| 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), |
| InvertedResidualConfig( 24, 3, 72, 24, False, "RE", 1, 1, 1), |
| InvertedResidualConfig( 24, 5, 72, 40, True, "RE", 2, 1, 1), |
| 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), |
| 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), |
| 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) |
|
|
