| | import torch |
| | from torch import nn |
| | from torchvision.models.resnet import ResNet, Bottleneck |
| |
|
| | class ResNet50Encoder(ResNet): |
| | def __init__(self, pretrained: bool = False): |
| | super().__init__( |
| | block=Bottleneck, |
| | layers=[3, 4, 6, 3], |
| | replace_stride_with_dilation=[False, False, True], |
| | norm_layer=None) |
| | |
| | if pretrained: |
| | self.load_state_dict(torch.hub.load_state_dict_from_url( |
| | 'https://download.pytorch.org/models/resnet50-0676ba61.pth')) |
| | |
| | del self.avgpool |
| | del self.fc |
| | |
| | def forward_single_frame(self, x): |
| | x = self.conv1(x) |
| | x = self.bn1(x) |
| | x = self.relu(x) |
| | f1 = x |
| | x = self.maxpool(x) |
| | x = self.layer1(x) |
| | f2 = x |
| | x = self.layer2(x) |
| | f3 = x |
| | x = self.layer3(x) |
| | x = self.layer4(x) |
| | f4 = x |
| | return [f1, f2, f3, f4] |
| | |
| | def forward_time_series(self, x): |
| | B, T = x.shape[:2] |
| | features = self.forward_single_frame(x.flatten(0, 1)) |
| | features = [f.unflatten(0, (B, T)) for f in features] |
| | return features |
| | |
| | def forward(self, x): |
| | if x.ndim == 5: |
| | return self.forward_time_series(x) |
| | else: |
| | return self.forward_single_frame(x) |
| |
|
