使用平台介绍
使用平台：启智AI协作平台 使用数据集：百度猫十二分类
数据集介绍
有cat_12_train和cat_12_test和train_list.txt train_list.txt内有每张图片所对应的标签
Minspore部分操作科普
数据集加载
Mindspore加载图片数据集就直接调整成这种格式就行，然后可以用这个函数加载，自动生成两个列，一列是图片，一列是标签；ImageFolderDataset函数会自动读取和处理数据集，标签就是文件夹的名称
数据处理
map函数里可以一键进行处理和映射，定义好数据处理函数，直接把路径和标签Map处理，后面可以带上是训练集还是测试集的标签；，要处理图片就指定 input_columns 参数为image，这个是前面数据读取形成的； 本项目这里用的是ImageFolderDataset，可以自动生成图片对应数据的两个列，要是相对数据处理就设置为数据列名即可
数据批处理和重复
batch就是批处理，把数据分成指定数量的一个个批次 最后repeat对数据进行重复
整体数据处理流程
就是读取数据形成数据和标签对应的列（读取数据函数有很多），然后定义数据预处理函数，在map函数里一键映射，指定要处理的列一键处理，最后对数据进行批次划分，就拿到可以放进训练网络函数的规范数据集了。当然使用时候还要用create_tuple_iterator或者create_dict_iterator函数形成可迭代的数据集。
迁移学习
在迁移学习中，固定特征训练和模型微调都是常用的技术
固定特征训练
在源任务上训练一个模型，并将其应用到目标任务上。在这个过程中，模型的特征提取器是固定的，只对输出层进行调整。这种方法可以利用源任务中已经学习到的特征，从而减少目标任务的训练时间和数据需求。固定特征训练通常适用于目标任务和源任务具有相似的特征空间，并且目标任务的数据量较小的情况。
模型微调
使用预训练模型作为初始模型，并在目标任务的数据集上进行进步训练。在微调阶段，可以根据目标任务的数据和特定要求调整模型的参数使其适应目标任务。模型微调的主要目的是通过在目标任务上的有限训练来调整预训练模型，以取得更好的性能。
举例
以训练一个猫、狗分类器为例，固定特征训练是指在一个大型的猫狗图片数据集上训练一个通用的图像识别模型，然后将该模型应用于特定的猫、狗分类任务。在这个过程中，我们只需要调整模型的输出层，使其能够正确地对猫和狗进行分类。而模型微调则是指使用一个已经在大型数据集上训练好的通用图像识别模型，然后在特定的猫、狗图片数据集上进行进一步训练，以优化模型的性能。在这个过程中我们可以根据猫、狗图片的特点来调整模型的参数，使其能够更好地识别猫和狗。
数据处理过程
由于本项目采用的数据集是百度所提供的猫十二分类，要使用ImageFolderDataset的话，形式不太匹配
现有形式
cat_12_train和cat_12_test里面都是一张一张的图片 train_list.txt内有每张图片所对应的标签
处理后形式
train和val文件夹内分别有十二个子文件夹，代表12类猫，每个子文件夹内又有一张张的图片
处理代码
这里有相关代码进行自动划分，但是对于训练集和测试集的划分，我直接采用了手动操作，也可以用代码来实现的；
# 处理异常图片
dir_lit = os.listdir('./work/cat_12_train/')
# dir_lit为一个列表，里面是一张张图片的名称
for list in dir_lit:
# list是图片名称，这里的操作是把这个图片形成一个个的路径
img_path=os.path.join('./work/cat_12_train/',list)
print(img_path)
# 如果不是RGB那就转换为RGB
img=Image.open(img_path)
if img.mode != 'RGB':
img = img.convert('RGB')
img.save(img_path)
dir_lit = os.listdir('./work/cat_12_test/')
for list in dir_lit:
img_path=os.path.join('./work/cat_12_test/',list)
img=Image.open(img_path)
if img.mode != 'RGB':
img = img.convert('RGB')
img.save(img_path)
# 整理数据格式
# 创建12个文件夹分别对应标签
path='./work/MyDataset/'
for i in range(12):
if not os.path.exists(path+str(i)):
os.mkdir(path+str(i))
else:
continue
#读取每一行
with open(f'./work/train_list.txt','r')as f:
img_path=f.readlines()
print(img_path)
# 里面是一个个的'cat_12_train/8GOkTtqw7E6IHZx4olYnhzvXLCiRsUfM.jpg\t0\n'
# 把对应文件放到对应标签文件夹下
for img in img_path:
# 拿取每一张图片路径
# print(img)
img_src= img.split('\t')[0]
# img_src为一个个图片路径
rel_src= img_src.split('cat_12_train/')[1]
# rel_src为图片名称
img_label = img.split('\t')[1]
img_label = img_label.split('\n')[0]
# img_label为图片标签
print(img_src)
print(rel_src)
print(img_label)
# os.system(f'cp ./work/{img_src} ./work/MyDataset/{img_label}/{rel_src}')
shutil.copy(f'./work/{img_src}',f'./work/MyDataset/{img_label}/{rel_src}')
print('图片处理完毕')
整体代码
# 解压上传的数据集压缩包并查看数据集结构
!unzip MyDataset.zip -d data/
import os
print(os.listdir("data"))
print(os.listdir("data/train"))
print(os.listdir('data/train/1'))
输出
['val', 'train']
['9', '10', '5', '8', '11', '2', '7', '3', '0', '1', '6', '4']
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超参数设置
batch_size = 18 # 批量大小
image_size = 224 # 训练图像空间大小
num_epochs = 10 # 训练周期数
lr = 0.001 # 学习率
momentum = 0.9 # 动量
workers = 4 # 并行线程个数
数据预处理
import mindspore as ms
import mindspore.dataset as ds
import mindspore.dataset.vision as vision
# 数据集目录路径
data_path_train = "data/train/"
data_path_val = "data/val/"
# 创建训练数据集
def create_dataset_canidae(dataset_path, usage):
"""数据加载"""
data_set = ds.ImageFolderDataset(dataset_path,
num_parallel_workers=workers,
shuffle=True,)
# 数据增强操作
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
scale = 32
if usage == "train":
# Define map operations for training dataset
trans = [
vision.RandomCropDecodeResize(size=image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
vision.RandomHorizontalFlip(prob=0.5),
vision.Normalize(mean=mean, std=std),
vision.HWC2CHW()
]
else:
# Define map operations for inference dataset
trans = [
vision.Decode(),
vision.Resize(image_size + scale),
vision.CenterCrop(image_size),
vision.Normalize(mean=mean, std=std),
vision.HWC2CHW()
]
# 数据映射操作
data_set = data_set.map(
operations=trans,
input_columns='image',
num_parallel_workers=workers)
# 批量操作
data_set = data_set.batch(batch_size)
return data_set
dataset_train = create_dataset_canidae(data_path_train, "train")
step_size_train = dataset_train.get_dataset_size()
dataset_val = create_dataset_canidae(data_path_val, "val")
step_size_val = dataset_val.get_dataset_size()
print(step_size_train)
print(step_size_val)
data = next(dataset_val.create_dict_iterator())
images = data["image"]
labels = data["label"]
print("Tensor of image", images.shape)
print("Labels:", labels)
输出
96
24
Tensor of image (18, 3, 224, 224)
Labels: [ 1 2 4 3 0 10 5 4 11 9 11 6 7 1 11 5 1 3]
数据集可视化查看
import matplotlib.pyplot as plt
import numpy as np
# class_name对应label，按文件夹字符串从小到大的顺序标记label
class_name = {0: "0", 1: "1",2: "2", 3: "3",4: "4", 5: "5",6: "6", 7: "7",8: "8", 9: "9",10: "10", 11: "11",12: "12"}
plt.figure(figsize=(5, 5))
for i in range(4):
# 获取图像及其对应的label
data_image = images[i].asnumpy()
data_label = labels[i]
# 处理图像供展示使用
data_image = np.transpose(data_image, (1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
data_image = std * data_image + mean
data_image = np.clip(data_image, 0, 1)
# 显示图像
plt.subplot(2, 2, i+1)
plt.imshow(data_image)
plt.title(class_name[int(labels[i].asnumpy())])
plt.axis("off")
plt.show()
网络结构搭建
from typing import Type, Union, List, Optional
from mindspore import nn, train
from mindspore.common.initializer import Normal
weight_init = Normal(mean=0, sigma=0.02)
gamma_init = Normal(mean=1, sigma=0.02)
class ResidualBlockBase(nn.Cell):
expansion: int = 1 # 最后一个卷积核数量与第一个卷积核数量相等
def __init__(self, in_channel: int, out_channel: int,
stride: int = 1, norm: Optional[nn.Cell] = None,
down_sample: Optional[nn.Cell] = None) -> None:
super(ResidualBlockBase, self).__init__()
if not norm:
self.norm = nn.BatchNorm2d(out_channel)
else:
self.norm = norm
self.conv1 = nn.Conv2d(in_channel, out_channel,
kernel_size=3, stride=stride,
weight_init=weight_init)
self.conv2 = nn.Conv2d(in_channel, out_channel,
kernel_size=3, weight_init=weight_init)
self.relu = nn.ReLU()
self.down_sample = down_sample
def construct(self, x):
"""ResidualBlockBase construct."""
identity = x # shortcuts分支
out = self.conv1(x) # 主分支第一层：3*3卷积层
out = self.norm(out)
out = self.relu(out)
out = self.conv2(out) # 主分支第二层：3*3卷积层
out = self.norm(out)
if self.down_sample is not None:
identity = self.down_sample(x)
out += identity # 输出为主分支与shortcuts之和
out = self.relu(out)
return out
class ResidualBlock(nn.Cell):
expansion = 4 # 最后一个卷积核的数量是第一个卷积核数量的4倍
def __init__(self, in_channel: int, out_channel: int,
stride: int = 1, down_sample: Optional[nn.Cell] = None) -> None:
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channel, out_channel,
kernel_size=1, weight_init=weight_init)
self.norm1 = nn.BatchNorm2d(out_channel)
self.conv2 = nn.Conv2d(out_channel, out_channel,
kernel_size=3, stride=stride,
weight_init=weight_init)
self.norm2 = nn.BatchNorm2d(out_channel)
self.conv3 = nn.Conv2d(out_channel, out_channel * self.expansion,
kernel_size=1, weight_init=weight_init)
self.norm3 = nn.BatchNorm2d(out_channel * self.expansion)
self.relu = nn.ReLU()
self.down_sample = down_sample
def construct(self, x):
identity = x # shortscuts分支
out = self.conv1(x) # 主分支第一层：1*1卷积层
out = self.norm1(out)
out = self.relu(out)
out = self.conv2(out) # 主分支第二层：3*3卷积层
out = self.norm2(out)
out = self.relu(out)
out = self.conv3(out) # 主分支第三层：1*1卷积层
out = self.norm3(out)
if self.down_sample is not None:
identity = self.down_sample(x)
out += identity # 输出为主分支与shortcuts之和
out = self.relu(out)
return out
def make_layer(last_out_channel, block: Type[Union[ResidualBlockBase, ResidualBlock]],
channel: int, block_nums: int, stride: int = 1):
down_sample = None # shortcuts分支
if stride != 1 or last_out_channel != channel * block.expansion:
down_sample = nn.SequentialCell([
nn.Conv2d(last_out_channel, channel * block.expansion,
kernel_size=1, stride=stride, weight_init=weight_init),
nn.BatchNorm2d(channel * block.expansion, gamma_init=gamma_init)
])
layers = []
layers.append(block(last_out_channel, channel, stride=stride, down_sample=down_sample))
in_channel = channel * block.expansion
# 堆叠残差网络
for _ in range(1, block_nums):
layers.append(block(in_channel, channel))
return nn.SequentialCell(layers)
from mindspore import load_checkpoint, load_param_into_net
class ResNet(nn.Cell):
def __init__(self, block: Type[Union[ResidualBlockBase, ResidualBlock]],
layer_nums: List[int], num_classes: int, input_channel: int) -> None:
super(ResNet, self).__init__()
self.relu = nn.ReLU()
# 第一个卷积层，输入channel为3（彩色图像），输出channel为64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, weight_init=weight_init)
self.norm = nn.BatchNorm2d(64)
# 最大池化层，缩小图片的尺寸
self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
# 各个残差网络结构块定义，
self.layer1 = make_layer(64, block, 64, layer_nums[0])
self.layer2 = make_layer(64 * block.expansion, block, 128, layer_nums[1], stride=2)
self.layer3 = make_layer(128 * block.expansion, block, 256, layer_nums[2], stride=2)
self.layer4 = make_layer(256 * block.expansion, block, 512, layer_nums[3], stride=2)
# 平均池化层
self.avg_pool = nn.AvgPool2d()
# flattern层
self.flatten = nn.Flatten()
# 全连接层
self.fc = nn.Dense(in_channels=input_channel, out_channels=num_classes)
def construct(self, x):
x = self.conv1(x)
x = self.norm(x)
x = self.relu(x)
x = self.max_pool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avg_pool(x)
x = self.flatten(x)
x = self.fc(x)
return x
def _resnet(model_url: str, block: Type[Union[ResidualBlockBase, ResidualBlock]],
layers: List[int], num_classes: int, pretrained: bool, pretrianed_ckpt: str,
input_channel: int):
model = ResNet(block, layers, num_classes, input_channel)
if pretrained:
# 加载预训练模型
# download(url=model_url, path=pretrianed_ckpt, replace=True)
param_dict = load_checkpoint(pretrianed_ckpt)
load_param_into_net(model, param_dict)
return model
def resnet50(num_classes: int = 1000, pretrained: bool = False):
"ResNet50模型"
resnet50_url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/models/application/resnet50_224_new.ckpt"
resnet50_ckpt = "./LoadPretrainedModel/resnet50_224_new.ckpt"
return _resnet(resnet50_url, ResidualBlock, [3, 4, 6, 3], num_classes,
pretrained, resnet50_ckpt, 2048)
形式一：模型微调
模型训练
from mindspore import nn, train
from mindspore.nn import Loss, Accuracy
!pip install download
import mindspore as ms
from download import download
network = resnet50(pretrained=True)
# 全连接层输入层的大小
in_channels = network.fc.in_channels
# 输出通道数大小为狼狗分类数2
head = nn.Dense(in_channels, 12)
# 重置全连接层
network.fc = head
# 平均池化层kernel size为7
avg_pool = nn.AvgPool2d(kernel_size=7)
# 重置平均池化层
network.avg_pool = avg_pool
import mindspore as ms
import mindspore
# 定义优化器和损失函数
opt = nn.Momentum(params=network.trainable_params(), learning_rate=lr, momentum=momentum)
loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
# 实例化模型
model = train.Model(network, loss_fn, opt, metrics={"Accuracy": Accuracy()})
def forward_fn(inputs, targets):
logits = network(inputs)
loss = loss_fn(logits, targets)
return loss
grad_fn = mindspore.ops.value_and_grad(forward_fn, None, opt.parameters)
def train_step(inputs, targets):
loss, grads = grad_fn(inputs, targets)
opt(grads)
return loss
# 创建迭代器
data_loader_train = dataset_train.create_tuple_iterator(num_epochs=num_epochs)
# 最佳模型保存路径
best_ckpt_dir = "./BestCheckpoint"
best_ckpt_path = "./BestCheckpoint/resnet50-best.ckpt"
import os
import time
# 开始循环训练
print("Start Training Loop ...")
best_acc = 0
for epoch in range(num_epochs):
losses = []
network.set_train()
epoch_start = time.time()
# 为每轮训练读入数据
for i, (images, labels) in enumerate(data_loader_train):
labels = labels.astype(ms.int32)
loss = train_step(images, labels)
losses.append(loss)
# 每个epoch结束后，验证准确率
acc = model.eval(dataset_val)['Accuracy']
epoch_end = time.time()
epoch_seconds = (epoch_end - epoch_start) * 1000
step_seconds = epoch_seconds/step_size_train
print("-" * 20)
print("Epoch: [%3d/%3d], Average Train Loss: [%5.3f], Accuracy: [%5.3f]" % (
epoch+1, num_epochs, sum(losses)/len(losses), acc
))
print("epoch time: %5.3f ms, per step time: %5.3f ms" % (
epoch_seconds, step_seconds
))
if acc > best_acc:
best_acc = acc
if not os.path.exists(best_ckpt_dir):
os.mkdir(best_ckpt_dir)
ms.save_checkpoint(network, best_ckpt_path)
print("=" * 80)
print(f"End of validation the best Accuracy is: {best_acc: 5.3f}, "
f"save the best ckpt file in {best_ckpt_path}", flush=True)
输出
Start Training Loop ...
--------------------
Epoch: [ 1/ 10], Average Train Loss: [1.774], Accuracy: [0.838]
epoch time: 60892.337 ms, per step time: 634.295 ms
--------------------
Epoch: [ 2/ 10], Average Train Loss: [0.762], Accuracy: [0.905]
epoch time: 8745.406 ms, per step time: 91.098 ms
--------------------
Epoch: [ 3/ 10], Average Train Loss: [0.568], Accuracy: [0.921]
epoch time: 8449.129 ms, per step time: 88.012 ms
--------------------
Epoch: [ 4/ 10], Average Train Loss: [0.508], Accuracy: [0.910]
epoch time: 8199.763 ms, per step time: 85.414 ms
--------------------
Epoch: [ 5/ 10], Average Train Loss: [0.459], Accuracy: [0.900]
epoch time: 7856.060 ms, per step time: 81.834 ms
--------------------
Epoch: [ 6/ 10], Average Train Loss: [0.405], Accuracy: [0.931]
epoch time: 8138.927 ms, per step time: 84.780 ms
--------------------
Epoch: [ 7/ 10], Average Train Loss: [0.368], Accuracy: [0.919]
epoch time: 8333.523 ms, per step time: 86.808 ms
--------------------
Epoch: [ 8/ 10], Average Train Loss: [0.354], Accuracy: [0.912]
epoch time: 8271.008 ms, per step time: 86.156 ms
--------------------
Epoch: [ 9/ 10], Average Train Loss: [0.338], Accuracy: [0.928]
epoch time: 8457.969 ms, per step time: 88.104 ms
--------------------
Epoch: [ 10/ 10], Average Train Loss: [0.338], Accuracy: [0.907]
epoch time: 8183.743 ms, per step time: 85.247 ms
================================================================================
End of validation the best Accuracy is: 0.931, save the best ckpt file in ./BestCheckpoint/resnet50-best.ckpt
模型评估
import matplotlib.pyplot as plt
import mindspore as ms
def visualize_model(best_ckpt_path, val_ds):
net = resnet50()
# 全连接层输入层的大小
in_channels = net.fc.in_channels
# 输出通道数大小为分类数12
head = nn.Dense(in_channels, 12)
# 重置全连接层
net.fc = head
# 平均池化层kernel size为7
avg_pool = nn.AvgPool2d(kernel_size=7)
# 重置平均池化层
net.avg_pool = avg_pool
# 加载模型参数
param_dict = ms.load_checkpoint(best_ckpt_path)
ms.load_param_into_net(net, param_dict)
model = train.Model(net)
#print(net)
# 加载验证集的数据进行验证
data = next(val_ds.create_dict_iterator())
images = data["image"].asnumpy()
print(type(images))
print(images.shape)
#print(images)
labels = data["label"].asnumpy()
#print(labels)
class_name = {0: "0", 1: "1",2: "2", 3: "3",4: "4", 5: "5",6: "6", 7: "7",8: "8", 9: "9",10: "10", 11: "11",12: "12"}
# 预测图像类别
data_pre=ms.Tensor(data["image"])
print(data_pre.shape)
print(type(data_pre))
output = model.predict(data_pre)
#print(output)
pred = np.argmax(output.asnumpy(), axis=1)
# 显示图像及图像的预测值
plt.figure(figsize=(5, 5))
for i in range(4):
plt.subplot(2, 2, i + 1)
# 若预测正确，显示为蓝色；若预测错误，显示为红色
color = 'blue' if pred[i] == labels[i] else 'red'
plt.title('predict:{}'.format(class_name[pred[i]]), color=color)
picture_show = np.transpose(images[i], (1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
picture_show = std * picture_show + mean
picture_show = np.clip(picture_show, 0, 1)
plt.imshow(picture_show)
plt.axis('off')
plt.show()
visualize_model('BestCheckpoint/resnet50-best.ckpt', dataset_val)
输出
形式二：固定特征训练
模型训练
net_work = resnet50(pretrained=True)
# 全连接层输入层的大小
in_channels = net_work.fc.in_channels
# 输出通道数大小为分类数12
head = nn.Dense(in_channels, 12)
# 重置全连接层
net_work.fc = head
# 平均池化层kernel size为7
avg_pool = nn.AvgPool2d(kernel_size=7)
# 重置平均池化层
net_work.avg_pool = avg_pool
# 冻结除最后一层外的所有参数
for param in net_work.get_parameters():
if param.name not in ["fc.weight", "fc.bias"]:
param.requires_grad = False
# 定义优化器和损失函数
opt = nn.Momentum(params=net_work.trainable_params(), learning_rate=lr, momentum=0.5)
loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
def forward_fn(inputs, targets):
logits = net_work(inputs)
loss = loss_fn(logits, targets)
return loss
grad_fn = ms.ops.value_and_grad(forward_fn, None, opt.parameters)
def train_step(inputs, targets):
loss, grads = grad_fn(inputs, targets)
opt(grads)
return loss
# 实例化模型
model1 = train.Model(net_work, loss_fn, opt, metrics={"Accuracy": Accuracy()})
dataset_train = create_dataset_canidae(data_path_train, "train")
step_size_train = dataset_train.get_dataset_size()
dataset_val = create_dataset_canidae(data_path_val, "val")
step_size_val = dataset_val.get_dataset_size()
num_epochs = 10
# 创建迭代器
data_loader_train = dataset_train.create_tuple_iterator(num_epochs=num_epochs)
data_loader_val = dataset_val.create_tuple_iterator(num_epochs=num_epochs)
best_ckpt_dir = "./BestCheckpoint"
best_ckpt_path = "./BestCheckpoint/resnet50-best-freezing-param.ckpt"
# 开始循环训练
print("Start Training Loop ...")
best_acc = 0
for epoch in range(num_epochs):
losses = []
net_work.set_train()
epoch_start = time.time()
# 为每轮训练读入数据
for i, (images, labels) in enumerate(data_loader_train):
labels = labels.astype(ms.int32)
loss = train_step(images, labels)
losses.append(loss)
# 每个epoch结束后，验证准确率
acc = model1.eval(dataset_val)['Accuracy']
epoch_end = time.time()
epoch_seconds = (epoch_end - epoch_start) * 1000
step_seconds = epoch_seconds/step_size_train
print("-" * 20)
print("Epoch: [%3d/%3d], Average Train Loss: [%5.3f], Accuracy: [%5.3f]" % (
epoch+1, num_epochs, sum(losses)/len(losses), acc
))
print("epoch time: %5.3f ms, per step time: %5.3f ms" % (
epoch_seconds, step_seconds
))
if acc > best_acc:
best_acc = acc
if not os.path.exists(best_ckpt_dir):
os.mkdir(best_ckpt_dir)
ms.save_checkpoint(net_work, best_ckpt_path)
print("=" * 80)
print(f"End of validation the best Accuracy is: {best_acc: 5.3f}, "
f"save the best ckpt file in {best_ckpt_path}", flush=True)
模型评估
visualize_model(best_ckpt_path, dataset_val)
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