内容介绍：
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。
具体内容：
- 导包
python
from download import download
import os
import mindspore as ms
from mindspore.dataset import ImageFolderDataset
import mindspore.dataset.vision as transforms
from mindspore import nn, ops
from typing import Optional, Dict
from mindspore.common.initializer import Normal
from mindspore.common.initializer import initializer
from mindspore import Parameter
from mindspore.nn import LossBase
from mindspore.train import LossMonitor, TimeMonitor, CheckpointConfig, ModelCheckpoint
from mindspore import train
import pathlib
import cv2
import numpy as np
from PIL import Image
from enum import Enum
from scipy import io- 下载数据
python
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)- 加载数据
python
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)- Multi-Head Attention
python
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- Encoder
python
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- 模型
python
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
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，这使得Transformer在自然语言处理领域非常好用，而二维图片矩阵如何与一维词向量进行转化就成为了Transformer进军图像处理领域的一个小门槛。
在ViT模型中：
-
通过将输入图像在每个channel上划分为16*16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。*
-
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。
python
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。
-
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。
-
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。
-
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。
-
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。
实际上，pos_embedding总共有4种方案。但是经过作者的论证，只有加上pos_embedding和不加pos_embedding有明显影响，至于pos_embedding是一维还是二维对分类结果影响不大，所以，在我们的代码中，也是采用了一维的pos_embedding，由于class_embedding是加在pos_embedding之前，所以pos_embedding的维度会比patch拉伸后的维度加1。
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。
- 整体构建ViT
python
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- 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。
python
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,)
- 模型验证
python
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)- 模型推理
python
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)- show
python
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")Vision Transformer将自然语言处理中的Transformer结构引入到图像识别领域，这一创新性的尝试打破了卷积神经网络（CNN）在图像分类中的主导地位。通过学习ViT，我深刻理解了Transformer在处理全局信息、捕捉长距离依赖方面的优势，这对于解决复杂图像识别任务具有重要意义。
在使用MindSpore和Vision Transformer进行图像分类的实践过程中，我遇到了不少挑战，如模型参数调整、训练稳定性控制、数据预处理优化等。这些挑战促使我不断学习和探索，也让我更加深刻地认识到深度学习技术的复杂性和多样性。
