updates

readme.md

@@ -22,5 +22,5 @@ python image_demo.py --help
 
 Requirements:
 ```
-pip install shedulefree tensorboard opencv-python-headless scipy pandas
+pip install schedulefree tensorboard opencv-python-headless scipy pandas matplotlib
 ```

src/common/layers.py

@@ -25,7 +25,7 @@ class PercievePattern():
         return x
 
 class UpscaleBlock(nn.Module):
-    def __init__(self, in_features=4, hidden_dim = 32, layers_count=5, upscale_factor=1):
+    def __init__(self, in_features=4, hidden_dim = 32, layers_count=4, upscale_factor=1):
         super(UpscaleBlock, self).__init__()   
         assert layers_count > 0     
         self.upscale_factor = upscale_factor 

src/common/losses.py

@@ -0,0 +1,152 @@
+
+import torch
+
+class FourierLoss(nn.Module):
+    def __init__(self, weight=None, size_average=True):
+        super(FourierLoss, self).__init__()
+
+    @staticmethod
+    def perm_roll(im, axis, amount):
+        permutation = torch.roll(torch.arange(im.shape[axis], device=im.device), amount, dims=0)
+        return torch.index_select(im, axis, permutation)
+
+    @staticmethod
+    def shift_left(im):
+        tt = perm_roll(im, axis=-2, amount=-(im.shape[-2]+1)//2)
+        tt = perm_roll(tt, axis=-1, amount=-(im.shape[-1]+1)//2)
+        return tt
+
+    @staticmethod
+    def shift_right(im):
+        tt = FourierLoss.perm_roll(im, axis=-2, amount=(im.shape[-2]+1)//2)
+        tt = FourierLoss.perm_roll(tt, axis=-1, amount=(im.shape[-1]+1)//2)
+        return tt
+
+    @staticmethod
+    def aperture(h, w, condition=None, low_pass_frequency=False):
+        """torch.where(condition(circle_dist), torch.ones_like(circle_dist), torch.zeros_like(circle_dist)) """
+        x, y = torch.meshgrid(torch.arange(-h//2, h//2), torch.arange(-w//2, w//2), indexing='ij')
+        circle_dist = torch.sqrt(x**2 + y**2)
+        if low_pass_frequency:
+            circle_aperture = torch.where(condition(circle_dist), torch.zeros_like(circle_dist), torch.ones_like(circle_dist))
+        else:
+            circle_aperture = torch.where(condition(circle_dist), torch.ones_like(circle_dist), torch.zeros_like(circle_dist))
+        return circle_aperture
+
+    @staticmethod
+    def fft(x, aperture):
+        r = FourierLoss.shift_right(torch.fft.fft2(x, norm='ortho'))*aperture
+        return r.real**2 + r.imag**2
+
+    def forward(self, inputs, targets):
+        b,c,h,w = inputs.shape
+        r = min(h, w)//12
+        low_pass = FourierLoss.aperture(h=h, w=w, condition=lambda circle_dist: circle_dist>r, low_pass_frequency=False).to(inputs.device)
+        high_pass = FourierLoss.aperture(h=h, w=w, condition=lambda circle_dist: circle_dist>r, low_pass_frequency=True).to(inputs.device)
+        low_frequency_loss = F.mse_loss(FourierLoss.fft(inputs, low_pass), FourierLoss.fft(targets, low_pass))*(low_pass.sum()/high_pass.sum())
+        high_frequency_loss = F.mse_loss(FourierLoss.fft(inputs, high_pass), FourierLoss.fft(targets, high_pass))
+        return low_frequency_loss + high_frequency_loss
+        
+
+class FocalFrequencyLoss(nn.Module):
+    """The torch.nn.Module class that implements focal frequency loss - a
+    frequency domain loss function for optimizing generative models.
+
+    Ref:
+    Focal Frequency Loss for Image Reconstruction and Synthesis. In ICCV 2021.
+    <https://arxiv.org/pdf/2012.12821.pdf>
+
+    Args:
+        loss_weight (float): weight for focal frequency loss. Default: 1.0
+        alpha (float): the scaling factor alpha of the spectrum weight matrix for flexibility. Default: 1.0
+        patch_factor (int): the factor to crop image patches for patch-based focal frequency loss. Default: 1
+        ave_spectrum (bool): whether to use minibatch average spectrum. Default: False
+        log_matrix (bool): whether to adjust the spectrum weight matrix by logarithm. Default: False
+        batch_matrix (bool): whether to calculate the spectrum weight matrix using batch-based statistics. Default: False
+    """
+
+    def __init__(self, loss_weight=1.0, alpha=1.0, patch_factor=1, ave_spectrum=False, log_matrix=False, batch_matrix=False):
+        super(FocalFrequencyLoss, self).__init__()
+        self.loss_weight = loss_weight
+        self.alpha = alpha
+        self.patch_factor = patch_factor
+        self.ave_spectrum = ave_spectrum
+        self.log_matrix = log_matrix
+        self.batch_matrix = batch_matrix
+
+    def tensor2freq(self, x):
+        # crop image patches
+        patch_factor = self.patch_factor
+        _, _, h, w = x.shape
+        assert h % patch_factor == 0 and w % patch_factor == 0, (
+            'Patch factor should be divisible by image height and width')
+        patch_list = []
+        patch_h = h // patch_factor
+        patch_w = w // patch_factor
+        for i in range(patch_factor):
+            for j in range(patch_factor):
+                patch_list.append(x[:, :, i * patch_h:(i + 1) * patch_h, j * patch_w:(j + 1) * patch_w])
+
+        # stack to patch tensor
+        y = torch.stack(patch_list, 1)
+
+        # perform 2D DFT (real-to-complex, orthonormalization)
+        freq = torch.fft.fft2(y, norm='ortho')
+        freq = torch.stack([freq.real, freq.imag], -1)
+        return freq
+
+    def loss_formulation(self, recon_freq, real_freq, matrix=None):
+        # spectrum weight matrix
+        if matrix is not None:
+            # if the matrix is predefined
+            weight_matrix = matrix.detach()
+        else:
+            # if the matrix is calculated online: continuous, dynamic, based on current Euclidean distance
+            matrix_tmp = (recon_freq - real_freq) ** 2
+            matrix_tmp = torch.sqrt(matrix_tmp[..., 0] + matrix_tmp[..., 1]) ** self.alpha
+
+            # whether to adjust the spectrum weight matrix by logarithm
+            if self.log_matrix:
+                matrix_tmp = torch.log(matrix_tmp + 1.0)
+
+            # whether to calculate the spectrum weight matrix using batch-based statistics
+            if self.batch_matrix:
+                matrix_tmp = matrix_tmp / matrix_tmp.max()
+            else:
+                matrix_tmp = matrix_tmp / matrix_tmp.max(-1).values.max(-1).values[:, :, :, None, None]
+
+            matrix_tmp[torch.isnan(matrix_tmp)] = 0.0
+            matrix_tmp = torch.clamp(matrix_tmp, min=0.0, max=1.0)
+            weight_matrix = matrix_tmp.clone().detach()
+
+        assert weight_matrix.min().item() >= 0 and weight_matrix.max().item() <= 1, (
+            'The values of spectrum weight matrix should be in the range [0, 1], '
+            'but got Min: %.10f Max: %.10f' % (weight_matrix.min().item(), weight_matrix.max().item()))
+
+        # frequency distance using (squared) Euclidean distance
+        tmp = (recon_freq - real_freq) ** 2
+        freq_distance = tmp[..., 0] + tmp[..., 1]
+
+        # dynamic spectrum weighting (Hadamard product)
+        loss = weight_matrix * freq_distance
+        return torch.mean(loss)
+
+    def forward(self, pred, target, matrix=None, **kwargs):
+        """Forward function to calculate focal frequency loss.
+
+        Args:
+            pred (torch.Tensor): of shape (N, C, H, W). Predicted tensor.
+            target (torch.Tensor): of shape (N, C, H, W). Target tensor.
+            matrix (torch.Tensor, optional): Element-wise spectrum weight matrix.
+                Default: None (If set to None: calculated online, dynamic).
+        """
+        pred_freq = self.tensor2freq(pred)
+        target_freq = self.tensor2freq(target)
+
+        # whether to use minibatch average spectrum
+        if self.ave_spectrum:
+            pred_freq = torch.mean(pred_freq, 0, keepdim=True)
+            target_freq = torch.mean(target_freq, 0, keepdim=True)
+
+        # calculate focal frequency loss
+        return self.loss_formulation(pred_freq, target_freq, matrix) * self.loss_weight

src/common/lut.py

@@ -8,12 +8,71 @@ import numpy as np
 
 ##################### TRANSFER ##########################
 
-class Domain4DValues(Dataset):
+class Domain2DValues(Dataset):
     def __init__(self, quantization_interval=1):
-        super(Domain4DValues, self).__init__()
+        super(Domain2DValues, self).__init__()
+        values1d = torch.arange(0, 256, quantization_interval, dtype=torch.uint8)
+        values1d = torch.cat([values1d, torch.tensor([256])])
+        self.quantization_interval = quantization_interval
+        self.values = torch.cartesian_prod(*([values1d]*2)).view(-1, 1, 2)
+        
+    def __getitem__(self, idx):
+        if isinstance(idx, slice):
+            ix1s, ix2s, batch = [], [], [], []
+            for i in range(idx.start, idx.stop):
+                ix1, ix2, values = self.__getitem__(i)                
+                ix1s.append(ix1)
+                ix2s.append(ix2)
+                batch.append(values)
+            return ix1s, ix2s, ix3s, batch
+        else:
+            v = self.values[idx]
+            ix = v[0]//self.quantization_interval
+            return ix[0], ix[1],  v
+
+    def __len__(self):
+        return len(self.values)
+
+    def __iter__(self):
+        for i in range(len(self.values)):
+            yield self.__getitem__(i)
+
+class Domain3DValues(Dataset):
+    def __init__(self, quantization_interval=1):
+        super(Domain3DValues, self).__init__()
         values1d = torch.arange(0, 256, quantization_interval, dtype=torch.uint8)
         values1d = torch.cat([values1d, torch.tensor([256])])
         self.quantization_interval = quantization_interval
+        self.values = torch.cartesian_prod(*([values1d]*3)).view(-1, 1, 3)
+        
+    def __getitem__(self, idx):
+        if isinstance(idx, slice):
+            ix1s, ix2s, ix3s, batch = [], [], [], []
+            for i in range(idx.start, idx.stop):
+                ix1, ix2, ix3,  values = self.__getitem__(i)                
+                ix1s.append(ix1)
+                ix2s.append(ix2)
+                ix3s.append(ix3)
+                batch.append(values)
+            return ix1s, ix2s, ix3s, batch
+        else:
+            v = self.values[idx]
+            ix = v[0]//self.quantization_interval
+            return ix[0], ix[1], ix[2], v
+
+    def __len__(self):
+        return len(self.values)
+
+    def __iter__(self):
+        for i in range(len(self.values)):
+            yield self.__getitem__(i)
+
+class Domain4DValues(Dataset):
+    def __init__(self, quantization_interval=1, max_value=255):
+        super(Domain4DValues, self).__init__()
+        values1d = torch.arange(0, max_value+1, quantization_interval, dtype=torch.uint8)
+        values1d = torch.cat([values1d, torch.tensor([max_value+1])])
+        self.quantization_interval = quantization_interval
         self.values = torch.cartesian_prod(*([values1d]*4)).view(-1, 1, 4)
         
     def __getitem__(self, idx):
@@ -54,11 +113,11 @@ def transfer_rc_conv(rc_conv, quantization_interval=1):
     print()
     return lut
 
-def transfer_2x2_input_SxS_output(block, quantization_interval=16, batch_size=2**10):
-    bucket_count = 256//quantization_interval    
+def transfer_2x2_input_SxS_output(block, quantization_interval=16, batch_size=2**10, max_value=255):
+    bucket_count = (max_value+1)//quantization_interval    
     scale = block.upscale_factor if hasattr(block, 'upscale_factor') else 1
-    lut = np.full((bucket_count+1, bucket_count+1, bucket_count+1, bucket_count+1, scale, scale), dtype=np.uint8, fill_value=255) # 4DLUT for simple input window 2x2
-    domain_values = Domain4DValues(quantization_interval=quantization_interval)
+    lut = np.full((bucket_count+1, bucket_count+1, bucket_count+1, bucket_count+1, scale, scale), dtype=np.uint8, fill_value=max_value) # 4DLUT for simple input window 2x2
+    domain_values = Domain4DValues(quantization_interval=quantization_interval, max_value=max_value)
     domain_values_loader = DataLoader(
         domain_values, 
         batch_size=batch_size if quantization_interval >= 16 else 2**16, 
@@ -68,7 +127,7 @@ def transfer_2x2_input_SxS_output(block, quantization_interval=16, batch_size=2*
     )
     counter = 0
     for idx, (ix1s, ix2s, ix3s, ix4s, batch) in enumerate(domain_values_loader):
-        inputs = batch.type(torch.float32).cuda()
+        inputs = batch.type(torch.float32).to(list(block.parameters())[0].device)
         with torch.no_grad():
             outputs = block(inputs)
         lut[ix1s, ix2s, ix3s, ix4s, :, :] = outputs.reshape(len(ix1s), scale, scale).cpu().numpy().astype(np.uint8)
@@ -77,7 +136,51 @@ def transfer_2x2_input_SxS_output(block, quantization_interval=16, batch_size=2*
     print()
     return lut
 
+def transfer_3_input_SxS_output(block, quantization_interval=16, batch_size=2**10):
+    bucket_count = 256//quantization_interval    
+    scale = block.upscale_factor if hasattr(block, 'upscale_factor') else 1
+    lut = np.full((bucket_count+1, bucket_count+1, bucket_count+1, scale, scale), dtype=np.uint8, fill_value=255) # 4DLUT for simple input window 2x2
+    domain_values = Domain3DValues(quantization_interval=quantization_interval)
+    domain_values_loader = DataLoader(
+        domain_values, 
+        batch_size=batch_size if quantization_interval >= 16 else 2**16, 
+        pin_memory=True, 
+        num_workers=1 if quantization_interval >= 16 else mp.cpu_count(),
+        shuffle=False,
+    )
+    counter = 0
+    for idx, (ix1s, ix2s, ix3s, batch) in enumerate(domain_values_loader):
+        inputs = batch.type(torch.float32).cuda()
+        with torch.no_grad():
+            outputs = block(inputs)
+        lut[ix1s, ix2s, ix3s, :, :] = outputs.reshape(len(ix1s), scale, scale).cpu().numpy().astype(np.uint8)
+        counter += inputs.shape[0]
+        print(f"\r {block.__class__.__name__} {counter}/{len(domain_values)}", end="    ")
+    print()
+    return lut
 
+def transfer_2_input_SxS_output(block, quantization_interval=16, batch_size=2**10):
+    bucket_count = 256//quantization_interval    
+    scale = block.upscale_factor if hasattr(block, 'upscale_factor') else 1
+    lut = np.full((bucket_count+1, bucket_count+1, scale, scale), dtype=np.uint8, fill_value=255) # 4DLUT for simple input window 2x2
+    domain_values = Domain2DValues(quantization_interval=quantization_interval)
+    domain_values_loader = DataLoader(
+        domain_values, 
+        batch_size=batch_size if quantization_interval >= 16 else 2**16, 
+        pin_memory=True, 
+        num_workers=1 if quantization_interval >= 16 else mp.cpu_count(),
+        shuffle=False,
+    )
+    counter = 0
+    for idx, (ix1s, ix2s, batch) in enumerate(domain_values_loader):
+        inputs = batch.type(torch.float32).cuda()
+        with torch.no_grad():
+            outputs = block(inputs)
+        lut[ix1s, ix2s, :, :] = outputs.reshape(len(ix1s), scale, scale).cpu().numpy().astype(np.uint8)
+        counter += inputs.shape[0]
+        print(f"\r {block.__class__.__name__} {counter}/{len(domain_values)}", end="    ")
+    print()
+    return lut
 
 ##################### FORWARD ##########################
 
@@ -140,11 +243,11 @@ def select_index_1dlut_linear(ixA, lut):
 def select_index_3dlut_tetrahedral(index, lut):
     b, hw, c = index.shape
     lut = torch.clamp(lut, 0, 255)
-    dimA, dimB, dimC, dimD = lut.shape[:4]
+    dimA, dimB, dimC = lut.shape[:4]
     q = 256/(dimA-1)
     L = dimA
     upscale = lut.shape[-1]
-    weight = lut.reshape(L**4, upscale, upscale)
+    weight = lut.reshape(L**3, upscale, upscale)
 
     msbA = torch.floor_divide(index, q).type(torch.int64)
     msbB = msbA + 1

src/common/validation.py

@@ -6,6 +6,12 @@ from pathlib import Path
 from PIL import Image
 import time
 from datetime import timedelta, datetime
+from matplotlib import pyplot as plt
+
+cmap = plt.get_cmap('viridis')
+cmaplist = [cmap(i) for i in range(cmap.N)]
+cmaplut = np.array(cmaplist)
+cmaplut = np.round(cmaplut[:, 0:3]*255).astype(np.uint8)
 
 def val_image_pair(model, hr_image, lr_image, color_model, output_image_path=None, device='cuda'):      
     with torch.inference_mode():
@@ -27,7 +33,7 @@ def val_image_pair(model, hr_image, lr_image, color_model, output_image_path=Non
             if pred_lr_image.shape[-1] == 3 and color_model == 'YCbCr':
                 Image.fromarray(pred_lr_image, mode=color_model).convert("RGB").save(output_image_path)
             if pred_lr_image.shape[-1] == 1:
-                Image.fromarray(pred_lr_image[:,:,0]).save(output_image_path)
+                Image.fromarray(cmaplut[pred_lr_image[:,:,0]]).save(output_image_path)
 
         # metrics
         hr_image = modcrop(hr_image, model.scale)
@@ -64,7 +70,7 @@ def valid_steps(model, datasets, config, log_prefix="", print_progress = False):
         if print_progress:
             start_datetime = datetime.now()
         for idx, (hr_image, lr_image, hr_image_path, lr_image_path) in enumerate(test_dataset):
-            output_image_path = predictions_path / f'{Path(hr_image_path).stem}.png' if config.save_predictions else None
+            output_image_path = predictions_path / f'{Path(hr_image_path).stem}_{config.current_iter:06d}.png' if config.save_predictions else None
             task = val_image_pair(model, hr_image, lr_image, color_model=config.color_model, output_image_path=output_image_path, device=config.device)
             tasks.append(task)
             if print_progress:
@@ -104,6 +110,8 @@ def valid_steps(model, datasets, config, log_prefix="", print_progress = False):
             'Total time'
         ]
         config.logger.info("\n" + str(pd.DataFrame([row], columns=column_names).set_index('Dataset').T))
+        config.writer.add_scalar(f'{dataset_name}_PSNR', np.mean(psnrs), config.current_iter)
+        config.writer.add_scalar(f'{dataset_name}_SSIM', np.mean(ssims), config.current_iter)
         config.writer.flush()
 
     results = pd.DataFrame(results, columns=column_names).set_index('Dataset')

src/image_demo.py

@@ -16,6 +16,7 @@ class ImageDemoOptions():
         self.parser.add_argument('--hr_image_path', '-a', type=str, default="../data/Set14/HR/monarch.png", help="HR image path")
         self.parser.add_argument('--lr_image_path', '-b', type=str, default="../data/Set14/LR/X4/monarch.png", help="LR image path")
         self.parser.add_argument('--output_path', type=str, default="../models/", help="Output path.")
+        self.parser.add_argument('--output_name', type=str, default="image_demo.png", help="Output name.")
         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')
@@ -92,6 +93,6 @@ for i in range(row_count):
         row.append(images[i*column_count + j])
     columns.append(np.concatenate(row, axis=1))
 canvas = np.concatenate(columns, axis=0).astype(np.uint8)
-Image.fromarray(canvas).save(config.output_path / 'image_demo.png')
+Image.fromarray(canvas).save(config.output_path / config.output_name)
 
 print(datetime.now() - start_script_time )

src/models/__init__.py

@@ -30,6 +30,9 @@ AVAILABLE_MODELS = {
     'SRLutY': srlut.SRLutY,
     'HDBNet': hdbnet.HDBNet,
     'HDBLut': hdblut.HDBLut,
+    'HDBLNet': hdbnet.HDBLNet,
+    'HDBHNet': hdbnet.HDBHNet,
+    'SRMsbLsb4R90Net': srnet.SRMsbLsb4R90Net,
     # 'RCNetCentered_3x3': rcnet.RCNetCentered_3x3, 'RCLutCentered_3x3': rclut.RCLutCentered_3x3,   
     # 'RCNetCentered_7x7': rcnet.RCNetCentered_7x7, 'RCLutCentered_7x7': rclut.RCLutCentered_7x7,   
     # 'RCNetRot90_3x3': rcnet.RCNetRot90_3x3, 'RCLutRot90_3x3': rclut.RCLutRot90_3x3,

src/models/sdylut.py

@@ -45,7 +45,7 @@ class SDYLutx1(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = x.view(b*c, 1, h, w).type(torch.float32)
         output = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device)
@@ -63,6 +63,11 @@ class SDYLutx1(nn.Module):
                f"\n  stageD size: {self.stageD.shape}" + \
                f"\n  stageY size: {self.stageY.shape}"
 
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+
 class SDYLutx2(nn.Module):
     def __init__(
         self, 
@@ -107,7 +112,7 @@ class SDYLutx2(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = x.view(b*c, 1, h, w).type(torch.float32)
         output = torch.zeros([b*c, 1, h, w], dtype=x.dtype, device=x.device)
@@ -135,6 +140,11 @@ class SDYLutx2(nn.Module):
                f"\n  stage2_D size: {self.stage2_D.shape}" + \
                f"\n  stage2_Y size: {self.stage2_Y.shape}"
 
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+
 
 class SDYLutx3(nn.Module):
     def __init__(
@@ -186,7 +196,7 @@ class SDYLutx3(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = x.view(b*c, 1, h, w).type(torch.float32)
         output = torch.zeros([b*c, 1, h, w], dtype=x.dtype, device=x.device)
@@ -224,6 +234,12 @@ class SDYLutx3(nn.Module):
                f"\n  stage3_D size: {self.stage3_D.shape}" + \
                f"\n  stage3_Y size: {self.stage3_Y.shape}"
 
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+
+
 class SDYLutR90x1(nn.Module):
     def __init__(
         self, 
@@ -262,7 +278,7 @@ class SDYLutR90x1(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = x.view(b*c, 1, h, w).type(torch.float32)
         output = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device)
@@ -285,6 +301,10 @@ class SDYLutR90x1(nn.Module):
                f"\n  stageD size: {self.stageD.shape}" + \
                f"\n  stageY size: {self.stageY.shape}"
 
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
 
 
 class SDYLutR90x2(nn.Module):
@@ -331,7 +351,7 @@ class SDYLutR90x2(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = x.view(b*c, 1, h, w).type(torch.float32)
         output = torch.zeros([b*c, 1, h, w], dtype=x.dtype, device=x.device) 
@@ -368,3 +388,8 @@ class SDYLutR90x2(nn.Module):
                f"\n  stage2_S size: {self.stage2_S.shape}" + \
                f"\n  stage2_D size: {self.stage2_D.shape}" + \
                f"\n  stage2_Y size: {self.stage2_Y.shape}"
+
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn

src/models/sdynet.py

@@ -30,7 +30,7 @@ class SDYNetx1(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = x.reshape(b*c, 1, h, w)
         output = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device)
@@ -50,6 +50,12 @@ class SDYNetx1(nn.Module):
         lut_model = sdylut.SDYLutx1.init_from_numpy(stageS, stageD, stageY)
         return lut_model
 
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+        
+
 class SDYNetx2(nn.Module):
     def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
         super(SDYNetx2, self).__init__()
@@ -74,7 +80,7 @@ class SDYNetx2(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = x.reshape(b*c, 1, h, w)
         output = torch.zeros([b*c, 1, h, w], dtype=x.dtype, device=x.device)
@@ -104,6 +110,11 @@ class SDYNetx2(nn.Module):
         lut_model = sdylut.SDYLutx2.init_from_numpy(stage1_S, stage1_D, stage1_Y, stage2_S, stage2_D, stage2_Y)
         return lut_model
 
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+
 class SDYNetx3(nn.Module):
     def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
         super(SDYNetx3, self).__init__()
@@ -131,7 +142,7 @@ class SDYNetx3(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = x.reshape(b*c, 1, h, w)
         output = torch.zeros([b*c, 1, h, w], dtype=x.dtype, device=x.device)
@@ -171,6 +182,11 @@ class SDYNetx3(nn.Module):
         lut_model = sdylut.SDYLutx3.init_from_numpy(stage1_S, stage1_D, stage1_Y, stage2_S, stage2_D, stage2_Y, stage3_S, stage3_D, stage3_Y)
         return lut_model
 
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+
 class SDYNetR90x1(nn.Module):
     def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
         super(SDYNetR90x1, self).__init__()
@@ -192,7 +208,7 @@ class SDYNetR90x1(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = x.reshape(b*c, 1, h, w)
         output = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device)
@@ -217,6 +233,11 @@ class SDYNetR90x1(nn.Module):
         lut_model = sdylut.SDYLutR90x1.init_from_numpy(stageS, stageD, stageY)
         return lut_model
 
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+
 class SDYNetR90x2(nn.Module):
     def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
         super(SDYNetR90x2, self).__init__()
@@ -241,7 +262,7 @@ class SDYNetR90x2(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = x.view(b*c, 1, h, w)
         output_1 = torch.zeros([b*c, 1, h, w], dtype=x.dtype, device=x.device)
@@ -278,3 +299,8 @@ class SDYNetR90x2(nn.Module):
         stage2_Y = lut.transfer_2x2_input_SxS_output(self.stage2_Y, quantization_interval=quantization_interval, batch_size=batch_size)
         lut_model = sdylut.SDYLutR90x2.init_from_numpy(stage1_S, stage1_D, stage1_Y, stage2_S, stage2_D, stage2_Y)
         return lut_model
+
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn

src/models/srlut.py

@@ -39,7 +39,7 @@ class SRLut(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = x.reshape(b*c, 1, h, w).type(torch.float32)
         x = self.forward_stage(x, self.scale, self._extract_pattern_S, self.stage_lut)
@@ -49,6 +49,11 @@ class SRLut(nn.Module):
     def __repr__(self):
         return f"{self.__class__.__name__}\n  lut size: {self.stage_lut.shape}"
 
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+
 class SRLutY(nn.Module):
     def __init__(
         self, 
@@ -83,7 +88,7 @@ class SRLutY(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = self.rgb_to_ycbcr(x)
         y = x[:,0:1,:,:]
@@ -98,6 +103,11 @@ class SRLutY(nn.Module):
     def __repr__(self):
         return f"{self.__class__.__name__}\n  lut size: {self.stage_lut.shape}"
 
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+
 class SRLutR90(nn.Module):
     def __init__(
         self, 
@@ -130,7 +140,7 @@ class SRLutR90(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = x.reshape(b*c, 1, h, w)
         output = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=torch.float32, device=x.device)
@@ -145,6 +155,11 @@ class SRLutR90(nn.Module):
     def __repr__(self):
         return f"{self.__class__.__name__}\n  lut size: {self.stage_lut.shape}"
 
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+
 
 class SRLutR90Y(nn.Module):
     def __init__(
@@ -180,7 +195,7 @@ class SRLutR90Y(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = self.rgb_to_ycbcr(x)
         y = x[:,0:1,:,:]
@@ -199,3 +214,8 @@ class SRLutR90Y(nn.Module):
 
     def __repr__(self):
         return f"{self.__class__.__name__}\n  lut size: {self.stage_lut.shape}"
+
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn

src/models/srnet.py

@@ -7,6 +7,8 @@ from common import lut
 from pathlib import Path
 from . import srlut 
 from common import layers
+from itertools import cycle
+from common import losses
 
 class SRNet(nn.Module):
     def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
@@ -29,7 +31,7 @@ class SRNet(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
             
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = x.reshape(b*c, 1, h, w)
         x = self.forward_stage(x, self.scale, self._extract_pattern_S, self.stage1_S)
@@ -41,6 +43,11 @@ class SRNet(nn.Module):
         lut_model = srlut.SRLut.init_from_numpy(stage_lut)
         return lut_model
 
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+
 class SRNetY(nn.Module):
     def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
         super(SRNetY, self).__init__()
@@ -64,7 +71,7 @@ class SRNetY(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = self.rgb_to_ycbcr(x)
         y = x[:,0:1,:,:]
@@ -82,6 +89,11 @@ class SRNetY(nn.Module):
         lut_model = srlut.SRLutY.init_from_numpy(stage_lut)
         return lut_model
 
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+
 class SRNetR90(nn.Module):
     def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
         super(SRNetR90, self).__init__()
@@ -103,7 +115,7 @@ class SRNetR90(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = x.reshape(b*c, 1, h, w)
         output = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device)
@@ -120,6 +132,11 @@ class SRNetR90(nn.Module):
         lut_model = srlut.SRLutR90.init_from_numpy(stage_lut)
         return lut_model
 
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+
 class SRNetR90Y(nn.Module):
     def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
         super(SRNetR90Y, self).__init__()
@@ -144,7 +161,7 @@ class SRNetR90Y(nn.Module):
         x = x.reshape(b, c, h*scale, w*scale)
         return x
 
-    def forward(self, x):
+    def forward(self, x, config=None):
         b,c,h,w = x.shape
         x = self.rgb_to_ycbcr(x)
         y = x[:,0:1,:,:]
@@ -166,3 +183,75 @@ class SRNetR90Y(nn.Module):
         stage_lut = lut.transfer_2x2_input_SxS_output(self.stage1_S, quantization_interval=quantization_interval, batch_size=batch_size)
         lut_model = srlut.SRLutR90Y.init_from_numpy(stage_lut)
         return lut_model
+
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+
+class SRMsbLsb4R90Net(nn.Module):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SRMsbLsb4R90Net, self).__init__()
+        self.scale = scale 
+        self.hidden_dim = hidden_dim
+        self.layers_count = layers_count
+
+        self.msb_fns = nn.ModuleList([layers.UpscaleBlock(
+            in_features=4,
+            hidden_dim=hidden_dim,
+            layers_count=layers_count,
+            upscale_factor=self.scale
+        ) for x in range(4)])
+        self.lsb_fns = nn.ModuleList([layers.UpscaleBlock(
+            in_features=4,
+            hidden_dim=hidden_dim,
+            layers_count=layers_count,
+            upscale_factor=self.scale
+        ) for x in range(4)])
+        self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
+
+    def forward_stage(self, x, scale, percieve_pattern, stage):
+        b,c,h,w = x.shape
+        x = percieve_pattern(x)   
+        x = stage(x)
+        x = round_func(x)
+        x = x.reshape(b, c, h, w, scale, scale)
+        x = x.permute(0,1,2,4,3,5)
+        x = x.reshape(b, c, h*scale, w*scale)
+        return x
+            
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+
+        lsb = x % 16
+        msb = x - lsb
+
+        output_msb = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device)
+        output_lsb = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device)
+        for rotations_count, msb_fn, lsb_fn in zip(range(4), cycle(self.msb_fns), cycle(self.lsb_fns)):
+           rotated_msb = torch.rot90(msb, k=rotations_count, dims=[2, 3])
+           rotated_lsb = torch.rot90(lsb, k=rotations_count, dims=[2, 3])
+           output_msb_r = self.forward_stage(rotated_msb, self.scale, self._extract_pattern_S, msb_fn)           
+           output_lsb_r = self.forward_stage(rotated_lsb, self.scale, self._extract_pattern_S, lsb_fn)
+           output_msb_r = round_func((output_msb_r / 255)*16) * 15 
+           output_lsb_r = (output_lsb_r / 255) * 15
+           output_msb += torch.rot90(output_msb_r, k=-rotations_count, dims=[2, 3])
+           output_lsb += torch.rot90(output_lsb_r, k=-rotations_count, dims=[2, 3])
+        output_msb /= 4
+        output_lsb /= 4
+        if not config is None and config.current_iter % config.display_step == 0:
+            config.writer.add_histogram('output_lsb', output_lsb.detach().cpu().numpy(), config.current_iter)
+            config.writer.add_histogram('output_msb', output_msb.detach().cpu().numpy(), config.current_iter)
+        x = output_msb + output_lsb
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+
+    def get_lut_model(self, quantization_interval=16, batch_size=2**10):
+        raise NotImplementedError
+    
+    def get_loss_fn(self):
+        fourier_loss_fn = losses.FocalFrequencyLoss()
+        def loss_fn(pred, target):
+            return fourier_loss_fn(pred/255, target/255) * 1e8
+        return loss_fn

src/test.py

@@ -41,7 +41,7 @@ class ValOptions():
         args.valout_dir = Path(args.exp_dir).resolve() /  'val'
         if not args.valout_dir.exists():
             args.valout_dir.mkdir()
-        args.current_iter = args.model_name.split('_')[-1]
+        args.current_iter = int(args.model_name.split('_')[-1])
         args.results_path = os.path.join(args.valout_dir, f'results_{args.model_name}_{args.device}.csv')
         # Tensorboard for monitoring
         writer = SummaryWriter(log_dir=args.valout_dir)

src/train.py

@@ -45,7 +45,7 @@ class TrainOptions:
         parser.add_argument('--save_step', type=int, default=2000, help='save models every N iteration')
         parser.add_argument('--worker_num', '-n', type=int, default=1, help="Number of dataloader workers")
         parser.add_argument('--prefetch_factor', '-p', type=int, default=16, help="Prefetch factor of dataloader workers.")
-        parser.add_argument('--save_predictions', action='store_true', default=False, help='Save model predictions to exp_dir/val/dataset_name')
+        parser.add_argument('--save_predictions', action='store_true', default=True, 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.")          
@@ -59,6 +59,8 @@ class TrainOptions:
         args.model_path = Path(args.model_path) if not args.model_path is None else None
         args.train_datasets = args.train_datasets.split(',')
         args.val_datasets = args.val_datasets.split(',')       
+        if not args.model_path is None:
+            args.start_iter = int(args.model_path.stem.split("_")[-1]) 
         return args
 
     def __repr__(self):
@@ -110,7 +112,8 @@ if __name__ == "__main__":
         if 'lut' in config.model.lower():
             model = AVAILABLE_MODELS[config.model]( quantization_interval = 2**(8-config.quantization_bits), scale = config.scale)
     model = model.to(torch.device(config.device))
-    optimizer = AdamWScheduleFree(model.parameters())
+    optimizer = AdamWScheduleFree(model.parameters(), lr=1e-2, betas=(0.9, 0.95))
+    # optimizer = optim.AdamW(model.parameters(), lr=1e-4, betas=(0.9, 0.95))
     print(optimizer)
 
     prepare_experiment_folder(config)
@@ -140,7 +143,7 @@ if __name__ == "__main__":
         dataset = train_dataset,
         batch_size = config.batch_size, 
         num_workers = config.worker_num,
-        shuffle = False, 
+        shuffle = True, 
         drop_last = False, 
         pin_memory = True,
         prefetch_factor = config.prefetch_factor
@@ -165,7 +168,9 @@ if __name__ == "__main__":
         config.current_iter = i            
         valid_steps(model=model, datasets=test_datasets, config=config, log_prefix=f"Iter {i}")
 
+    loss_fn = model.get_loss_fn()
     for i in range(config.start_iter + 1, config.total_iter + 1):
+        config.current_iter = i 
         torch.cuda.empty_cache()
         start_time = time.time()
         try:
@@ -179,8 +184,8 @@ if __name__ == "__main__":
 
         start_time = time.time()
 
-        pred = model(lr_patch)
-        loss = F.mse_loss(pred/255, hr_patch/255)
+        pred = model(x=lr_patch, config=config)
+        loss = loss_fn(pred=pred, target=hr_patch)
         loss.backward()
         optimizer.step()
         optimizer.zero_grad()
@@ -194,10 +199,15 @@ if __name__ == "__main__":
         # Show information
         if i % config.display_step == 0:
             config.writer.add_scalar('loss_Pixel', l_accum[0] / config.display_step, i)
-
-            config.logger.info("{} | Iter:{:6d}, Sample:{:6d}, GPixel:{:.2e}, prepare_data_time:{:.4f}, forward_backward_time:{:.4f}".format(
-                model.__class__.__name__, i, accum_samples, l_accum[0] / config.display_step, prepare_data_time / config.display_step,
-                                            forward_backward_time / config.display_step))
+            config.writer.add_scalar('loss', loss.item(), i)
+
+            config.logger.info("{} | Iter:{:6d}, Sample:{:6d}, loss:{:.2e}, prepare_data_time:{:.4f}, forward_backward_time:{:.4f}".format(
+                model.__class__.__name__, 
+                i, 
+                accum_samples, 
+                l_accum[0] / config.display_step, 
+                prepare_data_time / config.display_step,
+                forward_backward_time / config.display_step))
             l_accum = [0., 0., 0.]
             prepare_data_time = 0.
             forward_backward_time = 0.
@@ -208,7 +218,6 @@ if __name__ == "__main__":
 
         # Validation
         if i % config.val_step == 0:           
-            config.current_iter = i            
             valid_steps(model=model, datasets=test_datasets, config=config, log_prefix=f"Iter {i}")
 
     model_path = (Path(config.checkpoint_dir) / f"{model.__class__.__name__}_{i}.pth").resolve()