数据预处理部分：
- 数据增强：torchvision中transforms模块自带功能，比较实用
- 数据预处理：torchvision中transforms也帮我们实现好了，直接调用即可
- DataLoader模块直接读取batch数据
网络模块设置：
- 加载预训练模型，torchvision中有很多经典网络架构，调用起来十分方便，并且可以用人家训练好的权重参数来继续训练，也就是所谓的迁移学习
- 需要注意的是别人训练好的任务跟咱们的可不是完全一样，需要把最后的head层改一改，一般也就是最后的全连接层，改成咱们自己的任务
- 训练时可以全部重头训练，也可以只训练最后咱们任务的层，因为前几层都是做特征提取的，本质任务目标是一致的
网络模型保存与测试
- 模型保存的时候可以带有选择性，例如在验证集中如果当前效果好则保存
- 读取模型进行实际测试
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import os import matplotlib.pyplot as plt %matplotlib inline import numpy as np import torch from torch import nn import torch.optim as optim import torchvision #pip install torchvision from torchvision import transforms, models, datasets #https://pytorch.org/docs/stable/torchvision/index.html import imageio import time import warnings warnings.filterwarnings("ignore") import random import sys import copy import json from PIL import Image数据读取与预处理操作
data_dir = './flower_data/' train_dir = data_dir + '/train' valid_dir = data_dir + '/valid'制作好数据源：
- data_transforms中指定了所有图像预处理操作
- ImageFolder假设所有的文件按文件夹保存好，每个文件夹下面存贮同一类别的图片，文件夹的名字为分类的名字
data_transforms = { 'train': transforms.Compose([ transforms.Resize([96, 96]), transforms.RandomRotation(45),#随机旋转，-45到45度之间随机选 transforms.CenterCrop(64),#从中心开始裁剪 transforms.RandomHorizontalFlip(p=0.5),#随机水平翻转 选择一个概率概率 transforms.RandomVerticalFlip(p=0.5),#随机垂直翻转 transforms.ColorJitter(brightness=0.2, contrast=0.1, saturation=0.1, hue=0.1),#参数1为亮度，参数2为对比度，参数3为饱和度，参数4为色相 transforms.RandomGrayscale(p=0.025),#概率转换成灰度率，3通道就是R=G=B transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])#均值，标准差 ]), 'valid': transforms.Compose([ transforms.Resize([64, 64]), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), }batch_size = 128 image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'valid']} dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True) for x in ['train', 'valid']} dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'valid']} class_names = image_datasets['train'].classesimage_datasets{'train': Dataset ImageFolder Number of datapoints: 6552 Root location: ./flower_data/train StandardTransform Transform: Compose( Resize(size=[96, 96], interpolation=bilinear, max_size=None, antialias=None) RandomRotation(degrees=[-45.0, 45.0], interpolation=nearest, expand=False, fill=0) CenterCrop(size=(64, 64)) RandomHorizontalFlip(p=0.5) RandomVerticalFlip(p=0.5) ColorJitter(brightness=[0.8, 1.2], contrast=[0.9, 1.1], saturation=[0.9, 1.1], hue=[-0.1, 0.1]) RandomGrayscale(p=0.025) ToTensor() Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ), 'valid': Dataset ImageFolder Number of datapoints: 818 Root location: ./flower_data/valid StandardTransform Transform: Compose( Resize(size=[64, 64], interpolation=bilinear, max_size=None, antialias=None) ToTensor() Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) )}dataloaders{'train': <torch.utils.data.dataloader.DataLoader at 0x1e4c50b9400>, 'valid': <torch.utils.data.dataloader.DataLoader at 0x1e4c51ad128>}dataset_sizes{'train': 6552, 'valid': 818} -
读取标签对应的实际名字
with open('cat_to_name.json', 'r') as f: cat_to_name = json.load(f)cat_to_name{'1': 'pink primrose', '10': 'globe thistle', '100': 'blanket flower', '101': 'trumpet creeper', '102': 'blackberry lily', '11': 'snapdragon', '12': "colt's foot", '13': 'king protea', '14': 'spear thistle', '15': 'yellow iris', '16': 'globe-flower', '17': 'purple coneflower', '18': 'peruvian lily', '19': 'balloon flower', '2': 'hard-leaved pocket orchid', '20': 'giant white arum lily', '21': 'fire lily', '22': 'pincushion flower', '23': 'fritillary', '24': 'red ginger', '25': 'grape hyacinth', '26': 'corn poppy', '27': 'prince of wales feathers', '28': 'stemless gentian', '29': 'artichoke', '3': 'canterbury bells', '30': 'sweet william', '31': 'carnation', '32': 'garden phlox', '33': 'love in the mist', '34': 'mexican aster', '35': 'alpine sea holly', '36': 'ruby-lipped cattleya', '37': 'cape flower', '38': 'great masterwort', '39': 'siam tulip', '4': 'sweet pea', '40': 'lenten rose', '41': 'barbeton daisy', '42': 'daffodil', '43': 'sword lily', '44': 'poinsettia', '45': 'bolero deep blue', '46': 'wallflower', '47': 'marigold', '48': 'buttercup', '49': 'oxeye daisy', '5': 'english marigold', '50': 'common dandelion', '51': 'petunia', '52': 'wild pansy', '53': 'primula', '54': 'sunflower', '55': 'pelargonium', '56': 'bishop of llandaff', '57': 'gaura', '58': 'geranium', '59': 'orange dahlia', '6': 'tiger lily', '60': 'pink-yellow dahlia', '61': 'cautleya spicata', '62': 'japanese anemone', '63': 'black-eyed susan', '64': 'silverbush', '65': 'californian poppy', '66': 'osteospermum', '67': 'spring crocus', '68': 'bearded iris', '69': 'windflower', '7': 'moon orchid', '70': 'tree poppy', '71': 'gazania', '72': 'azalea', '73': 'water lily', '74': 'rose', '75': 'thorn apple', '76': 'morning glory', '77': 'passion flower', '78': 'lotus lotus', '79': 'toad lily', '8': 'bird of paradise', '80': 'anthurium', '81': 'frangipani', '82': 'clematis', '83': 'hibiscus', '84': 'columbine', '85': 'desert-rose', '86': 'tree mallow', '87': 'magnolia', '88': 'cyclamen', '89': 'watercress', '9': 'monkshood', '90': 'canna lily', '91': 'hippeastrum', '92': 'bee balm', '93': 'ball moss', '94': 'foxglove', '95': 'bougainvillea', '96': 'camellia', '97': 'mallow', '98': 'mexican petunia', '99': 'bromelia'} -
加载models中提供的模型，并且直接用训练的好权重当做初始化参数
- 第一次执行需要下载，可能会比较慢，我会提供给大家一份下载好的，可以直接放到相应路径
model_name = 'resnet' #可选的比较多 ['resnet', 'alexnet', 'vgg', 'squeezenet', 'densenet', 'inception'] #是否用人家训练好的特征来做 feature_extract = True #都用人家特征，咱先不更新# 是否用GPU训练 train_on_gpu = torch.cuda.is_available() if not train_on_gpu: print('CUDA is not available. Training on CPU ...') else: print('CUDA is available! Training on GPU ...') device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")CUDA is not available. Training on CPU ...
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模型参数要不要更新
- 有时候用人家模型，就一直用了，更不更新咱们可以自己定
def set_parameter_requires_grad(model, feature_extracting): if feature_extracting: for param in model.parameters(): param.requires_grad = Falsemodel_ft = models.resnet18()#18层的能快点，条件好点的也可以选152 model_ftResNet( (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (layer1): Sequential( (0): BasicBlock( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (1): BasicBlock( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (layer2): Sequential( (0): BasicBlock( (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (layer3): Sequential( (0): BasicBlock( (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (layer4): Sequential( (0): BasicBlock( (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (avgpool): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Linear(in_features=512, out_features=1000, bias=True) -
把模型输出层改成自己的
def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True): model_ft = models.resnet18(pretrained=use_pretrained) set_parameter_requires_grad(model_ft, feature_extract) num_ftrs = model_ft.fc.in_features model_ft.fc = nn.Linear(num_ftrs, 102)#类别数自己根据自己任务来 input_size = 64#输入大小根据自己配置来 return model_ft, input_size设置哪些层需要训练
model_ft, input_size = initialize_model(model_name, 102, feature_extract, use_pretrained=True) #GPU还是CPU计算 model_ft = model_ft.to(device) # 模型保存，名字自己起 filename='checkpoint.pth' # 是否训练所有层 params_to_update = model_ft.parameters() print("Params to learn:") if feature_extract: params_to_update = [] for name,param in model_ft.named_parameters(): if param.requires_grad == True: params_to_update.append(param) print("\t",name) else: for name,param in model_ft.named_parameters(): if param.requires_grad == True: print("\t",name)Params to learn: fc.weight fc.bias
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model_ftResNet( (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (layer1): Sequential( (0): BasicBlock( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (1): BasicBlock( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (layer2): Sequential( (0): BasicBlock( (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (layer3): Sequential( (0): BasicBlock( (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (layer4): Sequential( (0): BasicBlock( (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (avgpool): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Linear(in_features=512, out_features=102, bias=True) ) -
优化器设置
# 优化器设置 optimizer_ft = optim.Adam(params_to_update, lr=1e-2)#要训练啥参数，你来定 scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=10, gamma=0.1)#学习率每7个epoch衰减成原来的1/10 criterion = nn.CrossEntropyLoss()训练模块
def train_model(model, dataloaders, criterion, optimizer, num_epochs=25,filename='best.pt'): #咱们要算时间的 since = time.time() #也要记录最好的那一次 best_acc = 0 #模型也得放到你的CPU或者GPU model.to(device) #训练过程中打印一堆损失和指标 val_acc_history = [] train_acc_history = [] train_losses = [] valid_losses = [] #学习率 LRs = [optimizer.param_groups[0]['lr']] #最好的那次模型，后续会变的，先初始化 best_model_wts = copy.deepcopy(model.state_dict()) #一个个epoch来遍历 for epoch in range(num_epochs): print('Epoch {}/{}'.format(epoch, num_epochs - 1)) print('-' * 10) # 训练和验证 for phase in ['train', 'valid']: if phase == 'train': model.train() # 训练 else: model.eval() # 验证 running_loss = 0.0 running_corrects = 0 # 把数据都取个遍 for inputs, labels in dataloaders[phase]: inputs = inputs.to(device)#放到你的CPU或GPU labels = labels.to(device) # 清零 optimizer.zero_grad() # 只有训练的时候计算和更新梯度 outputs = model(inputs) loss = criterion(outputs, labels) _, preds = torch.max(outputs, 1) # 训练阶段更新权重 if phase == 'train': loss.backward() optimizer.step() # 计算损失 running_loss += loss.item() * inputs.size(0)#0表示batch那个维度 running_corrects += torch.sum(preds == labels.data)#预测结果最大的和真实值是否一致 epoch_loss = running_loss / len(dataloaders[phase].dataset)#算平均 epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset) time_elapsed = time.time() - since#一个epoch我浪费了多少时间 print('Time elapsed {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60)) print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc)) # 得到最好那次的模型 if phase == 'valid' and epoch_acc > best_acc: best_acc = epoch_acc best_model_wts = copy.deepcopy(model.state_dict()) state = { 'state_dict': model.state_dict(),#字典里key就是各层的名字，值就是训练好的权重 'best_acc': best_acc, 'optimizer' : optimizer.state_dict(), } torch.save(state, filename) if phase == 'valid': val_acc_history.append(epoch_acc) valid_losses.append(epoch_loss) #scheduler.step(epoch_loss)#学习率衰减 if phase == 'train': train_acc_history.append(epoch_acc) train_losses.append(epoch_loss) print('Optimizer learning rate : {:.7f}'.format(optimizer.param_groups[0]['lr'])) LRs.append(optimizer.param_groups[0]['lr']) print() scheduler.step()#学习率衰减 time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60)) print('Best val Acc: {:4f}'.format(best_acc)) # 训练完后用最好的一次当做模型最终的结果,等着一会测试 model.load_state_dict(best_model_wts) return model, val_acc_history, train_acc_history, valid_losses, train_losses, LRs开始训练！
- 我们现在只训练了输出层
model_ft, val_acc_history, train_acc_history, valid_losses, train_losses, LRs = train_model(model_ft, dataloaders, criterion, optimizer_ft, num_epochs=20)Epoch 0/19 ---------- Time elapsed 0m 39s train Loss: 4.0874 Acc: 0.2355 Time elapsed 0m 43s valid Loss: 3.5746 Acc: 0.2531 Optimizer learning rate : 0.0100000 Epoch 1/19 ---------- Time elapsed 1m 22s train Loss: 2.8185 Acc: 0.3953 Time elapsed 1m 26s valid Loss: 3.5450 Acc: 0.3142 Optimizer learning rate : 0.0100000 Epoch 2/19 ---------- Time elapsed 2m 5s train Loss: 2.7673 Acc: 0.4174 Time elapsed 2m 9s valid Loss: 3.9110 Acc: 0.2653 Optimizer learning rate : 0.0100000 Epoch 3/19 ---------- Time elapsed 2m 48s train Loss: 2.7962 Acc: 0.4255 Time elapsed 2m 52s valid Loss: 3.6922 Acc: 0.3142 Optimizer learning rate : 0.0100000 Epoch 4/19 ---------- Time elapsed 3m 32s train Loss: 2.7453 Acc: 0.4428 Time elapsed 3m 36s valid Loss: 3.9310 Acc: 0.3044 Optimizer learning rate : 0.0100000 Epoch 5/19 ---------- Time elapsed 4m 14s train Loss: 2.2935 Acc: 0.5043 Time elapsed 4m 18s valid Loss: 3.3299 Acc: 0.3435 Optimizer learning rate : 0.0010000 Epoch 6/19 ---------- Time elapsed 4m 57s train Loss: 2.0654 Acc: 0.5258 Time elapsed 5m 1s valid Loss: 3.2608 Acc: 0.3411 Optimizer learning rate : 0.0010000 Epoch 7/19 ---------- Time elapsed 5m 40s train Loss: 1.9603 Acc: 0.5369 Time elapsed 5m 44s valid Loss: 3.2618 Acc: 0.3472 Optimizer learning rate : 0.0010000 Epoch 8/19 ---------- Time elapsed 6m 23s train Loss: 1.9216 Acc: 0.5401 Time elapsed 6m 27s valid Loss: 3.1651 Acc: 0.3386 Optimizer learning rate : 0.0010000 Epoch 9/19 ---------- Time elapsed 7m 5s train Loss: 1.9203 Acc: 0.5458 Time elapsed 7m 9s valid Loss: 3.0449 Acc: 0.3680 Optimizer learning rate : 0.0010000 Epoch 10/19 ---------- Time elapsed 7m 48s train Loss: 1.8366 Acc: 0.5553 Time elapsed 7m 52s valid Loss: 3.0722 Acc: 0.3545 Optimizer learning rate : 0.0001000 Epoch 11/19 ---------- Time elapsed 8m 31s train Loss: 1.8324 Acc: 0.5546 Time elapsed 8m 35s valid Loss: 3.0115 Acc: 0.3643 Optimizer learning rate : 0.0001000 Epoch 12/19 ---------- Time elapsed 9m 13s train Loss: 1.8054 Acc: 0.5553 Time elapsed 9m 17s valid Loss: 3.0688 Acc: 0.3619 Optimizer learning rate : 0.0001000 Epoch 13/19 ---------- Time elapsed 9m 56s train Loss: 1.8436 Acc: 0.5534 Time elapsed 10m 0s valid Loss: 3.0100 Acc: 0.3631 Optimizer learning rate : 0.0001000 Epoch 14/19 ---------- Time elapsed 10m 39s train Loss: 1.7417 Acc: 0.5614 Time elapsed 10m 43s valid Loss: 3.0129 Acc: 0.3655 Optimizer learning rate : 0.0001000 Epoch 15/19 ---------- Time elapsed 11m 22s train Loss: 1.7610 Acc: 0.5672 Time elapsed 11m 26s valid Loss: 3.0220 Acc: 0.3606 Optimizer learning rate : 0.0000100 Epoch 16/19 ---------- Time elapsed 12m 6s train Loss: 1.7788 Acc: 0.5676 Time elapsed 12m 10s valid Loss: 3.0104 Acc: 0.3557 Optimizer learning rate : 0.0000100 Epoch 17/19 ---------- Time elapsed 12m 49s train Loss: 1.8033 Acc: 0.5638 Time elapsed 12m 53s valid Loss: 3.0428 Acc: 0.3606 Optimizer learning rate : 0.0000100 Epoch 18/19 ---------- Time elapsed 13m 33s train Loss: 1.8294 Acc: 0.5568 Time elapsed 13m 37s valid Loss: 3.0307 Acc: 0.3509 Optimizer learning rate : 0.0000100 Epoch 19/19 ---------- Time elapsed 14m 16s train Loss: 1.7949 Acc: 0.5612 Time elapsed 14m 20s valid Loss: 3.0396 Acc: 0.3643 Optimizer learning rate : 0.0000100 Training complete in 14m 20s Best val Acc: 0.367971
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再继续训练所有层
for param in model_ft.parameters(): param.requires_grad = True # 再继续训练所有的参数，学习率调小一点 optimizer = optim.Adam(model_ft.parameters(), lr=1e-3) scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1) # 损失函数 criterion = nn.CrossEntropyLoss()# 加载之前训练好的权重参数 checkpoint = torch.load(filename) best_acc = checkpoint['best_acc'] model_ft.load_state_dict(checkpoint['state_dict'])model_ft, val_acc_history, train_acc_history, valid_losses, train_losses, LRs = train_model(model_ft, dataloaders, criterion, optimizer, num_epochs=10,)Epoch 0/9 ---------- Time elapsed 1m 32s train Loss: 2.2451 Acc: 0.4846 Time elapsed 1m 36s valid Loss: 2.3190 Acc: 0.4633 Optimizer learning rate : 0.0010000 Epoch 1/9 ---------- Time elapsed 2m 54s train Loss: 1.2920 Acc: 0.6505 Time elapsed 2m 58s valid Loss: 2.2263 Acc: 0.4670 Optimizer learning rate : 0.0010000 Epoch 2/9 ---------- Time elapsed 4m 15s train Loss: 1.1026 Acc: 0.6993 Time elapsed 4m 19s valid Loss: 1.8115 Acc: 0.5452 Optimizer learning rate : 0.0010000 Epoch 3/9 ---------- Time elapsed 5m 35s train Loss: 0.9062 Acc: 0.7515 Time elapsed 5m 39s valid Loss: 2.0045 Acc: 0.5403 Optimizer learning rate : 0.0010000 Epoch 4/9 ---------- Time elapsed 6m 56s train Loss: 0.8392 Acc: 0.7643 Time elapsed 7m 0s valid Loss: 2.1381 Acc: 0.5171 Optimizer learning rate : 0.0010000 Epoch 5/9 ---------- Time elapsed 8m 17s train Loss: 0.7081 Acc: 0.7953 Time elapsed 8m 21s valid Loss: 2.0461 Acc: 0.5599 Optimizer learning rate : 0.0010000 Epoch 6/9 ---------- Time elapsed 9m 38s train Loss: 0.6400 Acc: 0.8147 Time elapsed 9m 42s valid Loss: 2.2603 Acc: 0.5452 Optimizer learning rate : 0.0010000 Epoch 7/9 ---------- Time elapsed 10m 59s train Loss: 0.6406 Acc: 0.8117 Time elapsed 11m 3s valid Loss: 1.4649 Acc: 0.6406 Optimizer learning rate : 0.0010000 Epoch 8/9 ---------- Time elapsed 12m 20s train Loss: 0.5686 Acc: 0.8300 Time elapsed 12m 24s valid Loss: 1.7538 Acc: 0.6100 Optimizer learning rate : 0.0010000 Epoch 9/9 ---------- Time elapsed 13m 41s train Loss: 0.5978 Acc: 0.8245 Time elapsed 13m 45s valid Loss: 1.6953 Acc: 0.6161 Optimizer learning rate : 0.0010000 Training complete in 13m 45s Best val Acc: 0.640587
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加载训练好的模型
model_ft, input_size = initialize_model(model_name, 102, feature_extract, use_pretrained=True) # GPU模式 model_ft = model_ft.to(device) # 保存文件的名字 filename='best.pt' # 加载模型 checkpoint = torch.load(filename) best_acc = checkpoint['best_acc'] model_ft.load_state_dict(checkpoint['state_dict'])测试数据预处理
- 测试数据处理方法需要跟训练时一直才可以
- crop操作的目的是保证输入的大小是一致的
- 标准化操作也是必须的，用跟训练数据相同的mean和std,但是需要注意一点训练数据是在0-1上进行标准化，所以测试数据也需要先归一化
- 最后一点，PyTorch中颜色通道是第一个维度，跟很多工具包都不一样，需要转换
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# 得到一个batch的测试数据 dataiter = iter(dataloaders['valid']) images, labels = dataiter.next() model_ft.eval() if train_on_gpu: output = model_ft(images.cuda()) else: output = model_ft(images)output表示对一个batch中每一个数据得到其属于各个类别的可能性
output.shape得到概率最大的那个
_, preds_tensor = torch.max(output, 1) preds = np.squeeze(preds_tensor.numpy()) if not train_on_gpu else np.squeeze(preds_tensor.cpu().numpy()) predsarray([ 34, 49, 43, 54, 20, 14, 49, 43, 50, 20, 19, 100, 78, 96, 96, 62, 62, 63, 32, 38, 82, 43, 88, 73, 6, 51, 43, 89, 55, 75, 55, 11, 46, 82, 48, 82, 20, 100, 48, 20, 24, 49, 76, 93, 49, 46, 90, 75, 89, 75, 76, 99, 56, 48, 77, 66, 60, 72, 89, 97, 76, 73, 17, 48, 39, 31, 19, 74, 61, 46, 93, 80, 27, 11, 91, 18, 23, 47, 29, 54, 18, 93, 1, 50, 79, 96, 39, 53, 63, 60, 49, 23, 23, 52, 99, 89, 3, 50, 64, 15, 19, 60, 19, 75, 50, 78, 82, 18, 75, 18, 82, 53, 3, 52, 60, 38, 62, 47, 21, 59, 81, 48, 89, 64, 60, 55, 100, 60], dtype=int64)展示预测结果
def im_convert(tensor): """ 展示数据""" image = tensor.to("cpu").clone().detach() image = image.numpy().squeeze() image = image.transpose(1,2,0) image = image * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406)) image = image.clip(0, 1) return imagefig=plt.figure(figsize=(20, 20)) columns =4 rows = 2 for idx in range (columns*rows): ax = fig.add_subplot(rows, columns, idx+1, xticks=[], yticks=[]) plt.imshow(im_convert(images[idx])) ax.set_title("{} ({})".format(cat_to_name[str(preds[idx])], cat_to_name[str(labels[idx].item())]), color=("green" if cat_to_name[str(preds[idx])]==cat_to_name[str(labels[idx].item())] else "red")) plt.show()
