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train.py
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train.py
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import torch
import torch.nn as nn
import torchvision
import torch.backends.cudnn as cudnn
import torch.optim
import os
import sys
import argparse
import time
import dataloader
import net
import numpy as np
from torchvision import transforms
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
def train(config):
dehaze_net = net.dehaze_net().cuda()
dehaze_net.apply(weights_init)
train_dataset = dataloader.dehazing_loader(config.orig_images_path,
config.hazy_images_path)
val_dataset = dataloader.dehazing_loader(config.orig_images_path,
config.hazy_images_path, mode="val")
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=config.train_batch_size, shuffle=True, num_workers=config.num_workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=config.val_batch_size, shuffle=True, num_workers=config.num_workers, pin_memory=True)
criterion = nn.MSELoss().cuda()
optimizer = torch.optim.Adam(dehaze_net.parameters(), lr=config.lr, weight_decay=config.weight_decay)
dehaze_net.train()
for epoch in range(config.num_epochs):
for iteration, (img_orig, img_haze) in enumerate(train_loader):
img_orig = img_orig.cuda()
img_haze = img_haze.cuda()
clean_image = dehaze_net(img_haze)
loss = criterion(clean_image, img_orig)
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm(dehaze_net.parameters(),config.grad_clip_norm)
optimizer.step()
if ((iteration+1) % config.display_iter) == 0:
print("Loss at iteration", iteration+1, ":", loss.item())
if ((iteration+1) % config.snapshot_iter) == 0:
torch.save(dehaze_net.state_dict(), config.snapshots_folder + "Epoch" + str(epoch) + '.pth')
# Validation Stage
for iter_val, (img_orig, img_haze) in enumerate(val_loader):
img_orig = img_orig.cuda()
img_haze = img_haze.cuda()
clean_image = dehaze_net(img_haze)
torchvision.utils.save_image(torch.cat((img_haze, clean_image, img_orig),0), config.sample_output_folder+str(iter_val+1)+".jpg")
torch.save(dehaze_net.state_dict(), config.snapshots_folder + "dehazer.pth")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Input Parameters
parser.add_argument('--orig_images_path', type=str, default="data/images/")
parser.add_argument('--hazy_images_path', type=str, default="data/data/")
parser.add_argument('--lr', type=float, default=0.0001)
parser.add_argument('--weight_decay', type=float, default=0.0001)
parser.add_argument('--grad_clip_norm', type=float, default=0.1)
parser.add_argument('--num_epochs', type=int, default=10)
parser.add_argument('--train_batch_size', type=int, default=8)
parser.add_argument('--val_batch_size', type=int, default=8)
parser.add_argument('--num_workers', type=int, default=4)
parser.add_argument('--display_iter', type=int, default=10)
parser.add_argument('--snapshot_iter', type=int, default=200)
parser.add_argument('--snapshots_folder', type=str, default="snapshots/")
parser.add_argument('--sample_output_folder', type=str, default="samples/")
config = parser.parse_args()
if not os.path.exists(config.snapshots_folder):
os.mkdir(config.snapshots_folder)
if not os.path.exists(config.sample_output_folder):
os.mkdir(config.sample_output_folder)
train(config)