For users to load in one key.

BiRefNet_pipe.py

@@ -0,0 +1,10 @@
+import torch
+from transformers import Pipeline
+
+
+class BiRefNetPipe(Pipeline):
+    def __init__(self, **kwargs):
+        Pipeline.__init__(self, **kwargs)
+        self.model.to(['cpu', 0][torch.cuda.is_available()])
+        self.model.eval()
+        

MyPipe.py

@@ -1,76 +0,0 @@
-import torch, os
-import torch.nn.functional as F
-from torchvision.transforms.functional import normalize
-import numpy as np
-from transformers import Pipeline
-from transformers.image_utils import load_image
-from skimage import io
-from PIL import Image
-
-class RMBGPipe(Pipeline):
-  def __init__(self,**kwargs):
-    Pipeline.__init__(self,**kwargs)
-    self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    self.model.to(self.device)
-    self.model.eval()
-
-  def _sanitize_parameters(self, **kwargs):
-    # parse parameters
-    preprocess_kwargs = {}
-    postprocess_kwargs = {}
-    if "model_input_size" in kwargs : 
-      preprocess_kwargs["model_input_size"] = kwargs["model_input_size"]
-    if "return_mask" in kwargs: 
-      postprocess_kwargs["return_mask"] = kwargs["return_mask"]
-    return preprocess_kwargs, {}, postprocess_kwargs
-
-  def preprocess(self,input_image,model_input_size: list=[1024,1024]):
-      # preprocess the input 
-      orig_im = load_image(input_image)
-      orig_im = np.array(orig_im)
-      orig_im_size = orig_im.shape[0:2]
-      preprocessed_image = self.preprocess_image(orig_im, model_input_size).to(self.device)
-      inputs = {
-          "preprocessed_image":preprocessed_image,
-          "orig_im_size":orig_im_size,
-          "input_image" : input_image
-      }
-      return inputs
-
-  def _forward(self,inputs):
-    result = self.model(inputs.pop("preprocessed_image"))
-    inputs["result"] = result
-    return inputs
-  
-  def postprocess(self,inputs,return_mask:bool=False ):
-    result = inputs.pop("result")
-    orig_im_size = inputs.pop("orig_im_size")
-    input_image = inputs.pop("input_image")
-    result_image = self.postprocess_image(result[0][0], orig_im_size)
-    pil_im = Image.fromarray(result_image)
-    if return_mask ==True : 
-      return pil_im
-    no_bg_image = Image.new("RGBA", pil_im.size, (0,0,0,0))
-    input_image = load_image(input_image)
-    no_bg_image.paste(input_image, mask=pil_im)
-    return no_bg_image
-    
-  # utilities functions
-  def preprocess_image(self,im: np.ndarray, model_input_size: list=[1024,1024]) -> torch.Tensor:
-    # same as utilities.py with minor modification
-    if len(im.shape) < 3:
-        im = im[:, :, np.newaxis]
-    im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
-    im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=model_input_size, mode='bilinear')
-    image = torch.divide(im_tensor,255.0)
-    image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
-    return image
-  
-  def postprocess_image(self,result: torch.Tensor, im_size: list)-> np.ndarray:
-      result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear') ,0)
-      ma = torch.max(result)
-      mi = torch.min(result)
-      result = (result-mi)/(ma-mi)
-      im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
-      im_array = np.squeeze(im_array)
-      return im_array

config.json

@@ -9,6 +9,7 @@
   },
   "custom_pipelines": {
     "image-segmentation": {
+      "impl": "BiRefNet_pipe.BiRefNetPipe",
       "pt": [
         "AutoModelForImageSegmentation"
       ],