Merge remote-tracking branch 'origin/dygraph' into dygraph

configs/det/ch_ppocr_v2.1/ch_det_lite_train_cml_v2.1.yml

@@ -8,7 +8,7 @@ Global:
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [3000, 2000]
   cal_metric_during_train: False
-  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+  pretrained_model: ./pretrain_models/ch_ppocr_mobile_v2.1_det_distill_train/best_accuracy
   checkpoints:
   save_inference_dir:
   use_visualdl: False
@@ -19,30 +19,26 @@ Architecture:
   name: DistillationModel
   algorithm: Distillation
   Models:
-    Student:
-      pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
-      freeze_params: false
+    Teacher:
+      freeze_params: true
       return_all_feats: false
       model_type: det
       algorithm: DB
+      Transform:
       Backbone:
-        name: MobileNetV3
-        scale: 0.5
-        model_name: large
-        disable_se: True
+        name: ResNet
+        layers: 18
       Neck:
         name: DBFPN
-        out_channels: 96
+        out_channels: 256
       Head:
         name: DBHead
         k: 50
-    Student2:
-      pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+    Student:
       freeze_params: false
       return_all_feats: false
       model_type: det
       algorithm: DB
-      Transform:
       Backbone:
         name: MobileNetV3
         scale: 0.5
@@ -54,23 +50,24 @@ Architecture:
       Head:
         name: DBHead
         k: 50
-    Teacher:
-      pretrained: ./pretrain_models/ch_ppocr_server_v2.0_det_train/best_accuracy
-      freeze_params: true
+    Student2:
+      freeze_params: false
       return_all_feats: false
       model_type: det
       algorithm: DB
       Transform:
       Backbone:
-        name: ResNet
-        layers: 18
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: True
       Neck:
         name: DBFPN
-        out_channels: 256
+        out_channels: 96
       Head:
         name: DBHead
         k: 50
-
+    
 Loss:
   name: CombinedLoss
   loss_config_list:

configs/det/ch_ppocr_v2.1/ch_det_mv3_db_v2.1_student.yml

+Global:
+  use_gpu: true
+  epoch_num: 1200
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/ch_db_mv3/
+  save_epoch_step: 1200
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 400]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/student.pdparams
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_db/predicts_db.txt
+
+Architecture:
+  model_type: det
+  algorithm: DB
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+    disable_se: True
+  Neck:
+    name: DBFPN
+    out_channels: 96
+  Head:
+    name: DBHead
+    k: 50
+
+Loss:
+  name: DBLoss
+  balance_loss: true
+  main_loss_type: DiceLoss
+  alpha: 5
+  beta: 10
+  ohem_ratio: 3
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 2
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+PostProcess:
+  name: DBPostProcess
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5
+
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [1.0]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
+            - { 'type': Resize, 'args': { 'size': [0.5, 3] } }
+      - EastRandomCropData:
+          size: [960, 960]
+          max_tries: 50
+          keep_ratio: true
+      - MakeBorderMap:
+          shrink_ratio: 0.4
+          thresh_min: 0.3
+          thresh_max: 0.7
+      - MakeShrinkMap:
+          shrink_ratio: 0.4
+          min_text_size: 8
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+#           image_shape: [736, 1280]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 2

tools/eval.py

@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model, load_pretrained_params
+from ppocr.utils.save_load import init_model, load_dygraph_params
 from ppocr.utils.utility import print_dict
 import tools.program as program
 
@@ -60,7 +60,7 @@ def main():
     else:
         model_type = None
 
-    best_model_dict = init_model(config, model)
+    best_model_dict = load_dygraph_params(config, model, logger, None)
     if len(best_model_dict):
         logger.info('metric in ckpt ***************')
         for k, v in best_model_dict.items():
@@ -71,7 +71,7 @@ def main():
 
     # start eval
     metric = program.eval(model, valid_dataloader, post_process_class,
-                        eval_class, model_type, use_srn)
+                          eval_class, model_type, use_srn)
     logger.info('metric eval ***************')
     for k, v in metric.items():
         logger.info('{}:{}'.format(k, v))

tools/infer_det.py

@@ -34,23 +34,21 @@ import paddle
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import init_model, load_dygraph_params
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 
 
-def draw_det_res(dt_boxes, config, img, img_name):
+def draw_det_res(dt_boxes, config, img, img_name, save_path):
     if len(dt_boxes) > 0:
         import cv2
         src_im = img
         for box in dt_boxes:
             box = box.astype(np.int32).reshape((-1, 1, 2))
             cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
-        save_det_path = os.path.dirname(config['Global'][
-            'save_res_path']) + "/det_results/"
-        if not os.path.exists(save_det_path):
-            os.makedirs(save_det_path)
-        save_path = os.path.join(save_det_path, os.path.basename(img_name))
+        if not os.path.exists(save_path):
+            os.makedirs(save_path)
+        save_path = os.path.join(save_path, os.path.basename(img_name))
         cv2.imwrite(save_path, src_im)
         logger.info("The detected Image saved in {}".format(save_path))
 
@@ -61,8 +59,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    init_model(config, model)
-
+    _ = load_dygraph_params(config, model, logger, None)
     # build post process
     post_process_class = build_post_process(config['PostProcess'])
 
@@ -96,17 +93,41 @@ def main():
             images = paddle.to_tensor(images)
             preds = model(images)
             post_result = post_process_class(preds, shape_list)
-            boxes = post_result[0]['points']
-            # write result
+
+            src_img = cv2.imread(file)
+
             dt_boxes_json = []
-            for box in boxes:
-                tmp_json = {"transcription": ""}
-                tmp_json['points'] = box.tolist()
-                dt_boxes_json.append(tmp_json)
+            # parser boxes if post_result is dict
+            if isinstance(post_result, dict):
+                det_box_json = {}
+                for k in post_result.keys():
+                    boxes = post_result[k][0]['points']
+                    dt_boxes_list = []
+                    for box in boxes:
+                        tmp_json = {"transcription": ""}
+                        tmp_json['points'] = box.tolist()
+                        dt_boxes_list.append(tmp_json)
+                    det_box_json[k] = dt_boxes_list
+                    save_det_path = os.path.dirname(config['Global'][
+                        'save_res_path']) + "/det_results_{}/".format(k)
+                    draw_det_res(boxes, config, src_img, file, save_det_path)
+            else:
+                boxes = post_result[0]['points']
+                dt_boxes_json = []
+                # write result
+                for box in boxes:
+                    tmp_json = {"transcription": ""}
+                    tmp_json['points'] = box.tolist()
+                    dt_boxes_json.append(tmp_json)
+                save_det_path = os.path.dirname(config['Global'][
+                    'save_res_path']) + "/det_results/"
+                draw_det_res(boxes, config, src_img, file, save_det_path)
             otstr = file + "\t" + json.dumps(dt_boxes_json) + "\n"
             fout.write(otstr.encode())
-            src_img = cv2.imread(file)
-            draw_det_res(boxes, config, src_img, file)
+
+            save_det_path = os.path.dirname(config['Global'][
+                'save_res_path']) + "/det_results/"
+            draw_det_res(boxes, config, src_img, file, save_det_path)
     logger.info("success!")