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

Vision Transformer（ViT）简介

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。

ViT则是自然语言处理和计算机视觉两个领域的融合结晶。在不依赖卷积操作的情况下，依然可以在图像分类任务上达到很好的效果。

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[1]如下：

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：

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。
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。
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。
下面将通过代码实例来详细解释基于ViT实现ImageNet分类任务。

注意，本教程在CPU上运行时间过长，不建议使用CPU运行。

实验环境搭建

%%capture captured_output
# 实验环境已经预装了mindspore==2.2.14，如需更换mindspore版本，可更改下面mindspore的版本号
!pip uninstall mindspore -y
!pip install -i https://pypi.mirrors.ustc.edu.cn/simple mindspore==2.2.14

from download import download

dataset_url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/vit_imagenet_dataset.zip"
path = "./"

path = download(dataset_url, path, kind="zip", replace=True)

import os

import mindspore as ms
from mindspore.dataset import ImageFolderDataset
import mindspore.dataset.vision as transforms


data_path = './dataset/'
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]

dataset_train = ImageFolderDataset(os.path.join(data_path, "train"), shuffle=True)

trans_train = [
    transforms.RandomCropDecodeResize(size=224,
                                      scale=(0.08, 1.0),
                                      ratio=(0.75, 1.333)),
    transforms.RandomHorizontalFlip(prob=0.5),
    transforms.Normalize(mean=mean, std=std),
    transforms.HWC2CHW()
]

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

模型解析

下面将通过代码来细致剖析ViT模型的内部结构。

Transformer基本原理

Transformer模型源于2017年的一篇文章[2]。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。模型结构如下图所示：

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，实际应用时建议根据项目需要调整epoch_size，当正常输出每个Epoch的step信息时，意味着训练正在进行，通过模型输出可以查看当前训练的loss值和时间等指标。

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,)

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。该方法可以对我们的推理图片进行resize和normalize处理，这样才能与我们训练时的输入数据匹配。

本案例采用了一张Doberman的图片作为推理图片来测试模型表现，期望模型可以给出正确的预测结果。

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


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")

学习心得

通过学习Vision Transformer（ViT），我认识到该模型如何将Transformer架构应用于图像分类领域，并实现了在无卷积操作的情况下依然能取得优异的性能。ViT将输入图像划分为固定大小的patch，并将其视为序列输入到Transformer中。通过多头自注意力机制，模型能够有效地捕捉图像中的全局特征。在实践中，我了解了如何使用MindSpore框架进行数据处理、模型训练和推理，包括数据增强、定义损失函数、优化器、以及模型的评估与可视化。通过对ViT的实现和调试，我不仅掌握了其理论基础，还提升了在深度学习和计算机视觉领域的实战技能。

原文地址：https://blog.csdn.net/weixin_56595425/article/details/140263018

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