Upload encoders/senet.py

encoders/senet.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.senet import (
+    SENet,
+    SEBottleneck,
+    SEResNetBottleneck,
+    SEResNeXtBottleneck,
+    pretrained_settings,
+)
+from ._base import EncoderMixin
+
+
+class SENetEncoder(SENet, 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.last_linear
+        del self.avg_pool
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            self.layer0[:-1],
+            nn.Sequential(self.layer0[-1], self.layer1),
+            self.layer2,
+            self.layer3,
+            self.layer4,
+        ]
+
+    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)
+
+
+senet_encoders = {
+    "senet154": {
+        "encoder": SENetEncoder,
+        "pretrained_settings": pretrained_settings["senet154"],
+        "params": {
+            "out_channels": (3, 128, 256, 512, 1024, 2048),
+            "block": SEBottleneck,
+            "dropout_p": 0.2,
+            "groups": 64,
+            "layers": [3, 8, 36, 3],
+            "num_classes": 1000,
+            "reduction": 16,
+        },
+    },
+    "se_resnet50": {
+        "encoder": SENetEncoder,
+        "pretrained_settings": pretrained_settings["se_resnet50"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": SEResNetBottleneck,
+            "layers": [3, 4, 6, 3],
+            "downsample_kernel_size": 1,
+            "downsample_padding": 0,
+            "dropout_p": None,
+            "groups": 1,
+            "inplanes": 64,
+            "input_3x3": False,
+            "num_classes": 1000,
+            "reduction": 16,
+        },
+    },
+    "se_resnet101": {
+        "encoder": SENetEncoder,
+        "pretrained_settings": pretrained_settings["se_resnet101"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": SEResNetBottleneck,
+            "layers": [3, 4, 23, 3],
+            "downsample_kernel_size": 1,
+            "downsample_padding": 0,
+            "dropout_p": None,
+            "groups": 1,
+            "inplanes": 64,
+            "input_3x3": False,
+            "num_classes": 1000,
+            "reduction": 16,
+        },
+    },
+    "se_resnet152": {
+        "encoder": SENetEncoder,
+        "pretrained_settings": pretrained_settings["se_resnet152"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": SEResNetBottleneck,
+            "layers": [3, 8, 36, 3],
+            "downsample_kernel_size": 1,
+            "downsample_padding": 0,
+            "dropout_p": None,
+            "groups": 1,
+            "inplanes": 64,
+            "input_3x3": False,
+            "num_classes": 1000,
+            "reduction": 16,
+        },
+    },
+    "se_resnext50_32x4d": {
+        "encoder": SENetEncoder,
+        "pretrained_settings": pretrained_settings["se_resnext50_32x4d"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": SEResNeXtBottleneck,
+            "layers": [3, 4, 6, 3],
+            "downsample_kernel_size": 1,
+            "downsample_padding": 0,
+            "dropout_p": None,
+            "groups": 32,
+            "inplanes": 64,
+            "input_3x3": False,
+            "num_classes": 1000,
+            "reduction": 16,
+        },
+    },
+    "se_resnext101_32x4d": {
+        "encoder": SENetEncoder,
+        "pretrained_settings": pretrained_settings["se_resnext101_32x4d"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": SEResNeXtBottleneck,
+            "layers": [3, 4, 23, 3],
+            "downsample_kernel_size": 1,
+            "downsample_padding": 0,
+            "dropout_p": None,
+            "groups": 32,
+            "inplanes": 64,
+            "input_3x3": False,
+            "num_classes": 1000,
+            "reduction": 16,
+        },
+    },
+}