2021-1-capstone-design1 / KHY_Project1

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Showing 6 changed files with 1404 additions and 80 deletions
......@@ -151,7 +151,7 @@ def compute_psnr(a, b):
mse = torch.mean((a - b)**2).item()
return -10 * math.log10(mse)
def _encode(path, image, model, metric, quality, coder, i, ref,total_bpp, ff, output, log_path):
def _encode(seq, path, image, model, metric, quality, coder, i, ref,total_bpp, ff, output, log_path):
compressai.set_entropy_coder(coder)
enc_start = time.time()
......@@ -182,16 +182,16 @@ def _encode(path, image, model, metric, quality, coder, i, ref,total_bpp, ff, ou
strings.append([s[0]])
with torch.no_grad():
recon_out = net.decompress(strings, (shape[0], shape[1], len(out["strings"])))
recon_out = net.decompress(strings, shape)
x_recon = crop(recon_out["x_hat"], (h, w))
psnr=compute_psnr(x, x_recon)
if i==False:
diff=x-ref
diff1=torch.clamp(diff, min=-0.5, max=0.5)+0.5
diff_img = torch2img(diff1)
diff_img.save(path+"recon/diff_v1_"+str(ff)+"_q"+str(quality)+".png")
#if i==False:
# diff=x-ref
# diff1=torch.clamp(diff, min=-0.5, max=0.5)+0.5
# diff_img = torch2img(diff1)
# diff_img.save("../Data/train/"+seq+str(ff)+"_train_v1_q"+str(quality)+".png")
enc_time = time.time() - enc_start
size = filesize(output)
......@@ -336,15 +336,15 @@ def encode(argv):
total_psnr=0.0
total_bpp=0.0
total_time=0.0
args.image =path + args.image+"_768x768_"+str(args.framerate)+"_8bit_444"
img=args.image+"_frame"+str(0)+".png"
total_psnr, total_bpp, ref,total_time = _encode(path, img, args.model, args.metric, args.quality, args.coder, True, 0, total_bpp, 0, args.output, log_path)
img_path =path + args.image+"_768x768_"+str(args.framerate)+"_8bit_444"
img=img_path+"_frame"+str(0)+".png"
total_psnr, total_bpp, ref,total_time = _encode(args.image, path, img, args.model, args.metric, args.quality, args.coder, True, 0, total_bpp, 0, args.output, log_path)
for ff in range(1, args.frame):
with Path(log_path).open("a") as f:
f.write(f" {ff:3d} | ")
img=args.image+"_frame"+str(ff)+".png"
img=img_path+"_frame"+str(ff)+".png"
psnr, total_bpp, ref,time = _encode(path, img, args.model, args.metric, args.quality, args.coder, False, ref, total_bpp, ff, args.output, log_path)
psnr, total_bpp, ref,time = _encode(args.image, path, img, args.model, args.metric, args.quality, args.coder, False, ref, total_bpp, ff, args.output, log_path)
total_psnr+=psnr
total_time+=time
......
......@@ -213,7 +213,7 @@ def _encode(path, image, model, metric, quality, coder, i, ref, total_bpp, ff, o
strings.append([s[0]])
with torch.no_grad():
recon_out1 = net.decompress(strings, (shape1[0], shape1[1], len(out1["strings"])))
recon_out1 = net.decompress(strings,shape)
x_hat1 = crop(recon_out1["x_hat"], (h, w))
with torch.no_grad():
......@@ -231,7 +231,7 @@ def _encode(path, image, model, metric, quality, coder, i, ref, total_bpp, ff, o
strings.append([s[0]])
with torch.no_grad():
recon_out = net.decompress(strings, (shape[0], shape[1], len(out["strings"])))
recon_out = net.decompress(strings, shape)
x_hat2 = crop(recon_out["x_hat"], (h, w))
x_recon=ref+x_hat1-x_hat2
......
......@@ -17,6 +17,7 @@ import struct
import sys
import time
import math
from pytorch_msssim import ms_ssim
from pathlib import Path
......@@ -27,7 +28,12 @@ from PIL import Image
from torchvision.transforms import ToPILImage, ToTensor
import compressai
from compressai.transforms.functional import (
rgb2ycbcr,
ycbcr2rgb,
yuv_420_to_444,
yuv_444_to_420,
)
from compressai.zoo import models
model_ids = {k: i for i, k in enumerate(models.keys())}
......@@ -151,13 +157,28 @@ def compute_psnr(a, b):
mse = torch.mean((a - b)**2).item()
return -10 * math.log10(mse)
def _encode(path, image, model, metric, quality, coder, i, ref, total_bpp, ff, output, log_path):
def compute_msssim(a, b):
return ms_ssim(a, b, data_range=1.).item()
def ycbcr_psnr(a, b):
yuv_a=rgb2ycbcr(a)
yuv_b=rgb2ycbcr(b)
a_y, a_cb, a_cr = yuv_a.chunk(3, -3)
b_y, b_cb, b_cr = yuv_b.chunk(3, -3)
y=compute_psnr(a_y, b_y)
cb=compute_psnr(a_cb, b_cb)
cr=compute_psnr(a_cr, b_cr)
return (4*y+cb+cr)/6
def _encode(checkpoint, path, seq, image, model, metric, quality, coder, i, ref, total_bpp, ff, output, log_path):
compressai.set_entropy_coder(coder)
enc_start = time.time()
img = load_image(image)
img = load_image(image+"_frame"+str(ff)+".png")
start = time.time()
net = models[model](quality=quality, metric=metric, pretrained=True).eval()
net = models[model](quality=quality, metric=metric, pretrained=True)
net.eval()
load_time = time.time() - start
x = img2torch(img)
......@@ -182,45 +203,26 @@ def _encode(path, image, model, metric, quality, coder, i, ref, total_bpp, ff, o
strings.append([s[0]])
with torch.no_grad():
recon_out = net.decompress(strings, (shape[0], shape[1], len(out["strings"])))
recon_out = net.decompress(strings,shape)
x_recon = crop(recon_out["x_hat"], (h, w))
psnr=compute_psnr(x, x_recon)
ssim=compute_msssim(x, x_recon)
ycbcr=ycbcr_psnr(x, x_recon)
else:
if checkpoint: # load from previous checkpoint
checkpoint = torch.load(checkpoint)
#state_dict = load_state_dict(checkpoint["state_dict"])
net=models[model](quality=quality, metric=metric)
net.load_state_dict(checkpoint["state_dict"])
net.update(force=True)
else:
net = models[model](quality=quality, metric=metric, pretrained=True)
diff=x-ref
#1
diff1=torch.clamp(diff, min=-0.5, max=0.5)+0.5
#2
'''
diff1=torch.clamp(diff, min=0.0, max=1.0)
diff2=-torch.clamp(diff, min=-1.0, max=0.0)
diff1=pad(diff1, p)
diff2=pad(diff2, p)
'''
#1
with torch.no_grad():
out1 = net.compress(diff1)
shape1 = out1["shape"]
strings = []
with Path(output).open("ab") as f:
# write shape and number of encoded latents
write_uints(f, (shape1[0], shape1[1], len(out1["strings"])))
for s in out1["strings"]:
write_uints(f, (len(s[0]),))
write_bytes(f, s[0])
strings.append([s[0]])
with torch.no_grad():
recon_out = net.decompress(strings, (shape1[0], shape1[1], len(out1["strings"])))
x_hat1 = crop(recon_out["x_hat"], (h, w))
#2
'''
with torch.no_grad():
out1 = net.compress(diff1)
shape1 = out1["shape"]
......@@ -236,32 +238,17 @@ def _encode(path, image, model, metric, quality, coder, i, ref, total_bpp, ff, o
strings.append([s[0]])
with torch.no_grad():
recon_out = net.decompress(strings, (shape1[0], shape1[1], len(out1["strings"])))
recon_out = net.decompress(strings, shape1)
x_hat1 = crop(recon_out["x_hat"], (h, w))
with torch.no_grad():
out = net.compress(diff2)
shape = out["shape"]
strings = []
with Path(output).open("ab") as f:
# write shape and number of encoded latents
write_uints(f, (shape[0], shape[1], len(out["strings"])))
for s in out["strings"]:
write_uints(f, (len(s[0]),))
write_bytes(f, s[0])
strings.append([s[0]])
with torch.no_grad():
recon_out = net.decompress(strings, (shape[0], shape[1], len(out["strings"])))
x_hat2 = crop(recon_out["x_hat"], (h, w))
x_recon=ref+x_hat1-x_hat2
'''
x_recon=ref+x_hat1-0.5
psnr=compute_psnr(x, x_recon)
ssim=compute_msssim(x, x_recon)
ycbcr=ycbcr_psnr(x, x_recon)
diff_img = torch2img(diff1)
diff_img.save(path+"recon/diff"+str(ff)+"_q"+str(quality)+".png")
# diff_img.save(path+"recon/"+seq+str(ff)+"_q"+str(quality)+".png")
# diff_img.save("../Data/train/"+seq+str(ff)+"_train8_q"+str(quality)+".png")
enc_time = time.time() - enc_start
size = filesize(output)
......@@ -269,11 +256,13 @@ def _encode(path, image, model, metric, quality, coder, i, ref, total_bpp, ff, o
with Path(log_path).open("a") as f:
f.write( f" {bpp-total_bpp:.4f} | "
f" {psnr:.4f} |"
f" {ssim:.4f} |"
f" {ycbcr:.4f} |"
f" Encoded in {enc_time:.2f}s (model loading: {load_time:.2f}s)\n")
recon_img = torch2img(x_recon)
recon_img.save(path+"recon/recon"+str(ff)+"_q"+str(quality)+".png")
return psnr, bpp, x_recon, enc_time
return psnr, bpp, x_recon, enc_time, ssim, ycbcr
def _decode(inputpath, coder, show, frame, output=None):
......@@ -381,13 +370,19 @@ def encode(argv):
default=768,
help="hight setting (default: %(default))",
)
parser.add_argument(
"-check",
"--checkpoint",
type=str,
help="Path to a checkpoint",
)
parser.add_argument("-o", "--output", help="Output path")
args = parser.parse_args(argv)
path="examples/"+args.image+"/"
if not args.output:
#args.output = Path(Path(args.image).resolve().name).with_suffix(".bin")
args.output = path+args.image+"_"+args.model+"_q"+str(args.quality)+"_v2.bin"
log_path=path+args.image+"_"+args.model+"_q"+str(args.quality)+"_v2.txt"
args.output = path+args.image+"_q"+str(args.quality)+"_train_ssim.bin"
log_path=path+args.image+"_q"+str(args.quality)+"_train_ssim.txt"
header = get_header(args.model, args.metric, args.quality)
with Path(args.output).open("wb") as f:
......@@ -400,32 +395,43 @@ def encode(argv):
f"frames : {args.frame}\n")
f.write( f"frame | bpp | "
f" psnr |"
f" ssim |"
f" Encoded time (model loading)\n"
f" {0:3d} | ")
total_psnr=0.0
total_ssim=0.0
total_ycbcr=0.0
total_bpp=0.0
total_time=0.0
args.image =path + args.image+"_768x768_"+str(args.framerate)+"_8bit_444"
img=args.image+"_frame"+str(0)+".png"
total_psnr, total_bpp, ref, total_time = _encode(path, img, args.model, args.metric, args.quality, args.coder, True, 0, total_bpp, 0, args.output, log_path)
img =path + args.image+"_768x768_"+str(args.framerate)+"_8bit_444"
total_psnr, total_bpp, ref, total_time, total_ssim, total_ycbcr = _encode(args.checkpoint, path, args.image, img, args.model, args.metric, args.quality, args.coder, True, 0, total_bpp, 0, args.output, log_path)
for ff in range(1, args.frame):
with Path(log_path).open("a") as f:
f.write(f" {ff:3d} | ")
img=args.image+"_frame"+str(ff)+".png"
psnr, total_bpp, ref, time = _encode(path, img, args.model, args.metric, args.quality, args.coder, False, ref, total_bpp, ff, args.output, log_path)
if ff%25==0:
psnr, total_bpp, ref, time, ssim, ycbcr = _encode(args.checkpoint, path, args.image, img, args.model, args.metric, args.quality, args.coder, True, ref, total_bpp, ff, args.output, log_path)
else:
psnr, total_bpp, ref, time, ssim, ycbcr = _encode(args.checkpoint, path, args.image, img, args.model, args.metric, args.quality, args.coder, False, ref, total_bpp, ff, args.output, log_path)
total_psnr+=psnr
total_ssim+=ssim
total_ycbcr+=ycbcr
total_time+=time
total_psnr/=args.frame
total_ssim/=args.frame
total_ycbcr/=args.frame
total_bpp/=args.frame
with Path(log_path).open("a") as f:
f.write( f"\n Total Encoded time: {total_time:.2f}s\n"
f"\n Total PSNR: {total_psnr:.6f}\n"
f"\n Total SSIM: {total_ssim:.6f}\n"
f"\n Total ycbcr: {total_ycbcr:.6f}\n"
f" Total BPP: {total_bpp:.6f}\n")
print(total_psnr)
print(total_ssim)
print(total_ycbcr)
print(total_bpp)
......
# Copyright 2020 InterDigital Communications, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import math
import random
import shutil
import sys
import time
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms
from compressai.datasets import ImageFolder
from compressai.zoo import models
import csv
import cv2
import numpy as np
class RateDistortionLoss(nn.Module):
"""Custom rate distortion loss with a Lagrangian parameter."""
def __init__(self, lmbda=1e-2):
super().__init__()
self.mse = nn.MSELoss()
self.lmbda = lmbda
def forward(self, output, target):
N, _, H, W = target.size()
out = {}
num_pixels = N * H * W
out["bpp_loss"] = sum(
(torch.log(likelihoods).sum() / (-math.log(2) * num_pixels))
for likelihoods in output["likelihoods"].values()
)
out["mse_loss"] = self.mse(output["x_hat"], target)
out["loss"] = self.lmbda * 255 ** 2 * out["mse_loss"] + out["bpp_loss"]
return out
class AverageMeter:
"""Compute running average."""
def __init__(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
class CustomDataParallel(nn.DataParallel):
"""Custom DataParallel to access the module methods."""
def __getattr__(self, key):
try:
return super().__getattr__(key)
except AttributeError:
return getattr(self.module, key)
def configure_optimizers(net, args):
"""Separate parameters for the main optimizer and the auxiliary optimizer.
Return two optimizers"""
parameters = set(
n
for n, p in net.named_parameters()
if not n.endswith(".quantiles") and p.requires_grad
)
aux_parameters = set(
n
for n, p in net.named_parameters()
if n.endswith(".quantiles") and p.requires_grad
)
# Make sure we don't have an intersection of parameters
params_dict = dict(net.named_parameters())
inter_params = parameters & aux_parameters
union_params = parameters | aux_parameters
assert len(inter_params) == 0
assert len(union_params) - len(params_dict.keys()) == 0
optimizer = optim.Adam(
(params_dict[n] for n in sorted(list(parameters))),
lr=args.learning_rate,
)
aux_optimizer = optim.Adam(
(params_dict[n] for n in sorted(list(aux_parameters))),
lr=args.aux_learning_rate,
)
return optimizer, aux_optimizer
def train_one_epoch(
model, criterion, train_dataloader, optimizer, aux_optimizer, epoch, clip_max_norm
):
model.train()
device = next(model.parameters()).device
loss = AverageMeter()
bpp_loss = AverageMeter()
mse_loss = AverageMeter()
a_aux_loss = AverageMeter()
for i, d in enumerate(train_dataloader):
d = d.to(device)
optimizer.zero_grad()
aux_optimizer.zero_grad()
out_net = model(d)
out_criterion = criterion(out_net, d)
bpp_loss.update(out_criterion["bpp_loss"])
loss.update(out_criterion["loss"])
mse_loss.update(out_criterion["mse_loss"])
out_criterion["loss"].backward()
if clip_max_norm > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), clip_max_norm)
optimizer.step()
aux_loss = model.aux_loss()
a_aux_loss.update(aux_loss)
aux_loss.backward()
aux_optimizer.step()
if i % 10 == 0:
print(
f"Train epoch {epoch}: ["
f"{i*len(d)}/{len(train_dataloader.dataset)}"
f" ({100. * i / len(train_dataloader):.0f}%)]"
f'\tLoss: {out_criterion["loss"].item():.3f} |'
f'\tMSE loss: {out_criterion["mse_loss"].item():.3f} |'
f'\tBpp loss: {out_criterion["bpp_loss"].item():.2f} |'
f"\tAux loss: {aux_loss.item():.2f}"
)
return loss.avg, bpp_loss.avg, a_aux_loss.avg
def test_epoch(epoch, test_dataloader, model, criterion):
model.eval()
device = next(model.parameters()).device
loss = AverageMeter()
bpp_loss = AverageMeter()
mse_loss = AverageMeter()
aux_loss = AverageMeter()
with torch.no_grad():
for d in test_dataloader:
d = d.to(device)
out_net = model(d)
out_criterion = criterion(out_net, d)
aux_loss.update(model.aux_loss())
bpp_loss.update(out_criterion["bpp_loss"])
loss.update(out_criterion["loss"])
mse_loss.update(out_criterion["mse_loss"])
print(
f"Test epoch {epoch}: Average losses:"
f"\tLoss: {loss.avg:.3f} |"
f"\tMSE loss: {mse_loss.avg:.3f} |"
f"\tBpp loss: {bpp_loss.avg:.2f} |"
f"\tAux loss: {aux_loss.avg:.2f}\n"
)
return loss.avg, bpp_loss.avg, aux_loss.avg
def save_checkpoint(state, is_best, q, filename="checkpoint"):
torch.save(state, filename+q+".pth.tar")
if is_best:
shutil.copyfile( filename+q+".pth.tar", "checkpoint_best_loss"+q+".pth.tar")
def parse_args(argv):
parser = argparse.ArgumentParser(description="Example training script.")
parser.add_argument(
"-m",
"--model",
default="bmshj2018-hyperprior",
choices=models.keys(),
help="Model architecture (default: %(default)s)",
)
parser.add_argument(
"-d", "--dataset", type=str, required=True, help="Training dataset"
)
parser.add_argument(
"-e",
"--epochs",
default=100,
type=int,
help="Number of epochs (default: %(default)s)",
)
parser.add_argument(
"-lr",
"--learning-rate",
default=1e-4,
type=float,
help="Learning rate (default: %(default)s)",
)
parser.add_argument(
"-n",
"--num-workers",
type=int,
default=0,
help="Dataloaders threads (default: %(default)s)",
)
parser.add_argument(
"--lambda",
dest="lmbda",
type=float,
default=1e-2,
help="Bit-rate distortion parameter (default: %(default)s)",
)
parser.add_argument(
"--batch-size", type=int, default=16, help="Batch size (default: %(default)s)"
)
parser.add_argument(
"--test-batch-size",
type=int,
default=64,
help="Test batch size (default: %(default)s)",
)
parser.add_argument(
"--aux-learning-rate",
default=1e-3,
help="Auxiliary loss learning rate (default: %(default)s)",
)
parser.add_argument(
"--patch-size",
type=int,
nargs=2,
default=(256, 256),
help="Size of the patches to be cropped (default: %(default)s)",
)
parser.add_argument(
"-q",
"--quality",
type=int,
default=3,
help="Quality (default: %(default)s)",
)
parser.add_argument("--cuda", action="store_true", help="Use cuda")
parser.add_argument("--save", action="store_true", help="Save model to disk")
parser.add_argument(
"--seed", type=float, help="Set random seed for reproducibility"
)
parser.add_argument(
"--clip_max_norm",
default=1.0,
type=float,
help="gradient clipping max norm (default: %(default)s",
)
parser.add_argument("--checkpoint", type=str, help="Path to a checkpoint")
args = parser.parse_args(argv)
return args
class CSVLogger():
def __init__(self, fieldnames, filename='log.csv'):
self.filename = filename
self.csv_file = open(filename, 'a')
# Write model configuration at top of csv
writer = csv.writer(self.csv_file)
self.writer = csv.DictWriter(self.csv_file, fieldnames=fieldnames)
# self.writer.writeheader()
# self.csv_file.flush()
def writerow(self, row):
self.writer.writerow(row)
self.csv_file.flush()
def close(self):
self.csv_file.close()
class Blur(object):
def __init__(self, k, sig):
self.k = k
self.sig = sig
def __call__(self, img):
r=np.random.rand(1)
if r<0.5:
img=cv2.GaussianBlur(img.numpy(), (self.k,self.k), self.sig)
img=torch.from_numpy(img)
return img
def main(argv):
args = parse_args(argv)
if args.seed is not None:
torch.manual_seed(args.seed)
random.seed(args.seed)
train_transforms = transforms.Compose(
[transforms.RandomCrop(args.patch_size),
transforms.RandomRotation(30),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()]
)
#train_transforms.transforms.append(Blur(k=3, sig=5))
test_transforms = transforms.Compose(
[transforms.CenterCrop(args.patch_size), transforms.ToTensor()]
)
train_dataset = ImageFolder(args.dataset, split="train", transform=train_transforms)
test_dataset = ImageFolder(args.dataset, split="test", transform=test_transforms)
device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu"
print(torch.cuda.is_available())
print(device)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers,
shuffle=True,
pin_memory=(device == "cuda"),
)
test_dataloader = DataLoader(
test_dataset,
batch_size=args.test_batch_size,
num_workers=args.num_workers,
shuffle=False,
pin_memory=(device == "cuda"),
)
net = models[args.model](quality=args.quality, pretrained=False)
net = net.to(device)
#if args.cuda and torch.cuda.device_count() > 1:
# net = CustomDataParallel(net)
optimizer, aux_optimizer = configure_optimizers(net, args)
# lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, "min", patience=20)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[400, 700], gamma=0.2)
criterion = RateDistortionLoss(lmbda=args.lmbda)
filename = "train"+str(args.quality)+".csv"
csv_logger = CSVLogger(fieldnames=['epoch', 'train_loss', 'train_bpp_loss','train_aux', 'test_loss', 'test_bpp_loss', 'test_aux'], filename=filename)
last_epoch = 0
if args.checkpoint: # load from previous checkpoint
print("Loading", args.checkpoint)
checkpoint = torch.load(args.checkpoint, map_location=device)
last_epoch = checkpoint["epoch"] + 1
net.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
aux_optimizer.load_state_dict(checkpoint["aux_optimizer"])
lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])
for g in optimizer.param_groups:
g['lr'] = 0.00001
for g in aux_optimizer.param_groups:
g['lr'] = 0.00001
best_loss = float("inf")
for epoch in range(last_epoch, args.epochs):
start = time.time()
print(f"Learning rate: {optimizer.param_groups[0]['lr']}")
train_loss, train_bpp_loss, train_aux = train_one_epoch(
net,
criterion,
train_dataloader,
optimizer,
aux_optimizer,
epoch,
args.clip_max_norm,
)
loss, bpp_loss, aux = test_epoch(epoch, test_dataloader, net, criterion)
lr_scheduler.step(loss)
row = {'epoch': str(epoch), 'train_loss': str(train_loss.item()),'train_bpp_loss': str(train_bpp_loss.item()),'train_aux': str(train_aux.item()), 'test_loss': str(loss.item()), 'test_bpp_loss': str(bpp_loss.item()), 'test_aux': str(aux.item())}
csv_logger.writerow(row)###
is_best = loss < best_loss
best_loss = min(loss, best_loss)
if args.save:
save_checkpoint(
{
"epoch": epoch,
"state_dict": net.state_dict(),
"loss": loss,
"optimizer": optimizer.state_dict(),
"aux_optimizer": aux_optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
},
is_best,
str(args.quality)
)
print(f"Total TIme: {time.time() - start}")
csv_logger.close()###
if __name__ == "__main__":
main(sys.argv[1:])
# Copyright 2020 InterDigital Communications, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import math
import random
import shutil
import sys
import time
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms
from compressai.datasets import ImageFolder
from compressai.zoo import models
import csv
import cv2
import numpy as np
from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM
class RateDistortionLoss(nn.Module):
"""Custom rate distortion loss with a Lagrangian parameter."""
def __init__(self, lmbda=1e-2):
super().__init__()
self.mse = ms_ssim
self.lmbda = lmbda
def forward(self, output, target):
N, _, H, W = target.size()
out = {}
num_pixels = N * H * W
out["bpp_loss"] = sum(
(torch.log(likelihoods).sum() / (-math.log(2) * num_pixels))
for likelihoods in output["likelihoods"].values()
)
out["mse_loss"] = 1 - self.mse(output["x_hat"], target, data_range=1.)
out["loss"] = self.lmbda * 255 ** 2 * out["mse_loss"] + out["bpp_loss"]
return out
class AverageMeter:
"""Compute running average."""
def __init__(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
class CustomDataParallel(nn.DataParallel):
"""Custom DataParallel to access the module methods."""
def __getattr__(self, key):
try:
return super().__getattr__(key)
except AttributeError:
return getattr(self.module, key)
def configure_optimizers(net, args):
"""Separate parameters for the main optimizer and the auxiliary optimizer.
Return two optimizers"""
parameters = set(
n
for n, p in net.named_parameters()
if not n.endswith(".quantiles") and p.requires_grad
)
aux_parameters = set(
n
for n, p in net.named_parameters()
if n.endswith(".quantiles") and p.requires_grad
)
# Make sure we don't have an intersection of parameters
params_dict = dict(net.named_parameters())
inter_params = parameters & aux_parameters
union_params = parameters | aux_parameters
assert len(inter_params) == 0
assert len(union_params) - len(params_dict.keys()) == 0
optimizer = optim.Adam(
(params_dict[n] for n in sorted(list(parameters))),
lr=args.learning_rate,
)
aux_optimizer = optim.Adam(
(params_dict[n] for n in sorted(list(aux_parameters))),
lr=args.aux_learning_rate,
)
return optimizer, aux_optimizer
def train_one_epoch(
model, criterion, train_dataloader, optimizer, aux_optimizer, epoch, clip_max_norm
):
model.train()
device = next(model.parameters()).device
loss = AverageMeter()
bpp_loss = AverageMeter()
mse_loss = AverageMeter()
a_aux_loss = AverageMeter()
for i, d in enumerate(train_dataloader):
d = d.to(device)
optimizer.zero_grad()
aux_optimizer.zero_grad()
out_net = model(d)
out_criterion = criterion(out_net, d)
bpp_loss.update(out_criterion["bpp_loss"])
loss.update(out_criterion["loss"])
mse_loss.update(out_criterion["mse_loss"])
out_criterion["loss"].backward()
if clip_max_norm > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), clip_max_norm)
optimizer.step()
aux_loss = model.aux_loss()
a_aux_loss.update(aux_loss)
aux_loss.backward()
aux_optimizer.step()
if i % 10 == 0:
print(
f"Train epoch {epoch}: ["
f"{i*len(d)}/{len(train_dataloader.dataset)}"
f" ({100. * i / len(train_dataloader):.0f}%)]"
f'\tLoss: {out_criterion["loss"].item():.3f} |'
f'\tMSE loss: {out_criterion["mse_loss"].item():.3f} |'
f'\tBpp loss: {out_criterion["bpp_loss"].item():.2f} |'
f"\tAux loss: {aux_loss.item():.2f}"
)
return loss.avg, bpp_loss.avg, a_aux_loss.avg
def test_epoch(epoch, test_dataloader, model, criterion):
model.eval()
device = next(model.parameters()).device
loss = AverageMeter()
bpp_loss = AverageMeter()
mse_loss = AverageMeter()
aux_loss = AverageMeter()
with torch.no_grad():
for d in test_dataloader:
d = d.to(device)
out_net = model(d)
out_criterion = criterion(out_net, d)
aux_loss.update(model.aux_loss())
bpp_loss.update(out_criterion["bpp_loss"])
loss.update(out_criterion["loss"])
mse_loss.update(out_criterion["mse_loss"])
print(
f"Test epoch {epoch}: Average losses:"
f"\tLoss: {loss.avg:.3f} |"
f"\tMSE loss: {mse_loss.avg:.3f} |"
f"\tBpp loss: {bpp_loss.avg:.2f} |"
f"\tAux loss: {aux_loss.avg:.2f}\n"
)
return loss.avg, bpp_loss.avg, aux_loss.avg
def save_checkpoint(state, is_best, q, filename="checkpoint_msssim"):
torch.save(state, filename+q+".pth.tar")
if is_best:
shutil.copyfile( filename+q+".pth.tar", "checkpoint_best_loss_msssim"+q+".pth.tar")
def parse_args(argv):
parser = argparse.ArgumentParser(description="Example training script.")
parser.add_argument(
"-m",
"--model",
default="bmshj2018-hyperprior",
choices=models.keys(),
help="Model architecture (default: %(default)s)",
)
parser.add_argument(
"-d", "--dataset", type=str, required=True, help="Training dataset"
)
parser.add_argument(
"-e",
"--epochs",
default=100,
type=int,
help="Number of epochs (default: %(default)s)",
)
parser.add_argument(
"-lr",
"--learning-rate",
default=1e-4,
type=float,
help="Learning rate (default: %(default)s)",
)
parser.add_argument(
"-n",
"--num-workers",
type=int,
default=0,
help="Dataloaders threads (default: %(default)s)",
)
parser.add_argument(
"--lambda",
dest="lmbda",
type=float,
default=1e-2,
help="Bit-rate distortion parameter (default: %(default)s)",
)
parser.add_argument(
"--batch-size", type=int, default=16, help="Batch size (default: %(default)s)"
)
parser.add_argument(
"--test-batch-size",
type=int,
default=64,
help="Test batch size (default: %(default)s)",
)
parser.add_argument(
"--aux-learning-rate",
default=1e-3,
help="Auxiliary loss learning rate (default: %(default)s)",
)
parser.add_argument(
"--patch-size",
type=int,
nargs=2,
default=(256, 256),
help="Size of the patches to be cropped (default: %(default)s)",
)
parser.add_argument(
"-q",
"--quality",
type=int,
default=3,
help="Quality (default: %(default)s)",
)
parser.add_argument("--cuda", action="store_true", help="Use cuda")
parser.add_argument("--save", action="store_true", help="Save model to disk")
parser.add_argument(
"--seed", type=float, help="Set random seed for reproducibility"
)
parser.add_argument(
"--clip_max_norm",
default=1.0,
type=float,
help="gradient clipping max norm (default: %(default)s",
)
parser.add_argument("--checkpoint", type=str, help="Path to a checkpoint")
args = parser.parse_args(argv)
return args
class CSVLogger():
def __init__(self, fieldnames, filename='log.csv'):
self.filename = filename
self.csv_file = open(filename, 'a')
# Write model configuration at top of csv
writer = csv.writer(self.csv_file)
self.writer = csv.DictWriter(self.csv_file, fieldnames=fieldnames)
# self.writer.writeheader()
# self.csv_file.flush()
def writerow(self, row):
self.writer.writerow(row)
self.csv_file.flush()
def close(self):
self.csv_file.close()
class Blur(object):
def __init__(self, k, sig):
self.k = k
self.sig = sig
def __call__(self, img):
r=np.random.rand(1)
if r<0.5:
img=cv2.GaussianBlur(img.numpy(), (self.k,self.k), self.sig)
img=torch.from_numpy(img)
return img
def main(argv):
args = parse_args(argv)
if args.seed is not None:
torch.manual_seed(args.seed)
random.seed(args.seed)
train_transforms = transforms.Compose(
[transforms.RandomCrop(args.patch_size),
transforms.RandomRotation(30),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()]
)
#train_transforms.transforms.append(Blur(k=3, sig=5))
test_transforms = transforms.Compose(
[transforms.CenterCrop(args.patch_size), transforms.ToTensor()]
)
train_dataset = ImageFolder(args.dataset, split="train", transform=train_transforms)
test_dataset = ImageFolder(args.dataset, split="test", transform=test_transforms)
device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu"
print(torch.cuda.is_available())
print(device)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers,
shuffle=True,
pin_memory=(device == "cuda"),
)
test_dataloader = DataLoader(
test_dataset,
batch_size=args.test_batch_size,
num_workers=args.num_workers,
shuffle=False,
pin_memory=(device == "cuda"),
)
net = models[args.model](quality=args.quality, pretrained=False)
net = net.to(device)
#if args.cuda and torch.cuda.device_count() > 1:
# net = CustomDataParallel(net)
optimizer, aux_optimizer = configure_optimizers(net, args)
lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, "min", patience=20)
criterion = RateDistortionLoss(lmbda=args.lmbda)
filename = "train_msssim"+str(args.quality)+".csv"
csv_logger = CSVLogger(fieldnames=['epoch', 'train_loss', 'train_bpp_loss','train_aux', 'test_loss', 'test_bpp_loss', 'test_aux'], filename=filename)
last_epoch = 0
if args.checkpoint: # load from previous checkpoint
print("Loading", args.checkpoint)
checkpoint = torch.load(args.checkpoint, map_location=device)
last_epoch = checkpoint["epoch"] + 1
net.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
aux_optimizer.load_state_dict(checkpoint["aux_optimizer"])
# for g in optimizer.param_groups:
# g['lr'] = 0.0001
# for g in aux_optimizer.param_groups:
# g['lr'] = 0.0001
lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])
best_loss = float("inf")
for epoch in range(last_epoch, args.epochs):
start = time.time()
print(f"Learning rate: {optimizer.param_groups[0]['lr']}")
train_loss, train_bpp_loss, train_aux = train_one_epoch(
net,
criterion,
train_dataloader,
optimizer,
aux_optimizer,
epoch,
args.clip_max_norm,
)
loss, bpp_loss, aux = test_epoch(epoch, test_dataloader, net, criterion)
lr_scheduler.step(loss)
row = {'epoch': str(epoch), 'train_loss': str(train_loss.item()),'train_bpp_loss': str(train_bpp_loss.item()),'train_aux': str(train_aux.item()), 'test_loss': str(loss.item()), 'test_bpp_loss': str(bpp_loss.item()), 'test_aux': str(aux.item())}
csv_logger.writerow(row)###
is_best = loss < best_loss
best_loss = min(loss, best_loss)
if args.save:
save_checkpoint(
{
"epoch": epoch,
"state_dict": net.state_dict(),
"loss": loss,
"optimizer": optimizer.state_dict(),
"aux_optimizer": aux_optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
},
is_best,
str(args.quality)
)
print(f"Total TIme: {time.time() - start}")
csv_logger.close()###
if __name__ == "__main__":
main(sys.argv[1:])
# Copyright 2020 InterDigital Communications, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import math
import random
import shutil
import sys
import time
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms
from compressai.datasets import ImageFolder
from compressai.zoo import models
import csv
import cv2
import numpy as np
from compressai.transforms.functional import (
rgb2ycbcr,
ycbcr2rgb,
yuv_420_to_444,
yuv_444_to_420,
)
class RateDistortionLoss(nn.Module):
"""Custom rate distortion loss with a Lagrangian parameter."""
# mse 함수를 4:1:1로 바꾸기
def __init__(self, lmbda=1e-2):
super().__init__()
self.mse = nn.MSELoss()
self.lmbda = lmbda
def forward(self, output, target):
N, _, H, W = target.size()
out = {}
num_pixels = N * H * W
out["bpp_loss"] = sum(
(torch.log(likelihoods).sum() / (-math.log(2) * num_pixels))
for likelihoods in output["likelihoods"].values()
)
o_y, o_cb, o_cr = output["x_hat"].chunk(3, -3)
t_y, t_cb, t_cr = target.chunk(3, -3)
mse_y = self.mse(o_y, t_y)
mse_cb = self.mse(o_cb, t_cb)
mse_cr = self.mse(o_cr, t_cr)
out["mse_loss"] = (4*mse_y+mse_cb+mse_cr)/6
out["loss"] = self.lmbda * 255 ** 2 * out["mse_loss"] + out["bpp_loss"]
return out
class AverageMeter:
"""Compute running average."""
def __init__(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
class CustomDataParallel(nn.DataParallel):
"""Custom DataParallel to access the module methods."""
def __getattr__(self, key):
try:
return super().__getattr__(key)
except AttributeError:
return getattr(self.module, key)
def configure_optimizers(net, args):
"""Separate parameters for the main optimizer and the auxiliary optimizer.
Return two optimizers"""
parameters = set(
n
for n, p in net.named_parameters()
if not n.endswith(".quantiles") and p.requires_grad
)
aux_parameters = set(
n
for n, p in net.named_parameters()
if n.endswith(".quantiles") and p.requires_grad
)
# Make sure we don't have an intersection of parameters
params_dict = dict(net.named_parameters())
inter_params = parameters & aux_parameters
union_params = parameters | aux_parameters
assert len(inter_params) == 0
assert len(union_params) - len(params_dict.keys()) == 0
optimizer = optim.Adam(
(params_dict[n] for n in sorted(list(parameters))),
lr=args.learning_rate,
)
aux_optimizer = optim.Adam(
(params_dict[n] for n in sorted(list(aux_parameters))),
lr=args.aux_learning_rate,
)
return optimizer, aux_optimizer
def train_one_epoch(
model, criterion, train_dataloader, optimizer, aux_optimizer, epoch, clip_max_norm
):
model.train()
device = next(model.parameters()).device
loss = AverageMeter()
bpp_loss = AverageMeter()
mse_loss = AverageMeter()
a_aux_loss = AverageMeter()
for i, d in enumerate(train_dataloader):
d = d.to(device)
optimizer.zero_grad()
aux_optimizer.zero_grad()
out_net = model(d)
out_criterion = criterion(out_net, d)
bpp_loss.update(out_criterion["bpp_loss"])
loss.update(out_criterion["loss"])
mse_loss.update(out_criterion["mse_loss"])
out_criterion["loss"].backward()
if clip_max_norm > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), clip_max_norm)
optimizer.step()
aux_loss = model.aux_loss()
a_aux_loss.update(aux_loss)
aux_loss.backward()
aux_optimizer.step()
if i % 10 == 0:
print(
f"Train epoch {epoch}: ["
f"{i*len(d)}/{len(train_dataloader.dataset)}"
f" ({100. * i / len(train_dataloader):.0f}%)]"
f'\tLoss: {out_criterion["loss"].item():.3f} |'
f'\tMSE loss: {out_criterion["mse_loss"].item():.3f} |'
f'\tBpp loss: {out_criterion["bpp_loss"].item():.2f} |'
f"\tAux loss: {aux_loss.item():.2f}"
)
return loss.avg, bpp_loss.avg, a_aux_loss.avg
def test_epoch(epoch, test_dataloader, model, criterion):
model.eval()
device = next(model.parameters()).device
loss = AverageMeter()
bpp_loss = AverageMeter()
mse_loss = AverageMeter()
aux_loss = AverageMeter()
with torch.no_grad():
for d in test_dataloader:
d = d.to(device)
out_net = model(d)
out_criterion = criterion(out_net, d)
aux_loss.update(model.aux_loss())
bpp_loss.update(out_criterion["bpp_loss"])
loss.update(out_criterion["loss"])
mse_loss.update(out_criterion["mse_loss"])
print(
f"Test epoch {epoch}: Average losses:"
f"\tLoss: {loss.avg:.3f} |"
f"\tMSE loss: {mse_loss.avg:.3f} |"
f"\tBpp loss: {bpp_loss.avg:.2f} |"
f"\tAux loss: {aux_loss.avg:.2f}\n"
)
return loss.avg, bpp_loss.avg, aux_loss.avg
def save_checkpoint(state, is_best, q, filename="checkpoint"):
torch.save(state, filename+q+".pth.tar")
if is_best:
shutil.copyfile( filename+q+".pth.tar", "checkpoint_best_loss"+q+".pth.tar")
def parse_args(argv):
parser = argparse.ArgumentParser(description="Example training script.")
parser.add_argument(
"-m",
"--model",
default="bmshj2018-hyperprior",
choices=models.keys(),
help="Model architecture (default: %(default)s)",
)
parser.add_argument(
"-d", "--dataset", type=str, required=True, help="Training dataset"
)
parser.add_argument(
"-e",
"--epochs",
default=100,
type=int,
help="Number of epochs (default: %(default)s)",
)
parser.add_argument(
"-lr",
"--learning-rate",
default=1e-4,
type=float,
help="Learning rate (default: %(default)s)",
)
parser.add_argument(
"-n",
"--num-workers",
type=int,
default=0,
help="Dataloaders threads (default: %(default)s)",
)
parser.add_argument(
"--lambda",
dest="lmbda",
type=float,
default=1e-2,
help="Bit-rate distortion parameter (default: %(default)s)",
)
parser.add_argument(
"--batch-size", type=int, default=16, help="Batch size (default: %(default)s)"
)
parser.add_argument(
"--test-batch-size",
type=int,
default=64,
help="Test batch size (default: %(default)s)",
)
parser.add_argument(
"--aux-learning-rate",
default=1e-3,
help="Auxiliary loss learning rate (default: %(default)s)",
)
parser.add_argument(
"--patch-size",
type=int,
nargs=2,
default=(256, 256),
help="Size of the patches to be cropped (default: %(default)s)",
)
parser.add_argument(
"-q",
"--quality",
type=int,
default=3,
help="Quality (default: %(default)s)",
)
parser.add_argument("--cuda", action="store_true", help="Use cuda")
parser.add_argument("--save", action="store_true", help="Save model to disk")
parser.add_argument(
"--seed", type=float, help="Set random seed for reproducibility"
)
parser.add_argument(
"--clip_max_norm",
default=1.0,
type=float,
help="gradient clipping max norm (default: %(default)s",
)
parser.add_argument("--checkpoint", type=str, help="Path to a checkpoint")
args = parser.parse_args(argv)
return args
class CSVLogger():
def __init__(self, fieldnames, filename='log.csv'):
self.filename = filename
self.csv_file = open(filename, 'a')
# Write model configuration at top of csv
writer = csv.writer(self.csv_file)
self.writer = csv.DictWriter(self.csv_file, fieldnames=fieldnames)
# self.writer.writeheader()
# self.csv_file.flush()
def writerow(self, row):
self.writer.writerow(row)
self.csv_file.flush()
def close(self):
self.csv_file.close()
class Blur(object):
def __init__(self, k, sig):
self.k = k
self.sig = sig
def __call__(self, img):
r=np.random.rand(1)
if r<0.5:
img=cv2.GaussianBlur(img.numpy(), (self.k,self.k), self.sig)
img=torch.from_numpy(img)
return img
class RGB2YCbCr(object):
def __call__(self, img):
"""
Args:
img (Tensor): Tensor image of size (C, H, W).
Returns:
Tensor: Image with n_holes of dimension length x length cut out of it.
"""
img=rgb2ycbcr(img)
return img
def main(argv):
args = parse_args(argv)
if args.seed is not None:
torch.manual_seed(args.seed)
random.seed(args.seed)
train_transforms = transforms.Compose(
[transforms.RandomCrop(args.patch_size), #이미지 크기 조절
transforms.RandomRotation(30),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()] # numpy이미지에서 torch이미지로 변경
)
train_transforms.transforms.append(RGB2YCbCr())
# print(train_transforms.shape)
# train_transforms=rgb2ycbcr(train_transforms)
#train_transforms.transforms.append(Blur(k=3, sig=5))
test_transforms = transforms.Compose(
[transforms.CenterCrop(args.patch_size), transforms.ToTensor()]
)
train_dataset = ImageFolder(args.dataset, split="train", transform=train_transforms)
test_dataset = ImageFolder(args.dataset, split="test", transform=test_transforms)
device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu"
print(torch.cuda.is_available())
print(device)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers,
shuffle=True,
pin_memory=(device == "cuda"),
)
test_dataloader = DataLoader(
test_dataset,
batch_size=args.test_batch_size,
num_workers=args.num_workers,
shuffle=False,
pin_memory=(device == "cuda"),
)
net = models[args.model](quality=args.quality, pretrained=False)
net = net.to(device)
#if args.cuda and torch.cuda.device_count() > 1:
# net = CustomDataParallel(net)
optimizer, aux_optimizer = configure_optimizers(net, args)
# lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, "min", patience=20)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[400, 700], gamma=0.2)
criterion = RateDistortionLoss(lmbda=args.lmbda)
filename = "train"+str(args.quality)+".csv"
csv_logger = CSVLogger(fieldnames=['epoch', 'train_loss', 'train_bpp_loss','train_aux', 'test_loss', 'test_bpp_loss', 'test_aux'], filename=filename)
last_epoch = 0
if args.checkpoint: # load from previous checkpoint
print("Loading", args.checkpoint)
checkpoint = torch.load(args.checkpoint, map_location=device)
last_epoch = checkpoint["epoch"] + 1
net.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
aux_optimizer.load_state_dict(checkpoint["aux_optimizer"])
# for g in optimizer.param_groups:
# g['lr'] = 0.0001
# for g in aux_optimizer.param_groups:
# g['lr'] = 0.0001
lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])
best_loss = float("inf")
for epoch in range(last_epoch, args.epochs):
start = time.time()
print(f"Learning rate: {optimizer.param_groups[0]['lr']}")
train_loss, train_bpp_loss, train_aux = train_one_epoch(
net,
criterion,
train_dataloader,
optimizer,
aux_optimizer,
epoch,
args.clip_max_norm,
)
loss, bpp_loss, aux = test_epoch(epoch, test_dataloader, net, criterion)
lr_scheduler.step()
row = {'epoch': str(epoch), 'train_loss': str(train_loss.item()),'train_bpp_loss': str(train_bpp_loss.item()),'train_aux': str(train_aux.item()), 'test_loss': str(loss.item()), 'test_bpp_loss': str(bpp_loss.item()), 'test_aux': str(aux.item())}
csv_logger.writerow(row)###
is_best = loss < best_loss
best_loss = min(loss, best_loss)
if args.save:
save_checkpoint(
{
"epoch": epoch,
"state_dict": net.state_dict(),
"loss": loss,
"optimizer": optimizer.state_dict(),
"aux_optimizer": aux_optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
},
is_best,
str(args.quality)
)
print(f"Total TIme: {time.time() - start}")
csv_logger.close()###
if __name__ == "__main__":
main(sys.argv[1:])