GoogLeNet依靠两个辅助loss将网络撑到22层并取得2014年ILSVRC比赛的冠军，但增加辅助loss的方法似乎治标不治本，否则GoogLeNet也不会增加区区三层即止，给人一种吊着氧气瓶赢得马拉松的感觉。2015年ResNet横空出世，使用残差结构打破深度神经网络的任督二脉！从此DNN层数开始成百上千。
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肺炎给人民的健康生活带来巨大的风险和挑战，尤其在医疗技术不是很发达的地区，由于缺乏医疗资源和医护人员，肺炎患者不能及时得到诊断，从而错过治疗的最佳时期。
新型冠状病毒肺炎（Corona Virus Disease 2019，COVID-19），简称“新冠肺炎”。新冠肺炎的爆发，是一场世界性的灾难，经过三年全国人民的共同努力，我们终于取得抗疫的阶段性胜利。经此一役，人民对肺炎的关注达到空前高度，肺部CT检测是诊断肺炎的最佳方法，它在临床护理和流行病学研究中发挥着至关重要的作用，但是通过肺部CT影像来诊断肺炎是一项具有挑战性的任务，需要依赖放射科医师的专业能力。
肺炎患者肺部CT影像中有相应的特征表现。因此，准确识别出肺部CT影像中的肺炎影像，具有十分重要的现实意义。项目旨在使用肺炎CT数据集训练深度学习算法，以协助医生快速、准确地判断患者是否感染肺炎。
项目采用来自ka塔尔大学和孟加拉国达卡大学的一组研究人员以及来自巴基斯坦和马来西亚的合作者同医生合作，创建的一个肺炎CT数据集。包含3616例COVID-19阳性、10192例正常、6012例肺部感染和1345例病毒性肺炎。模型选用注意力卷积网络EPSANet50-S，采用热启动的余弦退火学习率优化策略，测试集准确度可达94%以上。
医学免责声明：94%仅为实验数据集上的结果，任何临床使用的算法需要在实际使用环境下进行测试，本模型结果不可作为临床诊疗依据。
众所周知，对于浅层网络，其模型性能会随着网络层的堆叠而提升，因为非线性层增多，特征提取的能力越强，即模型拟合数据的能力越强，所以从AlexNet到VGG，深度学习模型层数越来越多。但当继续加深时，模型性能不升反降，因为更深的网络会导致梯度消失问题，从而阻碍收敛，即模型退化问题。
作为2015年ILSVRC比赛的冠军，ResNet在分类、检测、定位均表现优异。为解决退化问题，ResNet采用跨层连接的方法，图-2是论文中介绍的ResNet基本残差块的结构：
一般plain网络层输出y = F(x)，而残差块residual block输出y = F(x) + x。残差块额外提供一条identity路径（short cut）。identity mapping称为恒等映射，即输入和输出是相等的。使用残差块的好处是：如果增加的层并未增加网络性能，则训练使得F(x)趋近于0，这样增加的层的输出y也趋近于输入x，相当于没有增加这个层。图-3是对比18层和34层的普通plain网络和残差块residual block的训练结果：
残差块分为两种，一种如图-4右侧所示的瓶颈结构(Bottleneck)，Bottleneck主要用于降低计算复杂度，输入数据先经过1x1卷积层减少通道数，再经过3x3卷积层提取特征，最后经过1x1卷积层恢复通道数。通道数先减少再恢复，就像一个中间细两头粗的瓶颈，所以被称为Bottleneck。另一种如图-4左侧所示的Basic Block，由2个3×3卷积层构成。Bottleneck Block被用于ResNet50、ResNet101和ResNet152，而Basic Block被用于ResNet18和ResNet34。
short cut路径也分为两种，如图-5所示，当残差路径输出与输入的通道数和特征图尺寸均相同时，short cut路径将输入x原封不动地输出。若残差路径输出与输入的通道数或特征图尺寸不同时，short cut路径使用1x1卷积层对输入x进行调整，使得short cut路径输出与残差路径输出的通道数和特征图尺寸均相同。
已有研究表明：将注意力模块嵌入至CNN中可以带来显著的性能提升，例如SENet、BAM、CBAM、ECANet、GCNet、FcaNet等。其中SENet的缺点是其忽略空间信息的重要性，BAM和CBAM兼顾通道注意力和空间注意力，但仍然存在两个具有挑战性的问题：一是如何有效地捕获和利用不同尺度的特征图的空间信息来丰富特征空间，二是通道注意力或空间注意力只能有效地捕获局部信息，而无法建立远距离的依赖关系。后续出现的PyConv、Res2Net和HS-ResNet均用于解决这两个问题，但计算负担太过沉重。因此一种轻量高效的注意力模块PSA（Pyramid Squeeze Attention）应运而生，PSA模块可以处理多尺度特征图的空间信息并能有效地建立远距离的依赖关系。
PSA模块主要分四个步骤实现：
SPC模块：
SEWeight模块：
即插即用的PSA模块替换Bottleneck Block中的3×3卷积层得到EPSA Block（Efficient Pyramid Squeeze Attention）。
基于EPSA Block构建的注意力卷积网络EPSANet可以提供强大的多尺度特征表示能力并能自适应地重新校准跨维度通道权重。EPSANet不仅在图像分类任务Top-1 Acc上表现优秀，而且计算更加高效。如图-10所示，EPSANet分为两个版本：Small和Large，EPSANet-S的卷积核尺寸和分组尺寸分别为（3, 5, 7, 9）和（1, 4, 8, 16），而EPSANet-L拥有更大的分组尺寸：（32, 32, 32, 32）。
import osimport cv2import globimport paddleimport numpy as npimport prettytableimport matplotlib.pyplot as pltimport paddle.nn.functional as Ffrom paddle.io import Datasetfrom paddle.optimizer.lr import LinearWarmup, CosineAnnealingDecayfrom paddle.vision.transforms import Compose, Resize, ToTensor, Normalizefrom paddle.nn import Sequential, Conv2D, BatchNorm2D, ReLU, MaxPool2D, AdaptiveAvgPool2D, Flatten, Linear, Sigmoid, Softmax
# 解压数据集!unzip /home/aistudio/data/data179597/dataset.zip -d work/
# 划分数据集base_dir = '/home/aistudio/work/dataset/'img_dirs = ['COVID', 'LungOpacity', 'Normal', 'ViralPneumonia']
file_names = ['train.txt', 'val.txt', 'test.txt']
splits = [0, 0.7, 0.9, 1] # 7 : 2 : 1 划分for split_idx, file_name in enumerate(file_names): with open(os.path.join('/home/aistudio/work/dataset', file_name), 'w') as f: for label, img_dir in enumerate(img_dirs):
imgs = os.listdir(os.path.join(base_dir, img_dir)) for idx in range(int(splits[split_idx] * len(imgs)), int(splits[split_idx + 1] * len(imgs))): print('{} {}'.format(img_dir + '/' + imgs[idx], label), file=f)# 计算均值和标准差def get_mean_std(img_paths):
print('Total images:', len(img_paths))
mean, std = np.zeros(3), np.zeros(3)
transform = Compose([Resize(size=[224, 224]), ToTensor()]) for img_path in img_paths:
img = cv2.imread(img_path)
img = transform(img) for c in range(3):
mean[c] += img[c, :, :].mean()
std[c] += img[c, :, :].std()
mean /= len(img_paths)
std /= len(img_paths) return mean, std
img_paths = []
img_paths.extend(glob.glob(os.path.join('work/dataset/COVID', '*.png')))
img_paths.extend(glob.glob(os.path.join('work/dataset/LungOpacity', '*.png')))
img_paths.extend(glob.glob(os.path.join('work/dataset/Normal', '*.png')))
img_paths.extend(glob.glob(os.path.join('work/dataset/ViralPneumonia', '*.png')))
mean, std = get_mean_std(img_paths)print('mean:', mean)print('std:', std)Total images: 21165 mean: [0.50897003 0.50897003 0.50897003] std: [0.23101362 0.23101362 0.23101362]
# 自定义数据集class CTDataset(Dataset):
def __init__(self, base_dir, label_path, transform=None):
super(CTDataset, self).__init__()
self.datas = [] with open(label_path) as f: for line in f.readlines():
img_path, label = line.strip().split(' ')
img_path = os.path.join(base_dir, img_path)
self.datas.append([img_path, label])
self.transform = transform # 数据处理方法
def __getitem__(self, idx):
img_path, label = self.datas[idx]
img = cv2.imread(img_path) if self.transform is not None:
img = self.transform(img)
label = np.array([int(label)]) # cross_entropy要求label格式为int
return img, label def __len__(self):
return len(self.datas)# 数据预处理transform = Compose([Resize(size=[224, 224]),
ToTensor(), # numpy.ndarray -> paddle.Tensor HWC -> CHW 0~255 -> 0~1
Normalize(mean=[0.509, 0.509, 0.509], std=[0.231, 0.231, 0.231], data_format='CHW')])
train_dataset = CTDataset('work/dataset', 'work/dataset/train.txt', transform)
val_dataset = CTDataset('work/dataset', 'work/dataset/val.txt', transform)
test_dataset = CTDataset('work/dataset', 'work/dataset/test.txt', transform)print('训练集图片数量: {}\n验证集图片数量: {}\n测试集图片数量: {}'.format(len(train_dataset), len(val_dataset), len(test_dataset)))训练集图片数量: 14814 验证集图片数量: 4232 测试集图片数量: 2119
# 定义BasicBlockclass BasicBlock(paddle.nn.Layer):
def __init__(self, in_channels, out_channels, stride):
super(BasicBlock, self).__init__()
self.conv1 = Sequential(
Conv2D(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias_attr=False), # bias_attr=False 不添加偏置
BatchNorm2D(out_channels),
ReLU()
)
self.conv2 = Sequential(
Conv2D(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias_attr=False),
BatchNorm2D(out_channels)
) # 当输入通道数和输出通道数不同或特征图尺寸不同时 shortcut路径使用1x1卷积层对输入进行调整
if stride != 1 or in_channels != out_channels:
self.shortcut = Sequential(
Conv2D(in_channels, out_channels, kernel_size=1, stride=stride, padding=0, bias_attr=False),
BatchNorm2D(out_channels)
) else:
self.shortcut = Sequential()
self.relu = ReLU()
def forward(self, inputs):
out_conv1 = self.conv1(inputs)
out_conv2 = self.conv2(out_conv1)
outputs = self.relu(out_conv2 + self.shortcut(inputs)) return outputs# 定义BottleneckBlockclass BottleneckBlock(paddle.nn.Layer):
def __init__(self, in_channels, out_channels, stride):
super(BottleneckBlock, self).__init__()
self.conv1 = Sequential(
Conv2D(in_channels, out_channels // 4, kernel_size=1, stride=1, padding=0, bias_attr=False),
BatchNorm2D(out_channels // 4),
ReLU()
)
self.conv2 = Sequential(
Conv2D(out_channels // 4, out_channels // 4, kernel_size=3, stride=stride, padding=1, bias_attr=False),
BatchNorm2D(out_channels // 4),
ReLU()
)
self.conv3 = Sequential(
Conv2D(out_channels // 4, out_channels, kernel_size=1, stride=1, padding=0, bias_attr=False),
BatchNorm2D(out_channels)
) # 当输入通道数和输出通道数不同或特征图尺寸不同时 shortcut路径使用1x1卷积层对输入进行调整
if stride != 1 or in_channels != out_channels:
self.shortcut = Sequential(
Conv2D(in_channels, out_channels, kernel_size=1, stride=stride, padding=0, bias_attr=False),
BatchNorm2D(out_channels)
) else:
self.shortcut = Sequential()
self.relu = ReLU() def forward(self, inputs):
out_conv1 = self.conv1(inputs)
out_conv2 = self.conv2(out_conv1)
out_conv3 = self.conv3(out_conv2)
outputs = self.relu(out_conv3 + self.shortcut(inputs)) return outputs# 定义ResNetclass ResNet(paddle.nn.Layer):
def __init__(self, layers, num_classes):
super(ResNet, self).__init__()
config = { 18: {'block_type': BasicBlock, 'num_blocks': [2, 2, 2, 2], 'out_channels': [64, 128, 256, 512]},
34: {'block_type': BasicBlock, 'num_blocks': [3, 4, 6, 3], 'out_channels': [64, 128, 256, 512]},
50: {'block_type': BottleneckBlock, 'num_blocks': [3, 4, 6, 3], 'out_channels': [256, 512, 1024, 2048]},
101: {'block_type': BottleneckBlock, 'num_blocks': [3, 4, 23, 3], 'out_channels': [256, 512, 1024, 2048]},
152: {'block_type': BottleneckBlock, 'num_blocks': [3, 8, 36, 3], 'out_channels': [256, 512, 1024, 2048]}
}
self.conv = Sequential(
Conv2D(in_channels=3, out_channels=64, kernel_size=7, stride=2, padding=3, bias_attr=False),
BatchNorm2D(64),
ReLU(),
)
self.max_pool = MaxPool2D(kernel_size=3, stride=2, padding=1)
in_channels = 64
block_list = [] for i, block_num in enumerate(config[layers]['num_blocks']): for order in range(block_num):
block_list.append(config[layers]['block_type'](in_channels, config[layers]['out_channels'][i], 2 if order == 0 and i != 0 else 1))
in_channels = config[layers]['out_channels'][i]
self.block = Sequential(*block_list)
self.avg_pool = AdaptiveAvgPool2D(1) # 自适应平均池化
self.flatten = Flatten() # 展平
self.fc = Linear(config[layers]['out_channels'][-1], num_classes)
def forward(self, inputs):
out_conv = self.conv(inputs)
out_max_pool = self.max_pool(out_conv)
out_block = self.block(out_max_pool)
out_avg_pool = self.avg_pool(out_block)
out_flatten = self.flatten(out_avg_pool)
outputs = self.fc(out_flatten) return outputs
# 查看ResNet50网络结构resnet50 = ResNet(50, 4) paddle.summary(resnet50, (1, 3, 224, 224))
-------------------------------------------------------------------------------
Layer (type) Input Shape Output Shape Param #
===============================================================================
Conv2D-1 [[1, 3, 224, 224]] [1, 64, 112, 112] 9,408
BatchNorm2D-1 [[1, 64, 112, 112]] [1, 64, 112, 112] 256
ReLU-1 [[1, 64, 112, 112]] [1, 64, 112, 112] 0
MaxPool2D-1 [[1, 64, 112, 112]] [1, 64, 56, 56] 0
Conv2D-2 [[1, 64, 56, 56]] [1, 64, 56, 56] 4,096
BatchNorm2D-2 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-2 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
Conv2D-3 [[1, 64, 56, 56]] [1, 64, 56, 56] 36,864
BatchNorm2D-3 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-3 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
Conv2D-4 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384
BatchNorm2D-4 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
Conv2D-5 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384
BatchNorm2D-5 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
ReLU-4 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
BottleneckBlock-1 [[1, 64, 56, 56]] [1, 256, 56, 56] 0
Conv2D-6 [[1, 256, 56, 56]] [1, 64, 56, 56] 16,384
BatchNorm2D-6 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-5 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
Conv2D-7 [[1, 64, 56, 56]] [1, 64, 56, 56] 36,864
BatchNorm2D-7 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-6 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
Conv2D-8 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384
BatchNorm2D-8 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
ReLU-7 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
BottleneckBlock-2 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
Conv2D-9 [[1, 256, 56, 56]] [1, 64, 56, 56] 16,384
BatchNorm2D-9 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-8 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
Conv2D-10 [[1, 64, 56, 56]] [1, 64, 56, 56] 36,864
BatchNorm2D-10 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-9 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
Conv2D-11 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384
BatchNorm2D-11 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
ReLU-10 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
BottleneckBlock-3 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
Conv2D-12 [[1, 256, 56, 56]] [1, 128, 56, 56] 32,768
BatchNorm2D-12 [[1, 128, 56, 56]] [1, 128, 56, 56] 512
ReLU-11 [[1, 128, 56, 56]] [1, 128, 56, 56] 0
Conv2D-13 [[1, 128, 56, 56]] [1, 128, 28, 28] 147,456
BatchNorm2D-13 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-12 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
Conv2D-14 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536
BatchNorm2D-14 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
Conv2D-15 [[1, 256, 56, 56]] [1, 512, 28, 28] 131,072
BatchNorm2D-15 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
ReLU-13 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
BottleneckBlock-4 [[1, 256, 56, 56]] [1, 512, 28, 28] 0
Conv2D-16 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536
BatchNorm2D-16 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-14 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
Conv2D-17 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456
BatchNorm2D-17 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-15 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
Conv2D-18 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536
BatchNorm2D-18 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
ReLU-16 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
BottleneckBlock-5 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
Conv2D-19 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536
BatchNorm2D-19 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-17 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
Conv2D-20 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456
BatchNorm2D-20 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-18 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
Conv2D-21 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536
BatchNorm2D-21 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
ReLU-19 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
BottleneckBlock-6 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
Conv2D-22 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536
BatchNorm2D-22 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-20 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
Conv2D-23 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456
BatchNorm2D-23 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-21 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
Conv2D-24 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536
BatchNorm2D-24 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
ReLU-22 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
BottleneckBlock-7 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
Conv2D-25 [[1, 512, 28, 28]] [1, 256, 28, 28] 131,072
BatchNorm2D-25 [[1, 256, 28, 28]] [1, 256, 28, 28] 1,024
ReLU-23 [[1, 256, 28, 28]] [1, 256, 28, 28] 0
Conv2D-26 [[1, 256, 28, 28]] [1, 256, 14, 14] 589,824
BatchNorm2D-26 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-24 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-27 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-27 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
Conv2D-28 [[1, 512, 28, 28]] [1, 1024, 14, 14] 524,288
BatchNorm2D-28 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
ReLU-25 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
BottleneckBlock-8 [[1, 512, 28, 28]] [1, 1024, 14, 14] 0
Conv2D-29 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-29 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-26 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-30 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824
BatchNorm2D-30 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-27 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-31 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-31 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
ReLU-28 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
BottleneckBlock-9 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-32 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-32 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-29 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-33 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824
BatchNorm2D-33 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-30 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-34 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-34 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
ReLU-31 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
BottleneckBlock-10 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-35 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-35 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-32 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-36 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824
BatchNorm2D-36 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-33 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-37 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-37 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
ReLU-34 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
BottleneckBlock-11 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-38 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-38 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-35 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-39 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824
BatchNorm2D-39 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-36 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-40 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-40 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
ReLU-37 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
BottleneckBlock-12 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-41 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-41 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-38 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-42 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824
BatchNorm2D-42 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-39 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-43 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-43 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
ReLU-40 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
BottleneckBlock-13 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-44 [[1, 1024, 14, 14]] [1, 512, 14, 14] 524,288
BatchNorm2D-44 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048
ReLU-41 [[1, 512, 14, 14]] [1, 512, 14, 14] 0
Conv2D-45 [[1, 512, 14, 14]] [1, 512, 7, 7] 2,359,296
BatchNorm2D-45 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
ReLU-42 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
Conv2D-46 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576
BatchNorm2D-46 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
Conv2D-47 [[1, 1024, 14, 14]] [1, 2048, 7, 7] 2,097,152
BatchNorm2D-47 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
ReLU-43 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
BottleneckBlock-14 [[1, 1024, 14, 14]] [1, 2048, 7, 7] 0
Conv2D-48 [[1, 2048, 7, 7]] [1, 512, 7, 7] 1,048,576
BatchNorm2D-48 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
ReLU-44 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
Conv2D-49 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,359,296
BatchNorm2D-49 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
ReLU-45 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
Conv2D-50 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576
BatchNorm2D-50 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
ReLU-46 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
BottleneckBlock-15 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
Conv2D-51 [[1, 2048, 7, 7]] [1, 512, 7, 7] 1,048,576
BatchNorm2D-51 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
ReLU-47 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
Conv2D-52 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,359,296
BatchNorm2D-52 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
ReLU-48 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
Conv2D-53 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576
BatchNorm2D-53 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
ReLU-49 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
BottleneckBlock-16 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
AdaptiveAvgPool2D-1 [[1, 2048, 7, 7]] [1, 2048, 1, 1] 0
Flatten-1 [[1, 2048, 1, 1]] [1, 2048] 0
Linear-1 [[1, 2048]] [1, 4] 8,196
===============================================================================
Total params: 23,569,348
Trainable params: 23,463,108
Non-trainable params: 106,240
-------------------------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 286.57
Params size (MB): 89.91
Estimated Total Size (MB): 377.05
-------------------------------------------------------------------------------{'total_params': 23569348, 'trainable_params': 23463108}# 定义SEWeightModuleclass SEWeightModule(paddle.nn.Layer):
def __init__(self, channels, reduction=16):
super(SEWeightModule, self).__init__()
self.avg_pool = AdaptiveAvgPool2D(1) # 自适应平均池化
self.conv1 = Sequential(
Conv2D(channels, channels // reduction, kernel_size=1, stride=1, padding=0, bias_attr=False),
ReLU()
)
self.conv2 = Sequential(
Conv2D(channels // reduction, channels, kernel_size=1, stride=1, padding=0, bias_attr=False),
Sigmoid()
) def forward(self, inputs):
out_avg_pool = self.avg_pool(inputs)
out_conv1 = self.conv1(out_avg_pool)
outputs = self.conv2(out_conv1) return outputs# 定义PSAModuleclass PSAModule(paddle.nn.Layer):
def __init__(self, in_channels, out_channels, stride, conv_kernels, conv_groups):
super(PSAModule, self).__init__()
self.conv1 = Conv2D(in_channels, out_channels // 4, kernel_size=conv_kernels[0], stride=stride, padding=conv_kernels[0] // 2, groups=conv_groups[0], bias_attr=False)
self.conv2 = Conv2D(in_channels, out_channels // 4, kernel_size=conv_kernels[1], stride=stride, padding=conv_kernels[1] // 2, groups=conv_groups[1], bias_attr=False)
self.conv3 = Conv2D(in_channels, out_channels // 4, kernel_size=conv_kernels[2], stride=stride, padding=conv_kernels[2] // 2, groups=conv_groups[2], bias_attr=False)
self.conv4 = Conv2D(in_channels, out_channels // 4, kernel_size=conv_kernels[3], stride=stride, padding=conv_kernels[3] // 2, groups=conv_groups[3], bias_attr=False)
self.se = SEWeightModule(out_channels // 4)
self.split_channel = out_channels // 4
self.softmax = Softmax(axis=1)
def forward(self, inputs):
# stage 1
batch_size = inputs.shape[0]
out_conv1 = self.conv1(inputs)
out_conv2 = self.conv2(inputs)
out_conv3 = self.conv3(inputs)
out_conv4 = self.conv4(inputs)
feature = paddle.concat((out_conv1, out_conv2, out_conv3, out_conv4), axis=1)
feature = feature.reshape([batch_size, 4, self.split_channel, feature.shape[2], feature.shape[3]])
# stage 2
out_se1 = self.se(out_conv1)
out_se2 = self.se(out_conv2)
out_se3 = self.se(out_conv3)
out_se4 = self.se(out_conv4)
attention = paddle.concat((out_se1, out_se2, out_se3, out_se4), axis=1)
attention = attention.reshape([batch_size, 4, self.split_channel, 1, 1]) # stage 3
attention = self.softmax(attention) # stage 4
weight_feature = feature * attention for i in range(4):
x = weight_feature[:, i, :, :, :] if i == 0:
outputs = x else:
outputs = paddle.concat((outputs, x), axis=1) return outputs# 定义EPSABlockclass EPSABlock(paddle.nn.Layer):
def __init__(self, in_channels, out_channels, stride, conv_kernels, conv_groups, reduction):
super(EPSABlock, self).__init__()
self.conv1 = Sequential(
Conv2D(in_channels, out_channels // reduction, kernel_size=1, stride=1, padding=0, bias_attr=False),
BatchNorm2D(out_channels // reduction),
ReLU()
)
self.conv2 = Sequential(
PSAModule(out_channels // reduction, out_channels // reduction, conv_kernels=conv_kernels, stride=stride, conv_groups=conv_groups), # 3x3卷积 -> PSAModule
BatchNorm2D(out_channels // reduction),
ReLU()
)
self.conv3 = Sequential(
Conv2D(out_channels // reduction, out_channels, kernel_size=1, stride=1, padding=0, bias_attr=False),
BatchNorm2D(out_channels)
) # 当输入通道数和输出通道数不同或特征图尺寸不同时 shortcut路径使用1x1卷积层对输入进行调整
if stride != 1 or in_channels != out_channels:
self.shortcut = Sequential(
Conv2D(in_channels, out_channels, kernel_size=1, stride=stride, padding=0, bias_attr=False),
BatchNorm2D(out_channels)
) else:
self.shortcut = Sequential()
self.relu = ReLU() def forward(self, inputs):
out_conv1 = self.conv1(inputs)
out_conv2 = self.conv2(out_conv1)
out_conv3 = self.conv3(out_conv2)
outputs = self.relu(out_conv3 + self.shortcut(inputs)) return outputs# 定义EPSANetclass EPSANet(paddle.nn.Layer):
def __init__(self, layers, num_classes, conv_kernels=[3, 5, 7, 9], conv_groups=[1, 4, 8, 16], reduction=4):
super(EPSANet, self).__init__()
config = { 50: {'block_type': EPSABlock, 'num_blocks': [3, 4, 6, 3], 'out_channels': [256, 512, 1024, 2048]},
101: {'block_type': EPSABlock, 'num_blocks': [3, 4, 23, 3], 'out_channels': [256, 512, 1024, 2048]},
152: {'block_type': EPSABlock, 'num_blocks': [3, 8, 36, 3], 'out_channels': [256, 512, 1024, 2048]}
}
self.conv = Sequential(
Conv2D(in_channels=3, out_channels=64, kernel_size=7, stride=2, padding=3, bias_attr=False),
BatchNorm2D(64),
ReLU(),
)
self.max_pool = MaxPool2D(kernel_size=3, stride=2, padding=1)
in_channels = 64
block_list = [] for i, block_num in enumerate(config[layers]['num_blocks']): for order in range(block_num):
block_list.append(config[layers]['block_type'](in_channels, config[layers]['out_channels'][i], 2 if order == 0 and i != 0 else 1, conv_kernels=conv_kernels, conv_groups=conv_groups, reduction=reduction))
in_channels = config[layers]['out_channels'][i]
self.block = Sequential(*block_list)
self.avg_pool = AdaptiveAvgPool2D(1) # 自适应平均池化
self.flatten = Flatten() # 展平
self.fc = Linear(config[layers]['out_channels'][-1], num_classes)
def forward(self, inputs):
out_conv = self.conv(inputs)
out_max_pool = self.max_pool(out_conv)
out_block = self.block(out_max_pool)
out_avg_pool = self.avg_pool(out_block)
out_flatten = self.flatten(out_avg_pool)
outputs = self.fc(out_flatten) return outputs
# 查看EPSANet50-S网络结构epsanet50 = EPSANet(50, 4) paddle.summary(epsanet50, (1, 3, 224, 224))
--------------------------------------------------------------------------------
Layer (type) Input Shape Output Shape Param #
================================================================================
Conv2D-54 [[1, 3, 224, 224]] [1, 64, 112, 112] 9,408
BatchNorm2D-54 [[1, 64, 112, 112]] [1, 64, 112, 112] 256
ReLU-50 [[1, 64, 112, 112]] [1, 64, 112, 112] 0
MaxPool2D-2 [[1, 64, 112, 112]] [1, 64, 56, 56] 0
Conv2D-55 [[1, 64, 56, 56]] [1, 64, 56, 56] 4,096
BatchNorm2D-55 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-51 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
Conv2D-56 [[1, 64, 56, 56]] [1, 16, 56, 56] 9,216
Conv2D-57 [[1, 64, 56, 56]] [1, 16, 56, 56] 6,400
Conv2D-58 [[1, 64, 56, 56]] [1, 16, 56, 56] 6,272
Conv2D-59 [[1, 64, 56, 56]] [1, 16, 56, 56] 5,184
AdaptiveAvgPool2D-2 [[1, 16, 56, 56]] [1, 16, 1, 1] 0
Conv2D-60 [[1, 16, 1, 1]] [1, 1, 1, 1] 16
ReLU-52 [[1, 1, 1, 1]] [1, 1, 1, 1] 0
Conv2D-61 [[1, 1, 1, 1]] [1, 16, 1, 1] 16
Sigmoid-1 [[1, 16, 1, 1]] [1, 16, 1, 1] 0
SEWeightModule-1 [[1, 16, 56, 56]] [1, 16, 1, 1] 0
Softmax-1 [[1, 4, 16, 1, 1]] [1, 4, 16, 1, 1] 0
PSAModule-1 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
BatchNorm2D-56 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-53 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
Conv2D-62 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384
BatchNorm2D-57 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
Conv2D-63 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384
BatchNorm2D-58 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
ReLU-54 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
EPSABlock-1 [[1, 64, 56, 56]] [1, 256, 56, 56] 0
Conv2D-64 [[1, 256, 56, 56]] [1, 64, 56, 56] 16,384
BatchNorm2D-59 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-55 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
Conv2D-65 [[1, 64, 56, 56]] [1, 16, 56, 56] 9,216
Conv2D-66 [[1, 64, 56, 56]] [1, 16, 56, 56] 6,400
Conv2D-67 [[1, 64, 56, 56]] [1, 16, 56, 56] 6,272
Conv2D-68 [[1, 64, 56, 56]] [1, 16, 56, 56] 5,184
AdaptiveAvgPool2D-3 [[1, 16, 56, 56]] [1, 16, 1, 1] 0
Conv2D-69 [[1, 16, 1, 1]] [1, 1, 1, 1] 16
ReLU-56 [[1, 1, 1, 1]] [1, 1, 1, 1] 0
Conv2D-70 [[1, 1, 1, 1]] [1, 16, 1, 1] 16
Sigmoid-2 [[1, 16, 1, 1]] [1, 16, 1, 1] 0
SEWeightModule-2 [[1, 16, 56, 56]] [1, 16, 1, 1] 0
Softmax-2 [[1, 4, 16, 1, 1]] [1, 4, 16, 1, 1] 0
PSAModule-2 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
BatchNorm2D-60 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-57 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
Conv2D-71 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384
BatchNorm2D-61 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
ReLU-58 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
EPSABlock-2 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
Conv2D-72 [[1, 256, 56, 56]] [1, 64, 56, 56] 16,384
BatchNorm2D-62 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-59 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
Conv2D-73 [[1, 64, 56, 56]] [1, 16, 56, 56] 9,216
Conv2D-74 [[1, 64, 56, 56]] [1, 16, 56, 56] 6,400
Conv2D-75 [[1, 64, 56, 56]] [1, 16, 56, 56] 6,272
Conv2D-76 [[1, 64, 56, 56]] [1, 16, 56, 56] 5,184
AdaptiveAvgPool2D-4 [[1, 16, 56, 56]] [1, 16, 1, 1] 0
Conv2D-77 [[1, 16, 1, 1]] [1, 1, 1, 1] 16
ReLU-60 [[1, 1, 1, 1]] [1, 1, 1, 1] 0
Conv2D-78 [[1, 1, 1, 1]] [1, 16, 1, 1] 16
Sigmoid-3 [[1, 16, 1, 1]] [1, 16, 1, 1] 0
SEWeightModule-3 [[1, 16, 56, 56]] [1, 16, 1, 1] 0
Softmax-3 [[1, 4, 16, 1, 1]] [1, 4, 16, 1, 1] 0
PSAModule-3 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
BatchNorm2D-63 [[1, 64, 56, 56]] [1, 64, 56, 56] 256
ReLU-61 [[1, 64, 56, 56]] [1, 64, 56, 56] 0
Conv2D-79 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384
BatchNorm2D-64 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
ReLU-62 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
EPSABlock-3 [[1, 256, 56, 56]] [1, 256, 56, 56] 0
Conv2D-80 [[1, 256, 56, 56]] [1, 128, 56, 56] 32,768
BatchNorm2D-65 [[1, 128, 56, 56]] [1, 128, 56, 56] 512
ReLU-63 [[1, 128, 56, 56]] [1, 128, 56, 56] 0
Conv2D-81 [[1, 128, 56, 56]] [1, 32, 28, 28] 36,864
Conv2D-82 [[1, 128, 56, 56]] [1, 32, 28, 28] 25,600
Conv2D-83 [[1, 128, 56, 56]] [1, 32, 28, 28] 25,088
Conv2D-84 [[1, 128, 56, 56]] [1, 32, 28, 28] 20,736
AdaptiveAvgPool2D-5 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Conv2D-85 [[1, 32, 1, 1]] [1, 2, 1, 1] 64
ReLU-64 [[1, 2, 1, 1]] [1, 2, 1, 1] 0
Conv2D-86 [[1, 2, 1, 1]] [1, 32, 1, 1] 64
Sigmoid-4 [[1, 32, 1, 1]] [1, 32, 1, 1] 0
SEWeightModule-4 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Softmax-4 [[1, 4, 32, 1, 1]] [1, 4, 32, 1, 1] 0
PSAModule-4 [[1, 128, 56, 56]] [1, 128, 28, 28] 0
BatchNorm2D-66 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-65 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
Conv2D-87 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536
BatchNorm2D-67 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
Conv2D-88 [[1, 256, 56, 56]] [1, 512, 28, 28] 131,072
BatchNorm2D-68 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
ReLU-66 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
EPSABlock-4 [[1, 256, 56, 56]] [1, 512, 28, 28] 0
Conv2D-89 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536
BatchNorm2D-69 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-67 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
Conv2D-90 [[1, 128, 28, 28]] [1, 32, 28, 28] 36,864
Conv2D-91 [[1, 128, 28, 28]] [1, 32, 28, 28] 25,600
Conv2D-92 [[1, 128, 28, 28]] [1, 32, 28, 28] 25,088
Conv2D-93 [[1, 128, 28, 28]] [1, 32, 28, 28] 20,736
AdaptiveAvgPool2D-6 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Conv2D-94 [[1, 32, 1, 1]] [1, 2, 1, 1] 64
ReLU-68 [[1, 2, 1, 1]] [1, 2, 1, 1] 0
Conv2D-95 [[1, 2, 1, 1]] [1, 32, 1, 1] 64
Sigmoid-5 [[1, 32, 1, 1]] [1, 32, 1, 1] 0
SEWeightModule-5 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Softmax-5 [[1, 4, 32, 1, 1]] [1, 4, 32, 1, 1] 0
PSAModule-5 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
BatchNorm2D-70 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-69 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
Conv2D-96 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536
BatchNorm2D-71 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
ReLU-70 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
EPSABlock-5 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
Conv2D-97 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536
BatchNorm2D-72 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-71 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
Conv2D-98 [[1, 128, 28, 28]] [1, 32, 28, 28] 36,864
Conv2D-99 [[1, 128, 28, 28]] [1, 32, 28, 28] 25,600
Conv2D-100 [[1, 128, 28, 28]] [1, 32, 28, 28] 25,088
Conv2D-101 [[1, 128, 28, 28]] [1, 32, 28, 28] 20,736
AdaptiveAvgPool2D-7 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Conv2D-102 [[1, 32, 1, 1]] [1, 2, 1, 1] 64
ReLU-72 [[1, 2, 1, 1]] [1, 2, 1, 1] 0
Conv2D-103 [[1, 2, 1, 1]] [1, 32, 1, 1] 64
Sigmoid-6 [[1, 32, 1, 1]] [1, 32, 1, 1] 0
SEWeightModule-6 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Softmax-6 [[1, 4, 32, 1, 1]] [1, 4, 32, 1, 1] 0
PSAModule-6 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
BatchNorm2D-73 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-73 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
Conv2D-104 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536
BatchNorm2D-74 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
ReLU-74 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
EPSABlock-6 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
Conv2D-105 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536
BatchNorm2D-75 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-75 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
Conv2D-106 [[1, 128, 28, 28]] [1, 32, 28, 28] 36,864
Conv2D-107 [[1, 128, 28, 28]] [1, 32, 28, 28] 25,600
Conv2D-108 [[1, 128, 28, 28]] [1, 32, 28, 28] 25,088
Conv2D-109 [[1, 128, 28, 28]] [1, 32, 28, 28] 20,736
AdaptiveAvgPool2D-8 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Conv2D-110 [[1, 32, 1, 1]] [1, 2, 1, 1] 64
ReLU-76 [[1, 2, 1, 1]] [1, 2, 1, 1] 0
Conv2D-111 [[1, 2, 1, 1]] [1, 32, 1, 1] 64
Sigmoid-7 [[1, 32, 1, 1]] [1, 32, 1, 1] 0
SEWeightModule-7 [[1, 32, 28, 28]] [1, 32, 1, 1] 0
Softmax-7 [[1, 4, 32, 1, 1]] [1, 4, 32, 1, 1] 0
PSAModule-7 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
BatchNorm2D-76 [[1, 128, 28, 28]] [1, 128, 28, 28] 512
ReLU-77 [[1, 128, 28, 28]] [1, 128, 28, 28] 0
Conv2D-112 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536
BatchNorm2D-77 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
ReLU-78 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
EPSABlock-7 [[1, 512, 28, 28]] [1, 512, 28, 28] 0
Conv2D-113 [[1, 512, 28, 28]] [1, 256, 28, 28] 131,072
BatchNorm2D-78 [[1, 256, 28, 28]] [1, 256, 28, 28] 1,024
ReLU-79 [[1, 256, 28, 28]] [1, 256, 28, 28] 0
Conv2D-114 [[1, 256, 28, 28]] [1, 64, 14, 14] 147,456
Conv2D-115 [[1, 256, 28, 28]] [1, 64, 14, 14] 102,400
Conv2D-116 [[1, 256, 28, 28]] [1, 64, 14, 14] 100,352
Conv2D-117 [[1, 256, 28, 28]] [1, 64, 14, 14] 82,944
AdaptiveAvgPool2D-9 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Conv2D-118 [[1, 64, 1, 1]] [1, 4, 1, 1] 256
ReLU-80 [[1, 4, 1, 1]] [1, 4, 1, 1] 0
Conv2D-119 [[1, 4, 1, 1]] [1, 64, 1, 1] 256
Sigmoid-8 [[1, 64, 1, 1]] [1, 64, 1, 1] 0
SEWeightModule-8 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Softmax-8 [[1, 4, 64, 1, 1]] [1, 4, 64, 1, 1] 0
PSAModule-8 [[1, 256, 28, 28]] [1, 256, 14, 14] 0
BatchNorm2D-79 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-81 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-120 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-80 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
Conv2D-121 [[1, 512, 28, 28]] [1, 1024, 14, 14] 524,288
BatchNorm2D-81 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
ReLU-82 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
EPSABlock-8 [[1, 512, 28, 28]] [1, 1024, 14, 14] 0
Conv2D-122 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-82 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-83 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-123 [[1, 256, 14, 14]] [1, 64, 14, 14] 147,456
Conv2D-124 [[1, 256, 14, 14]] [1, 64, 14, 14] 102,400
Conv2D-125 [[1, 256, 14, 14]] [1, 64, 14, 14] 100,352
Conv2D-126 [[1, 256, 14, 14]] [1, 64, 14, 14] 82,944
AdaptiveAvgPool2D-10 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Conv2D-127 [[1, 64, 1, 1]] [1, 4, 1, 1] 256
ReLU-84 [[1, 4, 1, 1]] [1, 4, 1, 1] 0
Conv2D-128 [[1, 4, 1, 1]] [1, 64, 1, 1] 256
Sigmoid-9 [[1, 64, 1, 1]] [1, 64, 1, 1] 0
SEWeightModule-9 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Softmax-9 [[1, 4, 64, 1, 1]] [1, 4, 64, 1, 1] 0
PSAModule-9 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
BatchNorm2D-83 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-85 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-129 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-84 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
ReLU-86 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
EPSABlock-9 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-130 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-85 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-87 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-131 [[1, 256, 14, 14]] [1, 64, 14, 14] 147,456
Conv2D-132 [[1, 256, 14, 14]] [1, 64, 14, 14] 102,400
Conv2D-133 [[1, 256, 14, 14]] [1, 64, 14, 14] 100,352
Conv2D-134 [[1, 256, 14, 14]] [1, 64, 14, 14] 82,944
AdaptiveAvgPool2D-11 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Conv2D-135 [[1, 64, 1, 1]] [1, 4, 1, 1] 256
ReLU-88 [[1, 4, 1, 1]] [1, 4, 1, 1] 0
Conv2D-136 [[1, 4, 1, 1]] [1, 64, 1, 1] 256
Sigmoid-10 [[1, 64, 1, 1]] [1, 64, 1, 1] 0
SEWeightModule-10 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Softmax-10 [[1, 4, 64, 1, 1]] [1, 4, 64, 1, 1] 0
PSAModule-10 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
BatchNorm2D-86 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-89 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-137 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-87 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
ReLU-90 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
EPSABlock-10 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-138 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-88 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-91 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-139 [[1, 256, 14, 14]] [1, 64, 14, 14] 147,456
Conv2D-140 [[1, 256, 14, 14]] [1, 64, 14, 14] 102,400
Conv2D-141 [[1, 256, 14, 14]] [1, 64, 14, 14] 100,352
Conv2D-142 [[1, 256, 14, 14]] [1, 64, 14, 14] 82,944
AdaptiveAvgPool2D-12 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Conv2D-143 [[1, 64, 1, 1]] [1, 4, 1, 1] 256
ReLU-92 [[1, 4, 1, 1]] [1, 4, 1, 1] 0
Conv2D-144 [[1, 4, 1, 1]] [1, 64, 1, 1] 256
Sigmoid-11 [[1, 64, 1, 1]] [1, 64, 1, 1] 0
SEWeightModule-11 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Softmax-11 [[1, 4, 64, 1, 1]] [1, 4, 64, 1, 1] 0
PSAModule-11 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
BatchNorm2D-89 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-93 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-145 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-90 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
ReLU-94 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
EPSABlock-11 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-146 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-91 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-95 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-147 [[1, 256, 14, 14]] [1, 64, 14, 14] 147,456
Conv2D-148 [[1, 256, 14, 14]] [1, 64, 14, 14] 102,400
Conv2D-149 [[1, 256, 14, 14]] [1, 64, 14, 14] 100,352
Conv2D-150 [[1, 256, 14, 14]] [1, 64, 14, 14] 82,944
AdaptiveAvgPool2D-13 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Conv2D-151 [[1, 64, 1, 1]] [1, 4, 1, 1] 256
ReLU-96 [[1, 4, 1, 1]] [1, 4, 1, 1] 0
Conv2D-152 [[1, 4, 1, 1]] [1, 64, 1, 1] 256
Sigmoid-12 [[1, 64, 1, 1]] [1, 64, 1, 1] 0
SEWeightModule-12 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Softmax-12 [[1, 4, 64, 1, 1]] [1, 4, 64, 1, 1] 0
PSAModule-12 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
BatchNorm2D-92 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-97 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-153 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-93 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
ReLU-98 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
EPSABlock-12 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-154 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144
BatchNorm2D-94 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-99 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-155 [[1, 256, 14, 14]] [1, 64, 14, 14] 147,456
Conv2D-156 [[1, 256, 14, 14]] [1, 64, 14, 14] 102,400
Conv2D-157 [[1, 256, 14, 14]] [1, 64, 14, 14] 100,352
Conv2D-158 [[1, 256, 14, 14]] [1, 64, 14, 14] 82,944
AdaptiveAvgPool2D-14 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Conv2D-159 [[1, 64, 1, 1]] [1, 4, 1, 1] 256
ReLU-100 [[1, 4, 1, 1]] [1, 4, 1, 1] 0
Conv2D-160 [[1, 4, 1, 1]] [1, 64, 1, 1] 256
Sigmoid-13 [[1, 64, 1, 1]] [1, 64, 1, 1] 0
SEWeightModule-13 [[1, 64, 14, 14]] [1, 64, 1, 1] 0
Softmax-13 [[1, 4, 64, 1, 1]] [1, 4, 64, 1, 1] 0
PSAModule-13 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
BatchNorm2D-95 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024
ReLU-101 [[1, 256, 14, 14]] [1, 256, 14, 14] 0
Conv2D-161 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144
BatchNorm2D-96 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
ReLU-102 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
EPSABlock-13 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0
Conv2D-162 [[1, 1024, 14, 14]] [1, 512, 14, 14] 524,288
BatchNorm2D-97 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048
ReLU-103 [[1, 512, 14, 14]] [1, 512, 14, 14] 0
Conv2D-163 [[1, 512, 14, 14]] [1, 128, 7, 7] 589,824
Conv2D-164 [[1, 512, 14, 14]] [1, 128, 7, 7] 409,600
Conv2D-165 [[1, 512, 14, 14]] [1, 128, 7, 7] 401,408
Conv2D-166 [[1, 512, 14, 14]] [1, 128, 7, 7] 331,776
AdaptiveAvgPool2D-15 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Conv2D-167 [[1, 128, 1, 1]] [1, 8, 1, 1] 1,024
ReLU-104 [[1, 8, 1, 1]] [1, 8, 1, 1] 0
Conv2D-168 [[1, 8, 1, 1]] [1, 128, 1, 1] 1,024
Sigmoid-14 [[1, 128, 1, 1]] [1, 128, 1, 1] 0
SEWeightModule-14 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Softmax-14 [[1, 4, 128, 1, 1]] [1, 4, 128, 1, 1] 0
PSAModule-14 [[1, 512, 14, 14]] [1, 512, 7, 7] 0
BatchNorm2D-98 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
ReLU-105 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
Conv2D-169 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576
BatchNorm2D-99 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
Conv2D-170 [[1, 1024, 14, 14]] [1, 2048, 7, 7] 2,097,152
BatchNorm2D-100 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
ReLU-106 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
EPSABlock-14 [[1, 1024, 14, 14]] [1, 2048, 7, 7] 0
Conv2D-171 [[1, 2048, 7, 7]] [1, 512, 7, 7] 1,048,576
BatchNorm2D-101 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
ReLU-107 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
Conv2D-172 [[1, 512, 7, 7]] [1, 128, 7, 7] 589,824
Conv2D-173 [[1, 512, 7, 7]] [1, 128, 7, 7] 409,600
Conv2D-174 [[1, 512, 7, 7]] [1, 128, 7, 7] 401,408
Conv2D-175 [[1, 512, 7, 7]] [1, 128, 7, 7] 331,776
AdaptiveAvgPool2D-16 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Conv2D-176 [[1, 128, 1, 1]] [1, 8, 1, 1] 1,024
ReLU-108 [[1, 8, 1, 1]] [1, 8, 1, 1] 0
Conv2D-177 [[1, 8, 1, 1]] [1, 128, 1, 1] 1,024
Sigmoid-15 [[1, 128, 1, 1]] [1, 128, 1, 1] 0
SEWeightModule-15 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Softmax-15 [[1, 4, 128, 1, 1]] [1, 4, 128, 1, 1] 0
PSAModule-15 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
BatchNorm2D-102 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
ReLU-109 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
Conv2D-178 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576
BatchNorm2D-103 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
ReLU-110 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
EPSABlock-15 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
Conv2D-179 [[1, 2048, 7, 7]] [1, 512, 7, 7] 1,048,576
BatchNorm2D-104 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
ReLU-111 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
Conv2D-180 [[1, 512, 7, 7]] [1, 128, 7, 7] 589,824
Conv2D-181 [[1, 512, 7, 7]] [1, 128, 7, 7] 409,600
Conv2D-182 [[1, 512, 7, 7]] [1, 128, 7, 7] 401,408
Conv2D-183 [[1, 512, 7, 7]] [1, 128, 7, 7] 331,776
AdaptiveAvgPool2D-17 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Conv2D-184 [[1, 128, 1, 1]] [1, 8, 1, 1] 1,024
ReLU-112 [[1, 8, 1, 1]] [1, 8, 1, 1] 0
Conv2D-185 [[1, 8, 1, 1]] [1, 128, 1, 1] 1,024
Sigmoid-16 [[1, 128, 1, 1]] [1, 128, 1, 1] 0
SEWeightModule-16 [[1, 128, 7, 7]] [1, 128, 1, 1] 0
Softmax-16 [[1, 4, 128, 1, 1]] [1, 4, 128, 1, 1] 0
PSAModule-16 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
BatchNorm2D-105 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048
ReLU-113 [[1, 512, 7, 7]] [1, 512, 7, 7] 0
Conv2D-186 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576
BatchNorm2D-106 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
ReLU-114 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
EPSABlock-16 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
AdaptiveAvgPool2D-18 [[1, 2048, 7, 7]] [1, 2048, 1, 1] 0
Flatten-2 [[1, 2048, 1, 1]] [1, 2048] 0
Linear-2 [[1, 2048]] [1, 4] 8,196
================================================================================
Total params: 20,573,028
Trainable params: 20,466,788
Non-trainable params: 106,240
--------------------------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 297.15
Params size (MB): 78.48
Estimated Total Size (MB): 376.21
--------------------------------------------------------------------------------{'total_params': 20573028, 'trainable_params': 20466788}Momentum优化器：
如果初始学习率设置得当并且迭代轮数充足，该优化器会在众多的优化器中脱颖而出，使其在验证集上获得更高的准确率。但Momentum优化器有两个缺点，一是收敛速度慢（较之Adam、AdamW等自适应优化器），二是初始学习率的设置需要依靠大量经验。
Warmup：
Warmup是在ResNet论文中提到的一种学习率预热方法，它在训练开始时先使用一个较小的学习率训练一些epochs或者steps，再修改为预先设置的学习率进行训练。由于刚开始训练时，模型的权重是随机初始化的，若此时选择一个较大的学习率，可能会导致模型的不稳定（振荡），选择Warmup预热学习率的方式，可以使得开始训练时的一些epochs或者steps内学习率较小，在小的学习率下，模型可以慢慢趋于稳定，等模型相对稳定后再选择预先设置的学习率进行训练，使得模型收敛速度更快，模型效果更佳。
余弦退火策略：
在使用梯度下降算法来优化目标函数时，当越来越接近loss的全局最小值时，学习率应该变得更小来使得模型尽可能接近这一最低点，而余弦退火（Cosine annealing）可以通过余弦函数来降低学习率。余弦函数中随着x的增加余弦值首先缓慢下降，然后加速下降，最后缓慢下降。这种下降模式能和学习率配合，以一种十分有效的计算方式来产生很好的效果。
热启动的余弦退火学习率优化策略CosineWarmup非常实用，本项目选择使用Momentum优化器加CosineWarmup策略的组合替换传统SGD优化器。
# 热启动的余弦退火学习率优化策略class Cosine(CosineAnnealingDecay):
def __init__(self, learning_rate, step_each_epoch, epoch_num, **kwargs):
super(Cosine, self).__init__(learning_rate=learning_rate, T_max=step_each_epoch * epoch_num)class CosineWarmup(LinearWarmup):
def __init__(self, learning_rate, step_each_epoch, epoch_num, warmup_epoch_num=5, **kwargs):
assert epoch_num > warmup_epoch_num, 'epoch_num({}) should be larger than warmup_epoch_num({}) in CosineWarmup.'.format(epoch_num, warmup_epoch_num)
warmup_steps = warmup_epoch_num * step_each_epoch
start_lr = 0.0
end_lr = learning_rate
learning_rate = Cosine(learning_rate, step_each_epoch, epoch_num - warmup_epoch_num) super(CosineWarmup, self).__init__(learning_rate=learning_rate, warmup_steps=warmup_steps, start_lr=start_lr, end_lr=end_lr)# 训练def train(model, name='model'):
epoch_num = 50
batch_size = 50
learning_rate = 0.01
train_loss_list = []
train_acc_list = []
eval_loss_list = []
eval_acc_list = [] iter = 0
iters = []
epochs = []
max_eval_acc = 0
model.train()
train_loader = paddle.io.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = paddle.io.DataLoader(val_dataset, batch_size=batch_size)
scheduler = CosineWarmup(learning_rate=learning_rate, step_each_epoch=int(len(train_dataset) / batch_size), epoch_num=epoch_num, verbose=True)
opt = paddle.optimizer.Momentum(learning_rate=scheduler, weight_decay=paddle.regularizer.L2Decay(0.0005), parameters=model.parameters()) # Momentum + CosineWarmup
for epoch_id in range(epoch_num): for batch_id, (images, labels) in enumerate(train_loader()):
predicts = model(images)
loss = F.cross_entropy(predicts, labels)
acc = paddle.metric.accuracy(predicts, labels) if batch_id % 10 == 0:
train_loss_list.append(loss.item())
train_acc_list.append(acc.item())
iters.append(iter) iter += 10
print('epoch: {}, batch: {}, learning_rate: {}, \ntrain loss is: {}, train acc is: {}'.format(epoch_id, batch_id, opt.get_lr(), loss.item(), acc.item()))
loss.backward() # 反向传播
opt.step() # 更新参数
opt.clear_grad() # 清除梯度
scheduler.step() # 更新参数
# 每个epoch评估一次
model.eval()
loss_list = []
acc_list = []
results = np.zeros([4, 4], dtype='int64') for batch_id, (images, labels) in enumerate(val_loader()):
predicts = model(images) for i in range(len(images)):
results[labels[i].item()][paddle.argmax(predicts[i]).item()] += 1
loss = F.cross_entropy(predicts, labels)
acc = paddle.metric.accuracy(predicts, labels)
loss_list.append(loss.item())
acc_list.append(acc.item())
eval_loss, eval_acc = np.mean(loss_list), np.mean(acc_list)
eval_loss_list.append(eval_loss)
eval_acc_list.append(eval_acc)
epochs.append(epoch_id)
model.train() print('eval loss: {}, eval acc: {}'.format(eval_loss, eval_acc)) # 保存最优模型
if eval_acc > max_eval_acc:
paddle.save(model.state_dict(), '{}.pdparams'.format(name))
max_eval_acc = eval_acc
results_table = prettytable.PrettyTable()
results_table.field_names = ['Type', 'Precision', 'Recall', 'F1_Score']
class_names = ['COVID', 'LungOpacity', 'Normal', 'ViralPneumonia'] for i in range(4):
precision = results[i][i] / results.sum(axis=0)[i]
recall = results[i][i] / results.sum(axis=1)[i]
results_table.add_row([class_names[i],
np.round(precision, 3),
np.round(recall, 3),
np.round(precision * recall * 2 / (precision + recall), 3)]) print(results_table) return train_loss_list, train_acc_list, eval_loss_list, eval_acc_list, iters, epochsresnet50 = ResNet(50, 4) resnet50_train_loss, resnet50_train_acc, resnet50_eval_loss, resnet50_eval_acc, iters, epochs = train(resnet50, 'resnet50')
epsanet50 = EPSANet(50, 4) epsanet50_train_loss, epsanet50_train_acc, epsanet50_eval_loss, epsanet50_eval_acc, iters, epochs = train(epsanet50, 'epsanet50')
# 训练过程可视化def plot(freq, list, color, name, xlabel, ylabel, title):
plt.figure()
plt.title(title, fontsize='x-large')
plt.xlabel(xlabel, fontsize='large')
plt.ylabel(ylabel, fontsize='large') for i in range(len(list)):
plt.plot(freq, list[i], color=color[i])
plt.legend(name)
plt.grid()
plt.show()
plot(iters, [resnet50_train_loss, epsanet50_train_loss], ['blue', 'green'], ['resnet50', 'epsanet50'], 'iter', 'loss', 'train loss')
plot(iters, [resnet50_train_acc, epsanet50_train_acc], ['blue', 'green'], ['resnet50', 'epsanet50'], 'iter', 'acc', 'train acc')
plot(epochs, [resnet50_eval_loss, epsanet50_eval_loss], ['blue', 'green'], ['resnet50', 'epsanet50'], 'epoch', 'loss', 'eval loss')
plot(epochs, [resnet50_eval_acc, epsanet50_eval_acc], ['blue', 'green'], ['resnet50', 'epsanet50'], 'epoch', 'acc', 'eval acc')<Figure size 640x480 with 1 Axes>
<Figure size 640x480 with 1 Axes>
<Figure size 640x480 with 1 Axes>
<Figure size 640x480 with 1 Axes>
# 评估def eval(model, name='model'):
batch_size = 50
loss_list = []
acc_list = []
results = np.zeros([4, 4], dtype='int64')
params_file_path = '{}.pdparams'.format(name) # 加载模型参数
param_dict = paddle.load(params_file_path)
model.load_dict(param_dict)
model.eval()
test_loader = paddle.io.DataLoader(test_dataset, batch_size=batch_size)
for batch_id, (images, labels) in enumerate(test_loader()):
predicts = model(images) for i in range(len(images)):
results[labels[i].item()][paddle.argmax(predicts[i]).item()] += 1
loss = F.cross_entropy(predicts, labels)
acc = paddle.metric.accuracy(predicts, labels)
loss_list.append(loss.item())
acc_list.append(acc.item())
eval_loss, eval_acc = np.mean(loss_list), np.mean(acc_list) print('eval_loss: {}, eval_acc: {}'.format(eval_loss, eval_acc))
results_table = prettytable.PrettyTable()
results_table.field_names = ['Type', 'Precision', 'Recall', 'F1_Score']
class_names = ['COVID', 'LungOpacity', 'Normal', 'ViralPneumonia'] for i in range(4):
precision = results[i][i] / results.sum(axis=0)[i]
recall = results[i][i] / results.sum(axis=1)[i]
results_table.add_row([class_names[i],
np.round(precision, 3),
np.round(recall, 3),
np.round(precision * recall * 2 / (precision + recall), 3)]) print(results_table)resnet50 = ResNet(50, 4)eval(resnet50, 'resnet50')
eval_loss: 0.4542655143670218, eval_acc: 0.9192411234212476 +----------------+-----------+--------+----------+ | Type | Precision | Recall | F1_Score | +----------------+-----------+--------+----------+ | COVID | 0.95 | 0.953 | 0.952 | | LungOpacity | 0.907 | 0.846 | 0.875 | | Normal | 0.906 | 0.949 | 0.927 | | ViralPneumonia | 0.992 | 0.926 | 0.958 | +----------------+-----------+--------+----------+
epsanet50 = EPSANet(50, 4)eval(epsanet50, 'epsanet50')
eval_loss: 0.44931766024769043, eval_acc: 0.941101589868235 +----------------+-----------+--------+----------+ | Type | Precision | Recall | F1_Score | +----------------+-----------+--------+----------+ | COVID | 0.962 | 0.975 | 0.968 | | LungOpacity | 0.921 | 0.895 | 0.908 | | Normal | 0.937 | 0.952 | 0.945 | | ViralPneumonia | 1.0 | 0.97 | 0.985 | +----------------+-----------+--------+----------+
# 预测图像def predict(img_path, model, name='model'):
# 加载模型参数
model.load_dict(paddle.load('{}.pdparams'.format(name)))
model.eval()
img = cv2.imread(img_path)
plt.imshow(img[:, :, ::-1]) # BGR -> RGB
plt.show()
img = paddle.reshape(transform(img), [-1, 3, 224, 224]) # 返回每个分类标签的对应概率
results = model(img) # 概率最大的标签作为预测结果
classes = ['COVID', 'LungOpacity', 'Normal', 'ViralPneumonia']
label = paddle.argmax(results).item()
predict_result = classes[label] print(predict_result)
predict('work/dataset/COVID/COVID-1949.png', EPSANet(50, 4), 'epsanet50')
predict('work/dataset/LungOpacity/LungOpacity-3118.png', EPSANet(50, 4), 'epsanet50')
predict('work/dataset/Normal/Normal-09331.png', EPSANet(50, 4), 'epsanet50')
predict('work/dataset/ViralPneumonia/ViralPneumonia-1195.png', EPSANet(50, 4), 'epsanet50')<Figure size 640x480 with 1 Axes>
COVID
<Figure size 640x480 with 1 Axes>
LungOpacity
<Figure size 640x480 with 1 Axes>
Normal
<Figure size 640x480 with 1 Axes>
ViralPneumonia
Grad-CAM（Gradient-weighted Class Activation Mapping）梯度加权类激活图，其前身为CAM（Class Activation Mapping）类激活图。CAM可以理解为对预测输出的贡献分布，分数越高的地方表示原始图片对应区域对网络的响应越高、贡献越大，即表示每个位置对该类别的重要程度。Grad-CAM是在CAM基础上的改进与泛化，使其能够用于更广泛的模型结构上，并进一步提升突出重点区域的能力。
一般DNN的结构如图-14所示：模型前面是堆叠在一起不断降低输出特征图尺寸、增加通道数的卷积层，用于提取图片各个粒度的特征，后面接一个GAP（全局平均池化）层得到各个通道特征图的均值，最后接一个Softmax激活的全连接层输出各个类别的判别概率。最终模型输出的每一个类别的判别概率就是最后全连接层对应此类别的权重乘以前面GAP层输出的特征图均值得到的。这个值越大模型最终输出此类别的概率就越大，是模型判别最终输出类别的关键。
CAM就是从这个值的意义出发来设计的。全连接层权重与GAP层输出的特征图均值的乘积能够决定模型最终输出的类别，但是为了最终输出一个代表概率的值，GAP层将最后一个卷积层提取的特征图从二维降至一维，失去了空间特征信息。如果我们将最后一个卷积层提取的二维特征图不经过GAP层直接与最后的全连接层的权重相乘，不就既能保留二维特征图的二维空间特性，又能反应特征图对当前分类输出的重要性了么？其实，这就是CAM，计算公式如下所示：
其中Mc(x,y)表示计算得到的针对类别C的类激活图，fk(x,y)表示最后一个卷积层提取的特征图，wkc表示最后一个全连接层计算类别c概率的权重。
既然CAM已经能够展现模型的重点关注区域，那为什么还要发展Grad-CAM呢？因为CAM要求模型结构中必须要包含一个GAP层，如果没有就要加入一个GAP层。这对一些已经训练好的模型很不方便，从而限制CAM的适用范围。而Grad-CAM正是为克服这一局限而设计的。
CAM公式如下：
Grad-CAM公式如下：
ReLU的目的是在最后加和各个通道的激活图时只加和权重为正值的，以消除激活图上一些与目标类别无关的干扰（仅关注对最终预测分类有正向影响的特征）。
Grad-CAM公式里的Ak和CAM公式里的fk(x,y)均表示最后一个卷积层提取的特征图。两个公式中剩下的唯一不同部分，也是最重要的部分就是特征图的激活加权方式。在CAM公式中是通过乘上wkc给各个通道的特征图进行激活加权的，其表示经过GAP后最后一个全连接层中激活目标类别c的k通道的权重，实现算法时将这部分权重从全连接层中剥离出来即可，在Grad-CAM公式中给特征图进行激活加权是通过αkc这部分实现的。
αkc是通过对最后一个卷积层的梯度进行GAP操作得到的，公式如下：
等式右边左半部份表示GAP操作，右半部份的∂Aijk∂yc表示针对目标类别c的loss对最后一个卷积层提取的特征图的梯度，其通过对模型的计算图进行反向梯度传播得到。
# GradCAMfrom gradcam import GradCAM
model = EPSANet(50, 4)
model.load_dict(paddle.load('epsanet50.pdparams'))# 查看网络层GradCAM.show_network(model)# 指定卷积层layer = 'block.15.conv3.1'gradcam = GradCAM(model, layer)# 批量生成GradCAMdef grad_cam(img_dir):
img_list = os.listdir(img_dir)
img_list = filter(lambda x: '.png' in x, img_list) for img_file in img_list:
img_path = os.path.join(img_dir, img_file)
img = cv2.imread(img_path)
save_dir = os.path.split(img_dir)[-1]
save_path = os.path.join('/home/aistudio/work/gradcam', f'{save_dir}') if not os.path.exists(save_path):
os.makedirs(save_path)
save_path = os.path.join(save_path, f'{img_file}')
gradcam.save(img, file=save_path)
grad_cam('/home/aistudio/work/dataset/ViralPneumonia')# 批量展示GradCAMdef show_cam(img_dir, cam_dir):
img_list = os.listdir(img_dir)
img_list = filter(lambda x: '.png' in x, img_list)
img_list = [os.path.join(img_dir, img_file) for img_file in img_list]
img_list.sort(key=lambda x : x[-8:])
cam_list = os.listdir(cam_dir)
cam_list = filter(lambda x: '.png' in x, cam_list)
cam_list = [os.path.join(cam_dir, cam_file) for cam_file in cam_list]
cam_list.sort(key=lambda x : x[-8:])
show_list = img_list[:8] + cam_list[:8] for i, path in enumerate(show_list):
img = cv2.imread(path)
img = img[:, :, ::-1] # BGR -> RGB
plt.subplot(4, 4, i + 1)
plt.imshow(img)
plt.show()
show_cam('/home/aistudio/work/dataset/ViralPneumonia', '/home/aistudio/work/gradcam/ViralPneumonia')<Figure size 640x480 with 16 Axes>
以上就是应用体验：肺炎CT图像识别（Grad-CAM）的详细内容，更多请关注php中文网其它相关文章！
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