今天分享ResNet50迁移学习。
在实际应用场景中，由于训练数据集不足，所以很少有人会从头开始训练整个网络。普遍的做法是，在一个非常大的基础数据集上训练得到一个预训练模型，然后使用该模型来初始化网络的权重参数或作为固定特征提取器应用于特定的任务中。本章将使用迁移学习的方法对ImageNet数据集中的狼和狗图像进行分类。
目录
一、 数据准备
1. 下载数据集
2. 数据集的目录结构
二、 加载数据集
1. 定义执行过程中的全局变量
2. 加载数据
3. 数据集可视化
三、训练模型
1. 构建ResNet50网络
2. 固定特征进行训练
3. 训练和评估
4. 可视化模型预测
一、 数据准备
1. 下载数据集
下载案例所用到的狗与狼分类数据集，数据集中的图像来自于ImageNet，每个分类有大约120张训练图像与30张验证图像。使用download接口下载数据集，并将下载后的数据集自动解压到当前目录下。
from download import download
dataset_url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/intermediate/Canidae_data.zip"
download(dataset_url, "./datasets-Canidae", kind="zip", replace=True)运行结果：
Creating data folder... Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/intermediate/Canidae_data.zip (11.3 MB) file_sizes: 100%|███████████████████████████| 11.9M/11.9M [00:00<00:00, 116MB/s] Extracting zip file... Successfully downloaded / unzipped to ./datasets-Canidae
'./datasets-Canidae'
2. 数据集的目录结构
二、 加载数据集
狼狗数据集提取自ImageNet分类数据集，使用mindspore.dataset.ImageFolderDataset接口来加载数据集，并进行相关图像增强操作。
1. 定义执行过程中的全局变量
# 为执行过程定义一些输入
batch_size = 18 # 批量大小
image_size = 224 # 训练图像空间大小
num_epochs = 5 # 训练周期数
lr = 0.001 # 学习率
momentum = 0.9 # 动量
workers = 4 # 并行线程个数2. 加载数据
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 = "./datasets-Canidae/data/Canidae/train/"
data_path_val = "./datasets-Canidae/data/Canidae/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()3. 数据集可视化
从mindspore.dataset.ImageFolderDataset接口中加载的训练数据集返回值为字典，用户可通过 create_dict_iterator 接口创建数据迭代器，使用 next 迭代访问数据集。本章中 batch_size 设为18，所以使用 next 一次可获取18个图像及标签数据。
data = next(dataset_train.create_dict_iterator())
images = data["image"]
labels = data["label"]
print("Tensor of image", images.shape)
print("Labels:", labels)运行结果：
Tensor of image (18, 3, 224, 224) Labels: [0 1 0 0 1 1 0 0 0 1 0 1 1 1 1 1 0 0]
对获取到的图像及标签数据进行可视化，标题为图像对应的label名称。
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: "dogs", 1: "wolves"}
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()运行结果：
三、训练模型
本章使用ResNet50模型进行训练。搭建好模型框架后，通过将pretrained参数设置为True来下载ResNet50的预训练模型并将权重参数加载到网络中。
1. 构建ResNet50网络
"""
定义ResidualBlockBase 类，这个类实现了一个基本的残差块（Residual Block）结构，
它是卷积神经网络（CNN）中常用的一种构建块，特别是在构建非常深的网络（如ResNet）时，
残差块的主要目的是通过引入“短路连接”（或称为“恒等映射”、“跳跃连接”）来解决深度网络
训练过程中的梯度消失或梯度爆炸问题，使得网络能够更容易地学习和优化。
"""
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"""
定义 ResidualBlock 类，它是用于构建深度神经网络中的一个残差块（Residual Block）。
残差块是深度残差网络（ResNet）的核心组成部分，旨在解决深度神经网络在训练过程中出现的
梯度消失或梯度爆炸问题，从而允许训练更深的网络。
"""
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)
# 将主分支的输出与shortcuts分支相加，实现残差连接
out += identity
out = self.relu(out)
return out"""
定义make_layer函数，它负责构建一个由多个残差块（block）组成的网络层。
这个函数首先检查是否需要创建下采样层（down_sample），这通常是为了匹配输入和输出
的维度或进行特征图的空间降维。然后，它添加第一个残差块（可能需要下采样层），
并基于第一个残差块的输出通道数（考虑扩展因子）堆叠剩余的残差块。最后，所有的残差块
被封装在一个nn.SequentialCell中，以便可以作为一个整体进行前向传播。
"""
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
# 如果步长不为1或者上一层的输出通道数与当前层首个残差块的期望输出通道数不匹配，则创建下采样层
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)
])
# 初始化残差块列表
layers = []
layers.append(block(last_out_channel, channel, stride=stride, down_sample=down_sample))
# 更新输入通道数为当前残差块的输出通道数（考虑扩展因子）
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)# 构建ResNet50网络
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)2. 固定特征进行训练
使用固定特征进行训练的时候，需要冻结除最后一层之外的所有网络层。通过设置 requires_grad == False 冻结参数，以便不在反向传播中计算梯度。
import mindspore as ms
import matplotlib.pyplot as plt
import os
import time
net_work = resnet50(pretrained=True)
# 全连接层输入层的大小
in_channels = net_work.fc.in_channels
# 输出通道数大小为狼狗分类数2
head = nn.Dense(in_channels, 2)
# 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.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": train.Accuracy()})3. 训练和评估
开始训练模型，与没有预训练模型相比，将节约一大半时间，因为此时可以不用计算部分梯度。保存评估精度最高的ckpt文件于当前路径的./BestCheckpoint/resnet50-best-freezing-param.ckpt。
import mindspore as ms
import matplotlib.pyplot as plt
import os
import time
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 = 5
# 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)运行结果：
Start Training Loop ... -------------------- Epoch: [ 1/ 5], Average Train Loss: [0.664], Accuracy: [0.800] epoch time: 50934.131 ms, per step time: 3638.152 ms -------------------- Epoch: [ 2/ 5], Average Train Loss: [0.556], Accuracy: [0.817] epoch time: 824.633 ms, per step time: 58.902 ms -------------------- Epoch: [ 3/ 5], Average Train Loss: [0.510], Accuracy: [0.967] epoch time: 767.022 ms, per step time: 54.787 ms -------------------- Epoch: [ 4/ 5], Average Train Loss: [0.438], Accuracy: [1.000] epoch time: 714.965 ms, per step time: 51.069 ms -------------------- Epoch: [ 5/ 5], Average Train Loss: [0.395], Accuracy: [1.000] epoch time: 734.045 ms, per step time: 52.432 ms ================================================================================ End of validation the best Accuracy is: 1.000, save the best ckpt file in ./BestCheckpoint/resnet50-best-freezing-param.ckpt
4. 可视化模型预测
使用固定特征得到的best.ckpt文件对验证集的狼和狗图像数据进行预测。若预测字体为蓝色即为预测正确，若预测字体为红色则预测错误。
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
# 输出通道数大小为狼狗分类数2
head = nn.Dense(in_channels, 2)
# 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)
# 加载验证集的数据进行验证
data = next(val_ds.create_dict_iterator())
images = data["image"].asnumpy()
labels = data["label"].asnumpy()
class_name = {0: "dogs", 1: "wolves"}
# 预测图像类别
output = model.predict(ms.Tensor(data['image']))
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(best_ckpt_path, dataset_val)运行结果：
![实验2-3-6 计算分段函数[3]](https://i-blog.csdnimg.cn/direct/e106bba0fbe045f4a221b594ac704eee.png)