Merge branch 'NVIDIA-AI-IOT:main' into main

Author: nv-vankit

examples/owl_predict.py

@@ -30,7 +30,7 @@
 
     parser = argparse.ArgumentParser()
     parser.add_argument("--image", type=str, default="../assets/owl_glove_small.jpg")
-    parser.add_argument("--prompt", type=str, default="an owl, a glove")
+    parser.add_argument("--prompt", type=str, default="[an owl, a glove]")
     parser.add_argument("--threshold", type=str, default="0.1,0.1")
     parser.add_argument("--output", type=str, default="../data/owl_predict_out.jpg")
     parser.add_argument("--model", type=str, default="google/owlvit-base-patch32")
@@ -45,7 +45,10 @@
 
     thresholds = args.threshold.strip("][()")
     thresholds = thresholds.split(',')
-    thresholds = [float(x) for x in thresholds]
+    if len(thresholds) == 1:
+        thresholds = float(thresholds[0])
+    else:
+        thresholds = [float(x) for x in thresholds]
     print(thresholds)
 
 

nanoowl/owl_predictor.py

@@ -278,7 +278,7 @@ def decode(self,
         ) -> OwlDecodeOutput:
 
         if isinstance(threshold, (int, float)):
-            threshold = [threshold]
+            threshold = [threshold] * len(text_output.text_embeds) #apply single threshold to all labels 
 
         num_input_images = image_output.image_class_embeds.shape[0]
 
@@ -468,7 +468,7 @@ def predict(self,
         if text_encodings is None:
             text_encodings = self.encode_text(text)
 
-        rois = torch.tensor([[0, 0, image.height, image.width]], dtype=image_tensor.dtype, device=image_tensor.device)
+        rois = torch.tensor([[0, 0, image.width, image.height]], dtype=image_tensor.dtype, device=image_tensor.device)
 
         image_encodings = self.encode_rois(image_tensor, rois, pad_square=pad_square)