MindSpore ResNet 人脸识别模型训练与实时识别 - 疑难解答
MindSpore ResNet 人脸识别模型训练与实时识别 - 疑难解答
本文介绍使用 MindSpore 框架训练 ResNet 人脸识别模型，并使用训练好的模型进行实时人脸识别，并针对代码中遇到的 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. 错误进行分析和解决。
代码分析
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)
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()
错误分析
报错信息表明：Conv2D 的输入 x 的形状大小应该为 4 维，但实际只有 3 维。代码中，输入到 Conv2D 的 x 是经过 cv2.resize 处理的。因此，问题可能出在 resize 处理后的形状大小不正确。
解决方法
- 检查
resize结果： 确保resize处理后的face的形状大小为(100, 100, 3)。 - 打印
face的形状： 在调用Conv2D之前，打印出face的形状大小，查看具体情况。
代码修改示例
face = gray[y:y + w, x:x + h].astype(np.float32) # 数值转换
face = cv2.resize(face, (100, 100))
print(face.shape) # 打印 face 的形状
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)
总结
通过以上分析和修改，可以解决 Conv2D 输入形状大小不符合要求的错误，从而使人脸识别模型能够正常训练和识别。
附加说明
cv2.resize的参数dsize指定的是输出图像的大小，如果图像有 3 个通道，则输出图像的形状大小应该为(height, width, channels)，即(100, 100, 3)。- 在代码中，使用
np.expand_dims(face, axis=0)对face进行了扩展维度，使其成为一个批次大小为 1 的数据，方便输入到Conv2D中进行处理。 - 如果仍然无法解决问题，可以尝试使用其他的图像处理库，例如
PIL或skimage，并进行调试。
原文地址: https://www.cveoy.top/t/topic/jqyL 著作权归作者所有。请勿转载和采集!