Upload Image3DPredictionPipeline

config.json

@@ -7,6 +7,15 @@
     "AutoConfig": "YoussefMoNader/timesformer-test4--timesformer_config.TimesformerScrollprizeConfig",
     "AutoModel": "YoussefMoNader/timesformer-test4--timesformer_model.TimesformerScrollprizeModel"
   },
+  "custom_pipelines": {
+    "ink-detection": {
+      "impl": "ink_detection_pipeline.Image3DPredictionPipeline",
+      "pt": [
+        "AutoModel"
+      ],
+      "tf": []
+    }
+  },
   "depth": 8,
   "dim": 512,
   "n_heads": 6,

ink_detection_pipeline.py

@@ -0,0 +1,146 @@
+import torch
+import torch.nn.functional as F
+import numpy as np
+from transformers import Pipeline,AutoModel
+from tqdm import tqdm
+
+
+
+class Image3DPredictionPipeline(Pipeline):
+    """
+    A custom pipeline that:
+    1. Takes in a 3D image: shape (m, n, d).
+    2. Cuts it into (64, 64, d) tiles with a given stride.
+    3. Runs the model inference on each tile (model is 2D-only).
+    4. Reconstructs the predictions into a full-size output.
+    """
+
+    def __init__(self, model, device='cuda', tile_size=64, stride=32, scale_factor=16,**kwargs):
+        super().__init__(model=model, tokenizer=None, device=0 if device=='cuda' else -1)
+        self.model = model.to(device)
+        self.device = device
+        self.tile_size = tile_size
+        self.stride = stride
+        self.scale_factor = scale_factor
+        self.batch_size=64
+    def preprocess(self, inputs):
+        """
+        inputs: np.ndarray of shape (m, n, d)
+        This function cuts the input volume into tiles of shape (tile_size, tile_size, d)
+        with a given stride.
+        Returns:
+          tiles: list of np arrays each (tile_size, tile_size, d)
+          coords: list of (x1, y1, x2, y2) coords
+        """
+        volume = inputs
+        m, n, d = volume.shape
+        tiles = []
+        coords = []
+        
+        # Extract patches with overlap
+        for y in range(0, m - self.tile_size + 1, self.stride):
+            for x in range(0, n - self.tile_size + 1, self.stride):
+                y1, y2 = y, y + self.tile_size
+                x1, x2 = x, x + self.tile_size
+                patch = volume[y1:y2, x1:x2]  # shape (64,64,d)
+                tiles.append(patch)
+                coords.append((x1, y1, x2, y2))
+        return tiles, coords, (m, n)
+    def _forward(self, model_inputs):
+        """
+        model_inputs: a list of patches (B, tile_size, tile_size, d)
+        The model expects input: (B, C=1, H=tile_size, W=tile_size)
+        and returns (B, 1, H=tile_size, W=tile_size).
+
+        We'll add batching using a for loop. We assume `self.batch_size` is defined.
+        """
+
+        patches = model_inputs
+        B = len(patches)
+
+        # Convert from list of (tile_size, tile_size, d) to (B, d, tile_size, tile_size)
+        batch = np.stack([p.transpose(2,0,1) for p in patches], axis=0,dtype=np.float32)  # (B, d, tile_size, tile_size)
+        batch = torch.from_numpy(batch).float()
+
+        all_preds = []
+        # Process in batches to save memory
+        for start_idx in tqdm(range(0, B, 64)):
+            end_idx = start_idx + self.batch_size
+            sub_batch = batch[start_idx:end_idx]  # shape: (subB, d, tile_size, tile_size)
+
+            # Add channel dimension: (subB, 1, tile_size, tile_size)
+            sub_batch = sub_batch.unsqueeze(1)
+
+            with torch.no_grad(), torch.autocast(self.device if self.device == 'cuda' else 'cpu'):
+                sub_y_preds = self.model(sub_batch.to(self.device))  # (subB, 1, tile_size, tile_size)
+
+            # Apply sigmoid
+            sub_y_preds = torch.sigmoid(sub_y_preds)
+
+            # Move to CPU and numpy
+            sub_y_preds = sub_y_preds.detach().cpu().numpy()
+            # shape (subB, 1, tile_size, tile_size)
+
+            all_preds.append(sub_y_preds)
+
+        # Concatenate along the batch dimension
+        y_preds = np.concatenate(all_preds, axis=0)  # (B, 1, tile_size, tile_size)
+
+        return y_preds
+
+    def postprocess(self, model_outputs, coords, full_shape):
+        """
+        model_outputs: np.ndarray of shape (B, 1, tile_size, tile_size)
+        coords: list of (x1, y1, x2, y2) for each tile
+        full_shape: (m,n)
+
+        We need to:
+        - Place each tile prediction into a full (m,n) array
+        - Use the kernel to weight and sum predictions
+        - Divide by count
+        - Optionally upsample by scale_factor if required
+        """
+        m, n = full_shape
+        # We will create mask_pred and mask_count to accumulate predictions
+        mask_pred = np.zeros((m, n), dtype=np.float32)
+        mask_count = np.zeros((m, n), dtype=np.float32)
+        B = model_outputs.shape[0]
+        # Interpolate (upsample) each prediction if needed
+        # Using PyTorch interpolate:
+        preds_tensor = torch.from_numpy(model_outputs.astype(np.float32))  # (B,1,64,64)
+        if self.scale_factor != 1:
+            preds_tensor = F.interpolate(
+                preds_tensor, scale_factor=self.scale_factor, mode='bilinear', align_corners=False
+            )
+        # shape after upsample: (B,1,64*scale_factor,64*scale_factor)
+        preds_tensor = preds_tensor.squeeze(1).numpy()  # (B, H_out, W_out)
+
+        # Adjust coords for upsampling
+        out_tile_size = self.tile_size 
+
+        for i, (x1, y1, x2, y2) in enumerate(coords):
+            # Adjust coords due to upsampling
+            y2_up = y1 + out_tile_size
+            x2_up = x1 + out_tile_size
+
+            mask_pred[y1:y2_up, x1:x2_up] += preds_tensor[i] 
+            mask_count[y1:y2_up, x1:x2_up] += np.ones((out_tile_size, out_tile_size), dtype=np.float32)
+
+        mask_pred = np.divide(mask_pred, mask_count, out=np.zeros_like(mask_pred), where=mask_count!=0)
+        
+        return mask_pred
+    def _sanitize_parameters(self,**kwargs):
+        return {},{},{}
+    def __call__(self, image: np.ndarray):
+        """
+        Args:
+            image: np.ndarray of shape (m, n, d) input volume.
+        Returns:
+            mask_pred: np.ndarray of shape (m_out, n_out) predicted mask.
+        """
+        tiles, coords, full_shape = self.preprocess(image)
+        # Process in batches if too large (optional). Here we do a single batch inference for simplicity.
+        # If large images, consider chunking tiles into smaller batches.
+        outputs = self._forward(tiles)
+        mask_pred = self.postprocess(outputs, coords, full_shape)
+        return mask_pred