From 0cc63cc343fef246a4cc6f0afe104be29af280fd Mon Sep 17 00:00:00 2001 From: Aurelius84 Date: Thu, 20 Aug 2020 13:53:18 +0800 Subject: [PATCH] [Paddle2.0] Rename hapi.Input and move `data` api (#26396) * Rename `Input` into `InputSpec` * fix argument place of Input api --- .../fluid/tests/unittests/test_compare_op.py | 5 +- .../fluid/tests/unittests/test_cumsum_op.py | 4 +- .../test_flatten_contiguous_range_op.py | 9 +- .../fluid/tests/unittests/test_max_op.py | 10 +- .../fluid/tests/unittests/test_maximum_op.py | 8 +- .../fluid/tests/unittests/test_min_op.py | 10 +- .../fluid/tests/unittests/test_minimum_op.py | 8 +- .../fluid/tests/unittests/test_multiply.py | 14 ++- .../unittests/test_nn_margin_rank_loss.py | 16 ++- .../tests/unittests/test_nn_sigmoid_op.py | 4 +- .../fluid/tests/unittests/test_randint_op.py | 2 +- .../fluid/tests/unittests/test_randn_op.py | 2 +- python/paddle/incubate/hapi/callbacks.py | 8 +- python/paddle/incubate/hapi/model.py | 113 ++++++++---------- .../hapi/tests/dist_hapi_mnist_dynamic.py | 4 +- .../hapi/tests/dist_hapi_mnist_static.py | 4 +- .../incubate/hapi/tests/test_callbacks.py | 2 +- .../paddle/incubate/hapi/tests/test_model.py | 40 ++++--- .../hapi/tests/test_pretrained_model.py | 2 +- .../paddle/incubate/hapi/tests/test_text.py | 58 ++++----- .../incubate/hapi/tests/test_vision_models.py | 4 +- python/paddle/nn/__init__.py | 1 - python/paddle/nn/input.py | 21 ---- python/paddle/static/__init__.py | 5 +- python/paddle/static/input.py | 54 +++++++++ 25 files changed, 227 insertions(+), 181 deletions(-) delete mode 100644 python/paddle/nn/input.py create mode 100644 python/paddle/static/input.py diff --git a/python/paddle/fluid/tests/unittests/test_compare_op.py b/python/paddle/fluid/tests/unittests/test_compare_op.py index 99d0c77fce..cfad504098 100644 --- a/python/paddle/fluid/tests/unittests/test_compare_op.py +++ b/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) diff --git a/python/paddle/fluid/tests/unittests/test_cumsum_op.py b/python/paddle/fluid/tests/unittests/test_cumsum_op.py index 57024e8ae5..ad121fac8c 100644 --- a/python/paddle/fluid/tests/unittests/test_cumsum_op.py +++ b/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) diff --git a/python/paddle/fluid/tests/unittests/test_flatten_contiguous_range_op.py b/python/paddle/fluid/tests/unittests/test_flatten_contiguous_range_op.py index 4bd56802ef..642044bb4b 100644 --- a/python/paddle/fluid/tests/unittests/test_flatten_contiguous_range_op.py +++ b/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) diff --git a/python/paddle/fluid/tests/unittests/test_max_op.py b/python/paddle/fluid/tests/unittests/test_max_op.py index e2bdaba91a..c9afc4bec6 100644 --- a/python/paddle/fluid/tests/unittests/test_max_op.py +++ b/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) diff --git a/python/paddle/fluid/tests/unittests/test_maximum_op.py b/python/paddle/fluid/tests/unittests/test_maximum_op.py index bed2b57ec5..5645597007 100644 --- a/python/paddle/fluid/tests/unittests/test_maximum_op.py +++ b/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, diff --git a/python/paddle/fluid/tests/unittests/test_min_op.py b/python/paddle/fluid/tests/unittests/test_min_op.py index e8bfe55f32..b9eff05c5e 100644 --- a/python/paddle/fluid/tests/unittests/test_min_op.py +++ b/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) diff --git a/python/paddle/fluid/tests/unittests/test_minimum_op.py b/python/paddle/fluid/tests/unittests/test_minimum_op.py index 550580407a..4c08b7386c 100644 --- a/python/paddle/fluid/tests/unittests/test_minimum_op.py +++ b/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, diff --git a/python/paddle/fluid/tests/unittests/test_multiply.py b/python/paddle/fluid/tests/unittests/test_multiply.py index f7f6e1f1aa..32d0f7b63b 100644 --- a/python/paddle/fluid/tests/unittests/test_multiply.py +++ b/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) diff --git a/python/paddle/fluid/tests/unittests/test_nn_margin_rank_loss.py b/python/paddle/fluid/tests/unittests/test_nn_margin_rank_loss.py index 4f60f3e39a..2e74ffa88f 100644 --- a/python/paddle/fluid/tests/unittests/test_nn_margin_rank_loss.py +++ b/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) diff --git a/python/paddle/fluid/tests/unittests/test_nn_sigmoid_op.py b/python/paddle/fluid/tests/unittests/test_nn_sigmoid_op.py index 451ab696fd..d52a1f5d5b 100644 --- a/python/paddle/fluid/tests/unittests/test_nn_sigmoid_op.py +++ b/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]) diff --git a/python/paddle/fluid/tests/unittests/test_randint_op.py b/python/paddle/fluid/tests/unittests/test_randint_op.py index 715d66aa33..88b07f5df8 100644 --- a/python/paddle/fluid/tests/unittests/test_randint_op.py +++ b/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') diff --git a/python/paddle/fluid/tests/unittests/test_randn_op.py b/python/paddle/fluid/tests/unittests/test_randn_op.py index 8b560f18f9..9d2c03f3bb 100644 --- a/python/paddle/fluid/tests/unittests/test_randn_op.py +++ b/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( diff --git a/python/paddle/incubate/hapi/callbacks.py b/python/paddle/incubate/hapi/callbacks.py index 741552511f..ccf1b6389b 100644 --- a/python/paddle/incubate/hapi/callbacks.py +++ b/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') diff --git a/python/paddle/incubate/hapi/model.py b/python/paddle/incubate/hapi/model.py index 0b12987b10..f9dec82bd7 100644 --- a/python/paddle/incubate/hapi/model.py +++ b/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() diff --git a/python/paddle/incubate/hapi/tests/dist_hapi_mnist_dynamic.py b/python/paddle/incubate/hapi/tests/dist_hapi_mnist_dynamic.py index b338f3310b..f57a039fa4 100644 --- a/python/paddle/incubate/hapi/tests/dist_hapi_mnist_dynamic.py +++ b/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( diff --git a/python/paddle/incubate/hapi/tests/dist_hapi_mnist_static.py b/python/paddle/incubate/hapi/tests/dist_hapi_mnist_static.py index 1484620a4e..25e02774d1 100644 --- a/python/paddle/incubate/hapi/tests/dist_hapi_mnist_static.py +++ b/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( diff --git a/python/paddle/incubate/hapi/tests/test_callbacks.py b/python/paddle/incubate/hapi/tests/test_callbacks.py index 2a8a470736..e49bf215c2 100644 --- a/python/paddle/incubate/hapi/tests/test_callbacks.py +++ b/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() diff --git a/python/paddle/incubate/hapi/tests/test_model.py b/python/paddle/incubate/hapi/tests/test_model.py index cf65951424..103ab30724 100644 --- a/python/paddle/incubate/hapi/tests/test_model.py +++ b/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() diff --git a/python/paddle/incubate/hapi/tests/test_pretrained_model.py b/python/paddle/incubate/hapi/tests/test_pretrained_model.py index 588797322f..334ebff449 100644 --- a/python/paddle/incubate/hapi/tests/test_pretrained_model.py +++ b/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) diff --git a/python/paddle/incubate/hapi/tests/test_text.py b/python/paddle/incubate/hapi/tests/test_text.py index 78f089b06a..bdc637997b 100644 --- a/python/paddle/incubate/hapi/tests/test_text.py +++ b/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 diff --git a/python/paddle/incubate/hapi/tests/test_vision_models.py b/python/paddle/incubate/hapi/tests/test_vision_models.py index 16dbe431be..2dc9355bcc 100644 --- a/python/paddle/incubate/hapi/tests/test_vision_models.py +++ b/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() diff --git a/python/paddle/nn/__init__.py b/python/paddle/nn/__init__.py index cc8658f9d7..72a9f8d5c9 100644 --- a/python/paddle/nn/__init__.py +++ b/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 diff --git a/python/paddle/nn/input.py b/python/paddle/nn/input.py deleted file mode 100644 index b5f591f44a..0000000000 --- a/python/paddle/nn/input.py +++ /dev/null @@ -1,21 +0,0 @@ -# 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' -] diff --git a/python/paddle/static/__init__.py b/python/paddle/static/__init__.py index d31e5173f8..93060c7865 100644 --- a/python/paddle/static/__init__.py +++ b/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 diff --git a/python/paddle/static/input.py b/python/paddle/static/input.py new file mode 100644 index 0000000000..0fe1d7e03f --- /dev/null +++ b/python/paddle/static/input.py @@ -0,0 +1,54 @@ +# 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) -- GitLab