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config.json

@@ -3,7 +3,8 @@
     "ResnetModelForImageClassification"
   ],
   "auto_map": {
-    "AutoConfig": "diy_config.ResnetConfig"
+    "AutoConfig": "diy_config.ResnetConfig",
+    "AutoModelForImageClassification": "diy_model.ResnetModelForImageClassification"
   },
   "avg_down": true,
   "base_width": 64,

diy_model.py

@@ -0,0 +1,64 @@
+from transformers import PreTrainedModel
+from timm.models.resnet import BasicBlock, Bottleneck, ResNet
+from diy_config import ResnetConfig
+
+
+BLOCK_MAPPING = {"basic": BasicBlock, "bottleneck": Bottleneck}
+
+
+class ResnetModel(PreTrainedModel):
+    config_class = ResnetConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        block_layer = BLOCK_MAPPING[config.block_type]
+        self.model = ResNet(
+            block_layer,
+            config.layers,
+            num_classes=config.num_classes,
+            in_chans=config.input_channels,
+            cardinality=config.cardinality,
+            base_width=config.base_width,
+            stem_width=config.stem_width,
+            stem_type=config.stem_type,
+            avg_down=config.avg_down,
+        )
+
+    def forward(self, tensor):
+        return self.model.forward_features(tensor)
+
+import torch
+
+
+class ResnetModelForImageClassification(PreTrainedModel):
+    config_class = ResnetConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        block_layer = BLOCK_MAPPING[config.block_type]
+        self.model = ResNet(
+            block_layer,
+            config.layers,
+            num_classes=config.num_classes,
+            in_chans=config.input_channels,
+            cardinality=config.cardinality,
+            base_width=config.base_width,
+            stem_width=config.stem_width,
+            stem_type=config.stem_type,
+            avg_down=config.avg_down,
+        )
+
+    def forward(self, tensor, labels=None):
+        logits = self.model(tensor)
+        if labels is not None:
+            loss = torch.nn.functional.cross_entropy(logits, labels)
+            return {"loss": loss, "logits": logits}
+        return {"logits": logits}
+
+# resnet50d_config = ResnetConfig.from_pretrained("custom-resnet")
+# resnet50d = ResnetModelForImageClassification(resnet50d_config)
+# print(resnet50d)
+# import timm
+#
+# pretrained_model = timm.create_model("resnet50d", pretrained=True)
+# resnet50d.model.load_state_dict(pretrained_model.state_dict())