MindSpore ResNet人脸识别模型训练与摄像头实时识别 - 错误解决
MindSpore ResNet人脸识别模型训练与摄像头实时识别 - 错误解决
本文介绍如何使用MindSpore框架训练ResNet模型进行人脸识别，并提供摄像头实时识别的代码示例。同时，解决在训练过程中遇到的错误：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。
错误原因:
这个错误是因为在使用network(face)时，输入的face的shape只有三个维度，而Conv2d需要输入四个维度（batch_size, channels, height, width）。
解决方法:
可以使用np.expand_dims或者mindspore.ops.ExpandDims来增加维度。例如：
face = np.expand_dims(face, axis=0) # 扩展维度，变成(batch_size, channels, height, width)
face = Tensor(face)
output = network(face)
代码示例:
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()
注意:
- 代码中的
D:/pythonproject2/ckpt/checkpoint_resnet_1-20_49.ckpt和D:/pythonproject2/digital_mindspore/dataset需要替换成你的实际路径。 - 代码中的
subjects列表需要根据你的数据集进行调整。 - 训练和识别需要安装相应的库，例如
opencv-python等。 - 你可以根据需要调整模型参数、训练参数和识别参数。
原文地址: https://www.cveoy.top/t/topic/jqzk 著作权归作者所有。请勿转载和采集!