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

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)
-    train_reader = paddle.batch(
+    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
+    train_reader = paddle.fluid.io.batch(
         train_reader, batch_size=args.batch_size, drop_last=True)
 
     train_feeder = feeder = fluid.DataFeeder([image, label], place)

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],
-#    [0.08, 0.02, 0.03, 0.13, 0.1, 0.06, 0.03, 0.04, 0.14, 0.02, 0.03, 0.02, 0.01],
-        ]
+    #    [0.06, 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],
+]
 
 
-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,
-        step_each_epoch=step,
-        epochs=args.num_epochs)
+        learning_rate=args.lr, 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
-    image_shape="3,224,224"
+    class_dim = 1000
+    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))
-            print("exist",exist)
+            exist = os.path.exists(
+                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)
-    train_reader = paddle.batch(
+    val_reader = paddle.fluid.io.batch(reader.val(), batch_size=args.batch_size)
+    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
@@ -117,80 +123,99 @@ def compress(args):
         acc_top5_ns = []
         for data in val_reader():
             start_time = time.time()
-            acc_top1_n, acc_top5_n = exe.run(program,
-                                           feed=train_feeder.feed(data),
-                                           fetch_list=[acc_top1.name, acc_top5.name])
+            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):
 
         build_strategy = fluid.BuildStrategy()
         exec_strategy = fluid.ExecutionStrategy()
         train_program = fluid.compiler.CompiledProgram(
-                                   program).with_data_parallel(
-                                   loss_name=avg_cost.name,
-                                   build_strategy=build_strategy,
-                                   exec_strategy=exec_strategy)
+            program).with_data_parallel(
+                loss_name=avg_cost.name,
+                build_strategy=build_strategy,
+                exec_strategy=exec_strategy)
 
         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,
-                                               feed=train_feeder.feed(data),
-                                               fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name,"learning_rate"])
+            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"
+                ])
             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 = []
     for param in fluid.default_main_program().global_block().all_parameters():
         #if "_weights" in  param.name and "conv1_weights" not in param.name:
-        if "_sep_weights" 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,
-                                  fluid.global_scope(),
-                                  params=params,
-                                  ratios=ratios,
-                                  place=place,
-                                  only_graph=True)
-
-
-        pruned_program_iter = pruner.prune(pruned_program_iter,
-                                  fluid.global_scope(),
-                                  params=params,
-                                  ratios=ratios,
-                                  place=place)
+        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,
+            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")):
-      os.remove( args.pretrained_model + "/learning_rate")
-    if(os.path.exists(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)
+    if (os.path.exists(args.pretrained_model + "/learning_rate")):
+        os.remove(args.pretrained_model + "/learning_rate")
+    if (os.path.exists(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)
 
     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()

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],
-#    [0.08, 0.02, 0.03, 0.13, 0.1, 0.06, 0.03, 0.04, 0.14, 0.02, 0.03, 0.02, 0.01],
-        ]
+    #    [0.06, 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],
+]
+
 
 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)
-    train_reader = paddle.batch(
+    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
+    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,
-                                  fluid.global_scope(),
-                                  params=params,
-                                  ratios=ratios,
-                                  place=place,
-                                  only_graph=True)
-
-
-        pruned_program_iter = pruner.prune(pruned_program_iter,
-                                  fluid.global_scope(),
-                                  params=params,
-                                  ratios=ratios,
-                                  place=place)
+        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,
+            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)

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)
 

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)"
    ]

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)

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",
@@ -160,4 +160,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}

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)

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)

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(

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(

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)

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())"
    ]

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)
-    train_reader = paddle.batch(
+    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
+    train_reader = paddle.fluid.io.batch(
         train_reader, batch_size=args.batch_size, drop_last=True)
 
     train_feeder = feeder = fluid.DataFeeder([image, label], place)

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())"
    ]

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)
-    train_reader = paddle.batch(
+    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
+    train_reader = paddle.fluid.io.batch(
         train_reader, batch_size=args.batch_size, drop_last=True)
 
     train_feeder = feeder = fluid.DataFeeder([image, label], place)

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)
 

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())"
    ]

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)"
    ]

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)

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)
-    train_reader = paddle.batch(
+    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
+    train_reader = paddle.fluid.io.batch(
         train_reader, batch_size=args.batch_size, drop_last=True)
 
     train_feeder = feeder = fluid.DataFeeder([image, label], place)

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)
-    train_reader = paddle.batch(
+    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
+    train_reader = paddle.fluid.io.batch(
         train_reader, batch_size=args.batch_size, drop_last=True)
 
     train_feeder = feeder = fluid.DataFeeder([image, label], place)

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():

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)

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)

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)
 ```

docs/en/quick_start/nas_tutorial_en.md

-# Nerual Architecture Search for Image Classification 
+# Nerual Architecture Search for Image Classification
 
 This tutorial shows how to use [API](../api/nas_api.md) about SANAS in PaddleSlim. We start experiment based on MobileNetV2 as example. The tutorial contains follow section.
 
 1. necessary imports
 2. initial SANAS instance
 3. define function about building program
-4. define function about input data 
+4. define function about input data
 5. define function about training
 6. define funciton about evaluation
 7. start search
@@ -52,7 +52,7 @@ def build_program(archs):
         acc_top1 = fluid.layers.accuracy(input=softmax_out, label=label, k=1)
         acc_top5 = fluid.layers.accuracy(input=softmax_out, label=label, k=5)
         test_program = fluid.default_main_program().clone(for_test=True)
-            
+
         optimizer = fluid.optimizer.Adam(learning_rate=0.1)
         optimizer.minimize(avg_cost)
 
@@ -62,13 +62,13 @@ def build_program(archs):
     return exe, train_program, test_program, (data, label), avg_cost, acc_top1, acc_top5
 ```
 
-## 4. define function about input data 
-The dataset we used is cifar10, and `paddle.dataset.cifar` in Paddle including the download and pre-read about cifar. 
+## 4. define function about input data
+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
 ```
@@ -76,7 +76,7 @@ def input_data(inputs):
 ## 5. define function about training
 Start training.
 ```python
-def start_train(program, data_reader, data_feeder): 
+def start_train(program, data_reader, data_feeder):
     outputs = [avg_cost.name, acc_top1.name, acc_top5.name]
     for data in data_reader():
         batch_reward = exe.run(program, feed=data_feeder.feed(data), fetch_list = outputs)
@@ -145,7 +145,7 @@ for step in range(3):
     current_flops = slim.analysis.flops(train_program)
     if current_flops > 321208544:
         continue
-    
+
     for epoch in range(7):
         start_train(train_program, train_reader, train_feeder)
 

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())
 ```

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())
 ```

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())
 ```

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)
 ```

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

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)
    

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)
 ```

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
 ```

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())
 ```

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())
 ```

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())
 ```

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"
    ]

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)
 ```

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",
-    "test_reader = paddle.batch(paddle.dataset.cifar.test10(cycle=False), batch_size=BATCH_SIZE, drop_last=False)\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.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)"
    ]

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)
-test_reader = paddle.batch(paddle.dataset.cifar.test10(cycle=False), batch_size=BATCH_SIZE, drop_last=False)
+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.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)
 ```

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

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

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

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(