added choice for color model

src/common/data.py

@@ -8,15 +8,16 @@ import torch
 from torch.utils.data import Dataset, DataLoader
 from torch.utils.data.distributed import DistributedSampler
 from pathlib import Path
+from common.utils import PIL_CONVERT_COLOR, pil2numpy
 
 image_extensions = ['.jpg', '.png']
-def load_images_cached(images_dir_path):
+def load_images_cached(images_dir_path, color_model):
     image_paths = sorted([f for f in Path(images_dir_path).glob("*") if f.suffix.lower() in image_extensions])
-    cache_path = Path(images_dir_path).parent / f"{Path(images_dir_path).stem}_cache.npy"
+    cache_path = Path(images_dir_path).parent / f"{Path(images_dir_path).stem}_{color_model}_cache.npy"
     cache_path = cache_path.resolve()
     if not Path(cache_path).exists():
         print("Caching to:", cache_path)
-        value = {f:np.array(Image.open(f)) for f in image_paths}
+        value = {f:pil2numpy(PIL_CONVERT_COLOR[color_model](Image.open(f))) for f in image_paths} 
         np.save(cache_path, value, allow_pickle=True)        
     else:
         value = np.load(cache_path, allow_pickle=True).item()
@@ -24,12 +25,12 @@ def load_images_cached(images_dir_path):
     return list(value.keys()), list(value.values())
 
 class SRTrainDataset(Dataset):
-    def __init__(self, hr_dir_path, lr_dir_path, patch_size, rigid_aug=True):
+    def __init__(self, hr_dir_path, lr_dir_path, patch_size, color_model = "RGB", rigid_aug=True):
         super(SRTrainDataset, self).__init__()
         self.sz = patch_size
         self.rigid_aug = rigid_aug
-        self.hr_image_names, self.hr_images = load_images_cached(hr_dir_path) 
-        self.lr_image_names, self.lr_images = load_images_cached(lr_dir_path) 
+        self.hr_image_names, self.hr_images = load_images_cached(hr_dir_path, color_model=color_model) 
+        self.lr_image_names, self.lr_images = load_images_cached(lr_dir_path, color_model=color_model) 
         assert len(self.hr_images) == len(self.lr_images)
         
     def __getitem__(self, idx):
@@ -48,8 +49,8 @@ class SRTrainDataset(Dataset):
 
             i = random.randint(0, lr_image.shape[0] - self.sz)
             j = random.randint(0, lr_image.shape[1] - self.sz)
-            # c = random.choice([0, 1, 2])
 
+            if len(hr_image.shape) == 3:
                 hr_patch = hr_image[
                     (i*scale):(i*scale + self.sz*scale),
                     (j*scale):(j*scale + self.sz*scale), 
@@ -61,6 +62,7 @@ class SRTrainDataset(Dataset):
                     :
                 ]
 
+
             if self.rigid_aug:
                 if random.uniform(0, 1) < 0.5:
                     hr_patch = np.fliplr(hr_patch)
@@ -85,10 +87,10 @@ class SRTrainDataset(Dataset):
         return len(self.hr_images)
 
 class SRTestDataset(Dataset):
-    def __init__(self, hr_dir_path, lr_dir_path):
+    def __init__(self, hr_dir_path, lr_dir_path, color_model):
         super(SRTestDataset, self).__init__()
-        self.hr_image_paths, self.hr_images = load_images_cached(hr_dir_path) 
-        self.lr_image_paths, self.lr_images = load_images_cached(lr_dir_path) 
+        self.hr_image_paths, self.hr_images = load_images_cached(hr_dir_path, color_model=color_model) 
+        self.lr_image_paths, self.lr_images = load_images_cached(lr_dir_path, color_model=color_model) 
         assert len(self.hr_images) == len(self.lr_images)
         
     def __getitem__(self, idx):

src/common/utils.py

@@ -6,6 +6,18 @@ from scipy import signal
 import torch
 import os
 
+PIL_CONVERT_COLOR = {
+    'RGB': lambda pil_image: pil_image.convert("RGB") if pil_image.mode != 'RGB' else pil_image,
+    'YCbCr': lambda pil_image: pil_image.convert("YCbCr") if pil_image.mode != 'YCbCr'  else pil_image,
+    'Y': lambda pil_image: pil_image.convert("YCbCr").getchannel(0) if pil_image.mode != 'YCbCr'  else pil_image.getchannel(0),
+    'L': lambda pil_image: pil_image.convert("L") if pil_image.mode != 'L'  else pil_image,
+}
+
+def pil2numpy(image):
+    np_image = np.array(image)
+    if len(np_image.shape) == 2:
+        np_image = np_image[:,:,None]
+    return np_image
 
 def round_func(input):
     # Backward Pass Differentiable Approximation (BPDA)
@@ -41,7 +53,7 @@ def modcrop(image, modulo):
         sz = image.shape
         sz = sz - np.mod(sz, modulo)
         image = image[0:sz[0], 0:sz[1]]
-    elif image.shape[2] == 3:
+    elif len(image.shape) == 3:
         sz = image.shape[0:2]
         sz = sz - np.mod(sz, modulo)
         image = image[0:sz[0], 0:sz[1], :]

src/common/validation.py

@@ -7,7 +7,7 @@ from PIL import Image
 import time
 from datetime import timedelta
 
-def val_image_pair(model, hr_image, lr_image, output_image_path=None, device='cuda'):            
+def val_image_pair(model, hr_image, lr_image, color_model, output_image_path=None, device='cuda'):      
     with torch.inference_mode():
         start_time = time.perf_counter_ns()
         # prepare lr_image
@@ -22,13 +22,24 @@ def val_image_pair(model, hr_image, lr_image, output_image_path=None, device='cu
         torch.cuda.empty_cache()
 
         if not output_image_path is None:
-            Image.fromarray(pred_lr_image).save(output_image_path)
+            if pred_lr_image.shape[-1] == 3:
+                Image.fromarray(pred_lr_image, mode=color_model).save(output_image_path)
+            if pred_lr_image.shape[-1] == 1:
+                Image.fromarray(pred_lr_image[:,:,0]).save(output_image_path)
 
         # metrics
         hr_image = modcrop(hr_image, model.scale)
+        if pred_lr_image.shape[-1] == 3 and color_model == 'RGB':
             Y_left, Y_right = _rgb2ycbcr(pred_lr_image)[:, :, 0], _rgb2ycbcr(hr_image)[:, :, 0]
+        if pred_lr_image.shape[-1] == 3 and color_model == 'YCbCr':
+            Y_left, Y_right = pred_lr_image[:, :, 0], hr_image[:, :, 0]
+        if pred_lr_image.shape[-1] == 1:
+            Y_left, Y_right = pred_lr_image[:, :, 0], hr_image[:, :, 0]
+
         lr_area = np.prod(lr_image.shape[-2:])
-        return PSNR(Y_left, Y_right, model.scale), cal_ssim(Y_left, Y_right), run_time_ns, lr_area
+        psnr = PSNR(Y_left, Y_right, model.scale)
+        ssim = cal_ssim(Y_left, Y_right)
+        return psnr, ssim, run_time_ns, lr_area
 
 def valid_steps(model, datasets, config, log_prefix=""):
     dataset_names = list(datasets.keys())
@@ -50,7 +61,7 @@ def valid_steps(model, datasets, config, log_prefix=""):
         tasks = []
         for hr_image, lr_image, hr_image_path, lr_image_path in test_dataset:
             output_image_path = predictions_path / f'{Path(hr_image_path).stem}.png' if config.save_predictions else None
-            task = val_image_pair(model, hr_image, lr_image, output_image_path, device=config.device)
+            task = val_image_pair(model, hr_image, lr_image, output_image_path, color_model=config.color_model, device=config.device)
             tasks.append(task)
 
         total_time = time.time() - start_time 

src/image_demo.py

@@ -1,6 +1,6 @@
 from pathlib import Path
 import sys
-
+from common.utils import PIL_CONVERT_COLOR, pil2numpy
 from models import LoadCheckpoint
 import torch
 import numpy as np
@@ -8,6 +8,7 @@ import cv2
 from PIL import Image
 from datetime import datetime
 import argparse 
+
 class ImageDemoOptions():
     def __init__(self):
         self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
@@ -18,6 +19,7 @@ class ImageDemoOptions():
         self.parser.add_argument('--batch_size', type=int, default=2**10, help="Size of the batch for the input domain values.")
         self.parser.add_argument('--mirror', action='store_true', default=False)
         self.parser.add_argument('--device', default='cuda', help='Device of the model')
+        self.parser.add_argument('--color_model', type=str, default="RGB", help="Color model for input image.") 
 
     def parse_args(self): 
         args = self.parser.parse_args()       
@@ -49,25 +51,26 @@ models = [LoadCheckpoint(x).to(config.device) for x in config.model_paths]
 for m in models:
     print(m)
 
-lr_image = cv2.imread(str(config.lr_image_path))[:,:,::-1]
-image_gt = cv2.imread(str(config.hr_image_path))[:,:,::-1]
+lr_image = pil2numpy(PIL_CONVERT_COLOR[config.color_model](Image.open(str(config.lr_image_path))))
+image_gt = pil2numpy(PIL_CONVERT_COLOR[config.color_model](Image.open(str(config.hr_image_path))))
+image_gt = np.concatenate([image_gt]*3, axis=-1) if image_gt.shape[-1] == 1 else image_gt
+
 if config.mirror:
-    lr_image = lr_image[:,::-1,:]
-    image_gt = image_gt[:,::-1,:]
-lr_image = lr_image.copy()
-image_gt = image_gt.copy()
+    lr_image = lr_image[:,::-1,:].copy()
+    image_gt = image_gt[:,::-1,:].copy()
 
 input_image = torch.tensor(lr_image).type(torch.float32).permute(2,0,1)[None,...].to(config.device)
 
 predictions = []
 for model in models:
     with torch.inference_mode():
-        prediction = model(input_image).cpu().type(torch.uint8).squeeze().permute(1,2,0).numpy().copy()
+        prediction = model(input_image).cpu().type(torch.uint8).squeeze(0).permute(1,2,0).numpy().copy()
         predictions.append(prediction)
 
 image_gt = cv2.putText(image_gt, 'GT', org=(20, 50) , fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=(255,255,255), thickness=2, lineType= cv2.LINE_AA) 
 images_predicted = []
 for model_path, model, prediction in zip(config.model_paths, models, predictions):
+    prediction = np.concatenate([prediction]*3, axis=-1) if prediction.shape[-1] == 1 else prediction
     prediction = cv2.putText(prediction, model_path.stem, org=(20, 50) , fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=(255,255,255), thickness=2, lineType= cv2.LINE_AA) 
     images_predicted.append(prediction)
 

src/train.py

@@ -47,6 +47,7 @@ class TrainOptions:
         parser.add_argument('--save_predictions', action='store_true', default=False, help='Save model predictions to exp_dir/val/dataset_name')
         parser.add_argument('--device', default='cuda', help='Device of the model')
         parser.add_argument('--quantization_bits', '-q', type=int, default=4, help="Used when model is LUT. Number of 4DLUT buckets defined as 2**bits. Value is in range [1, 8].")  
+        parser.add_argument('--color_model', type=str, default="RGB", help="Color model for train and test dataset.")          
         
         self.parser = parser
 
@@ -130,7 +131,8 @@ if __name__ == "__main__":
         train_datasets.append(SRTrainDataset(
             hr_dir_path = Path(config.datasets_dir) / train_dataset_name / "HR",
             lr_dir_path = Path(config.datasets_dir) / train_dataset_name / "LR" / f"X{config.scale}",
-            patch_size = config.crop_size
+            patch_size = config.crop_size,
+            color_model = config.color_model
         ))
     train_dataset = torch.utils.data.ConcatDataset(train_datasets)
     train_loader = DataLoader(
@@ -149,6 +151,7 @@ if __name__ == "__main__":
         test_datasets[test_dataset_name] = SRTestDataset(
             hr_dir_path = Path(config.datasets_dir) / test_dataset_name  / "HR",
             lr_dir_path = Path(config.datasets_dir) / test_dataset_name / "LR" / f"X{config.scale}",
+            color_model = config.color_model
         )
     l_accum = [0., 0., 0.]
     prepare_data_time = 0.
@@ -180,8 +183,6 @@ if __name__ == "__main__":
         loss.backward()
         optimizer.step()
         optimizer.zero_grad()
-        del hr_patch
-        del lr_patch
         torch.cuda.empty_cache()
         forward_backward_time += time.time() - start_time
 

src/validate.py

@@ -28,6 +28,7 @@ class ValOptions():
         self.parser.add_argument('--val_datasets', type=str, default='Set5,Set14', help="Names of validation datasets.")        
         self.parser.add_argument('--save_predictions', action='store_true', default=True, help='Save model predictions to exp_dir/val/dataset_name')
         self.parser.add_argument('--device', type=str, default='cuda', help='Device of the model')
+        self.parser.add_argument('--color_model', type=str, default="RGB", help="Color model for train and test dataset.") 
 
     def parse_args(self):         
         args = self.parser.parse_args()
@@ -82,6 +83,7 @@ if __name__ == "__main__":
         test_datasets[test_dataset_name] = SRTestDataset(
             hr_dir_path = Path(config.datasets_dir) / test_dataset_name  / "HR",
             lr_dir_path = Path(config.datasets_dir) / test_dataset_name / "LR" / f"X{model.scale}",
+            color_model=config.color_model
         )
 
     results = valid_steps(model=model, datasets=test_datasets, config=config, log_prefix=f"Model {config.model_name}")