# 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 the extention functions import numpy as np from ...fluid.data_feeder import check_dtype from ...fluid.layer_helper import LayerHelper from ...static import Variable from ...tensor.creation import assign from ...fluid import dygraph_utils from ...tensor.layer_function_generator import templatedoc from paddle import in_dynamic_mode from paddle import _C_ops from ...fluid.framework import _non_static_mode, _in_legacy_dygraph, in_dygraph_mode from ...fluid.data_feeder import check_variable_and_dtype, check_type from ...framework import core from ...common_ops_import import convert_np_dtype_to_dtype_ __all__ = [] def diag_embed(input, offset=0, dim1=-2, dim2=-1): """ This OP creates a tensor whose diagonals of certain 2D planes (specified by dim1 and dim2) are filled by ``input``. By default, a 2D plane formed by the last two dimensions of the returned tensor will be selected. The argument ``offset`` determines which diagonal is generated: - If offset = 0, it is the main diagonal. - If offset > 0, it is above the main diagonal. - If offset < 0, it is below the main diagonal. Args: input(Tensor|numpy.ndarray): The input tensor. Must be at least 1-dimensional. The input data type should be float32, float64, int32, int64. offset(int, optional): Which diagonal to consider. Default: 0 (main diagonal). dim1(int, optional): The first dimension with respect to which to take diagonal. Default: -2. dim2(int, optional): The second dimension with respect to which to take diagonal. Default: -1. Returns: Tensor, the output data type is the same as input data type. Examples: .. code-block:: python import paddle.nn.functional as F import numpy as np diag_embed = np.random.randn(2, 3).astype('float32') # [[ 0.7545889 , -0.25074545, 0.5929117 ], # [-0.6097662 , -0.01753256, 0.619769 ]] data1 = F.diag_embed(diag_embed) data1.numpy() # [[[ 0.7545889 , 0. , 0. ], # [ 0. , -0.25074545, 0. ], # [ 0. , 0. , 0.5929117 ]], # [[-0.6097662 , 0. , 0. ], # [ 0. , -0.01753256, 0. ], # [ 0. , 0. , 0.619769 ]]] data2 = F.diag_embed(diag_embed, offset=-1, dim1=0, dim2=2) data2.numpy() # [[[ 0. , 0. , 0. , 0. ], # [ 0.7545889 , 0. , 0. , 0. ], # [ 0. , -0.25074545, 0. , 0. ], # [ 0. , 0. , 0.5929117 , 0. ]], # # [[ 0. , 0. , 0. , 0. ], # [-0.6097662 , 0. , 0. , 0. ], # [ 0. , -0.01753256, 0. , 0. ], # [ 0. , 0. , 0.619769 , 0. ]]] data3 = F.diag_embed(diag_embed, offset=1, dim1=0, dim2=2) data3.numpy() # [[[ 0. , 0.7545889 , 0. , 0. ], # [ 0. , -0.6097662 , 0. , 0. ]], # # [[ 0. , 0. , -0.25074545, 0. ], # [ 0. , 0. , -0.01753256, 0. ]], # # [[ 0. , 0. , 0. , 0.5929117 ], # [ 0. , 0. , 0. , 0.619769 ]], # # [[ 0. , 0. , 0. , 0. ], # [ 0. , 0. , 0. , 0. ]]] """ inputs = {'Input': [input]} attrs = {'offset': offset, 'dim1': dim1, 'dim2': dim2} if not isinstance(input, Variable): input = assign(input) def __check_input(input, offset, dim1, dim2): check_dtype(input.dtype, 'Input', ['int32', 'int64', 'float16', 'float32', 'float64'], 'diag_embed') input_shape = list(input.shape) assert len(input_shape) >= 1, \ "Input must be at least 1-dimensional, " \ "But received Input's dimensional: %s.\n" % \ len(input_shape) assert np.abs(dim1) <= len(input_shape), \ "Dim1 is out of range (expected to be in range of [%d, %d], but got %d).\n" \ % (-(len(input_shape) + 1), len(input_shape), dim1) assert np.abs(dim2) <= len(input_shape), \ "Dim2 is out of range (expected to be in range of [%d, %d], but got %d).\n" \ % (-(len(input_shape) + 1), len(input_shape), dim2) dim1_ = dim1 if dim1 >= 0 else len(input_shape) + dim1 + 1 dim2_ = dim2 if dim2 >= 0 else len(input_shape) + dim2 + 1 assert dim1_ != dim2_, \ "dim1 and dim2 cannot be the same dimension." \ "But received dim1 = %d, dim2 = %d\n"%(dim1, dim2) if not in_dynamic_mode(): __check_input(input, offset, dim1, dim2) helper = LayerHelper("diag_embed", **locals()) out = helper.create_variable_for_type_inference(dtype=input.dtype) helper.append_op(type='diag_embed', inputs={'Input': [input]}, attrs={ 'offset': offset, 'dim1': dim1, 'dim2': dim2 }, outputs={'Out': [out]}) out.stop_gradient = True return out def sequence_mask(x, maxlen=None, dtype='int64', name=None): r""" **SequenceMask Layer** This layer outputs a mask according to the input :code:`x` and :code:`maxlen` with data type of :code:`dtype`. Supposing :code:`x` is a Tensor with shape [d_1, d_2, ..., d_n], the :code:`y` is a mask with shape [d_1, d_2, ..., d_n, maxlen], where: .. math:: y(i_1, i_2,..., i_n, j) = (j < x(i_1, i_2,..., i_n)) .. code-block:: text Case: Consider input: x = [3, 1, 1, 0] max_len = 4 then we get out: mask = [[1, 1, 1, 0], [1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 0, 0]] Args: x (Variable): Input tensor of sequence_mask layer, \ whose elements are integers less than :code:`maxlen`. \ Tensor or LodTensor with shape [d_1, d_2, ..., d_n]. maxlen (int, optional): Maximum length of the sequence. If :code:`maxlen` \ is None, it would be replace with :math:`max(x)`. dtype (np.dtype|paddle.dtype|str, optional): Data type of the output, \ ``int64`` by default. name(str, optional): For detailed information, please refer \ to :ref:`api_guide_Name`. Usually name is no need to set and \ None by default. Returns: The output sequence mask. Tensor with shape [d_1, d_2, ..., d_n, maxlen] \ and data type of :code:`dtype`. The data type should be bool, float32, float64, int8, \ int32 or int64. Return Type: Tensor Examples: .. code-block:: python import paddle lengths = paddle.to_tensor([10, 9, 8]) mask = paddle.nn.functional.sequence_mask(lengths) print(mask.numpy()) # [[1 1 1 1 1 1 1 1 1 1] # [1 1 1 1 1 1 1 1 1 0] # [1 1 1 1 1 1 1 1 0 0]] """ if in_dygraph_mode(): if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) if maxlen is not None: if isinstance(maxlen, core.eager.Tensor): attrs = ('out_dtype', dtype) out = _C_ops.sequence_mask(x, maxlen, *attrs) else: attrs = ('out_dtype', dtype, 'maxlen', maxlen) out = _C_ops.sequence_mask(x, None, *attrs) out.stop_gradient = True return out helper = LayerHelper('sequence_mask', **locals()) out = helper.create_variable_for_type_inference(dtype=dtype) inputs = {'X': [x]} attrs = {'out_dtype': out.dtype} if maxlen is not None: if isinstance(maxlen, Variable): inputs['MaxLenTensor'] = maxlen else: attrs['maxlen'] = maxlen helper.append_op(type='sequence_mask', inputs=inputs, outputs={'Y': out}, attrs=attrs) out.stop_gradient = True return out def gather_tree(ids, parents): r""" To be used after beam search. After beam search, we get selected ids at each time step and the corresponding parents in the search tree. Both ids and parents have the layout :attr:`[max_time, batch_size, beam_size]`. Then :attr:`gather_tree` is used to backtrace from the last time step and generate the full sequences by collecting selected ids. Here is an example: .. code-block:: text Given: ids = [[[2 2] [6 1]] [[3 9] [6 1]] [[0 1] [9 0]]] parents = [[[0 0] [1 1]] [[1 0] [1 0]] [[0 0] [0 1]]] Then: gather_tree(ids, parents) = [[[2 2] [1 6]] [[3 3] [6 1]] [[0 1] [9 0]]] Args: ids(Tensor): A Tensor with shape :attr:`[length, batch_size, beam_size]` and data type :attr:`int32` or :attr:`int64`. It contains the selected ids of all time steps. parents(Tensor): A Tensor with the same shape and data type as :attr:`ids`, It contains the parents corresponding to selected ids when searching among beams. Returns: A Tensor with the same shape and data type as :attr:`ids`. \ It contains the full sequences. The sequences are collected from \ :attr:`ids` by backtracing according to :attr:`parents`. Examples: .. code-block:: python import paddle ids = paddle.to_tensor([[[2, 2], [6, 1]], [[3, 9], [6, 1]], [[0, 1], [9, 0]]]) parents = paddle.to_tensor([[[0, 0], [1, 1]], [[1, 0], [1, 0]], [[0, 0], [0, 1]]]) final_sequences = paddle.nn.functional.gather_tree(ids, parents) # [[[2, 2], [1, 6]], [[3, 3], [6, 1]], [[0, 1], [9, 0]]] """ if in_dygraph_mode(): return _C_ops.final_state_gather_tree(ids, parents) else: if _in_legacy_dygraph(): return _C_ops.gather_tree(ids, parents) else: helper = LayerHelper('gather_tree', **locals()) check_variable_and_dtype(ids, 'ids', ['int32', 'int64'], 'gather_tree') check_variable_and_dtype(parents, 'parents', ['int32', 'int64'], 'gather_tree') out = helper.create_variable_for_type_inference(dtype=ids.dtype) helper.append_op(type="gather_tree", inputs={ "Ids": ids, "Parents": parents }, outputs={"Out": out}) return out @templatedoc() def temporal_shift(x, seg_num, shift_ratio=0.25, name=None, data_format="NCHW"): """ **Temporal Shift Operator** ${comment} Args: x(Tensor): ${x_comment} seg_num(int): ${seg_num_comment} shift_ratio(float): ${shift_ratio_comment} name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. data_format(str, optional): Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Default: "NCHW". Returns: out(Tensor): The temporal shifting result is a tensor with the same shape and same data type as the input. Raises: TypeError: seg_num must be int type. Examples: .. code-block:: python import paddle import paddle.nn.functional as F input = paddle.randn([6, 4, 2, 2]) out = F.temporal_shift(x=input, seg_num=2, shift_ratio=0.2) """ if data_format not in ["NCHW", "NHWC"]: raise ValueError("Attr(data_format) should be 'NCHW' or 'NHWC'. " "Received Attr(data_format): {}.".format(data_format)) if _non_static_mode(): return _C_ops.temporal_shift(x, 'seg_num', seg_num, 'shift_ratio', shift_ratio, 'data_format', data_format) helper = LayerHelper("temporal_shift", **locals()) check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'temporal_shift') check_type(seg_num, 'seg_num', int, 'temporal_shift') check_type(shift_ratio, 'shift_ratio', float, 'temporal_shift') out = helper.create_variable_for_type_inference(dtype=x.dtype) if not isinstance(seg_num, int): raise TypeError("seg_num must be int type.") helper.append_op(type="temporal_shift", inputs={"X": x}, outputs={"Out": out}, attrs={ "seg_num": seg_num, "shift_ratio": shift_ratio, "data_format": data_format }) return out