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import pytest
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import torch
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import torch.nn as nn
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from mmaction.models import ResNetTIN
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from mmaction.testing import generate_backbone_demo_inputs
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@pytest.mark.skipif(
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not torch.cuda.is_available(), reason='requires CUDA support')
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def test_resnet_tin_backbone():
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"""Test resnet_tin backbone."""
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with pytest.raises(AssertionError):
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resnet_tin = ResNetTIN(50, num_segments=-1)
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resnet_tin.init_weights()
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from mmaction.models.backbones.resnet_tin import (CombineNet,
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TemporalInterlace)
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resnet_tin = ResNetTIN(50)
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resnet_tin.init_weights()
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for layer_name in resnet_tin.res_layers:
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layer = getattr(resnet_tin, layer_name)
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blocks = list(layer.children())
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for block in blocks:
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assert isinstance(block.conv1.conv, CombineNet)
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assert isinstance(block.conv1.conv.net1, TemporalInterlace)
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assert (
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block.conv1.conv.net1.num_segments == resnet_tin.num_segments)
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assert block.conv1.conv.net1.shift_div == resnet_tin.shift_div
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resnet_tin_pbn = ResNetTIN(50, partial_bn=True)
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resnet_tin_pbn.train()
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count_bn = 0
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for m in resnet_tin_pbn.modules():
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if isinstance(m, nn.BatchNorm2d):
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count_bn += 1
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if count_bn >= 2:
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assert m.training is False
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assert m.weight.requires_grad is False
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assert m.bias.requires_grad is False
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else:
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assert m.training is True
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assert m.weight.requires_grad is True
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assert m.bias.requires_grad is True
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input_shape = (8, 3, 64, 64)
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imgs = generate_backbone_demo_inputs(input_shape).cuda()
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resnet_tin = resnet_tin.cuda()
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feat = resnet_tin(imgs)
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assert feat.shape == torch.Size([8, 2048, 2, 2])
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