Fix pretrained models load problem when load params saved use API save_params (#4471)

* fix ocr pretrain_model problem

* fix ocr pretrain_model problem

PaddleCV/ocr_recognition/eval.py

@@ -66,9 +66,11 @@ def evaluate(args):
     model_dir = args.model_path
     if os.path.isdir(args.model_path):
         raise Exception("{} should not be a directory".format(args.model_path))
-    fluid.load(program=fluid.default_main_program(),
+    fluid.load(
+        program=fluid.default_main_program(),
         model_path=model_dir,
-               executor=exe)
+        executor=exe,
+        var_list=fluid.io.get_program_parameter(fluid.default_main_program()))
     print("Init model from: %s." % args.model_path)
 
     evaluator.reset(exe)

PaddleCV/ocr_recognition/infer.py

@@ -87,7 +87,8 @@ def inference(args):
     fluid.load(
         program=fluid.default_main_program(),
         model_path=model_dir,
-        executor=exe)
+        executor=exe,
+        var_list=fluid.io.get_program_parameter(fluid.default_main_program()))
     print("Init model from: %s." % args.model_path)
 
     batch_times = []

PaddleCV/ocr_recognition/train.py

@@ -106,7 +106,11 @@ def train(args):
     # load init model
     if args.init_model is not None:
         model_dir = args.init_model
-        fluid.load(fluid.default_main_program(), model_dir)
+        fluid.load(
+            fluid.default_main_program(),
+            model_dir,
+            var_list=fluid.io.get_program_parameter(fluid.default_main_program(
+            )))
         print("Init model from: %s." % args.init_model)
 
     train_exe = exe
@@ -135,7 +139,8 @@ def train(args):
             exe.run(inference_program, feed=get_feeder_data(data, place))
         _, test_seq_error = error_evaluator.eval(exe)
         print("\n[%s] - Iter[%d]; Test seq error: %s.\n" %
-              (time.asctime( time.localtime(time.time())), iter_num, str(test_seq_error[0])))
+              (time.asctime(time.localtime(time.time())), iter_num,
+               str(test_seq_error[0])))
 
         #Note: The following logs are special for CE monitoring.
         #Other situations do not need to care about these logs.
@@ -175,15 +180,16 @@ def train(args):
             iter_num += 1
             # training log
             if iter_num % args.log_period == 0:
-                print("\n[%s] - Iter[%d]; Avg loss: %.3f; Avg seq err: %.3f"
-                      % (time.asctime( time.localtime(time.time())), iter_num,
+                print("\n[%s] - Iter[%d]; Avg loss: %.3f; Avg seq err: %.3f" %
+                      (time.asctime(time.localtime(time.time())), iter_num,
                        total_loss / (args.log_period * args.batch_size),
                        total_seq_error / (args.log_period * args.batch_size)))
                 if 'ce_mode' in os.environ:
-                    print("kpis	train_cost	%f" % (total_loss / (args.log_period *
-                                                            args.batch_size)))
-                    print("kpis	train_acc	%f" % (
-                        1 - total_seq_error / (args.log_period * args.batch_size)))
+                    print("kpis	train_cost	%f" %
+                          (total_loss / (args.log_period * args.batch_size)))
+                    print("kpis	train_acc	%f" %
+                          (1 - total_seq_error /
+                           (args.log_period * args.batch_size)))
                 total_loss = 0.0
                 total_seq_error = 0.0