fix hip config

Author: zhangyubo0722

ppdet/engine/export_utils.py

@@ -245,14 +245,12 @@ def _parse_reader(reader_cfg, dataset_cfg, metric, arch, image_shape):
 
     fuse_normalize = reader_cfg.get('fuse_normalize', False)
     sample_transforms = reader_cfg['sample_transforms']
-    hpi_dynamic_shape = None
     for st in sample_transforms[1:]:
         for key, value in st.items():
             p = {'type': key}
             if key == 'Resize':
                 if int(image_shape[1]) != -1:
                     value['target_size'] = image_shape[1:]
-                    hpi_dynamic_shape = image_shape[1:]
                 value['interp'] = value.get('interp', 1)  # cv2.INTER_LINEAR
             if fuse_normalize and key == 'NormalizeImage':
                 continue
@@ -277,7 +275,7 @@ def _parse_reader(reader_cfg, dataset_cfg, metric, arch, image_shape):
                     preprocess_list.append(p)
                     break
 
-    return preprocess_list, label_list, hpi_dynamic_shape
+    return preprocess_list, label_list
 
 
 def _parse_tracker(tracker_cfg):
@@ -287,7 +285,7 @@ def _parse_tracker(tracker_cfg):
     return tracker_params
 
 
-def _dump_infer_config(config, path, image_shape, model):
+def _dump_infer_config(config, path, image_shape, model, input_spec):
     arch_state = False
     from ppdet.core.config.yaml_helpers import setup_orderdict
     setup_orderdict()
@@ -381,34 +379,47 @@ def _dump_infer_config(config, path, image_shape, model):
         reader_cfg = config['TestReader']
         dataset_cfg = config['TestDataset']
 
-    infer_cfg['Preprocess'], infer_cfg['label_list'], hpi_dynamic_shape = _parse_reader(
+    infer_cfg['Preprocess'], infer_cfg['label_list'] = _parse_reader(
         reader_cfg, dataset_cfg, config['metric'], label_arch, image_shape[1:])
     if config.get("uniform_output_enabled", None):
+        for d in input_spec:
+            if 'image' in d:
+                hpi_dynamic_shape = list(d['image'].shape[2:])
         def get_dynamic_shapes(hpi_shape):
             return [[1, 3] + hpi_shape, [1, 3] + hpi_shape, [8, 3] + hpi_shape]
 
-        dynamic_shapes = get_dynamic_shapes(hpi_dynamic_shape) if hpi_dynamic_shape else [
+        dynamic_shapes = get_dynamic_shapes(hpi_dynamic_shape) if hpi_dynamic_shape != [-1, -1] else [
             [1, 3, 320, 320],
             [1, 3, 640, 640],
             [8, 3, 1280, 1280]
         ]
         shapes = {
             "image": dynamic_shapes,
-            "im_shape": [[1, 2], [1, 2], [8, 2]],
             "scale_factor": [[1, 2], [1, 2], [8, 2]]
         }
-        trt_dynamic_shape = [
-            [dim for _ in range(shape[0]) for dim in shape[2:]]
-            for shape in dynamic_shapes
-        ]
         trt_dynamic_shape_input_data = {
-            "im_shape": trt_dynamic_shape,
             "scale_factor": [
                 [2, 2],
                 [1, 1],
                 [0.67 for _ in range(2 * shapes["scale_factor"][-1][0])]
             ]
         }
+        model_names_required_imgsize = [
+            "DETR",
+            "DINO",
+            "RCNN",
+            "YOLOv3",
+            "CenterNet",
+            "BlazeFace",
+            "BlazeFace-FPN-SSH",
+        ]
+        if any(name in config.get('pdx_model_name', None) for name in model_names_required_imgsize):
+            shapes["im_shape"] = [[1, 2], [1, 2], [8, 2]]
+            trt_dynamic_shape = [
+                [dim for _ in range(shape[0]) for dim in shape[2:]]
+                for shape in dynamic_shapes
+            ]
+            trt_dynamic_shape_input_data["im_shape"] = trt_dynamic_shape
         hpi_config = OrderedDict({
             "backend_configs": OrderedDict({
                 "paddle_infer": OrderedDict({

ppdet/engine/trainer.py

@@ -1217,11 +1217,6 @@ def _get_infer_cfg_and_input_spec(self,
         if export_post_process and not export_benchmark:
             image_shape = [None] + image_shape[1:]
 
-        # Save infer cfg
-        _dump_infer_config(self.cfg,
-                           os.path.join(save_dir, yaml_name), image_shape,
-                           model)
-
         input_spec = [{
             "image": InputSpec(
                 shape=image_shape, name='image'),
@@ -1263,6 +1258,11 @@ def _get_infer_cfg_and_input_spec(self,
                     "image": InputSpec(
                         shape=image_shape, name='image')
                 }]
+        
+        # Save infer cfg
+        _dump_infer_config(self.cfg,
+                           os.path.join(save_dir, yaml_name), image_shape,
+                           model, input_spec)
 
         return static_model, pruned_input_spec, input_spec
 
@@ -1299,7 +1299,7 @@ def export(self, output_dir='output_inference', for_fd=False):
             try:
                 import encryption
             except ModuleNotFoundError:
-                print("failed to import encryption")
+                logger.info("Skipping import of the encryption module.")
             paddle_version = version.parse(paddle.__version__)
             if self.cfg.get("export_with_pir", False):
                 assert (paddle_version >= version.parse(