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train.py
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import os
import argparse
from glob import glob
import numpy as np
import torch.utils.data as data
from torchvision import transforms
from dataset import Low_Light_Dataset
from trainer_2 import Base_Trainer
import matplotlib.pyplot as plt
def main(epochs, batch_size, patch_size, lr, data_dir, ckpt_dir, gpu_id, vis_dir, model_type):
phase_name = ['Decom',
'Restore',
'Relight'
]
phase_epoch = [epochs, epochs, epochs]
phase_lr = [lr, lr, lr]
val_every_epoch = 100
# lr[20:] = lr[0] / 10.0
train_data_path = os.path.join(data_dir, 'train')
valid_data_path = os.path.join(data_dir, 'val')
train_low_data_names = glob(train_data_path + '/low/*.png')
# glob(data_dir + '/train/low/*.png')
train_low_data_names.sort()
train_high_data_names = glob(train_data_path + '/high/*.png')
# glob(data_dir + '/our485/high/*.png')
train_high_data_names.sort()
eval_low_data_names = glob(valid_data_path + '/low/*.*')
eval_low_data_names.sort()
eval_high_data_names = glob(valid_data_path + '/high/*.*')
eval_high_data_names.sort()
assert len(train_low_data_names) == len(train_high_data_names)
assert len(train_low_data_names) != 0
transform_low = transforms.Compose(
[
transforms.RandomCrop(patch_size),
transforms.ToTensor(),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomVerticalFlip(p=0.5),
transforms.RandomRotation(90)
#transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
)
transform_high = transforms.Compose(
[
transforms.RandomCrop(patch_size),
transforms.ToTensor(),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomVerticalFlip(p=0.5),
transforms.RandomRotation(90)
#transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
)
transform_val = transforms.Compose(
[
transforms.RandomCrop(224),
transforms.ToTensor(),
#transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
)
train_dataset = Low_Light_Dataset(train_data_path, transform_low, transform_high)
valid_dataset = Low_Light_Dataset(valid_data_path, transform_val, transform_val)
train_dataloader = data.DataLoader(train_dataset, batch_size, shuffle=True, num_workers=4)
print('Number of train data: %d, Batch of train data: %d' % (len(train_dataset), len(train_dataloader)))
valid_dataloader = data.DataLoader(valid_dataset, batch_size, shuffle=True, num_workers=4)
print('Number of valid data: %d, Batch of valid data: %d' % (len(valid_dataset), len(valid_dataloader)))
'''for id, item in enumerate(train_dataloader):
low = item[0]
high = item[1]
print(low.shape, high.shape)
low = low[0].permute(1, 2, 0)
high = high[0].permute(1, 2, 0)
print(low.shape, high.shape)
plt.subplot(2, 1, 1)
plt.imshow(low)
plt.axis('off')
plt.subplot(2, 1, 2)
plt.imshow(high)
plt.axis('off')
plt.show()
print('1')'''
if model_type == 'KinD_color':
from model_KinD_color import Mymodel
model = Mymodel(gpu_id)
model.summary()
elif model_type == 'KinD_color':
from model_KinD_color import Mymodel
model = Mymodel(gpu_id)
model.summary()
elif model_type == 'Retinex':
from model_ISSR_noseg import Mymodel
model = Mymodel(gpu_id)
model.summary()
else:
raise ValueError('False Model !')
model_trainer = Base_Trainer(model, ckpt_dir,
train_dataloader, valid_dataloader,
phase_lr, val_every_epoch, gpu_id,
phase_name, phase_epoch, vis_dir)
# model_trainer.valid('Decom', 1)
model_trainer.train()
'''train_low_data_names,
train_high_data_names,
eval_low_data_names,
eval_high_data_names,
batch_size=batch_size,
patch_size=patch_size,
epoch=Decom_epoch,
lr=lr,
# vis_dir=vis_dir,
ckpt_dir=ckpt_dir,
eval_every_epoch=20,
train_phase="Decom")
model_trainer.train(train_low_data_names,
train_high_data_names,
eval_low_data_names,
eval_high_data_names,
batch_size=batch_size,
patch_size=patch_size,
epoch=Relight_epoch,
lr=lr,
# vis_dir=vis_dir,
ckpt_dir=ckpt_dir,
eval_every_epoch=20,
train_phase="Relight")'''
if __name__ == '__main__':
# TODO logger
parser = argparse.ArgumentParser(description='Learning Low Light Image Enhancement')
parser.add_argument('--gpu_id', dest='gpu_id', default="7",
help='GPU ID (-1 for CPU)')
parser.add_argument('--epochs', dest='epochs', type=int, default=1000,
help='number of total epochs')
parser.add_argument('--batch_size', dest='batch_size', type=int, default=16,
help='number of samples in one batch')
parser.add_argument('--patch_size', dest='patch_size', type=int, default=96,
help='patch size')
parser.add_argument('--lr', dest='lr', type=float, default=0.0001,
help='initial learning rate')
parser.add_argument('--data_dir', dest='data_dir',
default='./LOL/',
help='directory storing the training data')
parser.add_argument('--ckpt_dir', dest='ckpt_dir', default='./ckpts/MyReNet_LOL_KinD_2/',
help='directory for checkpoints')
parser.add_argument('--model_type', dest='model_type', default='KinD_color',
help='directory for checkpoints')
args = parser.parse_args()
if args.gpu_id != "-1":
# Create directories for saving the checkpoints and visuals
args.vis_dir = args.ckpt_dir + '/visuals/'
if not os.path.exists(args.ckpt_dir):
os.makedirs(args.ckpt_dir)
if not os.path.exists(args.vis_dir):
os.makedirs(args.vis_dir)
# Setup the CUDA env
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
# Create the model
# model = RetinexNet(args.ckpt_dir).cuda()
# Train the model
main(args.epochs, args.batch_size, args.patch_size, args.lr, args.data_dir, args.ckpt_dir, args.gpu_id,
args.vis_dir, args.model_type)
else:
# CPU mode not supported at the moment!
raise NotImplementedError