73 lines
2.9 KiB
Python
73 lines
2.9 KiB
Python
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), # C1
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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), # C2
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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), # C3
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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), # C4
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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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