Upload modeling_resnet.py

modeling_resnet.py

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+from transformers import PreTrainedModel
+from torchvision.models.resnet import ResNet, Bottleneck, BasicBlock
+import torch.nn.functional as F
+from .configuration_resnet import ResnetConfig
+
+
+BLOCK_MAPPING = {'basic': BasicBlock, 'bottleneck': Bottleneck}
+
+
+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, config.num_classes)
+
+    def forward(self, tensor, labels=None):
+        logits = self.model(tensor)
+        if labels is not None:
+            loss = F.cross_entropy(logits, labels)
+            return {'loss': loss, 'logits': logits}
+        return {'logits': logits}