[Paddle2.0] Rename hapi.Input and move `data` api (#26396)

* Rename `Input` into `InputSpec`

* fix argument place of Input api

python/paddle/fluid/tests/unittests/test_compare_op.py

@@ -93,8 +93,9 @@ def create_paddle_case(op_type, callback):
 
         def test_broadcast_api_1(self):
             with program_guard(Program(), Program()):
-                x = paddle.nn.data(name='x', shape=[1, 2, 1, 3], dtype='int32')
-                y = paddle.nn.data(name='y', shape=[1, 2, 3], dtype='int32')
+                x = paddle.static.data(
+                    name='x', shape=[1, 2, 1, 3], dtype='int32')
+                y = paddle.static.data(name='y', shape=[1, 2, 3], dtype='int32')
                 op = eval("paddle.%s" % (self.op_type))
                 out = op(x, y)
                 exe = paddle.static.Executor(self.place)

python/paddle/fluid/tests/unittests/test_cumsum_op.py

@@ -54,7 +54,7 @@ class TestCumsumOp(unittest.TestCase):
     def run_static(self, use_gpu=False):
         with fluid.program_guard(fluid.Program()):
             data_np = np.random.random((100, 100)).astype(np.float32)
-            x = paddle.nn.data('X', [100, 100])
+            x = paddle.static.data('X', [100, 100])
             y = paddle.cumsum(x)
             y2 = paddle.cumsum(x, axis=0)
             y3 = paddle.cumsum(x, axis=-1)
@@ -100,7 +100,7 @@ class TestCumsumOp(unittest.TestCase):
 
     def test_name(self):
         with fluid.program_guard(fluid.Program()):
-            x = paddle.nn.data('x', [3, 4])
+            x = paddle.static.data('x', [3, 4])
             y = paddle.cumsum(x, name='out')
             self.assertTrue('out' in y.name)
 

python/paddle/fluid/tests/unittests/test_flatten_contiguous_range_op.py

@@ -145,19 +145,22 @@ class TestFlatten2OpError(unittest.TestCase):
         x = x.astype('float32')
 
         def test_ValueError1():
-            x_var = paddle.nn.data(name="x", shape=image_shape, dtype='float32')
+            x_var = paddle.static.data(
+                name="x", shape=image_shape, dtype='float32')
             out = paddle.flatten(x_var, start_axis=2, stop_axis=1)
 
         self.assertRaises(ValueError, test_ValueError1)
 
         def test_ValueError2():
-            x_var = paddle.nn.data(name="x", shape=image_shape, dtype='float32')
+            x_var = paddle.static.data(
+                name="x", shape=image_shape, dtype='float32')
             paddle.flatten(x_var, start_axis=10, stop_axis=1)
 
         self.assertRaises(ValueError, test_ValueError2)
 
         def test_ValueError3():
-            x_var = paddle.nn.data(name="x", shape=image_shape, dtype='float32')
+            x_var = paddle.static.data(
+                name="x", shape=image_shape, dtype='float32')
             paddle.flatten(x_var, start_axis=2, stop_axis=10)
 
         self.assertRaises(ValueError, test_ValueError3)

python/paddle/fluid/tests/unittests/test_max_op.py

@@ -32,7 +32,7 @@ class ApiMaxTest(unittest.TestCase):
         paddle.enable_static()
         with paddle.static.program_guard(paddle.static.Program(),
                                          paddle.static.Program()):
-            data = paddle.nn.data("data", shape=[10, 10], dtype="float32")
+            data = paddle.static.data("data", shape=[10, 10], dtype="float32")
             result_max = paddle.max(x=data, axis=1)
             exe = paddle.static.Executor(self.place)
             input_data = np.random.rand(10, 10).astype(np.float32)
@@ -41,7 +41,7 @@ class ApiMaxTest(unittest.TestCase):
 
         with paddle.static.program_guard(paddle.static.Program(),
                                          paddle.static.Program()):
-            data = paddle.nn.data("data", shape=[10, 10], dtype="int64")
+            data = paddle.static.data("data", shape=[10, 10], dtype="int64")
             result_max = paddle.max(x=data, axis=0)
             exe = paddle.static.Executor(self.place)
             input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
@@ -50,7 +50,7 @@ class ApiMaxTest(unittest.TestCase):
 
         with paddle.static.program_guard(paddle.static.Program(),
                                          paddle.static.Program()):
-            data = paddle.nn.data("data", shape=[10, 10], dtype="int64")
+            data = paddle.static.data("data", shape=[10, 10], dtype="int64")
             result_max = paddle.max(x=data, axis=(0, 1))
             exe = paddle.static.Executor(self.place)
             input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
@@ -71,8 +71,8 @@ class ApiMaxTest(unittest.TestCase):
         def test_axis_type():
             with paddle.static.program_guard(paddle.static.Program(),
                                              paddle.static.Program()):
-                data = paddle.nn.data("data", shape=[10, 10], dtype="int64")
-                axis = paddle.nn.data("axis", shape=[10, 10], dtype="int64")
+                data = paddle.static.data("data", shape=[10, 10], dtype="int64")
+                axis = paddle.static.data("axis", shape=[10, 10], dtype="int64")
                 result_min = paddle.min(data, axis)
 
         self.assertRaises(TypeError, test_axis_type)

python/paddle/fluid/tests/unittests/test_maximum_op.py

@@ -36,8 +36,8 @@ class ApiMaximumTest(unittest.TestCase):
         paddle.enable_static()
         with paddle.static.program_guard(paddle.static.Program(),
                                          paddle.static.Program()):
-            data_x = paddle.nn.data("x", shape=[10, 15], dtype="float32")
-            data_y = paddle.nn.data("y", shape=[10, 15], dtype="float32")
+            data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
+            data_y = paddle.static.data("y", shape=[10, 15], dtype="float32")
             result_max = paddle.maximum(data_x, data_y)
             exe = paddle.static.Executor(self.place)
             res, = exe.run(feed={"x": self.input_x,
@@ -48,8 +48,8 @@ class ApiMaximumTest(unittest.TestCase):
 
         with paddle.static.program_guard(paddle.static.Program(),
                                          paddle.static.Program()):
-            data_x = paddle.nn.data("x", shape=[10, 15], dtype="float32")
-            data_z = paddle.nn.data("z", shape=[15], dtype="float32")
+            data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
+            data_z = paddle.static.data("z", shape=[15], dtype="float32")
             result_max = paddle.maximum(data_x, data_z, axis=1)
             exe = paddle.static.Executor(self.place)
             res, = exe.run(feed={"x": self.input_x,

python/paddle/fluid/tests/unittests/test_min_op.py

@@ -32,7 +32,7 @@ class ApiMinTest(unittest.TestCase):
         paddle.enable_static()
         with paddle.static.program_guard(paddle.static.Program(),
                                          paddle.static.Program()):
-            data = paddle.nn.data("data", shape=[10, 10], dtype="float32")
+            data = paddle.static.data("data", shape=[10, 10], dtype="float32")
             result_min = paddle.min(x=data, axis=1)
             exe = paddle.static.Executor(self.place)
             input_data = np.random.rand(10, 10).astype(np.float32)
@@ -41,7 +41,7 @@ class ApiMinTest(unittest.TestCase):
 
         with paddle.static.program_guard(paddle.static.Program(),
                                          paddle.static.Program()):
-            data = paddle.nn.data("data", shape=[10, 10], dtype="int64")
+            data = paddle.static.data("data", shape=[10, 10], dtype="int64")
             result_min = paddle.min(x=data, axis=0)
             exe = paddle.static.Executor(self.place)
             input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
@@ -50,7 +50,7 @@ class ApiMinTest(unittest.TestCase):
 
         with paddle.static.program_guard(paddle.static.Program(),
                                          paddle.static.Program()):
-            data = paddle.nn.data("data", shape=[10, 10], dtype="int64")
+            data = paddle.static.data("data", shape=[10, 10], dtype="int64")
             result_min = paddle.min(x=data, axis=(0, 1))
             exe = paddle.static.Executor(self.place)
             input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
@@ -71,8 +71,8 @@ class ApiMinTest(unittest.TestCase):
         def test_axis_type():
             with paddle.static.program_guard(paddle.static.Program(),
                                              paddle.static.Program()):
-                data = paddle.nn.data("data", shape=[10, 10], dtype="int64")
-                axis = paddle.nn.data("axis", shape=[10, 10], dtype="int64")
+                data = paddle.static.data("data", shape=[10, 10], dtype="int64")
+                axis = paddle.static.data("axis", shape=[10, 10], dtype="int64")
                 result_min = paddle.min(data, axis)
 
         self.assertRaises(TypeError, test_axis_type)

python/paddle/fluid/tests/unittests/test_minimum_op.py

@@ -36,8 +36,8 @@ class ApiMinimumTest(unittest.TestCase):
         paddle.enable_static()
         with paddle.static.program_guard(paddle.static.Program(),
                                          paddle.static.Program()):
-            data_x = paddle.nn.data("x", shape=[10, 15], dtype="float32")
-            data_y = paddle.nn.data("y", shape=[10, 15], dtype="float32")
+            data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
+            data_y = paddle.static.data("y", shape=[10, 15], dtype="float32")
             result_min = paddle.minimum(data_x, data_y)
             exe = paddle.static.Executor(self.place)
             res, = exe.run(feed={"x": self.input_x,
@@ -48,8 +48,8 @@ class ApiMinimumTest(unittest.TestCase):
 
         with paddle.static.program_guard(paddle.static.Program(),
                                          paddle.static.Program()):
-            data_x = paddle.nn.data("x", shape=[10, 15], dtype="float32")
-            data_z = paddle.nn.data("z", shape=[15], dtype="float32")
+            data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
+            data_z = paddle.static.data("z", shape=[15], dtype="float32")
             result_min = paddle.minimum(data_x, data_z, axis=1)
             exe = paddle.static.Executor(self.place)
             res, = exe.run(feed={"x": self.input_x,

python/paddle/fluid/tests/unittests/test_multiply.py

@@ -26,8 +26,10 @@ class TestMultiplyAPI(unittest.TestCase):
 
     def __run_static_graph_case(self, x_data, y_data, axis=-1):
         with program_guard(Program(), Program()):
-            x = paddle.nn.data(name='x', shape=x_data.shape, dtype=x_data.dtype)
-            y = paddle.nn.data(name='y', shape=y_data.shape, dtype=y_data.dtype)
+            x = paddle.static.data(
+                name='x', shape=x_data.shape, dtype=x_data.dtype)
+            y = paddle.static.data(
+                name='y', shape=y_data.shape, dtype=y_data.dtype)
             res = tensor.multiply(x, y, axis=axis)
 
             place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
@@ -109,14 +111,14 @@ class TestMultiplyError(unittest.TestCase):
         # test static computation graph: dtype can not be int8
         paddle.enable_static()
         with program_guard(Program(), Program()):
-            x = paddle.nn.data(name='x', shape=[100], dtype=np.int8)
-            y = paddle.nn.data(name='y', shape=[100], dtype=np.int8)
+            x = paddle.static.data(name='x', shape=[100], dtype=np.int8)
+            y = paddle.static.data(name='y', shape=[100], dtype=np.int8)
             self.assertRaises(TypeError, tensor.multiply, x, y)
 
         # test static computation graph: inputs must be broadcastable 
         with program_guard(Program(), Program()):
-            x = paddle.nn.data(name='x', shape=[20, 50], dtype=np.float64)
-            y = paddle.nn.data(name='y', shape=[20], dtype=np.float64)
+            x = paddle.static.data(name='x', shape=[20, 50], dtype=np.float64)
+            y = paddle.static.data(name='y', shape=[20], dtype=np.float64)
             self.assertRaises(fluid.core.EnforceNotMet, tensor.multiply, x, y)
 
         np.random.seed(7)

python/paddle/fluid/tests/unittests/test_nn_margin_rank_loss.py

@@ -54,9 +54,11 @@ def create_test_case(margin, reduction):
                 margin=margin,
                 reduction=reduction)
             with program_guard(Program(), Program()):
-                x = paddle.nn.data(name="x", shape=[10, 10], dtype="float64")
-                y = paddle.nn.data(name="y", shape=[10, 10], dtype="float64")
-                label = paddle.nn.data(
+                x = paddle.static.data(
+                    name="x", shape=[10, 10], dtype="float64")
+                y = paddle.static.data(
+                    name="y", shape=[10, 10], dtype="float64")
+                label = paddle.static.data(
                     name="label", shape=[10, 10], dtype="float64")
                 result = paddle.nn.functional.margin_ranking_loss(
                     x, y, label, margin, reduction)
@@ -78,9 +80,11 @@ def create_test_case(margin, reduction):
                 margin=margin,
                 reduction=reduction)
             with program_guard(Program(), Program()):
-                x = paddle.nn.data(name="x", shape=[10, 10], dtype="float64")
-                y = paddle.nn.data(name="y", shape=[10, 10], dtype="float64")
-                label = paddle.nn.data(
+                x = paddle.static.data(
+                    name="x", shape=[10, 10], dtype="float64")
+                y = paddle.static.data(
+                    name="y", shape=[10, 10], dtype="float64")
+                label = paddle.static.data(
                     name="label", shape=[10, 10], dtype="float64")
                 margin_rank_loss = paddle.nn.loss.MarginRankingLoss(
                     margin=margin, reduction=reduction)

python/paddle/fluid/tests/unittests/test_nn_sigmoid_op.py

@@ -45,7 +45,7 @@ class TestNNSigmoidAPI(unittest.TestCase):
         main_program = paddle.static.Program()
         mysigmoid = nn.Sigmoid(name="api_sigmoid")
         with paddle.static.program_guard(main_program):
-            x = paddle.nn.data(name='x', shape=self.x_shape)
+            x = paddle.static.data(name='x', shape=self.x_shape)
             x.stop_gradient = False
             y = mysigmoid(x)
             fluid.backward.append_backward(paddle.mean(y))
@@ -86,7 +86,7 @@ class TestNNFunctionalSigmoidAPI(unittest.TestCase):
         paddle.enable_static()
         main_program = paddle.static.Program()
         with paddle.static.program_guard(main_program):
-            x = paddle.nn.data(name='x', shape=self.x_shape)
+            x = paddle.static.data(name='x', shape=self.x_shape)
             y = functional.sigmoid(x, name="api_sigmoid")
         exe = paddle.static.Executor(fluid.CPUPlace())
         out = exe.run(main_program, feed={'x': self.x}, fetch_list=[y])

python/paddle/fluid/tests/unittests/test_randint_op.py

@@ -125,7 +125,7 @@ class TestRandintAPI(unittest.TestCase):
             out4 = paddle.randint(
                 low=-100, high=100, shape=[dim_1, 5, dim_2], dtype='int32')
             # shape is a tensor and dtype is 'float64'
-            var_shape = paddle.nn.data(
+            var_shape = paddle.static.data(
                 name='var_shape', shape=[2], dtype="int64")
             out5 = paddle.randint(
                 low=1, high=1000, shape=var_shape, dtype='int64')

python/paddle/fluid/tests/unittests/test_randn_op.py

@@ -34,7 +34,7 @@ class TestRandnOp(unittest.TestCase):
             dim_2 = paddle.fill_constant([1], "int32", 50)
             x3 = paddle.randn([dim_1, dim_2, 784])
 
-            var_shape = paddle.nn.data('X', [2], 'int32')
+            var_shape = paddle.static.data('X', [2], 'int32')
             x4 = paddle.randn(var_shape)
 
         place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(

python/paddle/incubate/hapi/callbacks.py

@@ -295,8 +295,8 @@ class ProgBarLogger(Callback):
             import paddle.fluid as fluid
             import paddle.incubate.hapi as hapi
 
-            inputs = [hapi.Input('image', [-1, 1, 28, 28], 'float32')]
-            labels = [hapi.Input('label', [None, 1], 'int64')]
+            inputs = [hapi.Input([-1, 1, 28, 28], 'float32', 'image')]
+            labels = [hapi.Input([None, 1], 'int64', 'label')]
 
             train_dataset = hapi.datasets.MNIST(mode='train')
 
@@ -431,8 +431,8 @@ class ModelCheckpoint(Callback):
             import paddle.fluid as fluid
             import paddle.incubate.hapi as hapi
 
-            inputs = [hapi.Input('image', [-1, 1, 28, 28], 'float32')]
-            labels = [hapi.Input('label', [None, 1], 'int64')]
+            inputs = [hapi.Input([-1, 1, 28, 28], 'float32', 'image')]
+            labels = [hapi.Input([None, 1], 'int64', 'label')]
 
             train_dataset = hapi.datasets.MNIST(mode='train')
 

python/paddle/incubate/hapi/model.py

@@ -25,6 +25,8 @@ import warnings
 from collections import Iterable
 
 from paddle import fluid
+# Note: Use alias `Input` temporarily before releasing hapi feature.
+from paddle.static import InputSpec as Input
 from paddle.fluid.framework import in_dygraph_mode, Variable
 from paddle.fluid.executor import global_scope
 from paddle.fluid.io import is_belong_to_optimizer
@@ -47,40 +49,6 @@ __all__ = [
 ]
 
 
-class Input(fluid.dygraph.Layer):
-    """
-    Define inputs the model.
-
-    Args:
-        name (str): The name/alias of the variable, see :ref:`api_guide_Name`
-            for more details.
-        shape (tuple(integers)|list[integers]): List|Tuple of integers
-            declaring the shape. You can set "None" or -1 at a dimension
-            to indicate the dimension can be of any size. For example,
-            it is useful to set changeable batch size as "None" or -1.
-        dtype (np.dtype|VarType|str, optional): The type of the data. Supported
-            dtype: bool, float16, float32, float64, int8, int16, int32, int64,
-            uint8. Default: float32.
-
-    Examples:
-        .. code-block:: python
-
-        import paddle.incubate.hapi as hapi
-
-        input = hapi.Input('x', [None, 784], 'float32')
-        label = hapi.Input('label', [None, 1], 'int64')
-    """
-
-    def __init__(self, name, shape=None, dtype='float32'):
-        super(Input, self).__init__()
-        self.shape = shape
-        self.dtype = dtype
-        self.name = name
-
-    def forward(self):
-        return fluid.data(self.name, shape=self.shape, dtype=self.dtype)
-
-
 class StaticGraphAdapter(object):
     """
     Model traning/inference with a static graph.
@@ -388,8 +356,8 @@ class StaticGraphAdapter(object):
         with fluid.program_guard(prog, self._startup_prog):
             inputs = self.model._inputs
             labels = self.model._labels if self.model._labels else []
-            inputs = [k.forward() for k in to_list(inputs)]
-            labels = [k.forward() for k in to_list(labels)]
+            inputs = [k._create_feed_layer() for k in to_list(inputs)]
+            labels = [k._create_feed_layer() for k in to_list(labels)]
             self._label_vars[mode] = labels
             outputs = to_list(self.model.network.forward(*inputs))
 
@@ -704,8 +672,8 @@ class Model(object):
         fluid.enable_dygraph(device)
         
         # inputs and labels are not required for dynamic graph.
-        input = hapi.Input('x', [None, 784], 'float32')
-        label = hapi.Input('label', [None, 1], 'int64')
+        input = hapi.Input([None, 784], 'float32', 'x')
+        label = hapi.Input([None, 1], 'int64', 'label')
         
         model = hapi.Model(MyNet(), input, label)
         optim = fluid.optimizer.SGD(learning_rate=1e-3,
@@ -734,16 +702,8 @@ class Model(object):
             if not isinstance(inputs, (list, dict, Input)):
                 raise TypeError(
                     "'inputs' must be list or dict in static graph mode")
-        if inputs is None:
-            self._inputs = [Input(name=n) \
-                for n in extract_args(self.network.forward) if n != 'self']
-        elif isinstance(input, dict):
-            self._inputs = [inputs[n] \
-                for n in extract_args(self.network.forward) if n != 'self']
-        else:
-            self._inputs = to_list(inputs)
-
-        self._labels = to_list(labels)
+        self._inputs = self._verify_spec(inputs, True)
+        self._labels = self._verify_spec(labels)
 
         # init backend
         if fluid.in_dygraph_mode():
@@ -787,8 +747,8 @@ class Model(object):
               device = hapi.set_device('gpu')
               fluid.enable_dygraph(device)
 
-              input = hapi.Input('x', [None, 784], 'float32')
-              label = hapi.Input('label', [None, 1], 'int64')
+              input = hapi.Input([None, 784], 'float32', 'x')
+              label = hapi.Input([None, 1], 'int64', 'label')
               model = hapi.Model(MyNet(), input, label)
               optim = fluid.optimizer.SGD(learning_rate=1e-3,
                   parameter_list=model.parameters())
@@ -836,8 +796,8 @@ class Model(object):
               device = hapi.set_device('gpu')
               fluid.enable_dygraph(device)
 
-              input = hapi.Input('x', [None, 784], 'float32')
-              label = hapi.Input('label', [None, 1], 'int64')
+              input = hapi.Input([None, 784], 'float32', 'x')
+              label = hapi.Input([None, 1], 'int64', 'label')
               model = hapi.Model(MyNet(), input, label)
               optim = fluid.optimizer.SGD(learning_rate=1e-3,
                   parameter_list=model.parameters())
@@ -1194,8 +1154,8 @@ class Model(object):
               train_dataset = hapi.datasets.MNIST(mode='train')
               val_dataset = hapi.datasets.MNIST(mode='test')
            
-              input = hapi.Input('image', [None, 1, 28, 28], 'float32')
-              label = hapi.Input('label', [None, 1], 'int64')
+              input = hapi.Input([None, 1, 28, 28], 'float32', 'image')
+              label = hapi.Input([None, 1], 'int64', 'label')
            
               model = hapi.Model(hapi.vision.LeNet(classifier_activation=None),
                   input, label)
@@ -1231,8 +1191,8 @@ class Model(object):
               val_loader = fluid.io.DataLoader(val_dataset,
                   places=device, batch_size=64)
            
-              input = hapi.Input('image', [None, 1, 28, 28], 'float32')
-              label = hapi.Input('label', [None, 1], 'int64')
+              input = hapi.Input([None, 1, 28, 28], 'float32', 'image')
+              label = hapi.Input([None, 1], 'int64', 'label')
            
               model = hapi.Model(hapi.vision.LeNet(classifier_activation=None),
                   input, label)
@@ -1359,8 +1319,8 @@ class Model(object):
             # declarative mode
             val_dataset = hapi.datasets.MNIST(mode='test')
 
-            input = hapi.Input('image', [-1, 1, 28, 28], 'float32')
-            label = hapi.Input('label', [None, 1], 'int64')
+            input = hapi.Input([-1, 1, 28, 28], 'float32', 'image')
+            label = hapi.Input([None, 1], 'int64', 'label')
             model = hapi.Model(hapi.vision.LeNet(), input, label)
             model.prepare(metrics=hapi.metrics.Accuracy())
 
@@ -1433,12 +1393,13 @@ class Model(object):
             num_workers (int): The number of subprocess to load data, 0 for no subprocess 
                 used and loading data in main process. When train_data and eval_data are
                 both the instance of Dataloader, this argument will be ignored. Default: 0.
-            stack_output (bool): Whether stack output field like a batch, as for an output
+            stack_outputs (bool): Whether stack output field like a batch, as for an output
                 filed of a sample is in shape [X, Y], test_data contains N samples, predict
                 output field will be in shape [N, X, Y] if stack_output is True, and will
                 be a length N list in shape [[X, Y], [X, Y], ....[X, Y]] if stack_outputs
                 is False. stack_outputs as False is used for LoDTensor output situation,
                 it is recommended set as True if outputs contains no LoDTensor. Default: False.
+            callbacks(Callback): A Callback instance, default None.
         Returns:
             list: output of models.
 
@@ -1466,7 +1427,7 @@ class Model(object):
             test_dataset = MnistDataset(mode='test', return_label=False)
 
             # declarative mode
-            input = hapi.Input('image', [-1, 1, 28, 28], 'float32')
+            input = hapi.Input([-1, 1, 28, 28], 'float32', 'image')
             model = hapi.Model(hapi.vision.LeNet(), input)
             model.prepare()
 
@@ -1548,7 +1509,7 @@ class Model(object):
             import paddle.fluid as fluid
             import paddle.incubate.hapi as hapi
 
-            input = hapi.Input('image', [-1, 1, 28, 28], 'float32')
+            input = hapi.Input([-1, 1, 28, 28], 'float32', 'image')
             model = hapi.Model(hapi.vision.LeNet(), input)
             model.prepare()
 
@@ -1639,6 +1600,36 @@ class Model(object):
             return logs, outputs
         return logs
 
+    def _verify_spec(self, specs, is_input=False):
+        out_specs = []
+
+        if specs is None:
+            # If not specific specs of `Input`, using argument names of `forward` function
+            # to generate `Input`.
+            if is_input:
+                out_specs = [
+                    Input(name=n) for n in extract_args(self.network.forward)
+                    if n != 'self'
+                ]
+            else:
+                out_specs = to_list(specs)
+        elif isinstance(specs, dict):
+            assert is_input == False
+            out_specs = [specs[n] \
+                for n in extract_args(self.network.forward) if n != 'self']
+        else:
+            out_specs = to_list(specs)
+        # Note: checks each element has specificed `name`.
+        if out_specs is not None:
+            for i, spec in enumerate(out_specs):
+                assert isinstance(spec, Input)
+                if spec.name is None:
+                    raise ValueError(
+                        "Requires Input[{}].name != None, but receive `None` with {}.".
+                        format(i, spec))
+
+        return out_specs
+
     def _reset_metrics(self):
         for metric in self._metrics:
             metric.reset()

python/paddle/incubate/hapi/tests/dist_hapi_mnist_dynamic.py

@@ -64,8 +64,8 @@ class TestDistTraning(unittest.TestCase):
         im_shape = (-1, 1, 28, 28)
         batch_size = 128
 
-        inputs = [Input('image', im_shape, 'float32')]
-        labels = [Input('label', [None, 1], 'int64')]
+        inputs = [Input(im_shape, 'float32', 'image')]
+        labels = [Input([None, 1], 'int64', 'label')]
 
         model = Model(LeNet(classifier_activation=None), inputs, labels)
         optim = fluid.optimizer.Momentum(

python/paddle/incubate/hapi/tests/dist_hapi_mnist_static.py

@@ -63,8 +63,8 @@ class TestDistTraning(unittest.TestCase):
         im_shape = (-1, 1, 28, 28)
         batch_size = 128
 
-        inputs = [Input('image', im_shape, 'float32')]
-        labels = [Input('label', [None, 1], 'int64')]
+        inputs = [Input(im_shape, 'float32', 'image')]
+        labels = [Input([None, 1], 'int64', 'label')]
 
         model = Model(LeNet(classifier_activation=None), inputs, labels)
         optim = fluid.optimizer.Momentum(

python/paddle/incubate/hapi/tests/test_callbacks.py

@@ -36,7 +36,7 @@ class TestCallbacks(unittest.TestCase):
         freq = 2
         eval_steps = 20
 
-        inputs = [Input('image', [None, 1, 28, 28], 'float32')]
+        inputs = [Input([None, 1, 28, 28], 'float32', 'image')]
         lenet = Model(LeNet(), inputs)
         lenet.prepare()
 

python/paddle/incubate/hapi/tests/test_model.py

@@ -150,8 +150,8 @@ class TestModel(unittest.TestCase):
 
         cls.acc1 = dynamic_evaluate(dy_lenet, cls.val_loader)
 
-        cls.inputs = [Input('image', [-1, 1, 28, 28], 'float32')]
-        cls.labels = [Input('label', [None, 1], 'int64')]
+        cls.inputs = [Input([-1, 1, 28, 28], 'float32', 'image')]
+        cls.labels = [Input([None, 1], 'int64', 'label')]
 
         cls.save_dir = tempfile.mkdtemp()
         cls.weight_path = os.path.join(cls.save_dir, 'lenet')
@@ -330,8 +330,8 @@ class TestModelFunction(unittest.TestCase):
             optim2 = fluid.optimizer.SGD(learning_rate=0.001,
                                          parameter_list=net.parameters())
 
-            inputs = [Input('x', [None, dim], 'float32')]
-            labels = [Input('label', [None, 1], 'int64')]
+            inputs = [Input([None, dim], 'float32', 'x')]
+            labels = [Input([None, 1], 'int64', 'label')]
             model = Model(net, inputs, labels)
             model.prepare(
                 optim2, loss_function=CrossEntropyLoss(reduction="sum"))
@@ -359,7 +359,7 @@ class TestModelFunction(unittest.TestCase):
             fluid.enable_dygraph(device) if dynamic else None
             self.set_seed()
             net = MyModel()
-            inputs = [Input('x', [None, dim], 'float32')]
+            inputs = [Input([None, dim], 'float32', 'x')]
             model = Model(net, inputs)
             model.prepare()
             out, = model.test_batch([data])
@@ -373,8 +373,8 @@ class TestModelFunction(unittest.TestCase):
             device = hapi.set_device('cpu')
             fluid.enable_dygraph(device) if dynamic else None
             net = MyModel(classifier_activation=None)
-            inputs = [Input('x', [None, 20], 'float32')]
-            labels = [Input('label', [None, 1], 'int64')]
+            inputs = [Input([None, 20], 'float32', 'x')]
+            labels = [Input([None, 1], 'int64', 'label')]
             optim = fluid.optimizer.SGD(learning_rate=0.001,
                                         parameter_list=net.parameters())
             model = Model(net, inputs, labels)
@@ -399,8 +399,8 @@ class TestModelFunction(unittest.TestCase):
         model.save(path + '/test')
         fluid.disable_dygraph()
 
-        inputs = [Input('x', [None, 20], 'float32')]
-        labels = [Input('label', [None, 1], 'int64')]
+        inputs = [Input([None, 20], 'float32', 'x')]
+        labels = [Input([None, 1], 'int64', 'label')]
         model = Model(MyModel(classifier_activation=None), inputs, labels)
         optim = fluid.optimizer.SGD(learning_rate=0.001,
                                     parameter_list=model.parameters())
@@ -413,8 +413,8 @@ class TestModelFunction(unittest.TestCase):
         path = tempfile.mkdtemp()
 
         net = MyModel(classifier_activation=None)
-        inputs = [Input('x', [None, 20], 'float32')]
-        labels = [Input('label', [None, 1], 'int64')]
+        inputs = [Input([None, 20], 'float32', 'x')]
+        labels = [Input([None, 1], 'int64', 'label')]
         optim = fluid.optimizer.SGD(learning_rate=0.001,
                                     parameter_list=net.parameters())
         model = Model(net, inputs, labels)
@@ -426,8 +426,8 @@ class TestModelFunction(unittest.TestCase):
         fluid.enable_dygraph(device)  #if dynamic else None
 
         net = MyModel(classifier_activation=None)
-        inputs = [Input('x', [None, 20], 'float32')]
-        labels = [Input('label', [None, 1], 'int64')]
+        inputs = [Input([None, 20], 'float32', 'x')]
+        labels = [Input([None, 1], 'int64', 'label')]
         optim = fluid.optimizer.SGD(learning_rate=0.001,
                                     parameter_list=net.parameters())
         model = Model(net, inputs, labels)
@@ -442,7 +442,7 @@ class TestModelFunction(unittest.TestCase):
             device = hapi.set_device('cpu')
             fluid.enable_dygraph(device) if dynamic else None
             net = MyModel()
-            inputs = [Input('x', [None, 20], 'float32')]
+            inputs = [Input([None, 20], 'float32', 'x')]
             model = Model(net, inputs)
             model.prepare()
             params = model.parameters()
@@ -452,7 +452,7 @@ class TestModelFunction(unittest.TestCase):
 
     def test_export_deploy_model(self):
         net = LeNet()
-        inputs = [Input('image', [-1, 1, 28, 28], 'float32')]
+        inputs = [Input([-1, 1, 28, 28], 'float32', 'image')]
         model = Model(net, inputs)
         model.prepare()
         save_dir = tempfile.mkdtemp()
@@ -480,5 +480,15 @@ class TestModelFunction(unittest.TestCase):
         shutil.rmtree(save_dir)
 
 
+class TestRaiseError(unittest.TestCase):
+    def test_input_without_name(self):
+        net = MyModel(classifier_activation=None)
+
+        inputs = [Input([None, 10], 'float32')]
+        labels = [Input([None, 1], 'int64', 'label')]
+        with self.assertRaises(ValueError):
+            model = Model(net, inputs, labels)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/incubate/hapi/tests/test_pretrained_model.py

@@ -28,7 +28,7 @@ class TestPretrainedModel(unittest.TestCase):
             fluid.enable_dygraph()
 
         net = models.__dict__[arch](pretrained=True, classifier_activation=None)
-        inputs = [Input('image', [None, 3, 224, 224], 'float32')]
+        inputs = [Input([None, 3, 224, 224], 'float32', 'image')]
         model = Model(network=net, inputs=inputs)
         model.prepare()
         res = model.test_batch(x)

python/paddle/incubate/hapi/tests/test_text.py

@@ -142,7 +142,7 @@ class TestBasicLSTM(ModuleApiTest):
 
     def make_inputs(self):
         inputs = [
-            Input("input", [None, None, self.inputs[-1].shape[-1]], "float32"),
+            Input([None, None, self.inputs[-1].shape[-1]], "float32", "input"),
         ]
         return inputs
 
@@ -168,7 +168,7 @@ class TestBasicGRU(ModuleApiTest):
 
     def make_inputs(self):
         inputs = [
-            Input("input", [None, None, self.inputs[-1].shape[-1]], "float32"),
+            Input([None, None, self.inputs[-1].shape[-1]], "float32", "input"),
         ]
         return inputs
 
@@ -219,8 +219,8 @@ class TestBeamSearch(ModuleApiTest):
 
     def make_inputs(self):
         inputs = [
-            Input("init_hidden", [None, self.inputs[0].shape[-1]], "float32"),
-            Input("init_cell", [None, self.inputs[1].shape[-1]], "float32"),
+            Input([None, self.inputs[0].shape[-1]], "float32", "init_hidden"),
+            Input([None, self.inputs[1].shape[-1]], "float32", "init_cell"),
         ]
         return inputs
 
@@ -272,10 +272,10 @@ class TestTransformerEncoder(ModuleApiTest):
 
     def make_inputs(self):
         inputs = [
-            Input("enc_input", [None, None, self.inputs[0].shape[-1]],
-                  "float32"),
-            Input("attn_bias", [None, self.inputs[1].shape[1], None, None],
-                  "float32"),
+            Input([None, None, self.inputs[0].shape[-1]], "float32",
+                  "enc_input"),
+            Input([None, self.inputs[1].shape[1], None, None], "float32",
+                  "attn_bias"),
         ]
         return inputs
 
@@ -336,14 +336,14 @@ class TestTransformerDecoder(TestTransformerEncoder):
 
     def make_inputs(self):
         inputs = [
-            Input("dec_input", [None, None, self.inputs[0].shape[-1]],
-                  "float32"),
-            Input("enc_output", [None, None, self.inputs[0].shape[-1]],
-                  "float32"),
-            Input("self_attn_bias",
-                  [None, self.inputs[-1].shape[1], None, None], "float32"),
-            Input("cross_attn_bias",
-                  [None, self.inputs[-1].shape[1], None, None], "float32"),
+            Input([None, None, self.inputs[0].shape[-1]], "float32",
+                  "dec_input"),
+            Input([None, None, self.inputs[0].shape[-1]], "float32",
+                  "enc_output"),
+            Input([None, self.inputs[-1].shape[1], None, None], "float32",
+                  "self_attn_bias"),
+            Input([None, self.inputs[-1].shape[1], None, None], "float32",
+                  "cross_attn_bias"),
         ]
         return inputs
 
@@ -431,10 +431,10 @@ class TestTransformerBeamSearchDecoder(ModuleApiTest):
 
     def make_inputs(self):
         inputs = [
-            Input("enc_output", [None, None, self.inputs[0].shape[-1]],
-                  "float32"),
-            Input("trg_src_attn_bias",
-                  [None, self.inputs[1].shape[1], None, None], "float32"),
+            Input([None, None, self.inputs[0].shape[-1]], "float32",
+                  "enc_output"),
+            Input([None, self.inputs[1].shape[1], None, None], "float32",
+                  "trg_src_attn_bias"),
         ]
         return inputs
 
@@ -473,9 +473,9 @@ class TestSequenceTagging(ModuleApiTest):
 
     def make_inputs(self):
         inputs = [
-            Input("word", [None, None], "int64"),
-            Input("lengths", [None], "int64"),
-            Input("target", [None, None], "int64"),
+            Input([None, None], "int64", "word"),
+            Input([None], "int64", "lengths"),
+            Input([None, None], "int64", "target"),
         ]
         return inputs
 
@@ -517,7 +517,7 @@ class TestStackedRNN(ModuleApiTest):
 
     def make_inputs(self):
         inputs = [
-            Input("input", [None, None, self.inputs[-1].shape[-1]], "float32"),
+            Input([None, None, self.inputs[-1].shape[-1]], "float32", "input"),
         ]
         return inputs
 
@@ -543,7 +543,7 @@ class TestLSTM(ModuleApiTest):
 
     def make_inputs(self):
         inputs = [
-            Input("input", [None, None, self.inputs[-1].shape[-1]], "float32"),
+            Input([None, None, self.inputs[-1].shape[-1]], "float32", "input"),
         ]
         return inputs
 
@@ -579,7 +579,7 @@ class TestBiLSTM(ModuleApiTest):
 
     def make_inputs(self):
         inputs = [
-            Input("input", [None, None, self.inputs[-1].shape[-1]], "float32"),
+            Input([None, None, self.inputs[-1].shape[-1]], "float32", "input"),
         ]
         return inputs
 
@@ -609,7 +609,7 @@ class TestGRU(ModuleApiTest):
 
     def make_inputs(self):
         inputs = [
-            Input("input", [None, None, self.inputs[-1].shape[-1]], "float32"),
+            Input([None, None, self.inputs[-1].shape[-1]], "float32", "input"),
         ]
         return inputs
 
@@ -645,7 +645,7 @@ class TestBiGRU(ModuleApiTest):
 
     def make_inputs(self):
         inputs = [
-            Input("input", [None, None, self.inputs[-1].shape[-1]], "float32"),
+            Input([None, None, self.inputs[-1].shape[-1]], "float32", "input"),
         ]
         return inputs
 
@@ -680,7 +680,7 @@ class TestCNNEncoder(ModuleApiTest):
 
     def make_inputs(self):
         inputs = [
-            Input("input", [None, self.inputs[-1].shape[1], None], "float32"),
+            Input([None, self.inputs[-1].shape[1], None], "float32", "input"),
         ]
         return inputs
 

python/paddle/incubate/hapi/tests/test_vision_models.py

@@ -28,7 +28,7 @@ class TestVisonModels(unittest.TestCase):
         else:
             net = models.__dict__[arch](pretrained=pretrained)
 
-        input = hapi.Input('image', [None, 3, 224, 224], 'float32')
+        input = hapi.Input([None, 3, 224, 224], 'float32', 'image')
         model = hapi.Model(net, input)
         model.prepare()
 
@@ -71,7 +71,7 @@ class TestVisonModels(unittest.TestCase):
         self.models_infer('resnet152')
 
     def test_lenet(self):
-        input = hapi.Input('x', [None, 1, 28, 28], 'float32')
+        input = hapi.Input([None, 1, 28, 28], 'float32', 'x')
         lenet = hapi.Model(models.__dict__['LeNet'](), input)
         lenet.prepare()
 

python/paddle/nn/__init__.py

@@ -49,7 +49,6 @@ from .decode import beam_search_decode  #DEFINE_ALIAS
 # from .decode import ctc_greedy_decoder        #DEFINE_ALIAS
 # from .decode import dynamic_decode        #DEFINE_ALIAS
 from .decode import gather_tree  #DEFINE_ALIAS
-from .input import data  #DEFINE_ALIAS
 # from .input import Input        #DEFINE_ALIAS
 from .layer.activation import ELU
 from .layer.activation import GELU

python/paddle/nn/input.py

-#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# TODO: define input placeholders of neural network  
-from ..fluid import data  #DEFINE_ALIAS
-
-__all__ = [
-    'data',
-    #       'Input'
-]

python/paddle/static/__init__.py

@@ -17,9 +17,12 @@ __all__ = [
     'append_backward', 'gradients', 'Executor', 'global_scope', 'scope_guard',
     'BuildStrategy', 'CompiledProgram', 'Print', 'py_func', 'ExecutionStrategy',
     'name_scope', 'ParallelExecutor', 'program_guard', 'WeightNormParamAttr',
-    'default_main_program', 'default_startup_program', 'Program', 'save', 'load'
+    'default_main_program', 'default_startup_program', 'Program', 'save',
+    'load', 'data', 'InputSpec'
 ]
 
+from .input import data  #DEFINE_ALIAS
+from .input import InputSpec  #DEFINE_ALIAS
 from ..fluid.executor import Executor  #DEFINE_ALIAS
 from ..fluid.executor import global_scope  #DEFINE_ALIAS
 from ..fluid.executor import scope_guard  #DEFINE_ALIAS

python/paddle/static/input.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ..fluid.data import data
+
+__all__ = ['data', 'InputSpec']
+
+
+class InputSpec(object):
+    """
+    Define input specification of the model.
+
+    Args:
+        name (str): The name/alias of the variable, see :ref:`api_guide_Name`
+            for more details.
+        shape (tuple(integers)|list[integers]): List|Tuple of integers
+            declaring the shape. You can set "None" or -1 at a dimension
+            to indicate the dimension can be of any size. For example,
+            it is useful to set changeable batch size as "None" or -1.
+        dtype (np.dtype|VarType|str, optional): The type of the data. Supported
+            dtype: bool, float16, float32, float64, int8, int16, int32, int64,
+            uint8. Default: float32.
+
+    Examples:
+        .. code-block:: python
+
+        from paddle.static import InputSpec
+
+        input = InputSpec([None, 784], 'float32', 'x')
+        label = InputSpec([None, 1], 'int64', 'label')
+    """
+
+    def __init__(self, shape=None, dtype='float32', name=None):
+        self.shape = shape
+        self.dtype = dtype
+        self.name = name
+
+    def _create_feed_layer(self):
+        return data(self.name, shape=self.shape, dtype=self.dtype)
+
+    def __repr__(self):
+        return '{}(shape={}, dtype={}, name={})'.format(
+            type(self).__name__, self.shape, self.dtype, self.name)