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| import torch
| from torch import nn
| from torchvision.models.mobilenetv3 import MobileNetV3, InvertedResidualConfig
| from torchvision.transforms.functional import normalize
|
| class MobileNetV3LargeEncoder(MobileNetV3):
| def __init__(self, pretrained: bool = False):
| super().__init__(
| inverted_residual_setting=[
| InvertedResidualConfig( 16, 3, 16, 16, False, "RE", 1, 1, 1),
| InvertedResidualConfig( 16, 3, 64, 24, False, "RE", 2, 1, 1), # C1
| InvertedResidualConfig( 24, 3, 72, 24, False, "RE", 1, 1, 1),
| InvertedResidualConfig( 24, 5, 72, 40, True, "RE", 2, 1, 1), # C2
| InvertedResidualConfig( 40, 5, 120, 40, True, "RE", 1, 1, 1),
| InvertedResidualConfig( 40, 5, 120, 40, True, "RE", 1, 1, 1),
| InvertedResidualConfig( 40, 3, 240, 80, False, "HS", 2, 1, 1), # C3
| InvertedResidualConfig( 80, 3, 200, 80, False, "HS", 1, 1, 1),
| InvertedResidualConfig( 80, 3, 184, 80, False, "HS", 1, 1, 1),
| InvertedResidualConfig( 80, 3, 184, 80, False, "HS", 1, 1, 1),
| InvertedResidualConfig( 80, 3, 480, 112, True, "HS", 1, 1, 1),
| InvertedResidualConfig(112, 3, 672, 112, True, "HS", 1, 1, 1),
| InvertedResidualConfig(112, 5, 672, 160, True, "HS", 2, 2, 1), # C4
| InvertedResidualConfig(160, 5, 960, 160, True, "HS", 1, 2, 1),
| InvertedResidualConfig(160, 5, 960, 160, True, "HS", 1, 2, 1),
| ],
| last_channel=1280
| )
|
| if pretrained:
| self.load_state_dict(torch.hub.load_state_dict_from_url(
| 'https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth'))
|
| del self.avgpool
| del self.classifier
|
| def forward_single_frame(self, x):
| x = normalize(x, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
|
| x = self.features[0](x)
| x = self.features[1](x)
| f1 = x
| x = self.features[2](x)
| x = self.features[3](x)
| f2 = x
| x = self.features[4](x)
| x = self.features[5](x)
| x = self.features[6](x)
| f3 = x
| x = self.features[7](x)
| x = self.features[8](x)
| x = self.features[9](x)
| x = self.features[10](x)
| x = self.features[11](x)
| x = self.features[12](x)
| x = self.features[13](x)
| x = self.features[14](x)
| x = self.features[15](x)
| x = self.features[16](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)
|
|
