网站平台之间的关系,官网铺设,写网站教程,惠州网站制作案例文章目录 一、前言二、前期准备1.设置GPU2.划分数据集 三、搭建网络模型1.DenseLayer模块2.DenseBlock模块3.Transition模块4.构建DenseNet5.构建densenet121 四、训练模型1.编写训练函数2.编写测试函数3.正式训练 五、结果可视化1.Loss与Accuracy图2.模型评估 总结#xff1a… 文章目录 一、前言二、前期准备1.设置GPU2.划分数据集 三、搭建网络模型1.DenseLayer模块2.DenseBlock模块3.Transition模块4.构建DenseNet5.构建densenet121 四、训练模型1.编写训练函数2.编写测试函数3.正式训练 五、结果可视化1.Loss与Accuracy图2.模型评估 总结 本文为365天深度学习训练营 中的学习记录博客 原作者K同学啊 一、前言
二、前期准备
1.设置GPU
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import os, PIL, pathlib, warningswarnings.filterwarnings(ignore) ## 忽略警告信息device torch.device(cuda if torch.cuda.is_available() else cpu)
devicedevice(type‘cpu’)
import os, PIL, random, pathlibdata_dir ./J3-data/
data_dir pathlib.Path(data_dir)data_paths list(data_dir.glob(*))
classeNames [str(path).split(/)[1] for path in data_paths]
classeNames[‘.DS_Store’, ‘0’, ‘1’]
train_transforms transforms.Compose([transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸# transforms.RandomHorizontalFlip(), # 随机水平翻转transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换为tensor并归一化到[0,1]之间transforms.Normalize( # 标准化处理--转换为标准正太分布高斯分布使模型更容易收敛mean[0.485, 0.456, 0.406], std[0.229, 0.224, 0.225]) # 其中 mean[0.485,0.456,0.406]与std[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])test_transform transforms.Compose([transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换为tensor并归一化到[0,1]之间transforms.Normalize( # 标准化处理--转换为标准正太分布高斯分布使模型更容易收敛mean[0.485, 0.456, 0.406], std[0.229, 0.224, 0.225]) # 其中 mean[0.485,0.456,0.406]与std[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])total_data datasets.ImageFolder(data_dir,transformtrain_transforms)
total_data
Dataset ImageFolder Number of datapoints: 13403 Root location: J3-data StandardTransform Transform: Compose( Resize(size[224, 224], interpolationbilinear, max_sizeNone, antialiasTrue) ToTensor() Normalize(mean[0.485, 0.456, 0.406], std[0.229, 0.224, 0.225]) )
total_data.class_to_idx{‘0’: 0, ‘1’: 1}
2.划分数据集
train_size int(0.8 * len(total_data))
test_size len(total_data) - train_size
train_dataset, test_dataset torch.utils.data.random_split(total_data, [train_size, test_size])
train_dataset, test_dataset(torch.utils.data.dataset.Subset at 0x17fc70760, torch.utils.data.dataset.Subset at 0x17fc70430)
batch_size 32
train_dl torch.utils.data.DataLoader(train_dataset,batch_sizebatch_size,shuffleTrue)test_dl torch.utils.data.DataLoader(test_dataset,batch_sizebatch_size,shuffleTrue)for X, y in test_dl:print(Shape of X [N, C, H, W]:, X.shape)print(Shape of y:, y.shape, y.dtype)breakShape of X [N, C, H, W]: torch.Size([32, 3, 224, 224]) Shape of y: torch.Size([32]) torch.int64
三、搭建网络模型
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F1.DenseLayer模块
class DenseLayer(nn.Sequential):def __init__(self, in_channel, growth_rate, bn_size, drop_rate):super(DenseLayer, self).__init__()self.add_module(norm1, nn.BatchNorm2d(in_channel))self.add_module(relu1, nn.ReLU(inplaceTrue))self.add_module(conv1, nn.Conv2d(in_channel, bn_size*growth_rate,kernel_size1, stride1, biasFalse))self.add_module(norm2, nn.BatchNorm2d(bn_size*growth_rate))self.add_module(relu2, nn.ReLU(inplaceTrue))self.add_module(conv2, nn.Conv2d(bn_size*growth_rate, growth_rate,kernel_size3, stride1, padding1, biasFalse))self.drop_rate drop_ratedef forward(self, x):new_feature super(DenseLayer, self).forward(x)if self.drop_rate0:new_feature F.dropout(new_feature, pself.drop_rate, trainingself.training)return torch.cat([x, new_feature], 1)
2.DenseBlock模块 DenseBlock
class DenseBlock(nn.Sequential):def __init__(self, num_layers, in_channel, bn_size, growth_rate, drop_rate):super(DenseBlock, self).__init__()for i in range(num_layers):layer DenseLayer(in_channeli*growth_rate, growth_rate, bn_size, drop_rate)self.add_module(denselayer%d%(i1,), layer)
3.Transition模块 Transition layer between two adjacent DenseBlock
class Transition(nn.Sequential):def __init__(self, in_channel, out_channel):super(Transition, self).__init__()self.add_module(norm, nn.BatchNorm2d(in_channel))self.add_module(relu, nn.ReLU(inplaceTrue))self.add_module(conv, nn.Conv2d(in_channel, out_channel,kernel_size1, stride1, biasFalse))self.add_module(pool, nn.AvgPool2d(2, stride2))
4.构建DenseNet
class DenseNet(nn.Module):def __init__(self, growth_rate32, block_config(6,12,24,16), init_channel64, bn_size4, compression_rate0.5, drop_rate0, num_classes1000)::param growth_rate: (int) number of filters used in DenseLayer, k in the paper:param block_config: (list of 4 ints) number of layers in eatch DenseBlock:param init_channel: (int) number of filters in the first Conv2d:param bn_size: (int) the factor using in the bottleneck layer:param compression_rate: (float) the compression rate used in Transition Layer:param drop_rate: (float) the drop rate after each DenseLayer:param num_classes: (int) 待分类的类别数super(DenseNet, self).__init__()# first Conv2dself.features nn.Sequential(OrderedDict([(conv0, nn.Conv2d(3, init_channel, kernel_size7, stride2, padding3, biasFalse)),(norm0, nn.BatchNorm2d(init_channel)),(relu0, nn.ReLU(inplaceTrue)),(pool0, nn.MaxPool2d(3, stride2, padding1))]))# DenseBlocknum_features init_channelfor i, num_layers in enumerate(block_config):block DenseBlock(num_layers, num_features, bn_size, growth_rate, drop_rate)self.features.add_module(denseblock%d%(i1), block)num_features num_layers*growth_rateif i ! len(block_config)-1:transition Transition(num_features, int(num_features*compression_rate))self.features.add_module(transition%d%(i1), transition)num_features int(num_features*compression_rate)# final BNReLUself.features.add_module(norm5, nn.BatchNorm2d(num_features))self.features.add_module(relu5, nn.ReLU(inplaceTrue))# 分类层self.classifier nn.Linear(num_features, num_classes)# 参数初始化for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight)elif isinstance(m, nn.BatchNorm2d):nn.init.constant_(m.bias, 0)nn.init.constant_(m.weight, 1)elif isinstance(m, nn.Linear):nn.init.constant_(m.bias, 0)def forward(self, x):x self.features(x)x F.avg_pool2d(x, 7, stride1).view(x.size(0), -1)x self.classifier(x)return x
5.构建densenet121
device cuda if torch.cuda.is_available() else cpu
print(Using {} device.format(device))densenet121 DenseNet(init_channel64,growth_rate32,block_config(6,12,24,16),num_classeslen(classeNames)) model densenet121.to(device)
modelUsing cpu device
DenseNet( (features): Sequential( (conv0): Conv2d(3, 64, kernel_size(7, 7), stride(2, 2), padding(3, 3), biasFalse) (norm0): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu0): ReLU(inplaceTrue) (pool0): MaxPool2d(kernel_size3, stride2, padding1, dilation1, ceil_modeFalse) (denseblock1): DenseBlock( (denselayer1): DenseLayer( (norm1): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(64, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer2): DenseLayer( (norm1): BatchNorm2d(96, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(96, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer3): DenseLayer( (norm1): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(128, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer4): DenseLayer( (norm1): BatchNorm2d(160, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(160, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer5): DenseLayer( (norm1): BatchNorm2d(192, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(192, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer6): DenseLayer( (norm1): BatchNorm2d(224, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(224, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) ) (transition1): Transition( (norm): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu): ReLU(inplaceTrue) (conv): Conv2d(256, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (pool): AvgPool2d(kernel_size2, stride2, padding0) ) (denseblock2): DenseBlock( (denselayer1): DenseLayer( (norm1): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(128, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer2): DenseLayer( (norm1): BatchNorm2d(160, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(160, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer3): DenseLayer( (norm1): BatchNorm2d(192, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(192, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer4): DenseLayer( (norm1): BatchNorm2d(224, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(224, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer5): DenseLayer( (norm1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(256, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer6): DenseLayer( (norm1): BatchNorm2d(288, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(288, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer7): DenseLayer( (norm1): BatchNorm2d(320, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(320, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer8): DenseLayer( (norm1): BatchNorm2d(352, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(352, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer9): DenseLayer( (norm1): BatchNorm2d(384, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(384, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer10): DenseLayer( (norm1): BatchNorm2d(416, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(416, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer11): DenseLayer( (norm1): BatchNorm2d(448, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(448, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer12): DenseLayer( (norm1): BatchNorm2d(480, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(480, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) ) (transition2): Transition( (norm): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu): ReLU(inplaceTrue) (conv): Conv2d(512, 256, kernel_size(1, 1), stride(1, 1), biasFalse) (pool): AvgPool2d(kernel_size2, stride2, padding0) ) (denseblock3): DenseBlock( (denselayer1): DenseLayer( (norm1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(256, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer2): DenseLayer( (norm1): BatchNorm2d(288, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(288, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer3): DenseLayer( (norm1): BatchNorm2d(320, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(320, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer4): DenseLayer( (norm1): BatchNorm2d(352, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(352, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer5): DenseLayer( (norm1): BatchNorm2d(384, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(384, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer6): DenseLayer( (norm1): BatchNorm2d(416, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(416, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer7): DenseLayer( (norm1): BatchNorm2d(448, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(448, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer8): DenseLayer( (norm1): BatchNorm2d(480, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(480, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer9): DenseLayer( (norm1): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(512, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer10): DenseLayer( (norm1): BatchNorm2d(544, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(544, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer11): DenseLayer( (norm1): BatchNorm2d(576, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(576, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer12): DenseLayer( (norm1): BatchNorm2d(608, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(608, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer13): DenseLayer( (norm1): BatchNorm2d(640, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(640, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer14): DenseLayer( (norm1): BatchNorm2d(672, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(672, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer15): DenseLayer( (norm1): BatchNorm2d(704, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(704, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer16): DenseLayer( (norm1): BatchNorm2d(736, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(736, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer17): DenseLayer( (norm1): BatchNorm2d(768, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(768, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer18): DenseLayer( (norm1): BatchNorm2d(800, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(800, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer19): DenseLayer( (norm1): BatchNorm2d(832, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(832, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer20): DenseLayer( (norm1): BatchNorm2d(864, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(864, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer21): DenseLayer( (norm1): BatchNorm2d(896, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(896, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer22): DenseLayer( (norm1): BatchNorm2d(928, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(928, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer23): DenseLayer( (norm1): BatchNorm2d(960, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(960, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer24): DenseLayer( (norm1): BatchNorm2d(992, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(992, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) ) (transition3): Transition( (norm): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu): ReLU(inplaceTrue) (conv): Conv2d(1024, 512, kernel_size(1, 1), stride(1, 1), biasFalse) (pool): AvgPool2d(kernel_size2, stride2, padding0) ) (denseblock4): DenseBlock( (denselayer1): DenseLayer( (norm1): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(512, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer2): DenseLayer( (norm1): BatchNorm2d(544, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(544, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer3): DenseLayer( (norm1): BatchNorm2d(576, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(576, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer4): DenseLayer( (norm1): BatchNorm2d(608, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(608, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer5): DenseLayer( (norm1): BatchNorm2d(640, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(640, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer6): DenseLayer( (norm1): BatchNorm2d(672, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(672, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer7): DenseLayer( (norm1): BatchNorm2d(704, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(704, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer8): DenseLayer( (norm1): BatchNorm2d(736, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(736, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer9): DenseLayer( (norm1): BatchNorm2d(768, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(768, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer10): DenseLayer( (norm1): BatchNorm2d(800, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(800, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer11): DenseLayer( (norm1): BatchNorm2d(832, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(832, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer12): DenseLayer( (norm1): BatchNorm2d(864, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(864, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer13): DenseLayer( (norm1): BatchNorm2d(896, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(896, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer14): DenseLayer( (norm1): BatchNorm2d(928, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(928, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer15): DenseLayer( (norm1): BatchNorm2d(960, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(960, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) (denselayer16): DenseLayer( (norm1): BatchNorm2d(992, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu1): ReLU(inplaceTrue) (conv1): Conv2d(992, 128, kernel_size(1, 1), stride(1, 1), biasFalse) (norm2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu2): ReLU(inplaceTrue) (conv2): Conv2d(128, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) ) ) (norm5): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (relu5): ReLU(inplaceTrue) ) (classifier): Linear(in_features1024, out_features3, biasTrue) )
# 统计模型参数量以及其他指标
import torchsummary as summary
summary.summary(model, (3, 224, 224))----------------------------------------------------------------Layer (type) Output Shape Param #
Conv2d-1 [-1, 64, 112, 112] 9,408BatchNorm2d-2 [-1, 64, 112, 112] 128ReLU-3 [-1, 64, 112, 112] 0MaxPool2d-4 [-1, 64, 56, 56] 0BatchNorm2d-5 [-1, 64, 56, 56] 128ReLU-6 [-1, 64, 56, 56] 0Conv2d-7 [-1, 128, 56, 56] 8,192BatchNorm2d-8 [-1, 128, 56, 56] 256ReLU-9 [-1, 128, 56, 56] 0Conv2d-10 [-1, 32, 56, 56] 36,864BatchNorm2d-11 [-1, 96, 56, 56] 192ReLU-12 [-1, 96, 56, 56] 0Conv2d-13 [-1, 128, 56, 56] 12,288BatchNorm2d-14 [-1, 128, 56, 56] 256ReLU-15 [-1, 128, 56, 56] 0Conv2d-16 [-1, 32, 56, 56] 36,864BatchNorm2d-17 [-1, 128, 56, 56] 256ReLU-18 [-1, 128, 56, 56] 0Conv2d-19 [-1, 128, 56, 56] 16,384BatchNorm2d-20 [-1, 128, 56, 56] 256ReLU-21 [-1, 128, 56, 56] 0Conv2d-22 [-1, 32, 56, 56] 36,864BatchNorm2d-23 [-1, 160, 56, 56] 320ReLU-24 [-1, 160, 56, 56] 0Conv2d-25 [-1, 128, 56, 56] 20,480BatchNorm2d-26 [-1, 128, 56, 56] 256ReLU-27 [-1, 128, 56, 56] 0Conv2d-28 [-1, 32, 56, 56] 36,864BatchNorm2d-29 [-1, 192, 56, 56] 384ReLU-30 [-1, 192, 56, 56] 0Conv2d-31 [-1, 128, 56, 56] 24,576BatchNorm2d-32 [-1, 128, 56, 56] 256ReLU-33 [-1, 128, 56, 56] 0Conv2d-34 [-1, 32, 56, 56] 36,864BatchNorm2d-35 [-1, 224, 56, 56] 448ReLU-36 [-1, 224, 56, 56] 0Conv2d-37 [-1, 128, 56, 56] 28,672BatchNorm2d-38 [-1, 128, 56, 56] 256ReLU-39 [-1, 128, 56, 56] 0Conv2d-40 [-1, 32, 56, 56] 36,864BatchNorm2d-41 [-1, 256, 56, 56] 512ReLU-42 [-1, 256, 56, 56] 0Conv2d-43 [-1, 128, 56, 56] 32,768AvgPool2d-44 [-1, 128, 28, 28] 0BatchNorm2d-45 [-1, 128, 28, 28] 256ReLU-46 [-1, 128, 28, 28] 0Conv2d-47 [-1, 128, 28, 28] 16,384BatchNorm2d-48 [-1, 128, 28, 28] 256ReLU-49 [-1, 128, 28, 28] 0Conv2d-50 [-1, 32, 28, 28] 36,864BatchNorm2d-51 [-1, 160, 28, 28] 320ReLU-52 [-1, 160, 28, 28] 0Conv2d-53 [-1, 128, 28, 28] 20,480BatchNorm2d-54 [-1, 128, 28, 28] 256ReLU-55 [-1, 128, 28, 28] 0Conv2d-56 [-1, 32, 28, 28] 36,864BatchNorm2d-57 [-1, 192, 28, 28] 384ReLU-58 [-1, 192, 28, 28] 0Conv2d-59 [-1, 128, 28, 28] 24,576BatchNorm2d-60 [-1, 128, 28, 28] 256ReLU-61 [-1, 128, 28, 28] 0Conv2d-62 [-1, 32, 28, 28] 36,864BatchNorm2d-63 [-1, 224, 28, 28] 448ReLU-64 [-1, 224, 28, 28] 0Conv2d-65 [-1, 128, 28, 28] 28,672BatchNorm2d-66 [-1, 128, 28, 28] 256ReLU-67 [-1, 128, 28, 28] 0Conv2d-68 [-1, 32, 28, 28] 36,864BatchNorm2d-69 [-1, 256, 28, 28] 512ReLU-70 [-1, 256, 28, 28] 0Conv2d-71 [-1, 128, 28, 28] 32,768BatchNorm2d-72 [-1, 128, 28, 28] 256ReLU-73 [-1, 128, 28, 28] 0Conv2d-74 [-1, 32, 28, 28] 36,864BatchNorm2d-75 [-1, 288, 28, 28] 576ReLU-76 [-1, 288, 28, 28] 0Conv2d-77 [-1, 128, 28, 28] 36,864BatchNorm2d-78 [-1, 128, 28, 28] 256ReLU-79 [-1, 128, 28, 28] 0Conv2d-80 [-1, 32, 28, 28] 36,864BatchNorm2d-81 [-1, 320, 28, 28] 640ReLU-82 [-1, 320, 28, 28] 0Conv2d-83 [-1, 128, 28, 28] 40,960BatchNorm2d-84 [-1, 128, 28, 28] 256ReLU-85 [-1, 128, 28, 28] 0Conv2d-86 [-1, 32, 28, 28] 36,864BatchNorm2d-87 [-1, 352, 28, 28] 704ReLU-88 [-1, 352, 28, 28] 0Conv2d-89 [-1, 128, 28, 28] 45,056BatchNorm2d-90 [-1, 128, 28, 28] 256ReLU-91 [-1, 128, 28, 28] 0Conv2d-92 [-1, 32, 28, 28] 36,864BatchNorm2d-93 [-1, 384, 28, 28] 768ReLU-94 [-1, 384, 28, 28] 0Conv2d-95 [-1, 128, 28, 28] 49,152BatchNorm2d-96 [-1, 128, 28, 28] 256ReLU-97 [-1, 128, 28, 28] 0Conv2d-98 [-1, 32, 28, 28] 36,864BatchNorm2d-99 [-1, 416, 28, 28] 832ReLU-100 [-1, 416, 28, 28] 0Conv2d-101 [-1, 128, 28, 28] 53,248BatchNorm2d-102 [-1, 128, 28, 28] 256ReLU-103 [-1, 128, 28, 28] 0Conv2d-104 [-1, 32, 28, 28] 36,864BatchNorm2d-105 [-1, 448, 28, 28] 896ReLU-106 [-1, 448, 28, 28] 0Conv2d-107 [-1, 128, 28, 28] 57,344BatchNorm2d-108 [-1, 128, 28, 28] 256ReLU-109 [-1, 128, 28, 28] 0Conv2d-110 [-1, 32, 28, 28] 36,864BatchNorm2d-111 [-1, 480, 28, 28] 960ReLU-112 [-1, 480, 28, 28] 0Conv2d-113 [-1, 128, 28, 28] 61,440BatchNorm2d-114 [-1, 128, 28, 28] 256ReLU-115 [-1, 128, 28, 28] 0Conv2d-116 [-1, 32, 28, 28] 36,864BatchNorm2d-117 [-1, 512, 28, 28] 1,024ReLU-118 [-1, 512, 28, 28] 0Conv2d-119 [-1, 256, 28, 28] 131,072AvgPool2d-120 [-1, 256, 14, 14] 0BatchNorm2d-121 [-1, 256, 14, 14] 512ReLU-122 [-1, 256, 14, 14] 0Conv2d-123 [-1, 128, 14, 14] 32,768BatchNorm2d-124 [-1, 128, 14, 14] 256ReLU-125 [-1, 128, 14, 14] 0Conv2d-126 [-1, 32, 14, 14] 36,864BatchNorm2d-127 [-1, 288, 14, 14] 576ReLU-128 [-1, 288, 14, 14] 0Conv2d-129 [-1, 128, 14, 14] 36,864BatchNorm2d-130 [-1, 128, 14, 14] 256ReLU-131 [-1, 128, 14, 14] 0Conv2d-132 [-1, 32, 14, 14] 36,864BatchNorm2d-133 [-1, 320, 14, 14] 640ReLU-134 [-1, 320, 14, 14] 0Conv2d-135 [-1, 128, 14, 14] 40,960BatchNorm2d-136 [-1, 128, 14, 14] 256ReLU-137 [-1, 128, 14, 14] 0Conv2d-138 [-1, 32, 14, 14] 36,864BatchNorm2d-139 [-1, 352, 14, 14] 704ReLU-140 [-1, 352, 14, 14] 0Conv2d-141 [-1, 128, 14, 14] 45,056BatchNorm2d-142 [-1, 128, 14, 14] 256ReLU-143 [-1, 128, 14, 14] 0Conv2d-144 [-1, 32, 14, 14] 36,864BatchNorm2d-145 [-1, 384, 14, 14] 768ReLU-146 [-1, 384, 14, 14] 0Conv2d-147 [-1, 128, 14, 14] 49,152BatchNorm2d-148 [-1, 128, 14, 14] 256ReLU-149 [-1, 128, 14, 14] 0Conv2d-150 [-1, 32, 14, 14] 36,864BatchNorm2d-151 [-1, 416, 14, 14] 832ReLU-152 [-1, 416, 14, 14] 0Conv2d-153 [-1, 128, 14, 14] 53,248BatchNorm2d-154 [-1, 128, 14, 14] 256ReLU-155 [-1, 128, 14, 14] 0Conv2d-156 [-1, 32, 14, 14] 36,864BatchNorm2d-157 [-1, 448, 14, 14] 896ReLU-158 [-1, 448, 14, 14] 0Conv2d-159 [-1, 128, 14, 14] 57,344BatchNorm2d-160 [-1, 128, 14, 14] 256ReLU-161 [-1, 128, 14, 14] 0Conv2d-162 [-1, 32, 14, 14] 36,864BatchNorm2d-163 [-1, 480, 14, 14] 960ReLU-164 [-1, 480, 14, 14] 0Conv2d-165 [-1, 128, 14, 14] 61,440BatchNorm2d-166 [-1, 128, 14, 14] 256ReLU-167 [-1, 128, 14, 14] 0Conv2d-168 [-1, 32, 14, 14] 36,864BatchNorm2d-169 [-1, 512, 14, 14] 1,024ReLU-170 [-1, 512, 14, 14] 0Conv2d-171 [-1, 128, 14, 14] 65,536BatchNorm2d-172 [-1, 128, 14, 14] 256ReLU-173 [-1, 128, 14, 14] 0Conv2d-174 [-1, 32, 14, 14] 36,864BatchNorm2d-175 [-1, 544, 14, 14] 1,088ReLU-176 [-1, 544, 14, 14] 0Conv2d-177 [-1, 128, 14, 14] 69,632BatchNorm2d-178 [-1, 128, 14, 14] 256ReLU-179 [-1, 128, 14, 14] 0Conv2d-180 [-1, 32, 14, 14] 36,864BatchNorm2d-181 [-1, 576, 14, 14] 1,152ReLU-182 [-1, 576, 14, 14] 0Conv2d-183 [-1, 128, 14, 14] 73,728BatchNorm2d-184 [-1, 128, 14, 14] 256ReLU-185 [-1, 128, 14, 14] 0Conv2d-186 [-1, 32, 14, 14] 36,864BatchNorm2d-187 [-1, 608, 14, 14] 1,216ReLU-188 [-1, 608, 14, 14] 0Conv2d-189 [-1, 128, 14, 14] 77,824BatchNorm2d-190 [-1, 128, 14, 14] 256ReLU-191 [-1, 128, 14, 14] 0Conv2d-192 [-1, 32, 14, 14] 36,864BatchNorm2d-193 [-1, 640, 14, 14] 1,280ReLU-194 [-1, 640, 14, 14] 0Conv2d-195 [-1, 128, 14, 14] 81,920BatchNorm2d-196 [-1, 128, 14, 14] 256ReLU-197 [-1, 128, 14, 14] 0Conv2d-198 [-1, 32, 14, 14] 36,864BatchNorm2d-199 [-1, 672, 14, 14] 1,344ReLU-200 [-1, 672, 14, 14] 0Conv2d-201 [-1, 128, 14, 14] 86,016BatchNorm2d-202 [-1, 128, 14, 14] 256ReLU-203 [-1, 128, 14, 14] 0Conv2d-204 [-1, 32, 14, 14] 36,864BatchNorm2d-205 [-1, 704, 14, 14] 1,408ReLU-206 [-1, 704, 14, 14] 0Conv2d-207 [-1, 128, 14, 14] 90,112BatchNorm2d-208 [-1, 128, 14, 14] 256ReLU-209 [-1, 128, 14, 14] 0Conv2d-210 [-1, 32, 14, 14] 36,864BatchNorm2d-211 [-1, 736, 14, 14] 1,472ReLU-212 [-1, 736, 14, 14] 0Conv2d-213 [-1, 128, 14, 14] 94,208BatchNorm2d-214 [-1, 128, 14, 14] 256ReLU-215 [-1, 128, 14, 14] 0Conv2d-216 [-1, 32, 14, 14] 36,864BatchNorm2d-217 [-1, 768, 14, 14] 1,536ReLU-218 [-1, 768, 14, 14] 0Conv2d-219 [-1, 128, 14, 14] 98,304BatchNorm2d-220 [-1, 128, 14, 14] 256ReLU-221 [-1, 128, 14, 14] 0Conv2d-222 [-1, 32, 14, 14] 36,864BatchNorm2d-223 [-1, 800, 14, 14] 1,600ReLU-224 [-1, 800, 14, 14] 0Conv2d-225 [-1, 128, 14, 14] 102,400BatchNorm2d-226 [-1, 128, 14, 14] 256ReLU-227 [-1, 128, 14, 14] 0Conv2d-228 [-1, 32, 14, 14] 36,864BatchNorm2d-229 [-1, 832, 14, 14] 1,664ReLU-230 [-1, 832, 14, 14] 0Conv2d-231 [-1, 128, 14, 14] 106,496BatchNorm2d-232 [-1, 128, 14, 14] 256ReLU-233 [-1, 128, 14, 14] 0Conv2d-234 [-1, 32, 14, 14] 36,864BatchNorm2d-235 [-1, 864, 14, 14] 1,728ReLU-236 [-1, 864, 14, 14] 0Conv2d-237 [-1, 128, 14, 14] 110,592BatchNorm2d-238 [-1, 128, 14, 14] 256ReLU-239 [-1, 128, 14, 14] 0Conv2d-240 [-1, 32, 14, 14] 36,864BatchNorm2d-241 [-1, 896, 14, 14] 1,792ReLU-242 [-1, 896, 14, 14] 0Conv2d-243 [-1, 128, 14, 14] 114,688BatchNorm2d-244 [-1, 128, 14, 14] 256ReLU-245 [-1, 128, 14, 14] 0Conv2d-246 [-1, 32, 14, 14] 36,864BatchNorm2d-247 [-1, 928, 14, 14] 1,856ReLU-248 [-1, 928, 14, 14] 0Conv2d-249 [-1, 128, 14, 14] 118,784BatchNorm2d-250 [-1, 128, 14, 14] 256ReLU-251 [-1, 128, 14, 14] 0Conv2d-252 [-1, 32, 14, 14] 36,864BatchNorm2d-253 [-1, 960, 14, 14] 1,920ReLU-254 [-1, 960, 14, 14] 0Conv2d-255 [-1, 128, 14, 14] 122,880BatchNorm2d-256 [-1, 128, 14, 14] 256ReLU-257 [-1, 128, 14, 14] 0Conv2d-258 [-1, 32, 14, 14] 36,864BatchNorm2d-259 [-1, 992, 14, 14] 1,984ReLU-260 [-1, 992, 14, 14] 0Conv2d-261 [-1, 128, 14, 14] 126,976BatchNorm2d-262 [-1, 128, 14, 14] 256ReLU-263 [-1, 128, 14, 14] 0Conv2d-264 [-1, 32, 14, 14] 36,864BatchNorm2d-265 [-1, 1024, 14, 14] 2,048ReLU-266 [-1, 1024, 14, 14] 0Conv2d-267 [-1, 512, 14, 14] 524,288AvgPool2d-268 [-1, 512, 7, 7] 0BatchNorm2d-269 [-1, 512, 7, 7] 1,024ReLU-270 [-1, 512, 7, 7] 0Conv2d-271 [-1, 128, 7, 7] 65,536BatchNorm2d-272 [-1, 128, 7, 7] 256ReLU-273 [-1, 128, 7, 7] 0Conv2d-274 [-1, 32, 7, 7] 36,864BatchNorm2d-275 [-1, 544, 7, 7] 1,088ReLU-276 [-1, 544, 7, 7] 0Conv2d-277 [-1, 128, 7, 7] 69,632BatchNorm2d-278 [-1, 128, 7, 7] 256ReLU-279 [-1, 128, 7, 7] 0Conv2d-280 [-1, 32, 7, 7] 36,864BatchNorm2d-281 [-1, 576, 7, 7] 1,152ReLU-282 [-1, 576, 7, 7] 0Conv2d-283 [-1, 128, 7, 7] 73,728BatchNorm2d-284 [-1, 128, 7, 7] 256ReLU-285 [-1, 128, 7, 7] 0Conv2d-286 [-1, 32, 7, 7] 36,864BatchNorm2d-287 [-1, 608, 7, 7] 1,216ReLU-288 [-1, 608, 7, 7] 0Conv2d-289 [-1, 128, 7, 7] 77,824BatchNorm2d-290 [-1, 128, 7, 7] 256ReLU-291 [-1, 128, 7, 7] 0Conv2d-292 [-1, 32, 7, 7] 36,864BatchNorm2d-293 [-1, 640, 7, 7] 1,280ReLU-294 [-1, 640, 7, 7] 0Conv2d-295 [-1, 128, 7, 7] 81,920BatchNorm2d-296 [-1, 128, 7, 7] 256ReLU-297 [-1, 128, 7, 7] 0Conv2d-298 [-1, 32, 7, 7] 36,864BatchNorm2d-299 [-1, 672, 7, 7] 1,344ReLU-300 [-1, 672, 7, 7] 0Conv2d-301 [-1, 128, 7, 7] 86,016BatchNorm2d-302 [-1, 128, 7, 7] 256ReLU-303 [-1, 128, 7, 7] 0Conv2d-304 [-1, 32, 7, 7] 36,864BatchNorm2d-305 [-1, 704, 7, 7] 1,408ReLU-306 [-1, 704, 7, 7] 0Conv2d-307 [-1, 128, 7, 7] 90,112BatchNorm2d-308 [-1, 128, 7, 7] 256ReLU-309 [-1, 128, 7, 7] 0Conv2d-310 [-1, 32, 7, 7] 36,864BatchNorm2d-311 [-1, 736, 7, 7] 1,472ReLU-312 [-1, 736, 7, 7] 0Conv2d-313 [-1, 128, 7, 7] 94,208BatchNorm2d-314 [-1, 128, 7, 7] 256ReLU-315 [-1, 128, 7, 7] 0Conv2d-316 [-1, 32, 7, 7] 36,864BatchNorm2d-317 [-1, 768, 7, 7] 1,536ReLU-318 [-1, 768, 7, 7] 0Conv2d-319 [-1, 128, 7, 7] 98,304BatchNorm2d-320 [-1, 128, 7, 7] 256ReLU-321 [-1, 128, 7, 7] 0Conv2d-322 [-1, 32, 7, 7] 36,864BatchNorm2d-323 [-1, 800, 7, 7] 1,600ReLU-324 [-1, 800, 7, 7] 0Conv2d-325 [-1, 128, 7, 7] 102,400BatchNorm2d-326 [-1, 128, 7, 7] 256ReLU-327 [-1, 128, 7, 7] 0Conv2d-328 [-1, 32, 7, 7] 36,864BatchNorm2d-329 [-1, 832, 7, 7] 1,664ReLU-330 [-1, 832, 7, 7] 0Conv2d-331 [-1, 128, 7, 7] 106,496BatchNorm2d-332 [-1, 128, 7, 7] 256ReLU-333 [-1, 128, 7, 7] 0Conv2d-334 [-1, 32, 7, 7] 36,864BatchNorm2d-335 [-1, 864, 7, 7] 1,728ReLU-336 [-1, 864, 7, 7] 0Conv2d-337 [-1, 128, 7, 7] 110,592BatchNorm2d-338 [-1, 128, 7, 7] 256ReLU-339 [-1, 128, 7, 7] 0Conv2d-340 [-1, 32, 7, 7] 36,864BatchNorm2d-341 [-1, 896, 7, 7] 1,792ReLU-342 [-1, 896, 7, 7] 0Conv2d-343 [-1, 128, 7, 7] 114,688BatchNorm2d-344 [-1, 128, 7, 7] 256ReLU-345 [-1, 128, 7, 7] 0Conv2d-346 [-1, 32, 7, 7] 36,864BatchNorm2d-347 [-1, 928, 7, 7] 1,856ReLU-348 [-1, 928, 7, 7] 0Conv2d-349 [-1, 128, 7, 7] 118,784BatchNorm2d-350 [-1, 128, 7, 7] 256ReLU-351 [-1, 128, 7, 7] 0Conv2d-352 [-1, 32, 7, 7] 36,864BatchNorm2d-353 [-1, 960, 7, 7] 1,920ReLU-354 [-1, 960, 7, 7] 0Conv2d-355 [-1, 128, 7, 7] 122,880BatchNorm2d-356 [-1, 128, 7, 7] 256ReLU-357 [-1, 128, 7, 7] 0Conv2d-358 [-1, 32, 7, 7] 36,864BatchNorm2d-359 [-1, 992, 7, 7] 1,984ReLU-360 [-1, 992, 7, 7] 0Conv2d-361 [-1, 128, 7, 7] 126,976BatchNorm2d-362 [-1, 128, 7, 7] 256ReLU-363 [-1, 128, 7, 7] 0Conv2d-364 [-1, 32, 7, 7] 36,864BatchNorm2d-365 [-1, 1024, 7, 7] 2,048ReLU-366 [-1, 1024, 7, 7] 0Linear-367 [-1, 3] 3,075Total params: 6,956,931
Trainable params: 6,956,931
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 294.57
Params size (MB): 26.54
Estimated Total Size (MB): 321.69
----------------------------------------------------------------四、训练模型
1.编写训练函数
# 训练循环
def train(dataloader, model, loss_fn, optimizer):size len(dataloader.dataset) # 训练集的大小num_batches len(dataloader) # 批次数目, (size/batch_size向上取整)train_loss, train_acc 0, 0 # 初始化训练损失和正确率for X, y in dataloader: # 获取图片及其标签X, y X.to(device), y.to(device)# 计算预测误差pred model(X) # 网络输出loss loss_fn(pred, y) # 计算网络输出和真实值之间的差距targets为真实值计算二者差值即为损失# 反向传播optimizer.zero_grad() # grad属性归零loss.backward() # 反向传播optimizer.step() # 每一步自动更新# 记录acc与losstrain_acc (pred.argmax(1) y).type(torch.float).sum().item()train_loss loss.item()train_acc / sizetrain_loss / num_batchesreturn train_acc, train_loss
2.编写测试函数
def test (dataloader, model, loss_fn):size len(dataloader.dataset) # 测试集的大小num_batches len(dataloader) # 批次数目, (size/batch_size向上取整)test_loss, test_acc 0, 0# 当不进行训练时停止梯度更新节省计算内存消耗with torch.no_grad():for imgs, target in dataloader:imgs, target imgs.to(device), target.to(device)# 计算losstarget_pred model(imgs)loss loss_fn(target_pred, target)test_loss loss.item()test_acc (target_pred.argmax(1) target).type(torch.float).sum().item()test_acc / sizetest_loss / num_batchesreturn test_acc, test_loss
3.正式训练
import copyoptimizer torch.optim.Adam(model.parameters(), lr 1e-4)
loss_fn nn.CrossEntropyLoss() # 创建损失函数epochs 20train_loss []
train_acc []
test_loss []
test_acc []best_acc 0 # 设置一个最佳准确率作为最佳模型的判别指标for epoch in range(epochs):model.train()epoch_train_acc, epoch_train_loss train(train_dl, model, loss_fn, optimizer)model.eval()epoch_test_acc, epoch_test_loss test(test_dl, model, loss_fn)# 保存最佳模型到 best_modelif epoch_test_acc best_acc:best_acc epoch_test_accbest_model copy.deepcopy(model)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 获取当前的学习率lr optimizer.state_dict()[param_groups][0][lr]template (Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E})print(template.format(epoch1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss, lr))# 保存最佳模型到文件中
PATH ./best_model.pth # 保存的参数文件名
torch.save(best_model.state_dict(), PATH)print(Done)五、结果可视化
1.Loss与Accuracy图
import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.filterwarnings(ignore) #忽略警告信息
plt.rcParams[font.sans-serif] [SimHei] # 用来正常显示中文标签
plt.rcParams[axes.unicode_minus] False # 用来正常显示负号
plt.rcParams[figure.dpi] 100 #分辨率epochs_range range(epochs)plt.figure(figsize(12, 3))
plt.subplot(1, 2, 1)plt.plot(epochs_range, train_acc, labelTraining Accuracy)
plt.plot(epochs_range, test_acc, labelTest Accuracy)
plt.legend(loclower right)
plt.title(Training and Validation Accuracy)plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, labelTraining Loss)
plt.plot(epochs_range, test_loss, labelTest Loss)
plt.legend(locupper right)
plt.title(Training and Validation Loss)
plt.show()2.模型评估
# 将参数加载到model当中
best_model.load_state_dict(torch.load(PATH, map_locationdevice))
epoch_test_acc, epoch_test_loss test(test_dl, best_model, loss_fn)epoch_test_acc, epoch_test_loss总结
本周主要通过实际例子完整学习了DenseNet算法更加深入地了接到了DenseNet的结构。
