深度学习每周练习J部分源代码

import os, PIL, random, pathlib import torch import torch.nn as nn import torchvision.transforms as transforms import torchvision import torch.nn.functional as F from torchvision import transforms, datasets device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(device) data_dir = './data/' data_dir = pathlib.Path(data_dir) data_paths = list(data_dir.glob('*')) classeNames = [str(path).split("\\")[1] for path in data_paths] print(classeNames) image_count = len(list(data_dir.glob('*/*'))) print("图片总数为：", image_count) 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/", transform=train_transforms) print(total_data.class_to_idx) 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]) batch_size = 16 train_dl = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0) test_dl = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=0) for X, y in test_dl: print("Shape of X [N, C, H, W]: ", X.shape) print("Shape of y: ", y.shape, y.dtype) break #################### class BasicConv2d(nn.Module): def __init__(self, in_channel, out_channel, **kwargs): super(BasicConv2d, self).__init__() self.conv = nn.Conv2d(in_channel, out_channel, bias=False, **kwargs) self.norm = nn.BatchNorm2d(out_channel, eps=0.001) self.relu = nn.ReLU(inplace=True) def forward(self, x): x = self.conv(x) x = self.norm(x) x = self.relu(x) return x class InceptionA(nn.Module): def __init__(self, in_channels, pool_features): super(InceptionA, self).__init__() self.branch1x1 = BasicConv2d(in_channels, 64, kernel_size=1) # 1 self.branch5x5_1 = BasicConv2d(in_channels, 48, kernel_size=1) self.branch5x5_2 = BasicConv2d(48, 64, kernel_size=5, padding=2) self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1) self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1) self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, padding=1) self.branch_pool = BasicConv2d(in_channels, pool_features, kernel_size=1) def forward(self, x): branch1x1 = self.branch1x1(x) branch5x5 = self.branch5x5_1(x) branch5x5 = self.branch5x5_2(branch5x5) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] return torch.cat(outputs, 1) class InceptionB(nn.Module): def __init__(self, in_channels, channels_7x7): super(InceptionB, self).__init__() self.branch1x1 = BasicConv2d(in_channels, 192, kernel_size=1) c7 = channels_7x7 self.branch7x7_1 = BasicConv2d(in_channels, c7, kernel_size=1) self.branch7x7_2 = BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3)) self.branch7x7_3 = BasicConv2d(c7, 192, kernel_size=(7, 1), padding=(3, 0)) self.branch7x7dbl_1 = BasicConv2d(in_channels, c7, kernel_size=1) self.branch7x7dbl_2 = BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0)) self.branch7x7dbl_3 = BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3)) self.branch7x7dbl_4 = BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0)) self.branch7x7dbl_5 = BasicConv2d(c7, 192, kernel_size=(1, 7), padding=(0, 3)) self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1) def forward(self, x): branch1x1 = self.branch1x1(x) branch7x7 = self.branch7x7_1(x) branch7x7 = self.branch7x7_2(branch7x7) branch7x7 = self.branch7x7_3(branch7x7) branch7x7dbl = self.branch7x7dbl_1(x) branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] return torch.cat(outputs, 1) class InceptionC(nn.Module): def __init__(self, in_channels): super(InceptionC, self).__init__() self.branch1x1 = BasicConv2d(in_channels, 320, kernel_size=1) self.branch3x3_1 = BasicConv2d(in_channels, 384, kernel_size=1) self.branch3x3_2a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1)) self.branch3x3_2b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0)) self.branch3x3dbl_1 = BasicConv2d(in_channels, 448, kernel_size=1) self.branch3x3dbl_2 = BasicConv2d(448, 384, kernel_size=3, padding=1) self.branch3x3dbl_3a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1)) self.branch3x3dbl_3b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0)) self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1) def forward(self, x): branch1x1 = self.branch1x1(x) branch3x3 = self.branch3x3_1(x) branch3x3 = [ self.branch3x3_2a(branch3x3), self.branch3x3_2b(branch3x3), ] branch3x3 = torch.cat(branch3x3, 1) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = [ self.branch3x3dbl_3a(branch3x3dbl), self.branch3x3dbl_3b(branch3x3dbl), ] branch3x3dbl = torch.cat(branch3x3dbl, 1) branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] return torch.cat(outputs, 1) class ReductionA(nn.Module): def __init__(self, in_channels): super(ReductionA, self).__init__() self.branch3x3 = BasicConv2d(in_channels, 384, kernel_size=3, stride=2) self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1) self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1) self.