昇思25天学习打卡营第10天|Vision Transformer图像分类

Vision Transformer (ViT)简介

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），结构如如下：

vit-architecture

模型特点

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。

环境准备与数据读取

使用ImageNet数据集

from download import downloaddataset_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 osimport mindspore as ms
from mindspore.dataset import ImageFolderDataset
import mindspore.dataset.vision as transformsdata_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

Transformer基本原理

基于Attention机制的编码器-解码器型结构在自然语言处理领域具有重大意义。模型结构如下图所示：

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-decoder

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

Self-Attention的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。

在Self-Attention中:

1. 最初的输入向量首先会经过Embedding层映射成Q（Query），K（Key），V（Value）三个向量。

self-attention1

$\left\{\begin{matrix} q_{i}=W_{q}\cdot x_{i}\\ k_{i}=W_{k}\cdot x_{i}\\ v_{i}=W_{v}\cdot x_{i} \end{matrix}\right.,i=1,2,3...$

2. 自注意力机制的自注意主要体现在它的Q，K，V都来源于其自身，也就是该过程是在提取输入的不同顺序的向量的联系与特征，最终通过不同顺序向量之间的联系紧密性（Q与K乘积经过Softmax的结果）来表现出来。

self-attention3

$\left\{\begin{matrix} a_{1,1}=q_{1}\cdot k_{1}/\sqrt{d}\\ a_{1,2}=q_{1}\cdot k_{2}/\sqrt{d}\\ a_{1,3}=q_{1}\cdot k_{3}/\sqrt{d} \end{matrix}\right.$

$Softmax:\hat{a}_{1,i}=exp(a_{1,i})/\sum_{j}exp(a_{1,j}),j=1,2,3...$

self-attention2

3. 其最终输出则是通过V这个映射后的向量与Q，K经过Softmax结果进行weight sum获得，这个过程可以理解为在全局上进行自注意表示。

$b_{1}=\sum_{i}\hat{a}_{1,i}v_{i},i=1,2,3...$

self-attention

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，这一点也可以从代码中体现，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。

multi-head-attention

Multi-Head Attention代码实现

from mindspore import nn, opsclass 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_headshead_dim = dim // num_headsself.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.shapeqkv = 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

Transformer Encoder

Self-Attention与Feed Forward，Residual Connection等结构的拼接就可以形成Transformer的基础结构，代码实现如下：

from typing import Optional, Dictclass 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_featureshidden_features = hidden_features or in_featuresself.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 xclass ResidualCell(nn.Cell):def __init__(self, cell):super(ResidualCell, self).__init__()self.cell = celldef construct(self, x):"""ResidualCell construct."""return self.cell(x) + x

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

vit-encoder

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。

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。

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)

ViT模型的输入

1. 通过将输入图像在每个channel上划分为1616个patch

2. 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

具体Patch Embedding代码如下：

class PatchEmbedding(nn.Cell):MIN_NUM_PATCHES = 4def __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_sizeself.patch_size = patch_sizeself.num_patches = (image_size // patch_size) ** 2self.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.shapex = ops.reshape(x, (b, c, h * w))x = ops.transpose(x, (0, 2, 1))return x

整体构建ViT

from mindspore.common.initializer import Normal
from mindspore.common.initializer import initializer
from mindspore import Parameterdef 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_patchesself.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 = poolself.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_embeddingx = 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

整体流程图如下：

data-process

模型训练与推理

模型训练

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 = sparseself.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 lossnetwork_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,)

模型验证

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。本案例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)

输出结果：

{'Top_1_Accuracy': 0.7495, 'Top_5_Accuracy': 0.928}

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。

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。

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)

在推理过程中，通过index2label就可以获取对应标签，再通过自定义的show_result接口将结果写在对应图片上。

import os
import pathlib
import cv2
import numpy as np
from PIL import Image
from enum import Enum
from scipy import ioclass 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].valueif isinstance(color, Color):return color.valueif isinstance(color, tuple):assert len(color) == 3for channel in color:assert 0 <= channel <= 255return colorif isinstance(color, int):assert 0 <= color <= 255return color, color, colorif isinstance(color, np.ndarray):assert color.ndim == 1 and color.size == 3assert 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):passelif 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 imagedef 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 closedwhile True:ret = cv2.waitKey(1)closed = cv2.getWindowProperty(win_name, cv2.WND_PROP_VISIBLE) < 1# if user closed window or if some key pressedif closed or ret != -1:breakelse: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_widthtext_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_widthif out_file:show = Falseimwrite(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")

输出结果：