Upload processor

image_processing_basnet.py

@@ -239,34 +239,6 @@ class BASNetImageProcessor(BaseImageProcessor):
  dn = (d - mi) / ((ma - mi) + torch.finfo(torch.float32).eps)
  return dn
 
- # prediction = _norm_output(prediction)
- # prediction = prediction.squeeze()
- # prediction_np = prediction.cpu().numpy()
-
- # image = Image.fromarray(prediction_np * 255).convert("RGB")
- # image = image.resize((width, height), resample=Image.Resampling.BILINEAR)
-
- # return image
-
- # breakpoint()
-
- # output = F.interpolate(output, (height, width), mode="bilinear")
- # output = output.squeeze(dim=0)
-
- # output = _norm_output(output)
-
- # # Add 0.5 after unnormalizing to [0, 255] to round to nearest integer
- # output = output * 255 + 0.5
- # output = output.clamp(0, 255)
-
- # # shape: (C=1, W, H) -> (W, H, C=1)
- # output = output.permute(1, 2, 0)
- # # shape: (W, H, C=3)
- # output = output.repeat(1, 1, 3)
-
- # output_np = output.cpu().numpy().astype(np.uint8)
- # return Image.fromarray(output_np)
-
  prediction = _norm_prediction(prediction)
  prediction = prediction.squeeze()
  prediction = prediction * 255 + 0.5