文章目录
昇思MindSpore应用实践
本系列文章主要用于记录昇思25天学习打卡营的学习心得。
基于MindSpore的ResNet50迁移学习
1、迁移学习简介
迁移学习顾名思义，通常指将一个训练好的模型特性迁移至另一个模型中，用于处理有区别但又具备一定关联性的任务。
为什么要用迁移学习？
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（小样本训练的效果可能也不好）。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，然后使用该预训练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。
2、加载ImageNet数据集
完整的ImageNet是一个非常大的数据集，是机器学习中最著名的数据集之一，由计算机视觉教母 Fei-Fei Li 在2007年初启动ImageNet项目，为解决机器学习，尤其是视觉识别任务中过拟合和泛化的问题而牵头构建的数据集。其训练集有100多个G，本文当然不会用完整的ImageNet来训练，本文基于Mindspore深度学习框架，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。
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()
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)
# print_log
Tensor of image (18, 3, 224, 224)
Labels: [0 0 1 1 1 0 1 0 0 0 1 0 1 1 0 0 1 1]
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()
3、ResNet50 模型
ResNet-50是一种深度残差网络（Residual Network），是ResNet系列中的一种经典模型。它由微软研究院的Kaiming He等人于2015年提出，被广泛应用于计算机视觉任务，如图像分类、目标检测和图像分割等。其网络结构如下图所示：
主要解决的问题是：通过Kaiming He等人提出的基于残差连接的训练方式大大改善了网络深度增加时的梯度消失和梯度爆炸问题。
当有这条跳跃连接线（残差连接，Residual Connections）时，即使网络层次很深导致梯度消失时，在网络上堆叠这样的结构（f(x)=0，y=g(x)=relu(x)=x），我什么也学不到，但至少能把原来的样子恒等映射给下一层网络，相当于在浅层网络上堆叠了“复制层”，这样至少不会比浅层网络差。
当然，万一我“不小心”学到了什么，那就赚大了，由于网络中要用到很多次恒等映射，所以网络有效学习到东西的概率很大。这就是ResNet的灵魂汁子，浇给~
ResNet50在多个常用的数据集上（如ImageNet）都有预训练的模型可供下载和使用。这种预训练模型含有大量且多样化的图像特征，能够作为多种视觉任务的良好起点。因其强大的特性和灵活的适用性，在学术界和工业界得到了广泛的应用，尤其是在迁移学习的场景中。
关于迁移学习更新的相关研究，如One-shot或Zero-shot，感兴趣的同学可以去了解一下CLIP模型，迁移性和泛化能力也特别强大。
4、模型训练
搭建好模型框架后，使用ResNet50模型进行训练。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。
from typing import Type, Union, List, Optional
from mindspore import nn, train
from mindspore.common.initializer import Normal
from mindspore import load_checkpoint, load_param_into_net
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)
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)
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()})
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"
import mindspore as ms
import matplotlib.pyplot as plt
import os
import time
# 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)
# print_log
Start Training Loop ...
--------------------
Epoch: [ 1/ 5], Average Train Loss: [0.661], Accuracy: [0.967]
epoch time: 64226.290 ms, per step time: 4587.592 ms
--------------------
Epoch: [ 2/ 5], Average Train Loss: [0.574], Accuracy: [0.833]
epoch time: 887.579 ms, per step time: 63.398 ms
--------------------
Epoch: [ 3/ 5], Average Train Loss: [0.496], Accuracy: [1.000]
epoch time: 1051.744 ms, per step time: 75.125 ms
--------------------
Epoch: [ 4/ 5], Average Train Loss: [0.449], Accuracy: [1.000]
epoch time: 907.051 ms, per step time: 64.789 ms
--------------------
Epoch: [ 5/ 5], Average Train Loss: [0.392], Accuracy: [1.000]
epoch time: 915.519 ms, per step time: 65.394 ms
================================================================================
End of validation the best Accuracy is: 1.000, save the best ckpt file in ./BestCheckpoint/resnet50-best-freezing-param.ckpt
2024-07-05 16:01:55 Wayn_Fan-sail
5、模型推理
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)
Reference
[1] 昇思大模型平台
[2] 昇思官方文档-ResNet50迁移学习
[3] 深度学习（五）：pytorch迁移学习之resnet50
[4] Resnet-50网络结构详解