remove im_shape for evaluation (#1089)

ppdet/modeling/architecture/cascade_rcnn.py

@@ -122,6 +122,5 @@ class CascadeRCNN(BaseArch):
             'bbox_nums': self.gbd['predicted_bbox_nums'].numpy(),
             'mask': self.gbd['predicted_mask'].numpy(),
             'im_id': self.gbd['im_id'].numpy(),
-            'im_shape': self.gbd['im_shape'].numpy()
         }
         return inputs

ppdet/modeling/architecture/faster_rcnn.py

@@ -73,7 +73,6 @@ class FasterRCNN(BaseArch):
         outs = {
             "bbox": self.gbd['predicted_bbox'].numpy(),
             "bbox_nums": self.gbd['predicted_bbox_nums'].numpy(),
-            'im_id': self.gbd['im_id'].numpy(),
-            'im_shape': self.gbd['im_shape'].numpy()
+            'im_id': self.gbd['im_id'].numpy()
         }
         return outs

ppdet/modeling/architecture/mask_rcnn.py

@@ -96,7 +96,6 @@ class MaskRCNN(BaseArch):
             'bbox': self.gbd['predicted_bbox'].numpy(),
             'bbox_nums': self.gbd['predicted_bbox_nums'].numpy(),
             'mask': self.gbd['predicted_mask'].numpy(),
-            'im_id': self.gbd['im_id'].numpy(),
-            'im_shape': self.gbd['im_shape'].numpy()
+            'im_id': self.gbd['im_id'].numpy()
         }
         return inputs

ppdet/modeling/architecture/yolo.py

@@ -48,6 +48,7 @@ class YOLOv3(BaseArch):
     def infer(self, ):
         outs = {
             "bbox": self.gbd['predicted_bbox'].numpy(),
-            "bbox_nums": self.gbd['predicted_bbox_nums']
+            "bbox_nums": self.gbd['predicted_bbox_nums'],
+            'im_id': self.gbd['im_id'].numpy()
         }
         return outs

ppdet/py_op/post_process.py

@@ -120,12 +120,7 @@ def mask_post_process(bbox_nums, bboxes, masks, im_info):
 
 
 @jit
-def get_det_res(bbox_nums,
-                bbox,
-                image_id,
-                image_shape,
-                num_id_to_cat_id_map,
-                batch_size=1):
+def get_det_res(bbox_nums, bbox, image_id, num_id_to_cat_id_map, batch_size=1):
     det_res = []
     bbox_v = np.array(bbox)
     if bbox_v.shape == (
@@ -139,8 +134,6 @@ def get_det_res(bbox_nums,
     for i in range(batch_size):
         dt_num_this_img = bbox_nums[i + 1] - bbox_nums[i]
         image_id = int(image_id[i][0])
-        image_width = int(image_shape[i][1])  #int(data[i][-1][1])
-        image_height = int(image_shape[i][2])  #int(data[i][-1][2])
         for j in range(dt_num_this_img):
             dt = bbox_v[k]
             k = k + 1

ppdet/utils/eval_utils.py

@@ -2,6 +2,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
+
 
 def json_eval_results(metric, json_directory=None, dataset=None):
     """
@@ -39,14 +41,16 @@ def coco_eval_results(outs_res=None,
     from ppdet.py_op.post_process import get_det_res, get_seg_res
     anno_file = os.path.join(dataset.dataset_dir, dataset.anno_path)
     cocoGt = COCO(anno_file)
-    catid = {i + 1: v for i, v in enumerate(cocoGt.getCatIds())}
+    catid = {
+        i + dataset.with_background: v
+        for i, v in enumerate(cocoGt.getCatIds())
+    }
 
     if outs_res is not None and len(outs_res) > 0:
         det_res = []
         for outs in outs_res:
             det_res += get_det_res(outs['bbox_nums'], outs['bbox'],
-                                   outs['im_id'], outs['im_shape'], catid,
-                                   batch_size)
+                                   outs['im_id'], catid, batch_size)
 
         with io.open("bbox_eval.json", 'w') as outfile:
             encode_func = unicode if six.PY2 else str