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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.
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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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"""
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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):
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"""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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