机器学习大作业 ConvNeXt篇

机器学习大作业 ConvNeXt篇

Author: Kedreamix

ConvNeXt/2019044031-邓恺俊-大数据驱动的多种深度模型在图像分类中的对比（ConvNeXt篇）.doc

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+import torch
+import torchvision
+import torchvision.transforms as transforms
+
+# Data
+def get_training_dataloader(batch_size = 64, num_workers = 4, shuffle = True, resize = 32):
+    print('==> Preparing Train data..')
+    transform_train = transforms.Compose([
+        transforms.Resize(resize),
+        # transforms.RandomCrop(32, padding=4),
+        # transforms.RandomHorizontalFlip(),
+        transforms.ToTensor(),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+    ])
+    trainset = torchvision.datasets.CIFAR10(
+        root='./data', train=True, download=True, transform=transform_train)
+    trainloader = torch.utils.data.DataLoader(
+        trainset, batch_size=batch_size, shuffle=shuffle, num_workers= num_workers)
+    return trainloader
+    
+def get_test_dataloader(batch_size = 64, num_workers = 4, shuffle = True, resize = 32): 
+    print('==> Preparing Test data..')   
+    transform_test = transforms.Compose([
+        transforms.Resize(resize),
+        transforms.ToTensor(),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+    ])
+
+    testset = torchvision.datasets.CIFAR10(
+        root='./data', train=False, download=True, transform=transform_test)
+    testloader = torch.utils.data.DataLoader(
+        testset, batch_size=batch_size, shuffle=shuffle, num_workers= num_workers)
+    return testloader
+

ConvNeXt/2019044031-邓恺俊-大数据驱动的多种深度模型在图像分类中的对比（ConvNeXt篇）.pdf

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+
+import os
+import torch
+import torch.nn as nn
+import argparse
+import numpy as np
+from tqdm import tqdm
+from dataloader import get_test_dataloader
+import torch.backends.cudnn as cudnn
+
+def eval_top1(outputs, label):
+    total = outputs.shape[0]
+    outputs = torch.softmax(outputs, dim=-1)
+    _, pred_y = outputs.data.max(dim=1) # 得到概率
+    correct = (pred_y == label).sum().data
+    return correct / total
+
+def eval_top5(outputs, label):
+    total = outputs.shape[0]
+    outputs = torch.softmax(outputs, dim=-1)
+    pred_y = np.argsort(-outputs.cpu().numpy())
+    pred_y_top5 = pred_y[:,:5]
+    correct = 0
+    for i in range(total):
+        if label[i].cpu().numpy() in pred_y_top5[i]:
+            correct += 1
+    return correct / total
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Test')
+    parser.add_argument('--cuda', action='store_true', default=False, help =' use GPU?')
+    parser.add_argument('--batch-size', default=64, type=int, help = "Batch Size for Test")
+    parser.add_argument('--num-workers', default=2, type=int, help = 'num-workers')
+    parser.add_argument('--net', type = str, choices=['LeNet5', 'AlexNet', 'VGG16','VGG19','ResNet18','ResNet34',   
+                                                       'DenseNet','MobileNetv1','MobileNetv2','ResNeXt','ConvNeXt-T','ConvNeXt-S','ConvNeXt-B'], default='MobileNetv1', help='net type')
+    parser.add_argument('--resize',type=int,default=32)
+    args = parser.parse_args()
+    testloader = get_test_dataloader(batch_size = args.batch_size, num_workers = args.num_workers, shuffle=False, resize = args.resize)
+
+    # Model
+    print('==> Building model..')
+    if args.net == 'VGG16':
+        from nets.VGG import VGG
+        net = VGG('VGG16')
+    elif args.net == 'VGG19':
+        from nets.VGG import VGG
+        net = VGG('VGG19')
+    elif args.net == 'ResNet18':
+        from nets.ResNet import ResNet18
+        net = ResNet18()
+    elif args.net == 'ResNet34':
+        from nets.ResNet import ResNet34
+        net = ResNet34()
+    elif args.net == 'LeNet5':
+        from nets.LeNet5 import LeNet5
+        net = LeNet5()
+    elif args.net == 'AlexNet':
+        from nets.AlexNet import AlexNet
+        net = AlexNet()
+    elif args.net == 'DenseNet':
+        from nets.DenseNet import densenet_cifar
+        net = densenet_cifar()
+    elif args.net == 'MobileNetv1':
+        from nets.MobileNetv1 import MobileNet
+        net = MobileNet()
+    elif args.net == 'MobileNetv2':
+        from nets.MobileNetv2 import MobileNetV2
+        net = MobileNetV2()
+    elif args.net == 'ResNeXt':
+        from nets.ResNeXt import ResNeXt50
+        net = ResNeXt50(10)
+    elif args.net == 'ConvNeXt-T':
+        from nets.ConvNeXt import convnext_tiny
+        net = convnext_tiny(10)
+    elif args.net == 'ConvNeXt-S':
+        from nets.ConvNeXt import convnext_small
+        net = convnext_small(10)
+    elif args.net == 'ConvNeXt-B':
+        from nets.ConvNeXt import convnext_base
+        net = convnext_base(10)
+
+    from torchinfo import summary
+    summary(net,(2,3,224,224))
+
+    criterion = nn.CrossEntropyLoss()
+    
+    # Load checkpoint.
+    print('==> Resuming from checkpoint..')
+    assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
+    checkpoint = torch.load('./checkpoint/{}_ckpt.pth'.format(args.net))
+
+    weights_dict = {}
+    for k, v in checkpoint['net'].items():
+        new_k = k.replace('module.', '') if 'module' in k else k
+        weights_dict[new_k] = v
+
+    net.load_state_dict(weights_dict)
+
+    if args.cuda and torch.cuda.is_available():
+        device = 'cuda'
+        net = torch.nn.DataParallel(net)
+        cudnn.benchmark = True
+    else:
+        device = 'cpu'
+        
+    epoch_step_test = len(testloader)
+    if epoch_step_test == 0:
+            raise ValueError("数据集过小，无法进行训练，请扩充数据集，或者减小batchsize")
+        
+    net.eval()
+    test_acc_top1 = 0
+    test_acc_top5 = 0
+    print('Start Test')
+    #--------------------------------
+    #   相同方法，同train
+    #--------------------------------
+    with tqdm(total=epoch_step_test,desc=f'Test Acc',postfix=dict,mininterval=0.3) as pbar2:
+        for step,(im,label) in enumerate(testloader,start=0):
+            im = im.to(device)
+            label = label.to(device)
+            with torch.no_grad():
+                if step >= epoch_step_test:
+                    break
+                
+                # 释放内存
+                if hasattr(torch.cuda, 'empty_cache'):
+                    torch.cuda.empty_cache()
+                #----------------------#
+                #   前向传播
+                #----------------------#
+                outputs = net(im)
+                loss = criterion(outputs,label)
+                test_acc_top1 += eval_top1(outputs,label)
+                test_acc_top5 += eval_top5(outputs,label)
+                pbar2.set_postfix(**{'Test Acc Top1': test_acc_top1.item()/(step+1),
+                            'Test Acc Top5': test_acc_top5 / (step + 1)})
+                pbar2.update(1)
+                
+    top1 = test_acc_top1.item()/ len(testloader)
+    top5 = test_acc_top5 / len(testloader)
+    print("top-1 accuracy = %.2f%%" % (top1*100))
+    print("top-5 accuracy = %.2f%%" % (top5*100))