Merge branch 'main' of github.com:JavaZeroo/fakeji into main

Author: JavaZeroo

config.py

@@ -1,5 +1,8 @@
 from pathlib import Path
+
 import torch
+
+
 class Config:
     def __init__(self, ):
         self.DATA_DIR = Path('data')

data_gen.py

@@ -1,10 +1,12 @@
+import random
+from pathlib import Path
+
 import cv2
+import matplotlib.pyplot as plt
 import numpy as np
-import random
 import torch
+
 from utils import *
-from pathlib import Path
-import matplotlib.pyplot as plt
 
 DATA_DIR = Path('data')
 SOURCE_DIR = DATA_DIR / 'source'

model.py

@@ -1,21 +1,22 @@
-from turtle import forward
-import cv2
-import numpy as np
 import random
-from utils import *
 from pathlib import Path
-import matplotlib.pyplot as plt
 
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
 # Pytorch
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 import torch.optim as optim
 import torch.optim.lr_scheduler as lr_scheduler
-from torch.utils.data import Dataset, DataLoader
 import torchvision.transforms as transforms
-import torch.nn.functional as F
+from torch.utils.data import DataLoader, Dataset
 from tqdm.notebook import tqdm
 
+from utils import *
+
+
 class fujiModel(nn.Module):
     def __init__(self):
         super().__init__()

ssim.py

@@ -1,8 +1,10 @@
+from math import exp
+
+import numpy as np
 import torch
 import torch.nn.functional as F
 from torch.autograd import Variable
-import numpy as np
-from math import exp
+
 
 def gaussian(window_size, sigma):
     gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])

test.py

@@ -1,6 +1,7 @@
-from pkgutil import ModuleInfo
-import torch.nn as nn
 import torch
+import torch.nn as nn
+
+
 class fujiModel(nn.Module):
     def __init__(self):
         super().__init__()

train.ipynb

@@ -6,23 +6,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import cv2\n",
-    "import numpy as np\n",
     "import random\n",
-    "from utils import *\n",
     "from pathlib import Path\n",
-    "import matplotlib.pyplot as plt\n",
     "\n",
+    "import cv2\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "# Pytorch\n",
     "import torch\n",
     "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
     "import torch.optim as optim\n",
     "import torch.optim.lr_scheduler as lr_scheduler\n",
-    "from torch.utils.data import Dataset, DataLoader\n",
     "import torchvision.transforms as transforms\n",
-    "import torch.nn.functional as F\n",
+    "from torch.utils.data import DataLoader, Dataset\n",
     "from tqdm.notebook import tqdm\n",
     "\n",
+    "from utils import *\n",
+    "\n",
     "DATA_DIR = Path('data')\n",
     "SOURCE_DIR = DATA_DIR / 'source'\n",
     "TARGET_DIR = DATA_DIR / 'target'\n",
@@ -195,6 +196,8 @@
    ],
    "source": [
     "import random\n",
+    "\n",
+    "\n",
     "def generate_img(sources_croped, targets_croped, num=1000, output='data_gen', show=False):\n",
     "    assert (len(sources_croped) == len(targets_croped))\n",
     "    output = Path(output)\n",
@@ -241,7 +244,7 @@
     "        cv2.imwrite(str(target_path), target)\n",
     "generate_img(sources_croped, targets_croped)\n",
     "                \n",
-    "                \n"
+    "                "
    ]
   },
   {
@@ -329,8 +332,10 @@
     }
    ],
    "source": [
-    "from ssim import ssim\n",
     "import time\n",
+    "\n",
+    "from ssim import ssim\n",
+    "\n",
     "#test\n",
     "test1 = torch.Tensor([np.transpose(sources[0], (2, 0, 1))])\n",
     "test2 = torch.Tensor([np.transpose(targets[0], (2, 0, 1))])"

train.py

@@ -1,25 +1,26 @@
-import cv2
-import numpy as np
+import os
 import random
-from utils import *
 from pathlib import Path
+
+import cv2
 import matplotlib.pyplot as plt
-from config import Config
-from utils import *
-from model import fujiModel
-import wandb
-import os
+import numpy as np
 # Pytorch
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 import torch.optim as optim
 import torch.optim.lr_scheduler as lr_scheduler
-from torch.utils.data import Dataset, DataLoader
-from torch.cuda.amp import GradScaler, autocast
 import torchvision.transforms as transforms
-import torch.nn.functional as F
+from torch.cuda.amp import GradScaler, autocast
+from torch.utils.data import DataLoader, Dataset
 from tqdm import tqdm
+
+import wandb
+from config import Config
+from model import fujiModel
 from ssim import ssim
+from utils import *
 
 config = Config()
 os.environ['WANDB_API_KEY'] = '67c99389e1ae37b747c40634c51802a4bf019d49'

utils.py

@@ -1,10 +1,12 @@
-import numpy as np
+import gc
 import random
-import torch
-from config import Config
 from pathlib import Path
-import gc
+
 import cv2
+import numpy as np
+import torch
+
+from config import Config
 
 config = Config()