Upload encoders/timm_sknet.py

encoders/timm_sknet.py

@@ -0,0 +1,103 @@
+from ._base import EncoderMixin
+from timm.models.resnet import ResNet
+from timm.models.sknet import SelectiveKernelBottleneck, SelectiveKernelBasic
+import torch.nn as nn
+
+
+class SkNetEncoder(ResNet, 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.fc
+        del self.global_pool
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            nn.Sequential(self.conv1, self.bn1, self.act1),
+            nn.Sequential(self.maxpool, 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("fc.bias", None)
+        state_dict.pop("fc.weight", None)
+        super().load_state_dict(state_dict, **kwargs)
+
+
+sknet_weights = {
+    'timm-skresnet18': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet18_ra-4eec2804.pth'
+    },
+    'timm-skresnet34': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet34_ra-bdc0ccde.pth'
+    },
+    'timm-skresnext50_32x4d': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnext50_ra-f40e40bf.pth',
+    }
+}
+
+pretrained_settings = {}
+for model_name, sources in sknet_weights.items():
+    pretrained_settings[model_name] = {}
+    for source_name, source_url in sources.items():
+        pretrained_settings[model_name][source_name] = {
+            "url": source_url,
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+
+timm_sknet_encoders = {
+    'timm-skresnet18': {
+        'encoder': SkNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-skresnet18"],
+        'params': {
+            'out_channels': (3, 64, 64, 128, 256, 512),
+            'block': SelectiveKernelBasic,
+            'layers': [2, 2, 2, 2],
+            'zero_init_last_bn': False,
+            'block_args': {'sk_kwargs': {'rd_ratio': 1/8, 'split_input': True}}
+        }
+    },
+    'timm-skresnet34': {
+        'encoder': SkNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-skresnet34"],
+        'params': {
+            'out_channels': (3, 64, 64, 128, 256, 512),
+            'block': SelectiveKernelBasic,
+            'layers': [3, 4, 6, 3],
+            'zero_init_last_bn': False,
+            'block_args': {'sk_kwargs': {'rd_ratio': 1/8, 'split_input': True}}
+        }
+    },
+    'timm-skresnext50_32x4d': {
+        'encoder': SkNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-skresnext50_32x4d"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': SelectiveKernelBottleneck,
+            'layers': [3, 4, 6, 3],
+            'zero_init_last_bn': False,
+            'cardinality': 32,
+            'base_width': 4
+        }
+    }
+}