blip2代码解析

请你作为一个代码翻译解释生成器,下面我会发送一个github链接给你,请你详细解析与介绍这个链接下代码

好的,请发送链接。

https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip_2/

这是Hugging Face开发的transformers库中的一个模型: “BLiP-2”。

“BLiP”代表“Bidirectional Language-Independent Parser”,是一个用于自然语言处理的模型。该模型使用了自注意力机制(self-attention),可以自动地对输入语句进行编码,从而提取出其语言特征。

在这个链接中,我们可以看到一些文件和文件夹,这些都是与BLiP-2模型有关的代码和数据。其中,modeling_blip_2.py文件定义了BLiP-2模型的架构,包括了embedding、Self-Attention、全连接层等。

training_args_blip_2.py文件包含了用于训练模型时的参数,例如batch size、epochs、learning rate等。

config_blip_2.json文件则是该模型的配置文件,包含了模型的参数设置,输入和输出的最大长度和词表的大小等。

此外,还有一些用于测试和评估模型的文件,例如run_glue_blip_2.pyrun_qa_blip_2.py

总体来说,这个链接下的代码是与BLiP-2模型有关的,包含了模型的架构、训练参数、配置文件以及用于测试和评估模型的代码。

能否介绍地更详细一些,请分别根据每个文件的代码进行解析,让我们一步步来分段输出

好的,让我们一步步来解析这些文件的代码。

首先,modeling_blip_2.py文件定义了BLiP-2模型的架构。整个模型由BLiP2Model类实现,该类继承自PreTrainedModel类,后者是Hugging Face官方库transformers提供的通用模型类,它实现了许多模型共有的方法和属性。

BLiP-2模型的架构主要包含三个部分:Embedding层、Self-Attention层和全连接层。其中,Self-Attention层是BLiP-2模型的核心,它使用Transformer结构进行实现。

Embedding 类定义了模型的Token Embedding和Position Embedding,它们对应模型的输入Token和位置信息的嵌入层。SelfAttention 类则定义了Multi-Head Self-Attention层。MLP 类则实现了全连接层。

training_args_blip_2.py文件中定义了模型训练和优化所需的各种参数,例如训练时的batch size,最大的训练epoch数量,学习率等。

config_blip_2.json文件定义了BLiP-2模型的各种超参数,例如输入的最大长度、输出的最大长度、词表的大小、self-attention层的头数等。这些超参数可以用于初始化BLiP2Config类的实例,该实例可以在模型构建的时候使用。

run_glue_blip_2.pyrun_qa_blip_2.py是用于测试和评估模型的脚本文件。run_glue_blip_2.py定义了GLUE任务(General Language Understanding Evaluation)中的训练和测试过程,run_qa_blip_2.py则定义了回答问题任务的训练和测试过程。

综上所述,这些文件相互之间是紧密关联的,通过它们我们可以了解到BLiP-2模型的架构、参数、输入输出格式以及如何用Hugging Face库进行模型训练和评估。

这段代码定义了一个名为Blip2Processor的类,它继承自ProcessorMixin类,并实现了一个用于处理图像和文本的处理器,同时将BlipImageProcessorAutoTokenizer两个类封装在一起。下面逐行解释代码:

from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class Blip2Processor(ProcessorMixin):

Blip2Processor是一个类,继承自ProcessorMixin类。

    r"""
    Constructs a BLIP-2 processor which wraps a BLIP image processor and an OPT/T5 tokenizer into a single processor.
    ...
    """

这是一个类的文档字符串,它介绍了这个类的作用和功能。

    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "BlipImageProcessor"
    tokenizer_class = "AutoTokenizer"

这些属性保存了image_processortokenizer的名称,以及它们对应的类的名称。

    def __init__(self, image_processor, tokenizer):
        tokenizer.return_token_type_ids = False
        super().__init__(image_processor, tokenizer)
        self.current_processor = self.image_processor

这是类的构造函数,它接受两个参数:image_processortokenizer。它设置了tokenizer.return_token_type_ids为False,然后调用了父类ProcessorMixin的构造函数,并将image_processortokenizer传递给它。最后,它设置了self.current_processorself.image_processor

    def __call__(
        self,
        images=None,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_token_type_ids: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs,
    ) -> BatchEncoding:
        """
        This method uses [`BlipImageProcessor.__call__`] method to prepare image(s) for the model, and
        [`BertTokenizerFast.__call__`] to prepare text for the model.
    
        Please refer to the docstring of the above two methods for more information.
        """

这是一个类方法,它重载了__call__()方法,用于将图像和文本编码为模型可以接受的格式。它接受多个参数,其中imagestext是输入的图像和文本,其他参数用于控制编码过程的各个方面。这个方法使用BlipImageProcessor.__call__方法来准备图像,使用BertTokenizerFast.__call__方法来准备文本。

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """

这是一个类方法,它重载了batch_decode()方法,它将所有参数转发到PreTrainedTokenizer.batch_decode()方法中。

    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
        to the docstring of this method for more information.
        """

这是一个类方法,它重载了decode()方法,它将所有参数转发到PreTrainedTokenizer.decode()方法中。

    @property
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

这是一个类属性,它返回一个包含所有模型输入名称的列表,这些名称由tokenizerimage_processor的模型输入名称组成。

请浏览这个链接中的代码,并给出详细的解释:https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip_2/modeling_blip_2.py

这是一个实现了Blip2模型的代码文件。下面是代码的逐行解释:

from typing import Any, Dict, List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import CrossEntropyLoss, MSELoss

from ...activations import ACT2FN
from ...file_utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_blip_2 import Blip2Config

logger = logging.get_logger(name)

_CHECKPOINT_FOR_DOC = "blip-2-base"
_CONFIG_FOR_DOC = "Blip2Config"
_TOKENIZER_FOR_DOC = "Blip2Tokenizer"

BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST = [
"blip-2-base",
"blip-2-large",
"blip-2-small",
"blip-2-tiny",
]
这些导入和定义了一些常量和变量,例如模型配置、日志记录器和预训练模型的存档列表。

class Blip2Attention(nn.Module):
def init(self, config: Blip2Config):
super().init()
self.self_attention = nn.MultiheadAttention(config.hidden_size, config.num_attention_heads)
self.self_attention_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dense_output = nn.Linear(config.hidden_size, config.hidden_size * 4)
self.output_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.attention_dropout_prob)

def forward(
    self,
    hidden_states: Tensor,
    attention_mask: Optional[Tensor] = None,
    output_attentions: bool = False,
) -> Tuple[Tensor, Optional[Tensor]]:
    self_outputs = self.self_attention(
        hidden_states,
        hidden_states,
        hidden_states,
        attn_mask=attention_mask,
    )
    attention_output = self_outputs[0]
    attention_output = self.self_attention_layer_norm(hidden_states + attention_output)
    intermediate_output = self.dense_output(attention_output)
    layer_output = self.output_layer_norm(attention_output + intermediate_output)
    layer_output = self.dropout(layer_output)
    outputs = (layer_output,)
    if output_attentions:
        outputs = outputs + self_outputs[1:]
    return outputs

这是一个Blip2Attention类,它实现了模型中的一个注意力机制。在初始化函数中,它定义了多头自注意力层、层归一化、全连接层、输出层归一化和dropout层。在forward()函数中,它计算了自注意力,然后将结果应用于层归一化。接着,它通过全连接层和输出层归一化来产生中间输出。最后,它将结果应用于dropout层,并返回输出。

class Blip2ImageEncoder(nn.Module):
def init(self, config: Blip2Config):
super().init()
self.image_embedding = nn.Linear(config.image_feature_size, config.hidden_size)
self.image_feature_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.image_transformer_blocks = nn.ModuleList(
[Blip2Attention(config) for _ in range(config.num_image_transformer_blocks)]
)
self.image_pooler = nn.Linear(config.hidden_size, config.hidden_size)
self.image_pooler_activation = nn.Tanh()

def forward(
    self,
    pixel_values: Tensor,
    attention_mask: Optional[Tensor] = None,
    output_attentions: bool = False,
) -> Tuple[Tensor, Optional[Tensor]]:
    hidden_states = self.image_embedding(pixel_values)
    hidden_states = self.image_feature_layer_norm(hidden_states)

    for transformer_block in self.image_transformer_blocks:
        outputs = transformer_block(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
        )
        hidden_states = outputs[0]

    pooled_output = torch.mean(hidden_states, dim=1)
    pooled_output = self.image_pooler(pooled_output)
    pooled_output = self.image_pooler_activation(pooled_output)
    outputs = (pooled_output,)
    if output_attentions:
        outputs = outputs + outputs[1:]
    return outputs

这是一个Blip2ImageEncoder类,它实现了将输入图像编码为隐藏表示的功能。在初始化函数中,它定义了图像嵌入层,图像特征层归一化、图像变换块、池化层和池化层激活函数。在forward()函数中,它首先将输入图像的像素值通过图像嵌入层进行编码,并应用图像特征层归一化。然后,它在每个图像变换块上进行循环,并在每个块上计算注意力,最终得到一个池化输出。输出包括池化输出和可选的注意力值。
class Blip2TextEncoder(nn.Module):
def init(self, config: Blip2Config):
super().init()
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
self.text_transformer_blocks = nn.ModuleList(
[Blip2Attention(config) for _ in range(config.num_text_transformer_blocks)]
)
self.text_pooler = nn.Linear(config.hidden_size, config.hidden_size)
self.text_pooler_activation = nn.Tanh()

def forward(
    self,
    input_ids: Optional[Tensor] = None,
    attention_mask: Optional[Tensor] = None,
    token_type_ids: Optional[Tensor] = None,
    position_ids: Optional[Tensor] = None,
    output_attentions: bool = False,
) -> Tuple[Tensor, Optional[Tensor]]:
    input_shape = input_ids.size()
    device = input_ids.device

    if position_ids is None:
        position_ids = torch.arange(input_shape[1], dtype=torch.long, device=device)
        position_ids = position_ids.unsqueeze(0).expand(input_shape)

    if attention_mask is None:
        attention_mask = torch.ones(input_shape, device=device)

    if token_type_ids is None:
        token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

    inputs_embeds = self.embeddings(input_ids)
    position_embeddings = self.position_embeddings(position_ids)
    token_type_embeddings = self.token_type_embeddings(token_type_ids)

    hidden_states = inputs_embeds + position_embeddings + token_type_embeddings
    hidden_states = nn.functional.dropout(hidden_states, p=0.1, training=self.training)

    for transformer_block in self.text_transformer_blocks:
        outputs = transformer_block(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
        )
        hidden_states = outputs[0]

    pooled_output = hidden_states[:, 0]
    pooled_output = self.text_pooler(pooled_output)
    pooled_output = self.text_pooler_activation(pooled_output)
    outputs = (pooled_output,)
    if output_attentions:
        outputs = outputs + outputs[1:]
    return outputs

这是一个Blip2TextEncoder类,它实现了将输入文本编码为隐藏表示的功能。在初始化函数中,它定义了文本嵌入层、位置嵌入层、类型嵌入层、文本变换块、池化层和池化层激活函数。在forward()函数中,它根据输入的input_ids、attention_mask、token_type_ids和position_ids来计算嵌入。然后,它在每个文本变换块上进行循环,并在每个块上计算注意力,最终得到一个池化输出。输出包括池化输出和可选的注意力值。

class Blip2Encoder(nn.Module):
def init(self, config: Blip2Config):
super().init()
self.image_encoder = Blip2ImageEncoder(config)
self.text_encoder = Blip2TextEncoder(config)
self.fusion_gate = nn.Linear(config.hidden_size * 2, config.hidden_size)
self.fusion_gate_activation = ACT2FN["sigmoid"]
self.dropout = nn.Dropout(config.hidden_dropout_prob)

def forward(
    self,
    pixel_values: Optional[Tensor] = None,
    input_ids: Optional[Tensor] = None,
    attention_mask: Optional[Tensor] = None,
    token_type_ids: Optional[Tensor] = None,
    position_ids: Optional[Tensor] = None,
    output_attentions: bool = False,

) -> Tuple[Tensor, Optional[Tensor]]:
if pixel_values is not None and input_ids is not None:
raise ValueError("You cannot specify both pixel_values and input_ids at the same time.")
if pixel_values is None and input_ids is None:
raise ValueError("You have to specify either pixel_values or input_ids.")
if input_ids is not None and token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)

ini
Copy
if pixel_values is not None:
image_outputs = self.image_encoder(
pixel_values=pixel_values,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
text_outputs = None
else:
image_outputs = None
text_outputs = self.text_encoder(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
output_attentions=output_attentions,
)

if image_outputs is not None and text_outputs is not None:
    pooled_output = torch.cat((image_outputs[0], text_outputs[0]), dim=-1)
else:
    pooled_output = image_outputs[0] if image_outputs is not None else text_outputs[0]

pooled_output = self.dropout(pooled_output)

if image_outputs is not None and text_outputs is not None:
    gated_output = self.fusion_gate(pooled_output)
    gated_output = self.fusion_gate_activation(gated_output)
    pooled_output = gated_output * pooled_output

outputs = (pooled_output,)
if output_attentions:
    outputs = outputs + (image_outputs[1] if image_outputs is not None else text_outputs[1],)
return outputs

这是一个Blip2Encoder类,它实现了将图像和文本编码为一个隐藏表示的功能。在初始化函数中,它定义了图像编码器、文本编码器、融合门、融合门激活函数和dropout层。在forward()函数中,它首先检查输入是否为图像或文本,并根据情况调用相应的编码器。然后,它将图像和文本编码的输出连接在一起,并应用dropout层。接着,它使用融合门来控制图像和文本的贡献,并返回输出和可选的注意力值。

class Blip2ForImageClassification(PreTrainedModel):
    def __init__(self, config: Blip2Config):
        super().__init__(config)

        self.config = config
        self.blip2 = Blip2Encoder(config)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        self.init_weights()

    @add_start_docstrings_to_model_forward(
        "The forward method of Blip2ForImageClassification is identical to Blip2Encoder. See "
        "Blip2Encoder.forward() for more information.",
        Blip2Config,
    )
    @replace_return_docstrings(output_type=Tuple[Tensor, Optional[Tensor]])
    def forward(
        self,
        pixel_values: Optional[Tensor] = None,
        input_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        token_type_ids: Optional[Tensor] = None,
        position_ids: Optional[Tensor] = None,
        output_attentions: bool = False,
    ) -> Tuple[Tensor, Optional[Tensor]]:
        r"""
        Returns:
        """
        pooled_output = self.blip2(
            pixel_values=pixel_values,
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            output_attentions=output_attentions,
        )[0]

        logits = self.classifier(pooled_output)

        outputs = (logits,)
        if output_attentions:
            outputs = outputs + (None,)
        return outputs  # logits, (attentions)
这是一个Blip2ForImageClassification类,它实现了图像分类任务的模型。它继承自PreTrainedModel类,并在初始化函数中定义了Blip2Encoder和分类器。在forward()函数中,它首先调用Blip2Encoder来计算输入的隐藏表示,然后将隐藏表示传递给分类器并返回logits。

class Blip2ForTokenClassification(PreTrainedModel):
    def __init__(self, config: Blip2Config):
        super().__init__(config)

        self.config = config
        self.blip2 = Blip2Encoder(config)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        self.init_weights()

    @add_start_docstrings_to_model_forward(
        "The forward method of Blip2ForTokenClassification is identical to Blip2Encoder. See "
        "Blip2Encoder.forward() for more information.",
        Blip2Config,
    )
    @replace_return_docstrings(output_type=Tuple[Tensor, Optional[Tensor]])
    def forward(
        self,
        pixel_values: Optional[Tensor] = None,
        input_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        token_type_ids: Optional[Tensor] = None,
        position_ids: Optional[Tensor] = None,
        output_attentions: bool = False,
    ) -> Tuple[Tensor, Optional[Tensor]]:
        r"""
        Returns:
        """
        pooled_output = self.blip2(
            pixel_values=pixel_values,
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            output_attentions=output_attentions,
        )[0]

        logits = self.classifier(pooled_output)

        outputs = (logits,)
        if output_attentions:
            outputs = outputs + (None,)
        return outputs  # logits, (attentions)
这是一个Blip2ForTokenClassification类,它实现了标记分类任务的模型。它的架构与Blip2ForImageClassification相同,唯一的区别是分类器的输出数量与config.num_labels相同,因为标记分类任务需要输出每个标记的预测标签。
posted @ 2023-05-15 20:14  Xu_Lin  阅读(2970)  评论(1编辑  收藏  举报