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import re
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),},
},
}
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