标签:nn img 代码 torch 复现 DIR import self AlexNet
train.py
import torch import torch.nn as nn import torch.nn.functional as F import os from tensorboardX import SummaryWriter import torchvision.datasets as Datasets import torchvision.transforms as transforms import torch.utils.data.dataloader as Dataloader from tqdm import tqdm device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') NUM_EPOCHS = 10 BATCH_SIZE = 4 MOMENTUM = 0.9 LR_DECAY = 0.0005 LR_INIT = 0.01 IMAGE_DIM = 227 NUM_CLASSES = 10 DEVICE_DIS = [0, 1, 2, 3] #数据集和输出路径 INPUT_ROOT_DIR = 'alexnet_data_in' TRAIN_IMG_DIR = INPUT_ROOT_DIR + '/imagenet' OUTPUT_DIR = 'alexnet_data_out' LOG_DIR = OUTPUT_DIR + '/tblogs' CHECKPOINT_DIR = OUTPUT_DIR + '/models' os.makedirs(CHECKPOINT_DIR, exist_ok = True) class AlexNet(nn.Module): def __init__(self): super(AlexNet, self).__init__() self.conv1 = nn.Conv2d(3, 96, 11, stride = 4) self.conv2 = nn.Conv2d(96, 256, 5, padding = 2) self.conv3 = nn.Conv2d(256, 384, 2, padding = 1) self.conv4 = nn.Conv2d(384, 384, 3, padding = 1) self.conv5 = nn.Conv2d(384, 256, 3, padding = 1) self.fc1 = nn.Linear((256 * 6 * 6), 4096) self.fc2 = nn.Linear(4096, 4096) self.fc3 = nn.Linear(4096, NUM_CLASSES) def forward(self, x): x = F.max_pool2d(F.local_response_norm(F.relu(self.conv1(x)), size = 5, alpha = 0.0001, beta = 0.75, k = 2), kernel_size = 3, stride = 2) x = F.max_pool2d(F.local_response_norm(F.relu(self.conv2(x)), size = 5, alpha = 0.0001, beta = 0.75, k = 2), kernel_size = 3, stride = 2) x = F.relu(self.conv3(x)) x = F.relu(self.conv4(x)) x = F.max_pool2d(F.relu(self.conv5(x)), kernel_size = 3, stride = 2) x = x.view(x.size()[0], -1) x = F.relu(self.fc1(x)) x = F.dropout(x, p = 0.5, training = True, inplace = True) x = F.relu(self.fc2(x)) x = F.dropout(x, p = 0.5, training = True, inplace = True) x = self.fc3(x) return x if __name__ == '__main__': tbwrite = SummaryWriter(log_dir = LOG_DIR) print('tensorboardX summary write created') alexnet = AlexNet().to(device) print(alexnet) print('alexnet created') dataset = Datasets.ImageFolder(TRAIN_IMG_DIR, transform = transforms.Compose([ transforms.CenterCrop(IMAGE_DIM), #将读进来的图片转换为tensor transforms.ToTensor(), #对tensor进行归一化 transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])) print('dataset created') dataloader = Dataloader.DataLoader( dataset, shuffle = True, num_workers = 8, drop_last = True, batch_size = BATCH_SIZE) print('dataloader created') optimizer = torch.optim.Adam(alexnet.parameters(), lr = 0.0001) #每30轮将lr * 0.1 lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 30, gamma = 0.1) loss = nn.CrossEntropyLoss() print('Start Training...') cnt = 0 for epoch in range(NUM_EPOCHS): true_total = 0 img_total = 0 loss_total = 0 for imgs, classes in tqdm(dataloader): cnt += 1 optimizer.zero_grad() imgs, classes = imgs.to(device), classes.to(device) print(classes) output = alexnet(imgs) loss_value = loss(output, classes) loss_value.backward() optimizer.step() preds = torch.max(output, 1)[1] true_total += torch.sum(preds == classes) img_total += len(classes) loss_total += loss_value.item() loss_k = float(loss_total) / float(cnt) accuracy = float(true_total) / float(img_total) print('epoch: {} \t Loss: {:.4f} \t Acc: {:.2f}'.format(epoch + 1, loss_k, accuracy)) tbwrite.add_scalar('loss', loss_k, epoch + 1) tbwrite.add_scalar('accuracy', accuracy, epoch + 1) #log information and add to tensorboard lr_scheduler.step() if (epoch + 1) % 10 == 0: checkpoint_path = os.path.join(CHECKPOINT_DIR, 'alexnet_staes_e{}.pkl'.format(epoch + 1)) state = { 'epoch' : epoch, 'optimizer' : optimizer.state_dict(), 'model' : alexnet.state_dict(), } torch.save(state, checkpoint_path)
predict.py
import torch import torch.nn import torch.nn.functional as F import numpy as np import torchvision.datasets as Dataset import torchvision.transforms as transforms import torch.utils.data.dataloader as Dataloader from PIL import Image import matplotlib.pyplot as plt import os from train import AlexNet import cv2 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') IMG_DIM = 227 INPUT_ROOT_DIR = 'alexnet_data_in' OUTPUT_ROOT_DIR = 'alexnet_data_out' IMG_DIR = INPUT_ROOT_DIR + '/test_imagenet' checkpoint_dir = os.path.join(OUTPUT_ROOT_DIR, 'models', 'alexnet_staes_e10.pkl') checkpoint = torch.load(checkpoint_dir) model = AlexNet() model.to(device) model.load_state_dict(checkpoint['model']) def open_list(dir): for home, files, dirs in os.walk(dir): return dirs def show(name, img): cv2.imshow(name, img) cv2.waitKey(0) cv2.destroyAllWindows() transform = transforms.Compose([ transforms.CenterCrop(IMG_DIM), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])] ) img_list = open_list(IMG_DIR) for img_name in img_list: img_path = os.path.join(IMG_DIR, img_name) img = Image.open(img_path) img = transform(img) img.to(device) #注意要reshape一下,module是默认有批次的 img = img.reshape([-1, 3, 227, 227]) preds = model(img) preds = torch.max(preds, 1)[1] print(preds.item()) img = cv2.imread(img_path) show('{}'.format(preds.item()), img)
标签:nn,img,代码,torch,复现,DIR,import,self,AlexNet 来源: https://www.cnblogs.com/WTSRUVF/p/15325507.html
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