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import torch |
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from torch import nn |
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from torchvision.models.mobilenetv3 import MobileNetV3, InvertedResidualConfig |
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from torchvision.transforms.functional import normalize |
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class MobileNetV3LargeEncoder(MobileNetV3): |
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def __init__(self, pretrained: bool = False): |
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super().__init__( |
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inverted_residual_setting=[ |
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InvertedResidualConfig( 16, 3, 16, 16, False, "RE", 1, 1, 1), |
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InvertedResidualConfig( 16, 3, 64, 24, False, "RE", 2, 1, 1), |
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InvertedResidualConfig( 24, 3, 72, 24, False, "RE", 1, 1, 1), |
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InvertedResidualConfig( 24, 5, 72, 40, True, "RE", 2, 1, 1), |
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InvertedResidualConfig( 40, 5, 120, 40, True, "RE", 1, 1, 1), |
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InvertedResidualConfig( 40, 5, 120, 40, True, "RE", 1, 1, 1), |
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InvertedResidualConfig( 40, 3, 240, 80, False, "HS", 2, 1, 1), |
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InvertedResidualConfig( 80, 3, 200, 80, False, "HS", 1, 1, 1), |
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InvertedResidualConfig( 80, 3, 184, 80, False, "HS", 1, 1, 1), |
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InvertedResidualConfig( 80, 3, 184, 80, False, "HS", 1, 1, 1), |
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InvertedResidualConfig( 80, 3, 480, 112, True, "HS", 1, 1, 1), |
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InvertedResidualConfig(112, 3, 672, 112, True, "HS", 1, 1, 1), |
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InvertedResidualConfig(112, 5, 672, 160, True, "HS", 2, 2, 1), |
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InvertedResidualConfig(160, 5, 960, 160, True, "HS", 1, 2, 1), |
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InvertedResidualConfig(160, 5, 960, 160, True, "HS", 1, 2, 1), |
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], |
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last_channel=1280 |
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) |
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if pretrained: |
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self.load_state_dict(torch.hub.load_state_dict_from_url( |
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'https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth')) |
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del self.avgpool |
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del self.classifier |
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def forward_single_frame(self, x): |
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x = normalize(x, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) |
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x = self.features[0](x) |
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x = self.features[1](x) |
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f1 = x |
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x = self.features[2](x) |
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x = self.features[3](x) |
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f2 = x |
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x = self.features[4](x) |
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x = self.features[5](x) |
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x = self.features[6](x) |
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f3 = x |
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x = self.features[7](x) |
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x = self.features[8](x) |
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x = self.features[9](x) |
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x = self.features[10](x) |
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x = self.features[11](x) |
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x = self.features[12](x) |
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x = self.features[13](x) |
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x = self.features[14](x) |
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x = self.features[15](x) |
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x = self.features[16](x) |
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f4 = x |
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return [f1, f2, f3, f4] |
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def forward_time_series(self, x): |
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B, T = x.shape[:2] |
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features = self.forward_single_frame(x.flatten(0, 1)) |
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features = [f.unflatten(0, (B, T)) for f in features] |
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return features |
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def forward(self, x): |
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if x.ndim == 5: |
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return self.forward_time_series(x) |
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else: |
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return self.forward_single_frame(x) |
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