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# coding=utf-8 | |
# Copyright 2022 The Salesforce Team Authors and The HuggingFace 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 BLIP model.""" | |
import warnings | |
from dataclasses import dataclass | |
from typing import Any, Optional, Tuple, Union | |
import torch | |
import torch.utils.checkpoint | |
from torch import nn | |
from torch.nn.functional import normalize | |
from ...activations import ACT2FN | |
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling | |
from ...modeling_utils import PreTrainedModel | |
from ...utils import ( | |
ModelOutput, | |
add_start_docstrings, | |
add_start_docstrings_to_model_forward, | |
logging, | |
replace_return_docstrings, | |
) | |
from .configuration_blip import BlipConfig, BlipTextConfig, BlipVisionConfig | |
from .modeling_blip_text import BlipTextLMHeadModel, BlipTextModel | |
logger = logging.get_logger(__name__) | |
_CHECKPOINT_FOR_DOC = "Salesforce/blip-vqa-base" | |
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [ | |
"Salesforce/blip-vqa-base", | |
"Salesforce/blip-vqa-capfilt-large", | |
"Salesforce/blip-image-captioning-base", | |
"Salesforce/blip-image-captioning-large", | |
"Salesforce/blip-itm-base-coco", | |
"Salesforce/blip-itm-large-coco", | |
"Salesforce/blip-itm-base-flickr", | |
"Salesforce/blip-itm-large-flickr", | |
# See all BLIP models at https://huggingface.co/models?filter=blip | |
] | |
# Copied from transformers.models.clip.modeling_clip.contrastive_loss | |
def contrastive_loss(logits: torch.Tensor) -> torch.Tensor: | |
return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device)) | |
# Copied from transformers.models.clip.modeling_clip.clip_loss with clip->blip | |
def blip_loss(similarity: torch.Tensor) -> torch.Tensor: | |
caption_loss = contrastive_loss(similarity) | |
image_loss = contrastive_loss(similarity.t()) | |
return (caption_loss + image_loss) / 2.0 | |
class BlipForConditionalGenerationModelOutput(ModelOutput): | |
""" | |
Adapted from the base class for vision model's outputs that also contains image embeddings of the pooling of the | |
last hidden states. This class also adds the loss term from the text decoder. | |
Args: | |
loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): | |
Languge modeling loss from the text decoder. | |
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`, *optional*): | |
Prediction scores of the language modeling head of the text decoder model. | |
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*): | |
The image embeddings obtained after applying the Vision Transformer model to the input image. | |
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): | |
Sequence of hidden-states at the output of the last layer of the model. | |
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`): | |
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + | |
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. | |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. | |
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. | |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention | |
heads. | |
""" | |
loss: Optional[Tuple[torch.FloatTensor]] = None | |
logits: Optional[Tuple[torch.FloatTensor]] = None | |
image_embeds: Optional[torch.FloatTensor] = None | |
last_hidden_state: torch.FloatTensor = None | |
hidden_states: Optional[Tuple[torch.FloatTensor]] = None | |
attentions: Optional[Tuple[torch.FloatTensor]] = None | |
def decoder_logits(self): | |
warnings.warn( | |
"`decoder_logits` attribute is deprecated and will be removed in version 5 of Transformers." | |
" Please use the `logits` attribute to retrieve the final output instead.", | |
FutureWarning, | |
) | |
return self.logits | |
class BlipTextVisionModelOutput(ModelOutput): | |
""" | |
Adapted from the base class for vision model's outputs that also contains image embeddings of the pooling of the | |
last hidden states. This class also adds the loss term from the text decoder. | |
Args: | |
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): | |
Languge modeling loss from the text decoder. | |
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): | |
The image embeddings obtained by applying the projection layer to the pooler_output. | |
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): | |
Sequence of hidden-states at the output of the last layer of the model. | |
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): | |
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + | |
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. | |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. | |
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. | |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention | |
heads. | |
""" | |
loss: Optional[torch.FloatTensor] = None | |
image_embeds: Optional[torch.FloatTensor] = None | |
last_hidden_state: torch.FloatTensor = None | |
hidden_states: Optional[Tuple[torch.FloatTensor]] = None | |
attentions: Optional[Tuple[torch.FloatTensor]] = None | |
class BlipImageTextMatchingModelOutput(ModelOutput): | |
""" | |
Adapted from the base class for vision model's outputs that also contains image embeddings of the pooling of the | |
last hidden states. This class also adds the loss term from the text decoder as well as the image-text similarity | |
scores. | |
Args: | |
itm_score (`torch.FloatTensor`): | |
The image-text similarity scores. | |
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): | |
Languge modeling loss from the text decoder. | |
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): | |
The image embeddings obtained by applying the projection layer to the pooler_output. | |
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): | |
Sequence of hidden-states at the output of the last layer of the model. | |
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): | |
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + | |
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. | |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. | |
vision_pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*): | |
Last layer hidden-state of the vision of the vision-only branch of the model. | |
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. | |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention | |
heads. | |
question_embeds (`torch.FloatTensor`): | |
The question embeddings obtained by the text projection layer. | |
""" | |
itm_score: Optional[torch.FloatTensor] = None | |
loss: Optional[torch.FloatTensor] = None | |
image_embeds: Optional[torch.FloatTensor] = None | |
last_hidden_state: torch.FloatTensor = None | |
hidden_states: Optional[Tuple[torch.FloatTensor]] = None | |
vision_pooler_output: Optional[torch.FloatTensor] = None | |
attentions: Optional[Tuple[torch.FloatTensor]] = None | |
question_embeds: Optional[Tuple[torch.FloatTensor]] = None | |
class BlipOutput(ModelOutput): | |
""" | |
Args: | |
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): | |
Contrastive loss for image-text similarity. | |
logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): | |
The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text | |
similarity scores. | |
logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): | |
The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image | |
similarity scores. | |
text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): | |
The text embeddings obtained by applying the projection layer to the pooled output of [`BlipTextModel`]. | |
image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): | |
The image embeddings obtained by applying the projection layer to the pooled output of [`BlipVisionModel`]. | |
text_model_output(`BaseModelOutputWithPooling`): | |
The output of the [`BlipTextModel`]. | |
vision_model_output(`BaseModelOutputWithPooling`): | |
The output of the [`BlipVisionModel`]. | |
""" | |
loss: Optional[torch.FloatTensor] = None | |
logits_per_image: torch.FloatTensor = None | |
logits_per_text: torch.FloatTensor = None | |
text_embeds: torch.FloatTensor = None | |
image_embeds: torch.FloatTensor = None | |
text_model_output: BaseModelOutputWithPooling = None | |
vision_model_output: BaseModelOutputWithPooling = None | |
def to_tuple(self) -> Tuple[Any]: | |
return tuple( | |
self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() | |
for k in self.keys() | |
) | |
class BlipVisionEmbeddings(nn.Module): | |
def __init__(self, config: BlipVisionConfig): | |
super().__init__() | |
self.config = config | |
self.embed_dim = config.hidden_size | |
self.image_size = config.image_size | |
self.patch_size = config.patch_size | |
self.class_embedding = nn.Parameter(torch.randn(1, 1, self.embed_dim)) | |
self.patch_embedding = nn.Conv2d( | |
in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size | |
) | |
self.num_patches = (self.image_size // self.patch_size) ** 2 | |
self.num_positions = self.num_patches + 1 | |
self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim)) | |
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: | |
batch_size = pixel_values.shape[0] | |
target_dtype = self.patch_embedding.weight.dtype | |
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid] | |
patch_embeds = patch_embeds.flatten(2).transpose(1, 2) | |
class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype) | |
embeddings = torch.cat([class_embeds, patch_embeds], dim=1) | |
embeddings = embeddings + self.position_embedding[:, : embeddings.size(1), :].to(target_dtype) | |
return embeddings | |
# Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->Blip | |
class BlipTextEmbeddings(nn.Module): | |
def __init__(self, config: BlipTextConfig): | |
super().__init__() | |
embed_dim = config.hidden_size | |
self.token_embedding = nn.Embedding(config.vocab_size, embed_dim) | |
self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim) | |
# position_ids (1, len position emb) is contiguous in memory and exported when serialized | |
self.register_buffer( | |
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False | |
) | |
def forward( | |
self, | |
input_ids: Optional[torch.LongTensor] = None, | |
position_ids: Optional[torch.LongTensor] = None, | |
inputs_embeds: Optional[torch.FloatTensor] = None, | |
) -> torch.Tensor: | |
seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2] | |
if position_ids is None: | |
position_ids = self.position_ids[:, :seq_length] | |
if inputs_embeds is None: | |
inputs_embeds = self.token_embedding(input_ids) | |
position_embeddings = self.position_embedding(position_ids) | |
embeddings = inputs_embeds + position_embeddings | |
return embeddings | |
class BlipAttention(nn.Module): | |
"""Multi-headed attention from 'Attention Is All You Need' paper""" | |
def __init__(self, config): | |
super().__init__() | |
self.config = config | |
self.embed_dim = config.hidden_size | |
self.num_heads = config.num_attention_heads | |
self.head_dim = self.embed_dim // self.num_heads | |
if self.head_dim * self.num_heads != self.embed_dim: | |
raise ValueError( | |
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" | |
f" {self.num_heads})." | |
) | |
self.scale = self.head_dim**-0.5 | |
self.dropout = nn.Dropout(config.attention_dropout) | |
self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim) | |
self.projection = nn.Linear(self.embed_dim, self.embed_dim) | |
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): | |
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
head_mask: Optional[torch.Tensor] = None, | |
output_attentions: Optional[bool] = False, | |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: | |
"""Input shape: Batch x Time x Channel""" | |
bsz, tgt_len, embed_dim = hidden_states.size() | |
mixed_qkv = ( | |
self.qkv(hidden_states) | |
.reshape(bsz, tgt_len, 3, self.num_heads, embed_dim // self.num_heads) | |
.permute(2, 0, 3, 1, 4) | |
) | |
query_states, key_states, value_states = mixed_qkv[0], mixed_qkv[1], mixed_qkv[2] | |
# Take the dot product between "query" and "key" to get the raw attention scores. | |
attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2)) | |
attention_scores = attention_scores * self.scale | |
# Normalize the attention scores to probabilities. | |
attention_probs = nn.functional.softmax(attention_scores, dim=-1) | |
# This is actually dropping out entire tokens to attend to, which might | |
# seem a bit unusual, but is taken from the original Transformer paper. | |
attention_probs = self.dropout(attention_probs) | |
# Mask heads if we want to | |
if head_mask is not None: | |
attention_probs = attention_probs * head_mask | |
context_layer = torch.matmul(attention_probs, value_states).permute(0, 2, 1, 3) | |
new_context_layer_shape = context_layer.size()[:-2] + (self.embed_dim,) | |
context_layer = context_layer.reshape(new_context_layer_shape) | |
output = self.projection(context_layer) | |
outputs = (output, attention_probs) if output_attentions else (output, None) | |
return outputs | |
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Blip | |
class BlipMLP(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 BlipEncoderLayer(nn.Module): | |
def __init__(self, config: BlipConfig): | |
super().__init__() | |
self.embed_dim = config.hidden_size | |
self.self_attn = BlipAttention(config) | |
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) | |
self.mlp = BlipMLP(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, | |
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, | |
head_mask=attention_mask, | |
output_attentions=output_attentions, | |
) | |
hidden_states = hidden_states + residual | |
residual = hidden_states | |
hidden_states = self.layer_norm2(hidden_states) | |
hidden_states = self.mlp(hidden_states) | |
hidden_states = hidden_states + residual | |
outputs = (hidden_states,) | |
if output_attentions: | |
outputs += (attn_weights,) | |
return outputs | |
class BlipPreTrainedModel(PreTrainedModel): | |
""" | |
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
models. | |
""" | |
config_class = BlipConfig | |
base_model_prefix = "blip" | |
supports_gradient_checkpointing = True | |
def _init_weights(self, module): | |
"""Initialize the weights""" | |
factor = self.config.initializer_range | |
if isinstance(module, nn.Conv2d) or isinstance(module, nn.Embedding) or isinstance(module, nn.Linear): | |
module.weight.data.normal_(mean=0.0, std=factor) | |
if hasattr(module, "bias") and module.bias is not None: | |
module.bias.data.zero_() | |
if isinstance(module, BlipVisionEmbeddings): | |
if hasattr(self.config, "vision_config"): | |
factor = self.config.vision_config.initializer_range | |
nn.init.trunc_normal_( | |
module.position_embedding, | |
mean=0.0, | |
std=factor, | |
) | |
nn.init.trunc_normal_( | |
module.class_embedding, | |
mean=0.0, | |
std=factor, | |
) | |
elif isinstance(module, nn.LayerNorm): | |
module.bias.data.zero_() | |
module.weight.data.fill_(1.0) | |
elif isinstance(module, nn.Linear) and module.bias is not None: | |
module.bias.data.zero_() | |
def _set_gradient_checkpointing(self, module, value=False): | |
if isinstance(module, BlipEncoder): | |
module.gradient_checkpointing = value | |
BLIP_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 ([`BlipConfig`]): 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. | |
""" | |
BLIP_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 [`AutoProcessor`]. See [`BlipProcessor.__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. | |
""" | |
BLIP_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 | |
[`BlipImageProcessor`]. See [`BlipImageProcessor.__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. | |
""" | |
BLIP_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 [`AutoProcessor`]. See [`BlipProcessor.__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 | |
[`BlipImageProcessor`]. See [`BlipImageProcessor.__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 BlipEncoder(nn.Module): | |
""" | |
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a | |
[`BlipEncoderLayer`]. | |
Args: | |
config (`BlipConfig`): | |
The corresponding vision configuration for the `BlipEncoder`. | |
""" | |
def __init__(self, config: BlipConfig): | |
super().__init__() | |
self.config = config | |
self.layers = nn.ModuleList([BlipEncoderLayer(config) for _ in range(config.num_hidden_layers)]) | |
self.gradient_checkpointing = False | |
def forward( | |
self, | |
inputs_embeds, | |
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)`): | |
Embedded representation of the inputs. Should be float, not int tokens. | |
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) | |
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: | |
def create_custom_forward(module): | |
def custom_forward(*inputs): | |
return module(*inputs, output_attentions) | |
return custom_forward | |
layer_outputs = torch.utils.checkpoint.checkpoint( | |
create_custom_forward(encoder_layer), | |
hidden_states, | |
attention_mask, | |
) | |
else: | |
layer_outputs = encoder_layer( | |
hidden_states, | |
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 BlipVisionModel(BlipPreTrainedModel): | |
main_input_name = "pixel_values" | |
config_class = BlipVisionConfig | |
def __init__(self, config: BlipVisionConfig): | |
super().__init__(config) | |
self.config = config | |
embed_dim = config.hidden_size | |
self.embeddings = BlipVisionEmbeddings(config) | |
self.encoder = BlipEncoder(config) | |
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) | |
self.post_init() | |
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, 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 pixel_values is None: | |
raise ValueError("You have to specify pixel_values") | |
hidden_states = self.embeddings(pixel_values) | |
encoder_outputs = self.encoder( | |
inputs_embeds=hidden_states, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
last_hidden_state = encoder_outputs[0] | |
last_hidden_state = self.post_layernorm(last_hidden_state) | |
pooled_output = last_hidden_state[:, 0, :] | |
pooled_output = self.post_layernorm(pooled_output) | |
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, | |
) | |
def get_input_embeddings(self): | |
return self.embeddings | |
class BlipModel(BlipPreTrainedModel): | |
config_class = BlipConfig | |
def __init__(self, config: BlipConfig): | |
super().__init__(config) | |
if not isinstance(config.text_config, BlipTextConfig): | |
raise ValueError( | |
"config.text_config is expected to be of type BlipTextConfig but is of type" | |
f" {type(config.text_config)}." | |
) | |
if not isinstance(config.vision_config, BlipVisionConfig): | |
raise ValueError( | |
"config.vision_config is expected to be of type BlipVisionConfig but is of type" | |
f" {type(config.vision_config)}." | |
) | |
text_config = config.text_config | |
vision_config = config.vision_config | |
self.projection_dim = config.projection_dim | |
self.text_embed_dim = text_config.hidden_size | |
self.vision_embed_dim = vision_config.hidden_size | |
self.text_model = BlipTextModel(text_config) | |
self.vision_model = BlipVisionModel(vision_config) | |
self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False) | |
self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False) | |
self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value)) | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_text_features( | |
self, | |
input_ids: Optional[torch.Tensor] = None, | |
attention_mask: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.Tensor] = None, | |
return_dict: Optional[bool] = None, | |
) -> torch.FloatTensor: | |
r""" | |
Returns: | |
text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by | |
applying the projection layer to the pooled output of [`BlipTextModel`]. | |
Examples: | |
```python | |
>>> from transformers import AutoProcessor, BlipModel | |
>>> model = BlipModel.from_pretrained("Salesforce/blip-image-captioning-base") | |
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
>>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") | |
>>> text_features = model.get_text_features(**inputs) | |
```""" | |
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, | |
return_dict=return_dict, | |
) | |
pooled_output = text_outputs[1] | |
text_features = self.text_projection(pooled_output) | |
return text_features | |
def get_image_features( | |
self, | |
pixel_values: Optional[torch.FloatTensor] = 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 [`BlipVisionModel`]. | |
Examples: | |
```python | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import AutoProcessor, BlipModel | |
>>> model = BlipModel.from_pretrained("Salesforce/blip-image-captioning-base") | |
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
>>> 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) | |
```""" | |
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, return_dict=return_dict) | |
pooled_output = vision_outputs[1] # pooled_output | |
image_features = self.visual_projection(pooled_output) | |
return image_features | |
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, BlipOutput]: | |
r""" | |
Returns: | |
Examples: | |
```python | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import AutoProcessor, BlipModel | |
>>> model = BlipModel.from_pretrained("Salesforce/blip-image-captioning-base") | |
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
>>> 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 | |
```""" | |
# Use BLIP 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, | |
) | |
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) | |
# normalized features | |
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) | |
# cosine similarity as logits | |
logit_scale = self.logit_scale.exp() | |
logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale | |
logits_per_image = logits_per_text.t() | |
loss = None | |
if return_loss: | |
loss = blip_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 BlipOutput( | |
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, | |
) | |
class BlipForConditionalGeneration(BlipPreTrainedModel): | |
config_class = BlipConfig | |
_tied_weights_keys = ["text_decoder.cls.predictions.decoder.bias"] | |
main_input_name = "pixel_values" | |
def __init__(self, config: BlipConfig): | |
super().__init__(config) | |
self.vision_model = BlipVisionModel(config.vision_config) | |
self.text_decoder = BlipTextLMHeadModel(config.text_config) | |
self.decoder_input_ids = config.text_config.bos_token_id | |
self.decoder_pad_token_id = config.text_config.pad_token_id | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_input_embeddings(self) -> nn.Module: | |
return self.vision_model.embeddings.patch_embedding | |
def forward( | |
self, | |
pixel_values: torch.FloatTensor, | |
input_ids: Optional[torch.LongTensor] = None, | |
attention_mask: Optional[torch.LongTensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
labels: Optional[torch.LongTensor] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple, BlipForConditionalGenerationModelOutput]: | |
r""" | |
Returns: | |
Examples: | |
```python | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import AutoProcessor, BlipForConditionalGeneration | |
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
>>> model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
>>> image = Image.open(requests.get(url, stream=True).raw) | |
>>> text = "A picture of" | |
>>> inputs = processor(images=image, text=text, return_tensors="pt") | |
>>> outputs = model(**inputs) | |
```""" | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
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 | |
) | |
vision_outputs = self.vision_model( | |
pixel_values=pixel_values, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
image_embeds = vision_outputs[0] | |
outputs = self.text_decoder( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
encoder_hidden_states=image_embeds, | |
labels=labels, | |
return_dict=return_dict, | |
reduction="mean", | |
) | |
if not return_dict: | |
outputs = (outputs[0], outputs[1], image_embeds, vision_outputs[0]) + vision_outputs[2:] | |
return tuple(output for output in outputs if output is not None) | |
return BlipForConditionalGenerationModelOutput( | |
loss=outputs.loss, | |
logits=outputs.logits, | |
image_embeds=image_embeds, | |
last_hidden_state=vision_outputs.last_hidden_state, | |
hidden_states=vision_outputs.hidden_states, | |
attentions=vision_outputs.attentions, | |
) | |
def generate( | |
self, | |
pixel_values: torch.FloatTensor, | |
input_ids: Optional[torch.LongTensor] = None, | |
attention_mask: Optional[torch.LongTensor] = None, | |
**generate_kwargs, | |
) -> torch.LongTensor: | |
r""" | |
Overrides *generate* function to be able to use the model as a conditional generator | |
Parameters: | |
pixel_values (*torch.FloatTensor* of shape *(batch_size, num_channels, image_height, image_width)*: | |
Input image to be processed | |
input_ids (*torch.LongTensor* of shape *(batch_size, sequence_length)*, *optional*): | |
The sequence used as a prompt for the generation. | |
attention_mask (*torch.LongTensor* of shape *(batch_size, sequence_length)*, *optional*): | |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: | |
Examples: | |
```python | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import AutoProcessor, BlipForConditionalGeneration | |
>>> model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
>>> 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.generate(**inputs) | |
>>> print(processor.decode(outputs[0], skip_special_tokens=True)) | |
two cats sleeping on a couch | |
``` | |
""" | |
batch_size = pixel_values.shape[0] | |
vision_outputs = self.vision_model(pixel_values=pixel_values) | |
image_embeds = vision_outputs[0] | |
image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image_embeds.device) | |
if isinstance(input_ids, list): | |
input_ids = torch.LongTensor(input_ids) | |
elif input_ids is None: | |
input_ids = ( | |
torch.LongTensor([[self.decoder_input_ids, self.config.text_config.eos_token_id]]) | |
.repeat(batch_size, 1) | |
.to(image_embeds.device) | |
) | |
input_ids[:, 0] = self.config.text_config.bos_token_id | |
attention_mask = attention_mask[:, :-1] if attention_mask is not None else None | |
outputs = self.text_decoder.generate( | |
input_ids=input_ids[:, :-1], | |
eos_token_id=self.config.text_config.sep_token_id, | |
pad_token_id=self.config.text_config.pad_token_id, | |
attention_mask=attention_mask, | |
encoder_hidden_states=image_embeds, | |
encoder_attention_mask=image_attention_mask, | |
**generate_kwargs, | |
) | |
return outputs | |
class BlipForQuestionAnswering(BlipPreTrainedModel): | |
config_class = BlipConfig | |
_tied_weights_keys = ["text_decoder.cls.predictions.decoder.bias"] | |
def __init__(self, config: BlipConfig): | |
super().__init__(config) | |
self.vision_model = BlipVisionModel(config.vision_config) | |
self.text_encoder = BlipTextModel(config.text_config, add_pooling_layer=False) | |
self.text_decoder = BlipTextLMHeadModel(config.text_config) | |
self.decoder_pad_token_id = config.text_config.pad_token_id | |
self.decoder_start_token_id = config.text_config.bos_token_id | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_input_embeddings(self) -> nn.Module: | |
return self.vision_model.embeddings.patch_embedding | |
def forward( | |
self, | |
input_ids: torch.LongTensor, | |
pixel_values: torch.FloatTensor, | |
decoder_input_ids: Optional[torch.LongTensor] = None, | |
decoder_attention_mask: Optional[torch.LongTensor] = None, | |
attention_mask: Optional[torch.LongTensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
labels: Optional[torch.LongTensor] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple, BlipTextVisionModelOutput]: | |
r""" | |
Returns: | |
Examples: | |
```python | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import AutoProcessor, BlipForQuestionAnswering | |
>>> model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base") | |
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base") | |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
>>> image = Image.open(requests.get(url, stream=True).raw) | |
>>> # training | |
>>> text = "How many cats are in the picture?" | |
>>> label = "2" | |
>>> inputs = processor(images=image, text=text, return_tensors="pt") | |
>>> labels = processor(text=label, return_tensors="pt").input_ids | |
>>> inputs["labels"] = labels | |
>>> outputs = model(**inputs) | |
>>> loss = outputs.loss | |
>>> loss.backward() | |
>>> # inference | |
>>> text = "How many cats are in the picture?" | |
>>> inputs = processor(images=image, text=text, return_tensors="pt") | |
>>> outputs = model.generate(**inputs) | |
>>> print(processor.decode(outputs[0], skip_special_tokens=True)) | |
2 | |
```""" | |
if labels is None and decoder_input_ids is None: | |
raise ValueError( | |
"Either `decoder_input_ids` or `labels` should be passed when calling `forward` with" | |
" `BlipForQuestionAnswering`. if you are training the model make sure that `labels` is passed, if you" | |
" are using the model for inference make sure that `decoder_input_ids` is passed or call `generate`" | |
) | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
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 | |
) | |
vision_outputs = self.vision_model( | |
pixel_values=pixel_values, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
image_embeds = vision_outputs[0] | |
image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long) | |
question_embeds = self.text_encoder( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
encoder_hidden_states=image_embeds, | |
encoder_attention_mask=image_attention_mask, | |
return_dict=return_dict, | |
) | |
if labels is not None and decoder_input_ids is None: | |
# labels are already shifted right, see: https://github.com/huggingface/transformers/pull/23153 | |
decoder_input_ids = labels | |
question_embeds = question_embeds[0] if not return_dict else question_embeds.last_hidden_state | |
answer_output = self.text_decoder( | |
input_ids=decoder_input_ids, | |
attention_mask=decoder_attention_mask, | |
encoder_hidden_states=question_embeds, | |
encoder_attention_mask=attention_mask, | |
labels=labels, | |
return_dict=return_dict, | |
reduction="mean", | |
) | |
if labels is not None: | |
decoder_loss = answer_output.loss.mean() if return_dict else answer_output[0].mean() | |
else: | |
decoder_loss = None | |
if not return_dict: | |
outputs = (decoder_loss, image_embeds, vision_outputs[0]) + vision_outputs[2:] | |
return tuple(output for output in outputs if output is not None) | |
return BlipTextVisionModelOutput( | |
loss=decoder_loss, | |
image_embeds=image_embeds, | |
last_hidden_state=vision_outputs.last_hidden_state, | |
hidden_states=vision_outputs.hidden_states, | |
attentions=vision_outputs.attentions, | |
) | |
def generate( | |
self, | |
input_ids: torch.LongTensor, | |
pixel_values: torch.FloatTensor, | |
attention_mask: Optional[torch.LongTensor] = None, | |
**generate_kwargs, | |
) -> torch.LongTensor: | |
r""" | |
Overrides *generate* function to be able to use the model as a conditional generator | |
Parameters: | |
input_ids (*torch.LongTensor* of shape *(batch_size, sequence_length)*): | |
The sequence used as a prompt for the generation. | |
pixel_values (*torch.FloatTensor* of shape *(batch_size, num_channels, image_height, image_width)*: | |
Input image to be processed | |
attention_mask (*torch.LongTensor* 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 MASKED tokens. | |
**generate_kwargs: | |
Additional arguments passed to the *generate* function of the decoder | |
Examples: | |
```python | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import AutoProcessor, BlipForQuestionAnswering | |
>>> model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base") | |
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base") | |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
>>> image = Image.open(requests.get(url, stream=True).raw) | |
>>> text = "How many cats are in the picture?" | |
>>> inputs = processor(images=image, text=text, return_tensors="pt") | |
>>> outputs = model.generate(**inputs) | |
>>> print(processor.decode(outputs[0], skip_special_tokens=True)) | |
2 | |
``` | |
""" | |
vision_outputs = self.vision_model(pixel_values=pixel_values) | |
image_embeds = vision_outputs[0] | |
image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image_embeds.device) | |
if isinstance(input_ids, list): | |
input_ids = torch.LongTensor(input_ids) | |
question_outputs = self.text_encoder( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
encoder_hidden_states=image_embeds, | |
encoder_attention_mask=image_attention_mask, | |
return_dict=False, | |
) | |
question_embeds = question_outputs[0] | |
question_attention_mask = torch.ones(question_embeds.size()[:-1], dtype=torch.long).to(question_embeds.device) | |
bos_ids = torch.full( | |
(question_embeds.size(0), 1), fill_value=self.decoder_start_token_id, device=question_embeds.device | |
) | |
outputs = self.text_decoder.generate( | |
input_ids=bos_ids, | |
eos_token_id=self.config.text_config.sep_token_id, | |
pad_token_id=self.config.text_config.pad_token_id, | |
encoder_hidden_states=question_embeds, | |
encoder_attention_mask=question_attention_mask, | |
**generate_kwargs, | |
) | |
return outputs | |
class BlipForImageTextRetrieval(BlipPreTrainedModel): | |
config_class = BlipConfig | |
def __init__(self, config: BlipConfig): | |
super().__init__(config) | |
self.vision_model = BlipVisionModel(config.vision_config) | |
self.text_encoder = BlipTextModel(config.text_config, add_pooling_layer=False) | |
# vision projection layer | |
self.vision_proj = nn.Linear(config.vision_config.hidden_size, config.image_text_hidden_size) | |
# text projection layer | |
self.text_proj = nn.Linear(config.text_config.hidden_size, config.image_text_hidden_size) | |
# image text matching head | |
self.itm_head = nn.Linear(config.text_config.hidden_size, 2) | |
self.decoder_pad_token_id = ( | |
config.text_config.pad_token_id | |
if not hasattr(config, "decoder_pad_token_id") | |
else config.decoder_pad_token_id | |
) | |
self.decoder_start_token_id = ( | |
config.text_config.bos_token_id | |
if not hasattr(config, "decoder_start_token_id") | |
else config.decoder_start_token_id | |
) | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_input_embeddings(self) -> nn.Module: | |
return self.vision_model.embeddings.patch_embedding | |
def forward( | |
self, | |
input_ids: torch.LongTensor, | |
pixel_values: torch.FloatTensor, | |
use_itm_head: Optional[bool] = True, | |
attention_mask: Optional[torch.LongTensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple, BlipTextVisionModelOutput]: | |
r""" | |
Returns: | |
Examples: | |
```python | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import AutoProcessor, BlipForImageTextRetrieval | |
>>> model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco") | |
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip-itm-base-coco") | |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
>>> image = Image.open(requests.get(url, stream=True).raw) | |
>>> text = "an image of a cat" | |
>>> inputs = processor(images=image, text=text, return_tensors="pt") | |
>>> outputs = model(**inputs) | |
``` | |
""" | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
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 | |
) | |
vision_outputs = self.vision_model( | |
pixel_values=pixel_values, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
image_embeds = vision_outputs[0] | |
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long) | |
if use_itm_head: | |
question_embeds = self.text_encoder( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
encoder_hidden_states=image_embeds, | |
encoder_attention_mask=image_atts, | |
return_dict=return_dict, | |
) | |
question_embeds = question_embeds[0] if not return_dict else question_embeds.last_hidden_state | |
output = self.itm_head(question_embeds[:, 0, :]) | |
else: | |
question_embeds = self.text_encoder( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
return_dict=return_dict, | |
) | |
question_embeds = question_embeds[0] if not return_dict else question_embeds.last_hidden_state | |
image_feat = normalize(self.vision_proj(image_embeds[:, 0, :]), dim=-1) | |
text_feat = normalize(self.text_proj(question_embeds[:, 0, :]), dim=-1) | |
output = image_feat @ text_feat.t() | |
if not return_dict: | |
outputs = (output, vision_outputs[0]) + vision_outputs[2:] + (question_embeds,) | |
return tuple(output for output in outputs if output is not None) | |
return BlipImageTextMatchingModelOutput( | |
itm_score=output, | |
last_hidden_state=vision_outputs.last_hidden_state, | |
hidden_states=vision_outputs.hidden_states, | |
attentions=vision_outputs.attentions, | |
question_embeds=question_embeds, | |
) | |