (DatasetInfo) – A class containing all dataset info.
test_mode (bool) – Store True when building test or
dataset. Default (validation) – False.

__init__(ann_file, img_prefix, data_cfg, dataset_info=None, test_mode=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

easycv.datasets.pose.data_sources.top_down module¶

class easycv.datasets.pose.data_sources.top_down.DatasetInfo(dataset_info)[source]¶

Bases: object

__init__(dataset_info)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.pose.data_sources.top_down.PoseTopDownSource(ann_file, img_prefix, data_cfg, dataset_info, coco_style=True, test_mode=False)[source]¶

Bases: object

Class for keypoint 2D top-down pose estimation with single-view RGB image as the data source.

Parameters

ann_file (str) – Path to the annotation file.
img_prefix (str) – Path to a directory where images are held. Default: None.
data_cfg (dict) – config
dataset_info (DatasetInfo) – A class containing all dataset info.
coco_style (bool) – Whether the annotation json is coco-style. Default: True
test_mode (bool) – Store True when building test or validation dataset. Default: False.

__init__(ann_file, img_prefix, data_cfg, dataset_info, coco_style=True, test_mode=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

load_image(image_file)[source]¶

get_length()[source]¶: Get the size of the dataset.

get_sample(idx)[source]¶

easycv.datasets.pose.pipelines package¶

class easycv.datasets.pose.pipelines.PoseCollect(keys, meta_keys, meta_name='img_metas')[source]¶

Bases: object

Collect data from the loader relevant to the specific task.

This keeps the items in keys as it is, and collect items in meta_keys into a meta item called meta_name.This is usually the last stage of the data loader pipeline. For example, when keys=’imgs’, meta_keys=(‘filename’, ‘label’, ‘original_shape’), meta_name=’img_metas’, the results will be a dict with keys ‘imgs’ and ‘img_metas’, where ‘img_metas’ is a DataContainer of another dict with keys ‘filename’, ‘label’, ‘original_shape’.

Parameters

keys (Sequence[str|tuple]) – Required keys to be collected. If a tuple (key, key_new) is given as an element, the item retrieved by key will be renamed as key_new in collected data.
meta_name (str) – The name of the key that contains meta information. This key is always populated. Default: “img_metas”.
meta_keys (Sequence[str|tuple]) – Keys that are collected under meta_name. The contents of the meta_name dictionary depends on meta_keys.

__init__(keys, meta_keys, meta_name='img_metas')[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.pose.pipelines.TopDownRandomFlip(flip_prob=0.5)[source]¶

Bases: object

Data augmentation with random image flip.

Required keys: ‘img’, ‘joints_3d’, ‘joints_3d_visible’, ‘center’ and ‘ann_info’. Modifies key: ‘img’, ‘joints_3d’, ‘joints_3d_visible’, ‘center’ and ‘flipped’.

Parameters

flip (bool) – Option to perform random flip.
flip_prob (float) – Probability of flip.

__init__(flip_prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.pose.pipelines.TopDownHalfBodyTransform(num_joints_half_body=8, prob_half_body=0.3)[source]¶

Bases: object

Data augmentation with half-body transform. Keep only the upper body or the lower body at random.

Required keys: ‘joints_3d’, ‘joints_3d_visible’, and ‘ann_info’. Modifies key: ‘scale’ and ‘center’.

Parameters

num_joints_half_body (int) – Threshold of performing half-body transform. If the body has fewer number of joints (< num_joints_half_body), ignore this step.
prob_half_body (float) – Probability of half-body transform.

__init__(num_joints_half_body=8, prob_half_body=0.3)[source]¶: Initialize self. See help(type(self)) for accurate signature.

static half_body_transform(cfg, joints_3d, joints_3d_visible)[source]¶: Get center&scale for half-body transform.

class easycv.datasets.pose.pipelines.TopDownGetRandomScaleRotation(rot_factor=40, scale_factor=0.5, rot_prob=0.6)[source]¶

Bases: object

Data augmentation with random scaling & rotating.

Required key: ‘scale’. Modifies key: ‘scale’ and ‘rotation’.

Parameters

rot_factor (int) – Rotating to [-2*rot_factor, 2*rot_factor].
scale_factor (float) – Scaling to [1-scale_factor, 1+scale_factor].
rot_prob (float) – Probability of random rotation.

__init__(rot_factor=40, scale_factor=0.5, rot_prob=0.6)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.pose.pipelines.TopDownAffine(use_udp=False)[source]¶

Bases: object

Affine transform the image to make input.

Required keys:’img’, ‘joints_3d’, ‘joints_3d_visible’, ‘ann_info’,’scale’, ‘rotation’ and ‘center’. Modified keys:’img’, ‘joints_3d’, and ‘joints_3d_visible’.

Parameters: use_udp (bool) – To use unbiased data processing. Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).

__init__(use_udp=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.pose.pipelines.TopDownGenerateTarget(sigma=2, kernel=(11, 11), valid_radius_factor=0.0546875, target_type='GaussianHeatmap', encoding='MSRA', unbiased_encoding=False)[source]¶

Bases: object

Generate the target heatmap.

Required keys: ‘joints_3d’, ‘joints_3d_visible’, ‘ann_info’. Modified keys: ‘target’, and ‘target_weight’.

Parameters

sigma – Sigma of heatmap gaussian for ‘MSRA’ approach.
kernel – Kernel of heatmap gaussian for ‘Megvii’ approach.
encoding (str) – Approach to generate target heatmaps. Currently supported approaches: ‘MSRA’, ‘Megvii’, ‘UDP’. Default:’MSRA’
unbiased_encoding (bool) – Option to use unbiased encoding methods. Paper ref: Zhang et al. Distribution-Aware Coordinate Representation for Human Pose Estimation (CVPR 2020).
keypoint_pose_distance – Keypoint pose distance for UDP. Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).
target_type (str) – supported targets: ‘GaussianHeatmap’, ‘CombinedTarget’. Default:’GaussianHeatmap’ CombinedTarget: The combination of classification target (response map) and regression target (offset map). Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).

__init__(sigma=2, kernel=(11, 11), valid_radius_factor=0.0546875, target_type='GaussianHeatmap', encoding='MSRA', unbiased_encoding=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.pose.pipelines.TopDownGenerateTargetRegression[source]¶

Bases: object

Generate the target regression vector (coordinates).

Required keys: ‘joints_3d’, ‘joints_3d_visible’, ‘ann_info’. Modified keys: ‘target’, and ‘target_weight’.

__init__()[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.pose.pipelines.TopDownRandomTranslation(trans_factor=0.15, trans_prob=1.0)[source]¶

Bases: object

Data augmentation with random translation.

Required key: ‘scale’ and ‘center’. Modifies key: ‘center’.

Notes

bbox height: H bbox width: W

Parameters

trans_factor (float) – Translating center to [-trans_factor, trans_factor] * [W, H] + center.
trans_prob (float) – Probability of random translation.

__init__(trans_factor=0.15, trans_prob=1.0)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Submodules¶

easycv.datasets.pose.pipelines.transforms module¶

class easycv.datasets.pose.pipelines.transforms.PoseCollect(keys, meta_keys, meta_name='img_metas')[source]¶

Bases: object

Collect data from the loader relevant to the specific task.

This keeps the items in keys as it is, and collect items in meta_keys into a meta item called meta_name.This is usually the last stage of the data loader pipeline. For example, when keys=’imgs’, meta_keys=(‘filename’, ‘label’, ‘original_shape’), meta_name=’img_metas’, the results will be a dict with keys ‘imgs’ and ‘img_metas’, where ‘img_metas’ is a DataContainer of another dict with keys ‘filename’, ‘label’, ‘original_shape’.

Parameters

keys (Sequence[str|tuple]) – Required keys to be collected. If a tuple (key, key_new) is given as an element, the item retrieved by key will be renamed as key_new in collected data.
meta_name (str) – The name of the key that contains meta information. This key is always populated. Default: “img_metas”.
meta_keys (Sequence[str|tuple]) – Keys that are collected under meta_name. The contents of the meta_name dictionary depends on meta_keys.

__init__(keys, meta_keys, meta_name='img_metas')[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.pose.pipelines.transforms.TopDownRandomFlip(flip_prob=0.5)[source]¶

Bases: object

Data augmentation with random image flip.

Required keys: ‘img’, ‘joints_3d’, ‘joints_3d_visible’, ‘center’ and ‘ann_info’. Modifies key: ‘img’, ‘joints_3d’, ‘joints_3d_visible’, ‘center’ and ‘flipped’.

Parameters

flip (bool) – Option to perform random flip.
flip_prob (float) – Probability of flip.

__init__(flip_prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.pose.pipelines.transforms.TopDownHalfBodyTransform(num_joints_half_body=8, prob_half_body=0.3)[source]¶

Bases: object

Data augmentation with half-body transform. Keep only the upper body or the lower body at random.

Required keys: ‘joints_3d’, ‘joints_3d_visible’, and ‘ann_info’. Modifies key: ‘scale’ and ‘center’.

Parameters

num_joints_half_body (int) – Threshold of performing half-body transform. If the body has fewer number of joints (< num_joints_half_body), ignore this step.
prob_half_body (float) – Probability of half-body transform.

__init__(num_joints_half_body=8, prob_half_body=0.3)[source]¶: Initialize self. See help(type(self)) for accurate signature.

static half_body_transform(cfg, joints_3d, joints_3d_visible)[source]¶: Get center&scale for half-body transform.

class easycv.datasets.pose.pipelines.transforms.TopDownGetRandomScaleRotation(rot_factor=40, scale_factor=0.5, rot_prob=0.6)[source]¶

Bases: object

Data augmentation with random scaling & rotating.

Required key: ‘scale’. Modifies key: ‘scale’ and ‘rotation’.

Parameters

rot_factor (int) – Rotating to [-2*rot_factor, 2*rot_factor].
scale_factor (float) – Scaling to [1-scale_factor, 1+scale_factor].
rot_prob (float) – Probability of random rotation.

__init__(rot_factor=40, scale_factor=0.5, rot_prob=0.6)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.pose.pipelines.transforms.TopDownAffine(use_udp=False)[source]¶

Bases: object

Affine transform the image to make input.

Required keys:’img’, ‘joints_3d’, ‘joints_3d_visible’, ‘ann_info’,’scale’, ‘rotation’ and ‘center’. Modified keys:’img’, ‘joints_3d’, and ‘joints_3d_visible’.

Parameters: use_udp (bool) – To use unbiased data processing. Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).

__init__(use_udp=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.pose.pipelines.transforms.TopDownGenerateTarget(sigma=2, kernel=(11, 11), valid_radius_factor=0.0546875, target_type='GaussianHeatmap', encoding='MSRA', unbiased_encoding=False)[source]¶

Bases: object

Generate the target heatmap.

Required keys: ‘joints_3d’, ‘joints_3d_visible’, ‘ann_info’. Modified keys: ‘target’, and ‘target_weight’.

Parameters

sigma – Sigma of heatmap gaussian for ‘MSRA’ approach.
kernel – Kernel of heatmap gaussian for ‘Megvii’ approach.
encoding (str) – Approach to generate target heatmaps. Currently supported approaches: ‘MSRA’, ‘Megvii’, ‘UDP’. Default:’MSRA’
unbiased_encoding (bool) – Option to use unbiased encoding methods. Paper ref: Zhang et al. Distribution-Aware Coordinate Representation for Human Pose Estimation (CVPR 2020).
keypoint_pose_distance – Keypoint pose distance for UDP. Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).
target_type (str) – supported targets: ‘GaussianHeatmap’, ‘CombinedTarget’. Default:’GaussianHeatmap’ CombinedTarget: The combination of classification target (response map) and regression target (offset map). Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).

__init__(sigma=2, kernel=(11, 11), valid_radius_factor=0.0546875, target_type='GaussianHeatmap', encoding='MSRA', unbiased_encoding=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.pose.pipelines.transforms.TopDownGenerateTargetRegression[source]¶

Bases: object

Generate the target regression vector (coordinates).

Required keys: ‘joints_3d’, ‘joints_3d_visible’, ‘ann_info’. Modified keys: ‘target’, and ‘target_weight’.

__init__()[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.pose.pipelines.transforms.TopDownRandomTranslation(trans_factor=0.15, trans_prob=1.0)[source]¶

Bases: object

Data augmentation with random translation.

Required key: ‘scale’ and ‘center’. Modifies key: ‘center’.

Notes

bbox height: H bbox width: W

Parameters

trans_factor (float) – Translating center to [-trans_factor, trans_factor] * [W, H] + center.
trans_prob (float) – Probability of random translation.

__init__(trans_factor=0.15, trans_prob=1.0)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Submodules¶

easycv.datasets.pose.top_down module¶

class easycv.datasets.pose.top_down.PoseTopDownDataset(data_source, pipeline, profiling=False)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

PoseTopDownDataset dataset for top-down pose estimation. The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.

Parameters

data_source – Data_source config dict
pipeline – Pipeline config list
profiling – If set True, will print pipeline time

__init__(data_source, pipeline, profiling=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

evaluate(outputs, evaluators, **kwargs)[source]¶

easycv.datasets.selfsup package¶

Subpackages¶

easycv.datasets.selfsup.data_sources package¶

class easycv.datasets.selfsup.data_sources.SSLSourceImageList(list_file, root='', max_try=20)[source]¶

Bases: object

datasource for classification

Parameters

list_file – str / list(str), str means a input image list file path, this file contains records as image_path label in list_file list(str) means multi image list, each one contains some records as image_path label
root – str / list(str), root path for image_path, each list_file will need a root, if len(root) < len(list_file), we will use root[-1] to fill root list.
max_try – int, max try numbers of reading image

__init__(list_file, root='', max_try=20)[source]¶: Initialize self. See help(type(self)) for accurate signature.

static parse_list_file(list_file, root)[source]¶

get_length()[source]¶

get_sample(idx)[source]¶

class easycv.datasets.selfsup.data_sources.SSLSourceImageNetFeature(root_path, training=True, data_keyword='feat1', label_keyword='label', dynamic_load=True)[source]¶

Bases: object

__init__(root_path, training=True, data_keyword='feat1', label_keyword='label', dynamic_load=True)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_sample(idx)[source]¶

get_length()[source]¶

Submodules¶

easycv.datasets.selfsup.data_sources.image_list module¶

class easycv.datasets.selfsup.data_sources.image_list.SSLSourceImageList(list_file, root='', max_try=20)[source]¶

Bases: object

datasource for classification

Parameters

list_file – str / list(str), str means a input image list file path, this file contains records as image_path label in list_file list(str) means multi image list, each one contains some records as image_path label
root – str / list(str), root path for image_path, each list_file will need a root, if len(root) < len(list_file), we will use root[-1] to fill root list.
max_try – int, max try numbers of reading image

__init__(list_file, root='', max_try=20)[source]¶: Initialize self. See help(type(self)) for accurate signature.

static parse_list_file(list_file, root)[source]¶

get_length()[source]¶

get_sample(idx)[source]¶

easycv.datasets.selfsup.data_sources.imagenet_feature module¶

class easycv.datasets.selfsup.data_sources.imagenet_feature.SSLSourceImageNetFeature(root_path, training=True, data_keyword='feat1', label_keyword='label', dynamic_load=True)[source]¶

Bases: object

__init__(root_path, training=True, data_keyword='feat1', label_keyword='label', dynamic_load=True)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_sample(idx)[source]¶

get_length()[source]¶

easycv.datasets.selfsup.pipelines package¶

class easycv.datasets.selfsup.pipelines.RandomAppliedTrans(transforms, p=0.5)[source]¶

Bases: object

Randomly applied transformations. :param transforms: List of transformations in dictionaries. :type transforms: List[Dict]

__init__(transforms, p=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.selfsup.pipelines.Lighting[source]¶

Bases: object

Lighting noise(AlexNet - style PCA - based noise)

__init__()[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.selfsup.pipelines.Solarization(threshold=128)[source]¶

Bases: object

__init__(threshold=128)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Submodules¶

easycv.datasets.selfsup.pipelines.transforms module¶

class easycv.datasets.selfsup.pipelines.transforms.MAEFtAugment(input_size=None, color_jitter=None, auto_augment=None, interpolation=None, re_prob=None, re_mode=None, re_count=None, mean=None, std=None, is_train=True)[source]¶

Bases: object

RandAugment data augmentation method based on “RandAugment: Practical automated data augmentation with a reduced search space”. This code is borrowed from <https://github.com/pengzhiliang/MAE-pytorch> :param input_size: images input size :type input_size: int :param color_jitter: Color jitter factor :type color_jitter: float :param auto_augment: Use AutoAugment policy :param iterpolation: Training interpolation :param re_prob: Random erase prob :param re_mode: Random erase mode :param re_count: Random erase count :param mean: mean used for normalization :param std: std used for normalization :param is_train: If True use all augmentation strategy

__init__(input_size=None, color_jitter=None, auto_augment=None, interpolation=None, re_prob=None, re_mode=None, re_count=None, mean=None, std=None, is_train=True)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.selfsup.pipelines.transforms.RandomAppliedTrans(transforms, p=0.5)[source]¶

Bases: object

Randomly applied transformations. :param transforms: List of transformations in dictionaries. :type transforms: List[Dict]

__init__(transforms, p=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.selfsup.pipelines.transforms.Lighting[source]¶

Bases: object

Lighting noise(AlexNet - style PCA - based noise)

__init__()[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.selfsup.pipelines.transforms.Solarization(threshold=128)[source]¶

Bases: object

__init__(threshold=128)[source]¶: Initialize self. See help(type(self)) for accurate signature.

easycv.datasets.shared package¶

class easycv.datasets.shared.ConcatDataset(datasets)[source]¶

Bases: torch.utils.data.dataset.Dataset[torch.utils.data.dataset.T_co]

A wrapper of concatenated dataset.

Same as torch.utils.data.dataset.ConcatDataset, but concat the group flag for image aspect ratio.

Parameters: datasets (list[Dataset]) – A list of datasets.

__init__(datasets)[source]¶: Initialize self. See help(type(self)) for accurate signature.

datasets: List[torch.utils.data.dataset.Dataset[torch.utils.data.dataset.T_co]]¶

cumulative_sizes: List[int]¶

class easycv.datasets.shared.RepeatDataset(dataset, times)[source]¶

Bases: object

A wrapper of repeated dataset.

The length of repeated dataset will be times larger than the original dataset. This is useful when the data loading time is long but the dataset is small. Using RepeatDataset can reduce the data loading time between epochs.

Parameters

dataset (Dataset) – The dataset to be repeated.
times (int) – Repeat times.

__init__(dataset, times)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.shared.OdpsReader(table_name, selected_cols=[], excluded_cols=[], random_start=False, odps_io_config=None, image_col=['url_image'], image_type=['url'])[source]¶

Bases: object

__init__(table_name, selected_cols=[], excluded_cols=[], random_start=False, odps_io_config=None, image_col=['url_image'], image_type=['url'])[source]¶

Init odps reader and datasource set to load data from odps table

Parameters

table_name (str) – odps table to load
selected_cols (list(str)) – select column
excluded_cols (list(str)) – exclude column
random_start (bool) – random start for odps table
odps_io_config (dict) – odps config contains access_id, access_key, endpoint
image_col (list(str)) – image column names
image_type (list(str)) – image column types support url/base64, must be same length with image type or 0

Returns :: None

get_length()[source]¶

reset_reader(dataloader_workid, dataloader_worknum)[source]¶

get_sample(idx)[source]¶

b64_decode()[source]¶

class easycv.datasets.shared.RawDataset(data_source, pipeline)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

__init__(data_source, pipeline)[source]¶: Initialize self. See help(type(self)) for accurate signature.

evaluate(scores, keyword, logger=None)[source]¶

class easycv.datasets.shared.BaseDataset(data_source, pipeline, profiling=False)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

Base Dataset

__init__(data_source, pipeline, profiling=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

abstract evaluate(results, evaluators, logger=None, **kwargs)[source]¶

visualize(results, **kwargs)[source]¶

Visulaize the model output results on validation data. Returns: A dictionary

If add image visualization, return dict containing
images: List of visulaized images. img_metas: List of length number of test images,

dict of image meta info, containing filename, img_shape, origin_img_shape, scale_factor and so on.

class easycv.datasets.shared.MultiViewDataset(data_source, num_views, pipelines)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

The dataset outputs multiple views of an image. The number of views in the output dict depends on num_views. The image can be processed by one pipeline or multiple piepelines. :param num_views: The number of different views. :type num_views: list :param pipelines: A list of pipelines. :type pipelines: list[list[dict]]

__init__(data_source, num_views, pipelines)[source]¶: Initialize self. See help(type(self)) for accurate signature.

evaluate(results, evaluators, logger=None)[source]¶

Subpackages¶

easycv.datasets.shared.data_sources package¶

class easycv.datasets.shared.data_sources.ImageNpy(image_file, label_file=None, cache_root='data_cache/')[source]¶

Bases: object

__init__(image_file, label_file=None, cache_root='data_cache/')[source]¶: image_file: (local or oss) image data saved in one .npy data [cv2.img, cv2.img,…] label_file: (local or oss) label data saved in one .npy data

get_length()[source]¶

get_sample(idx)[source]¶

class easycv.datasets.shared.data_sources.SourceConcat(data_source_list)[source]¶

Bases: object

Concat multi data source config.

__init__(data_source_list)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_length()[source]¶

get_sample(idx)[source]¶

cumsum_length()[source]¶

Submodules¶

easycv.datasets.shared.data_sources.concat module¶

class easycv.datasets.shared.data_sources.concat.SourceConcat(data_source_list)[source]¶

Bases: object

Concat multi data source config.

__init__(data_source_list)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_length()[source]¶

get_sample(idx)[source]¶

cumsum_length()[source]¶

easycv.datasets.shared.data_sources.image_npy module¶

class easycv.datasets.shared.data_sources.image_npy.ImageNpy(image_file, label_file=None, cache_root='data_cache/')[source]¶

Bases: object

__init__(image_file, label_file=None, cache_root='data_cache/')[source]¶: image_file: (local or oss) image data saved in one .npy data [cv2.img, cv2.img,…] label_file: (local or oss) label data saved in one .npy data

get_length()[source]¶

get_sample(idx)[source]¶

easycv.datasets.shared.pipelines package¶

Submodules¶

easycv.datasets.shared.pipelines.dali_transforms module¶

class easycv.datasets.shared.pipelines.dali_transforms.DaliImageDecoder(device='mixed', output_type=DALIImageType.RGB, **kwargs)[source]¶

Bases: object

refer to: https://docs.nvidia.com/deeplearning/dali/archives/dali_0250/user-guide/docs/supported_ops.html#nvidia.dali.ops.ImageDecoder

__init__(device='mixed', output_type=DALIImageType.RGB, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.shared.pipelines.dali_transforms.DaliRandomResizedCrop(size, random_area, device='gpu', **kwargs)[source]¶

Bases: object

refer to: https://docs.nvidia.com/deeplearning/dali/archives/dali_0250/user-guide/docs/supported_ops.html#nvidia.dali.ops.RandomResizedCrop

__init__(size, random_area, device='gpu', **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.shared.pipelines.dali_transforms.DaliResize(resize_shorter, device='gpu', interp_type=DALIInterpType.INTERP_TRIANGULAR, **kwargs)[source]¶

Bases: object

refer to: https://docs.nvidia.com/deeplearning/dali/archives/dali_0250/user-guide/docs/supported_ops.html#nvidia.dali.ops.Resize

__init__(resize_shorter, device='gpu', interp_type=DALIInterpType.INTERP_TRIANGULAR, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.shared.pipelines.dali_transforms.DaliColorTwist(prob, saturation, contrast, brightness, hue, device='gpu', center=1)[source]¶

Bases: object

refer to: https://docs.nvidia.com/deeplearning/dali/archives/dali_0250/user-guide/docs/supported_ops.html#nvidia.dali.ops.ColorTwist

__init__(prob, saturation, contrast, brightness, hue, device='gpu', center=1)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.shared.pipelines.dali_transforms.DaliRandomGrayscale(prob, device='gpu')[source]¶

Bases: object

refer to: https://docs.nvidia.com/deeplearning/dali/archives/dali_0250/user-guide/docs/supported_ops.html#nvidia.dali.ops.Hsv Create RandomGrayscale op with ops.Hsv. when saturation=0, it represents a grayscale image

__init__(prob, device='gpu')[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.shared.pipelines.dali_transforms.DaliCropMirrorNormalize(crop, mean, std, prob=0.0, device='gpu', dtype=DALIDataType.FLOAT, output_layout='CHW', crop_pos_x=0.5, crop_pos_y=0.5, **kwargs)[source]¶

Bases: object

refer to: https://docs.nvidia.com/deeplearning/dali/archives/dali_0250/user-guide/docs/supported_ops.html#nvidia.dali.ops.CropMirrorNormalize

__init__(crop, mean, std, prob=0.0, device='gpu', dtype=DALIDataType.FLOAT, output_layout='CHW', crop_pos_x=0.5, crop_pos_y=0.5, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

easycv.datasets.shared.pipelines.format module¶

easycv.datasets.shared.pipelines.format.to_tensor(data)[source]¶

Convert objects of various python types to torch.Tensor.

Supported types are: numpy.ndarray, torch.Tensor, Sequence, int and float.

Parameters: data (torch.Tensor | numpy.ndarray | Sequence | int | float) – Data to be converted.

class easycv.datasets.shared.pipelines.format.ImageToTensor(keys)[source]¶

Bases: object

Convert image to torch.Tensor by given keys.

The dimension order of input image is (H, W, C). The pipeline will convert it to (C, H, W). If only 2 dimension (H, W) is given, the output would be (1, H, W).

Parameters: keys (Sequence[str]) – Key of images to be converted to Tensor.

__init__(keys)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.shared.pipelines.format.Collect(keys, meta_keys=('filename', 'ori_filename', 'ori_img_shape', 'img_shape', 'scale_factor', 'pad', 'flip', 'flip_direction', 'img_norm_cfg'))[source]¶

Bases: object

Collect data from the loader relevant to the specific task.

This is usually the last stage of the data loader pipeline. Typically keys is set to some subset of “img”, “proposals”, “gt_bboxes”, “gt_bboxes_ignore”, “gt_labels”, and/or “gt_masks”.

The “img_meta” item is always populated. The contents of the “img_meta” dictionary depends on “meta_keys”. By default this includes:

“img_shape”: shape of the image input to the network as a tuple (h, w). Note that images may be zero padded on the bottom/right if the batch tensor is larger than this shape.

“scale_factor”: a float indicating the preprocessing scale

“flip”: a boolean indicating if image flip transform was used

“filename”: path to the image file

“ori_img_shape”: original shape of the image as a tuple (h, w, c)

“img_norm_cfg”: a dict of normalization information:

mean - per channel mean subtraction

std - per channel std divisor

to_rgb - bool indicating if bgr was converted to rgb

Parameters

keys (Sequence[str]) – Keys of results to be collected in data.
meta_keys (Sequence[str], optional) –
Meta keys to be converted to mmcv.DataContainer and collected in data[img_metas]. Default: ``(‘filename’, ‘ori_filename’, ‘ori_img_shape’, ‘img_shape’,

’scale_factor’, ‘flip’, ‘flip_direction’, ‘img_norm_cfg’)``

__init__(keys, meta_keys=('filename', 'ori_filename', 'ori_img_shape', 'img_shape', 'scale_factor', 'pad', 'flip', 'flip_direction', 'img_norm_cfg'))[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.shared.pipelines.format.DefaultFormatBundle[source]¶

Bases: object

Default formatting bundle.

It simplifies the pipeline of formatting common fields, including “img”, “proposals”, “gt_bboxes”, “gt_labels”, “gt_masks” and “gt_semantic_seg”. These fields are formatted as follows.

img: (1)transpose, (2)to tensor, (3)to DataContainer (stack=True)
proposals: (1)to tensor, (2)to DataContainer
gt_bboxes: (1)to tensor, (2)to DataContainer
gt_bboxes_ignore: (1)to tensor, (2)to DataContainer
gt_labels: (1)to tensor, (2)to DataContainer
gt_masks: (1)to tensor, (2)to DataContainer (cpu_only=True)
gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor, (3)to DataContainer (stack=True)

easycv.datasets.shared.pipelines.third_transforms_wrapper module¶

easycv.datasets.shared.pipelines.third_transforms_wrapper.is_child_of(obj, cls)[source]¶

easycv.datasets.shared.pipelines.third_transforms_wrapper.get_args(obj)[source]¶

easycv.datasets.shared.pipelines.third_transforms_wrapper.wrap_torchvision_transforms(transform_obj)[source]¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.AutoAugment(policy: torchvision.transforms.autoaugment.AutoAugmentPolicy = <AutoAugmentPolicy.IMAGENET: 'imagenet'>, interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.NEAREST: 'nearest'>, fill: Optional[List[float]] = None)¶

Bases: torchvision.transforms.autoaugment.AutoAugment

forward(results)¶

img (PIL Image or Tensor): Image to be transformed.

Returns: AutoAugmented image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.CenterCrop(size)¶

Bases: torchvision.transforms.transforms.CenterCrop

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be cropped.
Returns: Cropped image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.ColorJitter(brightness=0, contrast=0, saturation=0, hue=0)¶

Bases: torchvision.transforms.transforms.ColorJitter

forward(results)¶

Parameters: img (PIL Image or Tensor) – Input image.
Returns: Color jittered image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.ConvertImageDtype(dtype: torch.dtype)¶

Bases: torchvision.transforms.transforms.ConvertImageDtype

forward(results)¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.FiveCrop(size)¶

Bases: torchvision.transforms.transforms.FiveCrop

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be cropped.
Returns: tuple of 5 images. Image can be PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.GaussianBlur(kernel_size, sigma=(0.1, 2.0))¶

Bases: torchvision.transforms.transforms.GaussianBlur

forward(results)¶

Parameters: img (PIL Image or Tensor) – image to be blurred.
Returns: Gaussian blurred image
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.Grayscale(num_output_channels=1)¶

Bases: torchvision.transforms.transforms.Grayscale

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be converted to grayscale.
Returns: Grayscaled image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.Lambda(lambd)¶: Bases: torchvision.transforms.transforms.Lambda

class easycv.datasets.shared.pipelines.third_transforms_wrapper.LinearTransformation(transformation_matrix, mean_vector)¶

Bases: torchvision.transforms.transforms.LinearTransformation

forward(results)¶

Parameters: tensor (Tensor) – Tensor image to be whitened.
Returns: Transformed image.
Return type: Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.Normalize(mean, std, inplace=False)¶

Bases: torchvision.transforms.transforms.Normalize

forward(results)¶

Parameters: tensor (Tensor) – Tensor image to be normalized.
Returns: Normalized Tensor image.
Return type: Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.PILToTensor¶: Bases: torchvision.transforms.transforms.PILToTensor

class easycv.datasets.shared.pipelines.third_transforms_wrapper.Pad(padding, fill=0, padding_mode='constant')¶

Bases: torchvision.transforms.transforms.Pad

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be padded.
Returns: Padded image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandAugment(num_ops: int = 2, magnitude: int = 9, num_magnitude_bins: int = 31, interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.NEAREST: 'nearest'>, fill: Optional[List[float]] = None)¶

Bases: torchvision.transforms.autoaugment.RandAugment

forward(results)¶

img (PIL Image or Tensor): Image to be transformed.

Returns: Transformed image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomAdjustSharpness(sharpness_factor, p=0.5)¶

Bases: torchvision.transforms.transforms.RandomAdjustSharpness

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be sharpened.
Returns: Randomly sharpened image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomAffine(degrees, translate=None, scale=None, shear=None, interpolation=<InterpolationMode.NEAREST: 'nearest'>, fill=0, fillcolor=None, resample=None, center=None)¶

Bases: torchvision.transforms.transforms.RandomAffine

forward(results)¶

img (PIL Image or Tensor): Image to be transformed.

Returns: Affine transformed image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomAutocontrast(p=0.5)¶

Bases: torchvision.transforms.transforms.RandomAutocontrast

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be autocontrasted.
Returns: Randomly autocontrasted image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomChoice(transforms, p=None)¶: Bases: torchvision.transforms.transforms.RandomChoice

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomCrop(size, padding=None, pad_if_needed=False, fill=0, padding_mode='constant')¶

Bases: torchvision.transforms.transforms.RandomCrop

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be cropped.
Returns: Cropped image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomEqualize(p=0.5)¶

Bases: torchvision.transforms.transforms.RandomEqualize

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be equalized.
Returns: Randomly equalized image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomErasing(p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, inplace=False)¶

Bases: torchvision.transforms.transforms.RandomErasing

forward(results)¶

Parameters: img (Tensor) – Tensor image to be erased.
Returns: Erased Tensor image.
Return type: img (Tensor)

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomGrayscale(p=0.1)¶

Bases: torchvision.transforms.transforms.RandomGrayscale

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be converted to grayscale.
Returns: Randomly grayscaled image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomHorizontalFlip(p=0.5)¶

Bases: torchvision.transforms.transforms.RandomHorizontalFlip

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be flipped.
Returns: Randomly flipped image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomInvert(p=0.5)¶

Bases: torchvision.transforms.transforms.RandomInvert

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be inverted.
Returns: Randomly color inverted image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomOrder(transforms)¶: Bases: torchvision.transforms.transforms.RandomOrder

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomPerspective(distortion_scale=0.5, p=0.5, interpolation=<InterpolationMode.BILINEAR: 'bilinear'>, fill=0)¶

Bases: torchvision.transforms.transforms.RandomPerspective

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be Perspectively transformed.
Returns: Randomly transformed image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomPosterize(bits, p=0.5)¶

Bases: torchvision.transforms.transforms.RandomPosterize

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be posterized.
Returns: Randomly posterized image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomResizedCrop(size, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=<InterpolationMode.BILINEAR: 'bilinear'>)¶

Bases: torchvision.transforms.transforms.RandomResizedCrop

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be cropped and resized.
Returns: Randomly cropped and resized image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomRotation(degrees, interpolation=<InterpolationMode.NEAREST: 'nearest'>, expand=False, center=None, fill=0, resample=None)¶

Bases: torchvision.transforms.transforms.RandomRotation

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be rotated.
Returns: Rotated image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomSolarize(threshold, p=0.5)¶

Bases: torchvision.transforms.transforms.RandomSolarize

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be solarized.
Returns: Randomly solarized image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.RandomVerticalFlip(p=0.5)¶

Bases: torchvision.transforms.transforms.RandomVerticalFlip

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be flipped.
Returns: Randomly flipped image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.Resize(size, interpolation=<InterpolationMode.BILINEAR: 'bilinear'>, max_size=None, antialias=None)¶

Bases: torchvision.transforms.transforms.Resize

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be scaled.
Returns: Rescaled image.
Return type: PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.TenCrop(size, vertical_flip=False)¶

Bases: torchvision.transforms.transforms.TenCrop

forward(results)¶

Parameters: img (PIL Image or Tensor) – Image to be cropped.
Returns: tuple of 10 images. Image can be PIL Image or Tensor

training: bool¶

class easycv.datasets.shared.pipelines.third_transforms_wrapper.ToPILImage(mode=None)¶: Bases: torchvision.transforms.transforms.ToPILImage

class easycv.datasets.shared.pipelines.third_transforms_wrapper.ToTensor¶: Bases: torchvision.transforms.transforms.ToTensor

class easycv.datasets.shared.pipelines.third_transforms_wrapper.TrivialAugmentWide(num_magnitude_bins: int = 31, interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.NEAREST: 'nearest'>, fill: Optional[List[float]] = None)¶

Bases: torchvision.transforms.autoaugment.TrivialAugmentWide

forward(results)¶

img (PIL Image or Tensor): Image to be transformed.

Returns: Transformed image.
Return type: PIL Image or Tensor

training: bool¶

easycv.datasets.shared.pipelines.transforms module¶

class easycv.datasets.shared.pipelines.transforms.Compose(transforms, profiling=False)[source]¶

Bases: object

Compose a data pipeline with a sequence of transforms. :param transforms: Either config dicts of transforms or transform objects. :type transforms: list[dict | callable]

__init__(transforms, profiling=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Submodules¶

easycv.datasets.shared.base module¶

class easycv.datasets.shared.base.BaseDataset(data_source, pipeline, profiling=False)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

Base Dataset

__init__(data_source, pipeline, profiling=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

abstract evaluate(results, evaluators, logger=None, **kwargs)[source]¶

visualize(results, **kwargs)[source]¶

Visulaize the model output results on validation data. Returns: A dictionary

If add image visualization, return dict containing
images: List of visulaized images. img_metas: List of length number of test images,

dict of image meta info, containing filename, img_shape, origin_img_shape, scale_factor and so on.

easycv.datasets.shared.dali_tfrecord_imagenet module¶

class easycv.datasets.shared.dali_tfrecord_imagenet.DaliLoaderWrapper(dali_loader, batch_size, label_offset=0)[source]¶

Bases: object

__init__(dali_loader, batch_size, label_offset=0)[source]¶: Initialize self. See help(type(self)) for accurate signature.

evaluate(results, evaluators, logger=None)[source]¶

evaluate classification task

Parameters

results – a dict of list of tensor, including prediction and groundtruth info, where prediction tensor is NxC，and the same with groundtruth labels.
evaluators – a list of evaluator

Returns

a dict of float, different metric values

Return type

eval_result

class easycv.datasets.shared.dali_tfrecord_imagenet.ImageNetTFRecordPipe(data_source, transforms, batch_size, distributed, random_shuffle=True, workers_per_gpu=2, device='gpu')[source]¶

Bases: nvidia.dali.pipeline.Pipeline

__init__(data_source, transforms, batch_size, distributed, random_shuffle=True, workers_per_gpu=2, device='gpu')[source]¶: Initialize self. See help(type(self)) for accurate signature.

define_graph()[source]¶

This function is defined by the user to construct the graph of operations for their pipeline.

It returns a list of outputs created by calling DALI Operators.

class easycv.datasets.shared.dali_tfrecord_imagenet.DaliImageNetTFRecordDataSet(data_source, pipeline, distributed, batch_size, label_offset=0, random_shuffle=True, workers_per_gpu=2)[source]¶

Bases: object

__init__(data_source, pipeline, distributed, batch_size, label_offset=0, random_shuffle=True, workers_per_gpu=2)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_dataloader()[source]¶

easycv.datasets.shared.dali_tfrecord_multi_view module¶

class easycv.datasets.shared.dali_tfrecord_multi_view.DaliLoaderWrapper(dali_loader, batch_size, return_list)[source]¶

Bases: object

__init__(dali_loader, batch_size, return_list)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.shared.dali_tfrecord_multi_view.DaliTFRecordMultiViewPipe(data_source, transforms_list, batch_size, distributed, random_shuffle=True, workers_per_gpu=2, device='gpu')[source]¶

Bases: nvidia.dali.pipeline.Pipeline

__init__(data_source, transforms_list, batch_size, distributed, random_shuffle=True, workers_per_gpu=2, device='gpu')[source]¶: Initialize self. See help(type(self)) for accurate signature.

define_graph()[source]¶

This function is defined by the user to construct the graph of operations for their pipeline.

It returns a list of outputs created by calling DALI Operators.

class easycv.datasets.shared.dali_tfrecord_multi_view.DaliTFRecordMultiViewDataset(data_source, num_views, pipelines, distributed, batch_size, random_shuffle=True, workers_per_gpu=2)[source]¶

Bases: object

Adapt to dali, the dataset outputs multiple views of an image. The number of views in the output dict depends on num_views. The image can be processed by one pipeline or multiple piepelines. :param num_views: The number of different views. :type num_views: list :param pipelines: A list of pipelines. :type pipelines: list[list[dict]]

__init__(data_source, num_views, pipelines, distributed, batch_size, random_shuffle=True, workers_per_gpu=2)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_dataloader()[source]¶

easycv.datasets.shared.dataset_wrappers module¶

class easycv.datasets.shared.dataset_wrappers.ConcatDataset(datasets)[source]¶

Bases: torch.utils.data.dataset.Dataset[torch.utils.data.dataset.T_co]

A wrapper of concatenated dataset.

Same as torch.utils.data.dataset.ConcatDataset, but concat the group flag for image aspect ratio.

Parameters: datasets (list[Dataset]) – A list of datasets.

__init__(datasets)[source]¶: Initialize self. See help(type(self)) for accurate signature.

datasets: List[torch.utils.data.dataset.Dataset[torch.utils.data.dataset.T_co]]¶

cumulative_sizes: List[int]¶

class easycv.datasets.shared.dataset_wrappers.RepeatDataset(dataset, times)[source]¶

Bases: object

A wrapper of repeated dataset.

The length of repeated dataset will be times larger than the original dataset. This is useful when the data loading time is long but the dataset is small. Using RepeatDataset can reduce the data loading time between epochs.

Parameters

dataset (Dataset) – The dataset to be repeated.
times (int) – Repeat times.

__init__(dataset, times)[source]¶: Initialize self. See help(type(self)) for accurate signature.

easycv.datasets.shared.multi_view module¶

class easycv.datasets.shared.multi_view.MultiViewDataset(data_source, num_views, pipelines)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

The dataset outputs multiple views of an image. The number of views in the output dict depends on num_views. The image can be processed by one pipeline or multiple piepelines. :param num_views: The number of different views. :type num_views: list :param pipelines: A list of pipelines. :type pipelines: list[list[dict]]

__init__(data_source, num_views, pipelines)[source]¶: Initialize self. See help(type(self)) for accurate signature.

evaluate(results, evaluators, logger=None)[source]¶

easycv.datasets.shared.odps_reader module¶

easycv.datasets.shared.odps_reader.set_dataloader_workid(value)[source]¶

easycv.datasets.shared.odps_reader.set_dataloader_worknum(value)[source]¶

easycv.datasets.shared.odps_reader.get_dist_image(img_url, max_try=10)[source]¶

class easycv.datasets.shared.odps_reader.OdpsReader(table_name, selected_cols=[], excluded_cols=[], random_start=False, odps_io_config=None, image_col=['url_image'], image_type=['url'])[source]¶

Bases: object

__init__(table_name, selected_cols=[], excluded_cols=[], random_start=False, odps_io_config=None, image_col=['url_image'], image_type=['url'])[source]¶

Init odps reader and datasource set to load data from odps table

Parameters

table_name (str) – odps table to load
selected_cols (list(str)) – select column
excluded_cols (list(str)) – exclude column
random_start (bool) – random start for odps table
odps_io_config (dict) – odps config contains access_id, access_key, endpoint
image_col (list(str)) – image column names
image_type (list(str)) – image column types support url/base64, must be same length with image type or 0

Returns :: None

get_length()[source]¶

reset_reader(dataloader_workid, dataloader_worknum)[source]¶

get_sample(idx)[source]¶

b64_decode()[source]¶

easycv.datasets.shared.raw module¶

class easycv.datasets.shared.raw.RawDataset(data_source, pipeline)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

__init__(data_source, pipeline)[source]¶: Initialize self. See help(type(self)) for accurate signature.

evaluate(scores, keyword, logger=None)[source]¶

easycv.datasets.utils package¶

Submodules¶

easycv.datasets.utils.tfrecord_util module¶

easycv.datasets.utils.tfrecord_util.get_path_and_index(file_list_or_path)[source]¶

easycv.datasets.utils.tfrecord_util.download_tfrecord(file_list_or_path, target_path, slice_count=1, slice_id=0, force=False)[source]¶

Download data from oss. Use the processes on the gpus to slice download, each gpu process downloads part of the data. The number of slices is the same as the number of gpu processes. Support tfrecord of ImageNet style. tfrecord_dir

|—train1 |—train1.idx |—train2 |—train2.idx |—…

Parameters

file_list_or_path – A list of absolute data path or a path str type(file_list) == list means this is the list type(file_list) == str means open(file_list).readlines()
target_path – A str, download path
slice_count – Download worker num
slice_id – Download worker ID
force – If false, skip download if the file already exists in the target path. If true, recopy and replace the original file.

Returns

list of str, download tfrecord path index_path: list of str, download tfrecord idx path

Return type

path

easycv.datasets.utils.type_util module¶

easycv.datasets.utils.type_util.is_dali_dataset_type(type_name)[source]¶

Submodules¶

easycv.datasets.builder module¶

easycv.datasets.builder.build_dataset(cfg, default_args=None)[source]¶

easycv.datasets.builder.build_dali_dataset(cfg, default_args=None)[source]¶

easycv.datasets.builder.build_datasource(cfg)[source]¶

easycv.datasets.registry module¶

easycv.hooks package¶

Submodules¶

easycv.hooks.best_ckpt_saver_hook module¶

class easycv.hooks.best_ckpt_saver_hook.BestCkptSaverHook(by_epoch=True, save_optimizer=True, best_metric_name=[], best_metric_type=[], **kwargs)[source]¶

Bases: mmcv.runner.hooks.hook.Hook

Save checkpoints periodically.

Parameters

by_epoch (bool) – Saving checkpoints by epoch or by iteration. Default: True.
save_optimizer (bool) – Whether to save optimizer state_dict in the checkpoint. It is usually used for resuming experiments. Default: True.
best_metric_name (List(str)) – metric name to save best, such as “neck_top1”… Default: [], do not save anything
best_metric_type (List(str)) – metric type to define best, should be “max”, “min” if len(best_metric_type) <= len(best_metric_type), use “max” to append.

__init__(by_epoch=True, save_optimizer=True, best_metric_name=[], best_metric_type=[], **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

before_run(runner)[source]¶

after_train_epoch(runner)[source]¶

easycv.hooks.builder module¶

easycv.hooks.builder.build_hook(cfg, default_args=None)[source]¶

easycv.hooks.byol_hook module¶

class easycv.hooks.byol_hook.BYOLHook(end_momentum=1.0, **kwargs)[source]¶

Bases: mmcv.runner.hooks.hook.Hook

Hook in BYOL

This hook including momentum adjustment in BYOL following:: m = 1 - ( 1- m_0) * (cos(pi * k / K) + 1) / 2, k: current step, K: total steps.

__init__(end_momentum=1.0, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

before_train_iter(runner)[source]¶

easycv.hooks.dino_hook module¶

easycv.hooks.dino_hook.cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0, start_warmup_value=0)[source]¶

class easycv.hooks.dino_hook.DINOHook(momentum_teacher=0.996, weight_decay=0.04, weight_decay_end=0.4, **kwargs)[source]¶

Bases: mmcv.runner.hooks.hook.Hook

Hook in DINO

__init__(momentum_teacher=0.996, weight_decay=0.04, weight_decay_end=0.4, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

before_run(runner)[source]¶

before_train_iter(runner)[source]¶

after_train_iter(runner)[source]¶

before_train_epoch(runner)[source]¶

easycv.hooks.ema_hook module¶

class easycv.hooks.ema_hook.ModelEMA(model, decay=0.9999, updates=0)[source]¶

Bases: object

Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models Keep a moving average of everything in the model state_dict (parameters and buffers). This is intended to allow functionality like https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage A smoothed version of the weights is necessary for some training schemes to perform well. This class is sensitive where it is initialized in the sequence of model init, GPU assignment and distributed training wrappers.

In Yolo5s, ema help increase mAP from 0.27 to 0.353

__init__(model, decay=0.9999, updates=0)[source]¶: Initialize self. See help(type(self)) for accurate signature.

update(model)[source]¶

update_attr(model, include=(), exclude=('process_group', 'reducer'))[source]¶

class easycv.hooks.ema_hook.EMAHook(decay=0.9999, copy_model_attr=())[source]¶

Bases: mmcv.runner.hooks.hook.Hook

Hook to carry out Exponential Moving Average

__init__(decay=0.9999, copy_model_attr=())[source]¶

Parameters

decay – decay rate for exponetial moving average
copy_model_attr – attribute to copy from origin model to ema model

before_run(runner)[source]¶

before_train_epoch(runner)[source]¶

after_train_iter(runner)[source]¶

easycv.hooks.eval_hook module¶

class easycv.hooks.eval_hook.EvalHook(dataloader, initial=False, interval=1, mode='test', flush_buffer=True, **eval_kwargs)[source]¶

Bases: mmcv.runner.hooks.hook.Hook

Evaluation hook.

dataloader¶

A PyTorch dataloader.

Type: DataLoader

interval¶

Evaluation interval (by epochs). Default: 1.

Type: int

mode¶

model forward mode

Type: str

flush_buffer¶

flush log buffer

Type: bool

__init__(dataloader, initial=False, interval=1, mode='test', flush_buffer=True, **eval_kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

before_run(runner)[source]¶

after_train_epoch(runner)[source]¶

add_visualization_info(runner, results)[source]¶

evaluate(runner, results)[source]¶

class easycv.hooks.eval_hook.DistEvalHook(dataloader, interval=1, mode='test', initial=False, gpu_collect=False, flush_buffer=True, **eval_kwargs)[source]¶

Bases: easycv.hooks.eval_hook.EvalHook

Distributed evaluation hook.

dataloader¶

A PyTorch dataloader.

Type: DataLoader

interval¶

Evaluation interval (by epochs). Default: 1.

Type: int

mode¶

model forward mode

Type: str

tmpdir¶

Temporary directory to save the results of all processes. Default: None.

Type: str | None

gpu_collect¶

Whether to use gpu or cpu to collect results. Default: False.

Type: bool

__init__(dataloader, interval=1, mode='test', initial=False, gpu_collect=False, flush_buffer=True, **eval_kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

before_run(runner)[source]¶

after_train_epoch(runner)[source]¶

easycv.hooks.export_hook module¶

class easycv.hooks.export_hook.ExportHook(cfg, ckpt_filename_tmpl='epoch_{}.pth', export_ckpt_filename_tmpl='epoch_{}_export.pt', export_after_each_ckpt=False)[source]¶

Bases: mmcv.runner.hooks.hook.Hook

export model when training on pai

__init__(cfg, ckpt_filename_tmpl='epoch_{}.pth', export_ckpt_filename_tmpl='epoch_{}_export.pt', export_after_each_ckpt=False)[source]¶

Parameters

cfg – config dict
ckpt_filename_tmpl – checkpoint filename template

export_model(runner, epoch)[source]¶

after_train_iter(runner)[source]¶

after_train_epoch(runner)[source]¶

after_run(runner)[source]¶

easycv.hooks.extractor module¶

class easycv.hooks.extractor.Extractor(dataset, imgs_per_gpu, workers_per_gpu, dist_mode=False)[source]¶

Bases: object

__init__(dataset, imgs_per_gpu, workers_per_gpu, dist_mode=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

easycv.hooks.optimizer_hook module¶

class easycv.hooks.optimizer_hook.OptimizerHook(update_interval=1, grad_clip=None, coalesce=True, bucket_size_mb=- 1, ignore_key=[], ignore_key_epoch=[], multiply_key=[], multiply_rate=[])[source]¶

Bases: mmcv.runner.hooks.optimizer.OptimizerHook

__init__(update_interval=1, grad_clip=None, coalesce=True, bucket_size_mb=- 1, ignore_key=[], ignore_key_epoch=[], multiply_key=[], multiply_rate=[])[source]¶

ignore_key: [str,…], ignore_key[i], name of parameters, which’s gradient will be set to zero before every optimizer step when epoch < ignore_key_epoch[i] ignore_key_epoch: [int,…], epoch < ignore_key_epoch[i], ignore_key[i]’s gradient will be set to zero.

multiply_key:[str,…] multiply_key[i], name of parameters, which will set different learning rate ratio by multipy_rate multiply_rate:[float,…] multiply_rate[i], different ratio

before_run(runner)[source]¶

after_train_iter(runner)[source]¶

class easycv.hooks.optimizer_hook.AMPFP16OptimizerHook(update_interval=1, grad_clip=None, coalesce=True, bucket_size_mb=- 1, ignore_key=[], ignore_key_epoch=[])[source]¶

Bases: easycv.hooks.optimizer_hook.OptimizerHook

__init__(update_interval=1, grad_clip=None, coalesce=True, bucket_size_mb=- 1, ignore_key=[], ignore_key_epoch=[])[source]¶: ignore_key: [str,…], ignore_key[i], name of parameters, which’s gradient will be set to zero before every optimizer step when epoch < ignore_key_epoch[i] ignore_key_epoch: [int,…], epoch < ignore_key_epoch[i], ignore_key[i]’s gradient will be set to zero.

before_run(runner)[source]¶

after_train_iter(runner)[source]¶

easycv.hooks.oss_sync_hook module¶

class easycv.hooks.oss_sync_hook.OSSSyncHook(work_dir, oss_work_dir, interval=1, ckpt_filename_tmpl='epoch_{}.pth', export_ckpt_filename_tmpl='epoch_{}_export.pt', other_file_list=[], iter_interval=None)[source]¶

Bases: mmcv.runner.hooks.hook.Hook

upload log files and checkpoints to oss when training on pai

__init__(work_dir, oss_work_dir, interval=1, ckpt_filename_tmpl='epoch_{}.pth', export_ckpt_filename_tmpl='epoch_{}_export.pt', other_file_list=[], iter_interval=None)[source]¶

Parameters

work_dir – work_dir in cfg
oss_work_dir – oss directory where to upload local files in work_dir
interval – upload frequency
ckpt_filename_tmpl – checkpoint filename template
other_file_list – other file need to be upload to oss
iter_interval – upload frequency by iter interval, default to be None, means do it with certain assignment

upload_file(runner)[source]¶

after_train_iter(runner)[source]¶

after_train_epoch(runner)[source]¶

after_run(runner)[source]¶

easycv.hooks.registry module¶

easycv.hooks.show_time_hook module¶

class easycv.hooks.show_time_hook.TIMEHook(end_momentum=1.0, **kwargs)[source]¶

Bases: mmcv.runner.hooks.hook.Hook

This hook to show time for runner running process

__init__(end_momentum=1.0, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

before_train_iter(runner)[source]¶

after_train_iter(runner)[source]¶

easycv.hooks.swav_hook module¶

class easycv.hooks.swav_hook.SWAVHook(gpu_batch_size=32, dump_path='data/', **kwargs)[source]¶

Bases: mmcv.runner.hooks.hook.Hook

Hook in SWAV

__init__(gpu_batch_size=32, dump_path='data/', **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

before_run(runner)[source]¶

before_train_epoch(runner)[source]¶

after_train_epoch(runner)[source]¶

easycv.hooks.sync_norm_hook module¶

easycv.hooks.sync_norm_hook.get_norm_states(module)[source]¶

class easycv.hooks.sync_norm_hook.SyncNormHook(no_aug_epochs=15, interval=1, **kwargs)[source]¶

Bases: mmcv.runner.hooks.hook.Hook

Synchronize Norm states after training epoch, currently used in YOLOX.

Parameters

no_aug_epochs (int) – The number of latter epochs in the end of the training to switch to synchronizing norm interval. Default: 15.
interval (int) – Synchronizing norm interval. Default: 1.

__init__(no_aug_epochs=15, interval=1, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

before_train_epoch(runner)[source]¶

after_train_epoch(runner)[source]¶: Synchronizing norm.

easycv.hooks.sync_random_size_hook module¶

class easycv.hooks.sync_random_size_hook.SyncRandomSizeHook(ratio_range=(14, 26), img_scale=(640, 640), interval=10, device='cuda', **kwargs)[source]¶

Bases: mmcv.runner.hooks.hook.Hook

Change and synchronize the random image size across ranks, currently used in YOLOX.

Parameters

ratio_range (tuple[int]) – Random ratio range. It will be multiplied by 32, and then change the dataset output image size. Default: (14, 26).
img_scale (tuple[int]) – Size of input image. Default: (640, 640).
interval (int) – The interval of change image size. Default: 10.
device (torch.device | str) – device for returned tensors. Default: ‘cuda’.

__init__(ratio_range=(14, 26), img_scale=(640, 640), interval=10, device='cuda', **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

after_train_iter(runner)[source]¶: Change the dataset output image size.

easycv.hooks.tensorboard module¶

class easycv.hooks.tensorboard.TensorboardLoggerHookV2(log_dir=None, interval=10, ignore_last=True, reset_flag=False, by_epoch=True)[source]¶

Bases: mmcv.runner.hooks.logger.tensorboard.TensorboardLoggerHook

visualization_log(runner)[source]¶: Images Visulization. visualization_buffer is a dictionary containing:

images (list): list of visulaized images. img_metas (list of dict, optional): dict containing ori_filename and so on.

ori_filename will be displayed as the tag of the image by default.

log(runner)[source]¶

after_train_iter(runner)[source]¶

easycv.hooks.wandb module¶

class easycv.hooks.wandb.WandbLoggerHookV2(init_kwargs=None, interval=10, ignore_last=True, reset_flag=False, commit=True, by_epoch=True, with_step=True)[source]¶

Bases: mmcv.runner.hooks.logger.wandb.WandbLoggerHook

visualization_log(runner)[source]¶: Images Visulization. visualization_buffer is a dictionary containing:

images (list): list of visulaized images. img_metas (list of dict, optional): dict containing ori_filename and so on.

ori_filename will be displayed as the tag of the image by default.

log(runner)[source]¶

after_train_iter(runner)[source]¶

easycv.hooks.yolox_lr_hook module¶

class easycv.hooks.yolox_lr_hook.YOLOXLrUpdaterHook(num_last_epochs, **kwargs)[source]¶

Bases: mmcv.runner.hooks.lr_updater.CosineAnnealingLrUpdaterHook

YOLOX learning rate scheme.

There are two main differences between YOLOXLrUpdaterHook and CosineAnnealingLrUpdaterHook.

When the current running epoch is greater than
max_epoch-last_epoch, a fixed learning rate will be used

The exp warmup scheme is different with LrUpdaterHook in MMCV

Parameters: num_last_epochs (int) – The number of epochs with a fixed learning rate before the end of the training.

__init__(num_last_epochs, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_warmup_lr(cur_iters)[source]¶

get_lr(runner, base_lr)[source]¶

easycv.hooks.yolox_mode_switch_hook module¶

class easycv.hooks.yolox_mode_switch_hook.YOLOXModeSwitchHook(no_aug_epochs=15, skip_type_keys=('MMMosaic', 'MMRandomAffine', 'MMMixUp'), **kwargs)[source]¶

Bases: mmcv.runner.hooks.hook.Hook

Switch the mode of YOLOX during training.

This hook turns off the mosaic and mixup data augmentation and switches to use L1 loss in bbox_head.

Parameters: no_aug_epochs – The number of latter epochs in the end of the training to close the data augmentation and switch to L1 loss. Default: 15.

__init__(no_aug_epochs=15, skip_type_keys=('MMMosaic', 'MMRandomAffine', 'MMMixUp'), **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

before_train_epoch(runner)[source]¶: Close mosaic and mixup augmentation and switches to use L1 loss.

easycv.predictors package¶

Submodules¶

easycv.predictors.base module¶

class easycv.predictors.base.NumpyToPIL[source]¶: Bases: object

class easycv.predictors.base.Predictor(model_path, numpy_to_pil=True)[source]¶

Bases: object

__init__(model_path, numpy_to_pil=True)[source]¶: Initialize self. See help(type(self)) for accurate signature.

preprocess(image_list)[source]¶

predict_batch(image_batch, **forward_kwargs)[source]¶

predict using batched data

Parameters

image_batch (torch.Tensor) – tensor with shape [N, 3, H, W]
forward_kwargs – kwargs for additional parameters

Returns

the output of model.forward, list or tuple

Return type

output

easycv.predictors.builder module¶

easycv.predictors.builder.build_predictor(cfg)[source]¶

easycv.predictors.classifier module¶

class easycv.predictors.classifier.TorchClassifier(model_path, model_config=None, topk=1, label_map_path=None)[source]¶

Bases: easycv.predictors.interface.PredictorInterface

__init__(model_path, model_config=None, topk=1, label_map_path=None)[source]¶

init model

Parameters

model_path – model file path
model_config – config string for model to init, in json format

get_output_type()[source]¶

in this function user should return a type dict, which indicates which type of data should the output of predictor be converted to * type json, data will be serialized to json str

type image, data will be converted to encode image binary and write to oss file, whose name is output_dir/${key}/${input_filename}_${idx}.jpg, where input_filename is the base filename extracted from url, key corresponds to the key in the dict of output_type, if the type of data indexed by key is a list, idx is the index of element in list, otherwhile ${idx} will be empty
type video, data will be converted to encode video binary and write to oss file,

:: return {: ‘image’: ‘image’, ‘feature’: ‘json’

}

indicating that the image data in the output dict will be save to image file and feature in output dict will be converted to json

batch(image_tensor_list)[source]¶

predict(input_data_list, batch_size=- 1)[source]¶

using session run predict a number of samples using batch_size

Parameters

input_data_list – a list of numpy array, each array is a sample to be predicted
batch_size – batch_size passed by the caller, you can also ignore this param and use a fixed number if you do not want to adjust batch_size in runtime

Returns

a list of dict, each dict is the prediction result of one sample: eg, {“output1”: value1, “output2”: value2}, the value type can be python int str float, and numpy array

Return type

result

easycv.predictors.detector module¶

class easycv.predictors.detector.TorchYoloXPredictor(model_path, max_det=100, score_thresh=0.5, model_config=None)[source]¶

Bases: easycv.predictors.interface.PredictorInterface

__init__(model_path, max_det=100, score_thresh=0.5, model_config=None)[source]¶

init model

Parameters

model_path – model file path
max_det – maximum number of detection
score_thresh – score_thresh to filter box
model_config – config string for model to init, in json format

predict(input_data_list, batch_size=- 1)[source]¶

using session run predict a number of samples using batch_size

Parameters

input_data_list – a list of numpy array(in rgb order), each array is a sample to be predicted
batch_size – batch_size passed by the caller, you can also ignore this param and use a fixed number if you do not want to adjust batch_size in runtime

Returns

a list of dict, each dict is the prediction result of one sample: eg, {“output1”: value1, “output2”: value2}, the value type can be python int str float, and numpy array

Return type

result

class easycv.predictors.detector.TorchFaceDetector(model_path=None, model_config=None)[source]¶

Bases: easycv.predictors.interface.PredictorInterface

__init__(model_path=None, model_config=None)[source]¶

init model, add a facedetect and align for img input.

Parameters

model_path – model file path
model_config – config string for model to init, in json format

get_output_type()[source]¶

in this function user should return a type dict, which indicates which type of data should the output of predictor be converted to * type json, data will be serialized to json str

type image, data will be converted to encode image binary and write to oss file, whose name is output_dir/${key}/${input_filename}_${idx}.jpg, where input_filename is the base filename extracted from url, key corresponds to the key in the dict of output_type, if the type of data indexed by key is a list, idx is the index of element in list, otherwhile ${idx} will be empty
type video, data will be converted to encode video binary and write to oss file,

:: return {: ‘image’: ‘image’, ‘feature’: ‘json’

} indicating that the image data in the output dict will be save to image file and feature in output dict will be converted to json

batch(image_tensor_list)[source]¶

predict(input_data_list, batch_size=- 1, threshold=0.95)[source]¶

using session run predict a number of samples using batch_size

Parameters

input_data_list – a list of numpy array, each array is a sample to be predicted
batch_size – batch_size passed by the caller, you can also ignore this param and use a fixed number if you do not want to adjust batch_size in runtime

Returns

a list of dict, each dict is the prediction result of one sample: eg, {“output1”: value1, “output2”: value2}, the value type can be python int str float, and numpy array

Return type

result

Raises

if detect !=1 face in a img, then do nothing for this image –

class easycv.predictors.detector.TorchYoloXClassifierPredictor(models_root_dir, max_det=100, cls_score_thresh=0.01, det_model_config=None, cls_model_config=None)[source]¶

Bases: easycv.predictors.interface.PredictorInterface

__init__(models_root_dir, max_det=100, cls_score_thresh=0.01, det_model_config=None, cls_model_config=None)[source]¶

init model, add a yolox and classification predictor for img input.

Parameters

models_root_dir – models_root_dir/detection/.pth and models_root_dir/classification/.pth
det_model_config – config string for detection model to init, in json format
cls_model_config – config string for classification model to init, in json format

predict(input_data_list, batch_size=- 1)[source]¶

using session run predict a number of samples using batch_size

Parameters

input_data_list – a list of numpy array(in rgb order), each array is a sample to be predicted
batch_size – batch_size passed by the caller, you can also ignore this param and use a fixed number if you do not want to adjust batch_size in runtime

Returns

a list of dict, each dict is the prediction result of one sample: eg, {“output1”: value1, “output2”: value2}, the value type can be python int str float, and numpy array

Return type

result

easycv.predictors.feature_extractor module¶

class easycv.predictors.feature_extractor.TorchFeatureExtractor(model_path, model_config=None)[source]¶

Bases: easycv.predictors.interface.PredictorInterface

__init__(model_path, model_config=None)[source]¶

init model

Parameters

model_path – model file path
model_config – config string for model to init, in json format

get_output_type()[source]¶

in this function user should return a type dict, which indicates which type of data should the output of predictor be converted to * type json, data will be serialized to json str

type image, data will be converted to encode image binary and write to oss file, whose name is output_dir/${key}/${input_filename}_${idx}.jpg, where input_filename is the base filename extracted from url, key corresponds to the key in the dict of output_type, if the type of data indexed by key is a list, idx is the index of element in list, otherwhile ${idx} will be empty
type video, data will be converted to encode video binary and write to oss file,

:: return {: ‘image’: ‘image’, ‘feature’: ‘json’

} indicating that the image data in the output dict will be save to image file and feature in output dict will be converted to json

batch(image_tensor_list)[source]¶

predict(input_data_list, batch_size=- 1)[source]¶

using session run predict a number of samples using batch_size

Parameters

input_data_list – a list of numpy array, each array is a sample to be predicted
batch_size – batch_size passed by the caller, you can also ignore this param and use a fixed number if you do not want to adjust batch_size in runtime

Returns

a list of dict, each dict is the prediction result of one sample: eg, {“output1”: value1, “output2”: value2}, the value type can be python int str float, and numpy array

Return type

result

class easycv.predictors.feature_extractor.TorchFaceFeatureExtractor(model_path, model_config=None)[source]¶

Bases: easycv.predictors.interface.PredictorInterface

__init__(model_path, model_config=None)[source]¶

init model, add a facedetect and align for img input.

Parameters

model_path – model file path
model_config – config string for model to init, in json format

get_output_type()[source]¶

in this function user should return a type dict, which indicates which type of data should the output of predictor be converted to * type json, data will be serialized to json str

type image, data will be converted to encode image binary and write to oss file, whose name is output_dir/${key}/${input_filename}_${idx}.jpg, where input_filename is the base filename extracted from url, key corresponds to the key in the dict of output_type, if the type of data indexed by key is a list, idx is the index of element in list, otherwhile ${idx} will be empty
type video, data will be converted to encode video binary and write to oss file,

:: return {: ‘image’: ‘image’, ‘feature’: ‘json’

} indicating that the image data in the output dict will be save to image file and feature in output dict will be converted to json

batch(image_tensor_list)[source]¶

predict(input_data_list, batch_size=- 1, detect_and_align=True)[source]¶

using session run predict a number of samples using batch_size

Parameters

input_data_list – a list of numpy array or PIL.Image, each array is a sample to be predicted
batch_size – batch_size passed by the caller, you can also ignore this param and use a fixed number if you do not want to adjust batch_size in runtime
detect_and_align – True to detect and align before feature extractor

Returns

a list of dict, each dict is the prediction result of one sample: eg, {“output1”: value1, “output2”: value2}, the value type can be python int str float, and numpy array

Return type

result

Raises

if detect !=1 face in a img, then do nothing for this image –

class easycv.predictors.feature_extractor.TorchMultiFaceFeatureExtractor(model_path, model_config=None)[source]¶

Bases: easycv.predictors.interface.PredictorInterface

__init__(model_path, model_config=None)[source]¶

init model, add a facedetect and align for img input.

Parameters

model_path – model file path
model_config – config string for model to init, in json format

get_output_type()[source]¶

in this function user should return a type dict, which indicates which type of data should the output of predictor be converted to * type json, data will be serialized to json str

type image, data will be converted to encode image binary and write to oss file, whose name is output_dir/${key}/${input_filename}_${idx}.jpg, where input_filename is the base filename extracted from url, key corresponds to the key in the dict of output_type, if the type of data indexed by key is a list, idx is the index of element in list, otherwhile ${idx} will be empty
type video, data will be converted to encode video binary and write to oss file,

:: return {: ‘image’: ‘image’, ‘feature’: ‘json’

} indicating that the image data in the output dict will be save to image file and feature in output dict will be converted to json

batch(image_tensor_list)[source]¶

predict(input_data_list, batch_size=- 1, detect_and_align=True)[source]¶

using session run predict a number of samples using batch_size

Parameters

input_data_list – a list of numpy array or PIL.Image, each array is a sample to be predicted
batch_size – batch_size passed by the caller, you can also ignore this param and use a fixed number if you do not want to adjust batch_size in runtime
detect_and_align – True to detect and align before feature extractor

Returns

a list of dict, each dict is the prediction result of one sample: eg, {“output1”: value1, “output2”: value2}, the value type can be python int str float, and numpy array

Return type

result

Raises

if detect !=1 face in a img, then do nothing for this image –

class easycv.predictors.feature_extractor.TorchFaceAttrExtractor(model_path, model_config=None, face_threshold=0.95, attr_method=['distribute_sum', 'softmax', 'softmax'], attr_name=['age', 'gender', 'emo'])[source]¶

Bases: easycv.predictors.interface.PredictorInterface

__init__(model_path, model_config=None, face_threshold=0.95, attr_method=['distribute_sum', 'softmax', 'softmax'], attr_name=['age', 'gender', 'emo'])[source]¶

init model

Parameters

model_path – model file path
model_config – config string for model to init, in json format
attr_method –
- softmax: do softmax for feature_dim 1
- distribute_sum: do softmax and prob sum

get_output_type()[source]¶

in this function user should return a type dict, which indicates which type of data should the output of predictor be converted to * type json, data will be serialized to json str

type image, data will be converted to encode image binary and write to oss file, whose name is output_dir/${key}/${input_filename}_${idx}.jpg, where input_filename is the base filename extracted from url, key corresponds to the key in the dict of output_type, if the type of data indexed by key is a list, idx is the index of element in list, otherwhile ${idx} will be empty
type video, data will be converted to encode video binary and write to oss file,

:: return {: ‘image’: ‘image’, ‘feature’: ‘json’

} indicating that the image data in the output dict will be save to image file and feature in output dict will be converted to json

batch(image_tensor_list)[source]¶

predict(input_data_list, batch_size=- 1)[source]¶

using session run predict a number of samples using batch_size

Parameters

input_data_list – a list of numpy array, each array is a sample to be predicted
batch_size – batch_size passed by the caller, you can also ignore this param and use a fixed number if you do not want to adjust batch_size in runtime

Returns

a list of dict, each dict is the prediction result of one sample: eg, {“output1”: value1, “output2”: value2}, the value type can be python int str float, and numpy array

Return type

result

easycv.predictors.interface module¶

class easycv.predictors.interface.PredictorInterface(model_path, model_config=None)[source]¶

Bases: object

version = 1¶

__init__(model_path, model_config=None)[source]¶

init model

Parameters

model_path – init model from this directory
model_config – config string for model to init, in json format

abstract predict(input_data, batch_size)[source]¶

using session run predict a number of samples using batch_size

Parameters

input_data – a list of numpy array, each array is a sample to be predicted
batch_size – batch_size passed by the caller, you can also ignore this param and use a fixed number if you do not want to adjust batch_size in runtime

Returns

a list of dict, each dict is the prediction result of one sample: eg, {“output1”: value1, “output2”: value2}, the value type can be python int str float, and numpy array

Return type

result

get_output_type()[source]¶

in this function user should return a type dict, which indicates which type of data should the output of predictor be converted to * type json, data will be serialized to json str

type image, data will be converted to encode image binary and write to oss file, whose name is output_dir/${key}/${input_filename}_${idx}.jpg, where input_filename is extracted from url, key corresponds to the key in the dict of output_type, if the type of data indexed by key is a list, idx is the index of element in list, otherwhile ${idx} will be empty
type video, data will be converted to encode video binary and write to oss file,

:: return {: ‘image’: ‘image’, ‘feature’: ‘json’

} indicating that the image data in the output dict will be save to image file and feature in output dict will be converted to json

class easycv.predictors.interface.PredictorInterfaceV2(model_path, model_config=None)[source]¶

Bases: easycv.predictors.interface.PredictorInterface

version = 2¶

__init__(model_path, model_config=None)[source]¶

init model

Parameters

model_path – init model from this directory
model_config – config string for model to init, in json format

get_output_type()[source]¶

in this function user should return a type dict, which indicates which type of data should the output of predictor be converted to * type json, data will be serialized to json str

type image, data will be converted to encode image binary and write to oss file, whose name is output_dir/${key}/${input_filename}_${idx}.jpg, where input_filename is the base filename extracted from url, key corresponds to the key in the dict of output_type, if the type of data indexed by key is a list, idx is the index of element in list, otherwhile ${idx} will be empty
type video, data will be converted to encode video binary and write to oss file,

:: return {: ‘image’: ‘image’, ‘feature’: ‘json’

} indicating that the image data in the output dict will be save to image file and feature in output dict will be converted to json

abstract predict(input_data_dict_list, batch_size)[source]¶

using session run predict a number of samples using batch_size

Parameters

input_data_dict_list – a list of dict, each dict is a sample data to be predicted
batch_size – batch_size passed by the caller, you can also ignore this param and use a fixed number if you do not want to adjust batch_size in runtime

Returns

a list of dict, each dict is the prediction result of one sample: eg, {“output1”: value1, “output2”: value2}, the value type can be python int str float, and numpy array

Return type

result

easycv.predictors.pose_predictor module¶

class easycv.predictors.pose_predictor.LoadImage(color_type='color', channel_order='rgb')[source]¶

Bases: object

A simple pipeline to load image.

__init__(color_type='color', channel_order='rgb')[source]¶: Initialize self. See help(type(self)) for accurate signature.

easycv.predictors.pose_predictor.rgetattr(obj, attr, *args)[source]¶

class easycv.predictors.pose_predictor.OutputHook(module, outputs=None, as_tensor=False)[source]¶

Bases: object

__init__(module, outputs=None, as_tensor=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

register(module)[source]¶

remove()[source]¶

class easycv.predictors.pose_predictor.TorchPoseTopDownPredictor(model_path, model_config=None)[source]¶

Bases: easycv.predictors.interface.PredictorInterface

Inference a single image with a list of bounding boxes.

__init__(model_path, model_config=None)[source]¶

init model

Parameters

model_path – model file path
model_config – config string for model to init, in json format

predict(input_data_list, batch_size=- 1, return_heatmap=False)[source]¶

Inference pose.

Parameters

input_data_list –
A list of image infos, like: [

{

‘img’ (str | np.ndarray, RGB):
Image filename or loaded image.

’detection_results’(list | np.ndarray):
All bounding boxes (with scores), shaped (N, 4) or (N, 5). (left, top, width, height, [score]) where N is number of bounding boxes.

]
batch_size – batch size
return_heatmap – return heatmap value or not, default false.

Returns

{: ‘pose_results’: list of ndarray[NxKx3]: Predicted pose x, y, score ‘pose_heatmap’ (optional): list of heatmap[N, K, H, W]: Model output heatmap

}

class easycv.predictors.pose_predictor.TorchPoseTopDownPredictorWithDetector(model_path, model_config={'detection': {'model_type': None, 'reserved_classes': [], 'score_thresh': 0.0}, 'pose': {'bbox_thr': 0.3, 'format': 'xywh'}})[source]¶

Bases: easycv.predictors.interface.PredictorInterface

SUPPORT_DETECTION_PREDICTORS = {'TorchYoloXPredictor': <class 'easycv.predictors.detector.TorchYoloXPredictor'>}¶

__init__(model_path, model_config={'detection': {'model_type': None, 'reserved_classes': [], 'score_thresh': 0.0}, 'pose': {'bbox_thr': 0.3, 'format': 'xywh'}})[source]¶

init model

Parameters

model_path – pose and detection model file path, split with ,, make sure the first is pose model, second is detection model
model_config – config string for model to init, in json format

process_det_results(outputs, input_data_list, reserved_classes=[])[source]¶

predict(input_data_list, batch_size=- 1, return_heatmap=False)[source]¶

Inference with pose model and detection model.

Parameters

input_data_list – A list of images(np.ndarray, RGB)
batch_size – batch size
return_heatmap – return heatmap value or not, default false.

Returns

{: ‘pose_results’: list of ndarray[NxKx3]: Predicted pose x, y, score ‘pose_heatmap’ (optional): list of heatmap[N, K, H, W]: Model output heatmap

}

easycv.predictors.pose_predictor.vis_pose_result(model, img, result, radius=4, thickness=1, kpt_score_thr=0.3, bbox_color='green', dataset_info=None, show=False, out_file=None)[source]¶

Visualize the detection results on the image.

Parameters

model (nn.Module) – The loaded detector.
img (str | np.ndarray) – Image filename or loaded image.
result (list[dict]) – The results to draw over img (bbox_result, pose_result).
radius (int) – Radius of circles.
thickness (int) – Thickness of lines.
kpt_score_thr (float) – The threshold to visualize the keypoints.
skeleton (list[tuple()]) – Default None.
show (bool) – Whether to show the image. Default True.
out_file (str|None) – The filename of the output visualization image.

easycv.core package¶

Subpackages¶

easycv.core.evaluation package¶

Subpackages¶

easycv.core.evaluation.custom_cocotools package¶

Submodules¶

easycv.core.evaluation.custom_cocotools.cocoeval module¶

class easycv.core.evaluation.custom_cocotools.cocoeval.COCOeval(cocoGt=None, cocoDt=None, iouType='segm', sigmas=None)[source]¶

Bases: object

__init__(cocoGt=None, cocoDt=None, iouType='segm', sigmas=None)[source]¶

Initialize CocoEval using coco APIs for gt and dt :param cocoGt: coco object with ground truth annotations :param cocoDt: coco object with detection results :param iouType: type of iou to be computed, bbox for detection task,

segm for segmentation task

Parameters: sigmas – keypoint labelling sigmas.
Returns: None

evaluate()[source]¶: Run per image evaluation on given images and store results (a list of dict) in self.evalImgs :returns: None

computeIoU(imgId, catId)[source]¶

computeOks(imgId, catId)[source]¶

evaluateImg(imgId, catId, aRng, maxDet)[source]¶

perform evaluation for single category and image :param imgId: image id, string :param catId: category id, string :param aRng: area range, tuple :param maxDet: maximum detection number

Returns: dict (single image results)

accumulate(p=None)[source]¶

Accumulate per image evaluation results and store the result in self.eval :param param p: input params for evaluation

Returns: None

summarize()[source]¶: Compute and display summary metrics for evaluation results. Note this functin can only be applied on the default parameter setting

summarize_per_category()[source]¶: Compute and display summary metrics for evaluation results per category. Note this functin can only be applied on the default parameter setting

filter_annotations(annotations, catIds)[source]¶

makeplot(recThrs, precisions, name, save_dir=None)[source]¶

analyze()[source]¶: Analyze errors

class easycv.core.evaluation.custom_cocotools.cocoeval.Params(iouType='segm')[source]¶

Bases: object

Params for coco evaluation api

setDetParams()[source]¶

setKpParams()[source]¶

__init__(iouType='segm')[source]¶: Initialize self. See help(type(self)) for accurate signature.

Submodules¶

easycv.core.evaluation.ap module¶

easycv.core.evaluation.ap.ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-recall_curve.png')[source]¶

Compute the average precision, given the recall and precision curves. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.

Parameters

tp – True positives (nparray, nx1 or nx10).
conf – Objectness value from 0-1 (nparray).
pred_cls – Predicted object classes (nparray).
target_cls – True object classes (nparray).
plot – Plot precision-recall curve at mAP@0.5
fname – Plot filename

Returns

The average precision as computed in py-faster-rcnn.

easycv.core.evaluation.ap.compute_ap(recall, precision)[source]¶

Compute the average precision, given the recall and precision curves. Source: https://github.com/rbgirshick/py-faster-rcnn.

Parameters

recall – The recall curve (list).
precision – The precision curve (list).

Returns

The average precision as computed in py-faster-rcnn.

easycv.core.evaluation.auc_eval module¶

class easycv.core.evaluation.auc_eval.AucEvaluator(dataset_name=None, metric_names=['neck_auc'], neck_num=None)[source]¶

Bases: easycv.core.evaluation.base_evaluator.Evaluator

AUC evaluator for binary classification only.

__init__(dataset_name=None, metric_names=['neck_auc'], neck_num=None)[source]¶

Parameters

dataset_name – eval dataset name
metric_names – eval metrics name
neck_num – some model contains multi-neck to support multitask, neck_num means use the no.neck_num neck output of model to eval

easycv.core.evaluation.base_evaluator module¶

class easycv.core.evaluation.base_evaluator.Evaluator(dataset_name=None, metric_names=[])[source]¶

Bases: object

Evaluator interface

__init__(dataset_name=None, metric_names=[])[source]¶

Construct eval ops from tensor

Parameters

dataset_name (str) – dataset name to be evaluated
metric_names (List[str]) – metric names this evaluator will return

evaluate(prediction_dict, groundtruth_dict, **kwargs)[source]¶

property metric_names¶

easycv.core.evaluation.builder module¶

easycv.core.evaluation.builder.build_evaluator(evaluator_cfg_list)[source]¶

build evaluator according to metric name

Parameters: evaluator_cfg_list – list of evaluator config dict
Returns: return a list of evaluator

easycv.core.evaluation.classification_eval module¶

class easycv.core.evaluation.classification_eval.ClsEvaluator(topk=(1, 5), dataset_name=None, metric_names=['neck_top1'], neck_num=None)[source]¶

Bases: easycv.core.evaluation.base_evaluator.Evaluator

Classification evaluator.

__init__(topk=(1, 5), dataset_name=None, metric_names=['neck_top1'], neck_num=None)[source]¶

Parameters

top_k – tuple of int, evaluate top_k acc
dataset_name – eval dataset name
metric_names – eval metrics name
neck_num – some model contains multi-neck to support multitask, neck_num means use the no.neck_num neck output of model to eval

easycv.core.evaluation.coco_evaluation module¶

Class for evaluating object detections with COCO metrics.

class easycv.core.evaluation.coco_evaluation.CocoDetectionEvaluator(classes, include_metrics_per_category=False, all_metrics_per_category=False, coco_analyze=False, dataset_name=None, metric_names=['DetectionBoxes_Precision/mAP'])[source]¶

Bases: easycv.core.evaluation.base_evaluator.Evaluator

Class to evaluate COCO detection metrics.

__init__(classes, include_metrics_per_category=False, all_metrics_per_category=False, coco_analyze=False, dataset_name=None, metric_names=['DetectionBoxes_Precision/mAP'])[source]¶

Constructor.

Parameters

classes – a list of class name
include_metrics_per_category – If True, include metrics for each category.
all_metrics_per_category – Whether to include all the summary metrics for each category in per_category_ap. Be careful with setting it to true if you have more than handful of ∏, because it will pollute your mldash.
coco_analyze – If True, will analyze the detection result using coco analysis.
dataset_name – If not None, dataset_name will be inserted to each metric name.

clear()[source]¶: Clears the state to prepare for a fresh evaluation.

add_single_ground_truth_image_info(image_id, groundtruth_dict)[source]¶

Adds groundtruth for a single image to be used for evaluation.

If the image has already been added, a warning is logged, and groundtruth is ignored.

Parameters

image_id – A unique string/integer identifier for the image.
groundtruth_dict –
A dictionary containing

InputDataFields.groundtruth_boxes

float32 numpy array of shape [num_boxes, 4] containing num_boxes groundtruth boxes of the format [ymin, xmin, ymax, xmax] in absolute image coordinates.

InputDataFields.groundtruth_classes

integer numpy array of shape [num_boxes] containing 1-indexed groundtruth classes for the boxes. InputDataFields.groundtruth_is_crowd (optional): integer numpy array of shape [num_boxes] containing iscrowd flag for groundtruth boxes.

add_single_detected_image_info(image_id, detections_dict)[source]¶

Adds detections for a single image to be used for evaluation.

If a detection has already been added for this image id, a warning is logged, and the detection is skipped.

Parameters

image_id – A unique string/integer identifier for the image.
detections_dict –
A dictionary containing

DetectionResultFields.detection_boxes

float32 numpy array of shape [num_boxes, 4] containing num_boxes detection boxes of the format [ymin, xmin, ymax, xmax] in absolute image coordinates.

DetectionResultFields.detection_scores

float32 numpy array of shape [num_boxes] containing detection scores for the boxes.

DetectionResultFields.detection_classes

integer numpy array of shape [num_boxes] containing 1-indexed detection classes for the boxes.

Raises

ValueError – If groundtruth for the image_id is not available.

class easycv.core.evaluation.coco_evaluation.CocoMaskEvaluator(classes, include_metrics_per_category=False, dataset_name=None, metric_names=['DetectionMasks_Precision/mAP'])[source]¶

Bases: easycv.core.evaluation.base_evaluator.Evaluator

Class to evaluate COCO detection metrics.

__init__(classes, include_metrics_per_category=False, dataset_name=None, metric_names=['DetectionMasks_Precision/mAP'])[source]¶

Constructor.

Parameters

categories – A list of dicts, each of which has the following keys :id: (required) an integer id uniquely identifying this category. :name: (required) string representing category name e.g., ‘cat’, ‘dog’.
include_metrics_per_category – If True, include metrics for each category.

clear()[source]¶: Clears the state to prepare for a fresh evaluation.

add_single_ground_truth_image_info(image_id, groundtruth_dict)[source]¶

Adds groundtruth for a single image to be used for evaluation.

If the image has already been added, a warning is logged, and groundtruth is ignored.

Parameters

image_id – A unique string/integer identifier for the image.
groundtruth_dict –
A dictionary containing :InputDataFields.groundtruth_boxes: float32 numpy array of shape

[num_boxes, 4] containing num_boxes groundtruth boxes of the format [ymin, xmin, ymax, xmax] in absolute image coordinates.

InputDataFields.groundtruth_classes

integer numpy array of shape [num_boxes] containing 1-indexed groundtruth classes for the boxes.

InputDataFields.groundtruth_instance_masks

uint8 numpy array of shape [num_boxes, image_height, image_width] containing groundtruth masks corresponding to the boxes. The elements of the array must be in {0, 1}.

add_single_detected_image_info(image_id, detections_dict)[source]¶

Adds detections for a single image to be used for evaluation.

If a detection has already been added for this image id, a warning is logged, and the detection is skipped.

Parameters

image_id – A unique string/integer identifier for the image.
detections_dict – A dictionary containing - DetectionResultFields.detection_scores: float32 numpy array of shape [num_boxes] containing detection scores for the boxes. DetectionResultFields.detection_classes: integer numpy array of shape [num_boxes] containing 1-indexed detection classes for the boxes. DetectionResultFields.detection_masks: optional uint8 numpy array of shape [num_boxes, image_height, image_width] containing instance masks corresponding to the boxes. The elements of the array must be in {0, 1}.

Raises

ValueError – If groundtruth for the image_id is not available or if spatial shapes of groundtruth_instance_masks and detection_masks are incompatible.

class easycv.core.evaluation.coco_evaluation.CoCoPoseTopDownEvaluator(dataset_name=None, metric_names=['AP'], **kwargs)[source]¶

Bases: easycv.core.evaluation.base_evaluator.Evaluator

Class to evaluate COCO keypoint topdown metrics.

__init__(dataset_name=None, metric_names=['AP'], **kwargs)[source]¶

Construct eval ops from tensor

Parameters

dataset_name (str) – dataset name to be evaluated
metric_names (List[str]) – metric names this evaluator will return

easycv.core.evaluation.coco_tools module¶

Wrappers for third party pycocotools to be used within object_detection.

Note that nothing in this file is tensorflow related and thus cannot be called directly as a slim metric, for example.

TODO(jonathanhuang): wrap as a slim metric in metrics.py

Usage example: given a set of images with ids in the list image_ids and corresponding lists of numpy arrays encoding groundtruth (boxes and classes) and detections (boxes, scores and classes), where elements of each list correspond to detections/annotations of a single image, then evaluation (in multi-class mode) can be invoked as follows:

groundtruth_dict = coco_tools.ExportGroundtruthToCOCO(
image_ids, groundtruth_boxes_list, groundtruth_classes_list, max_num_classes, output_path=None)

detections_list = coco_tools.ExportDetectionsToCOCO(
image_ids, detection_boxes_list, detection_scores_list, detection_classes_list, output_path=None)

groundtruth = coco_tools.COCOWrapper(groundtruth_dict) detections = groundtruth.LoadAnnotations(detections_list) evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections,

agnostic_mode=False)

metrics = evaluator.ComputeMetrics()

class easycv.core.evaluation.coco_tools.COCOWrapper(dataset, detection_type='bbox')[source]¶

Bases: xtcocotools.coco.COCO

Wrapper for the pycocotools COCO class.

__init__(dataset, detection_type='bbox')[source]¶

COCOWrapper constructor.

See http://mscoco.org/dataset/#format for a description of the format. By default, the coco.COCO class constructor reads from a JSON file. This function duplicates the same behavior but loads from a dictionary, allowing us to perform evaluation without writing to external storage.

Parameters

dataset – a dictionary holding bounding box annotations in the COCO format.
detection_type – type of detections being wrapped. Can be one of [‘bbox’, ‘segmentation’]

Raises

ValueError – if detection_type is unsupported.

LoadAnnotations(annotations)[source]¶

Load annotations dictionary into COCO datastructure.

See http://mscoco.org/dataset/#format for a description of the annotations format. As above, this function replicates the default behavior of the API but does not require writing to external storage.

Parameters

annotations – python list holding object detection results where each detection is encoded as a dict with required keys [‘image_id’, ‘category_id’, ‘score’] and one of [‘bbox’, ‘segmentation’] based on detection_type.

Returns

a coco.COCO datastructure holding object detection annotations results

Raises

ValueError – if annotations is not a list
ValueError – if annotations do not correspond to the images contained in self.

class easycv.core.evaluation.coco_tools.COCOEvalWrapper(groundtruth=None, detections=None, agnostic_mode=False, iou_type='bbox')[source]¶

Bases: easycv.core.evaluation.custom_cocotools.cocoeval.COCOeval

Wrapper for the pycocotools COCOeval class.

To evaluate, create two objects (groundtruth_dict and detections_list) using the conventions listed at http://mscoco.org/dataset/#format. Then call evaluation as follows:

groundtruth = coco_tools.COCOWrapper(groundtruth_dict) detections = groundtruth.LoadAnnotations(detections_list) evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections,

agnostic_mode=False)

metrics = evaluator.ComputeMetrics()

__init__(groundtruth=None, detections=None, agnostic_mode=False, iou_type='bbox')[source]¶

COCOEvalWrapper constructor.

Note that for the area-based metrics to be meaningful, detection and groundtruth boxes must be in image coordinates measured in pixels.

Parameters

groundtruth – a coco.COCO (or coco_tools.COCOWrapper) object holding groundtruth annotations
detections – a coco.COCO (or coco_tools.COCOWrapper) object holding detections
agnostic_mode – boolean (default: False). If True, evaluation ignores class labels, treating all detections as proposals.
iou_type – IOU type to use for evaluation. Supports bbox or segm.

GetCategory(category_id)[source]¶

Fetches dictionary holding category information given category id.

Parameters: category_id – integer id
Returns: dictionary holding ‘id’, ‘name’.

GetAgnosticMode()[source]¶: Returns true if COCO Eval is configured to evaluate in agnostic mode.

GetCategoryIdList()[source]¶: Returns list of valid category ids.

ComputeMetrics(include_metrics_per_category=False, all_metrics_per_category=False)[source]¶

Computes detection metrics.

Parameters

include_metrics_per_category – If True, will include metrics per category.
all_metrics_per_category – If true, include all the summery metrics for each category in per_category_ap. Be careful with setting it to true if you have more than handful of categories, because it will pollute your mldash.

Returns

a dictionary holding:

’Precision/mAP’: mean average precision over classes averaged over IOU: thresholds ranging from .5 to .95 with .05 increments

’Precision/mAP@.50IOU’: mean average precision at 50% IOU ‘Precision/mAP@.75IOU’: mean average precision at 75% IOU ‘Precision/mAP (small)’: mean average precision for small objects

(area < 32^2 pixels)

’Precision/mAP (medium)’: mean average precision for medium sized: objects (32^2 pixels < area < 96^2 pixels)
’Precision/mAP (large)’: mean average precision for large objects: (96^2 pixels < area < 10000^2 pixels)

’Recall/AR@1’: average recall with 1 detection ‘Recall/AR@10’: average recall with 10 detections ‘Recall/AR@100’: average recall with 100 detections ‘Recall/AR@100 (small)’: average recall for small objects with 100

detections

’Recall/AR@100 (medium)’: average recall for medium objects with 100: detections
’Recall/AR@100 (large)’: average recall for large objects with 100: detections

per_category_ap: a dictionary holding category specific results with

keys of the form: ‘Precision mAP ByCategory/category’ (without the supercategory part if no supercategories exist). For backward compatibility ‘PerformanceByCategory’ is included in the output regardless of all_metrics_per_category. If evaluating class-agnostic mode, per_category_ap is an empty dictionary.

Return type

summary_metrics

Raises

ValueError – If category_stats does not exist.

Analyze()[source]¶

Analyze detection results.

Args:

Returns

A dictionary containing images of analyzing result images,: key is the image name, value is a [H,W,3] numpy array which represent the image content. You can refer to http://cocodataset.org/#detection-eval section 4 Analysis code.

easycv.core.evaluation.coco_tools.ExportSingleImageGroundtruthToCoco(image_id, next_annotation_id, category_id_set, groundtruth_boxes, groundtruth_classes, groundtruth_masks=None, groundtruth_is_crowd=None, super_categories=None)[source]¶

Export groundtruth of a single image to COCO format.

This function converts groundtruth detection annotations represented as numpy arrays to dictionaries that can be ingested by the COCO evaluation API. Note that the image_ids provided here must match the ones given to ExportSingleImageDetectionsToCoco. We assume that boxes and classes are in correspondence - that is: groundtruth_boxes[i, :], and groundtruth_classes[i] are associated with the same groundtruth annotation.

In the exported result, “area” fields are always set to the area of the groundtruth bounding box.

Parameters

image_id – a unique image identifier either of type integer or string.
next_annotation_id – integer specifying the first id to use for the groundtruth annotations. All annotations are assigned a continuous integer id starting from this value.
category_id_set – A set of valid class ids. Groundtruth with classes not in category_id_set are dropped.
groundtruth_boxes – numpy array (float32) with shape [num_gt_boxes, 4]
groundtruth_classes – numpy array (int) with shape [num_gt_boxes]
groundtruth_masks – optional uint8 numpy array of shape [num_detections, image_height, image_width] containing detection_masks.
groundtruth_is_crowd – optional numpy array (int) with shape [num_gt_boxes] indicating whether groundtruth boxes are crowd.
super_categories – optional list of str indicating each box super category

Returns

a list of groundtruth annotations for a single image in the COCO format.

Raises

ValueError – if (1) groundtruth_boxes and groundtruth_classes do not have the right lengths or (2) if each of the elements inside these lists do not have the correct shapes or (3) if image_ids are not integers

easycv.core.evaluation.coco_tools.ExportGroundtruthToCOCO(image_ids, groundtruth_boxes, groundtruth_classes, categories, output_path=None)[source]¶

Export groundtruth detection annotations in numpy arrays to COCO API.

This function converts a set of groundtruth detection annotations represented as numpy arrays to dictionaries that can be ingested by the COCO API. Inputs to this function are three lists: image ids for each groundtruth image, groundtruth boxes for each image and groundtruth classes respectively. Note that the image_ids provided here must match the ones given to the ExportDetectionsToCOCO function in order for evaluation to work properly. We assume that for each image, boxes, scores and classes are in correspondence — that is: image_id[i], groundtruth_boxes[i, :] and groundtruth_classes[i] are associated with the same groundtruth annotation.

In the exported result, “area” fields are always set to the area of the groundtruth bounding box and “iscrowd” fields are always set to 0. TODO(jonathanhuang): pass in “iscrowd” array for evaluating on COCO dataset.

Parameters

image_ids – a list of unique image identifier either of type integer or string.
groundtruth_boxes – list of numpy arrays with shape [num_gt_boxes, 4] (note that num_gt_boxes can be different for each entry in the list)
groundtruth_classes – list of numpy arrays (int) with shape [num_gt_boxes] (note that num_gt_boxes can be different for each entry in the list)
categories –
a list of dictionaries representing all possible categories. Each dict in this list has the following keys:

’id’: (required) an integer id uniquely identifying this category ‘name’: (required) string representing category name

e.g., ‘cat’, ‘dog’, ‘pizza’

’supercategory’: (optional) string representing the supercategory
e.g., ‘animal’, ‘vehicle’, ‘food’, etc
output_path – (optional) path for exporting result to JSON

Returns

dictionary that can be read by COCO API

Raises

ValueError – if (1) groundtruth_boxes and groundtruth_classes do not have the right lengths or (2) if each of the elements inside these lists do not have the correct shapes or (3) if image_ids are not integers

easycv.core.evaluation.coco_tools.ExportSingleImageDetectionBoxesToCoco(image_id, category_id_set, detection_boxes, detection_scores, detection_classes)[source]¶

Export detections of a single image to COCO format.

This function converts detections represented as numpy arrays to dictionaries that can be ingested by the COCO evaluation API. Note that the image_ids provided here must match the ones given to the ExporSingleImageDetectionBoxesToCoco. We assume that boxes, and classes are in correspondence - that is: boxes[i, :], and classes[i] are associated with the same groundtruth annotation.

Parameters

image_id – unique image identifier either of type integer or string.
category_id_set – A set of valid class ids. Detections with classes not in category_id_set are dropped.
detection_boxes – float numpy array of shape [num_detections, 4] containing detection boxes.
detection_scores – float numpy array of shape [num_detections] containing scored for the detection boxes.
detection_classes – integer numpy array of shape [num_detections] containing the classes for detection boxes.

Returns

a list of detection annotations for a single image in the COCO format.

Raises

ValueError – if (1) detection_boxes, detection_scores and detection_classes do not have the right lengths or (2) if each of the elements inside these lists do not have the correct shapes or (3) if image_ids are not integers.

easycv.core.evaluation.coco_tools.ExportSingleImageDetectionMasksToCoco(image_id, category_id_set, detection_masks, detection_scores, detection_classes)[source]¶

Export detection masks of a single image to COCO format.

This function converts detections represented as numpy arrays to dictionaries that can be ingested by the COCO evaluation API. We assume that detection_masks, detection_scores, and detection_classes are in correspondence - that is: detection_masks[i, :], detection_classes[i] and detection_scores[i]

are associated with the same annotation.

Parameters

image_id – unique image identifier either of type integer or string.
category_id_set – A set of valid class ids. Detections with classes not in category_id_set are dropped.
detection_masks – uint8 numpy array of shape [num_detections, image_height, image_width] containing detection_masks.
detection_scores – float numpy array of shape [num_detections] containing scores for detection masks.
detection_classes – integer numpy array of shape [num_detections] containing the classes for detection masks.

Returns

a list of detection mask annotations for a single image in the COCO format.

Raises

ValueError – if (1) detection_masks, detection_scores and detection_classes do not have the right lengths or (2) if each of the elements inside these lists do not have the correct shapes or (3) if image_ids are not integers.

easycv.core.evaluation.coco_tools.ExportDetectionsToCOCO(image_ids, detection_boxes, detection_scores, detection_classes, categories, output_path=None)[source]¶

Export detection annotations in numpy arrays to COCO API.

This function converts a set of predicted detections represented as numpy arrays to dictionaries that can be ingested by the COCO API. Inputs to this function are lists, consisting of boxes, scores and classes, respectively, corresponding to each image for which detections have been produced. Note that the image_ids provided here must match the ones given to the ExportGroundtruthToCOCO function in order for evaluation to work properly.

We assume that for each image, boxes, scores and classes are in correspondence — that is: detection_boxes[i, :], detection_scores[i] and detection_classes[i] are associated with the same detection.

Parameters

image_ids – a list of unique image identifier either of type integer or string.
detection_boxes – list of numpy arrays with shape [num_detection_boxes, 4]
detection_scores – list of numpy arrays (float) with shape [num_detection_boxes]. Note that num_detection_boxes can be different for each entry in the list.
detection_classes – list of numpy arrays (int) with shape [num_detection_boxes]. Note that num_detection_boxes can be different for each entry in the list.
categories – a list of dictionaries representing all possible categories. Each dict in this list must have an integer ‘id’ key uniquely identifying this category.
output_path – (optional) path for exporting result to JSON

Returns

list of dictionaries that can be read by COCO API, where each entry corresponds to a single detection and has keys from: [‘image_id’, ‘category_id’, ‘bbox’, ‘score’].

Raises

ValueError – if (1) detection_boxes and detection_classes do not have the right lengths or (2) if each of the elements inside these lists do not have the correct shapes or (3) if image_ids are not integers.

easycv.core.evaluation.coco_tools.ExportSegmentsToCOCO(image_ids, detection_masks, detection_scores, detection_classes, categories, output_path=None)[source]¶

Export segmentation masks in numpy arrays to COCO API.

This function converts a set of predicted instance masks represented as numpy arrays to dictionaries that can be ingested by the COCO API. Inputs to this function are lists, consisting of segments, scores and classes, respectively, corresponding to each image for which detections have been produced.

Note this function is recommended to use for small dataset. For large dataset, it should be used with a merge function (e.g. in map reduce), otherwise the memory consumption is large.

We assume that for each image, masks, scores and classes are in correspondence — that is: detection_masks[i, :, :, :], detection_scores[i] and detection_classes[i] are associated with the same detection.

Parameters

image_ids – list of image ids (typically ints or strings)
detection_masks – list of numpy arrays with shape [num_detection, h, w, 1] and type uint8. The height and width should match the shape of corresponding image.
detection_scores – list of numpy arrays (float) with shape [num_detection]. Note that num_detection can be different for each entry in the list.
detection_classes – list of numpy arrays (int) with shape [num_detection]. Note that num_detection can be different for each entry in the list.
categories – a list of dictionaries representing all possible categories. Each dict in this list must have an integer ‘id’ key uniquely identifying this category.
output_path – (optional) path for exporting result to JSON

Returns

list of dictionaries that can be read by COCO API, where each entry corresponds to a single detection and has keys from: [‘image_id’, ‘category_id’, ‘segmentation’, ‘score’].

Raises

ValueError – if detection_masks and detection_classes do not have the right lengths or if each of the elements inside these lists do not have the correct shapes.

easycv.core.evaluation.coco_tools.ExportKeypointsToCOCO(image_ids, detection_keypoints, detection_scores, detection_classes, categories, output_path=None)[source]¶

Exports keypoints in numpy arrays to COCO API.

This function converts a set of predicted keypoints represented as numpy arrays to dictionaries that can be ingested by the COCO API. Inputs to this function are lists, consisting of keypoints, scores and classes, respectively, corresponding to each image for which detections have been produced.

We assume that for each image, keypoints, scores and classes are in correspondence — that is: detection_keypoints[i, :, :, :], detection_scores[i] and detection_classes[i] are associated with the same detection.

Parameters

image_ids – list of image ids (typically ints or strings)
detection_keypoints – list of numpy arrays with shape [num_detection, num_keypoints, 2] and type float32 in absolute x-y coordinates.
detection_scores – list of numpy arrays (float) with shape [num_detection]. Note that num_detection can be different for each entry in the list.
detection_classes – list of numpy arrays (int) with shape [num_detection]. Note that num_detection can be different for each entry in the list.
categories – a list of dictionaries representing all possible categories. Each dict in this list must have an integer ‘id’ key uniquely identifying this category and an integer ‘num_keypoints’ key specifying the number of keypoints the category has.
output_path – (optional) path for exporting result to JSON

Returns

list of dictionaries that can be read by COCO API, where each entry corresponds to a single detection and has keys from: [‘image_id’, ‘category_id’, ‘keypoints’, ‘score’].

Raises

ValueError – if detection_keypoints and detection_classes do not have the right lengths or if each of the elements inside these lists do not have the correct shapes.

easycv.core.evaluation.faceid_pair_eval module¶

easycv.core.evaluation.faceid_pair_eval.calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, pca=0)[source]¶

easycv.core.evaluation.faceid_pair_eval.calculate_accuracy(threshold, dist, actual_issame)[source]¶

easycv.core.evaluation.faceid_pair_eval.calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10)[source]¶

easycv.core.evaluation.faceid_pair_eval.calculate_val_far(threshold, dist, actual_issame)[source]¶

easycv.core.evaluation.faceid_pair_eval.faceid_evaluate(embeddings, actual_issame, nrof_folds=10, pca=0)[source]¶

Do Kfold=nrof_folds faceid pair-match test for embeddings :param embeddings: [N x C] inputs embedding of all dataset :param actual_issame: [N/2, 1] label of is match :param nrof_folds: KFold number :param pca: > 0 means, do pca and trans embedding to [N, pca] feature

Returns: KFold average best accuracy and best threshold

class easycv.core.evaluation.faceid_pair_eval.FaceIDPairEvaluator(dataset_name=None, metric_names=['acc'], kfold=10, pca=0)[source]¶

Bases: easycv.core.evaluation.base_evaluator.Evaluator

FaceIDPairEvaluator evaluator. Input nx2 pairs and label, kfold thresholds search and return average best accuracy

__init__(dataset_name=None, metric_names=['acc'], kfold=10, pca=0)[source]¶

Faceid small dataset evaluator, do pair match validation :param dataset_name: faceid small validate set name, include [lfw, agedb_30, cfp_ff, cfp_fw, calfw] :param kfold: Kfold for train/val split :param pca: pca dimensions, if > 0, do PCA for input feature, transfer to [n, pca]

Returns: None

easycv.core.evaluation.metric_registry module¶

class easycv.core.evaluation.metric_registry.MetricRegistry[source]¶

Bases: object

__init__()[source]¶: Initialize self. See help(type(self)) for accurate signature.

get(evaluator_type)[source]¶

register_default_best_metric(cls, metric_name, metric_cmp_op='max')[source]¶

Register default best metric for each evaluator

Parameters

cls (object) – class object
metric_name (str or List[str]) – default best metric name
metric_cmp_op (str or List[str]) – metric compare operation, should be one of [“max”, “min”]

easycv.core.evaluation.mse_eval module¶

class easycv.core.evaluation.mse_eval.MSEEvaluator(dataset_name=None, metric_names=['avg_mse'], neck_num=None)[source]¶

Bases: easycv.core.evaluation.base_evaluator.Evaluator

MSEEvaluator evaluator,

__init__(dataset_name=None, metric_names=['avg_mse'], neck_num=None)[source]¶

easycv.core.evaluation.retrival_topk_eval module¶

class easycv.core.evaluation.retrival_topk_eval.RetrivalTopKEvaluator(topk=(1, 2, 4, 8), norm=0, metric='cos', pca=0, dataset_name=None, metric_names=['R@K=1'], save_results=False, save_results_dir='', feature_keyword=['neck'])[source]¶

Bases: easycv.core.evaluation.base_evaluator.Evaluator

RetrivalTopK evaluator, Retrival evaluate do the topK retrival, by measuring the distance of every 1 vs other. get the topK nearest, and count the match of ID. if Retrival = 1, Miss = 0. Finally average all RetrivalRate.

__init__(topk=(1, 2, 4, 8), norm=0, metric='cos', pca=0, dataset_name=None, metric_names=['R@K=1'], save_results=False, save_results_dir='', feature_keyword=['neck'])[source]¶

Parameters: top_k – tuple of int, evaluate top_k acc

easycv.core.evaluation.top_down_eval module¶

easycv.core.evaluation.top_down_eval.pose_pck_accuracy(output, target, mask, thr=0.05, normalize=None)[source]¶

Calculate the pose accuracy of PCK for each individual keypoint and the averaged accuracy across all keypoints from heatmaps.

Note

PCK metric measures accuracy of the localization of the body joints. The distances between predicted positions and the ground-truth ones are typically normalized by the bounding box size. The threshold (thr) of the normalized distance is commonly set as 0.05, 0.1 or 0.2 etc.

batch_size: N num_keypoints: K heatmap height: H heatmap width: W

Parameters

output (np.ndarray[N, K, H, W]) – Model output heatmaps.
target (np.ndarray[N, K, H, W]) – Groundtruth heatmaps.
mask (np.ndarray[N, K]) – Visibility of the target. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation.
thr (float) – Threshold of PCK calculation. Default 0.05.
normalize (np.ndarray[N, 2]) – Normalization factor for H&W.

Returns

A tuple containing keypoint accuracy.

np.ndarray[K]: Accuracy of each keypoint.
float: Averaged accuracy across all keypoints.
int: Number of valid keypoints.

Return type

tuple

easycv.core.evaluation.top_down_eval.keypoint_pck_accuracy(pred, gt, mask, thr, normalize)[source]¶

Calculate the pose accuracy of PCK for each individual keypoint and the averaged accuracy across all keypoints for coordinates.

Note

PCK metric measures accuracy of the localization of the body joints. The distances between predicted positions and the ground-truth ones are typically normalized by the bounding box size. The threshold (thr) of the normalized distance is commonly set as 0.05, 0.1 or 0.2 etc.

batch_size: N num_keypoints: K

Parameters

pred (np.ndarray[N, K, 2]) – Predicted keypoint location.
gt (np.ndarray[N, K, 2]) – Groundtruth keypoint location.
mask (np.ndarray[N, K]) – Visibility of the target. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation.
thr (float) – Threshold of PCK calculation.
normalize (np.ndarray[N, 2]) – Normalization factor for H&W.

Returns

A tuple containing keypoint accuracy.

acc (np.ndarray[K]): Accuracy of each keypoint.
avg_acc (float): Averaged accuracy across all keypoints.
cnt (int): Number of valid keypoints.

Return type

tuple

easycv.core.evaluation.top_down_eval.post_dark_udp(coords, batch_heatmaps, kernel=3)[source]¶

DARK post-pocessing. Implemented by udp. Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020). Zhang et al. Distribution-Aware Coordinate Representation for Human Pose Estimation (CVPR 2020).

Note

batch size: B num keypoints: K num persons: N height of heatmaps: H width of heatmaps: W B=1 for bottom_up paradigm where all persons share the same heatmap. B=N for top_down paradigm where each person has its own heatmaps.

Parameters

coords (np.ndarray[N, K, 2]) – Initial coordinates of human pose.
batch_heatmaps (np.ndarray[B, K, H, W]) – batch_heatmaps
kernel (int) – Gaussian kernel size (K) for modulation.

Returns

Refined coordinates.

Return type

res (np.ndarray[N, K, 2])

easycv.core.evaluation.top_down_eval.keypoints_from_heatmaps(heatmaps, center, scale, unbiased=False, post_process='default', kernel=11, valid_radius_factor=0.0546875, use_udp=False, target_type='GaussianHeatmap')[source]¶

Get final keypoint predictions from heatmaps and transform them back to the image.

Note

batch size: N num keypoints: K heatmap height: H heatmap width: W

Parameters

heatmaps (np.ndarray[N, K, H, W]) – model predicted heatmaps.
center (np.ndarray[N, 2]) – Center of the bounding box (x, y).
scale (np.ndarray[N, 2]) – Scale of the bounding box wrt height/width.
post_process (str/None) – Choice of methods to post-process heatmaps. Currently supported: None, ‘default’, ‘unbiased’, ‘megvii’.
unbiased (bool) – Option to use unbiased decoding. Mutually exclusive with megvii. Note: this arg is deprecated and unbiased=True can be replaced by post_process=’unbiased’ Paper ref: Zhang et al. Distribution-Aware Coordinate Representation for Human Pose Estimation (CVPR 2020).
kernel (int) – Gaussian kernel size (K) for modulation, which should match the heatmap gaussian sigma when training. K=17 for sigma=3 and k=11 for sigma=2.
valid_radius_factor (float) – The radius factor of the positive area in classification heatmap for UDP.
use_udp (bool) – Use unbiased data processing.
target_type (str) – ‘GaussianHeatmap’ or ‘CombinedTarget’. GaussianHeatmap: Classification target with gaussian distribution. CombinedTarget: The combination of classification target (response map) and regression target (offset map). Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).

Returns

A tuple containing keypoint predictions and scores.

preds (np.ndarray[N, K, 2]): Predicted keypoint location in images.
maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.

Return type

tuple

easycv.core.optimizer package¶

Submodules¶

easycv.core.optimizer.lars module¶

class easycv.core.optimizer.lars.LARS(params, lr=<required parameter>, momentum=0, dampening=0, weight_decay=0, eta=0.001, nesterov=False)[source]¶

Bases: torch.optim.optimizer.Optimizer

Implements layer-wise adaptive rate scaling for SGD.

Parameters

params (iterable) – iterable of parameters to optimize or dicts defining parameter groups
lr (float) – base learning rate (gamma_0)
momentum (float, optional) – momentum factor (default: 0) (“m”)
weight_decay (float, optional) – weight decay (L2 penalty) (default: 0) (“beta”)
dampening (float, optional) – dampening for momentum (default: 0)
eta (float, optional) – LARS coefficient
nesterov (bool, optional) – enables Nesterov momentum (default: False)

Based on Algorithm 1 of the following paper by You, Gitman, and Ginsburg. Large Batch Training of Convolutional Networks:

https://arxiv.org/abs/1708.03888

Example

>>> optimizer = LARS(model.parameters(), lr=0.1, momentum=0.9,
>>>                  weight_decay=1e-4, eta=1e-3)
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> optimizer.step()

__init__(params, lr=<required parameter>, momentum=0, dampening=0, weight_decay=0, eta=0.001, nesterov=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

step(closure=None)[source]¶

Performs a single optimization step.

Parameters: closure (callable, optional) – A closure that reevaluates the model and returns the loss.

easycv.core.optimizer.ranger module¶

easycv.core.optimizer.ranger.centralized_gradient(x, use_gc=True, gc_conv_only=False)[source]¶: credit - https://github.com/Yonghongwei/Gradient-Centralization

class easycv.core.optimizer.ranger.Ranger(params, lr=0.001, alpha=0.5, k=6, N_sma_threshhold=5, betas=(0.95, 0.999), eps=1e-05, weight_decay=0, use_gc=True, gc_conv_only=False, gc_loc=True)[source]¶

Bases: torch.optim.optimizer.Optimizer

Adam+LookAhead: refer to https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer

__init__(params, lr=0.001, alpha=0.5, k=6, N_sma_threshhold=5, betas=(0.95, 0.999), eps=1e-05, weight_decay=0, use_gc=True, gc_conv_only=False, gc_loc=True)[source]¶: Initialize self. See help(type(self)) for accurate signature.

step(closure=None)[source]¶

Performs a single optimization step (parameter update).

Parameters: closure (callable) – A closure that reevaluates the model and returns the loss. Optional for most optimizers.

Note

Unless otherwise specified, this function should not modify the .grad field of the parameters.

easycv.core.post_processing package¶

easycv.core.post_processing.affine_transform(pt, trans_mat)[source]¶

Apply an affine transformation to the points.

Parameters

pt (np.ndarray) – a 2 dimensional point to be transformed
trans_mat (np.ndarray) – 2x3 matrix of an affine transform

Returns

Transformed points.

Return type

np.ndarray

easycv.core.post_processing.flip_back(output_flipped, flip_pairs, target_type='GaussianHeatmap')[source]¶

Flip the flipped heatmaps back to the original form.

Note

batch_size: N num_keypoints: K heatmap height: H heatmap width: W

Parameters

output_flipped (np.ndarray[N, K, H, W]) – The output heatmaps obtained from the flipped images.
flip_pairs (list[tuple()) – Pairs of keypoints which are mirrored (for example, left ear – right ear).
target_type (str) – GaussianHeatmap or CombinedTarget

Returns

heatmaps that flipped back to the original image

Return type

np.ndarray

easycv.core.post_processing.fliplr_joints(joints_3d, joints_3d_visible, img_width, flip_pairs)[source]¶

Flip human joints horizontally.

Note

num_keypoints: K

Parameters

joints_3d (np.ndarray([K, 3])) – Coordinates of keypoints.
joints_3d_visible (np.ndarray([K, 1])) – Visibility of keypoints.
img_width (int) – Image width.
flip_pairs (list[tuple()]) – Pairs of keypoints which are mirrored (for example, left ear – right ear).

Returns

Flipped human joints.

joints_3d_flipped (np.ndarray([K, 3])): Flipped joints.
joints_3d_visible_flipped (np.ndarray([K, 1])): Joint visibility.

Return type

tuple

easycv.core.post_processing.fliplr_regression(regression, flip_pairs, center_mode='static', center_x=0.5, center_index=0)[source]¶

Flip human joints horizontally.

Note

batch_size: N num_keypoint: K

Parameters

regression (np.ndarray([..., K, C])) –
Coordinates of keypoints, where K is the joint number and C is the dimension. Example shapes are: - [N, K, C]: a batch of keypoints where N is the batch size. - [N, T, K, C]: a batch of pose sequences, where T is the frame

number.
flip_pairs (list[tuple()]) – Pairs of keypoints which are mirrored (for example, left ear – right ear).
center_mode (str) – The mode to set the center location on the x-axis to flip around. Options are: - static: use a static x value (see center_x also) - root: use a root joint (see center_index also)
center_x (float) – Set the x-axis location of the flip center. Only used when center_mode=static.
center_index (int) – Set the index of the root joint, whose x location will be used as the flip center. Only used when center_mode=root.

Returns

Flipped human joints.

regression_flipped (np.ndarray([…, K, C])): Flipped joints.

Return type

tuple

easycv.core.post_processing.get_affine_transform(center, scale, rot, output_size, shift=(0.0, 0.0), inv=False)[source]¶

Get the affine transform matrix, given the center/scale/rot/output_size.

Parameters

center (np.ndarray[2, ]) – Center of the bounding box (x, y).
scale (np.ndarray[2, ]) – Scale of the bounding box wrt [width, height].
rot (float) – Rotation angle (degree).
output_size (np.ndarray[2, ] | list(2,)) – Size of the destination heatmaps.
shift (0-100%) – Shift translation ratio wrt the width/height. Default (0., 0.).
inv (bool) – Option to inverse the affine transform direction. (inv=False: src->dst or inv=True: dst->src)

Returns

The transform matrix.

Return type

np.ndarray

easycv.core.post_processing.get_warp_matrix(theta, size_input, size_dst, size_target)[source]¶

Calculate the transformation matrix under the constraint of unbiased. Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).

Parameters

theta (float) – Rotation angle in degrees.
size_input (np.ndarray) – Size of input image [w, h].
size_dst (np.ndarray) – Size of output image [w, h].
size_target (np.ndarray) – Size of ROI in input plane [w, h].

Returns

A matrix for transformation.

Return type

matrix (np.ndarray)

easycv.core.post_processing.rotate_point(pt, angle_rad)[source]¶

Rotate a point by an angle.

Parameters

pt (list[float]) – 2 dimensional point to be rotated
angle_rad (float) – rotation angle by radian

Returns

Rotated point.

Return type

list[float]

easycv.core.post_processing.transform_preds(coords, center, scale, output_size, use_udp=False)[source]¶

Get final keypoint predictions from heatmaps and apply scaling and translation to map them back to the image.

Note

num_keypoints: K

Parameters

coords (np.ndarray[K, ndims]) –
- If ndims=2, corrds are predicted keypoint location.
- If ndims=4, corrds are composed of (x, y, scores, tags)
- If ndims=5, corrds are composed of (x, y, scores, tags, flipped_tags)
center (np.ndarray[2, ]) – Center of the bounding box (x, y).
scale (np.ndarray[2, ]) – Scale of the bounding box wrt [width, height].
output_size (np.ndarray[2, ] | list(2,)) – Size of the destination heatmaps.
use_udp (bool) – Use unbiased data processing

Returns

Predicted coordinates in the images.

Return type

np.ndarray

easycv.core.post_processing.warp_affine_joints(joints, mat)[source]¶

Apply affine transformation defined by the transform matrix on the joints.

Parameters

joints (np.ndarray[..., 2]) – Origin coordinate of joints.
mat (np.ndarray[3, 2]) – The affine matrix.

Returns

Result coordinate of joints.

Return type

matrix (np.ndarray[…, 2])

easycv.core.post_processing.oks_nms(kpts_db, thr, sigmas=None, vis_thr=None)[source]¶

OKS NMS implementations.

Parameters

kpts_db – keypoints.
thr – Retain overlap < thr.
sigmas – standard deviation of keypoint labelling.
vis_thr – threshold of the keypoint visibility.

Returns

indexes to keep.

Return type

np.ndarray

easycv.core.post_processing.soft_oks_nms(kpts_db, thr, max_dets=20, sigmas=None, vis_thr=None)[source]¶

Soft OKS NMS implementations.

Parameters

kpts_db –
thr – retain oks overlap < thr.
max_dets – max number of detections to keep.
sigmas – Keypoint labelling uncertainty.

Returns

indexes to keep.

Return type

np.ndarray

Submodules¶

easycv.core.post_processing.nms module¶

easycv.core.post_processing.nms.oks_iou(g, d, a_g, a_d, sigmas=None, vis_thr=None)[source]¶

Calculate oks ious.

Parameters

g – Ground truth keypoints.
d – Detected keypoints.
a_g – Area of the ground truth object.
a_d – Area of the detected object.
sigmas – standard deviation of keypoint labelling.
vis_thr – threshold of the keypoint visibility.

Returns

The oks ious.

Return type

list

easycv.core.post_processing.nms.oks_nms(kpts_db, thr, sigmas=None, vis_thr=None)[source]¶

OKS NMS implementations.

Parameters

kpts_db – keypoints.
thr – Retain overlap < thr.
sigmas – standard deviation of keypoint labelling.
vis_thr – threshold of the keypoint visibility.

Returns

indexes to keep.

Return type

np.ndarray

easycv.core.post_processing.nms.soft_oks_nms(kpts_db, thr, max_dets=20, sigmas=None, vis_thr=None)[source]¶

Soft OKS NMS implementations.

Parameters

kpts_db –
thr – retain oks overlap < thr.
max_dets – max number of detections to keep.
sigmas – Keypoint labelling uncertainty.

Returns

indexes to keep.

Return type

np.ndarray

easycv.core.post_processing.pose_transforms module¶

easycv.core.post_processing.pose_transforms.fliplr_joints(joints_3d, joints_3d_visible, img_width, flip_pairs)[source]¶

Flip human joints horizontally.

Note

num_keypoints: K

Parameters

joints_3d (np.ndarray([K, 3])) – Coordinates of keypoints.
joints_3d_visible (np.ndarray([K, 1])) – Visibility of keypoints.
img_width (int) – Image width.
flip_pairs (list[tuple()]) – Pairs of keypoints which are mirrored (for example, left ear – right ear).

Returns

Flipped human joints.

joints_3d_flipped (np.ndarray([K, 3])): Flipped joints.
joints_3d_visible_flipped (np.ndarray([K, 1])): Joint visibility.

Return type

tuple

easycv.core.post_processing.pose_transforms.fliplr_regression(regression, flip_pairs, center_mode='static', center_x=0.5, center_index=0)[source]¶

Flip human joints horizontally.

Note

batch_size: N num_keypoint: K

Parameters

regression (np.ndarray([..., K, C])) –
Coordinates of keypoints, where K is the joint number and C is the dimension. Example shapes are: - [N, K, C]: a batch of keypoints where N is the batch size. - [N, T, K, C]: a batch of pose sequences, where T is the frame

number.
flip_pairs (list[tuple()]) – Pairs of keypoints which are mirrored (for example, left ear – right ear).
center_mode (str) – The mode to set the center location on the x-axis to flip around. Options are: - static: use a static x value (see center_x also) - root: use a root joint (see center_index also)
center_x (float) – Set the x-axis location of the flip center. Only used when center_mode=static.
center_index (int) – Set the index of the root joint, whose x location will be used as the flip center. Only used when center_mode=root.

Returns

Flipped human joints.

regression_flipped (np.ndarray([…, K, C])): Flipped joints.

Return type

tuple

easycv.core.post_processing.pose_transforms.flip_back(output_flipped, flip_pairs, target_type='GaussianHeatmap')[source]¶

Flip the flipped heatmaps back to the original form.

Note

batch_size: N num_keypoints: K heatmap height: H heatmap width: W

Parameters

output_flipped (np.ndarray[N, K, H, W]) – The output heatmaps obtained from the flipped images.
flip_pairs (list[tuple()) – Pairs of keypoints which are mirrored (for example, left ear – right ear).
target_type (str) – GaussianHeatmap or CombinedTarget

Returns

heatmaps that flipped back to the original image

Return type

np.ndarray

easycv.core.post_processing.pose_transforms.transform_preds(coords, center, scale, output_size, use_udp=False)[source]¶

Get final keypoint predictions from heatmaps and apply scaling and translation to map them back to the image.

Note

num_keypoints: K

Parameters

coords (np.ndarray[K, ndims]) –
- If ndims=2, corrds are predicted keypoint location.
- If ndims=4, corrds are composed of (x, y, scores, tags)
- If ndims=5, corrds are composed of (x, y, scores, tags, flipped_tags)
center (np.ndarray[2, ]) – Center of the bounding box (x, y).
scale (np.ndarray[2, ]) – Scale of the bounding box wrt [width, height].
output_size (np.ndarray[2, ] | list(2,)) – Size of the destination heatmaps.
use_udp (bool) – Use unbiased data processing

Returns

Predicted coordinates in the images.

Return type

np.ndarray

easycv.core.post_processing.pose_transforms.get_affine_transform(center, scale, rot, output_size, shift=(0.0, 0.0), inv=False)[source]¶

Get the affine transform matrix, given the center/scale/rot/output_size.

Parameters

center (np.ndarray[2, ]) – Center of the bounding box (x, y).
scale (np.ndarray[2, ]) – Scale of the bounding box wrt [width, height].
rot (float) – Rotation angle (degree).
output_size (np.ndarray[2, ] | list(2,)) – Size of the destination heatmaps.
shift (0-100%) – Shift translation ratio wrt the width/height. Default (0., 0.).
inv (bool) – Option to inverse the affine transform direction. (inv=False: src->dst or inv=True: dst->src)

Returns

The transform matrix.

Return type

np.ndarray

easycv.core.post_processing.pose_transforms.affine_transform(pt, trans_mat)[source]¶

Apply an affine transformation to the points.

Parameters

pt (np.ndarray) – a 2 dimensional point to be transformed
trans_mat (np.ndarray) – 2x3 matrix of an affine transform

Returns

Transformed points.

Return type

np.ndarray

easycv.core.post_processing.pose_transforms.rotate_point(pt, angle_rad)[source]¶

Rotate a point by an angle.

Parameters

pt (list[float]) – 2 dimensional point to be rotated
angle_rad (float) – rotation angle by radian

Returns

Rotated point.

Return type

list[float]

easycv.core.post_processing.pose_transforms.get_warp_matrix(theta, size_input, size_dst, size_target)[source]¶

Calculate the transformation matrix under the constraint of unbiased. Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).

Parameters

theta (float) – Rotation angle in degrees.
size_input (np.ndarray) – Size of input image [w, h].
size_dst (np.ndarray) – Size of output image [w, h].
size_target (np.ndarray) – Size of ROI in input plane [w, h].

Returns

A matrix for transformation.

Return type

matrix (np.ndarray)

easycv.core.post_processing.pose_transforms.warp_affine_joints(joints, mat)[source]¶

Apply affine transformation defined by the transform matrix on the joints.

Parameters

joints (np.ndarray[..., 2]) – Origin coordinate of joints.
mat (np.ndarray[3, 2]) – The affine matrix.

Returns

Result coordinate of joints.

Return type

matrix (np.ndarray[…, 2])

easycv.core.visualization package¶

easycv.core.visualization.imshow_bboxes(img, bboxes, labels=None, colors='green', text_color='white', font_size=20, thickness=1, font_scale=0.5, show=True, win_name='', wait_time=0, out_file=None)[source]¶

Draw bboxes with labels (optional) on an image. This is a wrapper of mmcv.imshow_bboxes.

Parameters

img (str or ndarray) – The image to be displayed.
bboxes (ndarray) – ndarray of shape (k, 4), each row is a bbox in format [x1, y1, x2, y2].
labels (str or list[str], optional) – labels of each bbox.
colors (list[str or tuple or Color]) – A list of colors.
text_color (str or tuple or Color) – Color of texts.
font_size (int) – Size of font.
thickness (int) – Thickness of lines.
font_scale (float) – Font scales of texts.
show (bool) – Whether to show the image.
win_name (str) – The window name.
wait_time (int) – Value of waitKey param.
out_file (str, optional) – The filename to write the image.

Returns

The image with bboxes drawn on it.

Return type

ndarray

easycv.core.visualization.imshow_keypoints(img, pose_result, skeleton=None, kpt_score_thr=0.3, pose_kpt_color=None, pose_link_color=None, radius=4, thickness=1, show_keypoint_weight=False)[source]¶

Draw keypoints and links on an image.

Parameters

img (str or Tensor) – The image to draw poses on. If an image array is given, id will be modified in-place.
pose_result (list[kpts]) – The poses to draw. Each element kpts is a set of K keypoints as an Kx3 numpy.ndarray, where each keypoint is represented as x, y, score.
kpt_score_thr (float, optional) – Minimum score of keypoints to be shown. Default: 0.3.
pose_kpt_color (np.array[Nx3]`) – Color of N keypoints. If None, the keypoint will not be drawn.
pose_link_color (np.array[Mx3]) – Color of M links. If None, the links will not be drawn.
thickness (int) – Thickness of lines.

easycv.core.visualization.imshow_label(img, labels, text_color='blue', font_size=20, thickness=1, font_scale=0.5, intervel=5, show=True, win_name='', wait_time=0, out_file=None)[source]¶

Draw images with labels on an image.

Parameters

img (str or ndarray) – The image to be displayed.
labels (str or list[str]) – labels of each image.
text_color (str or tuple or Color) – Color of texts.
font_size (int) – Size of font.
thickness (int) – Thickness of lines.
font_scale (float) – Font scales of texts.
intervel（int) – interval pixels between multiple labels
show (bool) – Whether to show the image.
win_name (str) – The window name.
wait_time (int) – Value of waitKey param.
out_file (str, optional) – The filename to write the image.

Returns

The image with bboxes drawn on it.

Return type

ndarray

Submodules¶

easycv.core.visualization.image module¶

easycv.core.visualization.image.get_font_path()[source]¶

easycv.core.visualization.image.put_text(img, xy, text, fill, size=20)[source]¶: support chinese text

easycv.core.visualization.image.imshow_label(img, labels, text_color='blue', font_size=20, thickness=1, font_scale=0.5, intervel=5, show=True, win_name='', wait_time=0, out_file=None)[source]¶

Draw images with labels on an image.

Parameters

img (str or ndarray) – The image to be displayed.
labels (str or list[str]) – labels of each image.
text_color (str or tuple or Color) – Color of texts.
font_size (int) – Size of font.
thickness (int) – Thickness of lines.
font_scale (float) – Font scales of texts.
intervel（int) – interval pixels between multiple labels
show (bool) – Whether to show the image.
win_name (str) – The window name.
wait_time (int) – Value of waitKey param.
out_file (str, optional) – The filename to write the image.

Returns

The image with bboxes drawn on it.

Return type

ndarray

easycv.core.visualization.image.imshow_bboxes(img, bboxes, labels=None, colors='green', text_color='white', font_size=20, thickness=1, font_scale=0.5, show=True, win_name='', wait_time=0, out_file=None)[source]¶

Draw bboxes with labels (optional) on an image. This is a wrapper of mmcv.imshow_bboxes.

Parameters

img (str or ndarray) – The image to be displayed.
bboxes (ndarray) – ndarray of shape (k, 4), each row is a bbox in format [x1, y1, x2, y2].
labels (str or list[str], optional) – labels of each bbox.
colors (list[str or tuple or Color]) – A list of colors.
text_color (str or tuple or Color) – Color of texts.
font_size (int) – Size of font.
thickness (int) – Thickness of lines.
font_scale (float) – Font scales of texts.
show (bool) – Whether to show the image.
win_name (str) – The window name.
wait_time (int) – Value of waitKey param.
out_file (str, optional) – The filename to write the image.

Returns

The image with bboxes drawn on it.

Return type

ndarray

easycv.core.visualization.image.imshow_keypoints(img, pose_result, skeleton=None, kpt_score_thr=0.3, pose_kpt_color=None, pose_link_color=None, radius=4, thickness=1, show_keypoint_weight=False)[source]¶

Draw keypoints and links on an image.

Parameters

img (str or Tensor) – The image to draw poses on. If an image array is given, id will be modified in-place.
pose_result (list[kpts]) – The poses to draw. Each element kpts is a set of K keypoints as an Kx3 numpy.ndarray, where each keypoint is represented as x, y, score.
kpt_score_thr (float, optional) – Minimum score of keypoints to be shown. Default: 0.3.
pose_kpt_color (np.array[Nx3]`) – Color of N keypoints. If None, the keypoint will not be drawn.
pose_link_color (np.array[Mx3]) – Color of M links. If None, the links will not be drawn.
thickness (int) – Thickness of lines.

Submodules¶

easycv.core.standard_fields module¶

Contains classes specifying naming conventions used for object detection.

Specifies:: InputDataFields: standard fields used by reader/preprocessor/batcher. DetectionResultFields: standard fields returned by object detector. BoxListFields: standard field used by BoxList TfExampleFields: standard fields for tf-example data format (go/tf-example).

class easycv.core.standard_fields.InputDataFields[source]¶

Bases: object

Names for the input tensors.

Holds the standard data field names to use for identifying input tensors. This should be used by the decoder to identify keys for the returned tensor_dict containing input tensors. And it should be used by the model to identify the tensors it needs.

image¶: image.

original_image¶: image in the original input size.

key¶: unique key corresponding to image.

source_id¶: source of the original image.

filename¶: original filename of the dataset (without common path).

groundtruth_image_classes¶: image-level class labels.

groundtruth_boxes¶: coordinates of the ground truth boxes in the image.

groundtruth_classes¶: box-level class labels.

groundtruth_label_types¶: box-level label types (e.g. explicit negative).

groundtruth_is_crowd¶: [DEPRECATED, use groundtruth_group_of instead] is the groundtruth a single object or a crowd.

groundtruth_area¶: area of a groundtruth segment.

groundtruth_difficult¶: is a difficult object

groundtruth_group_of¶: is a group_of objects, e.g. multiple objects of the same class, forming a connected group, where instances are heavily occluding each other.

proposal_boxes¶: coordinates of object proposal boxes.

proposal_objectness¶: objectness score of each proposal.

groundtruth_instance_masks¶: ground truth instance masks.

groundtruth_instance_boundaries¶: ground truth instance boundaries.

groundtruth_instance_classes¶: instance mask-level class labels.

groundtruth_keypoints¶: ground truth keypoints.

groundtruth_keypoint_visibilities¶: ground truth keypoint visibilities.

groundtruth_label_scores¶: groundtruth label scores.

groundtruth_weights¶: groundtruth weight factor for bounding boxes.

num_groundtruth_boxes¶: number of groundtruth boxes.

true_image_shapes¶: true shapes of images in the resized images, as resized images can be padded with zeros.

image = 'image'¶

mask = 'mask'¶

width = 'width'¶

height = 'height'¶

original_image = 'original_image'¶

optical_flow = 'optical_flow'¶

key = 'key'¶

source_id = 'source_id'¶

filename = 'filename'¶

dataset_name = 'dataset_name'¶

groundtruth_image_classes = 'groundtruth_image_classes'¶

groundtruth_image_classes_num = 'groundtruth_image_classes_num'¶

groundtruth_boxes = 'groundtruth_boxes'¶

groundtruth_classes = 'groundtruth_classes'¶

groundtruth_label_types = 'groundtruth_label_types'¶

groundtruth_is_crowd = 'groundtruth_is_crowd'¶

groundtruth_area = 'groundtruth_area'¶

groundtruth_difficult = 'groundtruth_difficult'¶

groundtruth_group_of = 'groundtruth_group_of'¶

proposal_boxes = 'proposal_boxes'¶

proposal_objectness = 'proposal_objectness'¶

groundtruth_instance_masks = 'groundtruth_instance_masks'¶

groundtruth_instance_boundaries = 'groundtruth_instance_boundaries'¶

groundtruth_instance_classes = 'groundtruth_instance_classes'¶

groundtruth_keypoints = 'groundtruth_keypoints'¶

groundtruth_keypoint_visibilities = 'groundtruth_keypoint_visibilities'¶

groundtruth_label_scores = 'groundtruth_label_scores'¶

groundtruth_weights = 'groundtruth_weights'¶

num_groundtruth_boxes = 'num_groundtruth_boxes'¶

true_image_shape = 'true_image_shape'¶

original_image_shape = 'original_image_shape'¶

original_instance_masks = 'original_instance_masks'¶

groundtruth_boxes_absolute = 'groundtruth_boxes_absolute'¶

groundtruth_keypoints_absolute = 'groundtruth_keypoints_absolute'¶

label_map = 'label_map'¶

char_dict = 'char_dict'¶

class easycv.core.standard_fields.DetectionResultFields[source]¶

Bases: object

Naming conventions for storing the output of the detector.

source_id¶: source of the original image.

key¶: unique key corresponding to image.

detection_boxes¶: coordinates of the detection boxes in the image.

detection_scores¶: detection scores for the detection boxes in the image.

detection_classes¶: detection-level class labels.

detection_masks¶: contains a segmentation mask for each detection box.

detection_boundaries¶: contains an object boundary for each detection box.

detection_keypoints¶: contains detection keypoints for each detection box.

num_detections¶: number of detections in the batch.

source_id = 'source_id'¶

key = 'key'¶

detection_boxes = 'detection_boxes'¶

detection_scores = 'detection_scores'¶

detection_classes = 'detection_classes'¶

detection_masks = 'detection_masks'¶

detection_boundaries = 'detection_boundaries'¶

detection_keypoints = 'detection_keypoints'¶

num_detections = 'num_detections'¶

class easycv.core.standard_fields.TfExampleFields[source]¶

Bases: object

TF-example proto feature names for object detection.

Holds the standard feature names to load from an Example proto for object detection.

image_encoded¶: JPEG encoded string

image_format¶: image format, e.g. “JPEG”

filename¶: filename

channels¶: number of channels of image

colorspace¶: colorspace, e.g. “RGB”

height¶: height of image in pixels, e.g. 462

width¶: width of image in pixels, e.g. 581

source_id¶: original source of the image

object_class_text¶: labels in text format, e.g. [“person”, “cat”]

object_class_label¶: labels in numbers, e.g. [16, 8]

object_bbox_xmin¶: xmin coordinates of groundtruth box, e.g. 10, 30

object_bbox_xmax¶: xmax coordinates of groundtruth box, e.g. 50, 40

object_bbox_ymin¶: ymin coordinates of groundtruth box, e.g. 40, 50

object_bbox_ymax¶: ymax coordinates of groundtruth box, e.g. 80, 70

object_view¶: viewpoint of object, e.g. [“frontal”, “left”]

object_truncated¶: is object truncated, e.g. [true, false]

object_occluded¶: is object occluded, e.g. [true, false]

object_difficult¶: is object difficult, e.g. [true, false]

object_group_of¶: is object a single object or a group of objects

object_depiction¶: is object a depiction

object_is_crowd¶: [DEPRECATED, use object_group_of instead] is the object a single object or a crowd

object_segment_area¶: the area of the segment.

object_weight¶: a weight factor for the object’s bounding box.

instance_masks¶: instance segmentation masks.

instance_boundaries¶: instance boundaries.

instance_classes¶: Classes for each instance segmentation mask.

detection_class_label¶: class label in numbers.

detection_bbox_ymin¶: ymin coordinates of a detection box.

detection_bbox_xmin¶: xmin coordinates of a detection box.

detection_bbox_ymax¶: ymax coordinates of a detection box.

detection_bbox_xmax¶: xmax coordinates of a detection box.

detection_score¶: detection score for the class label and box.

image_encoded = 'image/encoded'¶

image_format = 'image/format'¶

filename = 'image/filename'¶

channels = 'image/channels'¶

colorspace = 'image/colorspace'¶

height = 'image/height'¶

width = 'image/width'¶

source_id = 'image/source_id'¶

object_class_text = 'image/object/class/text'¶

object_class_label = 'image/object/class/label'¶

object_bbox_ymin = 'image/object/bbox/ymin'¶

object_bbox_xmin = 'image/object/bbox/xmin'¶

object_bbox_ymax = 'image/object/bbox/ymax'¶

object_bbox_xmax = 'image/object/bbox/xmax'¶

object_view = 'image/object/view'¶

object_truncated = 'image/object/truncated'¶

object_occluded = 'image/object/occluded'¶

object_difficult = 'image/object/difficult'¶

object_group_of = 'image/object/group_of'¶

object_depiction = 'image/object/depiction'¶

object_is_crowd = 'image/object/is_crowd'¶

object_segment_area = 'image/object/segment/area'¶

object_weight = 'image/object/weight'¶

instance_masks = 'image/segmentation/object'¶

instance_boundaries = 'image/boundaries/object'¶

instance_classes = 'image/segmentation/object/class'¶

detection_class_label = 'image/detection/label'¶

detection_bbox_ymin = 'image/detection/bbox/ymin'¶

detection_bbox_xmin = 'image/detection/bbox/xmin'¶

detection_bbox_ymax = 'image/detection/bbox/ymax'¶

detection_bbox_xmax = 'image/detection/bbox/xmax'¶

detection_score = 'image/detection/score'¶

easycv.models package¶

Subpackages¶

easycv.models.backbones package¶

Submodules¶

easycv.models.backbones.benchmark_mlp module¶

class easycv.models.backbones.benchmark_mlp.BenchMarkMLP(feature_num, num_classes=1000, avg_pool=False, **kwargs)[source]¶

Bases: torch.nn.modules.module.Module

__init__(feature_num, num_classes=1000, avg_pool=False, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(pretrained=None)[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.backbones.bninception module¶

This model is taken from the official PyTorch model zoo. - torchvision.models.mobilenet.py on 31th Aug, 2019

class easycv.models.backbones.bninception.BNInception(num_classes=0)[source]¶

Bases: torch.nn.modules.module.Module

__init__(num_classes=0)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(pretrained=None)[source]¶

features(input)[source]¶

logits(features)[source]¶

forward(input)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.backbones.darknet module¶

class easycv.models.backbones.darknet.Darknet(depth, in_channels=3, stem_out_channels=32, out_features=('dark3', 'dark4', 'dark5'))[source]¶

Bases: torch.nn.modules.module.Module

depth2blocks = {21: [1, 2, 2, 1], 53: [2, 8, 8, 4]}¶

__init__(depth, in_channels=3, stem_out_channels=32, out_features=('dark3', 'dark4', 'dark5'))[source]¶

Parameters

depth (int) – depth of darknet used in model, usually use [21, 53] for this param.
in_channels (int) – number of input channels, for example, use 3 for RGB image.
stem_out_channels (int) – number of output chanels of darknet stem. It decides channels of darknet layer2 to layer5.
out_features (Tuple[str]) – desired output layer name.

make_group_layer(in_channels: int, num_blocks: int, stride: int = 1)[source]¶: starts with conv layer then has num_blocks ResLayer

make_spp_block(filters_list, in_filters)[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.darknet.CSPDarknet(dep_mul, wid_mul, out_features=('dark3', 'dark4', 'dark5'), depthwise=False, act='silu')[source]¶

Bases: torch.nn.modules.module.Module

__init__(dep_mul, wid_mul, out_features=('dark3', 'dark4', 'dark5'), depthwise=False, act='silu')[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.backbones.genet module¶

easycv.models.backbones.genet.remove_bn_in_superblock(super_block)[source]¶

easycv.models.backbones.genet.fuse_bn(model)[source]¶

class easycv.models.backbones.genet.PlainNetBasicBlockClass(in_channels=0, out_channels=0, stride=1, no_create=False, block_name=None, **kwargs)[source]¶

Bases: torch.nn.modules.module.Module

__init__(in_channels=0, out_channels=0, stride=1, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.AdaptiveAvgPool(out_channels, output_size, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(out_channels, output_size, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.BN(out_channels=None, copy_from=None, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(out_channels=None, copy_from=None, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.ConvDW(out_channels=None, kernel_size=None, stride=None, copy_from=None, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(out_channels=None, kernel_size=None, stride=None, copy_from=None, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.ConvKX(in_channels=None, out_channels=None, kernel_size=None, stride=None, copy_from=None, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(in_channels=None, out_channels=None, kernel_size=None, stride=None, copy_from=None, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.Flatten(out_channels, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(out_channels, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.Linear(in_channels=None, out_channels=None, bias=None, copy_from=None, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(in_channels=None, out_channels=None, bias=None, copy_from=None, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.MaxPool(out_channels, kernel_size, stride, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(out_channels, kernel_size, stride, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.MultiSumBlock(inner_block_list, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(inner_block_list, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.RELU(out_channels, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(out_channels, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.ResBlock(inner_block_list, in_channels=None, stride=None, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

ResBlock(in_channles, inner_blocks_str). If in_channels is missing, use inner_block_list[0].in_channels as in_channels

__init__(inner_block_list, in_channels=None, stride=None, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.Sequential(inner_block_list, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(inner_block_list, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.SuperResKXKX(in_channels=0, out_channels=0, kernel_size=3, stride=1, expansion=1.0, sublayers=1, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(in_channels=0, out_channels=0, kernel_size=3, stride=1, expansion=1.0, sublayers=1, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.SuperResK1KX(in_channels=0, out_channels=0, kernel_size=3, stride=1, expansion=1.0, sublayers=1, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(in_channels=0, out_channels=0, kernel_size=3, stride=1, expansion=1.0, sublayers=1, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.SuperResK1KXK1(in_channels=0, out_channels=0, kernel_size=3, stride=1, expansion=1.0, sublayers=1, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(in_channels=0, out_channels=0, kernel_size=3, stride=1, expansion=1.0, sublayers=1, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.SuperResK1DWK1(in_channels=0, out_channels=0, kernel_size=3, stride=1, expansion=1.0, sublayers=1, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(in_channels=0, out_channels=0, kernel_size=3, stride=1, expansion=1.0, sublayers=1, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

training: bool¶

class easycv.models.backbones.genet.SuperResK1DW(in_channels=0, out_channels=0, kernel_size=3, stride=1, expansion=1.0, sublayers=1, no_create=False, block_name=None, **kwargs)[source]¶

Bases: easycv.models.backbones.genet.PlainNetBasicBlockClass

__init__(in_channels=0, out_channels=0, kernel_size=3, stride=1, expansion=1.0, sublayers=1, no_create=False, block_name=None, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

training: bool¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static create_from_str(s, no_create=False)[source]¶

static is_instance_from_str(s)[source]¶

class easycv.models.backbones.genet.PlainNet(plainnet_struct_idx=None, num_classes=0, no_create=False, **kwargs)[source]¶

Bases: torch.nn.modules.module.Module

training: bool¶

__init__(plainnet_struct_idx=None, num_classes=0, no_create=False, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(pretrained=None)[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

easycv.models.backbones.hrnet module¶

easycv.models.backbones.hrnet.get_expansion(block, expansion=None)[source]¶

Get the expansion of a residual block.

The block expansion will be obtained by the following order:

If expansion is given, just return it.
If block has the attribute expansion, then return block.expansion.
Return the default value according the the block type: 1 for BasicBlock and 4 for Bottleneck.

Parameters

block (class) – The block class.
expansion (int | None) – The given expansion ratio.

Returns

The expansion of the block.

Return type

int

class easycv.models.backbones.hrnet.Bottleneck(in_channels, out_channels, expansion=4, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'})[source]¶

Bases: torch.nn.modules.module.Module

Bottleneck block for ResNet.

Parameters

in_channels (int) – Input channels of this block.
out_channels (int) – Output channels of this block.
expansion (int) – The ratio of out_channels/mid_channels where mid_channels is the input/output channels of conv2. Default: 4.
stride (int) – stride of the block. Default: 1
dilation (int) – dilation of convolution. Default: 1
downsample (nn.Module) – downsample operation on identity branch. Default: None.
style (str) – "pytorch" or "caffe". If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. Default: “pytorch”.
with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed.
conv_cfg (dict) – dictionary to construct and config conv layer. Default: None
norm_cfg (dict) – dictionary to construct and config norm layer. Default: dict(type=’BN’)

__init__(in_channels, out_channels, expansion=4, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'})[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

property norm1¶

the normalization layer named “norm1”

Type: nn.Module

property norm2¶

the normalization layer named “norm2”

Type: nn.Module

property norm3¶

the normalization layer named “norm3”

Type: nn.Module

forward(x)[source]¶: Forward function.

training: bool¶

class easycv.models.backbones.hrnet.HRModule(num_branches, blocks, num_blocks, in_channels, num_channels, multiscale_output=False, with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'}, upsample_cfg={'align_corners': None, 'mode': 'nearest'})[source]¶

Bases: torch.nn.modules.module.Module

High-Resolution Module for HRNet.

In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange is in this module.

__init__(num_branches, blocks, num_blocks, in_channels, num_channels, multiscale_output=False, with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'}, upsample_cfg={'align_corners': None, 'mode': 'nearest'})[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶: Forward function.

training: bool¶

class easycv.models.backbones.hrnet.HRNet(arch='w32', extra=None, in_channels=3, conv_cfg=None, norm_cfg={'type': 'BN'}, norm_eval=False, with_cp=False, zero_init_residual=False, multi_scale_output=False)[source]¶

Bases: torch.nn.modules.module.Module

HRNet backbone.

High-Resolution Representations for Labeling Pixels and Regions

Parameters

extra (dict) – detailed configuration for each stage of HRNet.
in_channels (int) – Number of input image channels. Default: 3.
conv_cfg (dict) – dictionary to construct and config conv layer.
norm_cfg (dict) – dictionary to construct and config norm layer.
norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False
with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed.
zero_init_residual (bool) – whether to use zero init for last norm layer in resblocks to let them behave as identity.

Example

>>> from mmpose.models import HRNet
>>> import torch
>>> extra = dict(
>>>     stage1=dict(
>>>         num_modules=1,
>>>         num_branches=1,
>>>         block='BOTTLENECK',
>>>         num_blocks=(4, ),
>>>         num_channels=(64, )),
>>>     stage2=dict(
>>>         num_modules=1,
>>>         num_branches=2,
>>>         block='BASIC',
>>>         num_blocks=(4, 4),
>>>         num_channels=(32, 64)),
>>>     stage3=dict(
>>>         num_modules=4,
>>>         num_branches=3,
>>>         block='BASIC',
>>>         num_blocks=(4, 4, 4),
>>>         num_channels=(32, 64, 128)),
>>>     stage4=dict(
>>>         num_modules=3,
>>>         num_branches=4,
>>>         block='BASIC',
>>>         num_blocks=(4, 4, 4, 4),
>>>         num_channels=(32, 64, 128, 256)))
>>> self = HRNet(extra, in_channels=1)
>>> self.eval()
>>> inputs = torch.rand(1, 1, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 32, 8, 8)

blocks_dict = {'BASIC': <class 'easycv.models.backbones.resnet.BasicBlock'>, 'BOTTLENECK': <class 'easycv.models.backbones.hrnet.Bottleneck'>}¶

arch_zoo = {'w18': [[1, 1, 'BOTTLENECK', (4,), (64,)], [1, 2, 'BASIC', (4, 4), (18, 36)], [4, 3, 'BASIC', (4, 4, 4), (18, 36, 72)], [3, 4, 'BASIC', (4, 4, 4, 4), (18, 36, 72, 144)]], 'w30': [[1, 1, 'BOTTLENECK', (4,), (64,)], [1, 2, 'BASIC', (4, 4), (30, 60)], [4, 3, 'BASIC', (4, 4, 4), (30, 60, 120)], [3, 4, 'BASIC', (4, 4, 4, 4), (30, 60, 120, 240)]], 'w32': [[1, 1, 'BOTTLENECK', (4,), (64,)], [1, 2, 'BASIC', (4, 4), (32, 64)], [4, 3, 'BASIC', (4, 4, 4), (32, 64, 128)], [3, 4, 'BASIC', (4, 4, 4, 4), (32, 64, 128, 256)]], 'w40': [[1, 1, 'BOTTLENECK', (4,), (64,)], [1, 2, 'BASIC', (4, 4), (40, 80)], [4, 3, 'BASIC', (4, 4, 4), (40, 80, 160)], [3, 4, 'BASIC', (4, 4, 4, 4), (40, 80, 160, 320)]], 'w44': [[1, 1, 'BOTTLENECK', (4,), (64,)], [1, 2, 'BASIC', (4, 4), (44, 88)], [4, 3, 'BASIC', (4, 4, 4), (44, 88, 176)], [3, 4, 'BASIC', (4, 4, 4, 4), (44, 88, 176, 352)]], 'w48': [[1, 1, 'BOTTLENECK', (4,), (64,)], [1, 2, 'BASIC', (4, 4), (48, 96)], [4, 3, 'BASIC', (4, 4, 4), (48, 96, 192)], [3, 4, 'BASIC', (4, 4, 4, 4), (48, 96, 192, 384)]], 'w64': [[1, 1, 'BOTTLENECK', (4,), (64,)], [1, 2, 'BASIC', (4, 4), (64, 128)], [4, 3, 'BASIC', (4, 4, 4), (64, 128, 256)], [3, 4, 'BASIC', (4, 4, 4, 4), (64, 128, 256, 512)]]}¶

__init__(arch='w32', extra=None, in_channels=3, conv_cfg=None, norm_cfg={'type': 'BN'}, norm_eval=False, with_cp=False, zero_init_residual=False, multi_scale_output=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

property norm1¶

the normalization layer named “norm1”

Type: nn.Module

property norm2¶

the normalization layer named “norm2”

Type: nn.Module

init_weights(pretrained=None)[source]¶

Initialize the weights in backbone.

Parameters: pretrained (str, optional) – Path to pre-trained weights. Defaults to None.

forward(x)[source]¶: Forward function.

train(mode=True)[source]¶: Convert the model into training mode.

training: bool¶

parse_arch(arch, extra=None)[source]¶

easycv.models.backbones.inceptionv3 module¶

This model is taken from the official PyTorch model zoo. - torchvision.models.inception.py on 31th Aug, 2019

class easycv.models.backbones.inceptionv3.Inception3(num_classes: int = 0, aux_logits: bool = True, transform_input: bool = False)[source]¶

Bases: torch.nn.modules.module.Module

__init__(num_classes: int = 0, aux_logits: bool = True, transform_input: bool = False) → None[source]¶

Parameters

num_classes – number of classes based on dataset.
aux_logits – If True, adds two auxiliary branches that can improve training. Default: False when pretrained is True otherwise True
transform_input – If True, preprocesses the input according to the method with which it was trained on ImageNet. Default: False

init_weights(pretrained=None)[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.backbones.lighthrnet module¶

easycv.models.backbones.lighthrnet.channel_shuffle(x, groups)[source]¶

Channel Shuffle operation.

This function enables cross-group information flow for multiple groups convolution layers.

Parameters

x (Tensor) – The input tensor.
groups (int) – The number of groups to divide the input tensor in the channel dimension.

Returns

The output tensor after channel shuffle operation.

Return type

Tensor

class easycv.models.backbones.lighthrnet.SpatialWeighting(channels, ratio=16, conv_cfg=None, norm_cfg=None, act_cfg=({'type': 'ReLU'}, {'type': 'Sigmoid'}))[source]¶

Bases: torch.nn.modules.module.Module

Spatial weighting module.

Parameters

channels (int) – The channels of the module.
ratio (int) – channel reduction ratio.
conv_cfg (dict) – Config dict for convolution layer. Default: None, which means using conv2d.
norm_cfg (dict) – Config dict for normalization layer. Default: None.
act_cfg (dict) – Config dict for activation layer. Default: (dict(type=’ReLU’), dict(type=’Sigmoid’)). The last ConvModule uses Sigmoid by default.

__init__(channels, ratio=16, conv_cfg=None, norm_cfg=None, act_cfg=({'type': 'ReLU'}, {'type': 'Sigmoid'}))[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.lighthrnet.CrossResolutionWeighting(channels, ratio=16, conv_cfg=None, norm_cfg=None, act_cfg=({'type': 'ReLU'}, {'type': 'Sigmoid'}))[source]¶

Bases: torch.nn.modules.module.Module

Cross-resolution channel weighting module.

Parameters

channels (int) – The channels of the module.
ratio (int) – channel reduction ratio.
conv_cfg (dict) – Config dict for convolution layer. Default: None, which means using conv2d.
norm_cfg (dict) – Config dict for normalization layer. Default: None.
act_cfg (dict) – Config dict for activation layer. Default: (dict(type=’ReLU’), dict(type=’Sigmoid’)). The last ConvModule uses Sigmoid by default.

__init__(channels, ratio=16, conv_cfg=None, norm_cfg=None, act_cfg=({'type': 'ReLU'}, {'type': 'Sigmoid'}))[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.lighthrnet.ConditionalChannelWeighting(in_channels, stride, reduce_ratio, conv_cfg=None, norm_cfg={'type': 'BN'}, with_cp=False)[source]¶

Bases: torch.nn.modules.module.Module

Conditional channel weighting block.

Parameters

in_channels (int) – The input channels of the block.
stride (int) – Stride of the 3x3 convolution layer.
reduce_ratio (int) – channel reduction ratio.
conv_cfg (dict) – Config dict for convolution layer. Default: None, which means using conv2d.
norm_cfg (dict) – Config dict for normalization layer. Default: dict(type=’BN’).
with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

__init__(in_channels, stride, reduce_ratio, conv_cfg=None, norm_cfg={'type': 'BN'}, with_cp=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.lighthrnet.Stem(in_channels, stem_channels, out_channels, expand_ratio, conv_cfg=None, norm_cfg={'type': 'BN'}, with_cp=False)[source]¶

Bases: torch.nn.modules.module.Module

Stem network block.

Parameters

in_channels (int) – The input channels of the block.
stem_channels (int) – Output channels of the stem layer.
out_channels (int) – The output channels of the block.
expand_ratio (int) – adjusts number of channels of the hidden layer in InvertedResidual by this amount.
conv_cfg (dict) – Config dict for convolution layer. Default: None, which means using conv2d.
norm_cfg (dict) – Config dict for normalization layer. Default: dict(type=’BN’).
with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

__init__(in_channels, stem_channels, out_channels, expand_ratio, conv_cfg=None, norm_cfg={'type': 'BN'}, with_cp=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.lighthrnet.IterativeHead(in_channels, norm_cfg={'type': 'BN'})[source]¶

Bases: torch.nn.modules.module.Module

Extra iterative head for feature learning.

Parameters

in_channels (int) – The input channels of the block.
norm_cfg (dict) – Config dict for normalization layer. Default: dict(type=’BN’).

__init__(in_channels, norm_cfg={'type': 'BN'})[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.lighthrnet.ShuffleUnit(in_channels, out_channels, stride=1, conv_cfg=None, norm_cfg={'type': 'BN'}, act_cfg={'type': 'ReLU'}, with_cp=False)[source]¶

Bases: torch.nn.modules.module.Module

InvertedResidual block for ShuffleNetV2 backbone.

Parameters

in_channels (int) – The input channels of the block.
out_channels (int) – The output channels of the block.
stride (int) – Stride of the 3x3 convolution layer. Default: 1
conv_cfg (dict) – Config dict for convolution layer. Default: None, which means using conv2d.
norm_cfg (dict) – Config dict for normalization layer. Default: dict(type=’BN’).
act_cfg (dict) – Config dict for activation layer. Default: dict(type=’ReLU’).
with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

__init__(in_channels, out_channels, stride=1, conv_cfg=None, norm_cfg={'type': 'BN'}, act_cfg={'type': 'ReLU'}, with_cp=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.lighthrnet.LiteHRModule(num_branches, num_blocks, in_channels, reduce_ratio, module_type, multiscale_output=False, with_fuse=True, conv_cfg=None, norm_cfg={'type': 'BN'}, with_cp=False)[source]¶

Bases: torch.nn.modules.module.Module

High-Resolution Module for LiteHRNet.

It contains conditional channel weighting blocks and shuffle blocks.

Parameters

num_branches (int) – Number of branches in the module.
num_blocks (int) – Number of blocks in the module.
in_channels (list(int)) – Number of input image channels.
reduce_ratio (int) – Channel reduction ratio.
module_type (str) – ‘LITE’ or ‘NAIVE’
multiscale_output (bool) – Whether to output multi-scale features.
with_fuse (bool) – Whether to use fuse layers.
conv_cfg (dict) – dictionary to construct and config conv layer.
norm_cfg (dict) – dictionary to construct and config norm layer.
with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed.

__init__(num_branches, num_blocks, in_channels, reduce_ratio, module_type, multiscale_output=False, with_fuse=True, conv_cfg=None, norm_cfg={'type': 'BN'}, with_cp=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶: Forward function.

training: bool¶

class easycv.models.backbones.lighthrnet.LiteHRNet(extra, in_channels=3, conv_cfg=None, norm_cfg={'type': 'BN'}, norm_eval=False, with_cp=False)[source]¶

Bases: torch.nn.modules.module.Module

Lite-HRNet backbone.

Lite-HRNet: A Lightweight High-Resolution Network

Code adapted from ‘https://github.com/HRNet/Lite-HRNet/’ ‘blob/hrnet/models/backbones/litehrnet.py’

Parameters

extra (dict) – detailed configuration for each stage of HRNet.
in_channels (int) – Number of input image channels. Default: 3.
conv_cfg (dict) – dictionary to construct and config conv layer.
norm_cfg (dict) – dictionary to construct and config norm layer.
norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False
with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed.

Example

>>> from mmpose.models import LiteHRNet
>>> import torch
>>> extra=dict(
>>>    stem=dict(stem_channels=32, out_channels=32, expand_ratio=1),
>>>    num_stages=3,
>>>    stages_spec=dict(
>>>        num_modules=(2, 4, 2),
>>>        num_branches=(2, 3, 4),
>>>        num_blocks=(2, 2, 2),
>>>        module_type=('LITE', 'LITE', 'LITE'),
>>>        with_fuse=(True, True, True),
>>>        reduce_ratios=(8, 8, 8),
>>>        num_channels=(
>>>            (40, 80),
>>>            (40, 80, 160),
>>>            (40, 80, 160, 320),
>>>        )),
>>>    with_head=False)
>>> self = LiteHRNet(extra, in_channels=1)
>>> self.eval()
>>> inputs = torch.rand(1, 1, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 40, 8, 8)

__init__(extra, in_channels=3, conv_cfg=None, norm_cfg={'type': 'BN'}, norm_eval=False, with_cp=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(pretrained=None)[source]¶

Initialize the weights in backbone.

Parameters: pretrained (str, optional) – Path to pre-trained weights. Defaults to None.

forward(x)[source]¶: Forward function.

train(mode=True)[source]¶: Convert the model into training mode.

training: bool¶

easycv.models.backbones.mae_vit_transformer module¶

Mostly copy-paste from https://github.com/facebookresearch/mae/blob/main/models_mae.py

class easycv.models.backbones.mae_vit_transformer.MaskedAutoencoderViT(img_size=224, patch_size=16, in_chans=3, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4.0, norm_layer=functools.partial(<class 'torch.nn.modules.normalization.LayerNorm'>, eps=1e-06))[source]¶

Bases: torch.nn.modules.module.Module

Masked Autoencoder with VisionTransformer backbone.: MaskedAutoencoderViT is mostly same as vit_tranformer_dynamic, but with a random_masking func. MaskedAutoencoderViT model can be loaded by vit_tranformer_dynamic.

Parameters

img_size (int) – input image size
patch_size (int) – patch size
in_chans (int) – input image channels
embed_dim (int) – feature dimensions
depth (int) – number of encoder layers
num_heads (int) – Parallel attention heads
mlp_ratio (float) – mlp ratio
norm_layer – type of normalization layer

__init__(img_size=224, patch_size=16, in_chans=3, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4.0, norm_layer=functools.partial(<class 'torch.nn.modules.normalization.LayerNorm'>, eps=1e-06))[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

random_masking(x, mask_ratio)[source]¶: Perform per-sample random masking by per-sample shuffling. Per-sample shuffling is done by argsort random noise. x: [N, L, D], sequence

forward(x, mask_ratio)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.backbones.mnasnet module¶

This model is taken from the official PyTorch model zoo. - torchvision.models.mnasnet.py on 31th Aug, 2019

class easycv.models.backbones.mnasnet.MNASNet(alpha, num_classes=0, dropout=0.2)[source]¶

Bases: torch.nn.modules.module.Module

MNASNet, as described in https://arxiv.org/pdf/1807.11626.pdf. >>> model = MNASNet(1000, 1.0) >>> x = torch.rand(1, 3, 224, 224) >>> y = model(x) >>> y.dim() 1 >>> y.nelement() 1000

__init__(alpha, num_classes=0, dropout=0.2)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

init_weights(pretrained=None)[source]¶

training: bool¶

easycv.models.backbones.mobilenetv2 module¶

This model is taken from the official PyTorch model zoo. - torchvision.models.mobilenet.py on 31th Aug, 2019

class easycv.models.backbones.mobilenetv2.MobileNetV2(num_classes=0, width_multi=1.0, inverted_residual_setting=None, round_nearest=8)[source]¶

Bases: torch.nn.modules.module.Module

__init__(num_classes=0, width_multi=1.0, inverted_residual_setting=None, round_nearest=8)[source]¶: MobileNet V2 main class :param num_classes: Number of classes :type num_classes: int :param width_multi: Width multiplier - adjusts number of channels in each layer by this amount :type width_multi: float :param inverted_residual_setting: Network structure :param round_nearest: Round the number of channels in each layer to be a multiple of this number :type round_nearest: int :param Set to 1 to turn off rounding:

init_weights(pretrained=None)[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.backbones.network_blocks module¶

class easycv.models.backbones.network_blocks.SiLU(inplace=True)[source]¶

Bases: torch.nn.modules.module.Module

export-friendly inplace version of nn.SiLU()

__init__(inplace=True)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

static forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.network_blocks.HSiLU(inplace=True)[source]¶

Bases: torch.nn.modules.module.Module

export-friendly inplace version of nn.SiLU() hardsigmoid is better than sigmoid when used for edge model

__init__(inplace=True)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

static forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.backbones.network_blocks.get_activation(name='silu', inplace=True)[source]¶

class easycv.models.backbones.network_blocks.BaseConv(in_channels, out_channels, ksize, stride, groups=1, bias=False, act='silu')[source]¶

Bases: torch.nn.modules.module.Module

A Conv2d -> Batchnorm -> silu/leaky relu block

__init__(in_channels, out_channels, ksize, stride, groups=1, bias=False, act='silu')[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

fuseforward(x)[source]¶

training: bool¶

class easycv.models.backbones.network_blocks.DWConv(in_channels, out_channels, ksize, stride=1, act='silu')[source]¶

Bases: torch.nn.modules.module.Module

Depthwise Conv + Conv

__init__(in_channels, out_channels, ksize, stride=1, act='silu')[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.network_blocks.Bottleneck(in_channels, out_channels, shortcut=True, expansion=0.5, depthwise=False, act='silu')[source]¶

Bases: torch.nn.modules.module.Module

__init__(in_channels, out_channels, shortcut=True, expansion=0.5, depthwise=False, act='silu')[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.network_blocks.ResLayer(in_channels: int)[source]¶

Bases: torch.nn.modules.module.Module

Residual layer with in_channels inputs.

__init__(in_channels: int)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.network_blocks.SPPBottleneck(in_channels, out_channels, kernel_sizes=(5, 9, 13), activation='silu')[source]¶

Bases: torch.nn.modules.module.Module

Spatial pyramid pooling layer used in YOLOv3-SPP

__init__(in_channels, out_channels, kernel_sizes=(5, 9, 13), activation='silu')[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.network_blocks.CSPLayer(in_channels, out_channels, n=1, shortcut=True, expansion=0.5, depthwise=False, act='silu')[source]¶

Bases: torch.nn.modules.module.Module

CSP Bottleneck with 3 convolutions

__init__(in_channels, out_channels, n=1, shortcut=True, expansion=0.5, depthwise=False, act='silu')[source]¶

Parameters

in_channels (int) – input channels.
out_channels (int) – output channels.
n (int) – number of Bottlenecks. Default value: 1.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.network_blocks.Focus(in_channels, out_channels, ksize=1, stride=1, act='silu')[source]¶

Bases: torch.nn.modules.module.Module

Focus width and height information into channel space.

__init__(in_channels, out_channels, ksize=1, stride=1, act='silu')[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.backbones.pytorch_image_models_wrapper module¶

class easycv.models.backbones.pytorch_image_models_wrapper.PytorchImageModelWrapper(model_name='resnet50', pretrained=False, checkpoint_path=None, scriptable=None, exportable=None, no_jit=None, **kwargs)[source]¶

Bases: torch.nn.modules.module.Module

Support Backbones From pytorch-image-models.

The PyTorch community has lots of awesome contributions for image models. PyTorch Image Models (timm) is a collection of image models, aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results.

Model pages can be found at https://rwightman.github.io/pytorch-image-models/models/

References: https://github.com/rwightman/pytorch-image-models

__init__(model_name='resnet50', pretrained=False, checkpoint_path=None, scriptable=None, exportable=None, no_jit=None, **kwargs)[source]¶: Inits PytorchImageModelWrapper by timm.create_models :param model_name: name of model to instantiate :type model_name: str :param pretrained: load pretrained ImageNet-1k weights if true :type pretrained: bool :param checkpoint_path: path of checkpoint to load after model is initialized :type checkpoint_path: str :param scriptable: set layer config so that model is jit scriptable (not working for all models yet) :type scriptable: bool :param exportable: set layer config so that model is traceable / ONNX exportable (not fully impl/obeyed yet) :type exportable: bool :param no_jit: set layer config so that model doesn’t utilize jit scripted layers (so far activations only) :type no_jit: bool

init_weights(pretrained=None)[source]¶

training: bool¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

easycv.models.backbones.resnest module¶

ResNet variants

class easycv.models.backbones.resnest.SplAtConv2d(in_channels, channels, kernel_size, stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, radix=2, reduction_factor=4, rectify=False, rectify_avg=False, norm_layer=None, dropblock_prob=0.0, **kwargs)[source]¶

Bases: torch.nn.modules.module.Module

Split-Attention Conv2d

__init__(in_channels, channels, kernel_size, stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, radix=2, reduction_factor=4, rectify=False, rectify_avg=False, norm_layer=None, dropblock_prob=0.0, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.resnest.rSoftMax(radix, cardinality)[source]¶

Bases: torch.nn.modules.module.Module

__init__(radix, cardinality)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.resnest.DropBlock2D(*args, **kwargs)[source]¶

Bases: object

__init__(*args, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.models.backbones.resnest.GlobalAvgPool2d[source]¶

Bases: torch.nn.modules.module.Module

__init__()[source]¶: Global average pooling over the input’s spatial dimensions

forward(inputs)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.resnest.Bottleneck(inplanes, planes, stride=1, downsample=None, radix=1, cardinality=1, bottleneck_width=64, avd=False, avd_first=False, dilation=1, is_first=False, rectified_conv=False, rectify_avg=False, norm_layer=None, dropblock_prob=0.0, last_gamma=False)[source]¶

Bases: torch.nn.modules.module.Module

ResNet Bottleneck

expansion = 4¶

__init__(inplanes, planes, stride=1, downsample=None, radix=1, cardinality=1, bottleneck_width=64, avd=False, avd_first=False, dilation=1, is_first=False, rectified_conv=False, rectify_avg=False, norm_layer=None, dropblock_prob=0.0, last_gamma=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.resnest.ResNeSt(depth=None, block=<class 'easycv.models.backbones.resnest.Bottleneck'>, layers=[3, 4, 6, 3], radix=2, groups=1, bottleneck_width=64, num_classes=0, dilated=False, dilation=1, deep_stem=True, stem_width=32, avg_down=True, rectified_conv=False, rectify_avg=False, avd=False, avd_first=False, final_drop=0.0, dropblock_prob=0, last_gamma=False, norm_layer=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>)[source]¶

Bases: torch.nn.modules.module.Module

ResNet Variants

Parameters

block (Block) – Class for the residual block. Options are BasicBlockV1, BottleneckV1.
layers (list of int) – Numbers of layers in each block
classes (int, default 1000) – Number of classification classes.
dilated (bool, default False) – Applying dilation strategy to pretrained ResNet yielding a stride-8 model, typically used in Semantic Segmentation.
norm_layer (object) – Normalization layer used in backbone network (default: mxnet.gluon.nn.BatchNorm; for Synchronized Cross-GPU BachNormalization).
Reference –
- He, Kaiming, et al. “Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- Yu, Fisher, and Vladlen Koltun. “Multi-scale context aggregation by dilated convolutions.”

arch_settings = {50: ((3, 4, 6, 3), 32), 101: ((3, 4, 23, 3), 64), 200: ((3, 24, 36, 3), 64), 269: ((3, 30, 48, 8), 64)}¶

__init__(depth=None, block=<class 'easycv.models.backbones.resnest.Bottleneck'>, layers=[3, 4, 6, 3], radix=2, groups=1, bottleneck_width=64, num_classes=0, dilated=False, dilation=1, deep_stem=True, stem_width=32, avg_down=True, rectified_conv=False, rectify_avg=False, avd=False, avd_first=False, final_drop=0.0, dropblock_prob=0, last_gamma=False, norm_layer=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

training: bool¶

init_weights(pretrained=None)[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

easycv.models.backbones.resnet module¶

class easycv.models.backbones.resnet.BasicBlock(inplanes, planes, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'}, frelu=False)[source]¶

Bases: torch.nn.modules.module.Module

expansion = 1¶

__init__(inplanes, planes, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'}, frelu=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

property norm1¶

property norm2¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.resnet.Bottleneck(inplanes, planes, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'}, frelu=False)[source]¶

Bases: torch.nn.modules.module.Module

expansion = 4¶

__init__(inplanes, planes, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'}, frelu=False)[source]¶: Bottleneck block for ResNet. If style is “pytorch”, the stride-two layer is the 3x3 conv layer, if it is “caffe”, the stride-two layer is the first 1x1 conv layer.

property norm1¶

property norm2¶

property norm3¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.backbones.resnet.make_res_layer(block, inplanes, planes, blocks, stride=1, dilation=1, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'}, frelu=False)[source]¶

class easycv.models.backbones.resnet.ResNet(depth, in_channels=3, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(0, 1, 2, 3, 4), style='pytorch', num_classes=0, frozen_stages=- 1, conv_cfg=None, norm_cfg={'requires_grad': True, 'type': 'BN'}, norm_eval=False, with_cp=False, frelu=False, original_inplanes=64, zero_init_residual=False)[source]¶

Bases: torch.nn.modules.module.Module

ResNet backbone.

Parameters

depth (int) – Depth of resnet, from {18, 34, 50, 101, 152}.
in_channels (int) – Number of input image channels. Normally 3.
num_stages (int) – Resnet stages, normally 4.
strides (Sequence[int]) – Strides of the first block of each stage.
dilations (Sequence[int]) – Dilation of each stage.
out_indices (Sequence[int]) – Output from which stages.
style (str) – pytorch or caffe. If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer.
frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters.
norm_cfg (dict) – dictionary to construct and config norm layer.
norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only.
with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed.
original_inplanes – start channel for first block, default=64
zero_init_residual (bool) – whether to use zero init for last norm layer in resblocks to let them behave as identity.

Example

>>> from easycv.models import ResNet
>>> import torch
>>> self = ResNet(depth=18)
>>> self.eval()
>>> inputs = torch.rand(1, 3, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 64, 8, 8)
(1, 128, 4, 4)
(1, 256, 2, 2)
(1, 512, 1, 1)

arch_settings = {10: (<class 'easycv.models.backbones.resnet.BasicBlock'>, (1, 1, 1, 1)), 18: (<class 'easycv.models.backbones.resnet.BasicBlock'>, (2, 2, 2, 2)), 34: (<class 'easycv.models.backbones.resnet.BasicBlock'>, (3, 4, 6, 3)), 50: (<class 'easycv.models.backbones.resnet.Bottleneck'>, (3, 4, 6, 3)), 101: (<class 'easycv.models.backbones.resnet.Bottleneck'>, (3, 4, 23, 3)), 152: (<class 'easycv.models.backbones.resnet.Bottleneck'>, (3, 8, 36, 3))}¶

__init__(depth, in_channels=3, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(0, 1, 2, 3, 4), style='pytorch', num_classes=0, frozen_stages=- 1, conv_cfg=None, norm_cfg={'requires_grad': True, 'type': 'BN'}, norm_eval=False, with_cp=False, frelu=False, original_inplanes=64, zero_init_residual=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

property norm1¶

init_weights(pretrained=None)[source]¶

training: bool¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

train(mode=True)[source]¶

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Parameters: mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.
Returns: self
Return type: Module

easycv.models.backbones.resnet_jit module¶

class easycv.models.backbones.resnet_jit.BasicBlock(inplanes, planes, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'})[source]¶

Bases: torch.nn.modules.module.Module

expansion = 1¶

__init__(inplanes, planes, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'})[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

property norm1¶

property norm2¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.resnet_jit.Bottleneck(inplanes, planes, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'})[source]¶

Bases: torch.nn.modules.module.Module

expansion = 4¶

__init__(inplanes, planes, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'})[source]¶: Bottleneck block for ResNet. If style is “pytorch”, the stride-two layer is the 3x3 conv layer, if it is “caffe”, the stride-two layer is the first 1x1 conv layer.

property norm1¶

property norm2¶

property norm3¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.backbones.resnet_jit.make_res_layer(block, inplanes, planes, blocks, stride=1, dilation=1, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'})[source]¶

class easycv.models.backbones.resnet_jit.ResNetJIT(depth, in_channels=3, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(0, 1, 2, 3, 4), style='pytorch', frozen_stages=- 1, conv_cfg=None, norm_cfg={'requires_grad': True, 'type': 'BN'}, norm_eval=False, with_cp=False, zero_init_residual=False)[source]¶

Bases: torch.nn.modules.module.Module

ResNet backbone.

Parameters

depth (int) – Depth of resnet, from {18, 34, 50, 101, 152}.
in_channels (int) – Number of input image channels. Normally 3.
num_stages (int) – Resnet stages, normally 4.
strides (Sequence[int]) – Strides of the first block of each stage.
dilations (Sequence[int]) – Dilation of each stage.
out_indices (Sequence[int]) – Output from which stages.
style (str) – pytorch or caffe. If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer.
frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters.
norm_cfg (dict) – dictionary to construct and config norm layer.
norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only.
with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed.
zero_init_residual (bool) – whether to use zero init for last norm layer in resblocks to let them behave as identity.

Example

>>> from easycv.models import ResNet
>>> import torch
>>> self = ResNet(depth=18)
>>> self.eval()
>>> inputs = torch.rand(1, 3, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 64, 8, 8)
(1, 128, 4, 4)
(1, 256, 2, 2)
(1, 512, 1, 1)

arch_settings = {18: (<class 'easycv.models.backbones.resnet_jit.BasicBlock'>, (2, 2, 2, 2)), 34: (<class 'easycv.models.backbones.resnet_jit.BasicBlock'>, (3, 4, 6, 3)), 50: (<class 'easycv.models.backbones.resnet_jit.Bottleneck'>, (3, 4, 6, 3)), 101: (<class 'easycv.models.backbones.resnet_jit.Bottleneck'>, (3, 4, 23, 3)), 152: (<class 'easycv.models.backbones.resnet_jit.Bottleneck'>, (3, 8, 36, 3))}¶

__init__(depth, in_channels=3, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(0, 1, 2, 3, 4), style='pytorch', frozen_stages=- 1, conv_cfg=None, norm_cfg={'requires_grad': True, 'type': 'BN'}, norm_eval=False, with_cp=False, zero_init_residual=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

property norm1¶

init_weights(pretrained=None)[source]¶

training: bool¶

forward(x: torch.Tensor) → List[torch.Tensor][source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

train(mode=True)[source]¶

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Parameters: mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.
Returns: self
Return type: Module

easycv.models.backbones.resnext module¶

class easycv.models.backbones.resnext.Bottleneck(inplanes, planes, groups=1, base_width=4, **kwargs)[source]¶

Bases: easycv.models.backbones.resnet.Bottleneck

__init__(inplanes, planes, groups=1, base_width=4, **kwargs)[source]¶: Bottleneck block for ResNeXt. If style is “pytorch”, the stride-two layer is the 3x3 conv layer, if it is “caffe”, the stride-two layer is the first 1x1 conv layer.

training: bool¶

easycv.models.backbones.resnext.make_res_layer(block, inplanes, planes, blocks, stride=1, dilation=1, groups=1, base_width=4, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg={'type': 'BN'})[source]¶

class easycv.models.backbones.resnext.ResNeXt(groups=1, base_width=4, **kwargs)[source]¶

Bases: easycv.models.backbones.resnet.ResNet

ResNeXt backbone.

Parameters

depth (int) – Depth of resnet, from {18, 34, 50, 101, 152}.
in_channels (int) – Number of input image channels. Normally 3.
num_stages (int) – Resnet stages, normally 4.
groups (int) – Group of resnext.
base_width (int) – Base width of resnext.
strides (Sequence[int]) – Strides of the first block of each stage.
dilations (Sequence[int]) – Dilation of each stage.
out_indices (Sequence[int]) – Output from which stages.
style (str) – pytorch or caffe. If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer.
frozen_stages (int) – Stages to be frozen (all param fixed). -1 means not freezing any parameters.
norm_cfg (dict) – dictionary to construct and config norm layer.
norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only.
with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed.
zero_init_residual (bool) – whether to use zero init for last norm layer in resblocks to let them behave as identity.

Example

>>> from easycv.models import ResNeXt
>>> import torch
>>> self = ResNeXt(depth=50)
>>> self.eval()
>>> inputs = torch.rand(1, 3, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 256, 8, 8)
(1, 512, 4, 4)
(1, 1024, 2, 2)
(1, 2048, 1, 1)

arch_settings = {50: (<class 'easycv.models.backbones.resnext.Bottleneck'>, (3, 4, 6, 3)), 101: (<class 'easycv.models.backbones.resnext.Bottleneck'>, (3, 4, 23, 3)), 152: (<class 'easycv.models.backbones.resnext.Bottleneck'>, (3, 8, 36, 3))}¶

__init__(groups=1, base_width=4, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

training: bool¶

easycv.models.backbones.shuffle_transformer module¶

class easycv.models.backbones.shuffle_transformer.Mlp(in_features, hidden_features=None, out_features=None, act_layer=<class 'torch.nn.modules.activation.ReLU6'>, drop=0.0, stride=False)[source]¶

Bases: torch.nn.modules.module.Module

__init__(in_features, hidden_features=None, out_features=None, act_layer=<class 'torch.nn.modules.activation.ReLU6'>, drop=0.0, stride=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.shuffle_transformer.Attention(dim, num_heads, window_size=1, shuffle=False, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, relative_pos_embedding=False)[source]¶

Bases: torch.nn.modules.module.Module

__init__(dim, num_heads, window_size=1, shuffle=False, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, relative_pos_embedding=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.shuffle_transformer.Block(dim, out_dim, num_heads, window_size=1, shuffle=False, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=<class 'torch.nn.modules.activation.ReLU6'>, norm_layer=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>, stride=False, relative_pos_embedding=False)[source]¶

Bases: torch.nn.modules.module.Module

__init__(dim, out_dim, num_heads, window_size=1, shuffle=False, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=<class 'torch.nn.modules.activation.ReLU6'>, norm_layer=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>, stride=False, relative_pos_embedding=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.shuffle_transformer.PatchMerging(dim, out_dim, norm_layer=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>)[source]¶

Bases: torch.nn.modules.module.Module

__init__(dim, out_dim, norm_layer=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

extra_repr() → str[source]¶

Set the extra representation of the module

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

training: bool¶

class easycv.models.backbones.shuffle_transformer.StageModule(layers, dim, out_dim, num_heads, window_size=1, shuffle=True, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=<class 'torch.nn.modules.activation.ReLU6'>, norm_layer=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>, relative_pos_embedding=False)[source]¶

Bases: torch.nn.modules.module.Module

__init__(layers, dim, out_dim, num_heads, window_size=1, shuffle=True, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=<class 'torch.nn.modules.activation.ReLU6'>, norm_layer=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>, relative_pos_embedding=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.shuffle_transformer.PatchEmbedding(inter_channel=32, out_channels=48)[source]¶

Bases: torch.nn.modules.module.Module

__init__(inter_channel=32, out_channels=48)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.shuffle_transformer.ShuffleTransformer(img_size=224, in_chans=3, num_classes=1000, token_dim=32, embed_dim=96, mlp_ratio=4.0, layers=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], relative_pos_embedding=True, shuffle=True, window_size=7, qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, has_pos_embed=False, **kwargs)[source]¶

Bases: torch.nn.modules.module.Module

__init__(img_size=224, in_chans=3, num_classes=1000, token_dim=32, embed_dim=96, mlp_ratio=4.0, layers=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], relative_pos_embedding=True, shuffle=True, window_size=7, qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, has_pos_embed=False, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(pretrained=None)[source]¶

no_weight_decay()[source]¶

no_weight_decay_keywords()[source]¶

get_classifier()[source]¶

reset_classifier(num_classes, global_pool='')[source]¶

forward_features(x)[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.backbones.shuffle_transformer.shuffletrans_base_p4_w7_224(pretrained=False, **kwargs)[source]¶

easycv.models.backbones.shuffle_transformer.shuffletrans_small_p4_w7_224(pretrained=False, **kwargs)[source]¶

easycv.models.backbones.shuffle_transformer.shuffletrans_tiny_p4_w7_224(pretrained=False, **kwargs)[source]¶

easycv.models.backbones.swin_transformer_dynamic module¶

Borrow this code from https://github.com/microsoft/esvit/blob/main/models/swin_transformer.py To support dynamic swin-transformer for ssl!

class easycv.models.backbones.swin_transformer_dynamic.Mlp(in_features, hidden_features=None, out_features=None, act_layer=<class 'torch.nn.modules.activation.GELU'>, drop=0.0)[source]¶

Bases: torch.nn.modules.module.Module

__init__(in_features, hidden_features=None, out_features=None, act_layer=<class 'torch.nn.modules.activation.GELU'>, drop=0.0)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.backbones.swin_transformer_dynamic.window_partition(x, window_size)[source]¶

Parameters

x – (B, H, W, C)
window_size (int) – window size

Returns

(num_windows*B, window_size, window_size, C)

Return type

windows

easycv.models.backbones.swin_transformer_dynamic.window_reverse(windows, window_size, H, W)[source]¶

Parameters

windows – (num_windows*B, window_size, window_size, C)
window_size (int) – Window size
H (int) – Height of image
W (int) – Width of image

Returns

(B, H, W, C)

Return type

x

class easycv.models.backbones.swin_transformer_dynamic.WindowAttention(dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0)[source]¶

Bases: torch.nn.modules.module.Module

Window based multi-head self attention (W-MSA) module with relative position bias. It supports both of shifted and non-shifted window.

Parameters

dim (int) – Number of input channels.
window_size (tuple[int]) – The height and width of the window.
num_heads (int) – Number of attention heads.
qkv_bias (bool, optional) – If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional) – Override default qk scale of head_dim ** -0.5 if set
attn_drop (float, optional) – Dropout ratio of attention weight. Default: 0.0
proj_drop (float, optional) – Dropout ratio of output. Default: 0.0

__init__(dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x, mask=None)[source]¶

Parameters

x – input features with shape of (num_windows*B, N, C)
mask – (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None

extra_repr() → str[source]¶

Set the extra representation of the module

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

flops(N)[source]¶

static compute_macs(module, input, output)[source]¶

training: bool¶

class easycv.models.backbones.swin_transformer_dynamic.SwinTransformerBlock(dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=<class 'torch.nn.modules.activation.GELU'>, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>)[source]¶

Bases: torch.nn.modules.module.Module

Swin Transformer Block.

Parameters

dim (int) – Number of input channels.
input_resolution (tuple[int]) – Input resulotion.
num_heads (int) – Number of attention heads.
window_size (int) – Window size.
shift_size (int) – Shift size for SW-MSA.
mlp_ratio (float) – Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional) – If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional) – Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional) – Dropout rate. Default: 0.0
attn_drop (float, optional) – Attention dropout rate. Default: 0.0
drop_path (float, optional) – Stochastic depth rate. Default: 0.0
act_layer (nn.Module, optional) – Activation layer. Default: nn.GELU
norm_layer (nn.Module, optional) – Normalization layer. Default: nn.LayerNorm

__init__(dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=<class 'torch.nn.modules.activation.GELU'>, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

create_attn_mask(H, W)[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

extra_repr() → str[source]¶

Set the extra representation of the module

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

flops()[source]¶

training: bool¶

class easycv.models.backbones.swin_transformer_dynamic.PatchMerging(input_resolution, dim, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>)[source]¶

Bases: torch.nn.modules.module.Module

Patch Merging Layer.

Parameters

input_resolution (tuple[int]) – Resolution of input feature.
dim (int) – Number of input channels.
norm_layer (nn.Module, optional) – Normalization layer. Default: nn.LayerNorm

__init__(input_resolution, dim, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶: Forward function. :param x: Input feature, tensor size (B, H*W, C). :param H: Spatial resolution of the input feature. :param W: Spatial resolution of the input feature.

extra_repr() → str[source]¶

Set the extra representation of the module

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

flops()[source]¶

training: bool¶

class easycv.models.backbones.swin_transformer_dynamic.BasicLayer(dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>, downsample=None)[source]¶

Bases: torch.nn.modules.module.Module

A basic Swin Transformer layer for one stage.

Parameters

dim (int) – Number of input channels.
input_resolution (tuple[int]) – Input resulotion.
depth (int) – Number of blocks.
num_heads (int) – Number of attention heads.
window_size (int) – Window size.
mlp_ratio (float) – Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional) – If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional) – Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional) – Dropout rate. Default: 0.0
attn_drop (float, optional) – Attention dropout rate. Default: 0.0
drop_path (float | tuple[float], optional) – Stochastic depth rate. Default: 0.0
norm_layer (nn.Module, optional) – Normalization layer. Default: nn.LayerNorm
downsample (nn.Module | None, optional) – Downsample layer at the end of the layer. Default: None

__init__(dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>, downsample=None)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

forward_with_features(x)[source]¶

forward_with_attention(x)[source]¶

extra_repr() → str[source]¶

Set the extra representation of the module

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

flops()[source]¶

training: bool¶

class easycv.models.backbones.swin_transformer_dynamic.PatchEmbed(img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None)[source]¶

Bases: torch.nn.modules.module.Module

Image to Patch Embedding

__init__(img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

flops()[source]¶

training: bool¶

class easycv.models.backbones.swin_transformer_dynamic.SwinTransformer(img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>, ape=False, patch_norm=True, use_dense_prediction=False, **kwargs)[source]¶

Bases: torch.nn.modules.module.Module

Swin Transformer

A PyTorch impl ofSwin Transformer: Hierarchical Vision Transformer using Shifted Windows -: https://arxiv.org/pdf/2103.14030

Parameters

img_size (int | tuple(int)) – Input image size.
patch_size (int | tuple(int)) – Patch size.
in_chans (int) – Number of input channels.
num_classes (int) – Number of classes for classification head.
embed_dim (int) – Embedding dimension.
depths (tuple(int)) – Depth of Swin Transformer layers.
num_heads (tuple(int)) – Number of attention heads in different layers.
window_size (int) – Window size.
mlp_ratio (float) – Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool) – If True, add a learnable bias to query, key, value. Default: Truee
qk_scale (float) – Override default qk scale of head_dim ** -0.5 if set.
drop_rate (float) – Dropout rate.
attn_drop_rate (float) – Attention dropout rate.
drop_path_rate (float) – Stochastic depth rate.
norm_layer (nn.Module) – normalization layer.
ape (bool) – If True, add absolute position embedding to the patch embedding.
patch_norm (bool) – If True, add normalization after patch embedding.

__init__(img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>, ape=False, patch_norm=True, use_dense_prediction=False, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights_unused(pretrained=None)[source]¶

no_weight_decay()[source]¶

no_weight_decay_keywords()[source]¶

forward_features(x)[source]¶

forward_feature_maps(x)[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

forward_selfattention(x, n=1)[source]¶

forward_last_selfattention(x)[source]¶

forward_all_selfattention(x)[source]¶

forward_return_n_last_blocks(x, n=1, return_patch_avgpool=False, depth=[])[source]¶

flops()[source]¶

init_weights(pretrained='', pretrained_layers=[], verbose=True)[source]¶

freeze_pretrained_layers(frozen_layers=[])[source]¶

training: bool¶

easycv.models.backbones.swin_transformer_dynamic.dynamic_swin_tiny_p4_w7_224(pretrained=False, **kwargs)[source]¶

easycv.models.backbones.swin_transformer_dynamic.dynamic_swin_small_p4_w7_224(pretrained=False, **kwargs)[source]¶

easycv.models.backbones.swin_transformer_dynamic.dynamic_swin_base_p4_w7_224(pretrained=False, **kwargs)[source]¶

easycv.models.backbones.vit_transfomer_dynamic module¶

Mostly copy-paste from timm library. https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py

dynamic Input support borrow from https://github.com/microsoft/esvit/blob/main/models/vision_transformer.py

easycv.models.backbones.vit_transfomer_dynamic.drop_path(x, drop_prob: float = 0.0, training: bool = False)[source]¶

class easycv.models.backbones.vit_transfomer_dynamic.DropPath(drop_prob=None)[source]¶

Bases: torch.nn.modules.module.Module

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

__init__(drop_prob=None)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.vit_transfomer_dynamic.Mlp(in_features, hidden_features=None, out_features=None, act_layer=<class 'torch.nn.modules.activation.GELU'>, drop=0.0)[source]¶

Bases: torch.nn.modules.module.Module

__init__(in_features, hidden_features=None, out_features=None, act_layer=<class 'torch.nn.modules.activation.GELU'>, drop=0.0)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.vit_transfomer_dynamic.Attention(dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0)[source]¶

Bases: torch.nn.modules.module.Module

__init__(dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.vit_transfomer_dynamic.Block(dim, num_heads, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=<class 'torch.nn.modules.activation.GELU'>, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>)[source]¶

Bases: torch.nn.modules.module.Module

__init__(dim, num_heads, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=<class 'torch.nn.modules.activation.GELU'>, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x, return_attention=False)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

forward_fea_and_attn(x)[source]¶

training: bool¶

class easycv.models.backbones.vit_transfomer_dynamic.PatchEmbed(img_size=224, patch_size=16, in_chans=3, embed_dim=768)[source]¶

Bases: torch.nn.modules.module.Module

Image to Patch Embedding

__init__(img_size=224, patch_size=16, in_chans=3, embed_dim=768)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.vit_transfomer_dynamic.VisionTransformer(img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>, use_dense_prediction=False, global_pool=False, **kwargs)[source]¶

Bases: torch.nn.modules.module.Module

Vision Transformer

__init__(img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>, use_dense_prediction=False, global_pool=False, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(pretrained=None)[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

forward_features(x)[source]¶

forward_feature_maps(x)[source]¶

interpolate_pos_encoding(x, pos_embed)[source]¶

forward_selfattention(x, n=1)[source]¶

forward_last_selfattention(x)[source]¶

forward_all_selfattention(x)[source]¶

forward_return_n_last_blocks(x, n=1, return_patch_avgpool=False, depths=[])[source]¶

training: bool¶

easycv.models.backbones.vit_transfomer_dynamic.dynamic_deit_tiny_p16(patch_size=16, **kwargs)[source]¶

easycv.models.backbones.vit_transfomer_dynamic.dynamic_deit_small_p16(patch_size=16, **kwargs)[source]¶

easycv.models.backbones.vit_transfomer_dynamic.dynamic_vit_base_p16(patch_size=16, **kwargs)[source]¶

easycv.models.backbones.vit_transfomer_dynamic.dynamic_vit_large_p16(patch_size=16, **kwargs)[source]¶

easycv.models.backbones.vit_transfomer_dynamic.dynamic_vit_huge_p14(patch_size=14, **kwargs)[source]¶

easycv.models.backbones.xcit_transformer module¶

Implementation of Cross-Covariance Image Transformer (XCiT) Based on timm and DeiT code bases https://github.com/rwightman/pytorch-image-models/tree/master/timm https://github.com/facebookresearch/deit/

XCiT Transformer. Part of the code is borrowed from: https://github.com/facebookresearch/xcit/blob/master/xcit.py

class easycv.models.backbones.xcit_transformer.PositionalEncodingFourier(hidden_dim=32, dim=768, temperature=10000)[source]¶

Bases: torch.nn.modules.module.Module

Positional encoding relying on a fourier kernel matching the one used in the “Attention is all of Need” paper. The implementation builds on DeTR code https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py

__init__(hidden_dim=32, dim=768, temperature=10000)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(B, H, W)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.backbones.xcit_transformer.conv3x3(in_planes, out_planes, stride=1)[source]¶: 3x3 convolution with padding

class easycv.models.backbones.xcit_transformer.ConvPatchEmbed(img_size=224, patch_size=16, in_chans=3, embed_dim=768)[source]¶

Bases: torch.nn.modules.module.Module

Image to Patch Embedding using multiple convolutional layers

__init__(img_size=224, patch_size=16, in_chans=3, embed_dim=768)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x, padding_size=None)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.xcit_transformer.LPI(in_features, hidden_features=None, out_features=None, act_layer=<class 'torch.nn.modules.activation.GELU'>, drop=0.0, kernel_size=3)[source]¶

Bases: torch.nn.modules.module.Module

Local Patch Interaction module that allows explicit communication between tokens in 3x3 windows to augment the implicit communcation performed by the block diagonal scatter attention. Implemented using 2 layers of separable 3x3 convolutions with GeLU and BatchNorm2d

__init__(in_features, hidden_features=None, out_features=None, act_layer=<class 'torch.nn.modules.activation.GELU'>, drop=0.0, kernel_size=3)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x, H, W)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.xcit_transformer.ClassAttention(dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0)[source]¶

Bases: torch.nn.modules.module.Module

Class Attention Layer as in CaiT https://arxiv.org/abs/2103.17239

__init__(dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.xcit_transformer.ClassAttentionBlock(dim, num_heads, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=<class 'torch.nn.modules.activation.GELU'>, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>, eta=None, tokens_norm=False)[source]¶

Bases: torch.nn.modules.module.Module

Class Attention Layer as in CaiT https://arxiv.org/abs/2103.17239

__init__(dim, num_heads, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=<class 'torch.nn.modules.activation.GELU'>, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>, eta=None, tokens_norm=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x, H, W, mask=None)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.xcit_transformer.XCA(dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0)[source]¶

Bases: torch.nn.modules.module.Module

Cross-Covariance Attention (XCA) operation where the channels are updated using a weighted sum.

The weights are obtained from the (softmax normalized) Cross-covariance matrix (Q^T K in d_h times d_h)

__init__(dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

no_weight_decay()[source]¶

training: bool¶

class easycv.models.backbones.xcit_transformer.XCABlock(dim, num_heads, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=<class 'torch.nn.modules.activation.GELU'>, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>, num_tokens=196, eta=None)[source]¶

Bases: torch.nn.modules.module.Module

__init__(dim, num_heads, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=<class 'torch.nn.modules.activation.GELU'>, norm_layer=<class 'torch.nn.modules.normalization.LayerNorm'>, num_tokens=196, eta=None)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x, H, W)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.backbones.xcit_transformer.XCiT(img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, norm_layer=None, cls_attn_layers=2, use_pos=True, patch_proj='linear', eta=None, tokens_norm=False)[source]¶

Bases: torch.nn.modules.module.Module

Based on timm and DeiT code bases https://github.com/rwightman/pytorch-image-models/tree/master/timm https://github.com/facebookresearch/deit/

__init__(img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, norm_layer=None, cls_attn_layers=2, use_pos=True, patch_proj='linear', eta=None, tokens_norm=False)[source]¶

Parameters

img_size (int, tuple) – input image size
patch_size (int, tuple) – patch size
in_chans (int) – number of input channels
num_classes (int) – number of classes for classification head
embed_dim (int) – embedding dimension
depth (int) – depth of transformer
num_heads (int) – number of attention heads
mlp_ratio (int) – ratio of mlp hidden dim to embedding dim
qkv_bias (bool) – enable bias for qkv if True
qk_scale (float) – override default qk scale of head_dim ** -0.5 if set
drop_rate (float) – dropout rate
attn_drop_rate (float) – attention dropout rate
drop_path_rate (float) – stochastic depth rate
norm_layer – (nn.Module): normalization layer
cls_attn_layers – (int) Depth of Class attention layers
use_pos – (bool) whether to use positional encoding
eta – (float) layerscale initialization value
tokens_norm – (bool) Whether to normalize all tokens or just the cls_token in the CA

init_weights(pretrained=None)[source]¶

no_weight_decay()[source]¶

forward_features(x)[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.backbones.xcit_transformer.xcit_small_12_p16(pretrained=False, **kwargs)[source]¶

easycv.models.backbones.xcit_transformer.xcit_small_24_p16(pretrained=False, **kwargs)[source]¶

easycv.models.backbones.xcit_transformer.xcit_medium_24_p16(pretrained=False, **kwargs)[source]¶

easycv.models.backbones.xcit_transformer.xcit_small_12_p8(pretrained=False, **kwargs)[source]¶

easycv.models.backbones.xcit_transformer.xcit_small_24_p8(pretrained=False, **kwargs)[source]¶

easycv.models.backbones.xcit_transformer.xcit_medium_24_p8(pretrained=False, **kwargs)[source]¶

easycv.models.backbones.xcit_transformer.xcit_large_24_p8(pretrained=False, **kwargs)[source]¶

easycv.models.classification package¶

Submodules¶

easycv.models.classification.classification module¶

easycv.models.classification.classification.distill_loss(cls_score, teacher_score, tempreature=1.0)[source]¶: Soft cross entropy loss

class easycv.models.classification.classification.Classification(backbone, train_preprocess=[], with_sobel=False, head=None, neck=None, teacher=None, pretrained=None, mixup_cfg=None)[source]¶

Bases: easycv.models.base.BaseModel

__init__(backbone, train_preprocess=[], with_sobel=False, head=None, neck=None, teacher=None, pretrained=None, mixup_cfg=None)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(pretrained=None)[source]¶

forward_backbone(img: torch.Tensor) → List[torch.Tensor][source]¶

Forward backbone

Returns: backbone outputs
Return type: x (tuple)

forward_train(img, gt_labels) → Dict[str, torch.Tensor][source]¶: In forward train, model will forward backbone + neck / multi-neck, get alist of output tensor, and put this list to head / multi-head, to compute each loss

forward_test(img: torch.Tensor) → Dict[str, torch.Tensor][source]¶: forward_test means generate prob/class from image only support one neck + one head

forward_test_label(img, gt_labels) → Dict[str, torch.Tensor][source]¶: forward_test_label means generate prob/class from image only support one neck + one head ps : head init need set the input feature idx

training: bool¶

aug_test(imgs)[source]¶

forward_feature(img) → Dict[str, torch.Tensor][source]¶

Forward feature means forward backbone + neck/multineck ,get dict of output feature,: self.neck_num = 0: means only forward backbone, output backbone feature with avgpool, with key neck, self.neck_num > 0: means has 1/multi neck, output neck’s feature with key neck_neckidx_featureidx, suck as neck_0_0

Returns: feature tensor
Return type: x (torch.Tensor)

update_extract_list(key)[source]¶

forward(img: torch.Tensor, mode: str = 'train', gt_labels: Optional[torch.Tensor] = None, img_metas: Optional[torch.Tensor] = None) → Dict[str, torch.Tensor][source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

easycv.models.classification.necks module¶

class easycv.models.classification.necks.LinearNeck(in_channels, out_channels, with_avg_pool=True, with_norm=False)[source]¶

Bases: torch.nn.modules.module.Module

Linear neck: fc only

__init__(in_channels, out_channels, with_avg_pool=True, with_norm=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(init_linear='normal')[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.classification.necks.RetrivalNeck(in_channels, out_channels, with_avg_pool=True, cdg_config=['G', 'M'])[source]¶

Bases: torch.nn.modules.module.Module

RetrivalNeck: refer, Combination of Multiple Global Descriptors for Image Retrieval: https://arxiv.org/pdf/1903.10663.pdf

CGD feature : only use avg pool + gem pooling + max pooling, by pool -> fc -> norm -> concat -> norm Avg feature : use avg pooling, avg pool -> syncbn -> fc

len(cgd_config) > 0: return [CGD, Avg] len(cgd_config) = 0 : return [Avg]

__init__(in_channels, out_channels, with_avg_pool=True, cdg_config=['G', 'M'])[source]¶

Init RetrivalNeck, faceid neck doesn’t pool for input feature map, doesn’t support dynamic input

Parameters

in_channels – Int - input feature map channels
out_channels – Int - output feature map channels
with_avg_pool – bool do avg pool for BNneck or not
cdg_config – list(‘G’,’M’,’S’), to configure output feature, CGD = [gempooling] + [maxpooling] + [meanpooling], if len(cgd_config) > 0: return [CGD, Avg] if len(cgd_config) = 0 : return [Avg]

init_weights(init_linear='normal')[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.classification.necks.FaceIDNeck(in_channels, out_channels, map_shape=1, dropout_ratio=0.4, with_norm=False, bn_type='SyncBN')[source]¶

Bases: torch.nn.modules.module.Module

FaceID neck: Include BN, dropout, flatten, linear, bn

__init__(in_channels, out_channels, map_shape=1, dropout_ratio=0.4, with_norm=False, bn_type='SyncBN')[source]¶

Init FaceIDNeck, faceid neck doesn’t pool for input feature map, doesn’t support dynamic input

Parameters

in_channels – Int - input feature map channels
out_channels – Int - output feature map channels
map_shape – Int or list(int,…), input feature map (w,h) or w when w=h,
dropout_ratio – float, drop out ratio
with_norm – normalize output feature or not
bn_type – SyncBN or BN

init_weights(init_linear='normal')[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.classification.necks.MultiLinearNeck(in_channels, out_channels, num_layers=1, with_avg_pool=True)[source]¶

Bases: torch.nn.modules.module.Module

MultiLinearNeck neck: MultiFc head

__init__(in_channels, out_channels, num_layers=1, with_avg_pool=True)[source]¶

Parameters

in_channels – int or list[int]
out_channels – int or list[int]
num_layers – total fc num
with_avg_pool – input will be avgPool if True

Returns

None

Raises

len(in_channel) != len(out_channels) –
len(in_channel) != len(num_layers) –

init_weights(init_linear='normal')[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.classification.necks.HRFuseScales(in_channels, out_channels=2048, norm_cfg={'momentum': 0.1, 'type': 'BN'})[source]¶

Bases: torch.nn.modules.module.Module

Fuse feature map of multiple scales in HRNet. :param in_channels: The input channels of all scales. :type in_channels: list[int] :param out_channels: The channels of fused feature map.

Defaults to 2048.

Parameters

norm_cfg (dict) – dictionary to construct norm layers. Defaults to dict(type='BN', momentum=0.1).
init_cfg (dict | list[dict], optional) – Initialization config dict. Defaults to dict(type='Normal', layer='Linear', std=0.01)).

__init__(in_channels, out_channels=2048, norm_cfg={'momentum': 0.1, 'type': 'BN'})[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

training: bool¶

init_weights(init_linear='normal')[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

easycv.models.detection package¶

Subpackages¶

easycv.models.detection.utils package¶

Submodules¶

easycv.models.detection.utils.boxes module¶

easycv.models.detection.utils.boxes.bboxes_iou(bboxes_a, bboxes_b, xyxy=True)[source]¶

easycv.models.detection.utils.boxes.postprocess(prediction, num_classes, conf_thre=0.7, nms_thre=0.45)[source]¶

easycv.models.detection.yolox package¶

Submodules¶

easycv.models.detection.yolox.yolo_head module¶

class easycv.models.detection.yolox.yolo_head.YOLOXHead(num_classes, width=1.0, strides=[8, 16, 32], in_channels=[256, 512, 1024], act='silu', depthwise=False, stage='CLOUD')[source]¶

Bases: torch.nn.modules.module.Module

__init__(num_classes, width=1.0, strides=[8, 16, 32], in_channels=[256, 512, 1024], act='silu', depthwise=False, stage='CLOUD')[source]¶

Parameters

num_classes (int) – detection class numbers.
width (float) – model width. Default value: 1.0.
strides (list) – expanded strides. Default value: [8, 16, 32].
in_channels (list) – model conv channels set. Default value: [256, 512, 1024].
act (str) – activation type of conv. Defalut value: “silu”.
depthwise (bool) – whether apply depthwise conv in conv branch. Default value: False.
stage (str) – model stage, distinguish edge head to cloud head. Default value: CLOUD.

initialize_biases(prior_prob)[source]¶

forward(xin, labels=None, imgs=None)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

get_output_and_grid(output, k, stride, dtype)[source]¶

decode_outputs(outputs, dtype)[source]¶

get_losses(imgs, x_shifts, y_shifts, expanded_strides, labels, outputs, origin_preds, dtype)[source]¶

get_l1_target(l1_target, gt, stride, x_shifts, y_shifts, eps=1e-08)[source]¶

get_assignments(batch_idx, num_gt, total_num_anchors, gt_bboxes_per_image, gt_classes, bboxes_preds_per_image, expanded_strides, x_shifts, y_shifts, cls_preds, bbox_preds, obj_preds, labels, imgs, mode='gpu')[source]¶

get_in_boxes_info(gt_bboxes_per_image, expanded_strides, x_shifts, y_shifts, total_num_anchors, num_gt)[source]¶

dynamic_k_matching(cost, pair_wise_ious, gt_classes, num_gt, fg_mask)[source]¶

training: bool¶

easycv.models.detection.yolox.yolo_pafpn module¶

class easycv.models.detection.yolox.yolo_pafpn.YOLOPAFPN(depth=1.0, width=1.0, in_features=('dark3', 'dark4', 'dark5'), in_channels=[256, 512, 1024], depthwise=False, act='silu')[source]¶

Bases: torch.nn.modules.module.Module

YOLOv3 model. Darknet 53 is the default backbone of this model.

__init__(depth=1.0, width=1.0, in_features=('dark3', 'dark4', 'dark5'), in_channels=[256, 512, 1024], depthwise=False, act='silu')[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input)[source]¶

Parameters: inputs – input images.
Returns: FPN feature.
Return type: Tuple[Tensor]

training: bool¶

easycv.models.detection.yolox.yolox module¶

easycv.models.detection.yolox.yolox.init_yolo(M)[source]¶

class easycv.models.detection.yolox.yolox.YOLOX(model_type: str = 's', num_classes: int = 80, test_size: tuple = (640, 640), test_conf: float = 0.01, nms_thre: float = 0.65, pretrained: Optional[str] = None)[source]¶

Bases: easycv.models.base.BaseModel

YOLOX model module. The module list is defined by create_yolov3_modules function. The network returns loss values from three YOLO layers during training and detection results during test.

param_map = {'l': [1.0, 1.0], 'm': [0.67, 0.75], 'nano': [0.33, 0.25], 's': [0.33, 0.5], 'tiny': [0.33, 0.375], 'x': [1.33, 1.25]}¶

__init__(model_type: str = 's', num_classes: int = 80, test_size: tuple = (640, 640), test_conf: float = 0.01, nms_thre: float = 0.65, pretrained: Optional[str] = None)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward_train(img: torch.Tensor, gt_bboxes: torch.Tensor, gt_labels: torch.Tensor, img_metas=None, scale=None) → Dict[str, torch.Tensor][source]¶

Abstract interface for model forward in training

Parameters

img (Tensor) – image tensor, NxCxHxW
target (List[Tensor]) – list of target tensor, NTx5 [class,x_c,y_c,w,h]

forward_test(img: torch.Tensor, img_metas=None) → torch.Tensor[source]¶

Abstract interface for model forward in training

Parameters

img (Tensor) – image tensor, NxCxHxW
target (List[Tensor]) – list of target tensor, NTx5 [class,x_c,y_c,w,h]

forward(img, mode='compression', **kwargs)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

forward_compression(x)[source]¶

training: bool¶

easycv.models.detection.yolox_edge package¶

Submodules¶

easycv.models.detection.yolox_edge.yolox_edge module¶

easycv.models.detection.yolox_edge.yolox_edge.init_yolo(M)[source]¶

class easycv.models.detection.yolox_edge.yolox_edge.YOLOX_EDGE(stage: str = 'EDGE', model_type: str = 's', num_classes: int = 80, test_size: tuple = (640, 640), test_conf: float = 0.01, nms_thre: float = 0.65, pretrained: Optional[str] = None, depth: float = 1.0, width: float = 1.0, max_model_params: float = 0.0, max_model_flops: float = 0.0, activation: str = 'silu', in_channels: list = [256, 512, 1024], backbone=None, head=None)[source]¶

Bases: easycv.models.detection.yolox.yolox.YOLOX

YOLOX model module. The module list is defined by create_yolov3_modules function. The network returns loss values from three YOLO layers during training and detection results during test.

__init__(stage: str = 'EDGE', model_type: str = 's', num_classes: int = 80, test_size: tuple = (640, 640), test_conf: float = 0.01, nms_thre: float = 0.65, pretrained: Optional[str] = None, depth: float = 1.0, width: float = 1.0, max_model_params: float = 0.0, max_model_flops: float = 0.0, activation: str = 'silu', in_channels: list = [256, 512, 1024], backbone=None, head=None)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

training: bool¶

easycv.models.heads package¶

Submodules¶

easycv.models.heads.cls_head module¶

class easycv.models.heads.cls_head.ClsHead(with_avg_pool=False, label_smooth=0.0, in_channels=2048, with_fc=True, num_classes=1000, loss_config={'type': 'CrossEntropyLossWithLabelSmooth'}, input_feature_index=[0])[source]¶

Bases: torch.nn.modules.module.Module

Simplest classifier head, with only one fc layer. Should Notice Evtorch module design input always be feature_list = [tensor, tensor,…]

__init__(with_avg_pool=False, label_smooth=0.0, in_channels=2048, with_fc=True, num_classes=1000, loss_config={'type': 'CrossEntropyLossWithLabelSmooth'}, input_feature_index=[0])[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(pretrained=None, init_linear='normal', std=0.01, bias=0.0)[source]¶

forward(x: List[torch.Tensor]) → List[torch.Tensor][source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

loss(cls_score: List[torch.Tensor], labels: torch.Tensor) → Dict[str, torch.Tensor][source]¶

Parameters

cls_score – [N x num_classes]
labels – if don’t use mixup, shape is [N],else [N x num_classes]

mixup_loss(cls_score, labels_1, labels_2, lam) → Dict[str, torch.Tensor][source]¶

training: bool¶

easycv.models.heads.contrastive_head module¶

class easycv.models.heads.contrastive_head.ContrastiveHead(temperature=0.1)[source]¶

Bases: torch.nn.modules.module.Module

Head for contrastive learning.

__init__(temperature=0.1)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(pos, neg)[source]¶

Parameters

pos (Tensor) – Nx1 positive similarity
neg (Tensor) – Nxk negative similarity

training: bool¶

class easycv.models.heads.contrastive_head.DebiasedContrastiveHead(temperature=0.1, tau=0.1)[source]¶

Bases: torch.nn.modules.module.Module

__init__(temperature=0.1, tau=0.1)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(pos, neg)[source]¶

Parameters

pos (Tensor) – Nx1 positive similarity
neg (Tensor) – Nxk negative similarity

training: bool¶

easycv.models.heads.latent_pred_head module¶

class easycv.models.heads.latent_pred_head.LatentPredictHead(predictor, size_average=True)[source]¶

Bases: torch.nn.modules.module.Module

Head for contrastive learning.

__init__(predictor, size_average=True)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(init_linear='normal')[source]¶

forward(input, target)[source]¶

Parameters

input (Tensor) – NxC input features.
target (Tensor) – NxC target features.

training: bool¶

class easycv.models.heads.latent_pred_head.LatentClsHead(predictor)[source]¶

Bases: torch.nn.modules.module.Module

Head for contrastive learning.

__init__(predictor)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(init_linear='normal')[source]¶

forward(input, target)[source]¶

Parameters

input (Tensor) – NxC input features.
target (Tensor) – NxC target features.

training: bool¶

easycv.models.heads.mp_metric_head module¶

easycv.models.heads.mp_metric_head.EmbeddingExplansion(embs, labels, explanion_rate=4, alpha=1.0)[source]¶

Expand embedding: CVPR refer to https://github.com/clovaai/embedding-expansion combine PK sampled data, mixup anchor positive pair to generate more features, always combine with BatchHardminer. result on SOP and CUB need to be add

Parameters

embs – [N , dims] tensor
labels – [N] tensor
explanion_rate – to expand N to explanion_rate * N
alpha – beta distribution parameter for mixup

Returns

[N*explanion_rate , dims]

Return type

embs

class easycv.models.heads.mp_metric_head.MpMetrixHead(with_avg_pool=False, in_channels=2048, loss_config=[{'type': 'CircleLoss', 'loss_weight': 1.0, 'norm': True, 'ddp': True, 'm': 0.4, 'gamma': 80}], input_feature_index=[0], input_label_index=0, ignore_label=None)[source]¶

Bases: torch.nn.modules.module.Module

Simplest classifier head, with only one fc layer.

__init__(with_avg_pool=False, in_channels=2048, loss_config=[{'type': 'CircleLoss', 'loss_weight': 1.0, 'norm': True, 'ddp': True, 'm': 0.4, 'gamma': 80}], input_feature_index=[0], input_label_index=0, ignore_label=None)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(pretrained=None, init_linear='normal', std=0.01, bias=0.0)[source]¶

forward(x: List[torch.Tensor]) → List[torch.Tensor][source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

loss(cls_score, labels) → Dict[str, torch.Tensor][source]¶

training: bool¶

easycv.models.heads.multi_cls_head module¶

class easycv.models.heads.multi_cls_head.MultiClsHead(pool_type='adaptive', in_indices=(0), with_last_layer_unpool=False, backbone='resnet50', norm_cfg={'type': 'BN'}, num_classes=1000)[source]¶

Bases: torch.nn.modules.module.Module

Multiple classifier heads.

FEAT_CHANNELS = {'resnet50': [64, 256, 512, 1024, 2048]}¶

FEAT_LAST_UNPOOL = {'resnet50': 100352}¶

__init__(pool_type='adaptive', in_indices=(0), with_last_layer_unpool=False, backbone='resnet50', norm_cfg={'type': 'BN'}, num_classes=1000)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights()[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

loss(cls_score, labels)[source]¶

training: bool¶

easycv.models.loss package¶

Submodules¶

easycv.models.loss.iou_loss module¶

class easycv.models.loss.iou_loss.IOUloss(reduction='none', loss_type='iou')[source]¶

Bases: torch.nn.modules.module.Module

__init__(reduction='none', loss_type='iou')[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(pred, target)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.loss.mse_loss module¶

class easycv.models.loss.mse_loss.JointsMSELoss(use_target_weight=False, loss_weight=1.0)[source]¶

Bases: torch.nn.modules.module.Module

MSE loss for heatmaps.

Parameters

use_target_weight (bool) – Option to use weighted MSE loss. Different joint types may have different target weights.
loss_weight (float) – Weight of the loss. Default: 1.0.

__init__(use_target_weight=False, loss_weight=1.0)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(output, target, target_weight)[source]¶: Forward function.

training: bool¶

easycv.models.loss.pytorch_metric_learning module¶

class easycv.models.loss.pytorch_metric_learning.FocalLoss2d(gamma=2, weight=None, size_average=None, reduce=None, reduction='mean', num_classes=2)[source]¶

Bases: torch.nn.modules.loss._WeightedLoss

__init__(gamma=2, weight=None, size_average=None, reduce=None, reduction='mean', num_classes=2)[source]¶

FocalLoss2d, loss solve 2-class classification unbalance problem

Parameters

gamma – focal loss param Gamma
weight – weight same as loss._WeightedLoss
size_average – size_average same as loss._WeightedLoss
reduce – reduce same as loss._WeightedLoss
reduction – reduce same as loss._WeightedLoss
num_classes – fix num 2

Returns

Focalloss nn.module.loss object

forward(input, target)[source]¶: input: [N * num_classes] target : [N * num_classes] one-hot

reduction: str¶

class easycv.models.loss.pytorch_metric_learning.DistributeMSELoss[source]¶

Bases: torch.nn.modules.module.Module

__init__()[source]¶: DistributeMSELoss : for faceid age, score predict (regression by softmax)

forward(input, target)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.loss.pytorch_metric_learning.CrossEntropyLossWithLabelSmooth(label_smooth=0.1, temperature=1.0, with_cls=False, embedding_size=512, num_classes=10000)[source]¶

Bases: torch.nn.modules.module.Module

__init__(label_smooth=0.1, temperature=1.0, with_cls=False, embedding_size=512, num_classes=10000)[source]¶

A softmax loss , with label_smooth and fc(to fit pytorch metric learning interface) :param label_smooth: label_smooth args, default=0.1 :param with_cls: if True, will generate a nn.Linear to trans input embedding from embedding_size to num_classes :param embedding_size: if input is feature not logits, then need this to indicate embedding shape :param num_classes: if input is feature not logits, then need this to indicate classification num_classes

Returns: None
Raises: IOError – An error occurred accessing the bigtable.Table object.

forward(input, target)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.loss.pytorch_metric_learning.AMSoftmaxLoss(embedding_size=512, num_classes=100000, margin=0.35, scale=30)[source]¶

Bases: torch.nn.modules.module.Module

__init__(embedding_size=512, num_classes=100000, margin=0.35, scale=30)[source]¶: AMsoftmax loss , with fc(to fit pytorch metric learning interface), paper: https://arxiv.org/pdf/1801.05599.pdf :param embedding_size: forward input [N, embedding_size ] :param num_classes: classification num_classes :param margin: AMSoftmax param :param scale: AMSoftmax param, should increase num_classes

forward(x, lb)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.loss.pytorch_metric_learning.ModelParallelSoftmaxLoss(embedding_size=512, num_classes=100000, scale=None, margin=None, bias=True)[source]¶

Bases: torch.nn.modules.module.Module

__init__(embedding_size=512, num_classes=100000, scale=None, margin=None, bias=True)[source]¶: ModelParallel Softmax by sailfish :param embedding_size: forward input [N, embedding_size ] :param num_classes: classification num_classes

forward(x, lb)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.loss.pytorch_metric_learning.ModelParallelAMSoftmaxLoss(embedding_size=512, num_classes=100000, margin=0.35, scale=30)[source]¶

Bases: torch.nn.modules.module.Module

__init__(embedding_size=512, num_classes=100000, margin=0.35, scale=30)[source]¶: ModelParallel AMSoftmax by sailfish :param embedding_size: forward input [N, embedding_size ] :param num_classes: classification num_classes

forward(x, lb)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.loss.pytorch_metric_learning.SoftTargetCrossEntropy(num_classes=1000, **kwargs)[source]¶

Bases: torch.nn.modules.module.Module

__init__(num_classes=1000, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x: torch.Tensor, target: torch.Tensor) → torch.Tensor[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.pose package¶

Subpackages¶

easycv.models.pose.heads package¶

Submodules¶

easycv.models.pose.heads.topdown_heatmap_base_head module¶

class easycv.models.pose.heads.topdown_heatmap_base_head.TopdownHeatmapBaseHead[source]¶

Bases: torch.nn.modules.module.Module

Base class for top-down heatmap heads.

All top-down heatmap heads should subclass it. All subclass should overwrite:

Methods:get_loss, supporting to calculate loss. Methods:get_accuracy, supporting to calculate accuracy. Methods:forward, supporting to forward model. Methods:inference_model, supporting to inference model.

abstract get_loss(**kwargs)[source]¶: Gets the loss.

abstract get_accuracy(**kwargs)[source]¶: Gets the accuracy.

abstract forward(**kwargs)[source]¶: Forward function.

abstract inference_model(**kwargs)[source]¶: Inference function.

decode(img_metas, output, **kwargs)[source]¶

Decode keypoints from heatmaps.

Parameters

img_metas (list(dict)) – Information about data augmentation By default this includes: - “image_file: path to the image file - “center”: center of the bbox - “scale”: scale of the bbox - “rotation”: rotation of the bbox - “bbox_score”: score of bbox
output (np.ndarray[N, K, H, W]) – model predicted heatmaps.

training: bool¶

easycv.models.pose.heads.topdown_heatmap_simple_head module¶

class easycv.models.pose.heads.topdown_heatmap_simple_head.TopdownHeatmapSimpleHead(in_channels, out_channels, num_deconv_layers=3, num_deconv_filters=(256, 256, 256), num_deconv_kernels=(4, 4, 4), extra=None, in_index=0, input_transform=None, align_corners=False, loss_keypoint=None, train_cfg=None, test_cfg=None)[source]¶

Bases: easycv.models.pose.heads.topdown_heatmap_base_head.TopdownHeatmapBaseHead

Top-down heatmap simple head. paper ref: Bin Xiao et al. Simple Baselines for Human Pose Estimation and Tracking.

TopdownHeatmapSimpleHead is consisted of (>=0) number of deconv layers and a simple conv2d layer.

Parameters

in_channels (int) – Number of input channels
out_channels (int) – Number of output channels
num_deconv_layers (int) – Number of deconv layers. num_deconv_layers should >= 0. Note that 0 means no deconv layers.
num_deconv_filters (list|tuple) – Number of filters. If num_deconv_layers > 0, the length of
num_deconv_kernels (list|tuple) – Kernel sizes.
in_index (int|Sequence[int]) – Input feature index. Default: 0
input_transform (str|None) –
Transformation type of input features. Options: ‘resize_concat’, ‘multiple_select’, None. Default: None.
- ’resize_concat’: Multiple feature maps will be resized to the
  same size as the first one and then concat together. Usually used in FCN head of HRNet.
- ’multiple_select’: Multiple feature maps will be bundle into
  a list and passed into decode head.
- None: Only one select feature map is allowed.
align_corners (bool) – align_corners argument of F.interpolate. Default: False.
loss_keypoint (dict) – Config for keypoint loss. Default: None.

__init__(in_channels, out_channels, num_deconv_layers=3, num_deconv_filters=(256, 256, 256), num_deconv_kernels=(4, 4, 4), extra=None, in_index=0, input_transform=None, align_corners=False, loss_keypoint=None, train_cfg=None, test_cfg=None)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

get_loss(output, target, target_weight)[source]¶

Calculate top-down keypoint loss.

Note

batch_size: N num_keypoints: K heatmaps height: H heatmaps weight: W

Parameters

output (torch.Tensor[NxKxHxW]) – Output heatmaps.
target (torch.Tensor[NxKxHxW]) – Target heatmaps.
target_weight (torch.Tensor[NxKx1]) – Weights across different joint types.

get_accuracy(output, target, target_weight)[source]¶

Calculate accuracy for top-down keypoint loss.

Note

batch_size: N num_keypoints: K heatmaps height: H heatmaps weight: W

Parameters

output (torch.Tensor[NxKxHxW]) – Output heatmaps.
target (torch.Tensor[NxKxHxW]) – Target heatmaps.
target_weight (torch.Tensor[NxKx1]) – Weights across different joint types.

forward(x)[source]¶: Forward function.

inference_model(x, flip_pairs=None)[source]¶

Inference function.

Returns

Output heatmaps.

Return type

output_heatmap (np.ndarray)

Parameters

x (torch.Tensor[NxKxHxW]) – Input features.
flip_pairs (None | list[tuple()) – Pairs of keypoints which are mirrored.

training: bool¶

init_weights()[source]¶: Initialize model weights.

Submodules¶

easycv.models.pose.top_down module¶

class easycv.models.pose.top_down.TopDown(backbone, neck=None, keypoint_head=None, train_cfg=None, test_cfg=None, pretrained=None, loss_pose=None)[source]¶

Bases: easycv.models.base.BaseModel

Top-down pose detectors.

Parameters

backbone (dict) – Backbone modules to extract feature.
keypoint_head (dict) – Keypoint head to process feature.
train_cfg (dict) – Config for training. Default: None.
test_cfg (dict) – Config for testing. Default: None.
pretrained (str) – Path to the pretrained models.
loss_pose (None) – Deprecated arguments. Please use loss_keypoint for heads instead.

__init__(backbone, neck=None, keypoint_head=None, train_cfg=None, test_cfg=None, pretrained=None, loss_pose=None)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

property with_neck¶: Check if has keypoint_head.

property with_keypoint¶: Check if has keypoint_head.

init_weights(pretrained=None)[source]¶: Weight initialization for model.

forward_train(img, target, target_weight, img_metas, **kwargs)[source]¶: Defines the computation performed at every call when training.

forward_test(img, img_metas, return_heatmap=False, **kwargs)[source]¶: Defines the computation performed at every call when testing.

show_result(img, result, skeleton=None, kpt_score_thr=0.3, bbox_color='green', pose_kpt_color=None, pose_link_color=None, text_color='white', radius=4, thickness=1, font_scale=0.5, bbox_thickness=1, win_name='', show=False, show_keypoint_weight=False, wait_time=0, out_file=None)[source]¶

Draw result over img.

Parameters

img (str or Tensor) – The image to be displayed.
result (list[dict]) – The results to draw over img (bbox_result, pose_result).
skeleton (list[list]) – The connection of keypoints. skeleton is 0-based indexing.
kpt_score_thr (float, optional) – Minimum score of keypoints to be shown. Default: 0.3.
bbox_color (str or tuple or Color) – Color of bbox lines.
pose_kpt_color (np.array[Nx3]`) – Color of N keypoints. If None, do not draw keypoints.
pose_link_color (np.array[Mx3]) – Color of M links. If None, do not draw links.
text_color (str or tuple or Color) – Color of texts.
radius (int) – Radius of circles.
thickness (int) – Thickness of lines.
font_scale (float) – Font scales of texts.
win_name (str) – The window name.
show (bool) – Whether to show the image. Default: False.
show_keypoint_weight (bool) – Whether to change the transparency using the predicted confidence scores of keypoints.
wait_time (int) – Value of waitKey param. Default: 0.
out_file (str or None) – The filename to write the image. Default: None.

Returns

Visualized img, only if not show or out_file.

Return type

Tensor

training: bool¶

easycv.models.selfsup package¶

Submodules¶

easycv.models.selfsup.byol module¶

class easycv.models.selfsup.byol.BYOL(backbone, neck=None, head=None, pretrained=None, base_momentum=0.996, **kwargs)[source]¶

Bases: easycv.models.base.BaseModel

BYOL unofficial implementation. Paper: https://arxiv.org/abs/2006.07733

__init__(backbone, neck=None, head=None, pretrained=None, base_momentum=0.996, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(pretrained=None)[source]¶

forward_train(img, **kwargs)[source]¶

Abstract interface for model forward in training

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward_test(img, **kwargs)[source]¶

Abstract interface for model forward in testing

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward(img, mode='train', **kwargs)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.selfsup.dino module¶

class easycv.models.selfsup.dino.MultiCropWrapper(backbone, head)[source]¶

Bases: torch.nn.modules.module.Module

Perform forward pass separately on each resolution input. The inputs corresponding to a single resolution are clubbed and single forward is run on the same resolution inputs. Hence we do several forward passes = number of different resolutions used. We then concatenate all the output features and run the head forward on these concatenated features.

__init__(backbone, head)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.selfsup.dino.DINOLoss(out_dim, ncrops, warmup_teacher_temp, teacher_temp, warmup_teacher_temp_epochs, nepochs, device, student_temp=0.1, center_momentum=0.9)[source]¶

Bases: torch.nn.modules.module.Module

__init__(out_dim, ncrops, warmup_teacher_temp, teacher_temp, warmup_teacher_temp_epochs, nepochs, device, student_temp=0.1, center_momentum=0.9)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(student_output, teacher_output, epoch)[source]¶: Cross-entropy between softmax outputs of the teacher and student networks.

update_center(teacher_output)[source]¶: Update center used for teacher output.

training: bool¶

easycv.models.selfsup.dino.has_batchnorms(model)[source]¶

easycv.models.selfsup.dino.get_params_groups(model)[source]¶

class easycv.models.selfsup.dino.DINO(backbone, train_preprocess=[], neck=None, config=None, pretrained=None)[source]¶

Bases: easycv.models.base.BaseModel

__init__(backbone, train_preprocess=[], neck=None, config=None, pretrained=None)[source]¶

Init Moby

Parameters

backbone – backbone config to build vision backbone
train_preprocess – [gaussBlur, mixUp, solarize]
neck – neck config to build Moby Neck
config – DINO parameter config

get_params_groups()[source]¶

init_weights(pretrained=None)[source]¶

init_before_train()[source]¶

momentum_update_key_encoder(m=0.999)[source]¶: ema for dino

forward_train(inputs)[source]¶

Abstract interface for model forward in training

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward_test(img, **kwargs)[source]¶

Abstract interface for model forward in testing

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward_feature(img, **kwargs)[source]¶

Forward backbone

Returns: feature tensor
Return type: x (torch.Tensor)

training: bool¶

forward(img, gt_label=None, mode='train', extract_list=['neck'], **kwargs)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

easycv.models.selfsup.mae module¶

class easycv.models.selfsup.mae.MAE(backbone, neck, mask_ratio=0.75, norm_pix_loss=True, **kwargs)[source]¶

Bases: easycv.models.base.BaseModel

__init__(backbone, neck, mask_ratio=0.75, norm_pix_loss=True, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

patchify(imgs)[source]¶

convert image to patch

Parameters: imgs – (N, 3, H, W)
Returns: (N, L, patch_size**2 *3)
Return type: x

forward_loss(imgs, pred, mask)[source]¶

compute loss

Parameters

imgs – (N, 3, H, W)
pred – (N, L, p*p*3)
mask – (N, L), 0 is keep, 1 is remove,

forward_train(img, **kwargs)[source]¶

Abstract interface for model forward in training

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward_test(img, **kwargs)[source]¶

Abstract interface for model forward in testing

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward(img, mode='train', **kwargs)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.selfsup.mixco module¶

class easycv.models.selfsup.mixco.MIXCO(backbone, train_preprocess=[], neck=None, head=None, mixco_head=None, pretrained=None, queue_len=65536, feat_dim=128, momentum=0.999, **kwargs)[source]¶

Bases: easycv.models.selfsup.moco.MOCO

MOCO.

A mixup version moco https://arxiv.org/pdf/2010.06300.pdf

__init__(backbone, train_preprocess=[], neck=None, head=None, mixco_head=None, pretrained=None, queue_len=65536, feat_dim=128, momentum=0.999, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward_train(img, **kwargs)[source]¶

Abstract interface for model forward in training

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

training: bool¶

easycv.models.selfsup.moby module¶

class easycv.models.selfsup.moby.MoBY(backbone, train_preprocess=[], neck=None, head=None, pretrained=None, queue_len=4096, contrast_temperature=0.2, momentum=0.99, online_drop_path_rate=0.2, target_drop_path_rate=0.0, **kwargs)[source]¶

Bases: easycv.models.base.BaseModel

MoBY. Part of the code is borrowed from: https://github.com/SwinTransformer/Transformer-SSL/blob/main/models/moby.py.

__init__(backbone, train_preprocess=[], neck=None, head=None, pretrained=None, queue_len=4096, contrast_temperature=0.2, momentum=0.99, online_drop_path_rate=0.2, target_drop_path_rate=0.0, **kwargs)[source]¶

Init Moby

Parameters

backbone – backbone config to build vision backbone
train_preprocess – [gaussBlur, mixUp, solarize]
neck – neck config to build Moby Neck
head – head config to build Moby Neck
pretrained – pretrained weight for backbone
queue_len – moby queue length
contrast_temperature – contrastive_loss temperature
momentum – ema target weights momentum
online_drop_path_rate – for transformer based backbone, set online model drop_path_rate
target_drop_path_rate – for transformer based backbone, set target model drop_path_rate

init_weights(pretrained=None)[source]¶

forward_backbone(img)[source]¶

contrastive_loss(q, k, queue)[source]¶

forward_train(img, **kwargs)[source]¶

Abstract interface for model forward in training

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward_test(img, **kwargs)[source]¶

Abstract interface for model forward in testing

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward_feature(img, **kwargs)[source]¶

Forward backbone

Returns: feature tensor
Return type: x (torch.Tensor)

forward(img, gt_label=None, mode='train', extract_list=['neck'], **kwargs)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.selfsup.moby.concat_all_gather(tensor)[source]¶: Performs all_gather operation on the provided tensors. * Warning *: torch.distributed.all_gather has no gradient.

easycv.models.selfsup.moco module¶

class easycv.models.selfsup.moco.MOCO(backbone, train_preprocess=[], neck=None, head=None, pretrained=None, queue_len=65536, feat_dim=128, momentum=0.999, **kwargs)[source]¶

Bases: easycv.models.base.BaseModel

MOCO. Part of the code is borrowed from: https://github.com/facebookresearch/moco/blob/master/moco/builder.py.

__init__(backbone, train_preprocess=[], neck=None, head=None, pretrained=None, queue_len=65536, feat_dim=128, momentum=0.999, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(pretrained=None)[source]¶

forward_backbone(img)[source]¶

forward_train(img, **kwargs)[source]¶

Abstract interface for model forward in training

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward_test(img, **kwargs)[source]¶

Abstract interface for model forward in testing

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward_feature(img, **kwargs)[source]¶

Forward backbone

Returns: feature tensor
Return type: x (torch.Tensor)

forward(img, gt_label=None, mode='train', extract_list=['neck'], **kwargs)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.selfsup.moco.concat_all_gather(tensor)[source]¶: Performs all_gather operation on the provided tensors. * Warning *: torch.distributed.all_gather has no gradient.

easycv.models.selfsup.necks module¶

class easycv.models.selfsup.necks.DINONeck(in_dim, out_dim, use_bn=False, norm_last_layer=True, nlayers=3, hidden_dim=2048, bottleneck_dim=256)[source]¶

Bases: torch.nn.modules.module.Module

__init__(in_dim, out_dim, use_bn=False, norm_last_layer=True, nlayers=3, hidden_dim=2048, bottleneck_dim=256)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.selfsup.necks.MoBYMLP(in_channels=256, hid_channels=4096, out_channels=256, num_layers=2, with_avg_pool=True)[source]¶

Bases: torch.nn.modules.module.Module

__init__(in_channels=256, hid_channels=4096, out_channels=256, num_layers=2, with_avg_pool=True)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

init_weights(init_linear='normal')[source]¶

training: bool¶

class easycv.models.selfsup.necks.NonLinearNeckSwav(in_channels, hid_channels, out_channels, with_avg_pool=True, export=False)[source]¶

Bases: torch.nn.modules.module.Module

The non-linear neck in byol: fc-syncbn-relu-fc

__init__(in_channels, hid_channels, out_channels, with_avg_pool=True, export=False)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(init_linear='normal')[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.selfsup.necks.NonLinearNeckV0(in_channels, hid_channels, out_channels, sync_bn=False, with_avg_pool=True)[source]¶

Bases: torch.nn.modules.module.Module

The non-linear neck in ODC, fc-bn-relu-dropout-fc-relu

__init__(in_channels, hid_channels, out_channels, sync_bn=False, with_avg_pool=True)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(init_linear='normal')[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.selfsup.necks.NonLinearNeckV1(in_channels, hid_channels, out_channels, with_avg_pool=True)[source]¶

Bases: torch.nn.modules.module.Module

The non-linear neck in MoCO v2: fc-relu-fc

__init__(in_channels, hid_channels, out_channels, with_avg_pool=True)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(init_linear='normal')[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.selfsup.necks.NonLinearNeckV2(in_channels, hid_channels, out_channels, with_avg_pool=True)[source]¶

Bases: torch.nn.modules.module.Module

The non-linear neck in byol: fc-bn-relu-fc

__init__(in_channels, hid_channels, out_channels, with_avg_pool=True)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(init_linear='normal')[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.selfsup.necks.NonLinearNeckSimCLR(in_channels, hid_channels, out_channels, num_layers=2, with_avg_pool=True)[source]¶

Bases: torch.nn.modules.module.Module

SimCLR non-linear neck.

Structure: fc(no_bias)-bn(has_bias)-[relu-fc(no_bias)-bn(no_bias)].: The substructures in [] can be repeated. For the SimCLR default setting, the repeat time is 1.
However, PyTorch does not support to specify (weight=True, bias=False).: It only support “affine” including the weight and bias. Hence, the second BatchNorm has bias in this implementation. This is different from the offical implementation of SimCLR.
Since SyncBatchNorm in pytorch<1.4.0 does not support 2D input, the input is: expanded to 4D with shape: (N,C,1,1). I am not sure if this workaround has no bugs. See the pull request here: https://github.com/pytorch/pytorch/pull/29626

Parameters

in_channels – input channel number
hid_channels – hidden channels
out_channels – output channel number
num_layers (int) – number of fc layers, it is 2 in the SimCLR default setting.
with_avg_pool – output with average pooling

__init__(in_channels, hid_channels, out_channels, num_layers=2, with_avg_pool=True)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(init_linear='normal')[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.selfsup.necks.RelativeLocNeck(in_channels, out_channels, sync_bn=False, with_avg_pool=True)[source]¶

Bases: torch.nn.modules.module.Module

Relative patch location neck: fc-bn-relu-dropout

__init__(in_channels, out_channels, sync_bn=False, with_avg_pool=True)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(init_linear='normal')[source]¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.selfsup.necks.MAENeck(num_patches, embed_dim=768, patch_size=16, in_chans=3, decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, mlp_ratio=4.0, norm_layer=functools.partial(<class 'torch.nn.modules.normalization.LayerNorm'>, eps=1e-06))[source]¶

Bases: torch.nn.modules.module.Module

MAE decoder

Parameters

num_patches (int) – number of patches from encoder
embed_dim (int) – encoder embedding dimension
patch_size (int) – encoder patch size
in_chans (int) – input image channels
decoder_embed_dim (int) – decoder embedding dimension
decoder_depth (int) – number of decoder layers
decoder_num_heads (int) – Parallel attention heads
mlp_ratio (float) – mlp ratio
norm_layer – type of normalization layer

__init__(num_patches, embed_dim=768, patch_size=16, in_chans=3, decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, mlp_ratio=4.0, norm_layer=functools.partial(<class 'torch.nn.modules.normalization.LayerNorm'>, eps=1e-06))[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

training: bool¶

forward(x, ids_restore)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

easycv.models.selfsup.simclr module¶

class easycv.models.selfsup.simclr.SimCLR(backbone, train_preprocess=[], neck=None, head=None, pretrained=None)[source]¶

Bases: easycv.models.base.BaseModel

__init__(backbone, train_preprocess=[], neck=None, head=None, pretrained=None)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(pretrained=None)[source]¶

forward_backbone(img)[source]¶

Forward backbone

Returns: backbone outputs
Return type: x (tuple)

forward_train(img, **kwargs)[source]¶

Abstract interface for model forward in training

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward_test(img, **kwargs)[source]¶

Abstract interface for model forward in testing

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward(img, mode='train', **kwargs)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.selfsup.swav module¶

class easycv.models.selfsup.swav.SWAV(backbone, train_preprocess=[], neck=None, config=None, pretrained=None)[source]¶

Bases: easycv.models.base.BaseModel

__init__(backbone, train_preprocess=[], neck=None, config=None, pretrained=None)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

init_weights(pretrained=None)[source]¶

forward_backbone(img)[source]¶

forward_train_model(inputs)[source]¶

forward_train(inputs)[source]¶

Abstract interface for model forward in training

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward_test(img, **kwargs)[source]¶

Abstract interface for model forward in testing

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward_feature(img, **kwargs)[source]¶

Forward backbone

Returns: feature tensor
Return type: x (torch.Tensor)

forward(img, gt_label=None, mode='train', extract_list=['neck'], **kwargs)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.selfsup.swav.MultiPrototypes(output_dim, nmb_prototypes)[source]¶

Bases: torch.nn.modules.module.Module

__init__(output_dim, nmb_prototypes)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.selfsup.swav.distributed_sinkhorn(Q, nmb_iters)[source]¶

easycv.models.utils package¶

Submodules¶

easycv.models.utils.accuracy module¶

easycv.models.utils.accuracy.accuracy(pred, target, topk=1)[source]¶

Parameters

pred – [N x num_classes]
target – [num_classes]

class easycv.models.utils.accuracy.Accuracy(topk=(1))[source]¶

Bases: torch.nn.modules.module.Module

__init__(topk=(1))[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(pred, target)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.utils.activation module¶

class easycv.models.utils.activation.FReLU(in_channel)[source]¶

Bases: torch.nn.modules.module.Module

__init__(in_channel)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.utils.conv_module module¶

easycv.models.utils.conv_module.build_conv_layer(cfg, *args, **kwargs)[source]¶

Build convolution layer

Parameters: cfg (None or dict) – cfg should contain: type (str): identify conv layer type. layer args: args needed to instantiate a conv layer.
Returns: created conv layer
Return type: layer (nn.Module)

class easycv.models.utils.conv_module.ConvModule(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias='auto', conv_cfg=None, norm_cfg=None, activation='relu', inplace=True, order=('conv', 'norm', 'act'))[source]¶

Bases: torch.nn.modules.module.Module

A conv block that contains conv/norm/activation layers.

Parameters

in_channels (int) – Same as nn.Conv2d.
out_channels (int) – Same as nn.Conv2d.
kernel_size (int or tuple[int]) – Same as nn.Conv2d.
stride (int or tuple[int]) – Same as nn.Conv2d.
padding (int or tuple[int]) – Same as nn.Conv2d.
dilation (int or tuple[int]) – Same as nn.Conv2d.
groups (int) – Same as nn.Conv2d.
bias (bool or str) – If specified as auto, it will be decided by the norm_cfg. Bias will be set as True if norm_cfg is None, otherwise False.
conv_cfg (dict) – Config dict for convolution layer.
norm_cfg (dict) – Config dict for normalization layer.
activation (str or None) – Activation type, “ReLU” by default.
inplace (bool) – Whether to use inplace mode for activation.
order (tuple[str]) – The order of conv/norm/activation layers. It is a sequence of “conv”, “norm” and “act”. Examples are (“conv”, “norm”, “act”) and (“act”, “conv”, “norm”).

__init__(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias='auto', conv_cfg=None, norm_cfg=None, activation='relu', inplace=True, order=('conv', 'norm', 'act'))[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

property norm¶

init_weights()[source]¶

forward(x, activate=True, norm=True)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.utils.conv_ws module¶

easycv.models.utils.conv_ws.conv_ws_2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1, eps=1e-05)[source]¶

class easycv.models.utils.conv_ws.ConvWS2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, eps=1e-05)[source]¶

Bases: torch.nn.modules.conv.Conv2d

__init__(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, eps=1e-05)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

bias: Optional[torch.Tensor]¶

out_channels: int¶

kernel_size: Tuple[int, ...]¶

stride: Tuple[int, ...]¶

padding: Union[str, Tuple[int, ...]]¶

dilation: Tuple[int, ...]¶

transposed: bool¶

output_padding: Tuple[int, ...]¶

groups: int¶

padding_mode: str¶

weight: torch.Tensor¶

easycv.models.utils.dist_utils module¶

easycv.models.utils.dist_utils.is_distributed()[source]¶

easycv.models.utils.dist_utils.all_gather(embeddings, labels)[source]¶

easycv.models.utils.dist_utils.all_gather_embeddings_labels(embeddings, labels)[source]¶

class easycv.models.utils.dist_utils.DistributedLossWrapper(loss, **kwargs)[source]¶

Bases: torch.nn.modules.module.Module

__init__(loss, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(embeddings, labels, *args, **kwargs)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.utils.dist_utils.DistributedMinerWrapper(miner)[source]¶

Bases: torch.nn.modules.module.Module

__init__(miner)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(embeddings, labels, ref_emb=None, ref_labels=None)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.utils.gather_layer module¶

class easycv.models.utils.gather_layer.GatherLayer(*args, **kwargs)[source]¶

Bases: torch.autograd.function.Function

Gather tensors from all process, supporting backward propagation.

static forward(ctx, input)[source]¶

Performs the operation.

This function is to be overridden by all subclasses.

It must accept a context ctx as the first argument, followed by any number of arguments (tensors or other types).

The context can be used to store arbitrary data that can be then retrieved during the backward pass. Tensors should not be stored directly on ctx (though this is not currently enforced for backward compatibility). Instead, tensors should be saved either with ctx.save_for_backward() if they are intended to be used in backward (equivalently, vjp) or ctx.save_for_forward() if they are intended to be used for in jvp.

static backward(ctx, *grads)[source]¶

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

It must accept a context ctx as the first argument, followed by as many outputs as the forward() returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs to forward(). Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.

The context can be used to retrieve tensors saved during the forward pass. It also has an attribute ctx.needs_input_grad as a tuple of booleans representing whether each input needs gradient. E.g., backward() will have ctx.needs_input_grad[0] = True if the first input to forward() needs gradient computated w.r.t. the output.

easycv.models.utils.init_weights module¶

easycv.models.utils.init_weights.trunc_normal_(tensor, mean=0.0, std=1.0, a=- 2.0, b=2.0)[source]¶

easycv.models.utils.multi_pooling module¶

class easycv.models.utils.multi_pooling.GeMPooling(p=3, eps=1e-06)[source]¶

Bases: torch.nn.modules.module.Module

GemPooling used for image retrival p = 1, avgpooling p > 1 : increases the contrast of the pooled feature map and focuses on the salient features of the image p = infinite : spatial max-pooling layer

__init__(p=3, eps=1e-06)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

gem(x, p=3, eps=1e-06)[source]¶

training: bool¶

class easycv.models.utils.multi_pooling.MultiPooling(pool_type='adaptive', in_indices=(0), backbone='resnet50')[source]¶

Bases: torch.nn.modules.module.Module

Pooling layers for features from multiple depth.

POOL_PARAMS = {'resnet50': [{'kernel_size': 10, 'stride': 10, 'padding': 4}, {'kernel_size': 16, 'stride': 8, 'padding': 0}, {'kernel_size': 13, 'stride': 5, 'padding': 0}, {'kernel_size': 8, 'stride': 3, 'padding': 0}, {'kernel_size': 6, 'stride': 1, 'padding': 0}]}¶

POOL_SIZES = {'resnet50': [12, 6, 4, 3, 2]}¶

POOL_DIMS = {'resnet50': [9216, 9216, 8192, 9216, 8192]}¶

__init__(pool_type='adaptive', in_indices=(0), backbone='resnet50')[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.utils.multi_pooling.MultiAvgPooling(pool_type='adaptive', in_indices=(0), backbone='resnet50')[source]¶

Bases: torch.nn.modules.module.Module

Pooling layers for features from multiple depth.

POOL_PARAMS = {'resnet50': [{'kernel_size': 10, 'stride': 10, 'padding': 4}, {'kernel_size': 16, 'stride': 8, 'padding': 0}, {'kernel_size': 13, 'stride': 5, 'padding': 0}, {'kernel_size': 8, 'stride': 3, 'padding': 0}, {'kernel_size': 7, 'stride': 1, 'padding': 0}]}¶

POOL_SIZES = {'resnet50': [12, 6, 4, 3, 1]}¶

POOL_DIMS = {'resnet50': [9216, 9216, 8192, 9216, 2048]}¶

__init__(pool_type='adaptive', in_indices=(0), backbone='resnet50')[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

training: bool¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

easycv.models.utils.norm module¶

class easycv.models.utils.norm.SyncIBN(planes, ratio=0.5, eps=1e-05)[source]¶

Bases: torch.nn.modules.module.Module

Instance-Batch Normalization layer from “Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net” <https://arxiv.org/pdf/1807.09441.pdf> :param planes: Number of channels for the input tensor :type planes: int :param ratio: Ratio of instance normalization in the IBN layer :type ratio: float

__init__(planes, ratio=0.5, eps=1e-05)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

class easycv.models.utils.norm.IBN(planes, ratio=0.5, eps=1e-05)[source]¶

Bases: torch.nn.modules.module.Module

Instance-Batch Normalization layer from “Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net” <https://arxiv.org/pdf/1807.09441.pdf> :param planes: Number of channels for the input tensor :type planes: int :param ratio: Ratio of instance normalization in the IBN layer :type ratio: float

__init__(planes, ratio=0.5, eps=1e-05)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.utils.norm.build_norm_layer(cfg, num_features, postfix='')[source]¶

Build normalization layer

Parameters

cfg (dict) – cfg should contain: type (str): identify norm layer type. layer args: args needed to instantiate a norm layer. requires_grad (bool): [optional] whether stop gradient updates
num_features (int) – number of channels from input.
postfix (int, str) – appended into norm abbreviation to create named layer.

Returns

abbreviation + postfix layer (nn.Module): created norm layer

Return type

name (str)

easycv.models.utils.ops module¶

easycv.models.utils.ops.resize_tensor(input, size=None, scale_factor=None, mode='nearest', align_corners=None, warning=True)[source]¶

Resize tensor with F.interpolate.

Parameters

input (Tensor) – the input tensor.
size (Tuple[int, int]) – output spatial size.
scale_factor (float or Tuple[float]) – multiplier for spatial size. If scale_factor is a tuple, its length has to match input.dim().
mode (str) – algorithm used for upsampling: ‘nearest’ | ‘linear’ | ‘bilinear’ | ‘bicubic’ | ‘trilinear’ | ‘area’. Default: ‘nearest’
align_corners (bool) –
Geometrically, we consider the pixels of the input and output as squares rather than points. If set to True, the input and output tensors are aligned by the center points of their corner pixels, preserving the values at the corner pixels.

If set to False, the input and output tensors are aligned by the corner points of their corner pixels, and the interpolation uses edge value padding for out-of-boundary values, making this operation independent of input size when scale_factor is kept the same. This only has an effect when mode is ‘linear’, ‘bilinear’, ‘bicubic’ or ‘trilinear’.

easycv.models.utils.pos_embed module¶

easycv.models.utils.pos_embed.get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False)[source]¶: grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)

easycv.models.utils.pos_embed.get_2d_sincos_pos_embed_from_grid(embed_dim, grid)[source]¶

easycv.models.utils.pos_embed.get_1d_sincos_pos_embed_from_grid(embed_dim, pos)[source]¶: embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)

easycv.models.utils.pos_embed.interpolate_pos_embed(model, checkpoint_model)[source]¶

easycv.models.utils.res_layer module¶

class easycv.models.utils.res_layer.ResLayer(block, num_blocks, in_channels, out_channels, expansion=None, stride=1, avg_down=False, conv_cfg=None, norm_cfg={'type': 'BN'}, **kwargs)[source]¶

Bases: torch.nn.modules.container.Sequential

ResLayer to build ResNet style backbone. :param block: Residual block used to build ResLayer. :type block: nn.Module :param num_blocks: Number of blocks. :type num_blocks: int :param in_channels: Input channels of this block. :type in_channels: int :param out_channels: Output channels of this block. :type out_channels: int :param expansion: The expansion for BasicBlock/Bottleneck.

If not specified, it will firstly be obtained via block.expansion. If the block has no attribute “expansion”, the following default values will be used: 1 for BasicBlock and 4 for Bottleneck. Default: None.

Parameters

stride (int) – stride of the first block. Default: 1.
avg_down (bool) – Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False
conv_cfg (dict, optional) – dictionary to construct and config conv layer. Default: None
norm_cfg (dict) – dictionary to construct and config norm layer. Default: dict(type=’BN’)

__init__(block, num_blocks, in_channels, out_channels, expansion=None, stride=1, avg_down=False, conv_cfg=None, norm_cfg={'type': 'BN'}, **kwargs)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

easycv.models.utils.scale module¶

class easycv.models.utils.scale.Scale(scale=1.0)[source]¶

Bases: torch.nn.modules.module.Module

A learnable scale parameter

__init__(scale=1.0)[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

easycv.models.utils.sobel module¶

class easycv.models.utils.sobel.Sobel[source]¶

Bases: torch.nn.modules.module.Module

__init__()[source]¶: Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool¶

Submodules¶

easycv.models.base module¶

class easycv.models.base.BaseModel[source]¶

Bases: torch.nn.modules.module.Module

base class for model.

abstract forward_train(img: torch.Tensor, **kwargs) → Dict[str, torch.Tensor][source]¶

Abstract interface for model forward in training

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward_test(img: torch.Tensor, **kwargs) → Dict[str, torch.Tensor][source]¶

Abstract interface for model forward in testing

Parameters

img (Tensor) – image tensor
kwargs (keyword arguments) – Specific to concrete implementation.

forward(img, mode='train', **kwargs)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

train_step(data, optimizer)[source]¶

The iteration step during training.

This method defines an iteration step during training, except for the back propagation and optimizer updating, which are done in an optimizer hook. Note that in some complicated cases or models, the whole process including back propagation and optimizer updating is also defined in this method, such as GAN.

Parameters

data (dict) – The output of dataloader.
optimizer (torch.optim.Optimizer | dict) – The optimizer of runner is passed to train_step(). This argument is unused and reserved.

Returns

It should contain at least 3 keys: loss, log_vars, num_samples.

loss is a tensor for back propagation, which can be a weighted sum of multiple losses.

log_vars contains all the variables to be sent to the logger.

num_samples indicates the batch size (when the model is DDP, it means the batch size on each GPU), which is used for averaging the logs.

Return type

dict

val_step(data, optimizer)[source]¶

The iteration step during validation.

This method shares the same signature as train_step(), but used during val epochs. Note that the evaluation after training epochs is not implemented with this method, but an evaluation hook.

show_result(**kwargs)[source]¶: Visualize the results.

training: bool¶

easycv.models.builder module¶

easycv.models.builder.build(cfg, registry, default_args=None)[source]¶

easycv.models.builder.build_backbone(cfg)[source]¶

easycv.models.builder.build_neck(cfg)[source]¶

easycv.models.builder.build_memory(cfg)[source]¶

easycv.models.builder.build_head(cfg)[source]¶

easycv.models.builder.build_loss(cfg)[source]¶

easycv.models.builder.build_model(cfg)[source]¶

easycv.models.modelzoo module¶

easycv.models.registry module¶

easycv.utils package¶

Submodules¶

easycv.utils.alias_multinomial module¶

class easycv.utils.alias_multinomial.AliasMethod(probs)[source]¶

Bases: object

https://hips.seas.harvard.edu/blog/2013/03/03/the-alias-method-efficient-sampling-with-many-discrete-outcomes/

__init__(probs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

cuda()[source]¶

draw(N)[source]¶: Draw N samples from multinomial

easycv.utils.bbox_util module¶

easycv.utils.bbox_util.bound_limits(v)[source]¶

easycv.utils.bbox_util.bound_limits_for_list(xywh)[source]¶

easycv.utils.bbox_util.xyxy2xywh_with_shape(x, shape)[source]¶

easycv.utils.bbox_util.batched_cxcywh2xyxy_with_shape(bboxes, shape)[source]¶

reverse of xyxy2xywh_with_shape transform normalized points [[x_center, y_center, box_w, box_h],…] to standard [[x1, y1, x2, y2],…] :param bboxes: np.array or tensor like [[x_center, y_center, box_w, box_h],…],

all value is normalized

Parameters: shape – img shape: [h, w]

return: np.array or tensor like [[x1, y1, x2, y2],…]

easycv.utils.bbox_util.batched_xyxy2cxcywh_with_shape(bboxes, shape)[source]¶

easycv.utils.bbox_util.xyxy2xywh_coco(bboxes, offset=0)[source]¶

easycv.utils.bbox_util.xywh2xyxy_coco(bboxes, offset=0)[source]¶

easycv.utils.bbox_util.xyxy2xywh(x)[source]¶

easycv.utils.bbox_util.xywh2xyxy(x)[source]¶

easycv.utils.bbox_util.bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-09)[source]¶

easycv.utils.bbox_util.box_iou(box1, box2)[source]¶

Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. :param box1: :type box1: Tensor[N, 4] :param box2: :type box2: Tensor[M, 4]

Returns

the NxM matrix containing the pairwise: IoU values for every element in boxes1 and boxes2

Return type

iou (Tensor[N, M])

easycv.utils.bbox_util.box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1)[source]¶: Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio

easycv.utils.bbox_util.clip_coords(boxes: torch.Tensor, img_shape: Tuple[int, int]) → None[source]¶

Clip bounding xyxy bounding boxes to image shape

Parameters

boxes – tensor with shape Nx4 (x1,y1,x2,y2)
img_shape – image size tuple, (height, width)

easycv.utils.bbox_util.scale_coords(img1_shape: Tuple[int, int], coords: torch.Tensor, img0_shape: Tuple[int, int], ratio_pad: Optional[Tuple[Tuple[float, float], Tuple[float, float]]] = None)[source]¶

easycv.utils.checkpoint module¶

easycv.utils.checkpoint.load_checkpoint(model, filename, map_location='cpu', strict=False, logger=None)[source]¶

Load checkpoint from a file or URI.

Parameters

model (Module) – Module to load checkpoint.
filename (str) – Accept local filepath, URL, torchvision://xxx, open-mmlab://xxx. Please refer to docs/model_zoo.md for details.
map_location (str) – Same as torch.load().
strict (bool) – Whether to allow different params for the model and checkpoint.
logger (logging.Logger or None) – The logger for error message.

Returns

The loaded checkpoint.

Return type

dict or OrderedDict

easycv.utils.checkpoint.save_checkpoint(model, filename, optimizer=None, meta=None)[source]¶

Save checkpoint to file.

The checkpoint will have 3 fields: meta, state_dict and optimizer. By default meta will contain version and time info.

Parameters

model (Module) – Module whose params are to be saved.
filename (str) – Checkpoint filename.
optimizer (Optimizer, optional) – Optimizer to be saved.
meta (dict, optional) – Metadata to be saved in checkpoint.

easycv.utils.collect module¶

easycv.utils.collect.nondist_forward_collect(func, data_loader, length)[source]¶

Forward and collect network outputs.

This function performs forward propagation and collects outputs. It can be used to collect results, features, losses, etc.

Parameters

func (function) – The function to process data. The output must be a dictionary of CPU tensors.
length (int) – Expected length of output arrays.

Returns

The concatenated outputs.

Return type

results_all (dict(np.ndarray))

easycv.utils.collect.dist_forward_collect(func, data_loader, rank, length, ret_rank=- 1)[source]¶

Forward and collect network outputs in a distributed manner.

This function performs forward propagation and collects outputs. It can be used to collect results, features, losses, etc.

Parameters

func (function) – The function to process data. The output must be a dictionary of CPU tensors.
rank (int) – This process id.
length (int) – Expected length of output arrays.
ret_rank (int) – The process that returns. Other processes will return None.

Returns

The concatenated outputs.

Return type

results_all (dict(np.ndarray))

easycv.utils.collect_env module¶

easycv.utils.collect_env.collect_env()[source]¶

easycv.utils.config_tools module¶

easycv.utils.config_tools.traverse_replace(d, key, value)[source]¶

easycv.utils.config_tools.check_base_cfg_path(base_cfg_name='configs/base.py', ori_filename=None)[source]¶

easycv.utils.config_tools.mmcv_file2dict_raw(ori_filename)[source]¶

easycv.utils.config_tools.mmcv_file2dict_base(ori_filename)[source]¶

easycv.utils.config_tools.mmcv_config_fromfile(ori_filename)[source]¶

easycv.utils.config_tools.get_config_class_value(cfg_dict, ori_key, dict_mem_helper)[source]¶

easycv.utils.config_tools.config_dict_edit(ori_cfg_dict, cfg_dict, reg, dict_mem_helper)[source]¶

edit ${configs.variables} in config dict to solve dependicies in config

ori_cfg_dict: to find the true value of ${configs.variables} cfg_dict: for find leafs of dict by recursive reg: Regular expression pattern for find all ${configs.variables} in leafs of dict dict_mem_helper: to store the true value of ${configs.variables} which have been found

easycv.utils.config_tools.rebuild_config(cfg, user_config_params)[source]¶

# rebuild config by user config params, modify config by user config params & replace ${configs.variables} by true value

return: Config

easycv.utils.config_tools.validate_export_config(cfg)[source]¶

easycv.utils.constant module¶

easycv.utils.dist_utils module¶

easycv.utils.dist_utils.is_master()[source]¶

easycv.utils.dist_utils.local_rank()[source]¶

easycv.utils.dist_utils.dist_zero_exec(rank=0)[source]¶

easycv.utils.dist_utils.get_num_gpu_per_node()[source]¶: get number of gpu per node

easycv.utils.dist_utils.barrier()[source]¶

easycv.utils.dist_utils.is_parallel(model)[source]¶

easycv.utils.dist_utils.obj2tensor(pyobj, device='cuda')[source]¶: Serialize picklable python object to tensor.

easycv.utils.dist_utils.tensor2obj(tensor)[source]¶: Deserialize tensor to picklable python object.

easycv.utils.dist_utils.all_reduce_dict(py_dict, op='sum', group=None, to_float=True)[source]¶

Apply all reduce function for python dict object.

The code is modified from https://github.com/Megvii- BaseDetection/YOLOX/blob/main/yolox/utils/allreduce_norm.py.

NOTE: make sure that py_dict in different ranks has the same keys and the values should be in the same shape.

Parameters

py_dict (dict) – Dict to be applied all reduce op.
op (str) – Operator, could be ‘sum’ or ‘mean’. Default: ‘sum’
group (torch.distributed.group, optional) – Distributed group, Default: None.
to_float (bool) – Whether to convert all values of dict to float. Default: True.

Returns

reduced python dict object.

Return type

OrderedDict

easycv.utils.eval_utils module¶

easycv.utils.eval_utils.generate_best_metric_name(evaluate_type, dataset_name, metric_names)[source]¶

Generate best metric name for different evaluator / different dataset / different metric_names evaluate_type: str dataset_name: None or str metric_names: None str or list[str] or tuple(str)

Returns: list[str]

easycv.utils.flops_counter module¶

easycv.utils.flops_counter.get_model_info(model, input_size, model_config, logger)[source]¶: get_model_info, check model parameters and Gflops

easycv.utils.flops_counter.get_model_complexity_info(model, input_res, print_per_layer_stat=True, as_strings=True, input_constructor=None, ost=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>)[source]¶

easycv.utils.flops_counter.flops_to_string(flops, units='GMac', precision=2)[source]¶

easycv.utils.flops_counter.params_to_string(params_num)[source]¶

converting number to string

Parameters: params_num (float) – number
Returns str: number

>>> params_to_string(1e9)
'1000.0 M'
>>> params_to_string(2e5)
'200.0 k'
>>> params_to_string(3e-9)
'3e-09'

easycv.utils.flops_counter.print_model_with_flops(model, units='GMac', precision=3, ost=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>)[source]¶

easycv.utils.flops_counter.get_model_parameters_number(model)[source]¶

easycv.utils.flops_counter.add_flops_counting_methods(net_main_module)[source]¶

easycv.utils.flops_counter.compute_average_flops_cost(self)[source]¶: A method that will be available after add_flops_counting_methods() is called on a desired net object. Returns current mean flops consumption per image.

easycv.utils.flops_counter.start_flops_count(self)[source]¶: A method that will be available after add_flops_counting_methods() is called on a desired net object. Activates the computation of mean flops consumption per image. Call it before you run the network.

easycv.utils.flops_counter.stop_flops_count(self)[source]¶: A method that will be available after add_flops_counting_methods() is called on a desired net object. Stops computing the mean flops consumption per image. Call whenever you want to pause the computation.

easycv.utils.flops_counter.reset_flops_count(self)[source]¶: A method that will be available after add_flops_counting_methods() is called on a desired net object. Resets statistics computed so far.

easycv.utils.flops_counter.add_flops_mask(module, mask)[source]¶

easycv.utils.flops_counter.remove_flops_mask(module)[source]¶

easycv.utils.flops_counter.is_supported_instance(module)[source]¶

easycv.utils.flops_counter.empty_flops_counter_hook(module, input, output)[source]¶

easycv.utils.flops_counter.upsample_flops_counter_hook(module, input, output)[source]¶

easycv.utils.flops_counter.relu_flops_counter_hook(module, input, output)[source]¶

easycv.utils.flops_counter.linear_flops_counter_hook(module, input, output)[source]¶

easycv.utils.flops_counter.pool_flops_counter_hook(module, input, output)[source]¶

easycv.utils.flops_counter.bn_flops_counter_hook(module, input, output)[source]¶

easycv.utils.flops_counter.gn_flops_counter_hook(module, input, output)[source]¶

easycv.utils.flops_counter.deconv_flops_counter_hook(conv_module, input, output)[source]¶

easycv.utils.flops_counter.conv_flops_counter_hook(conv_module, input, output)[source]¶

easycv.utils.flops_counter.batch_counter_hook(module, input, output)[source]¶

easycv.utils.flops_counter.add_batch_counter_variables_or_reset(module)[source]¶

easycv.utils.flops_counter.add_batch_counter_hook_function(module)[source]¶

easycv.utils.flops_counter.remove_batch_counter_hook_function(module)[source]¶

easycv.utils.flops_counter.add_flops_counter_variable_or_reset(module)[source]¶

easycv.utils.flops_counter.add_flops_counter_hook_function(module)[source]¶

easycv.utils.flops_counter.remove_flops_counter_hook_function(module)[source]¶

easycv.utils.flops_counter.add_flops_mask_variable_or_reset(module)[source]¶

easycv.utils.gather module¶

easycv.utils.gather.gather_tensors(input_array)[source]¶

easycv.utils.gather.gather_tensors_batch(input_array, part_size=100, ret_rank=- 1)[source]¶

easycv.utils.json_utils module¶

Utilities for dealing with writing json strings.

json_utils wraps json.dump and json.dumps so that they can be used to safely control the precision of floats when writing to json strings or files.

class easycv.utils.json_utils.MyEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)[source]¶

Bases: json.encoder.JSONEncoder

default(o)[source]¶

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return JSONEncoder.default(self, o)

iterencode(o, _one_shot=False)[source]¶

Encode the given object and yield each string representation as available.

For example:

for chunk in JSONEncoder().iterencode(bigobject):
    mysocket.write(chunk)

easycv.utils.json_utils.dump(obj, fid, float_digits=- 1, **params)[source]¶

Wrapper of json.dump that allows specifying the float precision used.

Parameters

obj – The object to dump.
fid – The file id to write to.
float_digits – The number of digits of precision when writing floats out.
**params – Additional parameters to pass to json.dumps.

easycv.utils.json_utils.dumps(obj, float_digits=- 1, **params)[source]¶

Wrapper of json.dumps that allows specifying the float precision used.

Parameters

obj – The object to dump.
float_digits – The number of digits of precision when writing floats out.
**params – Additional parameters to pass to json.dumps.

Returns

JSON string representation of obj.

Return type

output

easycv.utils.json_utils.compat_dumps(data, float_digits=- 1)[source]¶

handle json dumps chinese and numpy data :param data python data structure: :param float_digits: The number of digits of precision when writing floats out.

Returns

json str, in python2 , the str is encoded with utf8: in python3, the str is unicode type(python3 str)

easycv.utils.json_utils.PrettyParams(**params)[source]¶

Returns parameters for use with Dump and Dumps to output pretty json.

Example usage:: `json_str = json_utils.Dumps(obj, **json_utils.PrettyParams())` ```json_str = json_utils.Dumps(

obj, **json_utils.PrettyParams(allow_nans=False))```

Parameters

**params – Additional params to pass to json.dump or json.dumps.

Returns

Parameters that are compatible with json_utils.Dump and: json_utils.Dumps.

Return type

params

easycv.utils.logger module¶

easycv.utils.logger.get_root_logger(log_file=None, log_level=20)[source]¶

Get the root logger.

The logger will be initialized if it has not been initialized. By default a StreamHandler will be added. If log_file is specified, a FileHandler will also be added. The name of the root logger is the top-level package name, e.g., “easycv”.

Parameters

log_file (str | None) – The log filename. If specified, a FileHandler will be added to the root logger.
log_level (int) – The root logger level. Note that only the process of rank 0 is affected, while other processes will set the level to “Error” and be silent most of the time.

Returns

The root logger.

Return type

logging.Logger

easycv.utils.logger.print_log(msg, logger=None, level=20)[source]¶

Print a log message.

Parameters

msg (str) – The message to be logged.
logger (logging.Logger | str | None) – The logger to be used. Some special loggers are: - “root”: the root logger obtained with get_root_logger(). - “silent”: no message will be printed. - None: The print() method will be used to print log messages.
level (int) – Logging level. Only available when logger is a Logger object or “root”.

easycv.utils.metric_distance module¶

easycv.utils.metric_distance.LpDistance(query_emb, ref_emb, p=2)[source]¶

Input:: query_emb: [n, dims] tensor ref_emb: [m, dims] tensor p : p normalize
Output:: distance_matrix: [n, m] tensor

distance_matrix_i_j = (sigma_k(a_i_k**p - b_j_k**p))**(1/p)

easycv.utils.metric_distance.DotproductSimilarity(query_emb, ref_emb)[source]¶

easycv.utils.metric_distance.CosineSimilarity(query_emb, ref_emb)[source]¶

Input:: query_emb: [n, dims] tensor ref_emb: [m, dims] tensor
Output:: distance_matrix: [n, m] tensor

easycv.utils.misc module¶

easycv.utils.misc.tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True)[source]¶

easycv.utils.misc.multi_apply(func, *args, **kwargs)[source]¶

easycv.utils.misc.unmap(data, count, inds, fill=0)[source]¶: Unmap a subset of item (data) back to the original set of items (of size count)

easycv.utils.preprocess_function module¶

easycv.utils.preprocess_function.bninceptionPre(image, mean=[104, 117, 128], std=[1, 1, 1])[source]¶

Parameters

image – pytorch Image tensor from PIL (range 0~1), bgr format
mean – norm mean
std – norm val

Returns

A image norm in 0~255, rgb format

easycv.utils.preprocess_function.randomErasing(image, probability=0.5, sl=0.02, sh=0.2, r1=0.3, mean=[0.4914, 0.4822, 0.4465])[source]¶

easycv.utils.preprocess_function.solarize(tensor, threshold=0.5, apply_prob=0.2)[source]¶: tensor : pytorch tensor

easycv.utils.preprocess_function.gaussianBlurDynamic(image, apply_prob=0.5)[source]¶

easycv.utils.preprocess_function.gaussianBlur(image, kernel_size=22, apply_prob=0.5)[source]¶

easycv.utils.preprocess_function.randomGrayScale(image, apply_prob=0.2)[source]¶

easycv.utils.preprocess_function.mixUp(image, alpha=0.2)[source]¶

easycv.utils.preprocess_function.mixUpCls(data, alpha=0.2)[source]¶

easycv.utils.profiling module¶

easycv.utils.profiling.profile_time(trace_name, name, enabled=True, stream=None, end_stream=None)[source]¶

Print time spent by CPU and GPU.

Useful as a temporary context manager to find sweet spots of code suitable for async implementation.

easycv.utils.profiling.benchmark_torch_function(iters, f, *args)[source]¶

easycv.utils.profiling.time_synchronized()[source]¶

easycv.utils.py_util module¶

easycv.utils.py_util.copy_attr(a, b, include=(), exclude=())[source]¶

easycv.utils.py_util.get_parent_path(path: str)[source]¶: get parent path, support oss-style path

easycv.utils.registry module¶

class easycv.utils.registry.Registry(name)[source]¶

Bases: object

__init__(name)[source]¶: Initialize self. See help(type(self)) for accurate signature.

property name¶

property module_dict¶

get(key)[source]¶

register_module(cls=None, force=False)[source]¶

easycv.utils.registry.build_from_cfg(cfg, registry, default_args=None)[source]¶

Build a module from config dict.

Parameters

cfg (dict) – Config dict. It should at least contain the key “type”.
registry (Registry) – The registry to search the type from.
default_args (dict, optional) – Default initialization arguments.

Returns

The constructed object.

Return type

obj

easycv.utils.test_util module¶

Contains functions which are convenient for unit testing.

easycv.utils.test_util.get_tmp_dir()[source]¶

easycv.utils.test_util.clear_all_tmp_dirs()[source]¶

easycv.utils.test_util.replace_data_for_test(cfg)[source]¶

replace real data with test data

Parameters: cfg – Config object

easycv.utils.test_util.RunAsSubprocess(f)[source]¶

easycv.utils.test_util.clean_up(test_dir)[source]¶

easycv.utils.test_util.run_in_subprocess(cmd)[source]¶

easycv.utils.test_util.dist_exec_wrapper(cmd, nproc_per_node, node_rank=0, nnodes=1, port='29527', addr='127.0.0.1', python_path=None)[source]¶: donot forget init dist in your function or script of cmd `python from mmcv.runner import init_dist init_dist(launcher='pytorch') `

easycv.utils.test_util.is_port_used(port, host='127.0.0.1')[source]¶

easycv.utils.test_util.get_random_port()[source]¶

easycv.utils.test_util.pseudo_dist_init()[source]¶

easycv.utils.user_config_params_utils module¶

easycv.utils.user_config_params_utils.check_value_type(replacement, original)[source]¶: convert replacement’s type to original’s type, support converting str to int or float or list or tuple

easycv package¶

Subpackages¶

easycv.file package¶

Submodules¶

easycv.file.base module¶

class easycv.file.base.IOBase[source]¶

Bases: object

static register(options)[source]¶

open(path: str, mode: str = 'r')[source]¶

exists(path: str) → bool[source]¶

move(src: str, dst: str)[source]¶

copy(src: str, dst: str)[source]¶

copytree(src: str, dst: str)[source]¶

makedirs(path: str, exist_ok=True)[source]¶

remove(path: str)[source]¶

rmtree(path: str)[source]¶

listdir(path: str, recursive=False, full_path=False, contains=None)[source]¶

isdir(path: str) → bool[source]¶

isfile(path: str) → bool[source]¶

abspath(path: str) → str[source]¶

last_modified(path: str) → datetime.datetime[source]¶

last_modified_str(path: str) → str[source]¶

size(path: str) → int[source]¶

md5(path: str) → str[source]¶

re_remote = re.compile('(oss|https?)://')¶

islocal(path: str) → bool[source]¶

is_writable(path)[source]¶

class easycv.file.base.IOLocal[source]¶

Bases: easycv.file.base.IOBase

open(path, mode='r')[source]¶

exists(path)[source]¶

move(src, dst)[source]¶

copy(src, dst)[source]¶

copytree(src, dst)[source]¶

makedirs(path, exist_ok=True)[source]¶

remove(path)[source]¶

rmtree(path)[source]¶

listdir(path, recursive=False, full_path=False, contains: Optional[Union[str, List[str]]] = None)[source]¶

isdir(path)[source]¶

isfile(path)[source]¶

glob(path)[source]¶

abspath(path)[source]¶

last_modified(path)[source]¶

last_modified_str(path)[source]¶

size(path: str) → int[source]¶

easycv.file.file_io module¶

easycv.file.file_io.set_oss_env(ak_id: str, ak_secret: str, hosts: Union[str, List[str]], buckets: Union[str, List[str]])[source]¶

class easycv.file.file_io.IO(max_retry=10)[source]¶

Bases: easycv.file.base.IOLocal

IO module to support both local and oss io. If access oss file, you need to authorize OSS, please refer to IO.access_oss.

__init__(max_retry=10)[source]¶: Initialize self. See help(type(self)) for accurate signature.

access_oss(ak_id: str = '', ak_secret: str = '', hosts: Union[str, List[str]] = '', buckets: Union[str, List[str]] = '')[source]¶

If access oss file, you need to authorize OSS as follows:

Method1: from easycv.file import io io.access_oss(

ak_id=’your_accesskey_id’, ak_secret=’your_accesskey_secret’, hosts=’your endpoint’ or [‘your endpoint1’, ‘your endpoint2’], buckets=’your bucket’ or [‘your bucket1’, ‘your bucket2’])

Method2:

Add oss config to your local file ~/.ossutilconfig, as follows: More oss config information, please refer to: https://help.aliyun.com/document_detail/120072.html ``` [Credentials]

language = CH endpoint = your endpoint accessKeyID = your_accesskey_id accessKeySecret = your_accesskey_secret

[Bucket-Endpoint]: bucket1 = endpoint1 bucket2 = endpoint2

``` Then run the following command, the config file will be read by default to authorize oss.

from easycv.file import io io.access_oss()

open(full_path, mode='r')[source]¶

Same usage as the python build-in open. Support local path and oss path.

Example

from easycv.file import io io.access_oss(your oss config) # only oss file need, refer to IO.access_oss # Write something to a oss file. with io.open(‘oss://bucket_name/demo.txt’, ‘w’) as f:

f.write(“test”)

# Read from a oss file. with io.open(‘oss://bucket_name/demo.txt’, ‘r’) as f:

print(f.read())

Parameters: full_path – absolute oss path

exists(path)[source]¶

Whether the file exists, same usage as os.path.exists. Support local path and oss path.

Example

from easycv.file import io io.access_oss(your oss config) # only oss file need, refer to IO.access_oss ret = io.exists(‘oss://bucket_name/dir’) print(ret)

Parameters: path – oss path or local path

move(src, dst)[source]¶

Move src to dst, same usage as shutil.move. Support local path and oss path.

Example

from easycv.file import io io.access_oss(your oss config) # only oss file need, refer to IO.access_oss # move oss file to local io.move(‘oss://bucket_name/file.txt’, ‘/your/local/path/file.txt’) # move oss file to oss io.move(‘oss://bucket_name/file.txt’, ‘oss://bucket_name/file.txt’) # move local file to oss io.move(‘/your/local/file.txt’, ‘oss://bucket_name/file.txt’) # move directory io.move(‘oss://bucket_name/dir1’, ‘oss://bucket_name/dir2’)

Parameters

src – oss path or local path
dst – oss path or local path

safe_copy(src, dst, try_max=3)[source]¶: oss always bug, we need safe_copy!

copy(src, dst)[source]¶

Copy a file from src to dst. Same usage as shutil.copyfile. If you want to copy a directory, please use easycv.io.copytree

Example

from easycv.file import io io.access_oss(your oss config) # only oss file need, refer to IO.access_oss # Copy a file from local to oss: io.copy(‘/your/local/file.txt’, ‘oss://bucket/dir/file.txt’)

# Copy a oss file to local: io.copy(‘oss://bucket/dir/file.txt’, ‘/your/local/file.txt’)

# Copy a file from oss to oss:: io.copy(‘oss://bucket/dir/file.txt’, ‘oss://bucket/dir/file2.txt’)

Parameters

src – oss path or local path
dst – oss path or local path

copytree(src, dst)[source]¶

Copy files recursively from src to dst. Same usage as shutil.copytree. If you want to copy a file, please use easycv.io.copy.

Example

from easycv.file import io io.access_oss(your oss config) # only oss file need, refer to IO.access_oss # copy files from local to oss io.copytree(src=’/your/local/dir1’, dst=’oss://bucket_name/dir2’) # copy files from oss to local io.copytree(src=’oss://bucket_name/dir2’, dst=’/your/local/dir1’) # copy files from oss to oss io.copytree(src=’oss://bucket_name/dir1’, dst=’oss://bucket_name/dir2’)

Parameters

src – oss path or local path
dst – oss path or local path

listdir(path, recursive=False, full_path=False, contains: Optional[Union[str, List[str]]] = None)[source]¶

List all objects in path. Same usage as os.listdir.

Example

from easycv.file import io io.access_oss(your oss config) # only oss file need, refer to IO.access_oss ret = io.listdir(‘oss://bucket/dir’, recursive=True) print(ret)

Parameters

path – local file path or oss path.
recursive – If False, only list the top level objects. If True, recursively list all objects.
full_path – if full path, return files with path prefix.
contains – substr to filter list files.

return: A list of path.

remove(path)[source]¶

Remove a file or a directory recursively. Same usage as os.remove or shutil.rmtree.

Example

from easycv.file import io io.access_oss(your oss config) # only oss file need, refer to IO.access_oss # Remove a oss file io.remove(‘oss://bucket_name/file.txt’)

# Remove a oss directory io.remove(‘oss://bucket_name/dir/’)

Parameters: path – local or oss path, file or directory

rmtree(path)[source]¶

Remove directory recursively, same usage as shutil.rmtree

Example

from easycv.file import io io.access_oss(your oss config) # only oss file need, refer to IO.access_oss io.remove(‘oss://bucket_name/dir_name’) # Or io.remove(‘oss://bucket_name/dir_name/’)

Parameters: path – oss path

makedirs(path, exist_ok=True)[source]¶

Create directories recursively, same usage as os.makedirs

Example

from easycv.file import io io.access_oss(your oss config) # only oss file need, refer to IO.access_oss io.makedirs(‘oss://bucket/new_dir/’)

Parameters: path – local or oss dir path

isdir(path)[source]¶

Return whether a path is directory, same usage as os.path.isdir

Example

from easycv.file import io io.access_oss(your oss config) # only oss file need, refer to IO.access_oss io.isdir(‘oss://bucket/dir/’)

Parameters: path – local or oss path

Return: bool, True or False.

isfile(path)[source]¶

Return whether a path is file object, same usage as os.path.isfile

Example

from easycv.file import io io.access_oss(your oss config) # only oss file need, refer to IO.access_oss io.isfile(‘oss://bucket/file.txt’)

Parameters: path – local or oss path

Return: bool, True or False.

glob(file_path)[source]¶

Return a list of paths matching a pathname pattern. .. rubric:: Example

from easycv.file import io io.access_oss(your oss config) # only oss file need, refer to IO.access_oss io.glob(‘oss://bucket/dir/*.txt’)

Parameters: path – local or oss file pattern

Return: list, a list of paths.

abspath(path)[source]¶

authorize(path)[source]¶

last_modified(path)[source]¶

last_modified_str(path)[source]¶

size(path: str) → int[source]¶

Get the size of file path, same usage as os.path.getsize

Example

from easycv.file import io io.access_oss(your oss config) # only oss file need, refer to IO.access_oss size = io.size(‘oss://bucket/file.txt’) print(size)

Parameters: path – local or oss path.

Return: size of file in bytes

class easycv.file.file_io.OSSFile(bucket, path, position=0)[source]¶

Bases: object

__init__(bucket, path, position=0)[source]¶: Initialize self. See help(type(self)) for accurate signature.

write(content)[source]¶

flush(retry=0)[source]¶

close()[source]¶

seek(position)[source]¶

class easycv.file.file_io.BinaryOSSFile(bucket, path)[source]¶

Bases: object

__init__(bucket, path)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.file.file_io.NullContextWrapper(obj)[source]¶

Bases: object

__init__(obj)[source]¶: Initialize self. See help(type(self)) for accurate signature.

easycv.file.utils module¶

easycv.file.utils.create_namedtuple(**kwargs)[source]¶

easycv.file.utils.is_oss_path(s)[source]¶

easycv.file.utils.get_oss_config()[source]¶: Get oss config file from env OSS_CONFIG_FILE, default file is ~/.ossutilconfig.

easycv.file.utils.oss_progress(desc)[source]¶

easycv.file.utils.ignore_oss_error(msg='')[source]¶

easycv.file.utils.mute_stderr()[source]¶

easycv.runner package¶

Submodules¶

easycv.runner.ev_runner module¶

class easycv.runner.ev_runner.EVRunner(model, batch_processor=None, optimizer=None, work_dir=None, logger=None, meta=None)[source]¶

Bases: mmcv.runner.epoch_based_runner.EpochBasedRunner

__init__(model, batch_processor=None, optimizer=None, work_dir=None, logger=None, meta=None)[source]¶

Epoch Runner for easycv, add support for oss IO and file sync.

Parameters

model (torch.nn.Module) – The model to be run.
batch_processor (callable) – A callable method that process a data batch. The interface of this method should be batch_processor(model, data, train_mode) -> dict
optimizer (dict or torch.optim.Optimizer) – It can be either an optimizer (in most cases) or a dict of optimizers (in models that requires more than one optimizer, e.g., GAN).
work_dir (str, optional) – The working directory to save checkpoints and logs. Defaults to None.
logger (logging.Logger) – Logger used during training. Defaults to None. (The default value is just for backward compatibility)
meta (dict | None) – A dict records some import information such as environment info and seed, which will be logged in logger hook. Defaults to None.

run_iter(data_batch, train_mode, **kwargs)[source]¶

process for each iteration.

Parameters

data_batch – Batch of dict of data.
train_model (bool) – If set True, run training step else validation step.

train(data_loader, **kwargs)[source]¶

Training process for one epoch which will iterate through all training data and call hooks at different stages.

Parameters: data_loader – data loader object for training

val(data_loader, **kwargs)[source]¶: Validation step which Deprecated, using evaluation hook instead.

save_checkpoint(out_dir, filename_tmpl='epoch_{}.pth', save_optimizer=True, meta=None, create_symlink=True)[source]¶

Save checkpoint to file.

Parameters

out_dir – Directory where checkpoint files are to be saved.
filename_tmpl (str, optional) – Checkpoint filename pattern.
save_optimizer (bool, optional) – save optimizer state.
meta (dict, optional) – Metadata to be saved in checkpoint.

current_lr()[source]¶

Get current learning rates.

Returns

Current learning rates of all: param groups. If the runner has a dict of optimizers, this method will return a dict.

Return type

list[float] | dict[str, list[float]]

load_checkpoint(filename, map_location=device(type='cpu'), strict=False, logger=None)[source]¶

Load checkpoint from a file or URL.

Parameters

filename (str) – Accept local filepath, URL, torchvision://xxx, open-mmlab://xxx, oss://xxx. Please refer to docs/source/model_zoo.md for details.
map_location (str) – Same as torch.load().
strict (bool) – Whether to allow different params for the model and checkpoint.
logger (logging.Logger or None) – The logger for error message.

Returns

The loaded checkpoint.

Return type

dict or OrderedDict

resume(checkpoint, resume_optimizer=True, map_location='default')[source]¶

Resume state dict from checkpoint.

Parameters

checkpoint – Checkpoint path
resume_optimizer – Whether to resume optimizer state
map_location (str) – Same as torch.load().

easycv.toolkit package¶

Subpackages¶

easycv.toolkit.prune package¶

Submodules¶

easycv.toolkit.prune.prune_utils module¶

easycv.toolkit.quantize package¶

Submodules¶

easycv.toolkit.quantize.quantize_utils module¶

easycv.toolkit.quantize.quantize_utils.calib(model, data_loader)[source]¶