Upload encoders/timm_resnest.py

encoders/timm_resnest.py

@@ -0,0 +1,208 @@
+from ._base import EncoderMixin
+from timm.models.resnet import ResNet
+from timm.models.resnest import ResNestBottleneck
+import torch.nn as nn
+
+
+class ResNestEncoder(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 make_dilated(self, stage_list, dilation_list):
+        raise ValueError("ResNest encoders do not support dilated mode")
+
+    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)
+
+
+resnest_weights = {
+    'timm-resnest14d': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest14-9c8fe254.pth'
+    },
+    'timm-resnest26d': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest26-50eb607c.pth'
+    },
+    'timm-resnest50d': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50-528c19ca.pth',
+    },
+    'timm-resnest101e': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest101-22405ba7.pth',
+    },
+    'timm-resnest200e': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest200-75117900.pth',
+    },
+    'timm-resnest269e': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest269-0cc87c48.pth',
+    },
+    'timm-resnest50d_4s2x40d': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50_fast_4s2x40d-41d14ed0.pth',
+    },
+    'timm-resnest50d_1s4x24d': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50_fast_1s4x24d-d4a4f76f.pth',
+    }
+}
+
+pretrained_settings = {}
+for model_name, sources in resnest_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_resnest_encoders = {
+    'timm-resnest14d': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest14d"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [1, 1, 1, 1],
+            'stem_type': 'deep',
+            'stem_width': 32,
+            'avg_down': True,
+            'base_width': 64,
+            'cardinality': 1,
+            'block_args': {'radix': 2, 'avd': True, 'avd_first': False}
+        }
+    },
+    'timm-resnest26d': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest26d"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [2, 2, 2, 2],
+            'stem_type': 'deep',
+            'stem_width': 32,
+            'avg_down': True,
+            'base_width': 64,
+            'cardinality': 1,
+            'block_args': {'radix': 2, 'avd': True, 'avd_first': False}
+        }
+    },
+    'timm-resnest50d': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest50d"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [3, 4, 6, 3],
+            'stem_type': 'deep',
+            'stem_width': 32,
+            'avg_down': True,
+            'base_width': 64,
+            'cardinality': 1,
+            'block_args': {'radix': 2, 'avd': True, 'avd_first': False}
+        }
+    },
+    'timm-resnest101e': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest101e"],
+        'params': {
+            'out_channels': (3, 128, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [3, 4, 23, 3],
+            'stem_type': 'deep',
+            'stem_width': 64,
+            'avg_down': True,
+            'base_width': 64,
+            'cardinality': 1,
+            'block_args': {'radix': 2, 'avd': True, 'avd_first': False}
+        }
+    },
+    'timm-resnest200e': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest200e"],
+        'params': {
+            'out_channels': (3, 128, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [3, 24, 36, 3],
+            'stem_type': 'deep',
+            'stem_width': 64,
+            'avg_down': True,
+            'base_width': 64,
+            'cardinality': 1,
+            'block_args': {'radix': 2, 'avd': True, 'avd_first': False}
+        }
+    },
+    'timm-resnest269e': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest269e"],
+        'params': {
+            'out_channels': (3, 128, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [3, 30, 48, 8],
+            'stem_type': 'deep',
+            'stem_width': 64,
+            'avg_down': True,
+            'base_width': 64,
+            'cardinality': 1,
+            'block_args': {'radix': 2, 'avd': True, 'avd_first': False}
+        },
+    },
+    'timm-resnest50d_4s2x40d': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest50d_4s2x40d"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [3, 4, 6, 3],
+            'stem_type': 'deep',
+            'stem_width': 32,
+            'avg_down': True,
+            'base_width': 40,
+            'cardinality': 2,
+            'block_args': {'radix': 4, 'avd': True, 'avd_first': True}
+        }
+    },
+    'timm-resnest50d_1s4x24d': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest50d_1s4x24d"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [3, 4, 6, 3],
+            'stem_type': 'deep',
+            'stem_width': 32,
+            'avg_down': True,
+            'base_width': 24,
+            'cardinality': 4,
+            'block_args': {'radix': 1, 'avd': True, 'avd_first': True}
+        }
+    }
+}