wondervictor commited on
Commit
bddd2bc
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verified ·
1 Parent(s): aa489a7

Update mask_adapter/sam_maskadapter.py

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Files changed (1) hide show
  1. mask_adapter/sam_maskadapter.py +3 -3
mask_adapter/sam_maskadapter.py CHANGED
@@ -239,7 +239,7 @@ class SAMPointVisualizationDemo(object):
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  lvis_classes = [x[x.find(':')+1:] for x in lvis_classes]
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  self.class_names = thing_classes + stuff_classes + lvis_classes
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- #self.text_embedding = torch.from_numpy(np.load("./text_embedding/lvis_coco_text_embedding.npy"))
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  self.class_names = self._load_class_names()
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@@ -280,7 +280,7 @@ class SAMPointVisualizationDemo(object):
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  return clip_vis_dense
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- def run_on_image_with_points(self, ori_image, points, text_features):
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  height, width, _ = ori_image.shape
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  image = ori_image
@@ -311,7 +311,7 @@ class SAMPointVisualizationDemo(object):
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  # text_features = self.clip_model.encode_text(text.cuda())
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  # text_features /= text_features.norm(dim=-1, keepdim=True)
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  #np.save("/home/yongkangli/Mask-Adapter/text_embedding/lvis_coco_text_embedding.npy", text_features.cpu().numpy())
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- #text_features = self.text_embedding.to(self.clip_model.device)
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  features = self.extract_features_convnext(image.to(self.clip_model.device).float())
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  clip_feature = features['clip_vis_dense']
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  lvis_classes = [x[x.find(':')+1:] for x in lvis_classes]
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  self.class_names = thing_classes + stuff_classes + lvis_classes
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+ self.text_embedding = torch.from_numpy(np.load("./text_embedding/lvis_coco_text_embedding.npy"))
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  self.class_names = self._load_class_names()
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  return clip_vis_dense
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+ def run_on_image_with_points(self, ori_image, points):
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  height, width, _ = ori_image.shape
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  image = ori_image
 
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  # text_features = self.clip_model.encode_text(text.cuda())
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  # text_features /= text_features.norm(dim=-1, keepdim=True)
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  #np.save("/home/yongkangli/Mask-Adapter/text_embedding/lvis_coco_text_embedding.npy", text_features.cpu().numpy())
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+ text_features = self.text_embedding.to(self.clip_model.device)
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  features = self.extract_features_convnext(image.to(self.clip_model.device).float())
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  clip_feature = features['clip_vis_dense']
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