For users to load in one key.

BiRefNet_github/models/birefnet.py

@@ -114,7 +114,7 @@ class BiRefNet(
     def forward(self, x):
         scaled_preds, class_preds = self.forward_ori(x)
         class_preds_lst = [class_preds]
-        return [scaled_preds, class_preds_lst] if self.training and 0 else scaled_preds
+        return [scaled_preds, class_preds_lst] if self.training else scaled_preds
 
 
 class Decoder(nn.Module):

MyPipe.py

@@ -0,0 +1,76 @@
+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": "MyPipe.RMBGPipe",
       "pt": [
         "AutoModelForImageSegmentation"
       ],