Add Paddle exports to benchmarks

benchmarks.py

@@ -65,7 +65,7 @@ def run(
     model_type = type(attempt_load(weights, fuse=False))  # DetectionModel, SegmentationModel, etc.
     for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows():  # index, (name, file, suffix, CPU, GPU)
         try:
-            assert i not in (9, 10, 11), 'inference not supported'  # Edge TPU, TF.js and Paddle are unsupported
+            assert i not in (9, 10), 'inference not supported'  # Edge TPU and TF.js are unsupported
             assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13'  # CoreML
             if 'cpu' in device.type:
                 assert cpu, 'inference not supported on CPU'

models/common.py

@@ -460,8 +460,8 @@ def wrap_frozen_graph(gd, inputs, outputs):
             if cuda:
                 config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
             predictor = pdi.create_predictor(config)
-            input_names = predictor.get_input_names()
-            input_handle = predictor.get_input_handle(input_names[0])
+            input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
+            output_names = predictor.get_output_names()
         else:
             raise NotImplementedError(f'ERROR: {w} is not a supported format')
 
@@ -517,12 +517,10 @@ def forward(self, im, augment=False, visualize=False):
                 k = 'var_' + str(sorted(int(k.replace('var_', '')) for k in y)[-1])  # output key
                 y = y[k]  # output
         elif self.paddle:  # PaddlePaddle
-            im = im.cpu().numpy().astype("float32")
+            im = im.cpu().numpy().astype(np.float32)
             self.input_handle.copy_from_cpu(im)
             self.predictor.run()
-            output_names = self.predictor.get_output_names()
-            output_handle = self.predictor.get_output_handle(output_names[0])
-            y = output_handle.copy_to_cpu()
+            y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
         else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
             im = im.permute(0, 2, 3, 1).cpu().numpy()  # torch BCHW to numpy BHWC shape(1,320,192,3)
             if self.saved_model:  # SavedModel

utils/segment/plots.py

@@ -99,9 +99,9 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
                         if mh != h or mw != w:
                             mask = image_masks[j].astype(np.uint8)
                             mask = cv2.resize(mask, (w, h))
-                            mask = mask.astype(np.bool)
+                            mask = mask.astype(bool)
                         else:
-                            mask = image_masks[j].astype(np.bool)
+                            mask = image_masks[j].astype(bool)
                         with contextlib.suppress(Exception):
                             im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6
                 annotator.fromarray(im)