147 lines
5.0 KiB
Python
147 lines
5.0 KiB
Python
""" Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
|
|
|
|
Attributes:
|
|
|
|
_out_channels (list of int): specify number of channels for each encoder feature tensor
|
|
_depth (int): specify number of stages in decoder (in other words number of downsampling operations)
|
|
_in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
|
|
|
|
Methods:
|
|
|
|
forward(self, x: torch.Tensor)
|
|
produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
|
|
shape NCHW (features should be sorted in descending order according to spatial resolution, starting
|
|
with resolution same as input `x` tensor).
|
|
|
|
Input: `x` with shape (1, 3, 64, 64)
|
|
Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
|
|
[(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
|
|
(1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
|
|
|
|
also should support number of features according to specified depth, e.g. if depth = 5,
|
|
number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
|
|
depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
|
|
"""
|
|
|
|
import re
|
|
import torch.nn as nn
|
|
|
|
from pretrainedmodels.models.torchvision_models import pretrained_settings
|
|
from torchvision.models.densenet import DenseNet
|
|
|
|
from ._base import EncoderMixin
|
|
|
|
|
|
class TransitionWithSkip(nn.Module):
|
|
|
|
def __init__(self, module):
|
|
super().__init__()
|
|
self.module = module
|
|
|
|
def forward(self, x):
|
|
for module in self.module:
|
|
x = module(x)
|
|
if isinstance(module, nn.ReLU):
|
|
skip = x
|
|
return x, skip
|
|
|
|
|
|
class DenseNetEncoder(DenseNet, EncoderMixin):
|
|
def __init__(self, out_channels, depth=5, **kwargs):
|
|
super().__init__(**kwargs)
|
|
self._out_channels = out_channels
|
|
self._depth = depth
|
|
self._in_channels = 3
|
|
del self.classifier
|
|
|
|
def make_dilated(self, output_stride):
|
|
raise ValueError("DenseNet encoders do not support dilated mode "
|
|
"due to pooling operation for downsampling!")
|
|
|
|
def get_stages(self):
|
|
return [
|
|
nn.Identity(),
|
|
nn.Sequential(self.features.conv0, self.features.norm0, self.features.relu0),
|
|
nn.Sequential(self.features.pool0, self.features.denseblock1,
|
|
TransitionWithSkip(self.features.transition1)),
|
|
nn.Sequential(self.features.denseblock2, TransitionWithSkip(self.features.transition2)),
|
|
nn.Sequential(self.features.denseblock3, TransitionWithSkip(self.features.transition3)),
|
|
nn.Sequential(self.features.denseblock4, self.features.norm5)
|
|
]
|
|
|
|
def forward(self, x):
|
|
|
|
stages = self.get_stages()
|
|
|
|
features = []
|
|
for i in range(self._depth + 1):
|
|
x = stages[i](x)
|
|
if isinstance(x, (list, tuple)):
|
|
x, skip = x
|
|
features.append(skip)
|
|
else:
|
|
features.append(x)
|
|
|
|
return features
|
|
|
|
def load_state_dict(self, state_dict):
|
|
pattern = re.compile(
|
|
r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
|
|
)
|
|
for key in list(state_dict.keys()):
|
|
res = pattern.match(key)
|
|
if res:
|
|
new_key = res.group(1) + res.group(2)
|
|
state_dict[new_key] = state_dict[key]
|
|
del state_dict[key]
|
|
|
|
# remove linear
|
|
state_dict.pop("classifier.bias", None)
|
|
state_dict.pop("classifier.weight", None)
|
|
|
|
super().load_state_dict(state_dict)
|
|
|
|
|
|
densenet_encoders = {
|
|
"densenet121": {
|
|
"encoder": DenseNetEncoder,
|
|
"pretrained_settings": pretrained_settings["densenet121"],
|
|
"params": {
|
|
"out_channels": (3, 64, 256, 512, 1024, 1024),
|
|
"num_init_features": 64,
|
|
"growth_rate": 32,
|
|
"block_config": (6, 12, 24, 16),
|
|
},
|
|
},
|
|
"densenet169": {
|
|
"encoder": DenseNetEncoder,
|
|
"pretrained_settings": pretrained_settings["densenet169"],
|
|
"params": {
|
|
"out_channels": (3, 64, 256, 512, 1280, 1664),
|
|
"num_init_features": 64,
|
|
"growth_rate": 32,
|
|
"block_config": (6, 12, 32, 32),
|
|
},
|
|
},
|
|
"densenet201": {
|
|
"encoder": DenseNetEncoder,
|
|
"pretrained_settings": pretrained_settings["densenet201"],
|
|
"params": {
|
|
"out_channels": (3, 64, 256, 512, 1792, 1920),
|
|
"num_init_features": 64,
|
|
"growth_rate": 32,
|
|
"block_config": (6, 12, 48, 32),
|
|
},
|
|
},
|
|
"densenet161": {
|
|
"encoder": DenseNetEncoder,
|
|
"pretrained_settings": pretrained_settings["densenet161"],
|
|
"params": {
|
|
"out_channels": (3, 96, 384, 768, 2112, 2208),
|
|
"num_init_features": 96,
|
|
"growth_rate": 48,
|
|
"block_config": (6, 12, 36, 24),
|
|
},
|
|
},
|
|
}
|