MindSpore ResNet 模型训练和人脸识别实战：解决 Conv2D 输入维度错误
这个错误提示是因为在构建 ResNet 模型时，Conv2d 的输入数据维度不正确。Conv2d 的输入数据应该是四维的，即 (batch_size, channels, height, width)，但是在代码中输入的是三维的数据，因此需要检查代码中的数据维度是否正确。可能需要检查数据预处理部分，确保输入数据的维度正确。同时，也需要检查模型定义部分，确保输入和输出的维度正确。
from collections import defaultdict, Counter
from mindspore.train.serialization import load_checkpoint, load_param_into_net
import numpy as np
import mindspore.dataset as ds
import cv2
import mindspore.nn as nn
import os
from mindspore import context, ops, Tensor
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')
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.resize(data,(100,100))
data = data.transpose().astype(np.float32) / 255.
return data, label
def __len__(self):
return len(self.img_names)
def load_model_from_ckpt():
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
# 创建ResNet模型
network = ResNet(ResidualBlock,[2,2,2,2])
# 加载ckpt文件中的模型参数
param_dict = load_checkpoint('D:/pythonproject2/ckpt/checkpoint_resnet_1-20_49.ckpt')
#将模型参数加载到模型中
load_param_into_net(network, param_dict)
# 返回模型
return network
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)
network = load_model_from_ckpt()
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.001, 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 = 20
#print('============== Starting Training =============')
#model.train(epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()])
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml') # 加载检测器
# 训练阶段
cap = cv2.VideoCapture(0)
stop = False
while not stop:
success, img = cap.read()
subjects = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17',
'18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33']
# 生成图像的副本，这样就能保留原始图像
img1 = img.copy()
# 检测人脸
# 将测试图像转换为灰度图像，因为opencv人脸检测器需要灰度图像
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 检测多尺度图像，返回值是一张脸部区域信息的列表（x,y,宽,高）
rect = face_cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
# 如果未检测到面部
if len(rect) == 0:
txt = 'no face!'
cv2.putText(img1, txt, (10, 20), cv2.FONT_HERSHEY_COMPLEX, 1, (128, 128, 0), 2)
if not rect is None:
for (x, y, w, h) in rect:
face = gray[y:y + w, x:x + h].astype(np.float32) # 数值转换
face = cv2.resize(face, (100, 100))
face = face.transpose().astype(np.float32) / 255.
face = np.expand_dims(face, axis=0) # 扩展维度，变成(batch_size, channels, height, width)
face = Tensor(face)
print(face.shape)
cv2.rectangle(img1, (x, y), (x + w, y + h), (0, 255, 0), 2) # 画出矩形框
output =network(face)
predicted_class = np.argmax(output.asnumpy(),axis=1)
label = subjects[predicted_class[0]]
#if min_d < 200000000000:
cv2.putText(img1, label, (x, y), cv2.FONT_HERSHEY_COMPLEX, 1, (128, 128, 0), 2)
#else:
# label = 'unknown'
# cv2.putText(img1, label, (x, y), cv2.FONT_HERSHEY_COMPLEX, 1, (128, 128, 0), 2)
cv2.imshow('img', img1)
if (cv2.waitKey(1) & 0xFF == ord('q')): # 按下q程序结束
stop = True
cv2.destroyAllWindows() # 释放窗口
if __name__ == '__main__':
train_resnet()
ValueError: mindspore\core\utils\check_convert_utils.cc:367 CheckInteger] The primitive[Conv2D]'s x shape size must be equal to 4, but got 3.
解决方法：
- 检查数据预处理部分: 确保输入
Conv2d的数据是四维的 (batch_size, channels, height, width)。在代码中，face = np.expand_dims(face, axis=0)扩展了维度，但需要确保face的初始维度是 (channels, height, width)。 - 检查模型定义部分: 确保
Conv2d的输入和输出的维度正确。检查ResNet类中conv1的输入通道数是否正确。
示例：
# 在预处理部分，将 `face` 的维度改为 (channels, height, width)
face = gray[y:y + w, x:x + h].astype(np.float32) # 数值转换
face = cv2.resize(face, (100, 100))
face = face.transpose((2, 0, 1)).astype(np.float32) / 255.
face = np.expand_dims(face, axis=0) # 扩展维度，变成(batch_size, channels, height, width)
face = Tensor(face)
通过以上步骤，您可以解决 Conv2D 输入维度错误，并成功进行 MindSpore ResNet 模型训练和人脸识别。
原文地址: https://www.cveoy.top/t/topic/jqzj 著作权归作者所有。请勿转载和采集!