Create README.md

README.md

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+This model weight is identical to laion/CLIP-ViT-H-14-laion2B-s32B-b79K, but with the ViT component only.
+This is to support loading the model as a ClipModel. As a failed to load the original model using AutoModel (feedback appreciated)
+With this distribution, I was finally able to load from AutoModel, and further support image classification tasks using my self-defined class CLIPViTForImageClassification listed below.
+However, there is still a small issue that I cannot resolve, I can only load the model if I git clone this repo to local, if I load from web, the loading still fails. 
+```python
+from transformers.models.clip.modeling_clip import CLIPPreTrainedModel, CLIPConfig, CLIPVisionTransformer
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    BaseModelOutputWithPooling,
+    ImageClassifierOutput,
+    MaskedImageModelingOutput,
+)
+from typing import Dict, List, Optional, Set, Tuple, Union
+import torch
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+
+class CLIPViTForImageClassification(CLIPPreTrainedModel):
+    def __init__(self, config: CLIPConfig) -> None:
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        vision_config = config.vision_config
+        self.vision_model = CLIPVisionTransformer(vision_config)
+
+        # Classifier head
+        
+        self.classifier = nn.Linear(vision_config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        pixel_values: Optional[torch.Tensor] = None,
+        #head_mask: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        #interpolate_pos_encoding: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple, ImageClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.vision_model(
+            pixel_values,
+            #head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            #interpolate_pos_encoding=interpolate_pos_encoding,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.classifier(sequence_output[:, 0, :])
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return ImageClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+```