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This model weight is identical to laion/CLIP-ViT-H-14-laion2B-s32B-b79K, but with the pytorch_model component only (without open_clip_pytorch_model.bin).
This is to support loading the model as a ClipModel, as I 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.
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,
)
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