# Copyright (c) OpenMMLab. All rights reserved. import platform import pytest import torch from mmaction.registry import MODELS from mmaction.structures import ActionDataSample from mmaction.testing import get_recognizer_cfg from mmaction.utils import register_all_modules from mmaction.utils.gradcam_utils import GradCAM register_all_modules() def _get_target_shapes(input_shape, num_classes=400, model_type='2D'): if model_type not in ['2D', '3D']: raise ValueError(f'Data type {model_type} is not available') preds_target_shape = (input_shape[0], num_classes) if model_type == '3D': # input shape (batch_size, num_crops*num_clips, C, clip_len, H, W) # target shape (batch_size*num_crops*num_clips, clip_len, H, W, C) blended_imgs_target_shape = (input_shape[0] * input_shape[1], input_shape[3], input_shape[4], input_shape[5], input_shape[2]) else: # input shape (batch_size, num_segments, C, H, W) # target shape (batch_size, num_segments, H, W, C) blended_imgs_target_shape = (input_shape[0], input_shape[1], input_shape[3], input_shape[4], input_shape[2]) return blended_imgs_target_shape, preds_target_shape def _do_test_2D_models(recognizer, target_layer_name, input_shape, num_classes=400, device='cpu'): demo_data = { 'inputs': [torch.randint(0, 256, input_shape[1:])], 'data_samples': [ActionDataSample().set_gt_labels(2)] } recognizer = recognizer.to(device) gradcam = GradCAM(recognizer, target_layer_name) blended_imgs_target_shape, preds_target_shape = _get_target_shapes( input_shape, num_classes=num_classes, model_type='2D') blended_imgs, preds = gradcam(demo_data) assert blended_imgs.size() == blended_imgs_target_shape assert preds.size() == preds_target_shape blended_imgs, preds = gradcam(demo_data, True) assert blended_imgs.size() == blended_imgs_target_shape assert preds.size() == preds_target_shape def _do_test_3D_models(recognizer, target_layer_name, input_shape, num_classes=400): blended_imgs_target_shape, preds_target_shape = _get_target_shapes( input_shape, num_classes=num_classes, model_type='3D') demo_data = { 'inputs': [torch.randint(0, 256, input_shape[1:])], 'data_samples': [ActionDataSample().set_gt_labels(2)] } gradcam = GradCAM(recognizer, target_layer_name) blended_imgs, preds = gradcam(demo_data) assert blended_imgs.size() == blended_imgs_target_shape assert preds.size() == preds_target_shape blended_imgs, preds = gradcam(demo_data, True) assert blended_imgs.size() == blended_imgs_target_shape assert preds.size() == preds_target_shape def test_tsn(): config = get_recognizer_cfg( 'tsn/tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb.py') config.model['backbone']['pretrained'] = None recognizer = MODELS.build(config.model) recognizer.cfg = config input_shape = (1, 25, 3, 32, 32) target_layer_name = 'backbone/layer4/1/relu' _do_test_2D_models(recognizer, target_layer_name, input_shape) def test_i3d(): config = get_recognizer_cfg( 'i3d/i3d_imagenet-pretrained-r50_8xb8-32x2x1-100e_kinetics400-rgb.py') config.model['backbone']['pretrained2d'] = False config.model['backbone']['pretrained'] = None recognizer = MODELS.build(config.model) recognizer.cfg = config input_shape = (1, 1, 3, 32, 32, 32) target_layer_name = 'backbone/layer4/1/relu' _do_test_3D_models(recognizer, target_layer_name, input_shape) def test_r2plus1d(): config = get_recognizer_cfg( 'r2plus1d/r2plus1d_r34_8xb8-8x8x1-180e_kinetics400-rgb.py') config.model['backbone']['pretrained2d'] = False config.model['backbone']['pretrained'] = None config.model['backbone']['norm_cfg'] = dict(type='BN3d') recognizer = MODELS.build(config.model) recognizer.cfg = config input_shape = (1, 3, 3, 8, 16, 16) target_layer_name = 'backbone/layer4/1/relu' _do_test_3D_models(recognizer, target_layer_name, input_shape) def test_slowfast(): config = get_recognizer_cfg( 'slowfast/slowfast_r50_8xb8-4x16x1-256e_kinetics400-rgb.py') recognizer = MODELS.build(config.model) recognizer.cfg = config input_shape = (1, 1, 3, 32, 32, 32) target_layer_name = 'backbone/slow_path/layer4/1/relu' _do_test_3D_models(recognizer, target_layer_name, input_shape) @pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit') def test_tsm(): config = get_recognizer_cfg( 'tsm/tsm_imagenet-pretrained-r50_8xb16-1x1x8-50e_kinetics400-rgb.py') config.model['backbone']['pretrained'] = None target_layer_name = 'backbone/layer4/1/relu' # base config recognizer = MODELS.build(config.model) recognizer.cfg = config input_shape = (1, 8, 3, 32, 32) _do_test_2D_models(recognizer, target_layer_name, input_shape) # test twice sample + 3 crops, 2*3*8=48 config.model.test_cfg = dict(average_clips='prob') recognizer = MODELS.build(config.model) recognizer.cfg = config input_shape = (1, 48, 3, 32, 32) _do_test_2D_models(recognizer, target_layer_name, input_shape) @pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit') def test_csn(): config = get_recognizer_cfg( 'csn/ipcsn_ig65m-pretrained-r152-bnfrozen_32x2x1-58e_kinetics400-rgb.py' # noqa: E501 ) config.model['backbone']['pretrained2d'] = False config.model['backbone']['pretrained'] = None recognizer = MODELS.build(config.model) recognizer.cfg = config input_shape = (1, 1, 3, 32, 16, 16) target_layer_name = 'backbone/layer4/1/relu' _do_test_3D_models(recognizer, target_layer_name, input_shape) @pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit') def test_tpn(): target_layer_name = 'backbone/layer4/1/relu' config = get_recognizer_cfg( 'tpn/tpn-tsm_imagenet-pretrained-r50_8xb8-1x1x8-150e_sthv1-rgb.py') config.model['backbone']['pretrained'] = None config.model['backbone']['num_segments'] = 4 config.model.test_cfg['fcn_test'] = False recognizer = MODELS.build(config.model) recognizer.cfg = config input_shape = (1, 4, 3, 16, 16) _do_test_2D_models(recognizer, target_layer_name, input_shape, 174) config = get_recognizer_cfg( 'tpn/tpn-slowonly_r50_8xb8-8x8x1-150e_kinetics400-rgb.py') config.model['backbone']['pretrained'] = None config.model.test_cfg['fcn_test'] = False recognizer = MODELS.build(config.model) recognizer.cfg = config input_shape = (1, 3, 3, 4, 16, 16) _do_test_3D_models(recognizer, target_layer_name, input_shape) @pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit') def test_c3d(): config = get_recognizer_cfg( 'c3d/c3d_sports1m-pretrained_8xb30-16x1x1-45e_ucf101-rgb.py') config.model['backbone']['pretrained'] = None recognizer = MODELS.build(config.model) recognizer.cfg = config input_shape = (1, 1, 3, 16, 112, 112) target_layer_name = 'backbone/conv5a/activate' _do_test_3D_models(recognizer, target_layer_name, input_shape, 101) @pytest.mark.skipif( not torch.cuda.is_available(), reason='requires CUDA support') def test_tin(): config = get_recognizer_cfg( 'tin/tin_kinetics400-pretrained-tsm-r50_1x1x8-50e_kinetics400-rgb.py') config.model['backbone']['pretrained'] = None target_layer_name = 'backbone/layer4/1/relu' recognizer = MODELS.build(config.model) recognizer.cfg = config input_shape = (1, 8, 3, 64, 64) _do_test_2D_models( recognizer, target_layer_name, input_shape, device='cuda:0') @pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit') def test_x3d(): config = get_recognizer_cfg('x3d/x3d_s_13x6x1_facebook-kinetics400-rgb.py') config.model['backbone']['pretrained'] = None recognizer = MODELS.build(config.model) recognizer.cfg = config input_shape = (1, 1, 3, 13, 16, 16) target_layer_name = 'backbone/layer4/1/relu' _do_test_3D_models(recognizer, target_layer_name, input_shape)