Merge pull request #1133 from WenmuZhou/dygraph_rc

fix bug and update save_load to rc version

ppocr/utils/save_load.py

@@ -89,7 +89,8 @@ def init_model(config, model, logger, optimizer=None, lr_scheduler=None):
             "Given dir {}.pdparams not exist.".format(checkpoints)
         assert os.path.exists(checkpoints + ".pdopt"), \
             "Given dir {}.pdopt not exist.".format(checkpoints)
-        para_dict, opti_dict = paddle.load(checkpoints)
+        para_dict = paddle.load(checkpoints + '.pdparams')
+        opti_dict = paddle.load(checkpoints + '.pdopt')
         model.set_dict(para_dict)
         if optimizer is not None:
             optimizer.set_state_dict(opti_dict)
@@ -133,8 +134,8 @@ def save_model(net,
     """
     _mkdir_if_not_exist(model_path, logger)
     model_prefix = os.path.join(model_path, prefix)
-    paddle.save(net.state_dict(), model_prefix)
-    paddle.save(optimizer.state_dict(), model_prefix)
+    paddle.save(net.state_dict(), model_prefix + '.pdparams')
+    paddle.save(optimizer.state_dict(), model_prefix + '.pdopt')
 
     # save metric and config
     with open(model_prefix + '.states', 'wb') as f:

requirements.txt

@@ -2,6 +2,7 @@ shapely
 imgaug
 pyclipper
 lmdb
+opencv-python==4.2.0.32
 tqdm
 numpy
 visualdl

tools/eval.py

@@ -23,12 +23,8 @@ __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
 sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
 
-import paddle
-# paddle.manual_seed(2)
-
-from ppocr.utils.logging import get_logger
 from ppocr.data import build_dataloader
-from ppocr.modeling import build_model
+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
@@ -39,8 +35,7 @@ import tools.program as program
 def main():
     global_config = config['Global']
     # build dataloader
-    eval_loader, _ = build_dataloader(config['EVAL'], device, False,
-                                      global_config)
+    valid_dataloader = build_dataloader(config, 'Eval', device, logger)
 
     # build post process
     post_process_class = build_post_process(config['PostProcess'],
@@ -63,16 +58,13 @@ def main():
     eval_class = build_metric(config['Metric'])
 
     # start eval
-    metirc = program.eval(model, eval_loader, post_process_class, eval_class)
+    metirc = program.eval(model, valid_dataloader, post_process_class,
+                          eval_class)
     logger.info('metric eval ***************')
     for k, v in metirc.items():
         logger.info('{}:{}'.format(k, v))
 
 
 if __name__ == '__main__':
-    device, config = program.preprocess()
-    paddle.disable_static(device)
-
-    logger = get_logger()
-    print_dict(config, logger)
+    config, device, logger, vdl_writer = program.preprocess()
     main()

tools/program.py

@@ -231,7 +231,7 @@ def train(config,
             if global_step > start_eval_step and \
                     (global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
                 cur_metirc = eval(model, valid_dataloader, post_process_class,
-                                  eval_class, logger, print_batch_step)
+                                  eval_class)
                 cur_metirc_str = 'cur metirc, {}'.format(', '.join(
                     ['{}: {}'.format(k, v) for k, v in cur_metirc.items()]))
                 logger.info(cur_metirc_str)
@@ -293,8 +293,7 @@ def train(config,
     return
 
 
-def eval(model, valid_dataloader, post_process_class, eval_class, logger,
-         print_batch_step):
+def eval(model, valid_dataloader, post_process_class, eval_class):
     model.eval()
     with paddle.no_grad():
         total_frame = 0.0
@@ -315,9 +314,6 @@ def eval(model, valid_dataloader, post_process_class, eval_class, logger,
             eval_class(post_result, batch)
             pbar.update(1)
             total_frame += len(images)
-            # if idx % print_batch_step == 0 and dist.get_rank() == 0:
-            #     logger.info('tackling images for eval: {}/{}'.format(
-            #         idx, len(valid_dataloader)))
         # Get final metirc，eg. acc or hmean
         metirc = eval_class.get_metric()