Rename paddle.batch to paddle.io.batch (#290)

432639d7 · whs · GitHub · 567e90e4 · 432639d7 · 432639d7
46 changed file
--- a/demo/auto_prune/train.py
+++ b/demo/auto_prune/train.py
@@ -116,8 +116,8 @@ def compress(args):
        fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
-    val_reader = paddle.batch(val_reader, batch_size=args.batch_size)
+    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
-    train_reader = paddle.batch(
+    train_reader = paddle.fluid.io.batch(
        train_reader, batch_size=args.batch_size, drop_last=True)
    train_feeder = feeder = fluid.DataFeeder([image, label], place)

--- a/demo/auto_prune/train_finetune.py
+++ b/demo/auto_prune/train_finetune.py
@@ -34,12 +34,12 @@ add_arg('config_file',      str, None,                 "The config file for comp
 model_list = [m for m in dir(models) if "__" not in m]
 ratiolist = [
-#    [0.06, 0.0, 0.09, 0.03, 0.09, 0.02, 0.05, 0.03, 0.0, 0.07, 0.07, 0.05, 0.08],
+    #    [0.06, 0.0, 0.09, 0.03, 0.09, 0.02, 0.05, 0.03, 0.0, 0.07, 0.07, 0.05, 0.08],
-#    [0.08, 0.02, 0.03, 0.13, 0.1, 0.06, 0.03, 0.04, 0.14, 0.02, 0.03, 0.02, 0.01],
+    #    [0.08, 0.02, 0.03, 0.13, 0.1, 0.06, 0.03, 0.04, 0.14, 0.02, 0.03, 0.02, 0.01],
-        ]
+]
-def save_model(args, exe, train_prog, eval_prog,info):
+def save_model(args, exe, train_prog, eval_prog, info):
    model_path = os.path.join(args.model_save_dir, args.model, str(info))
    if not os.path.isdir(model_path):
        os.makedirs(model_path)
@@ -58,29 +58,31 @@ def piecewise_decay(args):
        regularization=fluid.regularizer.L2Decay(args.l2_decay))
    return optimizer
 def cosine_decay(args):
    step = int(math.ceil(float(args.total_images) / args.batch_size))
    learning_rate = fluid.layers.cosine_decay(
-        learning_rate=args.lr,
+        learning_rate=args.lr, step_each_epoch=step, epochs=args.num_epochs)
-        step_each_epoch=step,
-        epochs=args.num_epochs)
    optimizer = fluid.optimizer.Momentum(
        learning_rate=learning_rate,
        momentum=args.momentum_rate,
        regularization=fluid.regularizer.L2Decay(args.l2_decay))
    return optimizer
 def create_optimizer(args):
    if args.lr_strategy == "piecewise_decay":
        return piecewise_decay(args)
    elif args.lr_strategy == "cosine_decay":
        return cosine_decay(args)
 def compress(args):
-    class_dim=1000
+    class_dim = 1000
-    image_shape="3,224,224"
+    image_shape = "3,224,224"
    image_shape = [int(m) for m in image_shape.split(",")]
-    assert args.model in model_list, "{} is not in lists: {}".format(args.model, model_list)
+    assert args.model in model_list, "{} is not in lists: {}".format(
+        args.model, model_list)
    image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
    # model definition
@@ -98,18 +100,22 @@ def compress(args):
    exe.run(fluid.default_startup_program())
    if args.pretrained_model:
        def if_exist(var):
-            exist =  os.path.exists(os.path.join(args.pretrained_model, var.name))
+            exist = os.path.exists(
-            print("exist",exist)
+                os.path.join(args.pretrained_model, var.name))
+            print("exist", exist)
            return exist
        #fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
-    val_reader = paddle.batch(reader.val(), batch_size=args.batch_size)
+    val_reader = paddle.fluid.io.batch(reader.val(), batch_size=args.batch_size)
-    train_reader = paddle.batch(
+    train_reader = paddle.fluid.io.batch(
        reader.train(), batch_size=args.batch_size, drop_last=True)
    train_feeder = feeder = fluid.DataFeeder([image, label], place)
-    val_feeder = feeder = fluid.DataFeeder([image, label], place, program=val_program)
+    val_feeder = feeder = fluid.DataFeeder(
+        [image, label], place, program=val_program)
    def test(epoch, program):
        batch_id = 0
@@ -117,17 +123,23 @@ def compress(args):
        acc_top5_ns = []
        for data in val_reader():
            start_time = time.time()
-            acc_top1_n, acc_top5_n = exe.run(program,
+            acc_top1_n, acc_top5_n = exe.run(
+                program,
                feed=train_feeder.feed(data),
                fetch_list=[acc_top1.name, acc_top5.name])
            end_time = time.time()
-            print("Eval epoch[{}] batch[{}] - acc_top1: {}; acc_top5: {}; time: {}".format(epoch, batch_id, np.mean(acc_top1_n), np.mean(acc_top5_n), end_time-start_time))
+            print(
+                "Eval epoch[{}] batch[{}] - acc_top1: {}; acc_top5: {}; time: {}".
+                format(epoch, batch_id,
+                       np.mean(acc_top1_n),
+                       np.mean(acc_top5_n), end_time - start_time))
            acc_top1_ns.append(np.mean(acc_top1_n))
            acc_top5_ns.append(np.mean(acc_top5_n))
            batch_id += 1
-        print("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".format(epoch, np.mean(np.array(acc_top1_ns)), np.mean(np.array(acc_top5_ns))))
+        print("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".format(
+            epoch,
+            np.mean(np.array(acc_top1_ns)), np.mean(np.array(acc_top5_ns))))
    def train(epoch, program):
@@ -142,15 +154,22 @@ def compress(args):
        batch_id = 0
        for data in train_reader():
            start_time = time.time()
-            loss_n, acc_top1_n, acc_top5_n,lr_n = exe.run(train_program,
+            loss_n, acc_top1_n, acc_top5_n, lr_n = exe.run(
+                train_program,
                feed=train_feeder.feed(data),
-                                               fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name,"learning_rate"])
+                fetch_list=[
+                    avg_cost.name, acc_top1.name, acc_top5.name,
+                    "learning_rate"
+                ])
            end_time = time.time()
            loss_n = np.mean(loss_n)
            acc_top1_n = np.mean(acc_top1_n)
            acc_top5_n = np.mean(acc_top5_n)
            lr_n = np.mean(lr_n)
-            print("epoch[{}]-batch[{}] - loss: {}; acc_top1: {}; acc_top5: {};lrn: {}; time: {}".format(epoch, batch_id, loss_n, acc_top1_n, acc_top5_n, lr_n,end_time-start_time))
+            print(
+                "epoch[{}]-batch[{}] - loss: {}; acc_top1: {}; acc_top5: {};lrn: {}; time: {}".
+                format(epoch, batch_id, loss_n, acc_top1_n, acc_top5_n, lr_n,
+                       end_time - start_time))
            batch_id += 1
    params = []
@@ -158,39 +177,45 @@ def compress(args):
        #if "_weights" in  param.name and "conv1_weights" not in param.name:
        if "_sep_weights" in param.name:
            params.append(param.name)
-    print("fops before pruning: {}".format(flops(fluid.default_main_program())))
+    print("fops before pruning: {}".format(
+        flops(fluid.default_main_program())))
    pruned_program_iter = fluid.default_main_program()
    pruned_val_program_iter = val_program
    for ratios in ratiolist:
        pruner = Pruner()
-        pruned_val_program_iter = pruner.prune(pruned_val_program_iter,
+        pruned_val_program_iter = pruner.prune(
+            pruned_val_program_iter,
            fluid.global_scope(),
            params=params,
            ratios=ratios,
            place=place,
            only_graph=True)
+        pruned_program_iter = pruner.prune(
-        pruned_program_iter = pruner.prune(pruned_program_iter,
+            pruned_program_iter,
            fluid.global_scope(),
            params=params,
            ratios=ratios,
            place=place)
        print("fops after pruning: {}".format(flops(pruned_program_iter)))
    """ do not inherit learning rate """
-    if(os.path.exists(args.pretrained_model + "/learning_rate")):
+    if (os.path.exists(args.pretrained_model + "/learning_rate")):
-      os.remove( args.pretrained_model + "/learning_rate")
+        os.remove(args.pretrained_model + "/learning_rate")
-    if(os.path.exists(args.pretrained_model + "/@LR_DECAY_COUNTER@")):
+    if (os.path.exists(args.pretrained_model + "/@LR_DECAY_COUNTER@")):
-      os.remove( args.pretrained_model + "/@LR_DECAY_COUNTER@")
+        os.remove(args.pretrained_model + "/@LR_DECAY_COUNTER@")
-    fluid.io.load_vars(exe, args.pretrained_model , main_program = pruned_program_iter,  predicate=if_exist)
+    fluid.io.load_vars(
+        exe,
+        args.pretrained_model,
+        main_program=pruned_program_iter,
+        predicate=if_exist)
    pruned_program = pruned_program_iter
    pruned_val_program = pruned_val_program_iter
    for i in range(args.num_epochs):
        train(i, pruned_program)
        test(i, pruned_val_program)
-        save_model(args,exe,pruned_program,pruned_val_program,i)
+        save_model(args, exe, pruned_program, pruned_val_program, i)
 def main():
    args = parser.parse_args()

--- a/demo/auto_prune/train_iterator.py
+++ b/demo/auto_prune/train_iterator.py
@@ -41,9 +41,10 @@ add_arg('test_period',      int, 10,                 "Test period in epoches.")
 model_list = [m for m in dir(models) if "__" not in m]
 ratiolist = [
-#    [0.06, 0.0, 0.09, 0.03, 0.09, 0.02, 0.05, 0.03, 0.0, 0.07, 0.07, 0.05, 0.08],
+    #    [0.06, 0.0, 0.09, 0.03, 0.09, 0.02, 0.05, 0.03, 0.0, 0.07, 0.07, 0.05, 0.08],
-#    [0.08, 0.02, 0.03, 0.13, 0.1, 0.06, 0.03, 0.04, 0.14, 0.02, 0.03, 0.02, 0.01],
+    #    [0.08, 0.02, 0.03, 0.13, 0.1, 0.06, 0.03, 0.04, 0.14, 0.02, 0.03, 0.02, 0.01],
-        ]
+]
 def piecewise_decay(args):
    step = int(math.ceil(float(args.total_images) / args.batch_size))
@@ -121,8 +122,8 @@ def compress(args):
 #        fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
-    val_reader = paddle.batch(val_reader, batch_size=args.batch_size)
+    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
-    train_reader = paddle.batch(
+    train_reader = paddle.fluid.io.batch(
        train_reader, batch_size=args.batch_size, drop_last=True)
    train_feeder = feeder = fluid.DataFeeder([image, label], place)
@@ -194,21 +195,26 @@ def compress(args):
    for ratios in ratiolist:
        pruner = Pruner()
-        pruned_val_program_iter = pruner.prune(pruned_val_program_iter,
+        pruned_val_program_iter = pruner.prune(
+            pruned_val_program_iter,
            fluid.global_scope(),
            params=params,
            ratios=ratios,
            place=place,
            only_graph=True)
+        pruned_program_iter = pruner.prune(
-        pruned_program_iter = pruner.prune(pruned_program_iter,
+            pruned_program_iter,
            fluid.global_scope(),
            params=params,
            ratios=ratios,
            place=place)
        print("fops after pruning: {}".format(flops(pruned_program_iter)))
-    fluid.io.load_vars(exe, args.pretrained_model , main_program = pruned_program_iter,  predicate=if_exist)
+    fluid.io.load_vars(
+        exe,
+        args.pretrained_model,
+        main_program=pruned_program_iter,
+        predicate=if_exist)
    pruner = AutoPruner(
        pruned_val_program_iter,
@@ -238,8 +244,6 @@ def compress(args):
        pruner.reward(score)
 def main():
    args = parser.parse_args()
    print_arguments(args)

--- a/demo/distillation/distill.py
+++ b/demo/distillation/distill.py
@@ -133,9 +133,9 @@ def compress(args):
    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
    exe = fluid.Executor(place)
-    train_reader = paddle.batch(
+    train_reader = paddle.fluid.io.batch(
        train_reader, batch_size=args.batch_size, drop_last=True)
-    val_reader = paddle.batch(
+    val_reader = paddle.fluid.io.batch(
        val_reader, batch_size=args.batch_size, drop_last=True)
    val_program = student_program.clone(for_test=True)

--- a/demo/distillation/image_classification_distillation_tutorial.ipynb
+++ b/demo/distillation/image_classification_distillation_tutorial.ipynb
@@ -165,7 +165,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "train_reader = paddle.batch(\n",
+    "train_reader = paddle.fluid.io.batch(\n",
    "    paddle.dataset.mnist.train(), batch_size=128, drop_last=True)\n",
    "train_feeder = fluid.DataFeeder(['image', 'label'], fluid.CPUPlace(), student_program)"
   ]

--- a/demo/nas/block_sa_nas_mobilenetv2.py
+++ b/demo/nas/block_sa_nas_mobilenetv2.py
@@ -137,22 +137,22 @@ def search_mobilenetv2_block(config, args, image_size):
        exe.run(startup_program)
        if args.data == 'cifar10':
-            train_reader = paddle.batch(
+            train_reader = paddle.fluid.io.batch(
                paddle.reader.shuffle(
                    paddle.dataset.cifar.train10(cycle=False), buf_size=1024),
                batch_size=args.batch_size,
                drop_last=True)
-            test_reader = paddle.batch(
+            test_reader = paddle.fluid.io.batch(
                paddle.dataset.cifar.test10(cycle=False),
                batch_size=args.batch_size,
                drop_last=False)
        elif args.data == 'imagenet':
-            train_reader = paddle.batch(
+            train_reader = paddle.fluid.io.batch(
                imagenet_reader.train(),
                batch_size=args.batch_size,
                drop_last=True)
-            test_reader = paddle.batch(
+            test_reader = paddle.fluid.io.batch(
                imagenet_reader.val(),
                batch_size=args.batch_size,
                drop_last=False)

--- a/demo/nas/image_classification_nas_quick_start.ipynb
+++ b/demo/nas/image_classification_nas_quick_start.ipynb
@@ -114,9 +114,9 @@
    "    if current_flops > 321208544:\n",
    "        continue\n",
    "    \n",
-    "    train_reader = paddle.batch(paddle.reader.shuffle(paddle.dataset.cifar.train10(cycle=False),                          buf_size=1024),batch_size=256)\n",
+    "    train_reader = paddle.fluid.io.batch(paddle.reader.shuffle(paddle.dataset.cifar.train10(cycle=False),                          buf_size=1024),batch_size=256)\n",
    "    train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())\n",
-    "    test_reader = paddle.batch(paddle.dataset.cifar.test10(cycle=False),\n",
+    "    test_reader = paddle.fluid.io.batch(paddle.dataset.cifar.test10(cycle=False),\n",
    "               batch_size=256)\n",
    "    test_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())\n",
    "\n",

--- a/demo/nas/parl_nas_mobilenetv2.py
+++ b/demo/nas/parl_nas_mobilenetv2.py
@@ -105,22 +105,22 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
        exe.run(startup_program)
        if args.data == 'cifar10':
-            train_reader = paddle.batch(
+            train_reader = paddle.fluid.io.batch(
                paddle.reader.shuffle(
                    paddle.dataset.cifar.train10(cycle=False), buf_size=1024),
                batch_size=args.batch_size,
                drop_last=True)
-            test_reader = paddle.batch(
+            test_reader = paddle.fluid.io.batch(
                paddle.dataset.cifar.test10(cycle=False),
                batch_size=args.batch_size,
                drop_last=False)
        elif args.data == 'imagenet':
-            train_reader = paddle.batch(
+            train_reader = paddle.fluid.io.batch(
                imagenet_reader.train(),
                batch_size=args.batch_size,
                drop_last=True)
-            test_reader = paddle.batch(
+            test_reader = paddle.fluid.io.batch(
                imagenet_reader.val(),
                batch_size=args.batch_size,
                drop_last=False)

--- a/demo/nas/rl_nas_mobilenetv2.py
+++ b/demo/nas/rl_nas_mobilenetv2.py
@@ -109,22 +109,22 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
        exe.run(startup_program)
        if args.data == 'cifar10':
-            train_reader = paddle.batch(
+            train_reader = paddle.fluid.io.batch(
                paddle.reader.shuffle(
                    paddle.dataset.cifar.train10(cycle=False), buf_size=1024),
                batch_size=args.batch_size,
                drop_last=True)
-            test_reader = paddle.batch(
+            test_reader = paddle.fluid.io.batch(
                paddle.dataset.cifar.test10(cycle=False),
                batch_size=args.batch_size,
                drop_last=False)
        elif args.data == 'imagenet':
-            train_reader = paddle.batch(
+            train_reader = paddle.fluid.io.batch(
                imagenet_reader.train(),
                batch_size=args.batch_size,
                drop_last=True)
-            test_reader = paddle.batch(
+            test_reader = paddle.fluid.io.batch(
                imagenet_reader.val(),
                batch_size=args.batch_size,
                drop_last=False)

--- a/demo/nas/sa_nas_mobilenetv2.py
+++ b/demo/nas/sa_nas_mobilenetv2.py
@@ -102,22 +102,22 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
        exe.run(startup_program)
        if args.data == 'cifar10':
-            train_reader = paddle.batch(
+            train_reader = paddle.fluid.io.batch(
                paddle.reader.shuffle(
                    paddle.dataset.cifar.train10(cycle=False), buf_size=1024),
                batch_size=args.batch_size,
                drop_last=True)
-            test_reader = paddle.batch(
+            test_reader = paddle.fluid.io.batch(
                paddle.dataset.cifar.test10(cycle=False),
                batch_size=args.batch_size,
                drop_last=False)
        elif args.data == 'imagenet':
-            train_reader = paddle.batch(
+            train_reader = paddle.fluid.io.batch(
                imagenet_reader.train(),
                batch_size=args.batch_size,
                drop_last=True)
-            test_reader = paddle.batch(
+            test_reader = paddle.fluid.io.batch(
                imagenet_reader.val(),
                batch_size=args.batch_size,
                drop_last=False)
@@ -197,22 +197,22 @@ def test_search_result(tokens, image_size, args, config):
    exe.run(startup_program)
    if args.data == 'cifar10':
-        train_reader = paddle.batch(
+        train_reader = paddle.fluid.io.batch(
            paddle.reader.shuffle(
                paddle.dataset.cifar.train10(cycle=False), buf_size=1024),
            batch_size=args.batch_size,
            drop_last=True)
-        test_reader = paddle.batch(
+        test_reader = paddle.fluid.io.batch(
            paddle.dataset.cifar.test10(cycle=False),
            batch_size=args.batch_size,
            drop_last=False)
    elif args.data == 'imagenet':
-        train_reader = paddle.batch(
+        train_reader = paddle.fluid.io.batch(
            imagenet_reader.train(),
            batch_size=args.batch_size,
            drop_last=True)
-        test_reader = paddle.batch(
+        test_reader = paddle.fluid.io.batch(
            imagenet_reader.val(), batch_size=args.batch_size, drop_last=False)
    train_loader.set_sample_list_generator(

--- a/demo/one_shot/train.py
+++ b/demo/one_shot/train.py
@@ -113,7 +113,7 @@ def test_mnist(model, tokens=None):
    acc_set = []
    avg_loss_set = []
    batch_size = 64
-    test_reader = paddle.batch(
+    test_reader = paddle.fluid.io.batch(
        paddle.dataset.mnist.test(), batch_size=batch_size, drop_last=True)
    for batch_id, data in enumerate(test_reader()):
        dy_x_data = np.array([x[0].reshape(1, 28, 28)
@@ -145,7 +145,7 @@ def train_mnist(args, model, tokens=None):
    adam = AdamOptimizer(
        learning_rate=0.001, parameter_list=model.parameters())
-    train_reader = paddle.batch(
+    train_reader = paddle.fluid.io.batch(
        paddle.dataset.mnist.train(), batch_size=BATCH_SIZE, drop_last=True)
    if args.use_data_parallel:
        train_reader = fluid.contrib.reader.distributed_batch_reader(

--- a/demo/prune/eval.py
+++ b/demo/prune/eval.py
@@ -63,7 +63,7 @@ def eval(args):
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())
-    val_reader = paddle.batch(val_reader, batch_size=args.batch_size)
+    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
    val_feeder = feeder = fluid.DataFeeder(
        [image, label], place, program=val_program)

--- a/demo/prune/image_classification_pruning_quick_start.ipynb
+++ b/demo/prune/image_classification_pruning_quick_start.ipynb
@@ -161,7 +161,7 @@
   "outputs": [],
   "source": [
    "import paddle.dataset.mnist as reader\n",
-    "train_reader = paddle.batch(\n",
+    "train_reader = paddle.fluid.io.batch(\n",
    "        reader.train(), batch_size=128, drop_last=True)\n",
    "train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())"
   ]

--- a/demo/prune/train.py
+++ b/demo/prune/train.py
@@ -142,8 +142,8 @@ def compress(args):
            args.pretrained_model))
        fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
-    val_reader = paddle.batch(val_reader, batch_size=args.batch_size)
+    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
-    train_reader = paddle.batch(
+    train_reader = paddle.fluid.io.batch(
        train_reader, batch_size=args.batch_size, drop_last=True)
    train_feeder = feeder = fluid.DataFeeder([image, label], place)

--- a/demo/quant/quant_aware/image_classification_training_aware_quantization_quick_start.ipynb
+++ b/demo/quant/quant_aware/image_classification_training_aware_quantization_quick_start.ipynb
@@ -81,9 +81,9 @@
   "outputs": [],
   "source": [
    "import paddle.dataset.mnist as reader\n",
-    "train_reader = paddle.batch(\n",
+    "train_reader = paddle.fluid.io.batch(\n",
    "        reader.train(), batch_size=128, drop_last=True)\n",
-    "test_reader = paddle.batch(\n",
+    "test_reader = paddle.fluid.io.batch(\n",
    "        reader.train(), batch_size=128, drop_last=True)\n",
    "train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())"
   ]

--- a/demo/quant/quant_aware/train.py
+++ b/demo/quant/quant_aware/train.py
@@ -159,8 +159,8 @@ def compress(args):
        fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
-    val_reader = paddle.batch(val_reader, batch_size=args.batch_size)
+    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
-    train_reader = paddle.batch(
+    train_reader = paddle.fluid.io.batch(
        train_reader, batch_size=args.batch_size, drop_last=True)
    train_feeder = feeder = fluid.DataFeeder([image, label], place)

--- a/demo/quant/quant_post/eval.py
+++ b/demo/quant/quant_post/eval.py
@@ -46,7 +46,7 @@ def eval(args):
        exe,
        model_filename=args.model_name,
        params_filename=args.params_name)
-    val_reader = paddle.batch(reader.val(), batch_size=128)
+    val_reader = paddle.fluid.io.batch(reader.val(), batch_size=128)
    feeder = fluid.DataFeeder(
        place=place, feed_list=feed_target_names, program=val_program)

--- a/demo/quant/quant_post/image_classification_post_training_quantization_quick_start.ipynb
+++ b/demo/quant/quant_post/image_classification_post_training_quantization_quick_start.ipynb
@@ -79,9 +79,9 @@
   "outputs": [],
   "source": [
    "import paddle.dataset.mnist as reader\n",
-    "train_reader = paddle.batch(\n",
+    "train_reader = paddle.fluid.io.batch(\n",
    "        reader.train(), batch_size=128, drop_last=True)\n",
-    "test_reader = paddle.batch(\n",
+    "test_reader = paddle.fluid.io.batch(\n",
    "        reader.train(), batch_size=128, drop_last=True)\n",
    "train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())"
   ]

--- a/demo/sensitive/image_classification_sensitivity_analysis.ipynb
+++ b/demo/sensitive/image_classification_sensitivity_analysis.ipynb
@@ -73,9 +73,9 @@
   "outputs": [],
   "source": [
    "import paddle.dataset.mnist as reader\n",
-    "train_reader = paddle.batch(\n",
+    "train_reader = paddle.fluid.io.batch(\n",
    "        reader.train(), batch_size=128, drop_last=True)\n",
-    "test_reader = paddle.batch(\n",
+    "test_reader = paddle.fluid.io.batch(\n",
    "        reader.test(), batch_size=128, drop_last=True)\n",
    "data_feeder = fluid.DataFeeder(inputs, place)"
   ]

--- a/demo/sensitive/train.py
+++ b/demo/sensitive/train.py
@@ -68,7 +68,7 @@ def compress(args):
        fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
-    val_reader = paddle.batch(val_reader, batch_size=args.batch_size)
+    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
    val_feeder = feeder = fluid.DataFeeder(
        [image, label], place, program=val_program)

--- a/demo/sensitive_prune/greedy_prune.py
+++ b/demo/sensitive_prune/greedy_prune.py
@@ -119,8 +119,8 @@ def compress(args):
        fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
-    val_reader = paddle.batch(val_reader, batch_size=args.batch_size)
+    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
-    train_reader = paddle.batch(
+    train_reader = paddle.fluid.io.batch(
        train_reader, batch_size=args.batch_size, drop_last=True)
    train_feeder = feeder = fluid.DataFeeder([image, label], place)

--- a/demo/sensitive_prune/prune.py
+++ b/demo/sensitive_prune/prune.py
@@ -117,8 +117,8 @@ def compress(args):
        fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
-    val_reader = paddle.batch(val_reader, batch_size=args.batch_size)
+    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
-    train_reader = paddle.batch(
+    train_reader = paddle.fluid.io.batch(
        train_reader, batch_size=args.batch_size, drop_last=True)
    train_feeder = feeder = fluid.DataFeeder([image, label], place)

--- a/demo/slimfacenet/dataloader/casia.py
+++ b/demo/slimfacenet/dataloader/casia.py
@@ -79,7 +79,7 @@ if __name__ == '__main__':
        dataset = CASIA_Face(root=data_dir)
        print(len(dataset))
        print(dataset.class_nums)
-        trainloader = paddle.batch(
+        trainloader = paddle.fluid.io.batch(
            dataset.reader, batch_size=1, drop_last=False)
        for i in range(10):
            for data in trainloader():

--- a/demo/slimfacenet/lfw_eval.py
+++ b/demo/slimfacenet/lfw_eval.py
@@ -159,7 +159,7 @@ if __name__ == "__main__":
        train_dataset = CASIA_Face(root=args.train_data_dir)
        nl, nr, flods, flags = parse_filelist(args.test_data_dir)
        test_dataset = LFW(nl, nr)
-        test_reader = paddle.batch(
+        test_reader = paddle.fluid.io.batch(
            test_dataset.reader,
            batch_size=args.test_batchsize,
            drop_last=False)

--- a/demo/slimfacenet/train_eval.py
+++ b/demo/slimfacenet/train_eval.py
@@ -166,7 +166,7 @@ def build_program(program, startup, args, is_train=True):
                image = fluid.data(
                    name='image', shape=[-1, 3, 112, 96], dtype='float32')
                label = fluid.data(name='label', shape=[-1, 1], dtype='int64')
-                train_reader = paddle.batch(
+                train_reader = paddle.fluid.io.batch(
                    train_dataset.reader,
                    batch_size=args.train_batchsize // num_trainers,
                    drop_last=False)
@@ -187,7 +187,7 @@ def build_program(program, startup, args, is_train=True):
            else:
                nl, nr, flods, flags = parse_filelist(args.test_data_dir)
                test_dataset = LFW(nl, nr)
-                test_reader = paddle.batch(
+                test_reader = paddle.fluid.io.batch(
                    test_dataset.reader,
                    batch_size=args.test_batchsize,
                    drop_last=False)
@@ -231,7 +231,7 @@ def build_program(program, startup, args, is_train=True):
 def quant_val_reader_batch():
    nl, nr, flods, flags = parse_filelist(args.test_data_dir)
    test_dataset = LFW(nl, nr)
-    test_reader = paddle.batch(
+    test_reader = paddle.fluid.io.batch(
        test_dataset.reader, batch_size=1, drop_last=False)
    shuffle_index = args.seed if args.seed else np.random.randint(1000)
    print('shuffle_index: {}'.format(shuffle_index))
@@ -347,7 +347,7 @@ def main():
             executor=exe)
        nl, nr, flods, flags = parse_filelist(args.test_data_dir)
        test_dataset = LFW(nl, nr)
-        test_reader = paddle.batch(
+        test_reader = paddle.fluid.io.batch(
            test_dataset.reader,
            batch_size=args.test_batchsize,
            drop_last=False)

--- a/docs/en/quick_start/distillation_tutorial_en.md
+++ b/docs/en/quick_start/distillation_tutorial_en.md
@@ -100,7 +100,7 @@ The package `paddle.dataset.mnist` of Paddle define the downloading and reading
 Define training data reader and test data reader as below：
 ```python
-train_reader = paddle.batch(
+train_reader = paddle.fluid.io.batch(
    paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
 train_feeder = fluid.DataFeeder(['image', 'label'], fluid.CPUPlace(), student_program)
 ```

--- a/docs/en/quick_start/nas_tutorial_en.md
+++ b/docs/en/quick_start/nas_tutorial_en.md
@@ -66,9 +66,9 @@ def build_program(archs):
 The dataset we used is cifar10, and `paddle.dataset.cifar` in Paddle including the download and pre-read about cifar.
 ```python
 def input_data(inputs):
-    train_reader = paddle.batch(paddle.reader.shuffle(paddle.dataset.cifar.train10(cycle=False), buf_size=1024),batch_size=256)
+    train_reader = paddle.fluid.io.batch(paddle.reader.shuffle(paddle.dataset.cifar.train10(cycle=False), buf_size=1024),batch_size=256)
    train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
-    eval_reader = paddle.batch(paddle.dataset.cifar.test10(cycle=False), batch_size=256)
+    eval_reader = paddle.fluid.io.batch(paddle.dataset.cifar.test10(cycle=False), batch_size=256)
    eval_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
    return train_reader, train_feeder, eval_reader, eval_feeder
 ```

--- a/docs/en/quick_start/pruning_tutorial_en.md
+++ b/docs/en/quick_start/pruning_tutorial_en.md
@@ -74,7 +74,7 @@ Define training data reader and test data reader as below：
 ```
 import paddle.dataset.mnist as reader
-train_reader = paddle.batch(
+train_reader = paddle.fluid.io.batch(
        reader.train(), batch_size=128, drop_last=True)
 train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
 ```

--- a/docs/en/quick_start/quant_aware_tutorial_en.md
+++ b/docs/en/quick_start/quant_aware_tutorial_en.md
@@ -41,9 +41,9 @@ To speed up training process, we select MNIST dataset to train image classificat
 ```python
 import paddle.dataset.mnist as reader
-train_reader = paddle.batch(
+train_reader = paddle.fluid.io.batch(
        reader.train(), batch_size=128, drop_last=True)
-test_reader = paddle.batch(
+test_reader = paddle.fluid.io.batch(
        reader.train(), batch_size=128, drop_last=True)
 train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
 ```

--- a/docs/en/quick_start/quant_post_tutorial_en.md
+++ b/docs/en/quick_start/quant_post_tutorial_en.md
@@ -39,9 +39,10 @@ To speed up training process, we select MNIST dataset to train image classificat
 ```python
 import paddle.dataset.mnist as reader
-train_reader = paddle.batch(
+train_reader = paddle.fluid.io.batch(
        reader.train(), batch_size=128, drop_last=True)
-test_reader = paddle.batch(
+test_reader = paddle.fluid.io.batch(
+cs/en/quick_start/quant_aware_tutorial_en.md
        reader.train(), batch_size=128, drop_last=True)
 train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
 ```

--- a/docs/en/tutorials/image_classification_sensitivity_analysis_tutorial_en.md
+++ b/docs/en/tutorials/image_classification_sensitivity_analysis_tutorial_en.md
@@ -45,9 +45,9 @@ Show as below：
 ```python
 import paddle.dataset.mnist as reader
-train_reader = paddle.batch(
+train_reader = paddle.fluid.io.batch(
        reader.train(), batch_size=128, drop_last=True)
-test_reader = paddle.batch(
+test_reader = paddle.fluid.io.batch(
        reader.test(), batch_size=128, drop_last=True)
 data_feeder = fluid.DataFeeder(inputs, place)
 ```

--- a/docs/zh_cn/api_cn/pantheon_api.md
+++ b/docs/zh_cn/api_cn/pantheon_api.md
@@ -125,7 +125,7 @@ place = fluid.CPUPlace()
 exe = fluid.Executor(place)
 exe.run(startup)
-train_reader = paddle.batch(
+train_reader = paddle.fluid.io.batch(
        paddle.dataset.cifar.train10(), batch_size=32)
 teacher = Teacher(out_path="example_knowledge.dat", # offline mode

--- a/docs/zh_cn/api_cn/prune_api.rst
+++ b/docs/zh_cn/api_cn/prune_api.rst
@@ -270,7 +270,7 @@ sensitivity
   exe = fluid.Executor(place)
   exe.run(startup_program)
-   val_reader = paddle.batch(reader.test(), batch_size=128)
+   val_reader = paddle.fluid.io.batch(reader.test(), batch_size=128)
   val_feeder = feeder = fluid.DataFeeder(
           [image, label], place, program=main_program)

--- a/docs/zh_cn/quick_start/distillation_tutorial.md
+++ b/docs/zh_cn/quick_start/distillation_tutorial.md
@@ -101,7 +101,7 @@ exe.run(student_startup)
 为了快速执行该示例，我们选取简单的MNIST数据，Paddle框架的`paddle.dataset.mnist`包定义了MNIST数据的下载和读取。 代码如下：
 ```python
-train_reader = paddle.batch(
+train_reader = paddle.fluid.io.batch(
    paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
 train_feeder = fluid.DataFeeder(['image', 'label'], fluid.CPUPlace(), student_program)
 ```

--- a/docs/zh_cn/quick_start/nas_tutorial.md
+++ b/docs/zh_cn/quick_start/nas_tutorial.md
@@ -67,9 +67,9 @@ def build_program(archs):
 使用的数据集为cifar10，paddle框架中`paddle.dataset.cifar`包括了cifar数据集的下载和读取，代码如下：
 ```python
 def input_data(inputs):
-    train_reader = paddle.batch(paddle.reader.shuffle(paddle.dataset.cifar.train10(cycle=False), buf_size=1024),batch_size=256)
+    train_reader = paddle.fluid.io.batch(paddle.reader.shuffle(paddle.dataset.cifar.train10(cycle=False), buf_size=1024),batch_size=256)
    train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
-    eval_reader = paddle.batch(paddle.dataset.cifar.test10(cycle=False), batch_size=256)
+    eval_reader = paddle.fluid.io.batch(paddle.dataset.cifar.test10(cycle=False), batch_size=256)
    eval_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
    return train_reader, train_feeder, eval_reader, eval_feeder
 ```

--- a/docs/zh_cn/quick_start/pruning_tutorial.md
+++ b/docs/zh_cn/quick_start/pruning_tutorial.md
@@ -74,7 +74,7 @@ print("FLOPs: {}".format(FLOPs))
 ```
 import paddle.dataset.mnist as reader
-train_reader = paddle.batch(
+train_reader = paddle.fluid.io.batch(
        reader.train(), batch_size=128, drop_last=True)
 train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
 ```

--- a/docs/zh_cn/quick_start/quant_aware_tutorial.md
+++ b/docs/zh_cn/quick_start/quant_aware_tutorial.md
@@ -42,9 +42,9 @@ exe, train_program, val_program, inputs, outputs = \
 ```python
 import paddle.dataset.mnist as reader
-train_reader = paddle.batch(
+train_reader = paddle.fluid.io.batch(
        reader.train(), batch_size=128, drop_last=True)
-test_reader = paddle.batch(
+test_reader = paddle.fluid.io.batch(
        reader.train(), batch_size=128, drop_last=True)
 train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
 ```

--- a/docs/zh_cn/quick_start/quant_post_tutorial.md
+++ b/docs/zh_cn/quick_start/quant_post_tutorial.md
@@ -40,9 +40,9 @@ exe, train_program, val_program, inputs, outputs = \
 ```python
 import paddle.dataset.mnist as reader
-train_reader = paddle.batch(
+train_reader = paddle.fluid.io.batch(
        reader.train(), batch_size=128, drop_last=True)
-test_reader = paddle.batch(
+test_reader = paddle.fluid.io.batch(
        reader.train(), batch_size=128, drop_last=True)
 train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
 ```

--- a/docs/zh_cn/tutorials/image_classification_nas_quick_start.ipynb
+++ b/docs/zh_cn/tutorials/image_classification_nas_quick_start.ipynb
@@ -128,9 +128,9 @@
   "outputs": [],
   "source": [
    "def input_data(inputs):\n",
-    "    train_reader = paddle.batch(paddle.reader.shuffle(paddle.dataset.cifar.train10(cycle=False), buf_size=1024),batch_size=256)\n",
+    "    train_reader = paddle.fluid.io.batch(paddle.reader.shuffle(paddle.dataset.cifar.train10(cycle=False), buf_size=1024),batch_size=256)\n",
    "    train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())\n",
-    "    eval_reader = paddle.batch(paddle.dataset.cifar.test10(cycle=False), batch_size=256)\n",
+    "    eval_reader = paddle.fluid.io.batch(paddle.dataset.cifar.test10(cycle=False), batch_size=256)\n",
    "    eval_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())\n",
    "    return train_reader, train_feeder, eval_reader, eval_feeder"
   ]

--- a/docs/zh_cn/tutorials/image_classification_sensitivity_analysis_tutorial.md
+++ b/docs/zh_cn/tutorials/image_classification_sensitivity_analysis_tutorial.md
@@ -44,9 +44,9 @@ place = fluid.CUDAPlace(0)
 ```python
 import paddle.dataset.mnist as reader
-train_reader = paddle.batch(
+train_reader = paddle.fluid.io.batch(
        reader.train(), batch_size=128, drop_last=True)
-test_reader = paddle.batch(
+test_reader = paddle.fluid.io.batch(
        reader.test(), batch_size=128, drop_last=True)
 data_feeder = fluid.DataFeeder(inputs, place)
 ```

--- a/docs/zh_cn/tutorials/sanas_darts_space.ipynb
+++ b/docs/zh_cn/tutorials/sanas_darts_space.ipynb
@@ -264,8 +264,8 @@
    }
   ],
   "source": [
-    "train_reader = paddle.batch(paddle.reader.shuffle(paddle.dataset.cifar.train10(cycle=False), buf_size=1024), batch_size=BATCH_SIZE, drop_last=True)\n",
+    "train_reader = paddle.fluid.io.batch(paddle.reader.shuffle(paddle.dataset.cifar.train10(cycle=False), buf_size=1024), batch_size=BATCH_SIZE, drop_last=True)\n",
-    "test_reader = paddle.batch(paddle.dataset.cifar.test10(cycle=False), batch_size=BATCH_SIZE, drop_last=False)\n",
+    "test_reader = paddle.fluid.io.batch(paddle.dataset.cifar.test10(cycle=False), batch_size=BATCH_SIZE, drop_last=False)\n",
    "train_loader.set_sample_list_generator(train_reader, places=place)\n",
    "test_loader.set_sample_list_generator(test_reader, places=place)"
   ]

--- a/docs/zh_cn/tutorials/sanas_darts_space.md
+++ b/docs/zh_cn/tutorials/sanas_darts_space.md
@@ -236,8 +236,8 @@ exe.run(startup_program)
 **注意：**本示例为了简化代码直接调用`paddle.dataset.cifar10`定义训练数据和预测数据，实际训练需要使用自定义cifar10文件中的reader。
 ```python
-train_reader = paddle.batch(paddle.reader.shuffle(paddle.dataset.cifar.train10(cycle=False), buf_size=1024), batch_size=BATCH_SIZE, drop_last=True)
+train_reader = paddle.fluid.io.batch(paddle.reader.shuffle(paddle.dataset.cifar.train10(cycle=False), buf_size=1024), batch_size=BATCH_SIZE, drop_last=True)
-test_reader = paddle.batch(paddle.dataset.cifar.test10(cycle=False), batch_size=BATCH_SIZE, drop_last=False)
+test_reader = paddle.fluid.io.batch(paddle.dataset.cifar.test10(cycle=False), batch_size=BATCH_SIZE, drop_last=False)
 train_loader.set_sample_list_generator(train_reader, places=place)
 test_loader.set_sample_list_generator(test_reader, places=place)
 ```

--- a/tests/test_quant_aware.py
+++ b/tests/test_quant_aware.py
@@ -112,9 +112,10 @@ class TestQuantAwareCase2(unittest.TestCase):
        exe = fluid.Executor(place)
        exe.run(fluid.default_startup_program())
        feeder = fluid.DataFeeder([image, label], place, program=main_prog)
-        train_reader = paddle.batch(
+        train_reader = paddle.fluid.io.batch(
            paddle.dataset.mnist.train(), batch_size=64)
-        eval_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=64)
+        eval_reader = paddle.fluid.io.batch(
+            paddle.dataset.mnist.test(), batch_size=64)
        def train(program):
            iter = 0

--- a/tests/test_quant_post.py
+++ b/tests/test_quant_post.py
@@ -50,9 +50,10 @@ class TestQuantAwareCase1(unittest.TestCase):
        exe = fluid.Executor(place)
        exe.run(fluid.default_startup_program())
        feeder = fluid.DataFeeder([image, label], place, program=main_prog)
-        train_reader = paddle.batch(
+        train_reader = paddle.fluid.io.batch(
            paddle.dataset.mnist.train(), batch_size=64)
-        eval_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=64)
+        eval_reader = paddle.fluid.io.batch(
+            paddle.dataset.mnist.test(), batch_size=64)
        def train(program):
            iter = 0

--- a/tests/test_quant_post_only_weight.py
+++ b/tests/test_quant_post_only_weight.py
@@ -50,9 +50,10 @@ class TestQuantPostOnlyWeightCase1(unittest.TestCase):
        exe = fluid.Executor(place)
        exe.run(fluid.default_startup_program())
        feeder = fluid.DataFeeder([image, label], place, program=main_prog)
-        train_reader = paddle.batch(
+        train_reader = paddle.fluid.io.batch(
            paddle.dataset.mnist.train(), batch_size=64)
-        eval_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=64)
+        eval_reader = paddle.fluid.io.batch(
+            paddle.dataset.mnist.test(), batch_size=64)
        def train(program):
            iter = 0

--- a/tests/test_sensitivity.py
+++ b/tests/test_sensitivity.py
@@ -44,7 +44,8 @@ class TestSensitivity(unittest.TestCase):
        exe = fluid.Executor(place)
        exe.run(startup_program)
-        val_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
+        val_reader = paddle.fluid.io.batch(
+            paddle.dataset.mnist.test(), batch_size=128)
        def eval_func(program, scope):
            feeder = fluid.DataFeeder(