Delete image_processing_fast.py

image_processing_fast.py

@@ -1,62 +0,0 @@
-import numpy as np
-import torch
-from PIL import Image, ImageDraw
-from torch import Tensor
-from transformers import BaseImageProcessorFast
-
-
-class LSPDetrImageProcessorFast(BaseImageProcessorFast):
-    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