# 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 searching & indexing functions of a tensor __all__ = [ 'argmax', # 'argmin', # 'argsort', # 'has_inf', # 'has_nan', # 'masked_select', # 'topk', # 'where', # 'index_select', # 'nonzero', 'sort' ] from paddle.common_ops_import import * def argmax(input, axis=None, dtype=None, out=None, keepdims=False, name=None): """ This OP computes the indices of the max elements of the input tensor's element along the provided axis. Args: input(Variable): An input N-D Tensor with type float32, float64, int16, int32, int64, uint8. axis(int, optional): Axis to compute indices along. The effective range is [-R, R), where R is Rank(input). when axis<0, it works the same way as axis+R. Default is None, it will use the last dim to select indices of max value. dtype(np.dtype|core.VarDesc.VarType|str): Data type of the output tensor which can be int32, int64. The default value is None, and it will return the int64 indices. out(Variable, optional): Optional output which can be any created Variable that meets the requirements to store the result of operation. if out is None, a new Varibale will be create to store the result. Defalut is None. keepdims(bool, optional): Keep the axis that do the select max. name(str, optional): The name of output variable, normally there is no need for user to set this this property. Default value is None, the framework set the name of output variable. Returns: Variable: A Tensor with data type int64. Examples: .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np in1 = np.array([[[5,8,9,5], [0,0,1,7], [6,9,2,4]], [[5,2,4,2], [4,7,7,9], [1,7,0,6]]]) with fluid.dygraph.guard(): x = fluid.dygraph.to_variable(in1) out1 = paddle.argmax(input=x, axis=-1) out2 = paddle.argmax(input=x, axis=0) out3 = paddle.argmax(input=x, axis=1) out4 = paddle.argmax(input=x, axis=2) out5 = paddle.argmax(input=x, axis=2, keepdims=True) print(out1.numpy()) # [[2 3 1] # [0 3 1]] print(out2.numpy()) # [[0 0 0 0] # [1 1 1 1] # [0 0 0 1]] print(out3.numpy()) # [[2 2 0 1] # [0 1 1 1]] print(out4.numpy()) # [[2 3 1] # [0 3 1]] print(out5.numpy()) #array([[[2], # [3], # [1]], # [[0], # [3], # [1]]]) """ helper = LayerHelper("arg_max", **locals()) var_dtype = None attrs = {} if dtype is not None: check_dtype(dtype, 'create data type', ['int32', 'int64'], 'arg_max') var_dtype = convert_np_dtype_to_dtype_(dtype) attrs["dtype"] = var_dtype else: var_dtype = VarDesc.VarType.INT64 if out is None: out = helper.create_variable_for_type_inference(var_dtype) if axis is None: axis = -1 attrs['keepdims'] = keepdims attrs['axis'] = axis helper.append_op( type='arg_max', inputs={'X': input}, outputs={'Out': [out]}, attrs=attrs) out.stop_gradient = True return out def sort(input, axis=-1, descending=False, out=None, name=None): """ This OP sorts the input along the given axis, and returns sorted output data Varibale and its corresponding index Variable with the same shape as :attr:`input`. **NOTICE**: The Variable in the output of this OP has gradient. You could\ set Variable :attr:`stop_gradient`. Args: input(Variable): An input N-D Tensor with type float32, float64, int16, int32, int64, uint8. axis(int, optional): Axis to compute indices along. 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. descending(bool, optional) : Descending is a flag, if set to true, algorithm will sort by descending order, else sort by ascending order. Default is false. out(Variable, optional): The default value is None. Optional output which can be any created Variable that meets the requirements to store the result of operation. if out is None, a new Varibale will be create to store the result. 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`. Returns: tuple: A tuple of sorted data Variable(with the same shape and data type as input) and the sorted indices(with the same shape as input's and with data type int64). Examples: .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np in1 = np.array([[[5,8,9,5], [0,0,1,7], [6,9,2,4]], [[5,2,4,2], [4,7,7,9], [1,7,0,6]]]).astype(np.float32) with fluid.dygraph.guard(): x = fluid.dygraph.to_variable(in1) out1 = paddle.sort(input=x, axis=-1) out2 = paddle.sort(input=x, axis=0) out3 = paddle.sort(input=x, axis=1) print(out1[0].numpy()) # [[[5. 5. 8. 9.] # [0. 0. 1. 7.] # [2. 4. 6. 9.]] # [[2. 2. 4. 5.] # [4. 7. 7. 9.] # [0. 1. 6. 7.]]] print(out1[1].numpy()) # [[[0 3 1 2] # [0 1 2 3] # [2 3 0 1]] # [[1 3 2 0] # [0 1 2 3] # [2 0 3 1]]] print(out2[0].numpy()) # [[[5. 2. 4. 2.] # [0. 0. 1. 7.] # [1. 7. 0. 4.]] # [[5. 8. 9. 5.] # [4. 7. 7. 9.] # [6. 9. 2. 6.]]] print(out3[0].numpy()) # [[[0. 0. 1. 4.] # [5. 8. 2. 5.] # [6. 9. 9. 7.]] # [[1. 2. 0. 2.] # [4. 7. 4. 6.] # [5. 7. 7. 9.]]] """ helper = LayerHelper("sort", **locals()) if out is None: out = helper.create_variable_for_type_inference( dtype=input.dtype, stop_gradient=False) ids = helper.create_variable_for_type_inference( VarDesc.VarType.INT64, stop_gradient=True) helper.append_op( type='argsort', inputs={'X': input}, outputs={'Out': out, 'Indices': ids}, attrs={'axis': axis, 'descending': descending}) return out, ids