Fix slice image deploy infer index (#7317)

* remove duplicate backbone variant, test=document_fix

* fix slice_image deploy infer index

configs/smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025_slice_infer.yml

 _BASE_: [
-  './_base_/visdrone_sliced_640_025_detection.yml',
-  '../runtime.yml',
-  '../ppyoloe/_base_/optimizer_300e.yml',
-  '../ppyoloe/_base_/ppyoloe_crn.yml',
-  '../ppyoloe/_base_/ppyoloe_reader.yml',
+  'ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml',
 ]
-log_iter: 100
-snapshot_epoch: 10
-weights: output/ppyoloe_crn_l_80e_sliced_visdrone_640_025/model_final
+weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams
 
-pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
-depth_mult: 1.0
-width_mult: 1.0
 
-
-TrainReader:
-  batch_size: 8
-
-EvalReader:
-  batch_size: 1 # only support bs=1 when slice infer
-
-TestReader:
-  batch_size: 1
-  fuse_normalize: True
-
-
-epoch: 80
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-    - !CosineDecay
-      max_epochs: 96
-    - !LinearWarmup
-      start_factor: 0.
-      epochs: 1
-
-PPYOLOEHead:
-  static_assigner_epoch: -1
-  nms:
-    name: MultiClassNMS
-    nms_top_k: 10000
-    keep_top_k: 500
-    score_threshold: 0.01
-    nms_threshold: 0.6
-
-
-# very slow, preferly eval with a determined weights(xx.pdparams)
-# if you want to eval during training, change SlicedCOCODataSet to COCODataSet and delete sliced_size/overlap_ratio
+# very slow, better to use the determined weight (xx. pdparams) to evaluate separately, rather than evaling during training
+# if you want to eval during training, change SlicedCOCODataSet to COCODataSet, then delete sliced_size and overlap_ratio
 EvalDataset:
   !SlicedCOCODataSet
     image_dir: VisDrone2019-DET-val

deploy/python/infer.py

@@ -257,7 +257,7 @@ class Detector(object):
                 overlap_width_ratio=overlap_ratio[1])
             sub_img_num = len(slice_image_result)
             merged_bboxs = []
-            print('sub_img_num', sub_img_num)
+            print('slice to {} sub_samples.', sub_img_num)
 
             batch_image_list = [
                 slice_image_result.images[_ind] for _ind in range(sub_img_num)
@@ -306,7 +306,7 @@ class Detector(object):
             st, ed = 0, result['boxes_num'][0]  # start_index, end_index
             for _ind in range(sub_img_num):
                 boxes_num = result['boxes_num'][_ind]
-                ed = boxes_num
+                ed = st + boxes_num
                 shift_amount = slice_image_result.starting_pixels[_ind]
                 result['boxes'][st:ed][:, 2:4] = result['boxes'][
                     st:ed][:, 2:4] + shift_amount

ppdet/data/source/dataset.py

@@ -254,7 +254,8 @@ class ImageFolder(DetDataset):
                 records.append(rec)
             ct_sub += sub_img_num
             ct += 1
-        print('{} samples and slice to {} sub_samples'.format(ct, ct_sub))
+        logger.info('{} samples and slice to {} sub_samples.'.format(ct,
+                                                                     ct_sub))
         self.roidbs = records
 
     def get_label_list(self):

ppdet/engine/trainer.py

@@ -746,9 +746,11 @@ class Trainer(object):
                       output_dir='output',
                       save_results=False,
                       visualize=True):
+        if not os.path.exists(output_dir):
+            os.makedirs(output_dir)
+
         self.dataset.set_slice_images(images, slice_size, overlap_ratio)
         loader = create('TestReader')(self.dataset, 0)
-
         imid2path = self.dataset.get_imid2path()
 
         anno_file = self.dataset.get_anno()