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"""
@date: 2020/3/26 下午2:33
@file: classifier.py
@author: zj
@description:
"""
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.alexnet_spp as alexnet_spp
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, data_loaders, 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)
for phase in ['train', 'val']:
if phase == 'train':
model.train()
else:
model.eval()
running_loss = 0.0
running_corrects = 0
for inputs, labels in data_loaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
if phase == 'train':
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
if phase == 'train':
scheduler.step()
dataset_size = dataset_sizes[phase]
epoch_loss = running_loss / dataset_size
epoch_acc = running_corrects.double() / dataset_size
loss_dict[phase].append(epoch_loss)
acc_dict[phase].append(epoch_acc)
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
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))
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_spp', 'alexnet']:
if name == 'alexnet_spp':
model = alexnet_spp.AlexNet_SPP(num_classes=20)
else:
model = torchvision.models.AlexNet(num_classes=20)
device = util.get_device()
model = model.to(device)
criterion = nn.CrossEntropyLoss()
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)
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)
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