fix train fail problem and fix readme.

model_zoo/official/cv/retinaface_resnet50/README.md

@@ -218,9 +218,9 @@ Parameters for both training and evaluation can be set in config.py
   
   ```
   # grep "Val AP" eval.log
-  Easy   Val AP : 0.9437
-  Medium Val AP : 0.9334
-  Hard   Val AP : 0.8904
+  Easy   Val AP : 0.9413
+  Medium Val AP : 0.9325
+  Hard   Val AP : 0.8900
   ```
 
   OR,
@@ -233,9 +233,9 @@ Parameters for both training and evaluation can be set in config.py
   
   ```
   # grep "Val AP" eval.log
-  Easy   Val AP : 0.9437
-  Medium Val AP : 0.9334
-  Hard   Val AP : 0.8904
+  Easy   Val AP : 0.9413
+  Medium Val AP : 0.9325
+  Hard   Val AP : 0.8900
   ```
 
  
@@ -258,8 +258,8 @@ Parameters for both training and evaluation can be set in config.py
 | Loss Function              | MultiBoxLoss + Softmax Cross Entropy                         |
 | outputs                    | bounding box + confidence + landmark                         |
 | Loss                       | 1.200                                                        |
-| Speed                      | 3pcs: 550 ms/step                                            |
-| Total time                 | 3pcs: 8.2 hours                                              |
+| Speed                      | 3pcs: 566 ms/step                                            |
+| Total time                 | 3pcs: 8.43 hours                                              |
 | Parameters (M)             | 27.29M                                                            |
 | Checkpoint for Fine tuning | 336.3M (.ckpt file)                                          |
 | Scripts                    | [retinaface script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/retinaface) |

model_zoo/official/cv/retinaface_resnet50/eval.py

@@ -179,7 +179,7 @@ class DetectionEngine:
         for event in self.results:
             for name in self.results[event].keys():
                 bbox = np.array(self.results[event][name]['bboxes']).astype(np.float)
-                if not bool(bbox):
+                if bbox.shape[0] <= 0:
                     continue
                 max_score = max(max_score, np.max(bbox[:, -1]))
                 min_score = min(min_score, np.min(bbox[:, -1]))
@@ -188,7 +188,7 @@ class DetectionEngine:
         for event in self.results:
             for name in self.results[event].keys():
                 bbox = np.array(self.results[event][name]['bboxes']).astype(np.float)
-                if not bool(bbox):
+                if bbox.shape[0] <= 0:
                     continue
                 bbox[:, -1] -= min_score
                 bbox[:, -1] /= length
@@ -227,7 +227,7 @@ class DetectionEngine:
         for section in range(section_num):
             _thresh = 1 - (section + 1)/section_num
             over_score_index = np.where(predict[:, 4] >= _thresh)[0]
-            if not bool(over_score_index):
+            if over_score_index.shape[0] <= 0:
                 image_pr[section, 0] = 0
                 image_pr[section, 1] = 0
             else:
@@ -264,10 +264,10 @@ class DetectionEngine:
                     keep_index = event_gt_index_list[j][0]
                     count_gt += len(keep_index)
 
-                    if not bool(gt_boxes) or not bool(predict):
+                    if gt_boxes.shape[0] <= 0 or predict.shape[0] <= 0:
                         continue
                     keep = np.zeros(gt_boxes.shape[0])
-                    if bool(keep_index):
+                    if keep_index.shape[0] > 0:
                         keep[keep_index-1] = 1
 
                     image_pr = self._image_eval(predict, gt_boxes, keep,

model_zoo/official/cv/retinaface_resnet50/src/augmemtation.py

@@ -55,7 +55,7 @@ def _choose_candidate(max_trial, image_w, image_h, boxes):
         dx = int(_rand(0, image_w - nw))
         dy = int(_rand(0, image_h - nh))
 
-        if bool(boxes):
+        if boxes.shape[0] > 0:
             crop_box = np.array((dx, dy, dx + nw, dy + nh))
             if not _is_iof_satisfied_constraint(boxes, crop_box[np.newaxis]):
                 continue

model_zoo/official/cv/retinaface_resnet50/src/dataset.py

@@ -71,7 +71,7 @@ def read_dataset(img_path, annotation):
 
     labels = annotation
     anns = np.zeros((0, 15))
-    if not bool(labels):
+    if labels.shape[0] <= 0:
         return anns
     for _, label in enumerate(labels):
         ann = np.zeros((1, 15))