《昇思25天学习打卡营第15天|Vision Transformer图像分类》

发布于:2024-07-11 ⋅ 阅读:(31) ⋅ 点赞:(0)

#学习打卡第15天#

1. Vision Transformer(ViT)

        近些年,随着基于自注意(Self-Attention)结构的模型的发展,特别是Transformer模型的提出,极大地促进了自然语言处理模型的发展。由于Transformers的计算效率和可扩展性,它已经能够训练具有超过100B参数的空前规模的模型。

1.1 模型结构

ViT模型的主体结构是基于Transformer模型的Encoder部分(部分结构顺序有调整,如:Normalization的位置与标准Transformer不同),其结构图如下:

1.2 模型特点

ViT模型主要应用于图像分类领域。因此,其模型结构相较于传统的Transformer有以下几个特点:

  1. 数据集的原图像被划分为多个patch(图像块)后,将二维patch(不考虑channel)转换为一维向量,再加上类别向量与位置向量作为模型输入。
  2. 模型主体的Block结构是基于Transformer的Encoder结构,但是调整了Normalization的位置,其中,最主要的结构依然是Multi-head Attention结构。
  3. 模型在Blocks堆叠后接全连接层,接受类别向量的输出作为输入并用于分类。通常情况下,我们将最后的全连接层称为Head,Transformer Encoder部分为backbone。

2. 模型解析

2.1 Transformer基本原理

Transformer模型源于2017年的一篇文章。在这篇文章中提出的基于Attention机制的编码器-解码器型结构在自然语言处理领域获得了巨大的成功。其主要结构为多个Encoder和Decoder模块所组成,其中Encoder和Decoder的详细结构如下图所示:

        Encoder与Decoder由许多结构组成,如:多头注意力(Multi-Head Attention)层,Feed Forward层,Normaliztion层,甚至残差连接(Residual Connection,图中的“Add”)。不过,其中最重要的结构是多头注意力(Multi-Head Attention)结构,该结构基于自注意力(Self-Attention)机制,是多个Self-Attention的并行组成。

Attention模块

        核心内容是为输入向量的每个单词学习一个权重。通过给定一个任务相关的查询向量Query向量,计算Query和各个Key的相似性或者相关性得到注意力分布,即得到每个Key对应Value的权重系数,然后对Value进行加权求和得到最终的Attention数值。通过下图可以整体把握Self-Attention的全部过程:

        多头注意力机制就是将原本self-Attention处理的向量分割为多个Head进行处理,这也是attention结构可以进行并行加速的一个方面。

        总结来说,多头注意力机制在保持参数总量不变的情况下,将同样的query, key和value映射到原来的高维空间(Q,K,V)的不同子空间(Q_0,K_0,V_0)中进行自注意力的计算,最后再合并不同子空间中的注意力信息。

        所以,对于同一个输入向量,多个注意力机制可以同时对其进行处理,即利用并行计算加速处理过程,又在处理的时候更充分的分析和利用了向量特征。下图展示了多头注意力机制,其并行能力的主要体现在下图中的𝑎1和𝑎2是同一个向量进行分割获得的。

以下是Multi-Head Attention代码,结合上文的解释,代码清晰的展现了这一过程。

from mindspore import nn, ops


class Attention(nn.Cell):
    def __init__(self,
                 dim: int,
                 num_heads: int = 8,
                 keep_prob: float = 1.0,
                 attention_keep_prob: float = 1.0):
        super(Attention, self).__init__()

        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = ms.Tensor(head_dim ** -0.5)

        self.qkv = nn.Dense(dim, dim * 3)
        self.attn_drop = nn.Dropout(p=1.0-attention_keep_prob)
        self.out = nn.Dense(dim, dim)
        self.out_drop = nn.Dropout(p=1.0-keep_prob)
        self.attn_matmul_v = ops.BatchMatMul()
        self.q_matmul_k = ops.BatchMatMul(transpose_b=True)
        self.softmax = nn.Softmax(axis=-1)

    def construct(self, x):
        """Attention construct."""
        b, n, c = x.shape
        qkv = self.qkv(x)
        qkv = ops.reshape(qkv, (b, n, 3, self.num_heads, c // self.num_heads))
        qkv = ops.transpose(qkv, (2, 0, 3, 1, 4))
        q, k, v = ops.unstack(qkv, axis=0)
        attn = self.q_matmul_k(q, k)
        attn = ops.mul(attn, self.scale)
        attn = self.softmax(attn)
        attn = self.attn_drop(attn)
        out = self.attn_matmul_v(attn, v)
        out = ops.transpose(out, (0, 2, 1, 3))
        out = ops.reshape(out, (b, n, c))
        out = self.out(out)
        out = self.out_drop(out)

        return out

2.2 Transformer Encoder

在了解了Self-Attention结构之后,通过与Feed Forward,Residual Connection等结构的拼接就可以形成Transformer的基础结构,下面代码实现了Feed Forward,Residual Connection结构。

from typing import Optional, Dict


class FeedForward(nn.Cell):
    def __init__(self,
                 in_features: int,
                 hidden_features: Optional[int] = None,
                 out_features: Optional[int] = None,
                 activation: nn.Cell = nn.GELU,
                 keep_prob: float = 1.0):
        super(FeedForward, self).__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.dense1 = nn.Dense(in_features, hidden_features)
        self.activation = activation()
        self.dense2 = nn.Dense(hidden_features, out_features)
        self.dropout = nn.Dropout(p=1.0-keep_prob)

    def construct(self, x):
        """Feed Forward construct."""
        x = self.dense1(x)
        x = self.activation(x)
        x = self.dropout(x)
        x = self.dense2(x)
        x = self.dropout(x)

        return x


class ResidualCell(nn.Cell):
    def __init__(self, cell):
        super(ResidualCell, self).__init__()
        self.cell = cell

    def construct(self, x):
        """ResidualCell construct."""
        return self.cell(x) + x

接下来就利用Self-Attention来构建ViT模型中的TransformerEncoder部分,类似于构建了一个Transformer的编码器部分,将TransformerEncoder结构和一个多层感知器(MLP)结合,就构成了ViT模型的backbone部分。注意:

  1. ViT模型中的基础结构与标准Transformer有所不同,主要在于Normalization的位置是放在Self-Attention和Feed Forward之前,其他结构如Residual Connection,Feed Forward,Normalization都如Transformer中所设计。

  2. 从Transformer结构的图片可以发现,多个子encoder的堆叠就完成了模型编码器的构建,在ViT模型中,依然沿用这个思路,通过配置超参数num_layers,就可以确定堆叠层数。

  3. Residual Connection,Normalization的结构可以保证模型有很强的扩展性(保证信息经过深层处理不会出现退化的现象,这是Residual Connection的作用),Normalization和dropout的应用可以增强模型泛化能力。

class TransformerEncoder(nn.Cell):
    def __init__(self,
                 dim: int,
                 num_layers: int,
                 num_heads: int,
                 mlp_dim: int,
                 keep_prob: float = 1.,
                 attention_keep_prob: float = 1.0,
                 drop_path_keep_prob: float = 1.0,
                 activation: nn.Cell = nn.GELU,
                 norm: nn.Cell = nn.LayerNorm):
        super(TransformerEncoder, self).__init__()
        layers = []

        for _ in range(num_layers):
            normalization1 = norm((dim,))
            normalization2 = norm((dim,))
            attention = Attention(dim=dim,
                                  num_heads=num_heads,
                                  keep_prob=keep_prob,
                                  attention_keep_prob=attention_keep_prob)

            feedforward = FeedForward(in_features=dim,
                                      hidden_features=mlp_dim,
                                      activation=activation,
                                      keep_prob=keep_prob)

            layers.append(
                nn.SequentialCell([
                    ResidualCell(nn.SequentialCell([normalization1, attention])),
                    ResidualCell(nn.SequentialCell([normalization2, feedforward]))
                ])
            )
        self.layers = nn.SequentialCell(layers)

    def construct(self, x):
        """Transformer construct."""
        return self.layers(x)

2.3 ViT模型的输入

        传统的Transformer结构主要用于处理自然语言领域的词向量(Word Embedding or Word Vector),词向量与传统图像数据的主要区别在于,词向量通常是一维向量进行堆叠,而图片则是二维矩阵的堆叠,多头注意力机制在处理一维词向量的堆叠时会提取词向量之间的联系也就是上下文语义,所以在使用ViT模型处理图片数据,需要考虑将二维图片矩阵转换为一维向量:

  1. 通过将输入图像在每个channel上划分为1616个patch,这一步是通过卷积操作来完成的,当然也可以人工进行划分,但卷积操作也可以达到目的同时还可以进行一次而外的数据处理;*例如一幅输入224 x 224的图像,首先经过卷积处理得到16 x 16个patch,那么每一个patch的大小就是14 x 14。

  2. 再将每一个patch的矩阵拉伸成为一个一维向量,从而获得了近似词向量堆叠的效果。上一步得到的14 x 14的patch就转换为长度为196的向量。

class PatchEmbedding(nn.Cell):
    MIN_NUM_PATCHES = 4

    def __init__(self,
                 image_size: int = 224,
                 patch_size: int = 16,
                 embed_dim: int = 768,
                 input_channels: int = 3):
        super(PatchEmbedding, self).__init__()

        self.image_size = image_size
        self.patch_size = patch_size
        self.num_patches = (image_size // patch_size) ** 2
        self.conv = nn.Conv2d(input_channels, embed_dim, kernel_size=patch_size, stride=patch_size, has_bias=True)

    def construct(self, x):
        """Path Embedding construct."""
        x = self.conv(x)
        b, c, h, w = x.shape
        x = ops.reshape(x, (b, c, h * w))
        x = ops.transpose(x, (0, 2, 1))

        return x

输入图像在划分为patch之后,会经过pos_embedding 和 class_embedding两个过程。

  1. class_embedding主要借鉴了BERT模型的用于文本分类时的思想,在每一个word vector之前增加一个类别值,通常是加在向量的第一位,上一步得到的196维的向量加上class_embedding后变为197维。

  2. 增加的class_embedding是一个可以学习的参数,经过网络的不断训练,最终以输出向量的第一个维度的输出来决定最后的输出类别;由于输入是16 x 16个patch,所以输出进行分类时是取 16 x 16个class_embedding进行分类。

  3. pos_embedding也是一组可以学习的参数,会被加入到经过处理的patch矩阵中。

  4. 由于pos_embedding也是可以学习的参数,所以它的加入类似于全链接网络和卷积的bias。这一步就是创造一个长度维197的可训练向量加入到经过class_embedding的向量中。

        总的而言,ViT模型还是利用了Transformer模型在处理上下文语义时的优势,将图像转换为一种“变种词向量”然后进行处理,而这样转换的意义在于,多个patch之间本身具有空间联系,这类似于一种“空间语义”,从而获得了比较好的处理效果。

2.4 ViT模型的构建

整体流程图如下所示:

完整的模型构建代码:

from mindspore.common.initializer import Normal
from mindspore.common.initializer import initializer
from mindspore import Parameter


def init(init_type, shape, dtype, name, requires_grad):
    """Init."""
    initial = initializer(init_type, shape, dtype).init_data()
    return Parameter(initial, name=name, requires_grad=requires_grad)


class ViT(nn.Cell):
    def __init__(self,
                 image_size: int = 224,
                 input_channels: int = 3,
                 patch_size: int = 16,
                 embed_dim: int = 768,
                 num_layers: int = 12,
                 num_heads: int = 12,
                 mlp_dim: int = 3072,
                 keep_prob: float = 1.0,
                 attention_keep_prob: float = 1.0,
                 drop_path_keep_prob: float = 1.0,
                 activation: nn.Cell = nn.GELU,
                 norm: Optional[nn.Cell] = nn.LayerNorm,
                 pool: str = 'cls') -> None:
        super(ViT, self).__init__()

        self.patch_embedding = PatchEmbedding(image_size=image_size,
                                              patch_size=patch_size,
                                              embed_dim=embed_dim,
                                              input_channels=input_channels)
        num_patches = self.patch_embedding.num_patches

        self.cls_token = init(init_type=Normal(sigma=1.0),
                              shape=(1, 1, embed_dim),
                              dtype=ms.float32,
                              name='cls',
                              requires_grad=True)

        self.pos_embedding = init(init_type=Normal(sigma=1.0),
                                  shape=(1, num_patches + 1, embed_dim),
                                  dtype=ms.float32,
                                  name='pos_embedding',
                                  requires_grad=True)

        self.pool = pool
        self.pos_dropout = nn.Dropout(p=1.0-keep_prob)
        self.norm = norm((embed_dim,))
        self.transformer = TransformerEncoder(dim=embed_dim,
                                              num_layers=num_layers,
                                              num_heads=num_heads,
                                              mlp_dim=mlp_dim,
                                              keep_prob=keep_prob,
                                              attention_keep_prob=attention_keep_prob,
                                              drop_path_keep_prob=drop_path_keep_prob,
                                              activation=activation,
                                              norm=norm)
        self.dropout = nn.Dropout(p=1.0-keep_prob)
        self.dense = nn.Dense(embed_dim, num_classes)

    def construct(self, x):
        """ViT construct."""
        x = self.patch_embedding(x)
        cls_tokens = ops.tile(self.cls_token.astype(x.dtype), (x.shape[0], 1, 1))
        x = ops.concat((cls_tokens, x), axis=1)
        x += self.pos_embedding

        x = self.pos_dropout(x)
        x = self.transformer(x)
        x = self.norm(x)
        x = x[:, 0]
        if self.training:
            x = self.dropout(x)
        x = self.dense(x)

        return x

3 模型训练与推理

3.1 模型训练

        模型开始训练前,需要设定损失函数,优化器,回调函数等。完整训练ViT模型需要很长的时间,实际应用时,可以基于预训练模型权重进行初始化,然后根据项目需要调整epoch_size。

from mindspore.nn import LossBase
from mindspore.train import LossMonitor, TimeMonitor, CheckpointConfig, ModelCheckpoint
from mindspore import train

# define super parameter
epoch_size = 10
momentum = 0.9
num_classes = 1000
resize = 224
step_size = dataset_train.get_dataset_size()

# construct model
network = ViT()

# load ckpt
vit_url = "https://download.mindspore.cn/vision/classification/vit_b_16_224.ckpt"
path = "./ckpt/vit_b_16_224.ckpt"

vit_path = download(vit_url, path, replace=True)
param_dict = ms.load_checkpoint(vit_path)
ms.load_param_into_net(network, param_dict)

# define learning rate
lr = nn.cosine_decay_lr(min_lr=float(0),
                        max_lr=0.00005,
                        total_step=epoch_size * step_size,
                        step_per_epoch=step_size,
                        decay_epoch=10)

# define optimizer
network_opt = nn.Adam(network.trainable_params(), lr, momentum)


# define loss function
class CrossEntropySmooth(LossBase):
    """CrossEntropy."""

    def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
        super(CrossEntropySmooth, self).__init__()
        self.onehot = ops.OneHot()
        self.sparse = sparse
        self.on_value = ms.Tensor(1.0 - smooth_factor, ms.float32)
        self.off_value = ms.Tensor(1.0 * smooth_factor / (num_classes - 1), ms.float32)
        self.ce = nn.SoftmaxCrossEntropyWithLogits(reduction=reduction)

    def construct(self, logit, label):
        if self.sparse:
            label = self.onehot(label, ops.shape(logit)[1], self.on_value, self.off_value)
        loss = self.ce(logit, label)
        return loss


network_loss = CrossEntropySmooth(sparse=True,
                                  reduction="mean",
                                  smooth_factor=0.1,
                                  num_classes=num_classes)

# set checkpoint
ckpt_config = CheckpointConfig(save_checkpoint_steps=step_size, keep_checkpoint_max=100)
ckpt_callback = ModelCheckpoint(prefix='vit_b_16', directory='./ViT', config=ckpt_config)

# initialize model
# "Ascend + mixed precision" can improve performance
ascend_target = (ms.get_context("device_target") == "Ascend")
if ascend_target:
    model = train.Model(network, loss_fn=network_loss, optimizer=network_opt, metrics={"acc"}, amp_level="O2")
else:
    model = train.Model(network, loss_fn=network_loss, optimizer=network_opt, metrics={"acc"}, amp_level="O0")

# train model
model.train(epoch_size,
            dataset_train,
            callbacks=[ckpt_callback, LossMonitor(125), TimeMonitor(125)],
            dataset_sink_mode=False,)

3.2 模型验证

模型验证过程主要应用了ImageFolderDataset,CrossEntropySmooth和Model等接口。

ImageFolderDataset主要用于读取数据集。

CrossEntropySmooth是损失函数实例化接口。

Model主要用于编译模型。

与训练过程相似,首先进行数据增强,然后定义ViT网络结构,加载预训练模型参数。随后设置损失函数,评价指标等,编译模型后进行验证。下列代码采用了业界通用的评价标准Top_1_Accuracy和Top_5_Accuracy评价指标来评价模型表现。

dataset_val = ImageFolderDataset(os.path.join(data_path, "val"), shuffle=True)

trans_val = [
    transforms.Decode(),
    transforms.Resize(224 + 32),
    transforms.CenterCrop(224),
    transforms.Normalize(mean=mean, std=std),
    transforms.HWC2CHW()
]

dataset_val = dataset_val.map(operations=trans_val, input_columns=["image"])
dataset_val = dataset_val.batch(batch_size=16, drop_remainder=True)

# construct model
network = ViT()

# load ckpt
param_dict = ms.load_checkpoint(vit_path)
ms.load_param_into_net(network, param_dict)

network_loss = CrossEntropySmooth(sparse=True,
                                  reduction="mean",
                                  smooth_factor=0.1,
                                  num_classes=num_classes)

# define metric
eval_metrics = {'Top_1_Accuracy': train.Top1CategoricalAccuracy(),
                'Top_5_Accuracy': train.Top5CategoricalAccuracy()}

if ascend_target:
    model = train.Model(network, loss_fn=network_loss, optimizer=network_opt, metrics=eval_metrics, amp_level="O2")
else:
    model = train.Model(network, loss_fn=network_loss, optimizer=network_opt, metrics=eval_metrics, amp_level="O0")

# evaluate model
result = model.eval(dataset_val)
print(result)

3.3 模型推理

在进行模型推理之前,首先要定义一个对推理图片进行数据预处理的方法。该方法可以对推理图片进行resize和normalize处理,这样才能与训练时的输入数据匹配。

import os
import pathlib
import cv2
import numpy as np
from PIL import Image
from enum import Enum
from scipy import io

dataset_infer = ImageFolderDataset(os.path.join(data_path, "infer"), shuffle=True)

trans_infer = [
    transforms.Decode(),
    transforms.Resize([224, 224]),
    transforms.Normalize(mean=mean, std=std),
    transforms.HWC2CHW()
]

dataset_infer = dataset_infer.map(operations=trans_infer,
                                  input_columns=["image"],
                                  num_parallel_workers=1)
dataset_infer = dataset_infer.batch(1)


class Color(Enum):
    """dedine enum color."""
    red = (0, 0, 255)
    green = (0, 255, 0)
    blue = (255, 0, 0)
    cyan = (255, 255, 0)
    yellow = (0, 255, 255)
    magenta = (255, 0, 255)
    white = (255, 255, 255)
    black = (0, 0, 0)


def check_file_exist(file_name: str):
    """check_file_exist."""
    if not os.path.isfile(file_name):
        raise FileNotFoundError(f"File `{file_name}` does not exist.")


def color_val(color):
    """color_val."""
    if isinstance(color, str):
        return Color[color].value
    if isinstance(color, Color):
        return color.value
    if isinstance(color, tuple):
        assert len(color) == 3
        for channel in color:
            assert 0 <= channel <= 255
        return color
    if isinstance(color, int):
        assert 0 <= color <= 255
        return color, color, color
    if isinstance(color, np.ndarray):
        assert color.ndim == 1 and color.size == 3
        assert np.all((color >= 0) & (color <= 255))
        color = color.astype(np.uint8)
        return tuple(color)
    raise TypeError(f'Invalid type for color: {type(color)}')


def imread(image, mode=None):
    """imread."""
    if isinstance(image, pathlib.Path):
        image = str(image)

    if isinstance(image, np.ndarray):
        pass
    elif isinstance(image, str):
        check_file_exist(image)
        image = Image.open(image)
        if mode:
            image = np.array(image.convert(mode))
    else:
        raise TypeError("Image must be a `ndarray`, `str` or Path object.")

    return image


def imwrite(image, image_path, auto_mkdir=True):
    """imwrite."""
    if auto_mkdir:
        dir_name = os.path.abspath(os.path.dirname(image_path))
        if dir_name != '':
            dir_name = os.path.expanduser(dir_name)
            os.makedirs(dir_name, mode=777, exist_ok=True)

    image = Image.fromarray(image)
    image.save(image_path)


def imshow(img, win_name='', wait_time=0):
    """imshow"""
    cv2.imshow(win_name, imread(img))
    if wait_time == 0:  # prevent from hanging if windows was closed
        while True:
            ret = cv2.waitKey(1)

            closed = cv2.getWindowProperty(win_name, cv2.WND_PROP_VISIBLE) < 1
            # if user closed window or if some key pressed
            if closed or ret != -1:
                break
    else:
        ret = cv2.waitKey(wait_time)


def show_result(img: str,
                result: Dict[int, float],
                text_color: str = 'green',
                font_scale: float = 0.5,
                row_width: int = 20,
                show: bool = False,
                win_name: str = '',
                wait_time: int = 0,
                out_file: Optional[str] = None) -> None:
    """Mark the prediction results on the picture."""
    img = imread(img, mode="RGB")
    img = img.copy()
    x, y = 0, row_width
    text_color = color_val(text_color)
    for k, v in result.items():
        if isinstance(v, float):
            v = f'{v:.2f}'
        label_text = f'{k}: {v}'
        cv2.putText(img, label_text, (x, y), cv2.FONT_HERSHEY_COMPLEX,
                    font_scale, text_color)
        y += row_width
    if out_file:
        show = False
        imwrite(img, out_file)

    if show:
        imshow(img, win_name, wait_time)


def index2label():
    """Dictionary output for image numbers and categories of the ImageNet dataset."""
    metafile = os.path.join(data_path, "ILSVRC2012_devkit_t12/data/meta.mat")
    meta = io.loadmat(metafile, squeeze_me=True)['synsets']

    nums_children = list(zip(*meta))[4]
    meta = [meta[idx] for idx, num_children in enumerate(nums_children) if num_children == 0]

    _, wnids, classes = list(zip(*meta))[:3]
    clssname = [tuple(clss.split(', ')) for clss in classes]
    wnid2class = {wnid: clss for wnid, clss in zip(wnids, clssname)}
    wind2class_name = sorted(wnid2class.items(), key=lambda x: x[0])

    mapping = {}
    for index, (_, class_name) in enumerate(wind2class_name):
        mapping[index] = class_name[0]
    return mapping


# Read data for inference
for i, image in enumerate(dataset_infer.create_dict_iterator(output_numpy=True)):
    image = image["image"]
    image = ms.Tensor(image)
    prob = model.predict(image)
    label = np.argmax(prob.asnumpy(), axis=1)
    mapping = index2label()
    output = {int(label): mapping[int(label)]}
    print(output)
    show_result(img="./dataset/infer/n01440764/ILSVRC2012_test_00000279.JPEG",
                result=output,
                out_file="./dataset/infer/ILSVRC2012_test_00000279.JPEG")

4. 总结

        Vision Transformer (ViT) 是一种基于Transformer架构的深度学习模型。传统的Transformer模型主要用于自然语言处理(NLP),而ViT通过将图像分割成固定大小的块(patches),并将这些块展平成序列,从而将图像数据转换为适合Transformer处理的格式。关键步骤包括:

  1. 图像分块:将输入图像分割成多个小块。

  2. 线性嵌入:将每个小块展平并映射到一个固定维度的向量。

  3. 位置编码:为了保留图像中块的位置信息,添加位置编码到每个嵌入向量。

  4. Transformer编码器:使用标准的Transformer编码器层处理这些嵌入向量序列。

  5. 分类头:在Transformer的输出上添加一个全连接层,用于图像分类任务。

        基于Transformer架构,ViT可以方便地与其他NLP任务共享模型架构和训练技巧,并且可以轻松扩展到大规模数据集获取出色的效果。但ViT模型通常需要大量的计算资源和数据进行训练,而且在较小数据集上的表现不如预期。