{"id":61288,"date":"2026-01-17T11:05:39","date_gmt":"2026-01-17T03:05:39","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/61288.html"},"modified":"2026-01-17T11:05:39","modified_gmt":"2026-01-17T03:05:39","slug":"%e5%9f%ba%e4%ba%8epytorch%e6%a1%86%e6%9e%b6%e7%9a%84%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0vision-transformer%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%e8%9d%b4%e8%9d%b6%e5%88%86%e7%b1%bb%e8%af%86%e5%88%ab","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/61288.html","title":{"rendered":"\u57fa\u4e8ePytorch\u6846\u67b6\u7684\u6df1\u5ea6\u5b66\u4e60Vision Transformer\u795e\u7ecf\u7f51\u7edc\u8774\u8776\u5206\u7c7b\u8bc6\u522b\u7cfb\u7edf\u6e90\u7801"},"content":{"rendered":"

\u00a0\u7b2c\u4e00\u6b65：\u51c6\u5907\u6570\u636e<\/p>\n

6\u79cd\u8774\u8776\u6570\u636e：self.class_indict = ["\u66d9\u51e4\u8776", "\u9e9d\u51e4\u8776", "\u591a\u59ff\u9e9d\u51e4\u8776", "\u65d6\u51e4\u8776", "\u7ea2\u73e0\u51e4\u8776", "\u70ed\u6591\u51e4\u8776"]，\u603b\u5171\u6709900\u5f20\u56fe\u7247，\u6bcf\u4e2a\u6587\u4ef6\u5939\u5355\u72ec\u653e\u4e00\u79cd\u6570\u636e<\/p>\n

\"\"<\/p>\n<\/p>\n

\u7b2c\u4e8c\u6b65：\u642d\u5efa\u6a21\u578b<\/p>\n

\u672c\u6587\u9009\u62e9\u4e00\u4e2aVision Transformer\u7f51\u7edc，\u5176\u539f\u7406\u4ecb\u7ecd\u5982\u4e0b：<\/p>\n

Vision Transformer（ViT）\u662f\u4e00\u79cd\u57fa\u4e8eTransformer\u67b6\u6784\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b，\u7528\u4e8e\u56fe\u50cf\u8bc6\u522b\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u3002\u4e0e\u4f20\u7edf\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc（CNN）\u4e0d\u540c，ViT\u76f4\u63a5\u5c06\u56fe\u50cf\u89c6\u4e3a\u4e00\u4e2a\u5e8f\u5217\u5316\u7684\u8f93\u5165，\u5e76\u5229\u7528\u81ea\u6ce8\u610f\u529b\u673a\u5236\u6765\u5904\u7406\u56fe\u50cf\u4e2d\u7684\u50cf\u7d20\u5173\u7cfb\u3002<\/p>\n

ViT\u901a\u8fc7\u5c06\u56fe\u50cf\u5206\u6210\u4e00\u7cfb\u5217\u7684\u56fe\u5757（patches），\u5e76\u5c06\u6bcf\u4e2a\u56fe\u5757\u8f6c\u6362\u4e3a\u5411\u91cf\u8868\u793a\u4f5c\u4e3a\u8f93\u5165\u5e8f\u5217\u3002\u7136\u540e，\u8fd9\u4e9b\u5411\u91cf\u5c06\u901a\u8fc7\u591a\u5c42\u7684Transformer\u7f16\u7801\u5668\u8fdb\u884c\u5904\u7406，\u5176\u4e2d\u5305\u542b\u4e86\u81ea\u6ce8\u610f\u529b\u673a\u5236\u548c\u524d\u9988\u795e\u7ecf\u7f51\u7edc\u5c42\u3002\u8fd9\u6837\u53ef\u4ee5\u6355\u6349\u5230\u56fe\u50cf\u4e2d\u4e0d\u540c\u4f4d\u7f6e\u7684\u4e0a\u4e0b\u6587\u4f9d\u8d56\u5173\u7cfb\u3002\u6700\u540e，\u901a\u8fc7\u5bf9Transformer\u7f16\u7801\u5668\u8f93\u51fa\u8fdb\u884c\u5206\u7c7b\u6216\u56de\u5f52，\u53ef\u4ee5\u5b8c\u6210\u7279\u5b9a\u7684\u89c6\u89c9\u4efb\u52a1\u3002<\/p>\n

Vit model\u7ed3\u6784\u56fe Vit\u7684\u6a21\u578b\u7ed3\u6784\u5982\u4e0b\u56fe\u6240\u793a\u3002vit\u662f\u5c06\u56fe\u50cf\u5757\u5e94\u7528\u4e8etransformer\u3002CNN\u662f\u4ee5\u6ed1\u7a97\u7684\u601d\u60f3\u7528\u5377\u79ef\u6838\u5728\u56fe\u50cf\u4e0a\u8fdb\u884c\u5377\u79ef\u5f97\u5230\u7279\u5f81\u56fe\u3002\u4e3a\u4e86\u53ef\u4ee5\u4f7f\u56fe\u50cf\u4eff\u7167NLP\u7684\u8f93\u5165\u5e8f\u5217，\u6211\u4eec\u53ef\u4ee5\u5148\u5c06\u56fe\u50cf\u5206\u6210\u5757(patch)，\u518d\u5c06\u8fd9\u4e9b\u56fe\u50cf\u5757\u8fdb\u884c\u5e73\u94fa\u540e\u8f93\u5165\u5230\u7f51\u7edc\u4e2d(\u8fd9\u6837\u5c31\u53d8\u6210\u4e86\u56fe\u50cf\u5e8f\u5217)，\u7136\u540e\u901a\u8fc7transformer\u8fdb\u884c\u7279\u5f81\u63d0\u53d6，\u6700\u540e\u518d\u901a\u8fc7MLP\u5bf9\u8fd9\u4e9b\u7279\u5f81\u8fdb\u884c\u5206\u7c7b\u3010\u5176\u5b9e\u5c31\u53ef\u4ee5\u7406\u89e3\u4e3a\u5728\u4ee5\u5f80\u7684CNN\u5206\u7c7b\u4efb\u52a1\u4e2d，\u5c06backbone\u66ff\u6362\u4e3atransformer\u3011\u3002<\/p>\n

\"\"<\/p>\n<\/p>\n

\u7b2c\u4e09\u6b65：\u8bad\u7ec3\u4ee3\u7801<\/p>\n

1）\u635f\u5931\u51fd\u6570\u4e3a：\u4ea4\u53c9\u71b5\u635f\u5931\u51fd\u6570<\/p>\n

2）\u8bad\u7ec3\u4ee3\u7801：<\/p>\n

import os
\nimport math
\nimport argparse<\/p>\n

import torch
\nimport torch.optim as optim
\nimport torch.optim.lr_scheduler as lr_scheduler
\nfrom torch.utils.tensorboard import SummaryWriter
\nfrom torchvision import transforms<\/p>\n

from my_dataset import MyDataSet
\nfrom vit_model import vit_base_patch16_224_in21k as create_model
\nfrom utils import read_split_data, train_one_epoch, evaluate<\/p>\n

def main(args):
\n device = torch.device(args.device if torch.cuda.is_available() else "cpu")<\/p>\n

if os.path.exists(".\/weights") is False:
\n os.makedirs(".\/weights")<\/p>\n

tb_writer = SummaryWriter()<\/p>\n

train_images_path, train_images_label, val_images_path, val_images_label = read_split_data(args.data_path)<\/p>\n

data_transform = {
\n "train": transforms.Compose([transforms.RandomResizedCrop(224),
\n transforms.RandomHorizontalFlip(),
\n transforms.ToTensor(),
\n transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
\n "val": transforms.Compose([transforms.Resize(256),
\n transforms.CenterCrop(224),
\n transforms.ToTensor(),
\n transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])}<\/p>\n

# \u5b9e\u4f8b\u5316\u8bad\u7ec3\u6570\u636e\u96c6
\n train_dataset = MyDataSet(images_path=train_images_path,
\n images_class=train_images_label,
\n transform=data_transform["train"])<\/p>\n

# \u5b9e\u4f8b\u5316\u9a8c\u8bc1\u6570\u636e\u96c6
\n val_dataset = MyDataSet(images_path=val_images_path,
\n images_class=val_images_label,
\n transform=data_transform["val"])<\/p>\n

batch_size = args.batch_size
\n nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers
\n print('Using {} dataloader workers every process'.format(nw))
\n train_loader = torch.utils.data.DataLoader(train_dataset,
\n batch_size=batch_size,
\n shuffle=True,
\n pin_memory=True,
\n num_workers=nw,
\n collate_fn=train_dataset.collate_fn)<\/p>\n

val_loader = torch.utils.data.DataLoader(val_dataset,
\n batch_size=batch_size,
\n shuffle=False,
\n pin_memory=True,
\n num_workers=nw,
\n collate_fn=val_dataset.collate_fn)<\/p>\n

model = create_model(num_classes=args.num_classes, has_logits=False).to(device)<\/p>\n

if args.weights != "":
\n assert os.path.exists(args.weights), "weights file: '{}' not exist.".format(args.weights)
\n weights_dict = torch.load(args.weights, map_location=device)
\n # \u5220\u9664\u4e0d\u9700\u8981\u7684\u6743\u91cd
\n del_keys = ['head.weight', 'head.bias'] if model.has_logits \\\\
\n else ['pre_logits.fc.weight', 'pre_logits.fc.bias', 'head.weight', 'head.bias']
\n for k in del_keys:
\n del weights_dict[k]
\n print(model.load_state_dict(weights_dict, strict=False))<\/p>\n

if args.freeze_layers:
\n for name, para in model.named_parameters():
\n # \u9664head, pre_logits\u5916，\u5176\u4ed6\u6743\u91cd\u5168\u90e8\u51bb\u7ed3
\n if "head" not in name and "pre_logits" not in name:
\n para.requires_grad_(False)
\n else:
\n print("training {}".format(name))<\/p>\n

pg = [p for p in model.parameters() if p.requires_grad]
\n optimizer = optim.SGD(pg, lr=args.lr, momentum=0.9, weight_decay=5E-5)
\n # Scheduler https:\/\/arxiv.org\/pdf\/1812.01187.pdf
\n lf = lambda x: ((1 + math.cos(x * math.pi \/ args.epochs)) \/ 2) * (1 – args.lrf) + args.lrf # cosine
\n scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)<\/p>\n

for epoch in range(args.epochs):
\n # train
\n train_loss, train_acc = train_one_epoch(model=model,
\n optimizer=optimizer,
\n data_loader=train_loader,
\n device=device,
\n epoch=epoch)<\/p>\n

scheduler.step()<\/p>\n

# validate
\n val_loss, val_acc = evaluate(model=model,
\n data_loader=val_loader,
\n device=device,
\n epoch=epoch)<\/p>\n

tags = ["train_loss", "train_acc", "val_loss", "val_acc", "learning_rate"]
\n tb_writer.add_scalar(tags[0], train_loss, epoch)
\n tb_writer.add_scalar(tags[1], train_acc, epoch)
\n tb_writer.add_scalar(tags[2], val_loss, epoch)
\n tb_writer.add_scalar(tags[3], val_acc, epoch)
\n tb_writer.add_scalar(tags[4], optimizer.param_groups[0]["lr"], epoch)<\/p>\n

torch.save(model.state_dict(), ".\/weights\/model-{}.pth".format(epoch))<\/p>\n

if __name__ == '__main__':
\n parser = argparse.ArgumentParser()
\n parser.add_argument('–num_classes', type=int, default=6)
\n parser.add_argument('–epochs', type=int, default=100)
\n parser.add_argument('–batch-size', type=int, default=4)
\n parser.add_argument('–lr', type=float, default=0.001)
\n parser.add_argument('–lrf', type=float, default=0.01)<\/p>\n

# \u6570\u636e\u96c6\u6240\u5728\u6839\u76ee\u5f55
\n # https:\/\/storage.googleapis.com\/download.tensorflow.org\/example_images\/flower_photos.tgz
\n parser.add_argument('–data-path', type=str,
\n default=r"G:\\\\demo\\\\data\\\\Butterfly20")
\n parser.add_argument('–model-name', default='', help='create model name')<\/p>\n

# \u9884\u8bad\u7ec3\u6743\u91cd\u8def\u5f84，\u5982\u679c\u4e0d\u60f3\u8f7d\u5165\u5c31\u8bbe\u7f6e\u4e3a\u7a7a\u5b57\u7b26
\n parser.add_argument('–weights', type=str, default='.\/vit_base_patch16_224_in21k.pth',
\n help='initial weights path')
\n # \u662f\u5426\u51bb\u7ed3\u6743\u91cd
\n parser.add_argument('–freeze-layers', type=bool, default=True)
\n parser.add_argument('–device', default='cuda:0', help='device id (i.e. 0 or 0,1 or cpu)')<\/p>\n

opt = parser.parse_args()<\/p>\n

main(opt)<\/p>\n

\u7b2c\u56db\u6b65：\u7edf\u8ba1\u6b63\u786e\u7387<\/p>\n

\"\"<\/p>\n<\/p>\n

\u7b2c\u4e94\u6b65：\u642d\u5efaGUI\u754c\u9762<\/p>\n

\u6f14\u793a\u89c6\u9891：\u57fa\u4e8ePytorch\u6846\u67b6\u7684\u6df1\u5ea6\u5b66\u4e60Vision Transformer\u795e\u7ecf\u7f51\u7edc\u8774\u8776\u5206\u7c7b\u8bc6\u522b\u7cfb\u7edf\u6e90\u7801_\u54d4\u54e9\u54d4\u54e9_bilibili<\/span><\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n<\/p>\n

\u7b2c\u516d\u6b65：\u6574\u4e2a\u5de5\u7a0b\u7684\u5185\u5bb9<\/p>\n

\u6709\u8bad\u7ec3\u4ee3\u7801\u548c\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u8fc7\u7a0b，\u63d0\u4f9b\u6570\u636e，\u63d0\u4f9bGUI\u754c\u9762\u4ee3\u7801<\/p>\n

\"\"<\/p>\n<\/p>\n

\u9879\u76ee\u5b8c\u6574\u6587\u4ef6\u4e0b\u8f7d\u8bf7\u89c1\u6f14\u793a\u4e0e\u4ecb\u7ecd\u89c6\u9891\u7684\u7b80\u4ecb\u5904\u7ed9\u51fa：\u27b7\u27b7\u27b7<\/p>\n

https:\/\/www.bilibili.com\/video\/BV1WezAYRE1f\/<\/p>\n

\"\"<\/p>\n","protected":false},"excerpt":{"rendered":"

\u7b2c\u4e00\u6b65：\u51c6\u5907\u6570\u636e
\n6\u79cd\u8774\u8776\u6570\u636e：self.class_indict [\\”\u66d9\u51e4\u8776\\”, \\”\u9e9d\u51e4\u8776\\”, \\”\u591a\u59ff\u9e9d\u51e4\u8776\\”, \\”\u65d6\u51e4\u8776\\”, \\”\u7ea2\u73e0\u51e4\u8776\\”, \\”\u70ed\u6591\u51e4\u8776\\”]，\u603b\u5171\u6709900\u5f20\u56fe\u7247，\u6bcf\u4e2a\u6587\u4ef6\u5939\u5355\u72ec\u653e\u4e00\u79cd\u6570\u636e \u7b2c\u4e8c\u6b65：\u642d\u5efa\u6a21\u578b
\n\u672c\u6587\u9009\u62e9\u4e00\u4e2aVision Transformer\u7f51\u7edc，\u5176\u539f\u7406\u4ecb\u7ecd\u5982\u4e0b：
\nVision Transformer（ViT）\u662f<\/p>\n","protected":false},"author":2,"featured_media":61281,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[152,841,86],"topic":[],"class_list":["post-61288","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-pytorch","tag-transformer","tag-86"],"yoast_head":"\n\u57fa\u4e8ePytorch\u6846\u67b6\u7684\u6df1\u5ea6\u5b66\u4e60Vision Transformer\u795e\u7ecf\u7f51\u7edc\u8774\u8776\u5206\u7c7b\u8bc6\u522b\u7cfb\u7edf\u6e90\u7801 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/61288.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u57fa\u4e8ePytorch\u6846\u67b6\u7684\u6df1\u5ea6\u5b66\u4e60Vision Transformer\u795e\u7ecf\u7f51\u7edc\u8774\u8776\u5206\u7c7b\u8bc6\u522b\u7cfb\u7edf\u6e90\u7801 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"\u7b2c\u4e00\u6b65：\u51c6\u5907\u6570\u636e 6\u79cd\u8774\u8776\u6570\u636e：self.class_indict ["\u66d9\u51e4\u8776", "\u9e9d\u51e4\u8776", "\u591a\u59ff\u9e9d\u51e4\u8776", "\u65d6\u51e4\u8776", "\u7ea2\u73e0\u51e4\u8776", "\u70ed\u6591\u51e4\u8776"]，\u603b\u5171\u6709900\u5f20\u56fe\u7247，\u6bcf\u4e2a\u6587\u4ef6\u5939\u5355\u72ec\u653e\u4e00\u79cd\u6570\u636e \u7b2c\u4e8c\u6b65：\u642d\u5efa\u6a21\u578b \u672c\u6587\u9009\u62e9\u4e00\u4e2aVision Transformer\u7f51\u7edc，\u5176\u539f\u7406\u4ecb\u7ecd\u5982\u4e0b： Vision Transformer（ViT）\u662f\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wsisp.com\/helps\/61288.html\" \/>\n<meta property=\"og:site_name\" content=\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"article:published_time\" content=\"2026-01-17T03:05:39+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260117030535-696afc7f36105.png\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u4f5c\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 \u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/61288.html\",\"url\":\"https:\/\/www.wsisp.com\/helps\/61288.html\",\"name\":\"\u57fa\u4e8ePytorch\u6846\u67b6\u7684\u6df1\u5ea6\u5b66\u4e60Vision Transformer\u795e\u7ecf\u7f51\u7edc\u8774\u8776\u5206\u7c7b\u8bc6\u522b\u7cfb\u7edf\u6e90\u7801 - 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