easycv.apis package

Submodules

easycv.apis.export module

easycv.apis.export.export(cfg, ckpt_path, filename)

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)

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

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)

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)

easycv.apis.test.serialize_tensor(tensor_collection)

easycv.apis.test.collect_results_gpu(result_part, size)

easycv.apis.train module

easycv.apis.train.set_random_seed(seed, deterministic=False)

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)

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)

easycv.apis.train.build_optimizer(model, optimizer_cfg)

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)

build optimizer for yolo.