Pytorch实现Fashion-mnist分类任务全过程-世界热文
(资料图)
目录
数据概况Fashion-mnist类别标注数据处理对输入进行归一化数据增强定义Resnet网络训练与测试完整代码总结数据概况
Fashion-mnist
经典的MNIST数据集包含了大量的手写数字。十几年来,来自机器学习、机器视觉、人工智能、深度学习领域的研究员们把这个数据集作为衡量算法的基准之一。
你会在很多的会议,期刊的论文中发现这个数据集的身影。实际上,MNIST数据集已经成为算法作者的必测的数据集之一。
类别标注
在Fashion-mnist数据集中,每个训练样本都按照以下类别进行了标注:
数据处理
对输入进行归一化
归一化时需要统一进行 x = (x - mean) / std
train_trans = transforms.Compose([ transforms.RandomCrop(28, padding=2),#数据增强 transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize ]) test_trans = transforms.Compose([ transforms.ToTensor(), normalize ]) mnist_train = torchvision.datasets.FashionMNIST(root="../data",train=True,download=True,transform=train_trans) mnist_test = torchvision.datasets.FashionMNIST(root="../data",train=False,download=True,transform=test_trans) train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True) test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False) # 求整个数据集的均值 temp_sum = 0 cnt = 0 for X, y in train_iter: if y.shape[0] != batch_size: break # 最后一个batch不足batch_size,这里就忽略了 channel_mean = torch.mean(X, dim=(0,2,3)) # 按channel求均值(不过这里只有1个channel) cnt += 1 # cnt记录的是batch的个数,不是图像 temp_sum += channel_mean[0].item() dataset_global_mean = temp_sum / cnt print("整个数据集的像素均值:{}".format(dataset_global_mean)) # 求整个数据集的标准差 cnt = 0 temp_sum = 0 for X, y in train_iter: if y.shape[0] != batch_size: break # 最后一个batch不足batch_size,这里就忽略了 residual = (X - dataset_global_mean) ** 2 channel_var_mean = torch.mean(residual, dim=(0,2,3)) cnt += 1 # cnt记录的是batch的个数,不是图像 temp_sum += math.sqrt(channel_var_mean[0].item()) dataset_global_std = temp_sum / cnt print("整个数据集的像素标准差:{}".format(dataset_global_std))
整个数据集的像素均值:0.2860366729433025
整个数据集的像素标准差:0.35288708155778725
数据增强
加入随机裁剪和翻转
============================ step 1/6 数据 ============================ batch_size = 64 normalize = transforms.Normalize(mean=[0.286], std=[0.352])#对像素值归一化 train_trans = transforms.Compose([ transforms.RandomCrop(28, padding=2), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize ]) test_trans = transforms.Compose([ transforms.ToTensor(), normalize ]) mnist_train = torchvision.datasets.FashionMNIST(root="../data",train=True,download=True,transform=train_trans) mnist_test = torchvision.datasets.FashionMNIST(root="../data",train=False,download=True,transform=test_trans) train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True) test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False)
定义Resnet网络
class GlobalAvgPool2d(nn.Module): """ 全局平均池化层 可通过将普通的平均池化的窗口形状设置成输入的高和宽实现 """ def __init__(self): super(GlobalAvgPool2d, self).__init__() def forward(self, x): return F.avg_pool2d(x, kernel_size=x.size()[2:]) class FlattenLayer(torch.nn.Module): def __init__(self): super(FlattenLayer, self).__init__() def forward(self, x): # x shape: (batch, *, *, ...) return x.view(x.shape[0], -1) class Residual(nn.Module): def __init__(self, in_channels, out_channels, use_1x1conv=False, stride=1): """ use_1×1conv: 是否使用额外的1x1卷积层来修改通道数 stride: 卷积层的步幅, resnet使用步长为2的卷积来替代pooling的作用,是个很赞的idea """ super(Residual, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, stride=stride) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) if use_1x1conv: self.conv3 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride) else: self.conv3 = None self.bn1 = nn.BatchNorm2d(out_channels) self.bn2 = nn.BatchNorm2d(out_channels) def forward(self, X): Y = F.relu(self.bn1(self.conv1(X))) Y = self.bn2(self.conv2(Y)) if self.conv3: X = self.conv3(X) return F.relu(Y + X) def resnet_block(in_channels, out_channels, num_residuals, first_block=False): """ resnet block num_residuals: 当前block包含多少个残差块 first_block: 是否为第一个block 一个resnet block由num_residuals个残差块组成 其中第一个残差块起到了通道数的转换和pooling的作用 后面的若干残差块就是完成正常的特征提取 """ if first_block: assert in_channels == out_channels # 第一个模块的输出通道数同输入通道数一致 blk = [] for i in range(num_residuals): if i == 0 and not first_block: blk.append(Residual(in_channels, out_channels, use_1x1conv=True, stride=2)) else: blk.append(Residual(out_channels, out_channels)) return nn.Sequential(*blk) # 定义resnet模型结构 net = nn.Sequential( nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1), # TODO: 缩小感受野, 缩channel nn.BatchNorm2d(32), nn.ReLU()) # nn.ReLU(), # nn.MaxPool2d(kernel_size=2, stride=2)) # TODO:去掉maxpool缩小感受野 # 然后是连续4个block net.add_module("resnet_block1", resnet_block(32, 32, 2, first_block=True)) # TODO: channel统一减半 net.add_module("resnet_block2", resnet_block(32, 64, 2)) net.add_module("resnet_block3", resnet_block(64, 128, 2)) net.add_module("resnet_block4", resnet_block(128, 256, 2)) # global average pooling net.add_module("global_avg_pool", GlobalAvgPool2d()) # fc layer net.add_module("fc", nn.Sequential(FlattenLayer(), nn.Linear(256, 10)))
训练与测试
def evaluate_accuracy(data_iter, net, device=None): #评估模型在测试集的准确率 if device is None and isinstance(net, torch.nn.Module): # 如果没指定device就使用net的device device = list(net.parameters())[0].device net.eval() acc_sum, n = 0.0, 0 with torch.no_grad(): for X, y in data_iter: acc_sum += (net(X.to(device)).argmax(dim=1) == y.to(device)).float().sum().cpu().item() n += y.shape[0] net.train() # 改回训练模式 return acc_sum / n def train_model(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs): net = net.to(device) print("training on ", device) loss = torch.nn.CrossEntropyLoss() best_test_acc = 0 for epoch in range(num_epochs): train_l_sum, train_acc_sum, n, batch_count, start = 0.0, 0.0, 0, 0, time.time() for X, y in train_iter: X = X.to(device) y = y.to(device) y_hat = net(X) l = loss(y_hat, y) optimizer.zero_grad() l.backward() optimizer.step() train_l_sum += l.cpu().item() train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item() n += y.shape[0] batch_count += 1 test_acc = evaluate_accuracy(test_iter, net) print("epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec" % (epoch + 1, train_l_sum / batch_count, train_acc_sum / n, test_acc, time.time() - start)) if test_acc > best_test_acc: print("find best! save at model/best.pth") best_test_acc = test_acc torch.save(net.state_dict(), "model/best.pth") lr, num_epochs = 0.01, 10 optimizer = torch.optim.Adam(net.parameters(), lr=lr) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") train_model(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)
完整代码
import os import sys import time import torch from torch import nn, optim import torch.nn.functional as F import torchvision from torchvision import transforms class GlobalAvgPool2d(nn.Module): """ 全局平均池化层 可通过将普通的平均池化的窗口形状设置成输入的高和宽实现 """ def __init__(self): super(GlobalAvgPool2d, self).__init__() def forward(self, x): return F.avg_pool2d(x, kernel_size=x.size()[2:]) class FlattenLayer(torch.nn.Module): def __init__(self): super(FlattenLayer, self).__init__() def forward(self, x): # x shape: (batch, *, *, ...) return x.view(x.shape[0], -1) class Residual(nn.Module): def __init__(self, in_channels, out_channels, use_1x1conv=False, stride=1): """ use_1×1conv: 是否使用额外的1x1卷积层来修改通道数 stride: 卷积层的步幅, resnet使用步长为2的卷积来替代pooling的作用,是个很赞的idea """ super(Residual, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, stride=stride) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) if use_1x1conv: self.conv3 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride) else: self.conv3 = None self.bn1 = nn.BatchNorm2d(out_channels) self.bn2 = nn.BatchNorm2d(out_channels) def forward(self, X): Y = F.relu(self.bn1(self.conv1(X))) Y = self.bn2(self.conv2(Y)) if self.conv3: X = self.conv3(X) return F.relu(Y + X) def resnet_block(in_channels, out_channels, num_residuals, first_block=False): """ resnet block num_residuals: 当前block包含多少个残差块 first_block: 是否为第一个block 一个resnet block由num_residuals个残差块组成 其中第一个残差块起到了通道数的转换和pooling的作用 后面的若干残差块就是完成正常的特征提取 """ if first_block: assert in_channels == out_channels # 第一个模块的输出通道数同输入通道数一致 blk = [] for i in range(num_residuals): if i == 0 and not first_block: blk.append(Residual(in_channels, out_channels, use_1x1conv=True, stride=2)) else: blk.append(Residual(out_channels, out_channels)) return nn.Sequential(*blk) # 定义resnet模型结构 net = nn.Sequential( nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1), # TODO: 缩小感受野, 缩channel nn.BatchNorm2d(32), nn.ReLU()) # nn.ReLU(), # nn.MaxPool2d(kernel_size=2, stride=2)) # TODO:去掉maxpool缩小感受野 # 然后是连续4个block net.add_module("resnet_block1", resnet_block(32, 32, 2, first_block=True)) # TODO: channel统一减半 net.add_module("resnet_block2", resnet_block(32, 64, 2)) net.add_module("resnet_block3", resnet_block(64, 128, 2)) net.add_module("resnet_block4", resnet_block(128, 256, 2)) # global average pooling net.add_module("global_avg_pool", GlobalAvgPool2d()) # fc layer net.add_module("fc", nn.Sequential(FlattenLayer(), nn.Linear(256, 10))) def load_data_fashion_mnist(batch_size, root="../data"): """Download the fashion mnist dataset and then load into memory.""" normalize = transforms.Normalize(mean=[0.28], std=[0.35]) train_augs = transforms.Compose([ transforms.RandomCrop(28, padding=2), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize ]) test_augs = transforms.Compose([ transforms.ToTensor(), normalize ]) mnist_train = torchvision.datasets.FashionMNIST(root=root, train=True, download=True, transform=train_augs) mnist_test = torchvision.datasets.FashionMNIST(root=root, train=False, download=True, transform=test_augs) if sys.platform.startswith("win"): num_workers = 0 # 0表示不用额外的进程来加速读取数据 else: num_workers = 4 train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers) test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers) return train_iter, test_iter print("训练...") batch_size = 64 train_iter, test_iter = load_data_fashion_mnist(batch_size, root="../data") def evaluate_accuracy(data_iter, net, device=None): if device is None and isinstance(net, torch.nn.Module): # 如果没指定device就使用net的device device = list(net.parameters())[0].device net.eval() acc_sum, n = 0.0, 0 with torch.no_grad(): for X, y in data_iter: acc_sum += (net(X.to(device)).argmax(dim=1) == y.to(device)).float().sum().cpu().item() n += y.shape[0] net.train() # 改回训练模式 return acc_sum / n def train_model(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs, lr, lr_period, lr_decay): net = net.to(device) print("training on ", device) loss = torch.nn.CrossEntropyLoss() best_test_acc = 0 for epoch in range(num_epochs): train_l_sum, train_acc_sum, n, batch_count, start = 0.0, 0.0, 0, 0, time.time() if epoch > 0 and epoch % lr_period == 0: # 每lr_period个epoch,学习率衰减一次 lr = lr * lr_decay for param_group in optimizer.param_groups: param_group["lr"] = lr for X, y in train_iter: X = X.to(device) y = y.to(device) y_hat = net(X) l = loss(y_hat, y) optimizer.zero_grad() l.backward() optimizer.step() train_l_sum += l.cpu().item() train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item() n += y.shape[0] batch_count += 1 test_acc = evaluate_accuracy(test_iter, net) print("epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec" % (epoch + 1, train_l_sum / batch_count, train_acc_sum / n, test_acc, time.time() - start)) if test_acc > best_test_acc: print("find best! save at model/best.pth") best_test_acc = test_acc torch.save(net.state_dict(), "model/best.pth") # utils.save_model({ # "arch": args.model, # "state_dict": net.state_dict() # }, "saved-models/{}-run-{}.pth.tar".format(args.model, run)) lr, num_epochs, lr_period, lr_decay = 0.01, 50, 5, 0.1 #optimizer = optim.Adam(net.parameters(), lr=lr) optimizer = optim.SGD(net.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") train_model(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs, lr, lr_period, lr_decay) print("加载最优模型") net.load_state_dict(torch.load("model/best.pth")) net = net.to(device) print("inference测试集") net.eval() id = 0 preds_list = [] with torch.no_grad(): for X, y in test_iter: batch_pred = list(net(X.to(device)).argmax(dim=1).cpu().numpy()) for y_pred in batch_pred: preds_list.append((id, y_pred)) id += 1 print("生成测试集评估文件") with open("result.csv", "w") as f: f.write("ID,Prediction\n") for id, pred in preds_list: f.write("{},{}\n".format(id, pred))
总结
以上为个人经验,希望能给大家一个参考,也希望大家多多支持脚本之家。
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