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datasets.py

@@ -1,3 +1,4 @@
+# Modified for Adaptive Superpixels feature
 # Copyright (c) 2015-present, Facebook, Inc.
 # All rights reserved.
 from typing import Any, Callable, Optional
@@ -18,56 +19,173 @@
 from timm.data import create_transform
 
 
+# Superpixel generation function
+def generate_superpixels(
+    image_tensor_batch: torch.Tensor, 
+    k_values: torch.Tensor, 
+    m_values: torch.Tensor, 
+    denormalize_transform: Callable, 
+    downsample_factor: int, 
+    spix_method: str, 
+    device: torch.device
+) -> torch.Tensor:
+    """
+    Generates superpixel assignments for a batch of images.
+
+    Args:
+        image_tensor_batch (torch.Tensor): Batch of image tensors (N, C, H, W).
+                                           Assumed to be normalized.
+        k_values (torch.Tensor): Tensor of K values (number of superpixels) for each image (N,).
+        m_values (torch.Tensor): Tensor of m values (compactness) for each image (N,).
+        denormalize_transform (Callable): A callable transform (e.g., an instance of Denormalize)
+                                          to convert normalized image tensors back to approx. [0, 255] range.
+        downsample_factor (int): Factor by which to downsample the image before applying SLIC.
+                                 Higher values speed up computation but reduce detail.
+        spix_method (str): Superpixel algorithm to use, either 'fastslic' or 'slic'.
+        device (torch.device): The Torch device to which the final assignment tensor should be moved.
+
+    Returns:
+        torch.Tensor: A batch of superpixel assignment maps. 
+                      Shape (N, 1, H_spix, W_spix), where H_spix and W_spix are
+                      the dimensions of the downsampled image used for SLIC.
+    """
+    batch_assignments = []
+    for i in range(image_tensor_batch.shape[0]): # Iterate over each image in the batch
+        img_tensor = image_tensor_batch[i] # Single image tensor (C, H, W)
+        k_i = k_values[i].item() # K value for this specific image
+        m_i = m_values[i].item() # m value for this specific image
+
+        # Denormalize and prepare image for SLIC algorithm
+        # SLIC typically expects an image in a standard format (e.g., uint8, [0,255])
+        # Ensure tensor is on CPU for numpy conversion if it's not already.
+        img_for_spix_normalized_cpu = img_tensor.cpu()
+        img_for_spix_denormalized = denormalize_transform(img_for_spix_normalized_cpu)
+
+        # Convert to NumPy array, scale to [0, 255], and change layout from (C,H,W) to (H,W,C)
+        img_for_spix_numpy = np.array(img_for_spix_denormalized * 255).transpose(1, 2, 0)
+
+        # Rescale (downsample) the image
+        # anti_aliasing=True is important for quality when downscaling
+        # channel_axis=2 indicates the last axis is channels for skimage.transform.rescale
+        img_for_spix_rescaled = rescale(
+            img_for_spix_numpy, 
+            1 / downsample_factor, 
+            anti_aliasing=True, 
+            channel_axis=2
+        ).round().clip(0, 255).astype(np.uint8)
+
+        # Apply the chosen SLIC algorithm
+        if spix_method == 'fastslic':
+            slic_engine = SlicAvx2(num_components=int(k_i), compactness=m_i)
+            assignment = slic_engine.iterate(img_for_spix_rescaled)
+        elif spix_method == 'slic':
+            # skimage.segmentation.slic expects HWC format.
+            # channel_axis=-1 (or 2 for 3D HWC) might be needed depending on the version of skimage
+            # and if it correctly infers multichannel from the last dimension.
+            assignment = slic(
+                img_for_spix_rescaled, 
+                n_segments=int(k_i), 
+                compactness=m_i, 
+                channel_axis=-1, # Explicitly state channel axis for skimage
+                start_label=0 # Ensures segments start from 0
+            )
+        else:
+            raise NotImplementedError(f"Superpixel method {spix_method} not implemented.")
+
+        # Convert assignment map to tensor and add a channel dimension (1, H_spix, W_spix)
+        batch_assignments.append(torch.tensor(assignment, dtype=torch.int32).unsqueeze(0))
+
+        # Explicitly delete intermediate NumPy arrays and SLIC engine to help GC
+        del img_for_spix_normalized_cpu
+        del img_for_spix_denormalized
+        del img_for_spix_numpy
+        del img_for_spix_rescaled
+        del assignment # The NumPy array from SLIC
+        # slic_engine is minor, but can be deleted if it stores state and was created (fastslic path)
+        if 'slic_engine' in locals():
+            del slic_engine
+
+    # Stack all assignment maps into a single batch tensor (N, 1, H_spix, W_spix)
+    # and move to the specified device.
+    return torch.stack(batch_assignments).to(device)
+
+
 class SpixImageFolder(datasets.ImageFolder):
     def __init__(
         self,
         root: str,
-        n_segments=196,
-        compactness=10,
-        downsample=2,
+        n_segments=196, # Default number of superpixels (K) if not adaptive
+        compactness=10, # Default compactness (m) if not adaptive
+        downsample=2,   # Default downsample factor for superpixel generation
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
         loader: Callable[[str], Any] = datasets.folder.default_loader,
         is_valid_file: Optional[Callable[[str], bool]] = None,
-        spix_method = 'fastslic',
+        spix_method = 'fastslic', # Default superpixel algorithm
+        adaptive_superpixels = False, # If True, superpixels are not generated by this class
     ):
         super().__init__(root, transform, target_transform, loader, is_valid_file)
-        self.n_segments = n_segments
+        self.n_segments = n_segments # K for static superpixel mode
+        self.adaptive_superpixels = adaptive_superpixels # Flag to control behavior in __getitem__
+        # Denormalize transform is needed for superpixel generation as models operate on normalized images
         self.denormalize = Denormalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
-        self.compactness = compactness
+        self.compactness = compactness # m for static superpixel mode
         self.downsample = downsample
         self.spix_method = spix_method
 
     def __getitem__(self, index: int):
         """
         Args:
-            index (int): Index
+            index (int): Index of the data sample to retrieve.
 
         Returns:
-            tuple: (sample, assignment, target) where assignment is a map of superpixel indices for each pixel, and target is class_index of the target class.
+            tuple: 
+                - If `self.adaptive_superpixels` is `True` (adaptive mode): 
+                  `(sample, target)` 
+                  where `sample` is the transformed image tensor, and `target` is the class index.
+                - If `self.adaptive_superpixels` is `False` (static superpixel mode): 
+                  `(sample, assignment, target)`
+                  where `assignment` is the superpixel map tensor for the sample.
         """
-        path, target = self.samples[index]
-        sample = self.loader(path)
+        path, target = self.samples[index] # Get image path and class target
+        sample = self.loader(path) # Load image using the specified loader
+
+        # Apply image transformations (e.g., augmentations, normalization)
         if self.transform is not None:
-            # augmented sample
             sample = self.transform(sample)
-
-            # temporarily convert to [0, 255] and resize to acquire superpixels.
-            sample_for_spix = np.array(self.denormalize(sample) * 255).transpose(1, 2, 0)
-            sample_for_spix = rescale(sample_for_spix, 1 / self.downsample, anti_aliasing=True, channel_axis=2).round().clip(0, 255).astype(np.uint8)
-            if self.spix_method == 'fastslic':
-                slic_ = SlicAvx2(num_components=self.n_segments, compactness=self.compactness)
-                assignment = slic_.iterate(sample_for_spix)
-            elif self.spix_method == 'slic':
-                assignment = slic(sample_for_spix, n_segments=self.n_segments, compactness=self.compactness)
-            else:
-                raise NotImplementedError
-
-            assignment = torch.tensor(assignment).unsqueeze(0)
+
+        # Apply target transformations if any
         if self.target_transform is not None:
             target = self.target_transform(target)
-
-        return sample, assignment, target
+
+        if self.adaptive_superpixels:
+            # In adaptive mode, superpixels are generated on-the-fly in the training loop (engine.py).
+            # This class only returns the image and its target.
+            return sample, target
+        else:
+            # In static mode, superpixels are generated here using fixed K (n_segments) and m (compactness).
+            # The `generate_superpixels` function expects a batch of images,
+            # so we unsqueeze the single sample to create a batch of 1.
+
+            # Determine the device of the sample tensor. For datasets, this is typically CPU.
+            # The generate_superpixels function handles moving the result to a specified device,
+            # but its inputs (especially for denormalization and numpy conversion) are often expected on CPU.
+            current_device = sample.device 
+
+            # Call the helper function to generate superpixel assignments for the single image.
+            # K and m values are taken from self.n_segments and self.compactness.
+            assignment_batch = generate_superpixels(
+                image_tensor_batch=sample.unsqueeze(0),  # Convert (C,H,W) to (1,C,H,W)
+                k_values=torch.tensor([float(self.n_segments)], device=current_device), # K for this image
+                m_values=torch.tensor([float(self.compactness)], device=current_device),# m for this image
+                denormalize_transform=self.denormalize, # Pass the denormalization utility
+                downsample_factor=self.downsample,     # Pass the downsampling factor
+                spix_method=self.spix_method,          # Pass the SLIC algorithm choice
+                device=current_device                  # Superpixels generated on current_device (CPU)
+            )
+            # Remove the batch dimension from the assignment map (1,1,H_spix,W_spix) -> (1,H_spix,W_spix)
+            assignment = assignment_batch.squeeze(0) 
+            return sample, assignment, target
 
 
 class Denormalize(torch.nn.Module):
@@ -145,7 +263,17 @@ def build_dataset(is_train, args):
     elif args.data_set == 'IMNET':
         root = os.path.join(args.data_path, 'train' if is_train else 'val')
         if 'suit' in args.model:
-            dataset = SpixImageFolder(root, transform=transform, n_segments=args.n_spix_segments, compactness=args.compactness, downsample=args.downsample, spix_method=args.spix_method)
+            # Pass adaptive_superpixels flag, defaulting to False for now as args.adaptive_superpixels is not yet defined
+            # This will be updated later to use args.adaptive_superpixels
+            dataset = SpixImageFolder(
+                root, 
+                transform=transform, 
+                n_segments=args.n_spix_segments, 
+                compactness=args.compactness, 
+                downsample=args.downsample, 
+                spix_method=args.spix_method,
+                adaptive_superpixels=getattr(args, 'adaptive_superpixels', False) # Use getattr to avoid error if not present
+            )
         else:
             dataset = datasets.ImageFolder(root, transform=transform)
         nb_classes = 1000