|
|
| from mmaction.registry import MODELS
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| from mmaction.utils import get_str_type
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| from .resnet3d import ResNet3d
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|
|
|
|
| @MODELS.register_module()
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| class ResNet2Plus1d(ResNet3d):
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| """ResNet (2+1)d backbone.
|
|
|
| This model is proposed in `A Closer Look at Spatiotemporal Convolutions for
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| Action Recognition <https://arxiv.org/abs/1711.11248>`_
|
| """
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|
|
| def __init__(self, *args, **kwargs):
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| super().__init__(*args, **kwargs)
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| assert self.pretrained2d is False
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| assert get_str_type(self.conv_cfg['type']) == 'Conv2plus1d'
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|
|
| def _freeze_stages(self):
|
| """Prevent all the parameters from being optimized before
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| ``self.frozen_stages``."""
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| if self.frozen_stages >= 0:
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| self.conv1.eval()
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| for param in self.conv1.parameters():
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| param.requires_grad = False
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|
|
| for i in range(1, self.frozen_stages + 1):
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| m = getattr(self, f'layer{i}')
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| m.eval()
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| for param in m.parameters():
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| param.requires_grad = False
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|
|
| def forward(self, x):
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| """Defines the computation performed at every call.
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|
|
| Args:
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| x (torch.Tensor): The input data.
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|
|
| Returns:
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| torch.Tensor: The feature of the input
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| samples extracted by the backbone.
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| """
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| x = self.conv1(x)
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| x = self.maxpool(x)
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| for layer_name in self.res_layers:
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| res_layer = getattr(self, layer_name)
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|
|
| x = res_layer(x)
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|
|
| return x
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|
|
