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Train.py
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Train.py
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from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
from torchvision import datasets
import os
import argparse
import numpy as np
import shuffleNetV2
import matplotlib.pyplot as plt
import utills
import sys
device = 'cuda' if torch.cuda.is_available() else 'cpu'
best_acc = 0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
# Data
print('==> Preparing data..')
data_transforms = {
'train': transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.5, 0.5, 0.5])
]),
'val': transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.5, 0.5, 0.5])
]),
}
data_dir = 'data_crop'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=12,
shuffle=True, num_workers=0)
for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
classes = image_datasets['train'].classes
# Training
def train(epoch):
print('\nEpoch: %d' % epoch)
model_conv.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(dataloaders['train']):
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
outputs = model_conv(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
utills.progress_bar(batch_idx, len(dataloaders['train']), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (train_loss/(batch_idx+1), 100.*correct/total, correct, total))
def test(epoch):
global best_acc
model_conv.eval()
test_loss = 0
correct = 0
total = 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(dataloaders['val']):
inputs, targets = inputs.to(device), targets.to(device)
outputs = model_conv(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
print('Epoch : %d | val_Loss: %.3f | val_ Acc: %.3f%% (%d/%d)' % (epoch, test_loss/(batch_idx+1), 100.*correct/total, correct, total))
# Save checkpoint.
acc = 100.*correct/total
if acc > best_acc:
print('Saving..')
state = {
'net': model_conv.state_dict(),
'acc': acc,
'epoch': epoch,
}
if not os.path.isdir('model'):
os.mkdir('model')
torch.save(state, './model/model.t7')
best_acc = acc
# 이미지 출력 함수
def imshow(inp, title=None):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.5, 0.5, 0.5])
std = np.array([0.5, 0.5, 0.5])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
# 일부 예제에 대하여 예측한 값을 보여준다.
def visualize_model(model, num_images=90):
checkpoint = torch.load('./model/model.t7')
model.load_state_dict(checkpoint['net'])
best_acc = checkpoint['acc']
start_epoch = checkpoint['epoch']
was_training = model.training
model.eval()
images_so_far = 0
fig = plt.figure()
with torch.no_grad():
for i, (inputs, labels) in enumerate(dataloaders['val']):
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
for j in range(inputs.size()[0]):
images_so_far += 1
ax = plt.subplot(num_images//15, 15, images_so_far)
ax.axis('off')
ax.set_title('tag: {}'.format(classes[preds[j]]))
imshow(inputs.cpu().data[j])
if images_so_far == num_images:
model.train(mode=was_training)
return
if __name__ == "__main__":
# Model
print('==> Building model..')
model_conv = shuffleNetV2.ShuffleNetV2(0.5)
model_conv = model_conv.to(device)
if device == 'cuda':
model_conv = torch.nn.DataParallel(model_conv)
cudnn.benchmark = True
if True:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isdir('model'), 'Error: no checkpoint directory found!'
checkpoint = torch.load('./model/model.t7')
model_conv.load_state_dict(checkpoint['net'])
best_acc = checkpoint['acc']
start_epoch = checkpoint['epoch']
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model_conv.parameters(), lr=0.001, momentum=0.9, weight_decay=5e-4)
# for param in model_conv.parameters():
# param.requires_grad = False
for epoch in range(start_epoch, start_epoch+250):
train(epoch)
test(epoch)
# for param in model_conv.parameters():
# param.requires_grad = True
visualize_model(model_conv)
print("best Acc : {:2f}".format(best_acc))
plt.ioff()
plt.show()