Upload processor

image_processing.py

@@ -0,0 +1,68 @@
+import numpy as np
+import torch
+from PIL import Image, ImageDraw
+from torch import Tensor
+from transformers import BaseImageProcessorFast
+
+
+class LSPDetrImageProcessor(BaseImageProcessorFast):
+    image_mean = [0.485, 0.456, 0.406]
+    image_std = [0.229, 0.224, 0.225]
+    do_rescale = True
+    do_normalize = True
+    return_tensors = "pt"
+
+    def post_process(self, outputs: dict[str, Tensor]) -> list[dict[str, Tensor]]:
+        """Converts the raw output into polygons.
+
+        Returns:
+            A list of dictionaries, each containing:
+                - "polygons": A tensor of shape (N, num_radial_distances, 2) representing the polygons.
+                - "labels": A tensor of shape (N,) representing the labels for each polygon.
+        """
+        radial_distances = outputs["radial_distances"].expm1()
+
+        t = torch.linspace(0, 1, radial_distances.size(-1) + 1, device=self.device)[:-1]
+        cos = torch.cos(2 * torch.pi * t)
+        sin = torch.sin(2 * torch.pi * t)
+
+        polar = radial_distances.unsqueeze(-1) * torch.stack([sin, cos], dim=-1)
+        polygons = outputs["absolute_points"].unsqueeze(-2) + polar
+
+        labels = outputs["logits"].argmax(dim=-1)
+        non_no_object_indices = labels != outputs["logits"].size(-1) - 1
+
+        return [
+            {"polygons": polygons[b, indices], "labels": labels[b, indices]}
+            for b, indices in enumerate(non_no_object_indices)
+        ]
+
+    def post_process_instance(
+        self,
+        results: list[dict[str, Tensor]],
+        height: int,
+        width: int,
+    ) -> list[dict[str, Tensor]]:
+        """Converts the output into actual instance segmentation predictions.
+
+        Args:
+            results: Results list obtained by `post_process`, to which "masks" results will be added.
+            height: Height of the input image.
+            width: Width of the input image.
+        """
+        for i, result in enumerate(results):
+            masks = torch.zeros(
+                (len(result["polygons"]), height, width),
+                dtype=torch.bool,
+                device=result["polygons"].device,
+            )
+
+            for i, polygon in enumerate(result["polygons"]):
+                img = Image.fromarray(masks[i].cpu().numpy())
+                canvas = ImageDraw.Draw(img)
+                canvas.polygon(xy=polygon.flatten().tolist(), outline=1, fill=1)
+                masks[i] = torch.from_numpy(np.asarray(img))
+
+            results[i]["masks"] = masks
+
+        return results

preprocessor_config.json

@@ -1,6 +1,6 @@
 {
   "auto_map": {
-    "AutoImageProcessor": "image_processing_fast.LSPDetrImageProcessorFast"
+    "AutoImageProcessor": "image_processing.LSPDetrImageProcessor"
   },
   "crop_size": null,
   "data_format": "channels_first",
@@ -16,7 +16,7 @@
     0.456,
     0.406
   ],
-  "image_processor_type": "LSPDetrImageProcessorFast",
+  "image_processor_type": "LSPDetrImageProcessor",
   "image_std": [
     0.229,
     0.224,