# 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 __all__ = [ 'diag_embed', 'row_conv' ] import numpy as np from ...fluid.data_feeder import check_dtype from ...fluid.layer_helper import LayerHelper from ...fluid.framework import Variable, in_dygraph_mode from ...fluid.layers.tensor import assign from ...fluid import core, dygraph_utils from ...fluid.layers.layer_function_generator import templatedoc def diag_embed(input, offset=0, dim1=-2, dim2=-1): """ :alias_main: paddle.nn.functional.diag_embed :alias: paddle.nn.functional.diag_embed,paddle.nn.functional.extension.diag_embed 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(Variable|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: Variable, the output data type is the same as input data type. Examples: .. code-block:: python import paddle.nn.functional as F import paddle.fluid.dygraph as dg 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 ]] with dg.guard(): 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_dygraph_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 @templatedoc() def row_conv(input, weight, act=None): """ ${comment} Args: input (Variable): the input(X) is a LodTensor or tensor, LodTensor(X) supports variable time-length input sequences. The underlying tensor in this LoDTensor is a matrix with shape (T, D), where T is the total time steps in this mini-batch and D is the input data dimension. If the input is a padded minibatch, the shape of the input is (N, T, D), N is batch size, T is the max time steps in the batch, D is the input data dimension. weight (Variable): The weight. A Tensor with shape (future_context_size + 1, D), where future_context_size is the context size of the RowConv operator. act (str): Non-linear activation to be applied to output variable. Returns: ${out_comment}. Examples: .. code-block:: python from paddle import fluid, nn import paddle.fluid.dygraph as dg import paddle.nn.functional as F import numpy as np batch_size = 4 time_steps = 8 feature_size = 6 context_size = 4 x = np.random.randn(batch_size, time_steps, feature_size).astype(np.float32) weight = np.random.randn(context_size + 1, feature_size).astype(np.float32) place = fluid.CPUPlace() with dg.guard(place): x_var = dg.to_variable(x) w_var = dg.to_variable(weight) y_var = F.extension.row_conv(x_var, w_var) y_np = y_var.numpy() print(y_np.shape) # (4, 8, 6) """ if in_dygraph_mode(): pre_act = core.ops.row_conv(input, weight) out = dygraph_utils._append_activation_in_dygraph(pre_act, act) return out else: helper = LayerHelper('row_conv', **locals()) dtype = helper.input_dtype() inputs = {'X': [input], 'Filter': [weight]} pre_act = helper.create_variable_for_type_inference(dtype) outputs = {'Out': [pre_act]} helper.append_op(type='row_conv', inputs=inputs, outputs=outputs) out = helper.append_activation(pre_act) return out