remove unnecessary code and sort out code in deploy/infer (#1378)

deploy/python/infer.py

@@ -25,6 +25,24 @@ import numpy as np
 import paddle.fluid as fluid
 from visualize import visualize_box_mask
 
+# Global dictionary
+RESIZE_SCALE_SET = {
+    'RCNN',
+    'RetinaNet',
+    'FCOS',
+}
+
+SUPPORT_MODELS = {
+    'YOLO',
+    'SSD',
+    'RetinaNet',
+    'EfficientDet',
+    'RCNN',
+    'Face',
+    'TTF',
+    'FCOS',
+}
+
 
 def decode_image(im_file, im_info):
     """read rgb image
@@ -70,11 +88,10 @@ class Resize(object):
                  interp=cv2.INTER_LINEAR):
         self.target_size = target_size
         self.max_size = max_size
-        self.image_shape = image_shape,
+        self.image_shape = image_shape
         self.arch = arch
         self.use_cv2 = use_cv2
         self.interp = interp
-        self.scale_set = {'RCNN', 'RetinaNet', 'FCOS'}
 
     def __call__(self, im, im_info):
         """
@@ -124,12 +141,12 @@ class Resize(object):
         Args:
             im (np.ndarray): image (np.ndarray)
         Returns:
-            im_scale_x: the resize ratio of X 
-            im_scale_y: the resize ratio of Y 
+            im_scale_x: the resize ratio of X
+            im_scale_y: the resize ratio of Y
         """
         origin_shape = im.shape[:2]
         im_c = im.shape[2]
-        if self.max_size != 0 and self.arch in self.scale_set:
+        if self.max_size != 0 and self.arch in RESIZE_SCALE_SET:
             im_size_min = np.min(origin_shape[0:2])
             im_size_max = np.max(origin_shape[0:2])
             im_scale = float(self.target_size) / float(im_size_min)
@@ -255,7 +272,7 @@ def create_inputs(im, im_info, model_arch='YOLO'):
     if 'YOLO' in model_arch:
         im_size = np.array([origin_shape]).astype('int32')
         inputs['im_size'] = im_size
-    elif 'RetinaNet' in model_arch:
+    elif 'RetinaNet' or 'EfficientDet' in model_arch:
         scale = scale_x
         im_info = np.array([resize_shape + [scale]]).astype('float32')
         inputs['im_info'] = im_info
@@ -276,15 +293,6 @@ class Config():
     Args:
         model_dir (str): root path of model.yml
     """
-    support_models = [
-        'YOLO',
-        'SSD',
-        'RetinaNet',
-        'RCNN',
-        'Face',
-        'TTF',
-        'FCOS',
-    ]
 
     def __init__(self, model_dir):
         # parsing Yaml config for Preprocess
@@ -307,11 +315,11 @@ class Config():
         Raises:
             ValueError: loaded model not in supported model type 
         """
-        for support_model in self.support_models:
+        for support_model in SUPPORT_MODELS:
             if support_model in yml_conf['arch']:
                 return True
         raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
-            'arch'], self.support_models))
+            'arch'], SUPPORT_MODELS))
 
     def print_config(self):
         print('-----------  Model Configuration -----------')

tools/export_model.py

@@ -36,13 +36,29 @@ FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
 logging.basicConfig(level=logging.INFO, format=FORMAT)
 logger = logging.getLogger(__name__)
 
+# Global dictionary
+TRT_MIN_SUBGRAPH = {
+    'YOLO': 3,
+    'SSD': 3,
+    'RCNN': 40,
+    'RetinaNet': 40,
+    'EfficientDet': 40,
+    'Face': 3,
+    'TTFNet': 3,
+    'FCOS': 3,
+}
+RESIZE_SCALE_SET = {
+    'RCNN',
+    'RetinaNet',
+    'FCOS',
+}
+
 
 def parse_reader(reader_cfg, metric, arch):
     preprocess_list = []
 
     image_shape = reader_cfg['inputs_def'].get('image_shape', [3, None, None])
     has_shape_def = not None in image_shape
-    scale_set = {'RCNN', 'RetinaNet'}
 
     dataset = reader_cfg['dataset']
     anno_file = dataset.get_anno()
@@ -72,9 +88,9 @@ def parse_reader(reader_cfg, metric, arch):
         params.pop('_id')
         if p['type'] == 'Resize' and has_shape_def:
             params['target_size'] = min(image_shape[
-                1:]) if arch in scale_set else image_shape[1]
+                1:]) if arch in RESIZE_SCALE_SET else image_shape[1]
             params['max_size'] = max(image_shape[
-                1:]) if arch in scale_set else 0
+                1:]) if arch in RESIZE_SCALE_SET else 0
             params['image_shape'] = image_shape[1:]
             if 'target_dim' in params:
                 params.pop('target_dim')
@@ -114,19 +130,9 @@ def dump_infer_config(FLAGS, config):
         'draw_threshold': 0.5,
         'metric': config['metric']
     })
-    trt_min_subgraph = {
-        'YOLO': 3,
-        'SSD': 3,
-        'RCNN': 40,
-        'RetinaNet': 40,
-        'Face': 3,
-        'TTFNet': 3,
-        'FCOS': 3,
-    }
     infer_arch = config['architecture']
-    infer_arch = 'RetinaNet' if infer_arch == 'EfficientDet' else infer_arch
 
-    for arch, min_subgraph_size in trt_min_subgraph.items():
+    for arch, min_subgraph_size in TRT_MIN_SUBGRAPH.items():
         if arch in infer_arch:
             infer_cfg['arch'] = arch
             infer_cfg['min_subgraph_size'] = min_subgraph_size