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baseline.py
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baseline.py
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from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import TensorDataset, DataLoader, Dataset,SubsetRandomSampler
from torchvision import models
import time
from RS_Dataset import RS_Dataset
from tqdm import tqdm
import os
import shutil
from datetime import date
from torchvision.models import resnet50,alexnet,vgg16
def train(PARAMS, model, criterion, device, train_loader, optimizer, epoch):
t0 = time.time()
model.train()
correct = 0
for batch_idx, (img, target) in enumerate(tqdm(train_loader)):
img, target = img.to(device), target.to(device)
optimizer.zero_grad()
output = model(img)
loss = criterion(output, target )
loss.backward()
optimizer.step()
pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f} , {:.2f} seconds'.format(
epoch, batch_idx * len(img), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item(),time.time() - t0))
def test(PARAMS, model,criterion, device, test_loader,optimizer,epoch,best_acc):
model.eval()
test_loss = 0
correct = 0
example_images = []
with torch.no_grad():
for batch_idx, (img, target) in enumerate(tqdm(test_loader)):
img, target = img.to(device), target.to(device)
output = model(img)
test_loss += criterion(output, target).item() # sum up batch loss
pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
# Save the first input tensor in each test batch as an example image
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
current_acc = 100. * correct / len(test_loader.dataset)
return current_acc
def main():
parser = argparse.ArgumentParser(description='manual to this script')
parser.add_argument('--model', type=str, default = 'vgg16')
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--evaluate_model', type=str)
parser.add_argument('--dataset', type=str, default='rsscn7')
args = parser.parse_args()
PARAMS = {'DEVICE': torch.device("cuda" if torch.cuda.is_available() else "cpu"),
'bs': 8,
'epochs':50,
'lr': 0.0006,
'momentum': 0.5,
'log_interval':10,
'criterion':'cross_entropy',
'model_name': args.model,
'dataset': args.dataset,
}
# Training settings
train_transform = transforms.Compose(
[
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(0.4, 0.4, 0.4),
transforms.Resize((256,256)),
transforms.ToTensor(),
transforms.Normalize([0.4850, 0.4560, 0.4060], [0.2290, 0.2240, 0.2250])])
test_transform = transforms.Compose(
[
transforms.Resize((256,256)),
transforms.ToTensor(),
transforms.Normalize([0.4850, 0.4560, 0.4060], [0.2290, 0.2240, 0.2250])])
if args.dataset == 'rsscn7':
# train_dataset = datasets.ImageFolder(root='data/thick_removal',transform = train_transform)
# test_dataset = datasets.ImageFolder(root='data/thick_removal',transform = test_transform)
train_dataset = datasets.ImageFolder(root='data/rsscn7/train_dataset/',transform = train_transform)
test_dataset = datasets.ImageFolder(root='data/rsscn7/test_dataset/',transform = test_transform)
elif args.dataset == 'ucm':
train_dataset = datasets.ImageFolder(root='data/ucm/train_dataset/',transform = train_transform)
test_dataset = datasets.ImageFolder(root='data/ucm/test_dataset/',transform = test_transform)
print(PARAMS)
train_loader = DataLoader(train_dataset, batch_size=PARAMS['bs'], shuffle=True, num_workers=4, pin_memory = True )
test_loader = DataLoader(test_dataset, batch_size=PARAMS['bs'], shuffle=True, num_workers=4, pin_memory = True )
num_classes = len(train_dataset.classes)
if PARAMS['model_name'] == 'vgg16':
model = models.vgg16(pretrained=True)
model.classifier[-1] = nn.Linear(in_features=4096, out_features=num_classes, bias=True)
elif PARAMS['model_name'] == 'resnet50':
model = models.resnet50(pretrained=True)
model.fc = nn.Linear(in_features=2048, out_features=num_classes, bias=True)
elif PARAMS['model_name'] == 'alexnet':
model = models.alexnet(pretrained=True)
model.classifier[-1] = nn.Linear(in_features=4096, out_features=num_classes, bias=True)
model = model.to(PARAMS['DEVICE'])
optimizer = optim.SGD(model.parameters(), lr=PARAMS['lr'], momentum=PARAMS['momentum'])
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 7, gamma = 0.9)
criterion = F.cross_entropy
acc = 0
if not args.evaluate_model:
for epoch in range(1, PARAMS['epochs'] + 1):
train(PARAMS, model,criterion, PARAMS['DEVICE'], train_loader, optimizer, epoch)
acc = test(PARAMS, model,criterion, PARAMS['DEVICE'], test_loader,optimizer,epoch,acc)
scheduler.step()
torch.save(model.state_dict(), 'saved_models/{}_{}_{}_{}_baseline.pth'.format(args.dataset, date.today(), PARAMS['model_name'], round(acc,2)))
# torch.save(model, 'saved_models/{}_{}_{}_{}_baseline.pth'.format(args.dataset, date.today(), PARAMS['model_name'], round(acc,2)))
else:
model = torch.load(args.evaluate_model)
acc = test(PARAMS, model,criterion, PARAMS['DEVICE'], test_loader, optimizer, 0, acc)
print(f'the evalutaion acc is {acc}')
if __name__ == '__main__':
main()