Upload encoders/densenet.py

encoders/densenet.py

@@ -0,0 +1,146 @@
+""" 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, stage_list, dilation_list):
+        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),
+        },
+    },
+}