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# Copyright (c) OpenMMLab. All rights reserved.
import platform
from unittest.mock import MagicMock
import pytest
import torch
from mmengine.utils import digit_version
from mmaction.registry import MODELS
from mmaction.structures import ActionDataSample
from mmaction.testing import get_recognizer_cfg
from mmaction.utils import register_all_modules
def train_test_step(cfg, input_shape):
recognizer = MODELS.build(cfg.model)
num_classes = cfg.model.cls_head.num_classes
batch_size = input_shape[0]
input_shape = input_shape[1:]
data_batch = {
'inputs':
[torch.randint(0, 256, input_shape) for i in range(batch_size)],
'data_samples':
[ActionDataSample().set_gt_label(2) for i in range(batch_size)]
}
# test train_step
optim_wrapper = MagicMock()
loss_vars = recognizer.train_step(data_batch, optim_wrapper)
assert 'loss' in loss_vars
assert 'loss_cls' in loss_vars
optim_wrapper.update_params.assert_called_once()
# test test_step
with torch.no_grad():
predictions = recognizer.test_step(data_batch)
score = predictions[0].pred_score
assert len(predictions) == batch_size
assert score.shape == torch.Size([num_classes])
assert torch.min(score) >= 0
assert torch.max(score) <= 1
# test twice sample + 3 crops
num_views = input_shape[0] * 2 * 3
input_shape = (num_views, *input_shape[1:])
data_batch['inputs'] = [torch.randint(0, 256, input_shape)]
with torch.no_grad():
predictions = recognizer.test_step(data_batch)
score = predictions[0].pred_score
assert len(predictions) == batch_size
assert score.shape == torch.Size([num_classes])
return loss_vars, predictions
def test_tsn():
register_all_modules()
config = get_recognizer_cfg(
'tsn/tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb.py')
config.model['backbone']['pretrained'] = None
input_shape = (1, 3, 3, 32, 32)
train_test_step(config, input_shape)
def test_tsn_mmcls_backbone():
register_all_modules()
config = get_recognizer_cfg(
'tsn/tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb.py')
config.model['backbone']['pretrained'] = None
# test mmcls backbone
mmcls_backbone = dict(
type='mmcls.ResNeXt',
depth=101,
num_stages=4,
out_indices=(3, ),
groups=32,
width_per_group=4,
style='pytorch')
config.model['backbone'] = mmcls_backbone
input_shape = (1, 3, 3, 32, 32)
train_test_step(config, input_shape)
from mmcls.models import ResNeXt
mmcls_backbone['type'] = ResNeXt
config.model['backbone'] = mmcls_backbone
input_shape = (1, 3, 3, 32, 32)
train_test_step(config, input_shape)
def test_tsn_mobileone():
register_all_modules()
config = get_recognizer_cfg(
'tsn/custom_backbones/tsn_imagenet-pretrained-mobileone-s4_8xb32-1x1x8-100e_kinetics400-rgb.py' # noqa: E501
)
config.model['backbone']['init_cfg'] = None
input_shape = (1, 3, 3, 32, 32)
train_test_step(config, input_shape)
def test_tsn_timm_backbone():
# test tsn from timm
register_all_modules()
config = get_recognizer_cfg(
'tsn/tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb.py')
timm_backbone = dict(type='timm.efficientnet_b0', pretrained=False)
config.model['backbone'] = timm_backbone
config.model['cls_head']['in_channels'] = 1280
input_shape = (1, 3, 3, 32, 32)
train_test_step(config, input_shape)
import timm
if digit_version(timm.__version__) <= digit_version('0.6.7'):
feature_shape = 'NLC'
else:
feature_shape = 'NHWC'
timm_swin = dict(
type='timm.swin_base_patch4_window7_224',
pretrained=False,
feature_shape=feature_shape)
config.model['backbone'] = timm_swin
config.model['cls_head']['in_channels'] = 1024
input_shape = (1, 3, 3, 224, 224)
train_test_step(config, input_shape)
def test_tsn_tv_backbone():
register_all_modules()
config = get_recognizer_cfg(
'tsn/tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb.py')
config.model['backbone']['pretrained'] = None
# test tv backbone
tv_backbone = dict(type='torchvision.densenet161', pretrained=True)
config.model['backbone'] = tv_backbone
config.model['cls_head']['in_channels'] = 2208
input_shape = (1, 3, 3, 32, 32)
train_test_step(config, input_shape)
from torchvision.models import densenet161
tv_backbone = dict(type=densenet161, pretrained=True)
config.model['backbone'] = tv_backbone
config.model['cls_head']['in_channels'] = 2208
input_shape = (1, 3, 3, 32, 32)
train_test_step(config, input_shape)
def test_tsm():
register_all_modules()
# test tsm-mobilenetv2
config = get_recognizer_cfg(
'tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb.py' # noqa: E501
)
config.model['backbone']['pretrained'] = None
config.model['backbone']['pretrained2d'] = None
input_shape = (1, 8, 3, 32, 32)
train_test_step(config, input_shape)
# test tsm-res50
config = get_recognizer_cfg(
'tsm/tsm_imagenet-pretrained-r50_8xb16-1x1x8-50e_kinetics400-rgb.py')
config.model['backbone']['pretrained'] = None
config.model['backbone']['pretrained2d'] = None
input_shape = (1, 8, 3, 32, 32)
train_test_step(config, input_shape)
# test tsm-mobileone
config = get_recognizer_cfg(
'tsm/tsm_imagenet-pretrained-mobileone-s4_8xb16-1x1x16-50e_kinetics400-rgb.py' # noqa: E501
)
config.model['backbone']['init_cfg'] = None
config.model['backbone']['pretrained2d'] = None
input_shape = (1, 16, 3, 32, 32)
train_test_step(config, input_shape)
def test_trn():
register_all_modules()
config = get_recognizer_cfg(
'trn/trn_imagenet-pretrained-r50_8xb16-1x1x8-50e_sthv1-rgb.py')
config.model['backbone']['pretrained'] = None
input_shape = (1, 8, 3, 32, 32)
train_test_step(config, input_shape)
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
def test_tpn():
register_all_modules()
config = get_recognizer_cfg(
'tpn/tpn-tsm_imagenet-pretrained-r50_8xb8-1x1x8-150e_sthv1-rgb.py')
config.model['backbone']['pretrained'] = None
input_shape = (1, 8, 3, 64, 64)
train_test_step(config, input_shape)
def test_tanet():
register_all_modules()
config = get_recognizer_cfg('tanet/tanet_imagenet-pretrained-r50_8xb8-'
'dense-1x1x8-100e_kinetics400-rgb.py')
config.model['backbone']['pretrained'] = None
input_shape = (1, 8, 3, 32, 32)
train_test_step(config, input_shape)
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