MindSpore ResNet 模型训练：图像分类
import numpy as np
import mindspore.dataset as ds
import cv2
import mindspore.nn as nn
import os
from mindspore import context, ops
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train import Model
from mindspore.nn.metrics import Accuracy
np.random.seed(58)
class ResidualBlock(nn.Cell):
expansion = 1
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, pad_mode='same')
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, pad_mode='same')
self.bn2 = nn.BatchNorm2d(out_channels)
self.downsample = downsample
self.stride = stride
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 += identity
out = self.relu(out)
return out
class ResNet(nn.Cell):
def __init__(self, block, layers, num_classes=34):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, pad_mode='valid') # 修改输入通道数为3
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='valid')
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=3, stride=1, pad_mode='valid')
self.fc = nn.Dense(512 * block.expansion, num_classes)
def make_layer(self, block, out_channels, blocks, stride=1):
downsample = None
if (stride != 1) or (self.in_channels != out_channels * block.expansion):
downsample = nn.SequentialCell([
nn.Conv2d(self.in_channels, out_channels * block.expansion, kernel_size=1, stride=stride),
nn.BatchNorm2d(out_channels * block.expansion)
])
layers = []
layers.append(block(self.in_channels, out_channels, stride, downsample))
self.in_channels = out_channels * block.expansion
for _ in range(1, blocks):
layers.append(block(self.in_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 = ops.Reshape()(x, (ops.Shape()(x)[0], -1))
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=0):
data = cv2.imread(os.path.join(self.file_path, self.img_names[index]))
label = int(self.img_names[index].split('-')[0])
#data = cv2.cvtColor(data,cv2.COLOR_BGR2RGB)
data = cv2.resize(data,(224,224))
# label = np.array([label])
data = data.transpose().astype(np.float32) / 255.
# data = np.expand_dims(data, axis=0)
# data = Tensor(data)
# label = Tensor(label)
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:/pythonproject2/digital_mindspore/dataset')
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:/pythonproject2/test1')
ds_valid = ds.GeneratorDataset(valid_dataset_generator, ['data', 'label'], shuffle=True)
ds_valid = ds_valid.batch(batch_size=4, drop_remainder=True)
network = ResNet(ResidualBlock,[2,2,2,2])
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:/pythonproject2/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()
错误解释:
该错误是因为卷积层的输入通道数与卷积核的输入通道数不匹配导致的。在 ResNet 模型中，第一层卷积的输入通道数为 1，但是在训练数据中，图像是 RGB 三通道，因此需要将输入通道数修改为 3。
修改代码:
将 self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, pad_mode='valid') 修改为 self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, pad_mode='valid')。
其他建议:
- 可以根据实际情况调整模型参数，例如学习率、批次大小等。
- 可以使用更复杂的优化器，例如 Adam 或 RMSprop。
- 可以使用数据增强技术，例如随机旋转、裁剪等，提高模型的泛化能力。
- 可以使用 GPU 或其他硬件加速训练过程。
- 可以使用 TensorBoard 等工具监控训练过程。
参考链接:
- MindSpore 官方网站: https://www.mindspore.cn/
- MindSpore 文档: https://www.mindspore.cn/docs/zh-CN/master/
- ResNet 模型介绍: https://arxiv.org/abs/1512.03385
原文地址: https://www.cveoy.top/t/topic/mSRl 著作权归作者所有。请勿转载和采集!