1 系统环境
硬件环境(Ascend/GPU/CPU): Ascend/GPU/CPU
MindSpore版本: mindspore=2.1.0
执行模式（PyNative/ Graph）:不限
Python版本: Python=3.7.10
操作系统平台: 不限
2 报错信息
2.1脚本信息
from download import download
import mindspore as ms
import mindspore.dataset as ds
import mindspore.dataset.vision as vision
import mindspore.dataset.transforms as transforms
from mindspore import dtype as mstype
batch_size = 32 # Batch size
image_size = 128 # Image size of training data
workers = 2 # Number of parallel workers
num_classes = 1000 # Number of classes
def create_dataset(data_set, usage, resize, batch_size, workers):
trans = []
trans += [
vision.Resize((128, 128)),
vision.Rescale(1.0 / 255.0, 0.0),
vision.HWC2CHW()
]
target_trans = transforms.TypeCast(mstype.int32)
data_set = data_set.map(operations=trans,
input_columns='image',
num_parallel_workers=workers)
data_set = data_set.map(operations=target_trans,
input_columns='label',
num_parallel_workers=workers)
data_set = data_set.batch(batch_size)
return data_set
import mindspore
from mindspore.dataset import vision, transforms
import mindspore.dataset as ds
trainset = ds.ImageFolderDataset("./imagenet2012/train", decode=True)
testset = ds.ImageFolderDataset("./imagenet2012/val", decode=True)
dataset_train = create_dataset(trainset,
usage="train",
resize=image_size,
batch_size=batch_size,
workers=workers)
dataset_val = create_dataset(testset,
usage="test",
resize=image_size,
batch_size=batch_size,
workers=workers)
step_size_val = dataset_val.get_dataset_size()
step_size_train = dataset_train.get_dataset_size()
import matplotlib.pyplot as plt
import numpy as np
data_iter = next(dataset_train.create_dict_iterator())
images = data_iter["image"].asnumpy()
labels = data_iter["label"].asnumpy()
print(f"Image shape: {images.shape}, Label shape: {labels.shape}")
print(f"Labels: {labels[:6]}")
classes = []
with open('./imagenet2012/imagenet-classes.txt', "r") as f:
for line in f:
line = line.rstrip()
if line:
classes.append(line)
plt.figure()
for i in range(6):
plt.subplot(2, 3, i + 1)
image_trans = np.transpose(images[i], (1, 2, 0))
mean = np.array([0.4914, 0.4822, 0.4465])
std = np.array([0.2023, 0.1994, 0.2010])
image_trans = std * image_trans + mean
image_trans = np.clip(image_trans, 0, 1)
plt.title(f"{classes[labels[i]]}")
plt.imshow(image_trans)
plt.axis("off")
plt.show()
images.shape
from typing import Type, Union, List, Optional
import mindspore.nn as nn
from mindspore.common.initializer import Normal
# Initialize the parameters of the convolutional layer and BatchNorm layer
weight_init = Normal(mean=0, sigma=0.02)
gamma_init = Normal(mean=1, sigma=0.02)
class ResidualBlockBase(nn.Cell):
expansion: int = 1 # The number of convolution kernels at the last layer is the same as that of convolution kernels at the first layer.
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 # shortcut
out = self.conv1(x) # First layer of the main body: 3 x 3 convolutional layer
out = self.norm(out)
out = self.relu(out)
out = self.conv2(out) # Second layer of the main body: 3 x 3 convolutional layer
out = self.norm(out)
if self.down_sample is not None:
identity = self.down_sample(x)
out += identity # output the sum of the main body and the shortcuts
out = self.relu(out)
return out
class ResidualBlock(nn.Cell):
expansion = 4 # The number of convolution kernels at the last layer is four times that of convolution kernels at the first layer.
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 # shortcut
out = self.conv1(x) # First layer of the main body: 1 x 1 convolutional layer
out = self.norm1(out)
out = self.relu(out)
out = self.conv2(out) # Second layer of the main body: 3 x 3 convolutional layer
out = self.norm2(out)
out = self.relu(out)
out = self.conv3(out) # Third layer of the main body: 1 x 1 convolutional layer
out = self.norm3(out)
if self.down_sample is not None:
identity = self.down_sample(x)
out += identity # The output is the sum of the main body and the shortcut.
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
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
# Stack residual networks.
for _ in range(1, block_nums):
layers.append(block(in_channel, channel))
return nn.SequentialCell(layers)
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()
# At the first convolutional layer, the number of the input channels is 3 (color image) and that of the output channels is 64.
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, weight_init=weight_init)
self.norm = nn.BatchNorm2d(64)
# Maximum pooling layer, reducing the image size
self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
# Define each residual network structure block
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)
# average pooling layer
self.avg_pool = nn.AvgPool2d()
# flattern layer
self.flatten = nn.Flatten()
# fully-connected layer
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, pretrained_ckpt: str,
input_channel: int):
model = ResNet(block, layers, num_classes, input_channel)
if pretrained:
# load pre-trained models
download(url=model_url, path=pretrained_ckpt, replace=True)
param_dict = load_checkpoint(pretrained_ckpt)
load_param_into_net(model, param_dict)
return model
def resnet50(num_classes: int = 1000, pretrained: bool = False):
"ResNet50 model"
resnet50_url = "https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/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)
in_channel
# Define the ResNet50 network.
network = resnet50(pretrained=True)
# Size of the input layer of the fully-connected layer
in_channel = network.fc.in_channels
fc = nn.Dense(in_channels=in_channel, out_channels=1000)
# Reset the fully-connected layer.
network.fc = fc
# Set the learning rate
num_epochs = 5
lr = nn.cosine_decay_lr(min_lr=0.00001, max_lr=0.001, total_step=step_size_train * num_epochs,
step_per_epoch=step_size_train, decay_epoch=num_epochs)
# Define optimizer and loss function
opt = nn.Momentum(params=network.trainable_params(), learning_rate=lr, momentum=0.9)
loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
def forward_fn(inputs, targets):
logits = network(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
import os
# Creating Iterators
data_loader_train = dataset_train.create_tuple_iterator(num_epochs=num_epochs)
data_loader_val = dataset_val.create_tuple_iterator(num_epochs=num_epochs)
# Optimal model storage path
best_acc = 0
best_ckpt_dir = "./BestCheckpoint"
best_ckpt_path = "./BestCheckpoint/resnet50-best.ckpt"
if not os.path.exists(best_ckpt_dir):
os.mkdir(best_ckpt_dir)
import mindspore.ops as ops
def train(data_loader, epoch):
"""Model taining"""
losses = []
network.set_train(True)
for i, (images, labels) in enumerate(data_loader):
loss = train_step(images, labels)
if i % 100 == 0 or i == step_size_train - 1:
print('Epoch: [%3d/%3d], Steps: [%3d/%3d], Train Loss: [%5.3f]' %
(epoch + 1, num_epochs, i + 1, step_size_train, loss))
losses.append(loss)
return sum(losses) / len(losses)
def evaluate(data_loader):
"""Model Evaluation"""
network.set_train(False)
correct_num = 0.0 # Number of correct predictions
total_num = 0.0 # Total number of predictions
for images, labels in data_loader:
logits = network(images)
pred = logits.argmax(axis=1) # Prediction results
correct = ops.equal(pred, labels).reshape((-1, ))
correct_num += correct.sum().asnumpy()
total_num += correct.shape[0]
acc = correct_num / total_num # Accuracy
return acc
# Start training loop
print("Start Training Loop ...")
for epoch in range(num_epochs):
curr_loss = train(data_loader_train, epoch)
curr_acc = evaluate(data_loader_val)
print("-" * 50)
print("Epoch: [%3d/%3d], Average Train Loss: [%5.3f], Accuracy: [%5.3f]" % (
epoch+1, num_epochs, curr_loss, curr_acc
))
print("-" * 50)
# Save the model that has achieved the highest prediction accuracy
if curr_acc > best_acc:
best_acc = curr_acc
ms.save_checkpoint(network, 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)
2.2 报错信息
使用的imagenet数据，图像size设置为(128,128)，然后报错
ValueError: For 'MatMul' the input dimensions must be equal, but got 'x1_col': 32768 and 'x2_row': 2048.
3 根因分析
- 根据报错，可以看到是MatMul算子维度不匹配，脚本中只有ResNet网络中全连接层用到了Matmul：
self.fc = nn.Dense(in_channels=input_channel, out_channels=num_classes)
2）在脚本中加入打印，可以看到x的维度是(32, 32768), w的维度是(1000, 2048)
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)
print(f"x shape={x.shape}, w shape={self.fc.weight.shape}:", x.shape)
x = self.fc(x)
return x
- 由于x和w不满足矩阵乘法维度要求，最终脚本执行报错。这通常是由于修改了网络的输入（如数据预处理的图片size），但没有修改维度敏感算子导致。
4 解决方案
将脚本中将input_channel从2048改为32768即可
def resnet50(num_classes: int = 1000, pretrained: bool = False):
... ...
# 将input_channel从2048改为32768
return _resnet(resnet50_url, ResidualBlock, [3, 4, 6, 3], num_classes,
pretrained, resnet50_ckpt, 32768)