标签:img nn 街景 self torch train import 视觉 识别
import os, sys, glob, shutil, json import cv2 from PIL import Image import numpy as np import torch from torch.utils.data.dataset import Dataset import torchvision.transforms as transforms class SVHNDataset(Dataset): def __init__(self, img_path, img_label, transform=None): self.img_path = img_path self.img_label = img_label if transform is not None: self.transform = transform else: self.transform = None def __getitem__(self, index): img = Image.open(self.img_path[index]).convert('RGB') if self.transform is not None: img = self.transform(img) # 原始SVHN中类别10为数字0 lbl = np.array(self.img_label[index], dtype=np.int) lbl = list(lbl) + (5 - len(lbl)) * [10] return img, torch.from_numpy(np.array(lbl[:5])) def __len__(self): return len(self.img_path) train_path = glob.glob('./mchar_train/*.png') train_path.sort() train_json = json.load(open('train.json')) train_label = [train_json[x]['label'] for x in train_json] train_loader = torch.utils.data.DataLoader( SVHNDataset(train_path, train_label, transforms.Compose([ transforms.Resize((64, 128)), transforms.ColorJitter(0.3, 0.3, 0.2), transforms.RandomRotation(5), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ])), batch_size=10, # 每批样本个数 shuffle=False, # 是否打乱顺序 num_workers=0, # 读取的线程个数 ) torch.manual_seed(0) torch.backends.cudnn.deterministic = False torch.backends.cudnn.benchmark = True import torchvision.models as models import torchvision.transforms as transforms import torchvision.datasets as datasets import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.autograd import Variable from torch.utils.data.dataset import Dataset # 定义模型 class SVHN_Model1(nn.Module): def __init__(self): super(SVHN_Model1, self).__init__() # CNN提取特征模块 self.cnn = nn.Sequential( nn.Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2)), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2)), nn.ReLU(), nn.MaxPool2d(2), ) # self.fc1 = nn.Linear(32*3*7, 11) self.fc2 = nn.Linear(32*3*7, 11) self.fc3 = nn.Linear(32*3*7, 11) self.fc4 = nn.Linear(32*3*7, 11) self.fc5 = nn.Linear(32*3*7, 11) self.fc6 = nn.Linear(32*3*7, 11) def forward(self, img): feat = self.cnn(img) feat = feat.view(feat.shape[0], -1) c1 = self.fc1(feat) c2 = self.fc2(feat) c3 = self.fc3(feat) c4 = self.fc4(feat) c5 = self.fc5(feat) c6 = self.fc6(feat) return c1, c2, c3, c4, c5, c6 model = SVHN_Model1() device=torch.device("cuda")#设置GPU # 损失函数 criterion = nn.CrossEntropyLoss() criterion = criterion.to(device)#损失函数在GPU上运行 model= model.to(device)#模型放在GPU上 # 优化器 optimizer = torch.optim.Adam(model.parameters(), 0.005) optimizer loss_plot, c0_plot = [], [] # 迭代10个Epoch for epoch in range(10): for data in train_loader: img,target=data img=img.to(device)#输入图片放在GPU上 target=target.to(device)#输入的标签放在GPU上 c0, c1, c2, c3, c4, c5 = model(img) loss = criterion(c0, target[:, 0].long()) + \ criterion(c1, target[:, 1].long()) + \ criterion(c2, target[:, 2].long()) + \ criterion(c3, target[:, 3].long()) + \ criterion(c4, target[:, 4].long()) loss /= 5 optimizer.zero_grad() loss.backward() optimizer.step() loss_plot.append(loss.item()) c0_plot.append((c0.argmax(1) == target[:, 0]).sum().item()*1.0 / c0.shape[0]) print(epoch) torch.save(model,"moxing")
标签:img,nn,街景,self,torch,train,import,视觉,识别 来源: https://www.cnblogs.com/zhaoyids/p/15835475.html
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