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[6d389a]: / tests / test_models / test_recognizers / test_recognizer2d.py

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# Copyright (c) OpenMMLab. All rights reserved.

import torch



from mmaction.models import build_recognizer

from ..base import generate_recognizer_demo_inputs, get_recognizer_cfg





def test_tsn():

    config = get_recognizer_cfg('tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py')

    config.model['backbone']['pretrained'] = None



    recognizer = build_recognizer(config.model)



    input_shape = (1, 3, 3, 32, 32)

    demo_inputs = generate_recognizer_demo_inputs(input_shape)



    imgs = demo_inputs['imgs']

    gt_labels = demo_inputs['gt_labels']



    losses = recognizer(imgs, gt_labels)

    assert isinstance(losses, dict)



    # Test forward test

    with torch.no_grad():

        img_list = [img[None, :] for img in imgs]

        for one_img in img_list:

            recognizer(one_img, None, return_loss=False)



    # Test forward gradcam

    recognizer(imgs, gradcam=True)

    for one_img in img_list:

        recognizer(one_img, gradcam=True)



    # test forward dummy

    recognizer.forward_dummy(imgs, softmax=False)

    res = recognizer.forward_dummy(imgs, softmax=True)[0]

    assert torch.min(res) >= 0

    assert torch.max(res) <= 1



    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



    recognizer = build_recognizer(config.model)



    input_shape = (1, 3, 3, 32, 32)

    demo_inputs = generate_recognizer_demo_inputs(input_shape)



    imgs = demo_inputs['imgs']

    gt_labels = demo_inputs['gt_labels']



    losses = recognizer(imgs, gt_labels)

    assert isinstance(losses, dict)



    # Test forward test

    with torch.no_grad():

        img_list = [img[None, :] for img in imgs]

        for one_img in img_list:

            recognizer(one_img, None, return_loss=False)



    # test mixup forward

    config = get_recognizer_cfg(

        'tsn/tsn_r50_video_mixup_1x1x8_100e_kinetics400_rgb.py')

    config.model['backbone']['pretrained'] = None

    recognizer = build_recognizer(config.model)

    input_shape = (2, 8, 3, 32, 32)

    demo_inputs = generate_recognizer_demo_inputs(input_shape)

    imgs = demo_inputs['imgs']

    gt_labels = demo_inputs['gt_labels']

    losses = recognizer(imgs, gt_labels)

    assert isinstance(losses, dict)



    # test torchvision backbones

    tv_backbone = dict(type='torchvision.densenet161', pretrained=True)

    config.model['backbone'] = tv_backbone

    config.model['cls_head']['in_channels'] = 2208



    recognizer = build_recognizer(config.model)



    input_shape = (1, 3, 3, 32, 32)

    demo_inputs = generate_recognizer_demo_inputs(input_shape)



    imgs = demo_inputs['imgs']

    gt_labels = demo_inputs['gt_labels']



    losses = recognizer(imgs, gt_labels)

    assert isinstance(losses, dict)



    # Test forward test

    with torch.no_grad():

        img_list = [img[None, :] for img in imgs]

        for one_img in img_list:

            recognizer(one_img, None, return_loss=False)



    # test timm backbones

    timm_backbone = dict(type='timm.efficientnet_b0', pretrained=False)

    config.model['backbone'] = timm_backbone

    config.model['cls_head']['in_channels'] = 1280



    recognizer = build_recognizer(config.model)



    input_shape = (1, 3, 3, 32, 32)

    demo_inputs = generate_recognizer_demo_inputs(input_shape)



    imgs = demo_inputs['imgs']

    gt_labels = demo_inputs['gt_labels']



    losses = recognizer(imgs, gt_labels)

    assert isinstance(losses, dict)



    # Test forward test

    with torch.no_grad():

        img_list = [img[None, :] for img in imgs]

        for one_img in img_list:

            recognizer(one_img, None, return_loss=False)





def test_tsm():

    config = get_recognizer_cfg('tsm/tsm_r50_1x1x8_50e_kinetics400_rgb.py')

    config.model['backbone']['pretrained'] = None



    recognizer = build_recognizer(config.model)



    input_shape = (1, 8, 3, 32, 32)

    demo_inputs = generate_recognizer_demo_inputs(input_shape)



    imgs = demo_inputs['imgs']

    gt_labels = demo_inputs['gt_labels']



    losses = recognizer(imgs, gt_labels)

    assert isinstance(losses, dict)



    # Test forward test

    with torch.no_grad():

        img_list = [img[None, :] for img in imgs]

        for one_img in img_list:

            recognizer(one_img, None, return_loss=False)



    # test twice sample + 3 crops

    input_shape = (2, 48, 3, 32, 32)

    demo_inputs = generate_recognizer_demo_inputs(input_shape)

    imgs = demo_inputs['imgs']



    config.model.test_cfg = dict(average_clips='prob')

    recognizer = build_recognizer(config.model)



    # Test forward test

    with torch.no_grad():

        img_list = [img[None, :] for img in imgs]

        for one_img in img_list:

            recognizer(one_img, None, return_loss=False)



    # Test forward gradcam

    recognizer(imgs, gradcam=True)

    for one_img in img_list:

        recognizer(one_img, gradcam=True)





def test_trn():

    config = get_recognizer_cfg('trn/trn_r50_1x1x8_50e_sthv1_rgb.py')

    config.model['backbone']['pretrained'] = None



    recognizer = build_recognizer(config.model)



    input_shape = (1, 8, 3, 32, 32)

    demo_inputs = generate_recognizer_demo_inputs(input_shape)



    imgs = demo_inputs['imgs']

    gt_labels = demo_inputs['gt_labels']



    losses = recognizer(imgs, gt_labels)

    assert isinstance(losses, dict)



    # Test forward test

    with torch.no_grad():

        img_list = [img[None, :] for img in imgs]

        for one_img in img_list:

            recognizer(one_img, None, return_loss=False)



    # test twice sample + 3 crops

    input_shape = (2, 48, 3, 32, 32)

    demo_inputs = generate_recognizer_demo_inputs(input_shape)

    imgs = demo_inputs['imgs']



    config.model.test_cfg = dict(average_clips='prob')

    recognizer = build_recognizer(config.model)



    # Test forward test

    with torch.no_grad():

        img_list = [img[None, :] for img in imgs]

        for one_img in img_list:

            recognizer(one_img, None, return_loss=False)



    # Test forward gradcam

    recognizer(imgs, gradcam=True)

    for one_img in img_list:

        recognizer(one_img, gradcam=True)





def test_tpn():

    config = get_recognizer_cfg('tpn/tpn_tsm_r50_1x1x8_150e_sthv1_rgb.py')

    config.model['backbone']['pretrained'] = None



    recognizer = build_recognizer(config.model)



    input_shape = (1, 8, 3, 224, 224)

    demo_inputs = generate_recognizer_demo_inputs(input_shape)



    imgs = demo_inputs['imgs']

    gt_labels = demo_inputs['gt_labels']



    losses = recognizer(imgs, gt_labels)

    assert isinstance(losses, dict)

    assert 'loss_aux' in losses and 'loss_cls' in losses



    # Test forward test

    with torch.no_grad():

        img_list = [img[None, :] for img in imgs]

        for one_img in img_list:

            recognizer(one_img, None, return_loss=False)



    # Test forward gradcam

    recognizer(imgs, gradcam=True)

    for one_img in img_list:

        recognizer(one_img, gradcam=True)



    # Test forward dummy

    with torch.no_grad():

        _recognizer = build_recognizer(config.model)

        img_list = [img[None, :] for img in imgs]

        if hasattr(_recognizer, 'forward_dummy'):

            _recognizer.forward = _recognizer.forward_dummy

        for one_img in img_list:

            _recognizer(one_img)





def test_tanet():

    config = get_recognizer_cfg(

        'tanet/tanet_r50_dense_1x1x8_100e_kinetics400_rgb.py')

    config.model['backbone']['pretrained'] = None



    recognizer = build_recognizer(config.model)



    input_shape = (1, 8, 3, 32, 32)

    demo_inputs = generate_recognizer_demo_inputs(input_shape)



    imgs = demo_inputs['imgs']

    gt_labels = demo_inputs['gt_labels']



    losses = recognizer(imgs, gt_labels)

    assert isinstance(losses, dict)



    # Test forward test

    with torch.no_grad():

        img_list = [img[None, :] for img in imgs]

        for one_img in img_list:

            recognizer(one_img, None, return_loss=False)



    # test twice sample + 3 crops

    input_shape = (2, 48, 3, 32, 32)

    demo_inputs = generate_recognizer_demo_inputs(input_shape)

    imgs = demo_inputs['imgs']



    config.model.test_cfg = dict(average_clips='prob')

    recognizer = build_recognizer(config.model)



    # Test forward test

    with torch.no_grad():

        img_list = [img[None, :] for img in imgs]

        for one_img in img_list:

            recognizer(one_img, None, return_loss=False)



    # Test forward gradcam

    recognizer(imgs, gradcam=True)

    for one_img in img_list:

        recognizer(one_img, gradcam=True)