diff --git a/paddle/fluid/operators/concat_op.cc b/paddle/fluid/operators/concat_op.cc index 49fb1c6e17d0c68d1be3abe1f2e850ac2dc5b850..890f9beaee69915ed61a2526c8156f8f5af3b32b 100644 --- a/paddle/fluid/operators/concat_op.cc +++ b/paddle/fluid/operators/concat_op.cc @@ -208,10 +208,14 @@ REGISTER_OP_CPU_KERNEL( concat, ops::ConcatKernel, ops::ConcatKernel, ops::ConcatKernel, + ops::ConcatKernel, ops::ConcatKernel); REGISTER_OP_CPU_KERNEL( concat_grad, ops::ConcatGradKernel, ops::ConcatGradKernel, ops::ConcatGradKernel, + ops::ConcatGradKernel, ops::ConcatGradKernel); diff --git a/python/paddle/fluid/layers/tensor.py b/python/paddle/fluid/layers/tensor.py index 99cf77aed2609f311195e522ed3621b37d64ee34..f31c6a9db85d0f51f1fe1f2cf56bc388dbf5556c 100644 --- a/python/paddle/fluid/layers/tensor.py +++ b/python/paddle/fluid/layers/tensor.py @@ -265,24 +265,26 @@ def concat(input, axis=0, name=None): """ :alias_main: paddle.concat :alias: paddle.concat,paddle.tensor.concat,paddle.tensor.manipulation.concat - :old_api: paddle.fluid.layers.concat - - **Concat** This OP concatenates the input along the axis. Args: - input(list): List of input Tensors with data type float32, float64, int32, - int64. - axis(int32|Variable, optional): A scalar with type ``int32`` or a ``Tensor`` with shape [1] and type ``int32``. Axis to compute indices along. The effective range - is [-R, R), where R is Rank(x). when axis<0, it works the same way + input(list): List of input Tensors with data type float16, float32, float64, int32, + int64. All the Tensors in ``input`` must have the same data type. + axis(int|Variable, optional): Specify the axis to operate on the input Tensors. + It's a scalar with type ``int`` or a ``Tensor`` with shape [1] and data type ``int32`` or ``int64``. + The effective range is [-R, R), where R is Rank(x). When ``axis < 0``, it works the same way as axis+R. Default is 0. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. + Raises: + TypeError: The dtype of input must be one of float16, float32, float64, int32 and int64. + TypeError: The ``axis`` must be int or Variable. The dtype of ``axis`` must be int32 or int64 when it's a Tensor. + TypeError: All the Tensors in ``input`` must have the same data type. Returns: - Variable: A Tensor with the same data type as input's. + Variable: A Tensor with the same data type as ``input``. Examples: .. code-block:: python @@ -300,6 +302,8 @@ def concat(input, axis=0, name=None): x1 = fluid.dygraph.to_variable(in1) x2 = fluid.dygraph.to_variable(in2) x3 = fluid.dygraph.to_variable(in3) + # When the axis is negative, the real axis is (axis + Rank(x)). + # As follows, axis is -1, Rank(x) is 2, the real axis is 1 out1 = fluid.layers.concat(input=[x1,x2,x3], axis=-1) out2 = fluid.layers.concat(input=[x1,x2], axis=0) print(out1.numpy()) @@ -315,8 +319,6 @@ def concat(input, axis=0, name=None): if in_dygraph_mode(): if isinstance(axis, Variable): axis = axis.numpy() - assert axis.shape == ( - 1, ), "axis of type Variable should have shape [1]" axis = axis[0] return core.ops.concat(input, 'axis', axis) @@ -329,8 +331,16 @@ def concat(input, axis=0, name=None): check_variable_and_dtype( x, 'input[' + str(id) + ']', ['float16', 'float32', 'float64', 'int32', 'int64'], 'concat') + if x.dtype != input[0].dtype: + raise TypeError( + "All the Tensors in the input must have the same data type.") check_type(axis, 'axis', (int, Variable), 'concat') + if isinstance(axis, Variable): + check_dtype( + axis.dtype, 'axis', ['int32', 'int64'], 'concat', + "The data type of axis must be int32 or int64 when axis is a Tensor") + helper = LayerHelper('concat', **locals()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) @@ -652,7 +662,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None): Raises: TypeError: The dtype must be one of bool, float16, float32, float64, int32 and int64 - and the data type of out Tensor must be the same as the dtype. + and the data type of out Tensor must be the same as the dtype. TypeError: The shape must be one of list, tuple and Variable. Examples: diff --git a/python/paddle/fluid/tests/unittests/test_concat_op.py b/python/paddle/fluid/tests/unittests/test_concat_op.py index b84608889e087cae5f5e36459d160b7946628cac..48b597ab282351739fcca894aa69685a13a9688f 100644 --- a/python/paddle/fluid/tests/unittests/test_concat_op.py +++ b/python/paddle/fluid/tests/unittests/test_concat_op.py @@ -19,6 +19,7 @@ import numpy as np from op_test import OpTest, skip_check_grad_ci import paddle.fluid as fluid from paddle.fluid import compiler, Program, program_guard, core +import paddle class TestConcatOp(OpTest): @@ -175,8 +176,6 @@ create_test_AxisTensor(TestConcatOp6) def create_test_fp16(parent): - @unittest.skipIf(not core.is_compiled_with_cuda(), - "core is not compiled with CUDA") class TestConcatFp16(parent): def get_dtype(self): return np.float16 @@ -206,12 +205,13 @@ class TestConcatOpError(unittest.TestCase): x3 = fluid.create_lod_tensor( np.array([[-1]]), [[1]], fluid.CPUPlace()) self.assertRaises(TypeError, fluid.layers.concat, [x2]) - # The input dtype of concat_op must be float16(only support on GPU), float32, float64, int32, int64. + # The input dtype of concat_op must be float16, float32, float64, int32, int64. x4 = fluid.layers.data(shape=[4], dtype='uint8', name='x4') x5 = fluid.layers.data(shape=[4], dtype='uint8', name='x5') self.assertRaises(TypeError, fluid.layers.concat, [x4, x5]) x6 = fluid.layers.data(shape=[4], dtype='float16', name='x6') x7 = fluid.layers.data(shape=[4], dtype='float16', name='x7') + x8 = fluid.layers.data(shape=[4], dtype='float32', name='x8') fluid.layers.concat([x6, x7]) # The type of axis in concat_op should be int or Variable. @@ -220,9 +220,14 @@ class TestConcatOpError(unittest.TestCase): self.assertRaises(TypeError, test_axis_type) + def test_input_same_dtype(): + fluid.layers.concat([x7, x8]) + + self.assertRaises(TypeError, test_input_same_dtype) + class TestConcatAPI(unittest.TestCase): - def test_api(self): + def test_fluid_api(self): x_1 = fluid.data(shape=[None, 1, 4, 5], dtype='int32', name='x_1') fluid.layers.concat([x_1, x_1], 0) @@ -247,6 +252,77 @@ class TestConcatAPI(unittest.TestCase): assert np.array_equal(res_2, np.concatenate((input_2, input_3), axis=1)) assert np.array_equal(res_3, np.concatenate((input_2, input_3), axis=1)) + def test_api(self): + x_1 = paddle.data(shape=[None, 1, 4, 5], dtype='int32', name='x_1') + paddle.concat([x_1, x_1], 0) + + input_2 = np.random.random([2, 1, 4, 5]).astype("int32") + input_3 = np.random.random([2, 2, 4, 5]).astype("int32") + x_2 = fluid.data(shape=[2, 1, 4, 5], dtype='int32', name='x_2') + x_3 = fluid.data(shape=[2, 2, 4, 5], dtype='int32', name='x_3') + positive_1_int32 = paddle.fill_constant([1], "int32", 1) + positive_1_int64 = paddle.fill_constant([1], "int64", 1) + negative_int64 = paddle.fill_constant([1], "int64", -3) + out_1 = paddle.concat(x=[x_2, x_3], axis=1) + out_2 = paddle.concat(x=[x_2, x_3], axis=positive_1_int32) + out_3 = paddle.concat(x=[x_2, x_3], axis=positive_1_int64) + out_4 = paddle.concat(x=[x_2, x_3], axis=negative_int64) + + exe = paddle.Executor(place=paddle.CPUPlace()) + [res_1, res_2, res_3, res_4] = exe.run( + paddle.default_main_program(), + feed={"x_1": input_2, + "x_2": input_2, + "x_3": input_3}, + fetch_list=[out_1, out_2, out_3, out_4]) + assert np.array_equal(res_1, np.concatenate((input_2, input_3), axis=1)) + assert np.array_equal(res_2, np.concatenate((input_2, input_3), axis=1)) + assert np.array_equal(res_3, np.concatenate((input_2, input_3), axis=1)) + assert np.array_equal(res_4, np.concatenate((input_2, input_3), axis=1)) + + def test_imperative(self): + in1 = np.array([[1, 2, 3], [4, 5, 6]]) + in2 = np.array([[11, 12, 13], [14, 15, 16]]) + in3 = np.array([[21, 22], [23, 24]]) + with paddle.imperative.guard(): + x1 = paddle.imperative.to_variable(in1) + x2 = paddle.imperative.to_variable(in2) + x3 = paddle.imperative.to_variable(in3) + out1 = fluid.layers.concat(input=[x1, x2, x3], axis=-1) + out2 = paddle.concat(x=[x1, x2], axis=0) + np_out1 = np.concatenate([in1, in2, in3], axis=-1) + np_out2 = np.concatenate([in1, in2], axis=0) + self.assertEqual((out1.numpy() == np_out1).all(), True) + self.assertEqual((out2.numpy() == np_out2).all(), True) + + def test_errors(self): + with program_guard(Program(), Program()): + # The item in input must be Variable. + x2 = fluid.create_lod_tensor( + np.array([[-1]]), [[1]], fluid.CPUPlace()) + x3 = fluid.create_lod_tensor( + np.array([[-1]]), [[1]], fluid.CPUPlace()) + self.assertRaises(TypeError, paddle.concat, [x2]) + # The input dtype of concat_op must be float16, float32, float64, int32, int64. + x4 = paddle.data(shape=[4], dtype='uint8', name='x4') + x5 = paddle.data(shape=[4], dtype='uint8', name='x5') + self.assertRaises(TypeError, fluid.layers.concat, [x4, x5]) + + # The type of axis in concat_op should be int or Variable. + x6 = fluid.layers.data(shape=[4], dtype='float16', name='x6') + x7 = fluid.layers.data(shape=[4], dtype='float16', name='x7') + x8 = fluid.layers.data(shape=[4], dtype='float32', name='x8') + + def test_axis_type(): + paddle.concat([x6, x7], 3.2) + + self.assertRaises(TypeError, test_axis_type) + + def test_input_same_dtype(): + paddle.concat([x7, x8]) + + self.assertRaises(TypeError, test_input_same_dtype) + class TestConcatAPIWithLoDTensorArray(unittest.TestCase): """ diff --git a/python/paddle/tensor/creation.py b/python/paddle/tensor/creation.py index ed104b5f3e702143ca5a8767d2668a2f29f06aaf..42abe3ad0e8a4e10efa6fccda31a251969767e02 100644 --- a/python/paddle/tensor/creation.py +++ b/python/paddle/tensor/creation.py @@ -134,7 +134,7 @@ def ones(shape, dtype=None, name=None): Raises: TypeError: The dtype must be one of bool, float16, float32, float64, int32, int64 and None - and the data type of out Tensor must be the same as the dtype. + and the data type of out Tensor must be the same as the dtype. TypeError: The `shape` must be one of list, tuple and Variable. Examples: @@ -242,7 +242,7 @@ def zeros(shape, dtype=None, name=None): The OP creates a tensor of specified :attr:`shape` and :attr:`dtype`, and fills it with 0. Args: - shape(tuple|list): Shape of output tensor. + shape(tuple|list|Variable): Shape of output tensor. The data type of shape is int32 or int64. dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of output tensor, it supports bool, float16, float32, float64, int32 and int64. Default: if None, the date type is float32. name(str, optional): The default value is None. Normally there is no need for user to set this diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index 65d7eccb1a86c9c0e28f81bdddc30b1492cfd674..642b5db28379b499671d746280604de41f386004 100644 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -23,7 +23,6 @@ from ..fluid.layers import utils import numpy as np # TODO: define functions to manipulate a tensor from ..fluid.layers import cast #DEFINE_ALIAS -from ..fluid.layers import concat #DEFINE_ALIAS from ..fluid.layers import expand #DEFINE_ALIAS from ..fluid.layers import expand_as #DEFINE_ALIAS from ..fluid.layers import flatten #DEFINE_ALIAS @@ -41,6 +40,7 @@ from ..fluid.layers import scatter_nd #DEFINE_ALIAS from ..fluid.layers import shard_index #DEFINE_ALIAS from ..fluid.layers import unique_with_counts #DEFINE_ALIAS from ..fluid import layers +import paddle __all__ = [ 'cast', 'concat', 'expand', 'expand_as', 'flatten', 'gather', 'gather_nd', @@ -51,6 +51,65 @@ __all__ = [ ] +def concat(x, axis=0, name=None): + """ + :alias_main: paddle.concat + :alias: paddle.concat,paddle.tensor.concat,paddle.tensor.manipulation.concat + + This OP concatenates the input along the axis. + + Args: + x(list): List of input Tensors with data type float16, float32, float64, int32, int64. + All the Tensors in ``x`` must have same data type. + axis(int|Variable, optional): Specify the axis to operate on the input Tensors. + It's a scalar with type ``int`` or a ``Tensor`` with shape [1] and data type ``int32`` + or ``int64``. The effective range is [-R, R), where R is Rank(x). When ``axis < 0``, + it works the same way as axis+R. Default is 0. + name (str, optional): The default value is None. Normally there is no + need for user to set this property. For more information, please + refer to :ref:`api_guide_Name`. + Raises: + TypeError: The dtype of ``x`` must be one of float16, float32, float64, int32 and int64. + TypeError: The ``axis`` must be int or Variable. The dtype of ``axis`` must be int32 or int64 when it's a Tensor. + TypeError: All the Tensors in ``x`` must have the same data type. + + Returns: + Variable: A Tensor with the same data type as ``x``. + + Examples: + .. code-block:: python + + import paddle + import numpy as np + + paddle.enable_imperative() # Now we are in imperative mode + in1 = np.array([[1,2,3], + [4,5,6]]) + in2 = np.array([[11,12,13], + [14,15,16]]) + in3 = np.array([[21,22], + [23,24]]) + x1 = paddle.imperative.to_variable(in1) + x2 = paddle.imperative.to_variable(in2) + x3 = paddle.imperative.to_variable(in3) + zero = paddle.full(shape=[1], dtype='int32', fill_value=0) + # When the axis is negative, the real axis is (axis + Rank(x)) + # As follow, axis is -1, Rank(x) is 2, the real axis is 1 + out1 = paddle.concat(x=[x1,x2,x3], axis=-1) + out2 = paddle.concat(x=[x1,x2], axis=0) + out3 = paddle.concat(x=[x1,x2], axis=zero) + # out1 + # [[ 1 2 3 11 12 13 21 22] + # [ 4 5 6 14 15 16 23 24]] + # out2 out3 + # [[ 1 2 3] + # [ 4 5 6] + # [11 12 13] + # [14 15 16]] + """ + return paddle.fluid.layers.concat(input=x, axis=axis, name=name) + + def flip(x, axis, name=None): """ :alias_main: paddle.flip diff --git a/python/paddle/tensor/search.py b/python/paddle/tensor/search.py index d8874e47c357937020ffe2e392332599df6653c0..8ec48348c75224fc4e8cd91ef4d271775c1b8b3e 100644 --- a/python/paddle/tensor/search.py +++ b/python/paddle/tensor/search.py @@ -162,6 +162,7 @@ def index_select(x, index, axis=0, name=None): Examples: .. code-block:: python + import paddle import numpy as np