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# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch ZH-CLIP model."""
from typing import Optional, Tuple, Union
from torch import TensorType
import torch
from torch import nn
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging, ModelOutput
from transformers.models.auto.modeling_auto import AutoModel
from transformers.models.clip.modeling_clip import CLIPVisionConfig, CLIPVisionModel
from .configuration_zhclip import ZhCLIPConfig
from dataclasses import dataclass
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "ZhCLIPConfig"
@dataclass
class ZhCLIPModelOutput(ModelOutput):
text_features: torch.FloatTensor = None
image_features: torch.FloatTensor = None
class MeanPooler(nn.Module):
"""Mean pooling"""
def forward(self, last_hidden_state: TensorType, attention_mask: TensorType):
masked_output = last_hidden_state * attention_mask.unsqueeze(-1)
return masked_output.sum(dim=1) / attention_mask.sum(-1, keepdim=True)
class ZhCLIPPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization.
"""
config_class = ZhCLIPConfig
base_model_prefix = "zhclip"
supports_gradient_checkpointing = False
_keys_to_ignore_on_load_missing = [r"position_ids"]
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
class ZhCLIPModel(ZhCLIPPreTrainedModel):
def __init__(
self,
config: Optional[ZhCLIPConfig] = None,
vision_model: Optional[PreTrainedModel] = None,
text_model: Optional[PreTrainedModel] = None,
):
if config is None and (vision_model is None or text_model is None):
raise ValueError("Either a configuration or an vision and a text model has to be provided")
if config is None:
config = ZhCLIPConfig(vision_model.config, text_model.config)
else:
if not isinstance(config, self.config_class):
raise ValueError(f"config: {config} has to be of type {self.config_class}")
# initialize with config
super().__init__(config)
if vision_model is None:
if isinstance(config.vision_config, CLIPVisionConfig):
vision_model = CLIPVisionModel(config.vision_config).vision_model
else:
vision_model = AutoModel.from_config(config.vision_config)
if text_model is None:
text_model = AutoModel.from_config(config.text_config)
self.vision_model = vision_model
self.text_model = text_model
# make sure that the individual model's config refers to the shared config
# so that the updates to the config will be synced
self.vision_model.config = self.config.vision_config
self.text_model.config = self.config.text_config
self.vision_embed_dim = config.vision_config.hidden_size
self.text_embed_dim = config.text_config.hidden_size
self.coattention_dim = config.hidden_size
# add projection layers
mlp_hidden_size = (self.text_embed_dim + self.coattention_dim) // 2
self.text_projection = nn.Sequential(
nn.Linear(self.text_embed_dim, mlp_hidden_size, bias=False),
nn.GELU(),
nn.Linear(mlp_hidden_size, self.coattention_dim, bias=False),
)
self.text_pooler = MeanPooler()
self.visual_projection = nn.Linear(self.vision_embed_dim, self.coattention_dim)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
patch_ids = None,
extend_token_type_ids = None,
return_loss: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], ZhCLIPModelOutput]:
return_dict = return_dict if return_dict is not None else self.config.return_dict
image_features = self.get_image_features(
pixel_values=pixel_values,
return_dict=return_dict,
)
text_features = self.get_text_features(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
return_dict=return_dict,
)
return ZhCLIPModelOutput(
image_features = image_features,
text_features = text_features,
)
@classmethod
def from_pretrained(cls, *args, **kwargs):
# At the moment fast initialization is not supported
# for composite models
kwargs["_fast_init"] = False
return super().from_pretrained(*args, **kwargs)
def get_text_features(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
token_type_ids=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
token_type_ids=token_type_ids,
#output_attentions=output_attentions,
#output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if attention_mask is None:
attention_mask = (input_ids != self.config.pad_token_id).long()
text_pool = self.text_pooler(text_outputs[0], attention_mask)
text_feat = self.text_projection(text_pool)
return text_feat
def get_image_features(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> torch.FloatTensor:
r"""
Returns:
image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
applying the projection layer to the pooled output of [`CLIPVisionModel`].
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, CLIPModel
>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> image_features = model.get_image_features(**inputs)
```"""
# Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = vision_outputs[1] # pooled_output
image_features = self.visual_projection(pooled_output)
return image_features
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