Upload model

config.json

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+{
+  "architectures": [
+    "ResnetModelForImageClassification"
+  ],
+  "auto_map": {
+    "AutoConfig": "resnet_config.ResnetConfig",
+    "AutoModelForImageClassification": "model.ResnetModelForImageClassification"
+  },
+  "avg_down": true,
+  "base_width": 64,
+  "block_type": "bottleneck",
+  "cardinality": 1,
+  "input_channels": 3,
+  "layers": [
+    3,
+    4,
+    6,
+    3
+  ],
+  "model_type": "resnet",
+  "num_classes": 1000,
+  "stem_type": "deep",
+  "stem_width": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1"
+}

model.py

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+from transformers import PreTrainedModel
+from timm.models.resnet import BasicBlock, Bottleneck, ResNet
+from .resnet_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)
+
+    
+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 label is not None:
+            loss = torch.nn.cross_entropy(logits, labels)
+            return {"loss": loss, "logits": logits}
+        return {"logits": logits}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eecd20b2d48d10c073f948eaaed1f89dd1bd7157f33646c8291d679ce28ccf15
+size 102617769

resnet_config.py

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+from transformers import PretrainedConfig
+from typing import List
+
+class ResnetConfig(PretrainedConfig):
+    model_type = "resnet"
+
+    def __init__(
+        self,
+        block_type="bottleneck",
+        layers: List[int] = [3, 4, 6, 3],
+        num_classes: int = 1000,
+        input_channels: int = 3,
+        cardinality: int = 1,
+        base_width: int = 64,
+        stem_width: int = 64,
+        stem_type: str = "",
+        avg_down: bool = False,
+        **kwargs,
+    ):
+        if block_type not in ["basic", "bottleneck"]:
+            raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.")
+        if stem_type not in ["", "deep", "deep-tiered"]:
+            raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.")
+
+        self.block_type = block_type
+        self.layers = layers
+        self.num_classes = num_classes
+        self.input_channels = input_channels
+        self.cardinality = cardinality
+        self.base_width = base_width
+        self.stem_width = stem_width
+        self.stem_type = stem_type
+        self.avg_down = avg_down
+        super().__init__(**kwargs)