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train.py
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import os
import sys
import torch
import argparse
import datetime
from tqdm import tqdm
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
from dataset import *
from model import *
def train(device, loader, optimizer,criterion, epoch, writer):
epoch_loss = 0
epoch_accuracy = 0
total = 0 # Num of total ground truth in dataset
correct = 0 # Correct predictions
model.train()
progress_bar = tqdm(loader)
for (data, target) in progress_bar:
progress_bar.set_description('Train')
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
epoch_loss += loss
total += target.size(0)
calculate_accuracy(output, target)
correct += calculate_accuracy(output, target)
epoch_accuracy = correct/total
epoch_loss = epoch_loss / len(train_loader)
writer.add_scalar('Loss/train', epoch_loss, epoch) # Tensorboard
writer.add_scalar('Accuracy/train', epoch_accuracy, epoch)
return epoch_loss, epoch_accuracy
def test(device, loader, optimizer, criterion, epoch, writer):
with torch.no_grad():
epoch_loss = 0
epoch_accuracy = 0
total = 0
correct = 0
model.train()
progress_bar = tqdm(loader)
for (data, target) in progress_bar:
progress_bar.set_description('Test ')
data, target = data.to(device), target.to(device)
output = model(data)
loss = criterion(output, target)
epoch_loss += loss
total += target.size(0)
calculate_accuracy(output, target)
correct += calculate_accuracy(output, target)
epoch_accuracy = correct/total
epoch_loss = epoch_loss / len(train_loader)
writer.add_scalar('Loss/test', epoch_loss, epoch) # Tensorboard
writer.add_scalar('Accuracy/test', epoch_accuracy, epoch)
return epoch_loss, epoch_accuracy
def calculate_accuracy(output, target):
preds = torch.max(output.data, 1)[1] # Get prediction from softmax
return (preds == target).sum().item() # Compare ground truth with prediction
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--img_size', type=tuple, default=(32, 32), help='input size of image ')
parser.add_argument('--weights', type=str, default='weights', help='path for saving weight')
parser.add_argument('--num_workers', type=int, default=os.cpu_count(), help='number of threads for data loader, by default using all cores')
parser.add_argument('--dataset', type=str, default='dataset', help='path of dataset')
parser.add_argument('--learning_rate', type=float, default=0.0001, help='Learning rate for optimizer')
parser.add_argument('--device', type=str, default='cuda', help='Device for training')
args = parser.parse_args()
size = args.img_size
batch_size = args.batch_size
num_workers = args.num_workers
epochs = args.epochs
path = args.dataset
learning_rate = args.learning_rate
device = torch.device(args.device)
train_transformations = transforms.Compose([transforms.Resize((size[0],size[1])),
transforms.RandomRotation(15),
transforms.ColorJitter(brightness=.05, contrast=.05, saturation=.05, hue=.05),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
test_transformations = transforms.Compose([transforms.Resize((size[0],size[1])),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = ImageDataset('{}/train.json'.format(path), train_transformations)
test_dataset = ImageDataset('{}/val.json'.format(path), test_transformations)
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers
)
test_loader = DataLoader(test_dataset,
batch_size=batch_size,
num_workers=num_workers,
)
writer = SummaryWriter(flush_secs=13, log_dir='logs')
model = ImageClassifier(size).to(device)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss()
best_test_loss = 2
best_test_accuracy = 0
MODEL_SAVE_PATH = 'weights'
if os.path.isdir(MODEL_SAVE_PATH) == False:
os.mkdir(MODEL_SAVE_PATH)
for epoch in range(0, epochs):
print('Epoch {}'.format(epoch))
train_loss, train_accuracy = train(device, train_loader, optimizer, criterion, epoch, writer)
test_loss, test_accuracy = test(device, test_loader, optimizer, criterion, epoch, writer)
if test_loss < best_test_loss: # Choosing best epoch and saving
best_test_loss = test_loss
best_test_accuracy = test_accuracy
torch.save(model.state_dict(), MODEL_SAVE_PATH + '/last.pt')
print('Train loss: {0:.3f}|Train accuracy: {2:.3f}|Test loss: {1:.3f}|Test accuracy: {3:.3f}|'.format(train_loss, test_loss, train_accuracy, test_accuracy))
torch.save(model.state_dict(), MODEL_SAVE_PATH + '/best_loss{}_accuracy{}.pt'.format(round(best_test_loss.item(), 3), round(best_test_accuracy, 3))) # Saving the best epoch with loss and acc
print('| Best loss - {0:.3f}| Best accuracy - {1:.3f} |'.format(best_test_loss, best_test_accuracy))