Merge pull request #1290 from prickly-u/eval_fix

Pass preprocess_image function to generators in evaluate.py

keras_retinanet/bin/evaluate.py

@@ -28,16 +28,21 @@
 from .. import models
 from ..preprocessing.csv_generator import CSVGenerator
 from ..preprocessing.pascal_voc import PascalVocGenerator
+from ..utils.anchors import make_shapes_callback
 from ..utils.config import read_config_file, parse_anchor_parameters
 from ..utils.eval import evaluate
 from ..utils.gpu import setup_gpu
 from ..utils.keras_version import check_keras_version
 from ..utils.tf_version import check_tf_version
 
 
-def create_generator(args):
+def create_generator(args, preprocess_image):
     """ Create generators for evaluation.
     """
+    common_args = {
+        'preprocess_image': preprocess_image,
+    }
+
     if args.dataset_type == 'coco':
         # import here to prevent unnecessary dependency on cocoapi
         from ..preprocessing.coco import CocoGenerator
@@ -49,6 +54,7 @@ def create_generator(args):
             image_max_side=args.image_max_side,
             config=args.config,
             shuffle_groups=False,
+            **common_args
         )
     elif args.dataset_type == 'pascal':
         validation_generator = PascalVocGenerator(
@@ -59,6 +65,7 @@ def create_generator(args):
             image_max_side=args.image_max_side,
             config=args.config,
             shuffle_groups=False,
+            **common_args
         )
     elif args.dataset_type == 'csv':
         validation_generator = CSVGenerator(
@@ -68,6 +75,7 @@ def create_generator(args):
             image_max_side=args.image_max_side,
             config=args.config,
             shuffle_groups=False,
+            **common_args
         )
     else:
         raise ValueError('Invalid data type received: {}'.format(args.dataset_type))
@@ -131,7 +139,8 @@ def main(args=None):
         args.config = read_config_file(args.config)
 
     # create the generator
-    generator = create_generator(args)
+    backbone = models.backbone(args.backbone)
+    generator = create_generator(args, backbone.preprocess_image)
 
     # optionally load anchor parameters
     anchor_params = None
@@ -141,6 +150,7 @@ def main(args=None):
     # load the model
     print('Loading model, this may take a second...')
     model = models.load_model(args.model, backbone_name=args.backbone)
+    generator.compute_shapes = make_shapes_callback(model)
 
     # optionally convert the model
     if args.convert_model: