Upload encoders/mobilenet.py

encoders/mobilenet.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 torchvision
+import torch.nn as nn
+
+from ._base import EncoderMixin
+
+
+class MobileNetV2Encoder(torchvision.models.MobileNetV2, EncoderMixin):
+
+    def __init__(self, out_channels, depth=5, **kwargs):
+        super().__init__(**kwargs)
+        self._depth = depth
+        self._out_channels = out_channels
+        self._in_channels = 3
+        del self.classifier
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            self.features[:2],
+            self.features[2:4],
+            self.features[4:7],
+            self.features[7:14],
+            self.features[14:],
+        ]
+
+    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("classifier.1.bias", None)
+        state_dict.pop("classifier.1.weight", None)
+        super().load_state_dict(state_dict, **kwargs)
+
+
+mobilenet_encoders = {
+    "mobilenet_v2": {
+        "encoder": MobileNetV2Encoder,
+        "pretrained_settings": {
+            "imagenet": {
+                "mean": [0.485, 0.456, 0.406],
+                "std": [0.229, 0.224, 0.225],
+                "url": "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
+                "input_space": "RGB",
+                "input_range": [0, 1],
+            },
+        },
+        "params": {
+            "out_channels": (3, 16, 24, 32, 96, 1280),
+        },
+    },
+}