remove unused code in ml (#4781)

PaddleCV/metric_learning/train_elem.py

@@ -110,8 +110,7 @@ def build_program(is_train, main_prog, startup_prog, args):
         queue_capacity = 64
         image = fluid.data(
             name='image', shape=[None] + image_shape, dtype='float32')
-        label = fluid.data(
-                name='label', shape=[None, 1], dtype='int64')
+        label = fluid.data(name='label', shape=[None, 1], dtype='int64')
         loader = fluid.io.DataLoader.from_generator(
             feed_list=[image, label],
             capacity=queue_capacity,
@@ -187,17 +186,12 @@ def train_async(args):
 
     exe.run(startup_prog)
 
-    logging.debug('after run startup program')
-
     if checkpoint is not None:
         fluid.load(program=train_prog, model_path=checkpoint, executor=exe)
 
     if pretrained_model:
-
-        def if_exist(var):
-            return os.path.exists(os.path.join(pretrained_model, var.name))
-
-        fluid.load(program=train_prog, model_path=pretrained_model, executor=exe)
+        fluid.load(
+            program=train_prog, model_path=pretrained_model, executor=exe)
 
     if args.use_gpu:
         devicenum = get_gpu_num()
@@ -230,8 +224,7 @@ def train_async(args):
     while iter_no <= args.total_iter_num:
         for train_batch in train_loader():
             t1 = time.time()
-            lr, loss, acc1, acc5 = train_exe.run(
-                                       feed=train_batch,
+            lr, loss, acc1, acc5 = train_exe.run(feed=train_batch,
                                                  fetch_list=train_fetch_list)
             t2 = time.time()
             period = t2 - t1

PaddleCV/metric_learning/train_pair.py

@@ -117,8 +117,7 @@ def build_program(is_train, main_prog, startup_prog, args):
         queue_capacity = 64
         image = fluid.data(
             name='image', shape=[None] + image_shape, dtype='float32')
-        label = fluid.data(
-                name='label', shape=[None, 1], dtype='int64')
+        label = fluid.data(name='label', shape=[None, 1], dtype='int64')
         loader = fluid.io.DataLoader.from_generator(
             feed_list=[image, label],
             capacity=queue_capacity,
@@ -185,18 +184,12 @@ def train_async(args):
 
     exe.run(startup_prog)
 
-    logging.debug('after run startup program')
-
     if checkpoint is not None:
         fluid.load(program=train_prog, model_path=checkpoint, executor=exe)
 
     if pretrained_model:
-
-        def if_exist(var):
-            return os.path.exists(os.path.join(pretrained_model, var.name))
-
-        fluid.load(program=train_prog, model_path=pretrained_model, executor=exe)
-
+        fluid.load(
+            program=train_prog, model_path=pretrained_model, executor=exe)
 
     if args.use_gpu:
         devicenum = get_gpu_num()
@@ -229,8 +222,7 @@ def train_async(args):
     while iter_no <= args.total_iter_num:
         for train_batch in train_loader():
             t1 = time.time()
-            lr, loss, feas, label = train_exe.run(
-                                        feed=train_batch,
+            lr, loss, feas, label = train_exe.run(feed=train_batch,
                                                   fetch_list=train_fetch_list)
             t2 = time.time()
             period = t2 - t1