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"""PyTorch CLIP model.""" |
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|
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from dataclasses import dataclass |
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from typing import Any, Optional, Tuple, Union |
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|
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import torch |
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import torch.utils.checkpoint |
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from torch import nn |
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
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|
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from transformers.activations import ACT2FN |
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from transformers.modeling_attn_mask_utils import _create_4d_causal_attention_mask, _prepare_4d_attention_mask |
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from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput |
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from transformers.modeling_utils import PreTrainedModel |
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from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_2 |
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from transformers.utils import ( |
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ModelOutput, |
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add_code_sample_docstrings, |
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add_start_docstrings, |
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add_start_docstrings_to_model_forward, |
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is_flash_attn_2_available, |
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is_flash_attn_greater_or_equal_2_10, |
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logging, |
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replace_return_docstrings, |
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) |
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|
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from .configuration_clip import CLIPConfig, CLIPTextConfig, CLIPVisionConfig |
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|
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if is_flash_attn_2_available(): |
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from transformers.modeling_flash_attention_utils import _flash_attention_forward |
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logger = logging.get_logger(__name__) |
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_CONFIG_FOR_DOC = "CLIPConfig" |
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_CHECKPOINT_FOR_DOC = "openai/clip-vit-base-patch32" |
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_IMAGE_CLASS_CHECKPOINT = "openai/clip-vit-base-patch32" |
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_IMAGE_CLASS_EXPECTED_OUTPUT = "LABEL_0" |
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def contrastive_loss(logits: torch.Tensor) -> torch.Tensor: |
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return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device)) |
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|
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def clip_loss(similarity: torch.Tensor) -> torch.Tensor: |
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caption_loss = contrastive_loss(similarity) |
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image_loss = contrastive_loss(similarity.t()) |
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return (caption_loss + image_loss) / 2.0 |
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|
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@dataclass |
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class CLIPVisionModelOutput(ModelOutput): |
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""" |
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Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. |
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|
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Args: |
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image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): |
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The image embeddings obtained by applying the projection layer to the pooler_output. |
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): |
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Sequence of hidden-states at the output of the last layer of the model. |
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): |
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + |
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. |
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|
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. |
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): |
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, |
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sequence_length)`. |
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|
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention |
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heads. |
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""" |
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image_embeds: Optional[torch.FloatTensor] = None |
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last_hidden_state: torch.FloatTensor = None |
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None |
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None |
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@dataclass |
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class CLIPTextModelOutput(ModelOutput): |
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""" |
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Base class for text model's outputs that also contains a pooling of the last hidden states. |
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|
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Args: |
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text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): |
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The text embeddings obtained by applying the projection layer to the pooler_output. |
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): |
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Sequence of hidden-states at the output of the last layer of the model. |
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): |
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + |
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. |
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|
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. |
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): |
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, |
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sequence_length)`. |
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|
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention |
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heads. |
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""" |
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text_embeds: Optional[torch.FloatTensor] = None |
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last_hidden_state: torch.FloatTensor = None |
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None |
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None |
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@dataclass |
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class CLIPOutput(ModelOutput): |
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""" |
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Args: |
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): |
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Contrastive loss for image-text similarity. |
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logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): |
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The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text |
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similarity scores. |
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logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): |
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The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image |
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similarity scores. |
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text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): |
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The text embeddings obtained by applying the projection layer to the pooled output of [`CLIPTextModel`]. |
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image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): |
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The image embeddings obtained by applying the projection layer to the pooled output of [`CLIPVisionModel`]. |
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text_model_output(`BaseModelOutputWithPooling`): |
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The output of the [`CLIPTextModel`]. |
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vision_model_output(`BaseModelOutputWithPooling`): |
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The output of the [`CLIPVisionModel`]. |
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""" |
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loss: Optional[torch.FloatTensor] = None |
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logits_per_image: torch.FloatTensor = None |
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logits_per_text: torch.FloatTensor = None |
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text_embeds: torch.FloatTensor = None |
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image_embeds: torch.FloatTensor = None |
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text_model_output: BaseModelOutputWithPooling = None |
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vision_model_output: BaseModelOutputWithPooling = None |
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|
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def to_tuple(self) -> Tuple[Any]: |
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return tuple( |
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self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() |
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for k in self.keys() |
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) |
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class CLIPVisionEmbeddings(nn.Module): |
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def __init__(self, config: CLIPVisionConfig): |
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super().__init__() |
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self.config = config |
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self.embed_dim = config.hidden_size |
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self.image_size = config.image_size |
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self.patch_size = config.patch_size |
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self.class_embedding = nn.Parameter(torch.randn(self.embed_dim)) |
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self.patch_embedding = nn.Conv2d( |
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in_channels=config.num_channels, |
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out_channels=self.embed_dim, |
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kernel_size=self.patch_size, |
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stride=self.patch_size, |
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bias=False, |
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) |
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self.num_patches = (self.image_size // self.patch_size) ** 2 |
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self.num_positions = self.num_patches + 1 |
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self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) |
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self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) |
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|
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def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: |
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batch_size = pixel_values.shape[0] |
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target_dtype = self.patch_embedding.weight.dtype |
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patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) |
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patch_embeds = patch_embeds.flatten(2).transpose(1, 2) |
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class_embeds = self.class_embedding.expand(batch_size, 1, -1) |
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embeddings = torch.cat([class_embeds, patch_embeds], dim=1) |
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embeddings = embeddings + self.position_embedding(self.position_ids) |
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return embeddings |
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|
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class CLIPTextEmbeddings(nn.Module): |
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def __init__(self, config: CLIPTextConfig): |
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super().__init__() |
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embed_dim = config.hidden_size |
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self.token_embedding = nn.Embedding(config.vocab_size, embed_dim) |
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self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim) |
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self.register_buffer( |
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"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False |
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) |
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|
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def forward( |
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self, |
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input_ids: Optional[torch.LongTensor] = None, |
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position_ids: Optional[torch.LongTensor] = None, |
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inputs_embeds: Optional[torch.FloatTensor] = None, |
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) -> torch.Tensor: |
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seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2] |
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if position_ids is None: |
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position_ids = self.position_ids[:, :seq_length] |
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if inputs_embeds is None: |
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inputs_embeds = self.token_embedding(input_ids) |
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position_embeddings = self.position_embedding(position_ids) |
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embeddings = inputs_embeds + position_embeddings |
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return embeddings |
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|
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class CLIPAttention(nn.Module): |
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"""Multi-headed attention from 'Attention Is All You Need' paper""" |
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def __init__(self, config): |
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super().__init__() |
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self.config = config |
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self.embed_dim = config.hidden_size |
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self.num_heads = config.num_attention_heads |
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self.head_dim = self.embed_dim // self.num_heads |
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if self.head_dim * self.num_heads != self.embed_dim: |
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raise ValueError( |
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f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" |
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f" {self.num_heads})." |
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) |
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self.scale = self.head_dim**-0.5 |
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self.dropout = config.attention_dropout |
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|
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self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) |
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self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) |
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self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) |
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self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) |
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|
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): |
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() |
|
|
|
def forward( |
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self, |
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hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
causal_attention_mask: Optional[torch.Tensor] = None, |
|
output_attentions: Optional[bool] = False, |
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: |
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"""Input shape: Batch x Time x Channel""" |
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bsz, tgt_len, embed_dim = hidden_states.size() |
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|
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query_states = self.q_proj(hidden_states) * self.scale |
|
key_states = self._shape(self.k_proj(hidden_states), -1, bsz) |
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value_states = self._shape(self.v_proj(hidden_states), -1, bsz) |
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|
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proj_shape = (bsz * self.num_heads, -1, self.head_dim) |
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query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) |
|
key_states = key_states.view(*proj_shape) |
|
value_states = value_states.view(*proj_shape) |
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|
|
src_len = key_states.size(1) |
|
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) |
|
|
|
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): |
|
raise ValueError( |
|
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" |
|
f" {attn_weights.size()}" |
|
) |
|
|
|
|
|
if causal_attention_mask is not None: |
|
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len): |
|
raise ValueError( |
|
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is" |
|
f" {causal_attention_mask.size()}" |
|
) |
|
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask |
|
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) |
|
|
|
if attention_mask is not None: |
|
if attention_mask.size() != (bsz, 1, tgt_len, src_len): |
|
raise ValueError( |
|
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" |
|
) |
|
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask |
|
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) |
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|
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attn_weights = nn.functional.softmax(attn_weights, dim=-1) |
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|
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if output_attentions: |
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|
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|
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attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) |
|
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) |
|
else: |
|
attn_weights_reshaped = None |
|
|
|
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) |
|
|
|
attn_output = torch.bmm(attn_probs, value_states) |
|
|
|
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): |
|
raise ValueError( |
|
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" |
|
f" {attn_output.size()}" |
|
) |
|
|
|
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) |
|
attn_output = attn_output.transpose(1, 2) |
|
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim) |
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|
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attn_output = self.out_proj(attn_output) |
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|
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return attn_output, attn_weights_reshaped |
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|
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|
|
class CLIPFlashAttention2(CLIPAttention): |
|
""" |
|
CLIPAttention flash attention module. This module inherits from `CLIPAttention` as the weights of the module stays |
|
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of |
|
flash attention and deal with padding tokens in case the input contains any of them. |
|
""" |
|
|
|
|
|
def __init__(self, *args, **kwargs): |
|
super().__init__(*args, **kwargs) |
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|
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|
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self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() |
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|
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def forward( |
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self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
causal_attention_mask: Optional[torch.Tensor] = None, |
|
output_attentions: Optional[bool] = False, |
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: |
|
output_attentions = False |
|
|
|
batch_size, q_len, _ = hidden_states.size() |
|
|
|
query_states = self.q_proj(hidden_states) |
|
key_states = self.k_proj(hidden_states) |
|
value_states = self.v_proj(hidden_states) |
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|
|
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|
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query_states = query_states.view(batch_size, q_len, self.num_heads, self.head_dim) |
|
key_states = key_states.view(batch_size, q_len, self.num_heads, self.head_dim) |
|
value_states = value_states.view(batch_size, q_len, self.num_heads, self.head_dim) |
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|
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dropout_rate = self.dropout if self.training else 0.0 |
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|
|
input_dtype = query_states.dtype |
|
if input_dtype == torch.float32: |
|
if torch.is_autocast_enabled(): |
|
target_dtype = torch.get_autocast_gpu_dtype() |
|
|
|
elif hasattr(self.config, "_pre_quantization_dtype"): |
|
target_dtype = self.config._pre_quantization_dtype |
|
else: |
|
target_dtype = self.q_proj.weight.dtype |
|
|
|
logger.warning_once( |
|
f"The input hidden states seems to be silently casted in float32, this might be related to" |
|
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" |
|
f" {target_dtype}." |
|
) |
|
|
|
query_states = query_states.to(target_dtype) |
|
key_states = key_states.to(target_dtype) |
|
value_states = value_states.to(target_dtype) |
|
|
|
attn_output = _flash_attention_forward( |
|
query_states, |
|
key_states, |
|
value_states, |
|
attention_mask, |
|
q_len, |
|
dropout=dropout_rate, |
|
is_causal=causal_attention_mask is not None, |
|
use_top_left_mask=self._flash_attn_uses_top_left_mask, |
|
) |
|
|
|
attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim).contiguous() |
|
attn_output = self.out_proj(attn_output) |
|
|
|
if not output_attentions: |
|
attn_weights = None |
|
|
|
return attn_output, attn_weights |
|
|
|
|
|
class CLIPSdpaAttention(CLIPAttention): |
|
""" |
|
SDPA attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from |
|
`CLIPAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to |
|
SDPA API. |
|
""" |
|
|
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
causal_attention_mask: Optional[torch.Tensor] = None, |
|
output_attentions: Optional[bool] = False, |
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: |
|
if output_attentions: |
|
|
|
logger.warning_once( |
|
"CLIPModel is using CLIPSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not " |
|
"support `output_attentions=True`. Falling back to the manual attention implementation, but specifying " |
|
"the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can " |
|
'be removed using the argument `attn_implementation="eager"` when loading the model.' |
|
) |
|
return super().forward( |
|
hidden_states=hidden_states, |
|
attention_mask=attention_mask, |
|
causal_attention_mask=causal_attention_mask, |
|
output_attentions=output_attentions, |
|
) |
|
|
|
|
|
if attention_mask is not None and causal_attention_mask is not None: |
|
attn_mask = attention_mask + causal_attention_mask |
|
elif causal_attention_mask is not None: |
|
attn_mask = causal_attention_mask |
|
else: |
|
attn_mask = attention_mask |
|
|
|
bsz, tgt_len, embed_dim = hidden_states.size() |
|
|
|
query_states = self.q_proj(hidden_states) |
|
key_states = self.k_proj(hidden_states) |
|
value_states = self.v_proj(hidden_states) |
|
|
|
query_states = query_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2) |
|
key_states = key_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2) |
|
value_states = value_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2) |
|
|
|
|
|
|
|
if not is_torch_greater_or_equal_than_2_2 and query_states.device.type == "cuda" and attn_mask is not None: |
|
query_states = query_states.contiguous() |
|
key_states = key_states.contiguous() |
|
value_states = value_states.contiguous() |
|
|
|
|
|
attn_output = torch.nn.functional.scaled_dot_product_attention( |
|
query_states, |
|
key_states, |
|
value_states, |
|
attn_mask=attn_mask, |
|
dropout_p=self.dropout if self.training else 0.0, |
|
scale=self.scale, |
|
) |
|
|
|
attn_output = attn_output.transpose(1, 2) |
|
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim) |
|
|
|
attn_output = self.out_proj(attn_output) |
|
|
|
return attn_output, None |
|
|
|
|
|
CLIP_ATTENTION_CLASSES = { |
|
"eager": CLIPAttention, |
|
"sdpa": CLIPSdpaAttention, |
|
"flash_attention_2": CLIPFlashAttention2, |
|
} |
|
|
|
|
|
class CLIPMLP(nn.Module): |
|
def __init__(self, config): |
|
super().__init__() |
|
self.config = config |
|
self.activation_fn = ACT2FN[config.hidden_act] |
|
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) |
|
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) |
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: |
|
hidden_states = self.fc1(hidden_states) |
|
hidden_states = self.activation_fn(hidden_states) |
|
hidden_states = self.fc2(hidden_states) |
|
return hidden_states |
|
|
|
|
|
class CLIPEncoderLayer(nn.Module): |
|
def __init__(self, config: CLIPConfig): |
|
super().__init__() |
|
self.embed_dim = config.hidden_size |
|
self.self_attn = CLIP_ATTENTION_CLASSES[config._attn_implementation](config) |
|
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) |
|
self.mlp = CLIPMLP(config) |
|
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: torch.Tensor, |
|
causal_attention_mask: torch.Tensor, |
|
output_attentions: Optional[bool] = False, |
|
) -> Tuple[torch.FloatTensor]: |
|
""" |
|
Args: |
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` |
|
attention_mask (`torch.FloatTensor`): attention mask of size |
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. |
|
`(config.encoder_attention_heads,)`. |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under |
|
returned tensors for more detail. |
|
""" |
|
residual = hidden_states |
|
|
|
hidden_states = self.layer_norm1(hidden_states) |
|
hidden_states, attn_weights = self.self_attn( |
|
hidden_states=hidden_states, |
|
attention_mask=attention_mask, |
|
causal_attention_mask=causal_attention_mask, |
|
output_attentions=output_attentions, |
|
) |
|
hidden_states = residual + hidden_states |
|
|
|
residual = hidden_states |
|
hidden_states = self.layer_norm2(hidden_states) |
|
hidden_states = self.mlp(hidden_states) |
|
hidden_states = residual + hidden_states |
|
|
|
outputs = (hidden_states,) |
|
|
|
if output_attentions: |
|
outputs += (attn_weights,) |
|
|
|
return outputs |
|
|
|
|
|
class CLIPPreTrainedModel(PreTrainedModel): |
|
""" |
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained |
|
models. |
|
""" |
|
|
|
config_class = CLIPConfig |
|
base_model_prefix = "clip" |
|
supports_gradient_checkpointing = True |
|
_supports_sdpa = True |
|
_supports_flash_attn_2 = True |
|
|
|
def _init_weights(self, module): |
|
"""Initialize the weights""" |
|
factor = self.config.initializer_factor |
|
if isinstance(module, CLIPTextEmbeddings): |
|
module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02) |
|
module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02) |
|
elif isinstance(module, CLIPVisionEmbeddings): |
|
factor = self.config.initializer_factor |
|
nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor) |
|
nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor) |
|
nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor) |
|
elif isinstance(module, CLIPAttention): |
|
factor = self.config.initializer_factor |
|
in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor |
|
out_proj_std = (module.embed_dim**-0.5) * factor |
|
nn.init.normal_(module.q_proj.weight, std=in_proj_std) |
|
nn.init.normal_(module.k_proj.weight, std=in_proj_std) |
|
nn.init.normal_(module.v_proj.weight, std=in_proj_std) |
|
nn.init.normal_(module.out_proj.weight, std=out_proj_std) |
|
elif isinstance(module, CLIPMLP): |
|
factor = self.config.initializer_factor |
|
in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor |
|
fc_std = (2 * module.config.hidden_size) ** -0.5 * factor |
|
nn.init.normal_(module.fc1.weight, std=fc_std) |
|
nn.init.normal_(module.fc2.weight, std=in_proj_std) |
|
elif isinstance(module, LLM2CLIPModel): |
|
|
|
|
|
|
|
|
|
nn.init.normal_( |
|
module.visual_projection.weight, |
|
std=module.vision_embed_dim**-0.5 * self.config.initializer_factor, |
|
) |
|
elif isinstance(module, CLIPVisionModelWithProjection): |
|
nn.init.normal_( |
|
module.visual_projection.weight, |
|
std=self.config.hidden_size**-0.5 * self.config.initializer_factor, |
|
) |
|
elif isinstance(module, CLIPTextModelWithProjection): |
|
nn.init.normal_( |
|
module.text_projection.weight, |
|
std=self.config.hidden_size**-0.5 * self.config.initializer_factor, |
|
) |
|
elif isinstance(module, CLIPForImageClassification): |
|
nn.init.normal_( |
|
module.classifier.weight, |
|
std=self.config.vision_config.hidden_size**-0.5 * self.config.initializer_factor, |
|
) |
|
|
|
if isinstance(module, nn.LayerNorm): |
|
module.bias.data.zero_() |
|
module.weight.data.fill_(1.0) |
|
if isinstance(module, nn.Linear) and module.bias is not None: |
|
module.bias.data.zero_() |
|
|
|
|
|
CLIP_START_DOCSTRING = r""" |
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the |
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads |
|
etc.) |
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. |
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage |
|
and behavior. |
|
|
|
Parameters: |
|
config ([`CLIPConfig`]): Model configuration class with all the parameters of the model. |
|
Initializing with a config file does not load the weights associated with the model, only the |
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. |
|
""" |
|
|
|
CLIP_TEXT_INPUTS_DOCSTRING = r""" |
|
Args: |
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): |
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide |
|
it. |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
[What are input IDs?](../glossary#input-ids) |
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: |
|
|
|
- 1 for tokens that are **not masked**, |
|
- 0 for tokens that are **masked**. |
|
|
|
[What are attention masks?](../glossary#attention-mask) |
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, |
|
config.max_position_embeddings - 1]`. |
|
|
|
[What are position IDs?](../glossary#position-ids) |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned |
|
tensors for more detail. |
|
output_hidden_states (`bool`, *optional*): |
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for |
|
more detail. |
|
return_dict (`bool`, *optional*): |
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. |
|
""" |
|
|
|
CLIP_VISION_INPUTS_DOCSTRING = r""" |
|
Args: |
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): |
|
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using |
|
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned |
|
tensors for more detail. |
|
output_hidden_states (`bool`, *optional*): |
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for |
|
more detail. |
|
return_dict (`bool`, *optional*): |
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. |
|
""" |
|
|
|
CLIP_INPUTS_DOCSTRING = r""" |
|
Args: |
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): |
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide |
|
it. |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
[What are input IDs?](../glossary#input-ids) |
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: |
|
|
|
- 1 for tokens that are **not masked**, |
|
- 0 for tokens that are **masked**. |
|
|
|
[What are attention masks?](../glossary#attention-mask) |
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, |
|
config.max_position_embeddings - 1]`. |
|
|
|
[What are position IDs?](../glossary#position-ids) |
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): |
|
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using |
|
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. |
|
return_loss (`bool`, *optional*): |
|
Whether or not to return the contrastive loss. |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned |
|
tensors for more detail. |
|
output_hidden_states (`bool`, *optional*): |
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for |
|
more detail. |
|
return_dict (`bool`, *optional*): |
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. |
|
""" |
|
|
|
|
|
class CLIPEncoder(nn.Module): |
|
""" |
|
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a |
|
[`CLIPEncoderLayer`]. |
|
|
|
Args: |
|
config: CLIPConfig |
|
""" |
|
|
|
def __init__(self, config: CLIPConfig): |
|
super().__init__() |
|
self.config = config |
|
self.layers = nn.ModuleList([CLIPEncoderLayer(config) for _ in range(config.num_hidden_layers)]) |
|
self.gradient_checkpointing = False |
|
|
|
def forward( |
|
self, |
|
inputs_embeds, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
causal_attention_mask: Optional[torch.Tensor] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, BaseModelOutput]: |
|
r""" |
|
Args: |
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): |
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. |
|
This is useful if you want more control over how to convert `input_ids` indices into associated vectors |
|
than the model's internal embedding lookup matrix. |
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: |
|
|
|
- 1 for tokens that are **not masked**, |
|
- 0 for tokens that are **masked**. |
|
|
|
[What are attention masks?](../glossary#attention-mask) |
|
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Causal mask for the text model. Mask values selected in `[0, 1]`: |
|
|
|
- 1 for tokens that are **not masked**, |
|
- 0 for tokens that are **masked**. |
|
|
|
[What are attention masks?](../glossary#attention-mask) |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under |
|
returned tensors for more detail. |
|
output_hidden_states (`bool`, *optional*): |
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors |
|
for more detail. |
|
return_dict (`bool`, *optional*): |
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. |
|
""" |
|
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 |
|
|
|
encoder_states = () if output_hidden_states else None |
|
all_attentions = () if output_attentions else None |
|
|
|
hidden_states = inputs_embeds |
|
for idx, encoder_layer in enumerate(self.layers): |
|
if output_hidden_states: |
|
encoder_states = encoder_states + (hidden_states,) |
|
if self.gradient_checkpointing and self.training: |
|
layer_outputs = self._gradient_checkpointing_func( |
|
encoder_layer.__call__, |
|
hidden_states, |
|
attention_mask, |
|
causal_attention_mask, |
|
output_attentions, |
|
) |
|
else: |
|
layer_outputs = encoder_layer( |
|
hidden_states, |
|
attention_mask, |
|
causal_attention_mask, |
|
output_attentions=output_attentions, |
|
) |
|
|
|
hidden_states = layer_outputs[0] |
|
|
|
if output_attentions: |
|
all_attentions = all_attentions + (layer_outputs[1],) |
|
|
|
if output_hidden_states: |
|
encoder_states = encoder_states + (hidden_states,) |
|
|
|
if not return_dict: |
|
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) |
|
return BaseModelOutput( |
|
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions |
|
) |
|
|
|
|
|
class CLIPTextTransformer(nn.Module): |
|
def __init__(self, config: CLIPTextConfig): |
|
super().__init__() |
|
self.config = config |
|
embed_dim = config.hidden_size |
|
self.embeddings = CLIPTextEmbeddings(config) |
|
self.encoder = CLIPEncoder(config) |
|
self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) |
|
|
|
|
|
self.eos_token_id = config.eos_token_id |
|
|
|
|
|
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" |
|
|
|
@add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING) |
|
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPTextConfig) |
|
def forward( |
|
self, |
|
input_ids: Optional[torch.Tensor] = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.Tensor] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, BaseModelOutputWithPooling]: |
|
r""" |
|
Returns: |
|
|
|
""" |
|
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 |
|
|
|
if input_ids is None: |
|
raise ValueError("You have to specify input_ids") |
|
|
|
input_shape = input_ids.size() |
|
input_ids = input_ids.view(-1, input_shape[-1]) |
|
|
|
hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) |
|
|
|
|
|
|
|
causal_attention_mask = _create_4d_causal_attention_mask( |
|
input_shape, hidden_states.dtype, device=hidden_states.device |
|
) |
|
|
|
|
|
if attention_mask is not None and not self._use_flash_attention_2: |
|
|
|
attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype) |
|
|
|
encoder_outputs = self.encoder( |
|
inputs_embeds=hidden_states, |
|
attention_mask=attention_mask, |
|
causal_attention_mask=causal_attention_mask, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
|
|
last_hidden_state = encoder_outputs[0] |
|
last_hidden_state = self.final_layer_norm(last_hidden_state) |
|
|
|
if self.eos_token_id == 2: |
|
|
|
|
|
|
|
|
|
|
|
|
|
pooled_output = last_hidden_state[ |
|
torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device), |
|
input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1), |
|
] |
|
else: |
|
|
|
pooled_output = last_hidden_state[ |
|
torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device), |
|
|
|
|
|
(input_ids.to(dtype=torch.int, device=last_hidden_state.device) == self.eos_token_id) |
|
.int() |
|
.argmax(dim=-1), |
|
] |
|
|
|
if not return_dict: |
|
return (last_hidden_state, pooled_output) + encoder_outputs[1:] |
|
|
|
return BaseModelOutputWithPooling( |
|
last_hidden_state=last_hidden_state, |
|
pooler_output=pooled_output, |
|
hidden_states=encoder_outputs.hidden_states, |
|
attentions=encoder_outputs.attentions, |
|
) |
|
|
|
|
|
@add_start_docstrings( |
|
"""The text model from CLIP without any head or projection on top.""", |
|
CLIP_START_DOCSTRING, |
|
) |
|
class CLIPTextModel(CLIPPreTrainedModel): |
|
config_class = CLIPTextConfig |
|
|
|
_no_split_modules = ["CLIPTextEmbeddings", "CLIPEncoderLayer"] |
|
|
|
def __init__(self, config: CLIPTextConfig): |
|
super().__init__(config) |
|
self.text_model = CLIPTextTransformer(config) |
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self) -> nn.Module: |
|
return self.text_model.embeddings.token_embedding |
|
|
|
def set_input_embeddings(self, value): |
|
self.text_model.embeddings.token_embedding = value |
|
|
|
@add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING) |
|
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPTextConfig) |
|
def forward( |
|
self, |
|
input_ids: Optional[torch.Tensor] = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.Tensor] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, BaseModelOutputWithPooling]: |
|
r""" |
|
Returns: |
|
|
|
Examples: |
|
|
|
```python |
|
>>> from transformers import AutoTokenizer, CLIPTextModel |
|
|
|
>>> model = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32") |
|
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32") |
|
|
|
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") |
|
|
|
>>> outputs = model(**inputs) |
|
>>> last_hidden_state = outputs.last_hidden_state |
|
>>> pooled_output = outputs.pooler_output # pooled (EOS token) states |
|
```""" |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
return self.text_model( |
|
input_ids=input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
|
|
|
|
class CLIPVisionTransformer(nn.Module): |
|
def __init__(self, config: CLIPVisionConfig): |
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super().__init__() |
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self.config = config |
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embed_dim = config.hidden_size |
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|
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self.embeddings = CLIPVisionEmbeddings(config) |
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self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) |
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self.encoder = CLIPEncoder(config) |
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self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) |
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|
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@add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING) |
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@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPVisionConfig) |
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def forward( |
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self, |
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pixel_values: Optional[torch.FloatTensor] = None, |
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output_attentions: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, BaseModelOutputWithPooling]: |
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r""" |
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Returns: |
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|
|
""" |
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
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output_hidden_states = ( |
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
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) |
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
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|
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if pixel_values is None: |
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raise ValueError("You have to specify pixel_values") |
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|
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hidden_states = self.embeddings(pixel_values) |
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hidden_states = self.pre_layrnorm(hidden_states) |
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|
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encoder_outputs = self.encoder( |
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inputs_embeds=hidden_states, |
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output_attentions=output_attentions, |
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output_hidden_states=output_hidden_states, |
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return_dict=return_dict, |
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) |
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|
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last_hidden_state = encoder_outputs[0] |
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pooled_output = last_hidden_state[:, 0, :] |
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pooled_output = self.post_layernorm(pooled_output) |
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|
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if not return_dict: |
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return (last_hidden_state, pooled_output) + encoder_outputs[1:] |
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|
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return BaseModelOutputWithPooling( |
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last_hidden_state=last_hidden_state, |
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pooler_output=pooled_output, |
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hidden_states=encoder_outputs.hidden_states, |
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attentions=encoder_outputs.attentions, |
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) |
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@add_start_docstrings( |
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"""The vision model from CLIP without any head or projection on top.""", |
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CLIP_START_DOCSTRING, |
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) |
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class CLIPVisionModel(CLIPPreTrainedModel): |
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config_class = CLIPVisionConfig |
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main_input_name = "pixel_values" |
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_no_split_modules = ["CLIPEncoderLayer"] |
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|
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def __init__(self, config: CLIPVisionConfig): |
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super().__init__(config) |
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self.vision_model = CLIPVisionTransformer(config) |
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|
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self.post_init() |
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|
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def get_input_embeddings(self) -> nn.Module: |
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return self.vision_model.embeddings.patch_embedding |
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|
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@add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING) |
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@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPVisionConfig) |
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def forward( |
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self, |
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pixel_values: Optional[torch.FloatTensor] = None, |
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output_attentions: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, BaseModelOutputWithPooling]: |
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r""" |
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Returns: |
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|
|
Examples: |
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|
|
```python |
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>>> from PIL import Image |
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>>> import requests |
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>>> from transformers import AutoProcessor, CLIPVisionModel |
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|
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>>> model = CLIPVisionModel.from_pretrained("openai/clip-vit-base-patch32") |
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>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32") |
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|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
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>>> image = Image.open(requests.get(url, stream=True).raw) |
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|
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>>> inputs = processor(images=image, return_tensors="pt") |
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|
|
>>> outputs = model(**inputs) |
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>>> last_hidden_state = outputs.last_hidden_state |
|
>>> pooled_output = outputs.pooler_output # pooled CLS states |
|
```""" |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
return self.vision_model( |
|
pixel_values=pixel_values, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
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|
|
|
|
@add_start_docstrings(CLIP_START_DOCSTRING) |
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class LLM2CLIPModel(CLIPPreTrainedModel): |
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config_class = CLIPConfig |
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_no_split_modules = ["CLIPTextEmbeddings", "CLIPEncoderLayer", "CLIPVisionEmbeddings"] |
|
|
|
def __init__(self, config: CLIPConfig): |
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super().__init__(config) |
|
|
|
|
|
|
|
|
|
|
|
|
|
if not isinstance(config.vision_config, CLIPVisionConfig): |
|
raise TypeError( |
|
"config.vision_config is expected to be of type CLIPVisionConfig but is of type" |
|
f" {type(config.vision_config)}." |
|
) |
|
|
|
|
|
vision_config = config.vision_config |
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|
|
self.projection_dim = config.projection_dim |
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|
|
self.vision_embed_dim = vision_config.hidden_size |
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|
|
adapter = LLM2CLIP_Adapter() |
|
self.text_adapter = adapter |
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|
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|
|
vision_model = CLIPVisionModel._from_config(vision_config, attn_implementation=config._attn_implementation) |
|
self.vision_model = vision_model.vision_model |
|
|
|
self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False) |
|
|
|
self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value)) |
|
|
|
|
|
self.post_init() |
|
|
|
def get_text_features(self, inputs): |
|
|
|
return self.text_adapter(inputs) |
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|
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@add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING) |
|
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) |
|
```""" |
|
|
|
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] |
|
image_features = self.visual_projection(pooled_output) |
|
|
|
return image_features |
|
|
|
@add_start_docstrings_to_model_forward(CLIP_INPUTS_DOCSTRING) |
|
@replace_return_docstrings(output_type=CLIPOutput, config_class=CLIPConfig) |
|
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, |
|
return_loss: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, CLIPOutput]: |
|
r""" |
|
Returns: |
|
|
|
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( |
|
... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True |
|
... ) |
|
|
|
>>> outputs = model(**inputs) |
|
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score |
|
>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities |
|
```""" |
|
|
|
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, |
|
) |
|
|
|
text_outputs = self.text_model( |
|
input_ids=input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
|
|
image_embeds = vision_outputs[1] |
|
image_embeds = self.visual_projection(image_embeds) |
|
|
|
text_embeds = text_outputs[1] |
|
text_embeds = self.text_projection(text_embeds) |
|
|
|
|
|
image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True) |
|
text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True) |
|
|
|
|
|
logit_scale = self.logit_scale.exp() |
|
logits_per_text = torch.matmul(text_embeds, image_embeds.t().to(text_embeds.device)) * logit_scale.to( |
|
text_embeds.device |
|
) |
|
logits_per_image = logits_per_text.t() |
|
|
|
loss = None |
|
if return_loss: |
|
loss = clip_loss(logits_per_text) |
|
|
|
if not return_dict: |
|
output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) |
|
return ((loss,) + output) if loss is not None else output |
|
|
|
return CLIPOutput( |
|
loss=loss, |
|
logits_per_image=logits_per_image, |
|
logits_per_text=logits_per_text, |
|
text_embeds=text_embeds, |
|
image_embeds=image_embeds, |
|
text_model_output=text_outputs, |
|
vision_model_output=vision_outputs, |
|
) |
|
|
|
|
|
@add_start_docstrings( |
|
""" |
|
CLIP Text Model with a projection layer on top (a linear layer on top of the pooled output). |
|
""", |
|
CLIP_START_DOCSTRING, |
|
) |
|
class CLIPTextModelWithProjection(CLIPPreTrainedModel): |
|
config_class = CLIPTextConfig |
|
|
|
_no_split_modules = ["CLIPTextEmbeddings", "CLIPEncoderLayer"] |
|
|
|
def __init__(self, config: CLIPTextConfig): |
|
super().__init__(config) |
|
|
|
text_model = CLIPTextModel._from_config(config, attn_implementation=config._attn_implementation) |
|
self.text_model = text_model.text_model |
|
|
|
self.text_projection = nn.Linear(config.hidden_size, config.projection_dim, bias=False) |
|
|
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self) -> nn.Module: |
|
return self.text_model.embeddings.token_embedding |
|
|
|
def set_input_embeddings(self, value): |
|
self.text_model.embeddings.token_embedding = value |
|
|
|
@add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING) |
|
@replace_return_docstrings(output_type=CLIPTextModelOutput, config_class=CLIPTextConfig) |
|
def forward( |
|
self, |
|
input_ids: Optional[torch.Tensor] = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.Tensor] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, CLIPTextModelOutput]: |
|
r""" |
|
Returns: |
|
|
|
Examples: |
|
|
|
```python |
|
>>> from transformers import AutoTokenizer, CLIPTextModelWithProjection |
|
|
|
>>> model = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-base-patch32") |
|
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32") |
|
|
|
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") |
|
|
|
>>> outputs = model(**inputs) |
|
>>> text_embeds = outputs.text_embeds |
|
```""" |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
text_outputs = self.text_model( |
|
input_ids=input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
|
|
pooled_output = text_outputs[1] |
|
|
|
text_embeds = self.text_projection(pooled_output) |
|
|
|
if not return_dict: |
|
outputs = (text_embeds, text_outputs[0]) + text_outputs[2:] |
|
return tuple(output for output in outputs if output is not None) |
|
|
|
return CLIPTextModelOutput( |
|
text_embeds=text_embeds, |
|
last_hidden_state=text_outputs.last_hidden_state, |
|
hidden_states=text_outputs.hidden_states, |
|
attentions=text_outputs.attentions, |
|
) |
|
|
|
class LinearBlock(nn.Module): |
|
def __init__(self, dim, expansion_factor=4, dropout=0.,norm_layer=nn.LayerNorm): |
|
super().__init__() |
|
self.fn = nn.Sequential( |
|
nn.Linear(dim, int(expansion_factor * dim)), |
|
nn.GELU(), |
|
nn.Dropout(dropout), |
|
nn.Linear(int(expansion_factor * dim), dim), |
|
) |
|
self.ln = norm_layer(dim) |
|
|
|
def forward(self, x): |
|
return x + self.fn(self.ln(x)) |
|
|
|
class LLM2CLIP_Adapter(nn.Module): |
|
def __init__(self): |
|
super().__init__() |
|
|
|
|
|
text_embedding_dim = 4096 |
|
expansion_factor = 2 |
|
adaptor_num_layers = 4 |
|
proj_bias = True |
|
output_dim = 1280 |
|
self.adaptor = nn.Sequential( |
|
*[LinearBlock(text_embedding_dim, expansion_factor) for _ in range(adaptor_num_layers)], |
|
nn.LayerNorm(text_embedding_dim), |
|
nn.Linear(text_embedding_dim, output_dim, bias=proj_bias), |
|
) |
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: |
|
hidden_states = torch.nn.functional.normalize(hidden_states, p=2, dim=1) |
|
hidden_states = self.adaptor(hidden_states) |
|
return hidden_states |
|
|
|
@add_start_docstrings( |
|
""" |
|
CLIP Vision Model with a projection layer on top (a linear layer on top of the pooled output). |
|
""", |
|
CLIP_START_DOCSTRING, |
|
) |
|
class CLIPVisionModelWithProjection(CLIPPreTrainedModel): |
|
config_class = CLIPVisionConfig |
|
main_input_name = "pixel_values" |
|
|
|
def __init__(self, config: CLIPVisionConfig): |
|
super().__init__(config) |
|
|
|
vision_model = CLIPVisionModel._from_config(config, attn_implementation=config._attn_implementation) |
|
self.vision_model = vision_model.vision_model |
|
|
|
self.visual_projection = nn.Linear(config.hidden_size, config.projection_dim, bias=False) |
|
|
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self) -> nn.Module: |
|
return self.vision_model.embeddings.patch_embedding |
|
|
|
@add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING) |
|
@replace_return_docstrings(output_type=CLIPVisionModelOutput, config_class=CLIPVisionConfig) |
|
def forward( |
|
self, |
|
pixel_values: Optional[torch.FloatTensor] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, CLIPVisionModelOutput]: |
|
r""" |
|
Returns: |
|
|
|
Examples: |
|
|
|
```python |
|
>>> from PIL import Image |
|
>>> import requests |
|
>>> from transformers import AutoProcessor, CLIPVisionModelWithProjection |
|
|
|
>>> model = CLIPVisionModelWithProjection.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") |
|
|
|
>>> outputs = model(**inputs) |
|
>>> image_embeds = outputs.image_embeds |
|
```""" |
|
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] |
|
|
|
image_embeds = self.visual_projection(pooled_output) |
|
|
|
if not return_dict: |
|
outputs = (image_embeds, vision_outputs[0]) + vision_outputs[2:] |
|
return tuple(output for output in outputs if output is not None) |
|
|
|
return CLIPVisionModelOutput( |
|
image_embeds=image_embeds, |
|
last_hidden_state=vision_outputs.last_hidden_state, |
|
hidden_states=vision_outputs.hidden_states, |
|
attentions=vision_outputs.attentions, |
|
) |
|
|
|
|
|
@add_start_docstrings( |
|
""" |
|
CLIP vision encoder with an image classification head on top (a linear layer on top of the pooled final hidden states of |
|
the patch tokens) e.g. for ImageNet. |
|
""", |
|
CLIP_START_DOCSTRING, |
|
) |
|
class CLIPForImageClassification(CLIPPreTrainedModel): |
|
main_input_name = "pixel_values" |
|
|
|
def __init__(self, config: CLIPConfig) -> None: |
|
super().__init__(config) |
|
|
|
self.num_labels = config.num_labels |
|
vision_model = CLIPVisionModel._from_config( |
|
config.vision_config, attn_implementation=config._attn_implementation |
|
) |
|
self.vision_model = vision_model.vision_model |
|
|
|
|
|
self.classifier = ( |
|
nn.Linear(config.vision_config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() |
|
) |
|
|
|
|
|
self.post_init() |
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@add_start_docstrings_to_model_forward(CLIP_INPUTS_DOCSTRING) |
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@add_code_sample_docstrings( |
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checkpoint=_IMAGE_CLASS_CHECKPOINT, |
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output_type=ImageClassifierOutput, |
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config_class=_CONFIG_FOR_DOC, |
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expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, |
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) |
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def forward( |
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self, |
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pixel_values: Optional[torch.Tensor] = None, |
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labels: Optional[torch.Tensor] = None, |
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output_attentions: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
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) -> Union[tuple, ImageClassifierOutput]: |
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r""" |
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labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): |
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Labels for computing the image classification/regression loss. Indices should be in `[0, ..., |
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config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If |
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`config.num_labels > 1` a classification loss is computed (Cross-Entropy). |
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""" |
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
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output_hidden_states = ( |
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
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) |
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
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outputs = self.vision_model( |
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pixel_values, |
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output_attentions=output_attentions, |
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output_hidden_states=output_hidden_states, |
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return_dict=return_dict, |
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) |
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sequence_output = outputs[0] |
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sequence_output = torch.mean(sequence_output[:, 1:, :], dim=1) |
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logits = self.classifier(sequence_output) |
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loss = None |
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if labels is not None: |
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labels = labels.to(logits.device) |
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if self.config.problem_type is None: |
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if self.num_labels == 1: |
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self.config.problem_type = "regression" |
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elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): |
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self.config.problem_type = "single_label_classification" |
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else: |
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self.config.problem_type = "multi_label_classification" |
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if self.config.problem_type == "regression": |
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loss_fct = MSELoss() |
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if self.num_labels == 1: |
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loss = loss_fct(logits.squeeze(), labels.squeeze()) |
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else: |
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loss = loss_fct(logits, labels) |
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elif self.config.problem_type == "single_label_classification": |
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loss_fct = CrossEntropyLoss() |
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loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) |
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elif self.config.problem_type == "multi_label_classification": |
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loss_fct = BCEWithLogitsLoss() |
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loss = loss_fct(logits, labels) |
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if not return_dict: |
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output = (logits,) + outputs[2:] |
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return ((loss,) + output) if loss is not None else output |
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return ImageClassifierOutput( |
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loss=loss, |
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logits=logits, |
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hidden_states=outputs.hidden_states, |
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attentions=outputs.attentions, |
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) |