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| # -*- coding: utf-8 -*-
import os
import time
import copy
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from torchvision.datasets import ImageFolder
import torchvision
import models.zfnet as zfnet
import utils.util as util
data_root_dir = '../data/train_val/'
model_dir = '../data/models/'
def load_data(root_dir):
transform = transforms.Compose([
transforms.Resize((227, 227)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
data_loaders = {}
dataset_sizes = {}
for phase in ['train', 'val']:
phase_dir = os.path.join(root_dir, phase)
data_set = ImageFolder(phase_dir, transform=transform)
data_loader = DataLoader(data_set, batch_size=128, shuffle=True, num_workers=8)
data_loaders[phase] = data_loader
dataset_sizes[phase] = len(data_set)
return data_loaders, dataset_sizes
def train_model(model, criterion, optimizer, scheduler, dataset_sizes, dataloaders, num_epochs=25, device=None):
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
loss_dict = {'train': [], 'val': []}
acc_dict = {'train': [], 'val': []}
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
if phase == 'train':
scheduler.step()
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
loss_dict[phase].append(epoch_loss)
acc_dict[phase].append(epoch_acc)
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
print()
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))
# load best model weights
model.load_state_dict(best_model_wts)
return model, loss_dict, acc_dict
if __name__ == '__main__':
data_loaders, data_sizes = load_data(data_root_dir)
print(data_sizes)
res_loss = dict()
res_acc = dict()
for name in ['alexnet', 'zfnet']:
if name == 'alexnet':
model = torchvision.models.AlexNet(num_classes=20)
else:
model = zfnet.ZFNet(num_classes=20)
device = util.get_device()
model = model.to(device)
criterion = nn.CrossEntropyLoss()
# optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.9)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.1)
best_model, loss_dict, acc_dict = train_model(model, criterion, optimizer, lr_scheduler, data_sizes,
data_loaders, num_epochs=50,
device=device)
# 保存最好的模型参数
util.check_dir(model_dir)
torch.save(best_model.state_dict(), os.path.join(model_dir, '%s.pth' % name))
res_loss[name] = loss_dict
res_acc[name] = acc_dict
print('train %s done' % name)
print()
util.save_png('loss', res_loss)
util.save_png('acc', res_acc)
|
