fix TRT8 core bug

tools/infer_cls.py

@@ -32,7 +32,7 @@ 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 load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 
@@ -47,7 +47,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    init_model(config, model)
+    load_model(config, model)
 
     # create data ops
     transforms = []

tools/infer_det.py

@@ -34,7 +34,7 @@ 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, load_dygraph_params
+from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 
@@ -59,7 +59,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    _ = load_dygraph_params(config, model, logger, None)
+    load_model(config, model)
     # build post process
     post_process_class = build_post_process(config['PostProcess'])
 

tools/infer_e2e.py

@@ -34,7 +34,7 @@ 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 load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 
@@ -68,7 +68,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    init_model(config, model)
+    load_model(config, model)
 
     # build post process
     post_process_class = build_post_process(config['PostProcess'],

tools/infer_rec.py

@@ -33,7 +33,7 @@ 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 load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 
@@ -58,7 +58,7 @@ def main():
 
     model = build_model(config['Architecture'])
 
-    init_model(config, model)
+    load_model(config, model)
 
     # create data ops
     transforms = []
@@ -75,9 +75,7 @@ def main():
                     'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2'
                 ]
             elif config['Architecture']['algorithm'] == "SAR":
-                op[op_name]['keep_keys'] = [
-                    'image', 'valid_ratio'
-                ]
+                op[op_name]['keep_keys'] = ['image', 'valid_ratio']
             else:
                 op[op_name]['keep_keys'] = ['image']
         transforms.append(op)

tools/infer_table.py

@@ -34,11 +34,12 @@ from paddle.jit import to_static
 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 load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 import cv2
 
+
 def main(config, device, logger, vdl_writer):
     global_config = config['Global']
 
@@ -53,7 +54,7 @@ def main(config, device, logger, vdl_writer):
 
     model = build_model(config['Architecture'])
 
-    init_model(config, model, logger)
+    load_model(config, model)
 
     # create data ops
     transforms = []
@@ -104,4 +105,3 @@ def main(config, device, logger, vdl_writer):
 if __name__ == '__main__':
     config, device, logger, vdl_writer = program.preprocess()
     main(config, device, logger, vdl_writer)
-

tools/program.py

@@ -212,15 +212,15 @@ def train(config,
     for epoch in range(start_epoch, epoch_num + 1):
         train_dataloader = build_dataloader(
             config, 'Train', device, logger, seed=epoch)
-        train_batch_cost = 0.0
         train_reader_cost = 0.0
-        batch_sum = 0
-        batch_start = time.time()
+        train_run_cost = 0.0
+        total_samples = 0
+        reader_start = time.time()
         max_iter = len(train_dataloader) - 1 if platform.system(
         ) == "Windows" else len(train_dataloader)
         for idx, batch in enumerate(train_dataloader):
             profiler.add_profiler_step(profiler_options)
-            train_reader_cost += time.time() - batch_start
+            train_reader_cost += time.time() - reader_start
             if idx >= max_iter:
                 break
             lr = optimizer.get_lr()
@@ -228,6 +228,7 @@ def train(config,
             if use_srn:
                 model_average = True
 
+            train_start = time.time()
             # use amp
             if scaler:
                 with paddle.amp.auto_cast():
@@ -252,8 +253,8 @@ def train(config,
                 optimizer.step()
             optimizer.clear_grad()
 
-            train_batch_cost += time.time() - batch_start
-            batch_sum += len(images)
+            train_run_cost += time.time() - train_start
+            total_samples += len(images)
 
             if not isinstance(lr_scheduler, float):
                 lr_scheduler.step()
@@ -284,12 +285,13 @@ def train(config,
                 logs = train_stats.log()
                 strs = 'epoch: [{}/{}], iter: {}, {}, reader_cost: {:.5f} s, batch_cost: {:.5f} s, samples: {}, ips: {:.5f}'.format(
                     epoch, epoch_num, global_step, logs, train_reader_cost /
-                    print_batch_step, train_batch_cost / print_batch_step,
-                    batch_sum, batch_sum / train_batch_cost)
+                    print_batch_step, (train_reader_cost + train_run_cost) /
+                    print_batch_step, total_samples,
+                    total_samples / (train_reader_cost + train_run_cost))
                 logger.info(strs)
-                train_batch_cost = 0.0
                 train_reader_cost = 0.0
-                batch_sum = 0
+                train_run_cost = 0.0
+                total_samples = 0
             # eval
             if global_step > start_eval_step and \
                     (global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
@@ -342,7 +344,7 @@ def train(config,
                                           global_step)
             global_step += 1
             optimizer.clear_grad()
-            batch_start = time.time()
+            reader_start = time.time()
         if dist.get_rank() == 0:
             save_model(
                 model,
@@ -383,7 +385,11 @@ def eval(model,
     with paddle.no_grad():
         total_frame = 0.0
         total_time = 0.0
-        pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
+        pbar = tqdm(
+            total=len(valid_dataloader),
+            desc='eval model:',
+            position=0,
+            leave=True)
         max_iter = len(valid_dataloader) - 1 if platform.system(
         ) == "Windows" else len(valid_dataloader)
         for idx, batch in enumerate(valid_dataloader):
@@ -452,8 +458,6 @@ def get_center(model, eval_dataloader, post_process_class):
 
         batch = [item.numpy() for item in batch]
         # Obtain usable results from post-processing methods
-        total_time += time.time() - start
-        # Evaluate the results of the current batch
         post_result = post_process_class(preds, batch[1])
 
         #update char_center

tools/train.py

@@ -35,7 +35,7 @@ from ppocr.losses import build_loss
 from ppocr.optimizer import build_optimizer
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model, load_dygraph_params
+from ppocr.utils.save_load import load_model
 import tools.program as program
 
 dist.get_world_size()
@@ -97,7 +97,7 @@ def main(config, device, logger, vdl_writer):
     # build metric
     eval_class = build_metric(config['Metric'])
     # load pretrain model
-    pre_best_model_dict = load_dygraph_params(config, model, logger, optimizer)
+    pre_best_model_dict = load_model(config, model, optimizer)
     logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
     if valid_dataloader is not None:
         logger.info('valid dataloader has {} iters'.format(