MindSpore ResNet 模型训练 - 基于 CPU 的图像分类实战
import numpy as np
import mindspore.dataset as ds
import os
import cv2
import mindspore
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.initializer import Normal
from mindspore import context
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train import Model
from mindspore.nn.metrics import Accuracy
from mindspore.ops.operations import TensorAdd
from scipy.integrate._ivp.radau import P
np.random.seed(58)
class BasicBlock(nn.Cell):
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, pad_mode='pad',has_bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, pad_mode='pad', has_bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.downsample = downsample
self.add = TensorAdd()
def construct(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out = self.add(out, identity)
out = self.relu(out)
return out
class ResNet(nn.Cell):
def __init__(self, block, layers, num_classes=10):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad', has_bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
self.layer1 = self.make_layer(block, 64, layers[0])
self.layer2 = self.make_layer(block, 128, layers[1], stride=2)
self.layer3 = self.make_layer(block, 256, layers[2], stride=2)
self.layer4 = self.make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1) # 修改这里
self.flatten = nn.Flatten()
self.fc = nn.Dense(512, num_classes)
def make_layer(self, block, out_channels, blocks, stride=1):
downsample = None
if stride != 1 or self.in_channels != out_channels:
downsample = nn.SequentialCell([
nn.Conv2d(self.in_channels, out_channels, kernel_size=1, stride=stride, has_bias=False),
nn.BatchNorm2d(out_channels)
])
layers = []
layers.append(block(self.in_channels, out_channels, stride, downsample))
self.in_channels = out_channels
for _ in range(1, blocks):
layers.append(block(out_channels, out_channels))
return nn.SequentialCell(layers)
def construct(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = self.flatten(x)
x = self.fc(x)
return x
class TrainDatasetGenerator:
def __init__(self, file_path):
self.file_path = file_path
self.img_names = os.listdir(file_path)
def __getitem__(self, index):
data = cv2.imread(os.path.join(self.file_path, self.img_names[index]))
label = self.img_names[index].split('_')[0]
label = int(label)
data = cv2.cvtColor(data, cv2.COLOR_BGR2RGB)
data = cv2.resize(data, (224, 224))
data = data.transpose().astype(np.float32) / 255.
return data, label
def __len__(self):
return len(self.img_names)
def train_resnet():
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
train_dataset_generator = TrainDatasetGenerator('D:/pythonProject7/train')
ds_train = ds.GeneratorDataset(train_dataset_generator, ['data', 'label'], shuffle=True)
ds_train = ds_train.shuffle(buffer_size=10)
ds_train = ds_train.batch(batch_size=4, drop_remainder=True)
valid_dataset_generator = TrainDatasetGenerator('D:/pythonProject7/test')
ds_valid = ds.GeneratorDataset(valid_dataset_generator, ['data', 'label'], shuffle=True)
ds_valid = ds_valid.batch(batch_size=4, drop_remainder=True)
network = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=100)
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.01, momentum=0.9)
time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
config_ck = CheckpointConfig(save_checkpoint_steps=10, keep_checkpoint_max=10)
config_ckpt_path = 'D:/pythonProject7/ckpt/'
ckpoint_cb = ModelCheckpoint(prefix='checkpoint_resnet', directory=config_ckpt_path, config=config_ck)
model = Model(network, net_loss, net_opt, metrics={'Accuracy': Accuracy()})
epoch_size = 10
print('============== Starting Training ===============')
model.train(epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()])
acc = model.eval(ds_valid)
print('============== {} ==============='.format(acc))
epoch_size = 10
print('============== Starting Training ===============')
model.train(epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()])
acc = model.eval(ds_valid)
print('============== {} ==============='.format(acc))
epoch_size = 10
print('============== Starting Training ===============')
model.train(epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()])
acc = model.eval(ds_valid)
print('============== {} ==============='.format(acc))
if __name__ == '__main__':
train_resnet()
错误分析
在代码中，ResNet 类中的 AvgPool2d 层设置了 kernel_size=(10, 10)，而输入特征图大小为 7x7，导致 kernel_size 大于输入大小，引发错误。
解决方法
将 ResNet 类中的 AvgPool2d 层的 kernel_size 修改为 7 即可解决问题。
修改后的代码
class ResNet(nn.Cell):
def __init__(self, block, layers, num_classes=10):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad', has_bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
self.layer1 = self.make_layer(block, 64, layers[0])
self.layer2 = self.make_layer(block, 128, layers[1], stride=2)
self.layer3 = self.make_layer(block, 256, layers[2], stride=2)
self.layer4 = self.make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1) # 修改这里
self.flatten = nn.Flatten()
self.fc = nn.Dense(512, num_classes)
# 省略其它代码
修改后，运行代码将不再报错。
代码说明
- 代码使用 MindSpore 框架训练 ResNet 模型进行图像分类。
- 代码使用了 CPU 进行训练。
- 代码使用
GeneratorDataset加载数据。 - 代码中包含
BasicBlock和ResNet两个类，分别定义了 ResNet 模型的基本模块和整体结构。 - 代码中使用了
ModelCheckpoint和LossMonitor回调函数，用于保存模型和监控训练过程。 - 代码中使用
Accuracy度量函数评估模型性能。
注意
- 代码中的数据路径需要根据实际情况进行修改。
- 代码中使用的学习率、动量等参数可以根据实际情况进行调整。
- 代码中使用的批大小可以根据实际情况进行调整。
- 代码中使用的 epochs 数可以根据实际情况进行调整。
总结
本文介绍了如何使用 MindSpore 框架训练 ResNet 模型进行图像分类，并提供完整代码示例。代码示例使用了 CPU 进行训练，并使用 GeneratorDataset 加载数据。希望本文能够帮助您学习使用 MindSpore 框架进行深度学习模型的训练。
原文地址: https://www.cveoy.top/t/topic/mQ3v 著作权归作者所有。请勿转载和采集!