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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 # 1/2
x = self.maxpool(x)
x = self.layer1(x)
f2 = x # 1/4
x = self.layer2(x)
f3 = x # 1/8
x = self.layer3(x)
x = self.layer4(x)
f4 = x # 1/16
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)
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