fix the bug of visualization in tensorrt inference

deploy/TensorRT/Processor.py

@@ -120,7 +120,7 @@ def __init__(self, model, num_classes=80, num_layers=3, anchors=1, device=torch.
 
     def detect(self, img):
         """Detect objects in the input image."""
-        resized, _, _ = self.pre_process(img, self.input_shape)
+        resized, _ = self.pre_process(img, self.input_shape)
         outputs = self.inference(resized)
         return outputs
 
@@ -133,10 +133,10 @@ def pre_process(self, img_src, input_shape=None,):
         image = image.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
         image = torch.from_numpy(np.ascontiguousarray(image)).to(self.device).float()
         image = image / 255.  # 0 - 255 to 0.0 - 1.0
-        return image, pad, img_src
+        return image, pad
 
     def inference(self, inputs):
-        self.binding_addrs['image_arrays'] = int(inputs.data_ptr())
+        self.binding_addrs['images'] = int(inputs.data_ptr())
         #self.binding_addrs['x2paddle_image_arrays'] = int(inputs.data_ptr())
         self.context.execute_v2(list(self.binding_addrs.values()))
         output = self.bindings['outputs'].data
@@ -198,7 +198,6 @@ def non_max_suppression(self, prediction, conf_thres=0.25, iou_thres=0.45, class
         Returns:
              list of detections, echo item is one tensor with shape (num_boxes, 6), 6 is for [xyxy, conf, cls].
         """
-
         num_classes = prediction.shape[2] - 5  # number of classes
         pred_candidates = prediction[..., 4] > conf_thres  # candidates
 

deploy/TensorRT/eval_yolo_trt.py

@@ -81,9 +81,9 @@ def generate_results(processor, imgs_dir, jpgs, results_file, conf_thres, iou_th
         image_ids = []
         shapes = []
         for i in range(batch_size):
-            idx += 1
             if (idx == len(jpgs)): break
             img = cv2.imread(os.path.join(imgs_dir, jpgs[idx]))
+            img_src = img.copy()
             # shapes.append(img.shape)
             h0, w0 = img.shape[:2]
             r = test_load_size / max(h0, w0)
@@ -95,13 +95,14 @@ def generate_results(processor, imgs_dir, jpgs, results_file, conf_thres, iou_th
                     if r < 1 else cv2.INTER_LINEAR,
                 )
             h, w = img.shape[:2]
-            imgs[i], pad, img_src = processor.pre_process(img)
+            imgs[i], pad = processor.pre_process(img)
             source_imgs.append(img_src)
             shape = (h0, w0), ((h / h0, w / w0), pad)
             shapes.append(shape)
             image_ids.append(int(jpgs[idx].split('.')[0].split('_')[-1]))
+            idx += 1
         output = processor.inference(imgs)
-
+        
         for j in range(len(shapes)):
             pred = processor.post_process(output[j].unsqueeze(0), shapes[j], conf_thres=conf_thres, iou_thres=iou_thres)
             if visualize and num_visualized < num_imgs_to_visualize:

tools/qat/qat_export.py

@@ -138,7 +138,7 @@ def zero_scale_fix(model, device):
                           opset_version=13,
                           training=torch.onnx.TrainingMode.EVAL,
                           do_constant_folding=True,
-                          input_names=['image_arrays'],
+                          input_names=['images'],
                           output_names=['num_dets', 'det_boxes', 'det_scores', 'det_classes']
                           if args.end2end else ['outputs'],
                           dynamic_axes=dynamic_axes
@@ -157,7 +157,7 @@ def zero_scale_fix(model, device):
                           opset_version=13,
                           training=torch.onnx.TrainingMode.EVAL,
                           do_constant_folding=True,
-                          input_names=['image_arrays'],
+                          input_names=['images'],
                           output_names=['num_dets', 'det_boxes', 'det_scores', 'det_classes']
                           if args.end2end else ['outputs'],
                           )