code update

@@ -151,7 +151,7 @@ def compute_psnr(a, b):
     mse = torch.mean((a - b)**2).item()
     mse = torch.mean((a - b)**2).item()
     return -10 * math.log10(mse)
     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)
     compressai.set_entropy_coder(coder)
     enc_start = time.time()
     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]])
             strings.append([s[0]])
 
 
     with torch.no_grad():
     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))
     x_recon = crop(recon_out["x_hat"], (h, w))
 
 
     psnr=compute_psnr(x, x_recon)
     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
     enc_time = time.time() - enc_start
     size = filesize(output)
     size = filesize(output)
@@ -336,15 +336,15 @@ def encode(argv):
     total_psnr=0.0
     total_psnr=0.0
     total_bpp=0.0
     total_bpp=0.0
     total_time=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):
     for ff in range(1, args.frame):
         with Path(log_path).open("a") as f:
         with Path(log_path).open("a") as f:
             f.write(f"  {ff:3d}    |  ")
             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_psnr+=psnr
         total_time+=time
         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]])
                 strings.append([s[0]])
 
 
         with torch.no_grad():
         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))
         x_hat1 = crop(recon_out1["x_hat"], (h, w))
         
         
         with torch.no_grad():
         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]])
                 strings.append([s[0]])
 
 
         with torch.no_grad():
         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_hat2 = crop(recon_out["x_hat"], (h, w))
         x_recon=ref+x_hat1-x_hat2
         x_recon=ref+x_hat1-x_hat2
        
        

@@ -17,6 +17,7 @@ import struct
 import sys
 import sys
 import time
 import time
 import math
 import math
+from pytorch_msssim import ms_ssim
 
 
 from pathlib import Path
 from pathlib import Path
 
 
@@ -27,7 +28,12 @@ from PIL import Image
 from torchvision.transforms import ToPILImage, ToTensor
 from torchvision.transforms import ToPILImage, ToTensor
 
 
 import compressai
 import compressai
-
+from compressai.transforms.functional import (
+    rgb2ycbcr,
+    ycbcr2rgb,
+    yuv_420_to_444,
+    yuv_444_to_420,
+)
 from compressai.zoo import models
 from compressai.zoo import models
 
 
 model_ids = {k: i for i, k in enumerate(models.keys())}
 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()
     mse = torch.mean((a - b)**2).item()
     return -10 * math.log10(mse)
     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)
     compressai.set_entropy_coder(coder)
     enc_start = time.time()
     enc_start = time.time()
 
 
-    img = load_image(image)
+    img = load_image(image+"_frame"+str(ff)+".png")
     start = time.time()
     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
     load_time = time.time() - start
 
 
     x = img2torch(img)
     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]])
                 strings.append([s[0]])
 
 
         with torch.no_grad():
         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))
         x_recon = crop(recon_out["x_hat"], (h, w))
         
         
         psnr=compute_psnr(x, x_recon)
         psnr=compute_psnr(x, x_recon)
+        ssim=compute_msssim(x, x_recon)
+        ycbcr=ycbcr_psnr(x, x_recon)
     else:
     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
         diff=x-ref
-        #1
         diff1=torch.clamp(diff, min=-0.5, max=0.5)+0.5
         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():
         with torch.no_grad():
             out1 = net.compress(diff1)
             out1 = net.compress(diff1)
         shape1 = out1["shape"]
         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]])
                 strings.append([s[0]])
 
 
         with torch.no_grad():
         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))
         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
         x_recon=ref+x_hat1-0.5
         psnr=compute_psnr(x, x_recon)
         psnr=compute_psnr(x, x_recon)
+        ssim=compute_msssim(x, x_recon)
+        ycbcr=ycbcr_psnr(x, x_recon)
         diff_img = torch2img(diff1)
         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
     enc_time = time.time() - enc_start
     size = filesize(output)
     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:
     with Path(log_path).open("a") as f:
         f.write( f"  {bpp-total_bpp:.4f}   | "
         f.write( f"  {bpp-total_bpp:.4f}   | "
                 f"  {psnr:.4f}  |"
                 f"  {psnr:.4f}  |"
+                f"  {ssim:.4f}  |"
+                f"  {ycbcr:.4f}  |"
                f"  Encoded in {enc_time:.2f}s (model loading: {load_time:.2f}s)\n")
                f"  Encoded in {enc_time:.2f}s (model loading: {load_time:.2f}s)\n")
     recon_img = torch2img(x_recon)
     recon_img = torch2img(x_recon)
     recon_img.save(path+"recon/recon"+str(ff)+"_q"+str(quality)+".png")
     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):
 def _decode(inputpath, coder, show, frame, output=None):
@@ -381,13 +370,19 @@ def encode(argv):
         default=768,
         default=768,
         help="hight setting (default: %(default))",
         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")
     parser.add_argument("-o", "--output", help="Output path")
     args = parser.parse_args(argv)
     args = parser.parse_args(argv)
     path="examples/"+args.image+"/"
     path="examples/"+args.image+"/"
     if not args.output:
     if not args.output:
         #args.output = Path(Path(args.image).resolve().name).with_suffix(".bin")
         #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)
     header = get_header(args.model, args.metric, args.quality)
     with Path(args.output).open("wb") as f:
     with Path(args.output).open("wb") as f:
@@ -400,32 +395,43 @@ def encode(argv):
                 f"frames : {args.frame}\n")
                 f"frames : {args.frame}\n")
         f.write( f"frame  |     bpp     | "
         f.write( f"frame  |     bpp     | "
                 f"    psnr     |"
                 f"    psnr     |"
+               f"    ssim     |"
                f"  Encoded time (model loading)\n"
                f"  Encoded time (model loading)\n"
                f"  {0:3d}    |  ")
                f"  {0:3d}    |  ")
         
         
     total_psnr=0.0
     total_psnr=0.0
+    total_ssim=0.0
+    total_ycbcr=0.0
     total_bpp=0.0
     total_bpp=0.0
     total_time=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):
     for ff in range(1, args.frame):
         with Path(log_path).open("a") as f:
         with Path(log_path).open("a") as f:
             f.write(f"  {ff:3d}    |  ")
             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_psnr+=psnr
+        total_ssim+=ssim
+        total_ycbcr+=ycbcr
         total_time+=time
         total_time+=time
         
         
     total_psnr/=args.frame
     total_psnr/=args.frame
+    total_ssim/=args.frame
+    total_ycbcr/=args.frame
     total_bpp/=args.frame
     total_bpp/=args.frame
     
     
     with Path(log_path).open("a") as f:
     with Path(log_path).open("a") as f:
         f.write( f"\n Total Encoded time: {total_time:.2f}s\n"
         f.write( f"\n Total Encoded time: {total_time:.2f}s\n"
                 f"\n Total PSNR: {total_psnr:.6f}\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")
                 f" Total BPP: {total_bpp:.6f}\n")
     print(total_psnr)
     print(total_psnr)
+    print(total_ssim)
+    print(total_ycbcr)
     print(total_bpp)
     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:])