| |
|
| | import pytest
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| | import torch
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| | import torch.nn as nn
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| | from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
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| |
|
| | from mmaction.models import ResNet3dCSN
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| | from mmaction.testing import generate_backbone_demo_inputs
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| |
|
| |
|
| | def test_resnet_csn_backbone():
|
| | """Test resnet_csn backbone."""
|
| | with pytest.raises(ValueError):
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| |
|
| | ResNet3dCSN(152, None, bottleneck_mode='id')
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| |
|
| | input_shape = (2, 3, 6, 64, 64)
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| | imgs = generate_backbone_demo_inputs(input_shape)
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| |
|
| | resnet3d_csn_frozen = ResNet3dCSN(
|
| | 152, None, bn_frozen=True, norm_eval=True)
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| | resnet3d_csn_frozen.train()
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| | for m in resnet3d_csn_frozen.modules():
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| | if isinstance(m, _BatchNorm):
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| | for param in m.parameters():
|
| | assert param.requires_grad is False
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| |
|
| |
|
| | resnet3d_csn_ip = ResNet3dCSN(152, None, bottleneck_mode='ip')
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| | resnet3d_csn_ip.init_weights()
|
| | resnet3d_csn_ip.train()
|
| | for i, layer_name in enumerate(resnet3d_csn_ip.res_layers):
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| | layers = getattr(resnet3d_csn_ip, layer_name)
|
| | num_blocks = resnet3d_csn_ip.stage_blocks[i]
|
| | assert len(layers) == num_blocks
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| | for layer in layers:
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| | assert isinstance(layer.conv2, nn.Sequential)
|
| | assert len(layer.conv2) == 2
|
| | assert layer.conv2[1].groups == layer.planes
|
| | if torch.__version__ == 'parrots':
|
| | if torch.cuda.is_available():
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| | resnet3d_csn_ip = resnet3d_csn_ip.cuda()
|
| | imgs_gpu = imgs.cuda()
|
| | feat = resnet3d_csn_ip(imgs_gpu)
|
| | assert feat.shape == torch.Size([2, 2048, 1, 2, 2])
|
| | else:
|
| | feat = resnet3d_csn_ip(imgs)
|
| | assert feat.shape == torch.Size([2, 2048, 1, 2, 2])
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| |
|
| |
|
| | resnet3d_csn_ir = ResNet3dCSN(152, None, bottleneck_mode='ir')
|
| | resnet3d_csn_ir.init_weights()
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| | resnet3d_csn_ir.train()
|
| | for i, layer_name in enumerate(resnet3d_csn_ir.res_layers):
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| | layers = getattr(resnet3d_csn_ir, layer_name)
|
| | num_blocks = resnet3d_csn_ir.stage_blocks[i]
|
| | assert len(layers) == num_blocks
|
| | for layer in layers:
|
| | assert isinstance(layer.conv2, nn.Sequential)
|
| | assert len(layer.conv2) == 1
|
| | assert layer.conv2[0].groups == layer.planes
|
| | if torch.__version__ == 'parrots':
|
| | if torch.cuda.is_available():
|
| | resnet3d_csn_ir = resnet3d_csn_ir.cuda()
|
| | imgs_gpu = imgs.cuda()
|
| | feat = resnet3d_csn_ir(imgs_gpu)
|
| | assert feat.shape == torch.Size([2, 2048, 1, 2, 2])
|
| | else:
|
| | feat = resnet3d_csn_ir(imgs)
|
| | assert feat.shape == torch.Size([2, 2048, 1, 2, 2])
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| |
|
| |
|
| | resnet3d_csn_ip = ResNet3dCSN(152, None, bottleneck_mode='ip')
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| | resnet3d_csn_ip.init_weights()
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| | resnet3d_csn_ip.train(False)
|
| | for module in resnet3d_csn_ip.children():
|
| | assert module.training is False
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| |
|
