Upload encoders/inceptionresnetv2.py

encoders/inceptionresnetv2.py

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+""" 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 torch.nn as nn
+from pretrainedmodels.models.inceptionresnetv2 import InceptionResNetV2
+from pretrainedmodels.models.inceptionresnetv2 import pretrained_settings
+
+from ._base import EncoderMixin
+
+
+class InceptionResNetV2Encoder(InceptionResNetV2, EncoderMixin):
+    def __init__(self, out_channels, depth=5, **kwargs):
+        super().__init__(**kwargs)
+
+        self._out_channels = out_channels
+        self._depth = depth
+        self._in_channels = 3
+
+        # correct paddings
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                if m.kernel_size == (3, 3):
+                    m.padding = (1, 1)
+            if isinstance(m, nn.MaxPool2d):
+                m.padding = (1, 1)
+
+        # remove linear layers
+        del self.avgpool_1a
+        del self.last_linear
+
+    def make_dilated(self, stage_list, dilation_list):
+        raise ValueError("InceptionResnetV2 encoder does not support dilated mode "
+                         "due to pooling operation for downsampling!")
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            nn.Sequential(self.conv2d_1a, self.conv2d_2a, self.conv2d_2b),
+            nn.Sequential(self.maxpool_3a, self.conv2d_3b, self.conv2d_4a),
+            nn.Sequential(self.maxpool_5a, self.mixed_5b, self.repeat),
+            nn.Sequential(self.mixed_6a, self.repeat_1),
+            nn.Sequential(self.mixed_7a, self.repeat_2, self.block8, self.conv2d_7b),
+        ]
+
+    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, **kwargs):
+        state_dict.pop("last_linear.bias", None)
+        state_dict.pop("last_linear.weight", None)
+        super().load_state_dict(state_dict, **kwargs)
+
+
+inceptionresnetv2_encoders = {
+    "inceptionresnetv2": {
+        "encoder": InceptionResNetV2Encoder,
+        "pretrained_settings": pretrained_settings["inceptionresnetv2"],
+        "params": {"out_channels": (3, 64, 192, 320, 1088, 1536), "num_classes": 1000},
+    }
+}