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| import torch.nn as nn | |
| from pretrainedmodels.models.xception import pretrained_settings | |
| from pretrainedmodels.models.xception import Xception | |
| from ._base import EncoderMixin | |
| class XceptionEncoder(Xception, EncoderMixin): | |
| def __init__(self, out_channels, *args, depth=5, **kwargs): | |
| super().__init__(*args, **kwargs) | |
| self._out_channels = out_channels | |
| self._depth = depth | |
| self._in_channels = 3 | |
| # modify padding to maintain output shape | |
| self.conv1.padding = (1, 1) | |
| self.conv2.padding = (1, 1) | |
| del self.fc | |
| def make_dilated(self, *args, **kwargs): | |
| raise ValueError( | |
| "Xception encoder does not support dilated mode " | |
| "due to pooling operation for downsampling!" | |
| ) | |
| def get_stages(self): | |
| return [ | |
| nn.Identity(), | |
| nn.Sequential( | |
| self.conv1, self.bn1, self.relu, self.conv2, self.bn2, self.relu | |
| ), | |
| self.block1, | |
| self.block2, | |
| nn.Sequential( | |
| self.block3, | |
| self.block4, | |
| self.block5, | |
| self.block6, | |
| self.block7, | |
| self.block8, | |
| self.block9, | |
| self.block10, | |
| self.block11, | |
| ), | |
| nn.Sequential( | |
| self.block12, self.conv3, self.bn3, self.relu, self.conv4, self.bn4 | |
| ), | |
| ] | |
| def forward(self, x): | |
| stages = self.get_stages() | |
| features = [] | |
| for i in range(self._depth + 1): | |
| x = stages[i](x) | |
| features.append(x) | |
| return features | |
| def load_state_dict(self, state_dict): | |
| # remove linear | |
| state_dict.pop("fc.bias", None) | |
| state_dict.pop("fc.weight", None) | |
| super().load_state_dict(state_dict) | |
| xception_encoders = { | |
| "xception": { | |
| "encoder": XceptionEncoder, | |
| "pretrained_settings": pretrained_settings["xception"], | |
| "params": {"out_channels": (3, 64, 128, 256, 728, 2048)}, | |
| } | |
| } | |