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import numpy as np
import os, time, random
import argparse
import json
import torch.nn.functional as F
import torch
from torch.utils.data import Dataset, DataLoader
from torch.optim import lr_scheduler
from model.model import InvISPNet
from dataset.FiveK_dataset import FiveKDatasetTrain
from config.config import get_arguments
from utils.JPEG import DiffJPEG
os.system("nvidia-smi -q -d Memory |grep -A4 GPU|grep Free >tmp")
os.environ["CUDA_VISIBLE_DEVICES"] = str(
np.argmax([int(x.split()[2]) for x in open("tmp", "r").readlines()])
)
# os.environ['CUDA_VISIBLE_DEVICES'] = "1"
os.system("rm tmp")
DiffJPEG = DiffJPEG(differentiable=True, quality=90).cuda()
parser = get_arguments()
parser.add_argument(
"--out_path", type=str, default="./exps/", help="Path to save checkpoint. "
)
parser.add_argument(
"--resume", dest="resume", action="store_true", help="Resume training. "
)
parser.add_argument(
"--loss",
type=str,
default="L1",
choices=["L1", "L2"],
help="Choose which loss function to use. ",
)
parser.add_argument("--lr", type=float, default=0.0001, help="Learning rate")
parser.add_argument(
"--aug", dest="aug", action="store_true", help="Use data augmentation."
)
args = parser.parse_args()
print("Parsed arguments: {}".format(args))
os.makedirs(args.out_path, exist_ok=True)
os.makedirs(args.out_path + "%s" % args.task, exist_ok=True)
os.makedirs(args.out_path + "%s/checkpoint" % args.task, exist_ok=True)
with open(args.out_path + "%s/commandline_args.yaml" % args.task, "w") as f:
json.dump(args.__dict__, f, indent=2)
def main(args):
# ======================================define the model======================================
net = InvISPNet(channel_in=3, channel_out=3, block_num=8)
net.cuda()
# load the pretrained weight if there exists one
if args.resume:
net.load_state_dict(
torch.load(args.out_path + "%s/checkpoint/latest.pth" % args.task)
)
print("[INFO] loaded " + args.out_path + "%s/checkpoint/latest.pth" % args.task)
optimizer = torch.optim.Adam(net.parameters(), lr=args.lr)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[50, 80], gamma=0.5)
print("[INFO] Start data loading and preprocessing")
RAWDataset = FiveKDatasetTrain(opt=args)
dataloader = DataLoader(
RAWDataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
drop_last=True,
)
print("[INFO] Start to train")
step = 0
for epoch in range(0, 300):
epoch_time = time.time()
for i_batch, sample_batched in enumerate(dataloader):
step_time = time.time()
input, target_rgb, target_raw = (
sample_batched["input_raw"].cuda(),
sample_batched["target_rgb"].cuda(),
sample_batched["target_raw"].cuda(),
)
reconstruct_rgb = net(input)
reconstruct_rgb = torch.clamp(reconstruct_rgb, 0, 1)
rgb_loss = F.l1_loss(reconstruct_rgb, target_rgb)
reconstruct_rgb = DiffJPEG(reconstruct_rgb)
reconstruct_raw = net(reconstruct_rgb, rev=True)
raw_loss = F.l1_loss(reconstruct_raw, target_raw)
loss = args.rgb_weight * rgb_loss + raw_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(
"task: %s Epoch: %d Step: %d || loss: %.5f raw_loss: %.5f rgb_loss: %.5f || lr: %f time: %f"
% (
args.task,
epoch,
step,
loss.detach().cpu().numpy(),
raw_loss.detach().cpu().numpy(),
rgb_loss.detach().cpu().numpy(),
optimizer.param_groups[0]["lr"],
time.time() - step_time,
)
)
step += 1
torch.save(
net.state_dict(), args.out_path + "%s/checkpoint/latest.pth" % args.task
)
if (epoch + 1) % 10 == 0:
# os.makedirs(args.out_path+"%s/checkpoint/%04d"%(args.task,epoch), exist_ok=True)
torch.save(
net.state_dict(),
args.out_path + "%s/checkpoint/%04d.pth" % (args.task, epoch),
)
print(
"[INFO] Successfully saved "
+ args.out_path
+ "%s/checkpoint/%04d.pth" % (args.task, epoch)
)
scheduler.step()
print("[INFO] Epoch time: ", time.time() - epoch_time, "task: ", args.task)
if __name__ == "__main__":
torch.set_num_threads(4)
main(args)
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