MindSpore ResNet 实现图像分类 - 代码示例和错误解决
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',group=1)
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],stride=1)
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(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, 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()
# TypeError: __init__() takes from 3 to 5 positional arguments but 6 were given
# 解决错误代码示例
# 内容：将ResNet类中的第二个参数num_classes改为可选参数，即加上默认值34，代码如下：
# class ResNet(nn.Cell):
# def __init__(self, block, layers, num_classes=34):
# super(ResNet, self).__init__()
# # 省略代码
错误原因：
在 ResNet 类中，__init__ 方法的定义原本要求传入 5 个参数，但实际调用时传入 6 个参数。
解决方法：
将 ResNet 类中 __init__ 方法的第二个参数 num_classes 设置为可选参数，并为其提供一个默认值，例如 num_classes=34。这样，在调用 ResNet 类时，即使没有明确指定 num_classes 的值，也能正常执行。
修改后的代码：
class ResNet(nn.Cell):
def __init__(self, block, layers, num_classes=34):
# ...
其他可能的错误解决方法：
- 检查
ResNet类中__init__方法的定义，确保参数个数与实际调用时传入的参数个数一致。 - 检查
ResNet类中make_layer方法的调用方式，确保参数顺序和个数正确。 - 检查数据集的读取和处理过程，确保数据类型和形状符合要求。
代码示例：
以上代码示例展示了如何使用 MindSpore 实现 ResNet 模型，并解决了常见的错误。您可以根据自己的需求修改代码，例如更改数据路径、调整模型参数等。
参考文档：
原文地址: https://www.cveoy.top/t/topic/mSNZ 著作权归作者所有。请勿转载和采集!