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main.py
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main.py
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import time
import torch
import torch.backends.cudnn as cudnn
import torch.optim
from torch import nn
from models.SSRNET import SSRNET
from models.SingleCNN import SpatCNN, SpecCNN
from models.TFNet import TFNet, ResTFNet
from models.SSFCNN import SSFCNN, ConSSFCNN
from models.MSDCNN import MSDCNN
from utils import *
from data_loader import build_datasets
from validate import validate
from train import train
import pdb
import args_parser
from torch.nn import functional as F
args = args_parser.args_parser()
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
print (args)
def main():
# Custom dataloader
train_list, test_list = build_datasets(args.root,
args.dataset,
args.image_size,
args.n_select_bands,
args.scale_ratio)
if args.dataset == 'PaviaU':
args.n_bands = 103
elif args.dataset == 'Pavia':
args.n_bands = 102
elif args.dataset == 'Botswana':
args.n_bands = 145
elif args.dataset == 'KSC':
args.n_bands = 176
elif args.dataset == 'Urban':
args.n_bands = 162
elif args.dataset == 'IndianP':
args.n_bands = 200
elif args.dataset == 'Washington':
args.n_bands = 191
# Build the models
if args.arch == 'SSFCNN':
model = SSFCNN(args.scale_ratio,
args.n_select_bands,
args.n_bands).cuda()
elif args.arch == 'ConSSFCNN':
model = ConSSFCNN(args.scale_ratio,
args.n_select_bands,
args.n_bands).cuda()
elif args.arch == 'TFNet':
model = TFNet(args.scale_ratio,
args.n_select_bands,
args.n_bands).cuda()
elif args.arch == 'ResTFNet':
model = ResTFNet(args.scale_ratio,
args.n_select_bands,
args.n_bands).cuda()
elif args.arch == 'MSDCNN':
model = MSDCNN(args.scale_ratio,
args.n_select_bands,
args.n_bands).cuda()
elif args.arch == 'SSRNET' or args.arch == 'SpatRNET' or args.arch == 'SpecRNET':
model = SSRNET(args.arch,
args.scale_ratio,
args.n_select_bands,
args.n_bands).cuda()
elif args.arch == 'SpatCNN':
model = SpatCNN(args.scale_ratio,
args.n_select_bands,
args.n_bands).cuda()
elif args.arch == 'SpecCNN':
model = SpecCNN(args.scale_ratio,
args.n_select_bands,
args.n_bands).cuda()
# Loss and optimizer
criterion = nn.MSELoss().cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
# Load the trained model parameters
model_path = args.model_path.replace('dataset', args.dataset) \
.replace('arch', args.arch)
if os.path.exists(model_path):
model.load_state_dict(torch.load(model_path), strict=False)
print ('Load the chekpoint of {}'.format(model_path))
recent_psnr = validate(test_list,
args.arch,
model,
0,
args.n_epochs)
print ('psnr: ', recent_psnr)
best_psnr = 0
best_psnr = validate(test_list,
args.arch,
model,
0,
args.n_epochs)
print ('psnr: ', best_psnr)
# Epochs
print ('Start Training: ')
for epoch in range(args.n_epochs):
# One epoch's training
print ('Train_Epoch_{}: '.format(epoch))
train(train_list,
args.image_size,
args.scale_ratio,
args.n_bands,
args.arch,
model,
optimizer,
criterion,
epoch,
args.n_epochs)
# One epoch's validation
print ('Val_Epoch_{}: '.format(epoch))
recent_psnr = validate(test_list,
args.arch,
model,
epoch,
args.n_epochs)
print ('psnr: ', recent_psnr)
# # save model
is_best = recent_psnr > best_psnr
best_psnr = max(recent_psnr, best_psnr)
if is_best:
torch.save(model.state_dict(), model_path)
print ('Saved!')
print ('')
print ('best_psnr: ', best_psnr)
if __name__ == '__main__':
main()