# Copyright (c) 2018 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. """ All layers just related to the neural network. """ import os import inspect import warnings import numpy as np import paddle from ..layer_helper import LayerHelper from paddle.fluid.framework import _in_legacy_dygraph from ..initializer import Normal, Constant from ..framework import ( Variable, OpProtoHolder, _non_static_mode, dygraph_only, _dygraph_tracer, default_main_program, _varbase_creator, static_only, _global_flags, _in_legacy_dygraph, in_dygraph_mode, ) from ..framework import _current_expected_place from .. import dygraph_utils from ..param_attr import ParamAttr from .layer_function_generator import ( autodoc, templatedoc, _generate_doc_string_, ) from .tensor import concat, assign, fill_constant, zeros, tensor_array_to_tensor from . import utils from .. import unique_name from functools import reduce from .. import core from ...utils import deprecated from ..data_feeder import ( convert_dtype, check_variable_and_dtype, check_type, check_dtype, ) from paddle.utils import deprecated from paddle import _C_ops, _legacy_C_ops from collections.abc import Iterable __all__ = [ 'fc', 'embedding', 'linear_chain_crf', 'crf_decoding', 'conv2d', 'softmax', 'pool2d', 'batch_norm', 'reduce_all', 'reduce_any', 'dropout', 'split', 'ctc_greedy_decoder', 'l2_normalize', 'matmul', 'topk', 'im2sequence', 'row_conv', 'multiplex', 'layer_norm', 'spectral_norm', 'smooth_l1', 'one_hot', 'autoincreased_step_counter', 'unsqueeze', 'lod_reset', 'pad', 'image_resize', 'resize_bilinear', 'resize_trilinear', 'resize_nearest', 'relu', 'elementwise_add', 'elementwise_div', 'elementwise_sub', 'elementwise_mul', 'gaussian_random', 'sampling_id', 'shape', 'clip', 'clip_by_norm', 'mean', 'mul', 'hash', 'grid_sampler', 'log_loss', 'bilinear_tensor_product', 'merge_selected_rows', 'get_tensor_from_selected_rows', 'temporal_shift', 'py_func', 'continuous_value_model', 'unfold', 'deformable_roi_pooling', 'shard_index', 'hard_swish', 'mish', 'uniform_random', 'unbind', ] OP_NAMEMAPPING = { 'elementwise_max': 'maximum', 'elementwise_min': 'minimum', 'elementwise_pow': 'elementwise_pow', 'elementwise_floordiv': 'floor_divide', 'elementwise_add': 'add', 'elementwise_sub': 'subtract', 'elementwise_mul': 'multiply', 'elementwise_div': 'divide', 'elementwise_mod': 'remainder', } def _get_reduce_dim(dim, input): """ Internal function for reduce_sum, reduce_mean, reduce_prod. It computes the attribute reduce_all value based on axis. """ if dim is not None and not isinstance(dim, list): if isinstance(dim, (tuple, range)): dim = list(dim) elif isinstance(dim, int): dim = [dim] else: raise TypeError( "The type of dim must be int, list, tuple or range, but received {}".format( type(axis) ) ) if dim is None: dim = [] if dim == [] or len(dim) == len(input.shape): reduce_all = True else: reduce_all = False return reduce_all, dim @dygraph_only def _elementwise_op_in_dygraph( x, y, axis=-1, act=None, use_mkldnn=False, op_name=None ): def is_inplace(op_name): return op_name[-1] == "_" if op_name not in OP_NAMEMAPPING.keys() or axis != -1: op = getattr(_legacy_C_ops, op_name) out = op(x, y, 'axis', axis, 'use_mkldnn', use_mkldnn) else: if in_dygraph_mode(): op = getattr( _C_ops, OP_NAMEMAPPING[op_name] if not is_inplace(op_name) else op_name, ) out = op(x, y) if _in_legacy_dygraph(): op = getattr(_legacy_C_ops, op_name) out = op(x, y, 'axis', axis, 'use_mkldnn', use_mkldnn) return dygraph_utils._append_activation_in_dygraph( out, act, use_mkldnn=use_mkldnn ) def fc( input, size, num_flatten_dims=1, param_attr=None, bias_attr=None, act=None, name=None, ): r""" :api_attr: Static Graph **Fully Connected Layer** This operator creates a fully connected layer in the network. It can take a Tensor(or LoDTensor) or a list of Tensor(or LoDTensor) as its inputs(see Args in detail). It creates a variable called weight for each input Tensor, which represents a fully connected weight matrix from each input unit to each output unit. The fully connected layer multiplies each input Tensor with its corresponding weight to produce an output Tensor with shape :math:`[M, size]` , where M is batch size. If a list of Tensor is given, the results of multiple output Tensors with shape :math:`[M, size]` will be summed up. If :attr:`bias_attr` is not None, a bias variable will be created and added to the output. Finally, if :attr:`act` is not None, it will be applied to the output as well. When the input is a single Tensor(or LoDTensor): .. math:: Out = Act({XW + b}) When the input is a list of Tensor(or LoDTensor): .. math:: Out = Act({\sum_{i=0}^{N-1}X_iW_i + b}) In the above equation: * :math:`N`: Number of the input. N equals to len(input) if input is list of Variable. * :math:`X_i`: The i-th input tensor. * :math:`W_i`: The i-th weights matrix corresponding i-th input tensor. * :math:`b`: The bias parameter created by this layer (if needed). * :math:`Act`: The activation function. * :math:`Out`: The output Tensor. .. code-block:: text Case 1: Given a single Tensor data_1, and num_flatten_dims = 2: data_1.data = [[[0.1, 0.2], [0.3, 0.4]]] data_1.shape = (1, 2, 2) # 1 is batch_size out = fluid.layers.fc(input=data_1, size=1, num_flatten_dims=2) Then output is: out.data = [[0.83234344], [0.34936576]] out.shape = (1, 2, 1) Case 2: Given a list of Tensor: data_1.data = [[[0.1, 0.2], [0.3, 0.4]]] data_1.shape = (1, 2, 2) # 1 is batch_size data_2 = [[[0.1, 0.2, 0.3]]] data_2.shape = (1, 1, 3) out = fluid.layers.fc(input=[data_1, data_2], size=2) Then: out.data = [[0.18669507, 0.1893476]] out.shape = (1, 2) Args: input (Variable|list of Variable): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` or a list of Tensor(or LoDTensor). The dimensions of the input Tensor is at least 2 and the data type should be float32 or float64. size(int): The number of output units in this layer, which also means the feature size of output Tensor(or LoDTensor). num_flatten_dims (int): The fc layer can accept an input Tensor with more than two dimensions. If this happens, the multidimensional tensor will first be flattened into a 2-D matrix. The parameter :attr:`num_flatten_dims` determines how the input Tensor is flattened: the first :attr:`num_flatten_dims` (inclusive, index starts from 1) dimensions will be flatten to form the first dimension of the final matrix (height of the matrix), and the rest :math:`rank(X) - num\_flatten\_dims` dimensions are flattened to form the second dimension of the final matrix (width of the matrix). For example, assuming that X is a 5-dimensional Tensor with a shape [2, 3, 4, 5, 6], and :attr:`num_flatten_dims` = 3. Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30]. Default: 1. param_attr (ParamAttr): To specify the weight parameter property. Default: None, which means the default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . bias_attr (ParamAttr): To specify the bias parameter property. Default: None, which means the default bias parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . act (str): Activation to be applied to the output of this layer, such as tanh, softmax, sigmoid, relu. For more information, please refer to :ref:`api_guide_activations_en` . Default: None. 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: Variable: Tensor or LoDTensor calculated by fc layer. The data type is same with input. Raises: ValueError: If dimensions of the input Tensor is less than 2. Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() # when input is single tensor data = fluid.data(name="data", shape=[-1, 32], dtype="float32") fc = fluid.layers.fc(input=data, size=1000, act="tanh") # when input are multiple tensors data_1 = fluid.data(name="data_1", shape=[-1, 32], dtype="float32") data_2 = fluid.data(name="data_2", shape=[-1, 36], dtype="float32") fc = fluid.layers.fc(input=[data_1, data_2], size=1000, act="tanh") """ helper = LayerHelper("fc", **locals()) check_type(input, 'input', (list, tuple, Variable), 'fc') if isinstance(input, (list, tuple)): for i, input_x in enumerate(input): check_type(input_x, 'input[' + str(i) + ']', Variable, 'fc') dtype = helper.input_dtype() check_dtype( dtype, 'input', ['float16', 'uint16', 'float32', 'float64'], 'fc' ) mul_results = [] for input_var, param_attr in helper.iter_inputs_and_params(): input_shape = input_var.shape if num_flatten_dims == -1: num_flatten_dims = len(input_shape) - 1 param_shape = [ reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1) ] + [size] w = helper.create_parameter( attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False ) tmp = helper.create_variable_for_type_inference(dtype) helper.append_op( type="mul", inputs={"X": input_var, "Y": w}, outputs={"Out": tmp}, attrs={"x_num_col_dims": num_flatten_dims, "y_num_col_dims": 1}, ) mul_results.append(tmp) if len(mul_results) == 1: pre_bias = mul_results[0] else: pre_bias = helper.create_variable_for_type_inference(dtype) helper.append_op( type="sum", inputs={"X": mul_results}, outputs={"Out": pre_bias}, attrs={"use_mkldnn": False}, ) # add bias pre_activation = helper.append_bias_op(pre_bias, dim_start=num_flatten_dims) # add activation return helper.append_activation(pre_activation) @deprecated(since="2.0.0", update_to="paddle.nn.functional.embedding") def embedding( input, size, is_sparse=False, is_distributed=False, padding_idx=None, param_attr=None, dtype='float32', ): r""" :api_attr: Static Graph **WARING:** This OP will be deprecated in a future release. This OP requires the last dimension of Tensor shape must be equal to 1. It is recommended to use fluid. :ref:`api_fluid_embedding` . The operator is used to lookup embeddings vector of ids provided by :attr:`input` . It automatically constructs a 2D embedding matrix based on the input :attr:`size` (vocab_size, emb_size) and :attr:`dtype` . This OP requires the last dimension of Tensor shape must be equal to 1. The shape of output Tensor is generated by replacing the last dimension of the input Tensor shape with emb_size. **Note:** The id in :attr:`input` must satisfy :math:`0 =< id < size[0]` , otherwise the program will throw an exception and exit. .. code-block:: text Case 1: input is a Tensor. padding_idx = -1 input.data = [[[1], [3]], [[2], [4]], [[4], [127]]] input.shape = [3, 2, 1] Given size = [128, 16] output is a Tensor: out.shape = [3, 2, 16] out.data = [[[0.129435295, 0.244512452, ..., 0.436322452], [0.345421456, 0.524563927, ..., 0.144534654]], [[0.345249859, 0.124939536, ..., 0.194353745], [0.945345345, 0.435394634, ..., 0.435345365]], [[0.945345345, 0.435394634, ..., 0.435345365], [0.0, 0.0, ..., 0.0 ]]] # padding data The input padding_idx is less than 0, it is automatically converted to padding_idx = -1 + 128 = 127 It will pad all-zero data when ids is 127. Case 2: input is a LoDTensor with 1-level LoD. padding_idx = 0 input.lod = [[2, 3]] input.data = [[1], [3], [2], [4], [0]] input.shape = [5, 1] Given size = [128, 16] output is a LoDTensor: out.lod = [[2, 3]] out.shape = [5, 16] out.data = [[0.129435295, 0.244512452, ..., 0.436322452], [0.345421456, 0.524563927, ..., 0.144534654], [0.345249859, 0.124939536, ..., 0.194353745], [0.945345345, 0.435394634, ..., 0.435345365], [0.0, 0.0, ..., 0.0 ]] # padding data It will pad all-zero data when ids is 0. Args: input(Variable): A Tensor or LoDTensor with type int64, which contains the id information. The last dimension of Tensor shape must be equal to 1. The value of the input id should satisfy :math:`0<= id < size[0]` . size(tuple|list): The shape of lookup table parameter. It should have two elements which indicates the size of the dictionary of embeddings and the size of each embedding vector respectively. is_sparse(bool): The flag indicating whether to use sparse update. This parameter only affects the performance of the backwards gradient update. It is recommended to set True because sparse update is faster. But some optimizer does not support sparse update, such as :ref:`api_fluid_optimizer_AdadeltaOptimizer` , :ref:`api_fluid_optimizer_AdamaxOptimizer` , :ref:`api_fluid_optimizer_DecayedAdagradOptimizer` , :ref:`api_fluid_optimizer_FtrlOptimizer` , :ref:`api_fluid_optimizer_LambOptimizer` and :ref:`api_fluid_optimizer_LarsMomentumOptimizer` . In these case, is_sparse must be False. Default: False. is_distributed(bool): Whether to store the embedding matrix in a distributed manner. Only used in multi-machine distributed CPU training. Default: False. padding_idx(int|long|None): padding_idx needs to be in the interval [-vocab_size, vocab_size). If :math:`padding\_idx < 0`, the :math:`padding\_idx` will automatically be converted to :math:`vocab\_size + padding\_idx` . It will output all-zero padding data whenever lookup encounters :math:`padding\_idx` in id. And the padding data will not be updated while training. If set None, it makes no effect to output. Default: None. param_attr(ParamAttr): To specify the weight parameter property. Default: None, which means the default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . In addition, user-defined or pre-trained word vectors can be loaded with the :attr:`param_attr` parameter. The local word vector needs to be transformed into numpy format, and the shape of local word vector should be consistent with :attr:`size` . Then :ref:`api_fluid_initializer_NumpyArrayInitializer` is used to load custom or pre-trained word vectors. See code example 2 for details. dtype(str|core.VarDesc.VarType): It refers to the data type of output Tensor. It must be float32 or float64. Default: float32. Returns: Variable: Embedding Tensor or LoDTensor mapped by input. The data type is the same as :attr:`dtype` . Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle paddle.enable_static() data = fluid.data(name='x', shape=[None, 1], dtype='int64') # example 1 emb_1 = fluid.embedding(input=data, size=[128, 64]) # example 2: load custom or pre-trained word vectors weight_data = np.random.random(size=(128, 100)) # word vectors with numpy format w_param_attrs = fluid.ParamAttr( name="emb_weight", learning_rate=0.5, initializer=fluid.initializer.NumpyArrayInitializer(weight_data), trainable=True) emb_2 = fluid.layers.embedding(input=data, size=(128, 100), param_attr=w_param_attrs, dtype='float32') """ helper = LayerHelper('embedding', **locals()) check_variable_and_dtype( input, 'input', ['int64'], 'fluid.layers.embedding' ) check_dtype( dtype, 'dtype', ['uint16', 'float16', 'float32', 'float64'], 'fluid.layers.embedding', ) if is_distributed: is_distributed = False warnings.warn( "is_distributed is go out of use, `fluid.contrib.layers.sparse_embedding` is your needed" ) remote_prefetch = True if is_sparse else False w = helper.create_parameter( attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False ) tmp = helper.create_variable_for_type_inference(dtype) padding_idx = ( -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (size[0] + padding_idx) ) helper.append_op( type='lookup_table', inputs={'Ids': input, 'W': w}, outputs={'Out': tmp}, attrs={ 'is_sparse': is_sparse, 'is_distributed': is_distributed, 'remote_prefetch': remote_prefetch, 'padding_idx': padding_idx, }, ) return tmp def _pull_sparse( input, size, table_id, accessor_class, name="embedding", ctr_label_name="", padding_id=0, dtype='float32', scale_sparse_grad=True, ): r""" **Pull Fleet Sparse Layer** This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in Fleet lookup table. The result of this lookup is the embedding of each ID in the :attr:`input`. Args: input(Variable|list of Variable): Input is a Tensor Variable, which contains the IDs information. size(int): The embedding size parameter, which indicates the size of each embedding vector respectively. table_id(int): the fleet table id of this embedding. accessor_class(str): the pslib accessor of the table, default is DownpourCtrAccessor. ctr_label_name(str): the layer name of click. padding_id(int): the padding id during lookup, default is 0. dtype(str): The dtype refers to the data type of output tensor. Only supports float32 now. scale_sparse_grad(bool): whether to scale sparse gradient with batch size. default is True. Returns: Variable|list of Variable: The tensor variable storing the embeddings of the \ supplied inputs. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1) emb = fluid.layers.nn._pull_sparse( input=data, size=11, table_id=0, accessor_class="DownpourCtrAccessor") """ helper = LayerHelper(name, **locals()) inputs = helper.multiple_input() outs = [helper.create_variable_for_type_inference(dtype)] input_names = [i.name for i in inputs] attrs = { 'EmbeddingDim': size, 'TableId': table_id, 'AccessorClass': accessor_class, 'CtrLabelName': ctr_label_name, 'PaddingId': padding_id, 'ScaleSparseGrad': scale_sparse_grad, 'InputNames': input_names, # this is only for compatible with embedding op 'is_distributed': True, } # this is only for compatible with embedding op w, _ = helper.create_or_get_global_variable( name=name, shape=[size], dtype=dtype, is_bias=False, persistable=True ) helper.append_op( type='pull_sparse', inputs={'Ids': inputs, 'W': w}, outputs={'Out': outs}, attrs=attrs, ) if len(outs) == 1: return outs[0] return outs def _pull_sparse_v2( input, size, table_id, accessor_class, name="embedding", ctr_label_name="", padding_id=0, dtype='float32', scale_sparse_grad=True, ): r""" **Pull Fleet Sparse Layer** This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in Fleet lookup table. The result of this lookup is the embedding of each ID in the :attr:`input`. Args: input(Variable|list of Variable): Input is a Tensor Variable, which contains the IDs information. size(int): The embedding size parameter, which indicates the size of each embedding vector respectively. table_id(int): the pslib table id of this embedding. accessor_class(str): the fleet accessor of the table, default is DownpourCtrAccessor. ctr_label_name(str): the layer name of click. padding_id(int): the padding id during lookup, default is 0. dtype(str): The dtype refers to the data type of output tensor. Only supports float32 now. scale_sparse_grad(bool): whether to scale sparse gradient with batch size. default is True. Returns: Variable|list of Variable: The tensor variable storing the embeddings of the \ supplied inputs. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1) emb = fluid.layers.nn._pull_sparse_v2( input=data, size=11, table_id=0, accessor_class="DownpourCtrAccessor") """ helper = LayerHelper(name, **locals()) inputs = helper.multiple_input() outs = [helper.create_variable_for_type_inference(dtype)] input_names = [i.name for i in inputs] attrs = { 'EmbeddingDim': size, 'TableId': table_id, 'AccessorClass': accessor_class, 'CtrLabelName': ctr_label_name, 'PaddingId': padding_id, 'ScaleSparseGrad': scale_sparse_grad, 'InputNames': input_names, # this is only for compatible with embedding op 'is_distributed': True, } # this is only for compatible with embedding op w, _ = helper.create_or_get_global_variable( name=name, shape=[size], dtype=dtype, is_bias=False, persistable=True ) helper.append_op( type='pull_sparse_v2', inputs={'Ids': inputs, 'W': w}, outputs={'Out': outs}, attrs=attrs, ) if len(outs) == 1: return outs[0] return outs def _pull_gpups_sparse( input, size, dtype='float32', is_distributed=False, is_sparse=False ): r""" **Pull GpuPS Sparse Layer** This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in GpuPS lookup table. The result of this lookup is the embedding of each ID in the :attr:`input`. Args: input(Variable|list of Variable): Input is a Tensor Variable, which contains the IDs information. size(int|list of int): The embedding size parameter of each input, which indicates the size of each embedding vector respectively. dtype(str): The dtype refers to the data type of output tensor. Only supports float32 now. Returns: Variable|list of Variable: The tensor variable storing the embeddings of the \ supplied inputs, whose size are indicated by size respectively. Examples: .. code-block:: python import paddle.fluid as fluid slots = [] data_1 = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1) slots.append(data_1) data_2 = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1) slots.append(data_2) embs = fluid.layers.pull_gpups_sparse(input=slots, size=[11, 35]) """ helper = LayerHelper('pull_gpups_sparse', **locals()) if dtype != 'float32': raise ValueError( "GpuPS only support float type embedding now, and your type is: " + dtype ) helper.input_dtype() inputs = helper.multiple_input() outs = [ helper.create_variable_for_type_inference(dtype) for i in range(len(inputs)) ] w = helper.create_parameter( attr=helper.param_attr, shape=[size[0]], dtype=dtype, is_bias=False ) helper.append_op( type='pull_gpups_sparse', inputs={'Ids': inputs, 'W': w}, outputs={'Out': outs}, attrs={ 'size': size, 'is_distributed': is_distributed, 'is_sparse': is_sparse, }, ) if len(outs) == 1: return outs[0] return outs def _pull_box_sparse( input, size, dtype='float32', is_distributed=False, is_sparse=False ): r""" **Pull Box Sparse Layer** This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in BoxPS lookup table. The result of this lookup is the embedding of each ID in the :attr:`input`. Args: input(Variable|list of Variable): Input is a Tensor Variable, which contains the IDs information. size(int): The embedding size parameter, which indicates the size of each embedding vector respectively. dtype(str): The dtype refers to the data type of output tensor. Only supports float32 now. Returns: Variable|list of Variable: The tensor variable storing the embeddings of the \ supplied inputs. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1) emb = fluid.layers.pull_box_sparse(input=data, size=[11]) """ helper = LayerHelper('pull_box_sparse', **locals()) if dtype != 'float32': raise ValueError( "BoxPS only support float type embedding now, and your type is: " + dtype ) helper.input_dtype() inputs = helper.multiple_input() outs = [ helper.create_variable_for_type_inference(dtype) for i in range(len(inputs)) ] w = helper.create_parameter( attr=helper.param_attr, shape=[size], dtype=dtype, is_bias=False ) helper.append_op( type='pull_box_sparse', inputs={'Ids': inputs, 'W': w}, outputs={'Out': outs}, attrs={ 'size': size, 'is_distributed': is_distributed, 'is_sparse': is_sparse, }, ) if len(outs) == 1: return outs[0] return outs @templatedoc() def linear_chain_crf(input, label, param_attr=None, length=None): """ :api_attr: Static Graph Linear Chain CRF. ${comment} Args: input(${emission_type}): ${emission_comment} label(${label_type}): ${label_comment} Length(${length_type}): ${length_comment} param_attr(ParamAttr): The attribute of the learnable parameter for transition parameter. Returns: output(${emission_exps_type}): ${emission_exps_comment} \n output(${transition_exps_type}): ${transition_exps_comment} \n output(${log_likelihood_type}): ${log_likelihood_comment} \n Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle paddle.enable_static() #define net structure, using LodTensor train_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): input_data = fluid.data(name='input_data', shape=[-1,10], dtype='float32') label = fluid.data(name='label', shape=[-1,1], dtype='int') emission= fluid.layers.fc(input=input_data, size=10, act="tanh") crf_cost = fluid.layers.linear_chain_crf( input=emission, label=label, param_attr=fluid.ParamAttr( name='crfw', learning_rate=0.01)) use_cuda = False place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_program) #define data, using LoDTensor a = fluid.create_lod_tensor(np.random.rand(12,10).astype('float32'), [[3,3,4,2]], place) b = fluid.create_lod_tensor(np.array([[1],[1],[2],[3],[1],[1],[1],[3],[1],[1],[1],[1]]),[[3,3,4,2]] , place) feed1 = {'input_data':a,'label':b} loss= exe.run(train_program,feed=feed1, fetch_list=[crf_cost]) print(loss) #define net structure, using padding train_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): input_data2 = fluid.data(name='input_data2', shape=[-1,10,10], dtype='float32') label2 = fluid.data(name='label2', shape=[-1,10,1], dtype='int') label_length = fluid.data(name='length', shape=[-1,1], dtype='int') emission2= fluid.layers.fc(input=input_data2, size=10, act="tanh", num_flatten_dims=2) crf_cost2 = fluid.layers.linear_chain_crf( input=emission2, label=label2, length=label_length, param_attr=fluid.ParamAttr( name='crfw', learning_rate=0.01)) use_cuda = False place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_program) #define data, using padding cc=np.random.rand(4,10,10).astype('float32') dd=np.random.rand(4,10,1).astype('int64') ll=np.array([[3],[3],[4],[2]]) feed2 = {'input_data2':cc,'label2':dd,'length':ll} loss2= exe.run(train_program,feed=feed2, fetch_list=[crf_cost2]) print(loss2) #[array([[ 7.8902354], # [ 7.3602567], # [ 10.004011], # [ 5.86721 ]], dtype=float32)] #you can use find_var to get transition parameter. transition=np.array(fluid.global_scope().find_var('crfw').get_tensor()) print(transition) """ check_variable_and_dtype( input, 'input', ['float32', 'float64'], 'linear_chain_crf' ) check_variable_and_dtype(label, 'label', ['int64'], 'linear_chain_crf') helper = LayerHelper('linear_chain_crf', **locals()) size = input.shape[2] if length else input.shape[1] transition = helper.create_parameter( attr=helper.param_attr, shape=[size + 2, size], dtype=helper.input_dtype(), ) alpha = helper.create_variable_for_type_inference( dtype=helper.input_dtype() ) emission_exps = helper.create_variable_for_type_inference( dtype=helper.input_dtype() ) transition_exps = helper.create_variable_for_type_inference( dtype=helper.input_dtype() ) log_likelihood = helper.create_variable_for_type_inference( dtype=helper.input_dtype() ) this_inputs = { "Emission": [input], "Transition": transition, "Label": [label], } if length: this_inputs['Length'] = [length] helper.append_op( type='linear_chain_crf', inputs=this_inputs, outputs={ "Alpha": [alpha], "EmissionExps": [emission_exps], "TransitionExps": transition_exps, "LogLikelihood": log_likelihood, }, ) return log_likelihood @templatedoc() def crf_decoding(input, param_attr, label=None, length=None): """ :api_attr: Static Graph ${comment} Args: input(Tensor): ${emission_comment} param_attr (ParamAttr|None): To specify the weight parameter attribute. Default: None, which means the default weight parameter property is used. See usage for details in :ref:`api_paddle_fluid_param_attr_ParamAttr` . label(${label_type}, optional): ${label_comment} length(${length_type}, optional): ${length_comment} Returns: Tensor: ${viterbi_path_comment} Examples: .. code-block:: python import paddle paddle.enable_static() # LoDTensor-based example num_labels = 10 feature = paddle.static.data(name='word_emb', shape=[-1, 784], dtype='float32', lod_level=1) label = paddle.static.data(name='label', shape=[-1, 1], dtype='int64', lod_level=1) emission = paddle.static.nn.fc(feature, size=num_labels) crf_cost = paddle.fluid.layers.linear_chain_crf(input=emission, label=label, param_attr=paddle.ParamAttr(name="crfw")) crf_decode = paddle.static.nn.crf_decoding(input=emission, param_attr=paddle.ParamAttr(name="crfw")) # Common tensor example num_labels, max_len = 10, 20 feature = paddle.static.data(name='word_emb_pad', shape=[-1, max_len, 784], dtype='float32') label = paddle.static.data(name='label_pad', shape=[-1, max_len, 1], dtype='int64') length = paddle.static.data(name='length', shape=[-1, 1], dtype='int64') emission = paddle.static.nn.fc(feature, size=num_labels, num_flatten_dims=2) crf_cost = paddle.fluid.layers.linear_chain_crf(input=emission, label=label, length=length, param_attr=paddle.ParamAttr(name="crfw_pad")) crf_decode = paddle.static.nn.crf_decoding(input=emission, length=length, param_attr=paddle.ParamAttr(name="crfw_pad")) """ check_variable_and_dtype( input, 'input', ['float32', 'float64'], 'crf_decoding' ) helper = LayerHelper('crf_decoding', **locals()) transition = helper.get_parameter(param_attr.name) viterbi_path = helper.create_variable_for_type_inference( dtype=core.VarDesc.VarType.INT64 ) inputs = {"Emission": [input], "Transition": transition, "Label": label} if length: inputs['Length'] = length helper.append_op( type='crf_decoding', inputs=inputs, outputs={"ViterbiPath": [viterbi_path]}, ) return viterbi_path @deprecated(since="2.0.0", update_to="paddle.nn.functional.dropout") def dropout( x, dropout_prob, is_test=None, seed=None, name=None, dropout_implementation="downgrade_in_infer", ): """ Computes dropout. Drop or keep each element of `x` independently. Dropout is a regularization technique for reducing overfitting by preventing neuron co-adaption during training. The dropout operator randomly sets (according to the given dropout probability) the outputs of some units to zero, while others are remain unchanged. dropout op can be removed from the program to make the program more efficient. Args: x (Variable): The input tensor variable. The data type is float16 or float32 or float64. dropout_prob (float): Probability of setting units to zero. is_test (bool): A flag indicating whether it is in test phrase or not. Default None, in dynamic graph, it use global tracer mode; in static graph, it means False. seed (int): A Python integer used to create random seeds. If this parameter is set to None, a random seed is used. NOTE: If an integer seed is given, always the same output units will be dropped. DO NOT use a fixed seed in training.Default: None. name (str|None): A name for this layer(optional). If set None, the layer will be named automatically. dropout_implementation(string): ['downgrade_in_infer'(default)|'upscale_in_train'] 1. downgrade_in_infer(default), downgrade the outcome at inference - train: out = input * mask - inference: out = input * (1.0 - dropout_prob) (mask is a tensor same shape with input, value is 0 or 1 ratio of 0 is dropout_prob) 2. upscale_in_train, upscale the outcome at training time - train: out = input * mask / ( 1.0 - dropout_prob ) - inference: out = input (mask is a tensor same shape with input, value is 0 or 1 ratio of 0 is dropout_prob) Returns: A Variable holding Tensor representing the dropout, has same shape and data type with `x`. Examples: .. code-block:: python import paddle import paddle.fluid as fluid paddle.enable_static() x = fluid.data(name="data", shape=[None, 32, 32], dtype="float32") dropped = fluid.layers.dropout(x, dropout_prob=0.5) """ if not isinstance(dropout_prob, (float, int, Variable)): raise TypeError( "dropout_prob argument should be a number(int|float) or Variable" ) # fast return for p == 0 if isinstance(dropout_prob, (int, float)) and dropout_prob == 0: return x if _non_static_mode(): if ( seed is None or seed == 0 ) and default_main_program().random_seed != 0: seed = default_main_program().random_seed if is_test is None: is_test = not _dygraph_tracer()._train_mode out, mask = _legacy_C_ops.dropout( x, 'dropout_prob', dropout_prob, 'is_test', is_test, 'fix_seed', seed is not None, 'seed', seed if seed is not None else 0, 'dropout_implementation', dropout_implementation, ) return out def get_attrs(prog, dropout_prob, is_test, seed): if (seed is None or seed == 0) and prog.random_seed != 0: seed = prog.random_seed if isinstance(dropout_prob, Variable) and not dropout_prob.shape != [1]: raise TypeError( "Required dropout_prob.shape == [1] if type(dropout_prob) is Variable, but received dropout_prob.shape = {}".format( dropout_prob.shape ) ) attrs = { 'dropout_prob': dropout_prob, 'is_test': is_test, 'fix_seed': seed is not None, 'seed': seed if seed is not None else 0, 'dropout_implementation': dropout_implementation, } return attrs helper = LayerHelper('dropout', **locals()) check_variable_and_dtype( x, 'x', ['float16', 'float32', 'float64'], 'dropout' ) out = helper.create_variable_for_type_inference(dtype=x.dtype) mask = helper.create_variable_for_type_inference( dtype=core.VarDesc.VarType.UINT8, stop_gradient=True ) attrs = get_attrs(helper.main_program, dropout_prob, is_test, seed) helper.append_op( type='dropout', inputs={'X': [x]}, outputs={'Out': [out], 'Mask': [mask]}, attrs=attrs, ) return out @deprecated(since="2.0.0", update_to="paddle.nn.functional.softmax") def softmax(input, use_cudnn=True, name=None, axis=-1): r""" This operator implements the softmax layer. The calculation process is as follows: 1. The dimension :attr:`axis` of the ``input`` will be permuted to the last. 2. Then the input tensor will be logically flattened to a 2-D matrix. The matrix's second dimension(row length) is the same as the dimension :attr:`axis` of the input tensor, and the first dimension(column length) is the product of all other dimensions of the input tensor. For each row of the matrix, the softmax operator squashes the K-dimensional(K is the width of the matrix, which is also the size of the input tensor's dimension :attr:`axis`) vector of arbitrary real values to a K-dimensional vector of real values in the range [0, 1] that add up to 1. 3. After the softmax operation is completed, the inverse operations of steps 1 and 2 are performed to restore the two-dimensional matrix to the same dimension as the ``input``. It computes the exponential of the given dimension and the sum of exponential values of all the other dimensions in the K-dimensional vector input. Then the ratio of the exponential of the given dimension and the sum of exponential values of all the other dimensions is the output of the softmax operator. For each row :math:`i` and each column :math:`j` in the matrix, we have: .. math:: Out[i, j] = \\frac{\\exp(X[i, j])}{\\sum_j(exp(X[i, j])} Example: .. code-block:: text Case 1: Input: X.shape = [2, 3, 4] X.data = [[[2.0, 3.0, 4.0, 5.0], [3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 8.0, 9.0]], [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [6.0, 7.0, 8.0, 9.0]]] Attrs: axis = -1 Output: Out.shape = [2, 3, 4] Out.data = [[[0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.07232949, 0.19661193, 0.19661193, 0.53444665]], [[0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.0320586 , 0.08714432, 0.23688282, 0.64391426]]] Case 2: Input: X.shape = [2, 3, 4] X.data = [[[2.0, 3.0, 4.0, 5.0], [3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 8.0, 9.0]], [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [6.0, 7.0, 8.0, 9.0]]] Attrs: axis = 1 Output: Out.shape = [2, 3, 4] Out.data = [[[0.00657326, 0.00657326, 0.01714783, 0.01714783], [0.01786798, 0.01786798, 0.04661262, 0.04661262], [0.97555875, 0.97555875, 0.93623955, 0.93623955]], [[0.00490169, 0.00490169, 0.00490169, 0.00490169], [0.26762315, 0.26762315, 0.26762315, 0.26762315], [0.72747516, 0.72747516, 0.72747516, 0.72747516]]] Args: input (Tensor): The input tensor. A multi-dimension ``Tensor`` with type float32 or float64. use_cudnn (bool, optional): Use cudnn kernel or not, it is valid only when the cudnn \ library is installed. To improve performance, set use_cudnn to True by default. 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` . Default: None. will be named automatically. Default: None. axis (int, optional): The index of dimension to perform softmax calculations, it should be in range :math:`[-1, rank - 1]`, while :math:`rank` is the rank of input tensor. Default: -1. -1 means the last dimension. Returns: Tensor: ``Tensor`` indicates the output of softmax. The data type and shape are the same as ``input`` . Examples: .. code-block:: python import paddle import paddle.nn.functional as F x = paddle.to_tensor([[[2.0, 3.0, 4.0, 5.0], [3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 8.0, 9.0]], [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [6.0, 7.0, 8.0, 9.0]]], dtype='float32') y = F.softmax(x, axis=1) print(y) # [[[0.00657326, 0.00657326, 0.01714783, 0.01714783], # [0.01786798, 0.01786798, 0.04661262, 0.04661262], # [0.97555870, 0.97555870, 0.93623954, 0.93623954]], # [[0.00490169, 0.00490169, 0.00490169, 0.00490169], # [0.26762316, 0.26762316, 0.26762316, 0.26762316], # [0.72747517, 0.72747517, 0.72747517, 0.72747517]]] """ if in_dygraph_mode(): return _C_ops.softmax(input, axis) if _non_static_mode(): return _legacy_C_ops.softmax( input, 'axis', axis, 'use_cudnn', use_cudnn ) inputs = {"X": [input]} attrs = {"axis": axis, "use_cudnn": use_cudnn} helper = LayerHelper('softmax', **locals()) check_variable_and_dtype( input, 'input/x', ['float16', 'float32', 'float64'], 'softmax' ) dtype = helper.input_dtype() softmax_out = helper.create_variable_for_type_inference(dtype) helper.append_op( type="softmax", inputs={"X": input}, outputs={"Out": softmax_out}, attrs=attrs, ) return softmax_out def conv2d( input, num_filters, filter_size, stride=1, padding=0, dilation=1, groups=None, param_attr=None, bias_attr=None, use_cudnn=True, act=None, name=None, data_format="NCHW", ): r""" :api_attr: Static Graph The convolution2D layer calculates the output based on the input, filter and strides, paddings, dilations, groups parameters. Input and Output are in NCHW or NHWC format, where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature. Filter is in MCHW format, where M is the number of output image channels, C is the number of input image channels, H is the height of the filter, and W is the width of the filter. If the groups is greater than 1, C will equal the number of input image channels divided by the groups. Please refer to UFLDL's `convolution `_ for more details. If bias attribution and activation type are provided, bias is added to the output of the convolution, and the corresponding activation function is applied to the final result. For each input :math:`X`, the equation is: .. math:: Out = \sigma (W \\ast X + b) Where: * :math:`X`: Input value, a tensor with NCHW or NHWC format. * :math:`W`: Filter value, a tensor with MCHW format. * :math:`\\ast`: Convolution operation. * :math:`b`: Bias value, a 2-D tensor with shape [M, 1]. * :math:`\\sigma`: Activation function. * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different. Example: - Input: Input shape: :math:`(N, C_{in}, H_{in}, W_{in})` Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)` - Output: Output shape: :math:`(N, C_{out}, H_{out}, W_{out})` Where .. math:: H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\ W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1 Args: input (Tensor): The input is 4-D Tensor with shape [N, C, H, W], the data type of input is float16 or float32 or float64. num_filters(int): The number of filter. It is as same as the output image channel. filter_size (int|tuple): The filter size. If filter_size is a tuple, it must contain two integers, (filter_size_height, filter_size_width). Otherwise, filter_size_height = filter_size_width =\ filter_size. stride (int|tuple): The stride size. It means the stride in convolution. If stride is a tuple, it must contain two integers, (stride_height, stride_width). Otherwise, stride_height = stride_width = stride. Default: stride = 1. padding (string|int|list|tuple): The padding size. It means the number of zero-paddings on both sides for each dimension.If `padding` is a string, either 'VALID' or 'SAME' which is the padding algorithm. If padding size is a tuple or list, it could be in three forms: `[pad_height, pad_width]` or `[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `data_format` is `"NCHW"`, `padding` can be in the form `[[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`. when `data_format` is `"NHWC"`, `pool_padding` can be in the form `[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`. Default: padding = 0. dilation (int|tuple): The dilation size. It means the spacing between the kernel points. If dilation is a tuple, it must contain two integers, (dilation_height, dilation_width). Otherwise, dilation_height = dilation_width = dilation. Default: dilation = 1. groups (int): The groups number of the Conv2d Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only connected to the second half of the input channels. Default: groups=1. param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights of conv2d. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as param_attr. If the Initializer of the param_attr is not set, the parameter is initialized with :math:`Normal(0.0, std)`, and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None. bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d. If it is set to False, no bias will be added to the output units. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as bias_attr. If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None. use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn library is installed. Default: True act (str): Activation type, if it is set to None, activation is not appended. Default: None name(str|None): 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): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. Returns: A Tensor representing the conv2d, whose data type is the same with input. If act is None, the tensor storing the convolution result, and if act is not None, the tensor storing convolution and non-linearity activation result. Raises: ValueError: If the type of `use_cudnn` is not bool. ValueError: If `data_format` is not "NCHW" or "NHWC". ValueError: If the channel dimmention of the input is less than or equal to zero. ValueError: If `padding` is a string, but not "SAME" or "VALID". ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 or the element corresponding to the input's channel is not 0. ShapeError: If the input is not 4-D Tensor. ShapeError: If the input's dimension size and filter's dimension size not equal. ShapeError: If the dimension size of input minus the size of `stride` is not 2. ShapeError: If the number of input channels is not equal to filter's channels * groups. ShapeError: If the number of output channels is not be divided by groups. Examples: .. code-block:: python import paddle paddle.enable_static() data = paddle.static.data(name='data', shape=[None, 3, 32, 32], dtype='float32') conv2d = paddle.static.nn.conv2d(input=data, num_filters=2, filter_size=3, act="relu") print(conv2d.shape) # [-1, 2, 30, 30] """ check_variable_and_dtype( input, 'input', ['float16', 'float32', 'float64'], 'conv2d' ) if len(input.shape) != 4: raise ValueError( "Input size should be 4, " "but received {}".format(len(input.shape)) ) num_channels = input.shape[1] if not isinstance(use_cudnn, bool): raise ValueError( "Attr(use_cudnn) should be True or False. Received " "Attr(use_cudnn): %s. " % str(use_cudnn) ) if data_format not in ["NCHW", "NHWC"]: raise ValueError( "Attr(data_format) should be 'NCHW' or 'NHWC'. Received " "Attr(data_format): %s." % str(data_format) ) channel_last = data_format == "NHWC" num_channels = input.shape[3] if channel_last else input.shape[1] if num_channels < 0: raise ValueError( "The channel dimmention of the input(%s) should be defined. " "Received: %s." % (str(input.shape), str(num_channels)) ) assert param_attr is not False, "param_attr should not be False here." if groups is None: num_filter_channels = num_channels elif groups <= 0: raise ValueError( "the groups of input must be greater than 0, " "but received the groups of input is {}".format(groups) ) else: if num_channels % groups != 0: raise ValueError( "the channel of input must be divisible by groups," "received: the channel of input is {}, the shape of input is {}" ", the groups is {}".format(num_channels, input.shape, groups) ) num_filter_channels = num_channels // groups l_type = 'conv2d' if ( num_channels == groups and num_filters % num_channels == 0 and not use_cudnn ): l_type = 'depthwise_conv2d' if ( num_channels == groups and num_filters % num_channels == 0 and core.is_compiled_with_rocm() ): l_type = 'depthwise_conv2d' # NPU only supports depthwise_conv2d when "input_channel = output_channel = groups" if core.is_compiled_with_npu(): if num_channels == groups and num_channels == num_filters: l_type = 'depthwise_conv2d' else: l_type = 'conv2d' helper = LayerHelper(l_type, **locals()) dtype = helper.input_dtype() filter_size = utils.convert_to_list(filter_size, 2, 'filter_size') stride = utils.convert_to_list(stride, 2, 'stride') dilation = utils.convert_to_list(dilation, 2, 'dilation') # padding def _update_padding(padding, data_format): def is_list_or_tuple(ele): if isinstance(ele, list) or isinstance(ele, tuple): return True return False if is_list_or_tuple(padding) and len(padding) == 4: if is_list_or_tuple(padding[0]) and (data_format == "NCHW"): if not (padding[0] == [0, 0] and padding[1] == [0, 0]): raise ValueError( "Non-zero padding(%s) in the batch or channel dimensions " "is not supported." % str(padding) ) padding = padding[2:4] padding = [ele for a_list in padding for ele in a_list] elif is_list_or_tuple(padding[0]) and (data_format == "NHWC"): if not (padding[0] == [0, 0] and padding[3] == [0, 0]): raise ValueError( "Non-zero padding(%s) in the batch or channel dimensions " "is not supported." % str(padding) ) padding = padding[1:3] padding = [ele for a_list in padding for ele in a_list] padding = utils.convert_to_list(padding, 4, 'padding') if utils._is_symmetric_padding(padding, 2): padding = [padding[0], padding[2]] else: padding = utils.convert_to_list(padding, 2, 'padding') return padding padding_algorithm = "EXPLICIT" if isinstance(padding, str): padding = padding.upper() if padding not in ["SAME", "VALID"]: raise ValueError( "Unknown padding: '%s'. It can only be 'SAME' or 'VALID'." % str(padding) ) if padding == "VALID": padding_algorithm = "VALID" padding = [0, 0] elif padding == "SAME": padding_algorithm = "SAME" padding = [0, 0] padding = _update_padding(padding, data_format) filter_shape = [num_filters, int(num_filter_channels)] + filter_size def _get_default_param_initializer(): filter_elem_num = filter_size[0] * filter_size[1] * num_channels if filter_elem_num <= 0: raise ValueError( "Invalid filter number, excepted number is larger than 0, but" " received {}, please check the input shape and " "filter size.".format(filter_elem_num) ) std = (2.0 / filter_elem_num) ** 0.5 return Normal(0.0, std, 0) filter_param = helper.create_parameter( attr=helper.param_attr, shape=filter_shape, dtype=dtype, default_initializer=_get_default_param_initializer(), ) pre_bias = helper.create_variable_for_type_inference(dtype) if ( core.is_compiled_with_cuda() and paddle.fluid.get_flags("FLAGS_conv2d_disable_cudnn")[ "FLAGS_conv2d_disable_cudnn" ] ): use_cudnn = False helper.append_op( type=l_type, inputs={ 'Input': input, 'Filter': filter_param, }, outputs={"Output": pre_bias}, attrs={ 'strides': stride, 'paddings': padding, 'dilations': dilation, 'groups': groups, 'use_cudnn': use_cudnn, 'use_mkldnn': False, 'fuse_relu_before_depthwise_conv': False, "padding_algorithm": padding_algorithm, "data_format": data_format, }, ) if data_format == 'NCHW': pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2) else: pre_act = helper.append_bias_op(pre_bias, dim_start=3, dim_end=4) return helper.append_activation(pre_act) @templatedoc() def pool2d( input, pool_size=-1, pool_type="max", pool_stride=1, pool_padding=0, global_pooling=False, use_cudnn=True, ceil_mode=False, name=None, exclusive=True, data_format="NCHW", ): """ ${comment} Args: input (Variable): The input tensor of pooling operator which is a 4-D tensor with shape [N, C, H, W]. The format of input tensor is `"NCHW"` or `"NHWC"`, where `N` is batch size, `C` is the number of channels, `H` is the height of the feature, and `W` is the width of the feature. The data type if float32 or float64. pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain two integers, (pool_size_Height, pool_size_Width). Otherwise, the pool kernel size will be a square of an int. pool_type: ${pooling_type_comment} pool_stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list, it must contain two integers, (pool_stride_Height, pool_stride_Width). Otherwise, the pool stride size will be a square of an int. pool_padding (string|int|list|tuple): The pool padding. If `pool_padding` is a string, either 'VALID' or 'SAME' which is the padding algorithm. If pool padding size is a tuple or list, it could be in three forms: `[pad_height, pad_width]` or `[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `data_format` is `"NCHW"`, `pool_padding` can be in the form `[[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`. when `data_format` is `"NHWC"`, `pool_padding` can be in the form `[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`. Otherwise, the pool padding size will be a square of an int. global_pooling (bool): ${global_pooling_comment} use_cudnn (bool): ${use_cudnn_comment} ceil_mode (bool): ${ceil_mode_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. exclusive (bool): Whether to exclude padding points in average pooling mode, default is `true`. data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. Returns: Variable: The output tensor of pooling result. The data type is same as input tensor. Raises: ValueError: If `pool_type` is not "max" nor "avg". ValueError: If `global_pooling` is False and `pool_size` is -1. TypeError: If `use_cudnn` is not a bool value. ValueError: If `data_format` is not "NCHW" or "NHWC". ValueError: If `pool_padding` is a string, but not "SAME" or "VALID". ValueError: If `pool_padding` is "VALID", but `ceil_mode` is True. ValueError: If `pool_padding` is a list or tuple, but the elements in the batch or channel dimensions are non-zero. ShapeError: If the input is not a 4-D or 5-D Tensor. ShapeError: If the dimension of input minus the size of `pool_stride` is not 2. ShapeError: If the size of `pool_size` and `pool_stride` is not equal. ShapeError: If the output's shape calculated is not greater than 0. Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32') # max pool2d pool2d = fluid.layers.pool2d( input = data, pool_size = 2, pool_type = "max", pool_stride = 1, global_pooling=False) # average pool2d pool2d = fluid.layers.pool2d( input = data, pool_size = 2, pool_type = "avg", pool_stride = 1, global_pooling=False) # global average pool2d pool2d = fluid.layers.pool2d( input = data, pool_size = 2, pool_type = "avg", pool_stride = 1, global_pooling=True) # Attr(pool_padding) is a list with 4 elements, Attr(data_format) is "NCHW". out_1 = fluid.layers.pool2d( input = data, pool_size = 3, pool_type = "avg", pool_stride = 1, pool_padding = [1, 2, 1, 0], data_format = "NCHW") # Attr(pool_padding) is a string, Attr(data_format) is "NCHW". out_2 = fluid.layers.pool2d( input = data, pool_size = 3, pool_type = "avg", pool_stride = 1, pool_padding = "VALID", data_format = "NCHW") """ if pool_type not in ["max", "avg"]: raise ValueError( "Unknown Attr(pool_type): '%s'. It can only be 'max' or 'avg'.", str(pool_type), ) if global_pooling is False and pool_size == -1: raise ValueError( "When Attr(global_pooling) is False, Attr(pool_size) must be passed " "and be a valid value. Received pool_size: %s." % str(pool_size) ) if not isinstance(use_cudnn, bool): raise TypeError( "Attr(use_cudnn) should be True or False. Received " "Attr(use_cudnn): %s." % str(use_cudnn) ) if data_format not in ["NCHW", "NHWC"]: raise ValueError( "Attr(data_format) should be 'NCHW' or 'NHWC'. Received " "Attr(data_format): %s." % str(data_format) ) pool_size = utils.convert_to_list(pool_size, 2, 'pool_size') pool_stride = utils.convert_to_list(pool_stride, 2, 'pool_stride') def update_padding(padding, data_format): def is_list_or_tuple(ele): if isinstance(ele, list) or isinstance(ele, tuple): return True return False if is_list_or_tuple(padding) and len(padding) == 4: if is_list_or_tuple(padding[0]) and (data_format == "NCHW"): if not (padding[0] == [0, 0] and padding[1] == [0, 0]): raise ValueError( "Non-zero pool_padding(%s) in the batch or channel dimensions " "is not supported." % str(padding) ) padding = padding[2:4] padding = [ele for a_list in padding for ele in a_list] elif is_list_or_tuple(padding[0]) and (data_format == "NHWC"): if not (padding[0] == [0, 0] and padding[3] == [0, 0]): raise ValueError( "Non-zero pool_padding(%s) in the batch or channel dimensions " "is not supported." % str(padding) ) padding = padding[1:3] padding = [ele for a_list in padding for ele in a_list] padding = utils.convert_to_list(padding, 4, 'padding') if utils._is_symmetric_padding(padding, 2): padding = [padding[0], padding[2]] else: padding = utils.convert_to_list(padding, 2, 'padding') return padding padding_algorithm = "EXPLICIT" if isinstance(pool_padding, str): pool_padding = pool_padding.upper() if pool_padding not in ["SAME", "VALID"]: raise ValueError( "Unknown Attr(pool_padding): '%s'. It can only be 'SAME' or 'VALID'." % str(pool_padding) ) if pool_padding == "VALID": padding_algorithm = "VALID" pool_padding = [0, 0] if ceil_mode != False: raise ValueError( "When Attr(pool_padding) is \"VALID\", Attr(ceil_mode) must be False. " "Received ceil_mode: True." ) elif pool_padding == "SAME": padding_algorithm = "SAME" pool_padding = [0, 0] pool_padding = update_padding(pool_padding, data_format) if in_dygraph_mode(): input = input._use_gpudnn(use_cudnn) return _C_ops.pool2d( input, pool_size, pool_stride, pool_padding, ceil_mode, exclusive, data_format, pool_type, global_pooling, False, padding_algorithm, ) op_type = 'pool2d' helper = LayerHelper(op_type, **locals()) dtype = helper.input_dtype() pool_out = helper.create_variable_for_type_inference(dtype) helper.append_op( type=op_type, inputs={"X": input}, outputs={"Out": pool_out}, attrs={ "pooling_type": pool_type, "ksize": pool_size, "global_pooling": global_pooling, "strides": pool_stride, "paddings": pool_padding, "padding_algorithm": padding_algorithm, "use_cudnn": use_cudnn, "ceil_mode": ceil_mode, "use_mkldnn": False, "exclusive": exclusive, "data_format": data_format, }, ) return pool_out def batch_norm( input, act=None, is_test=False, momentum=0.9, epsilon=1e-05, param_attr=None, bias_attr=None, data_layout='NCHW', in_place=False, name=None, moving_mean_name=None, moving_variance_name=None, do_model_average_for_mean_and_var=True, use_global_stats=False, ): r""" :api_attr: Static Graph **Batch Normalization Layer** Can be used as a normalizer function for convolution or fully_connected operations. The required data format for this layer is one of the following: 1. NHWC `[batch, in_height, in_width, in_channels]` 2. NCHW `[batch, in_channels, in_height, in_width]` Refer to `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift `_ for more details. :math:`input` is the input features over a mini-batch. .. math:: \\mu_{\\beta} &\\gets \\frac{1}{m} \\sum_{i=1}^{m} x_i \\qquad &//\\ \ mini-batch\ mean \\\\ \\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\ \\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\ \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\ \\sigma_{\\beta}^{2} + \\epsilon}} \\qquad &//\ normalize \\\\ y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift moving\_mean = moving\_mean * momentum + mini-batch\_mean * (1. - momentum) \\\\ moving\_var = moving\_var * momentum + mini-batch\_var * (1. - momentum) moving_mean is global mean and moving_var is global variance. When use_global_stats = True, the :math:`\\mu_{\\beta}` and :math:`\\sigma_{\\beta}^{2}` are not the statistics of one mini-batch. They are global (or running) statistics. (It usually got from the pre-trained model.) The training and testing (or inference) have the same behavior: .. math:: \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\ \\sigma_{\\beta}^{2} + \\epsilon}} \\\\ y_i &\\gets \\gamma \\hat{x_i} + \\beta Note: if build_strategy.sync_batch_norm=True, the batch_norm in network will use sync_batch_norm automatically. `is_test = True` can only be used in test program and inference program, `is_test` CANNOT be set to True in train program, if you want to use global status from pre_train model in train program, please set `use_global_stats = True`. Args: input(Tensor): The rank of input Tensor can be 2, 3, 4, 5. The data type is float16 or float32 or float64. act(string, Default None): Activation type, linear|relu|prelu|... is_test (bool, Default False): A flag indicating whether it is in test phrase or not. momentum(float|Tensor, Default 0.9): The value used for the moving_mean and moving_var computation. This should be a float number or a Tensor with shape [1] and data type as float32. The updated formula is: :math:`moving\_mean = moving\_mean * momentum + new\_mean * (1. - momentum)` :math:`moving\_var = moving\_var * momentum + new\_var * (1. - momentum)` Default is 0.9. epsilon(float, Default 1e-05): A value added to the denominator for numerical stability. Default is 1e-5. param_attr(ParamAttr|None): The parameter attribute for Parameter `scale` of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm will create ParamAttr as param_attr, the name of scale can be set in ParamAttr. If the Initializer of the param_attr is not set, the parameter is initialized with Xavier. Default: None. bias_attr(ParamAttr|None): The parameter attribute for the bias of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None. data_layout (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. in_place(bool, Default False): Make the input and output of batch norm reuse memory. name(str|None): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. moving_mean_name(str, Default None): The name of moving_mean which store the global Mean. If it is set to None, batch_norm will save global mean with a random name, otherwise, batch_norm will save global mean with the string. moving_variance_name(str, Default None): The name of the moving_variance which store the global Variance. If it is set to None, batch_norm will save global variance with a random name, otherwise, batch_norm will save global variance with the string. do_model_average_for_mean_and_var(bool, Default True): Whether parameter mean and variance should do model average when model average is enabled. use_global_stats(bool, Default False): Whether to use global mean and variance. In inference or test mode, set use_global_stats to true or is_test to true, and the behavior is equivalent. In train mode, when setting use_global_stats True, the global mean and variance are also used during train period. Returns: A Tensor which is the result after applying batch normalization on the input, has same shape and data type with input. Examples: .. code-block:: python import paddle paddle.enable_static() x = paddle.static.data(name='x', shape=[3, 7, 3, 7], dtype='float32') hidden1 = paddle.static.nn.fc(x=x, size=200) print(hidden1.shape) # [3, 200] hidden2 = paddle.static.nn.batch_norm(input=hidden1) print(hidden2.shape) # [3, 200] """ assert ( bias_attr is not False ), "bias_attr should not be False in batch_norm." helper = LayerHelper('batch_norm', **locals()) check_variable_and_dtype( input, 'input', ['float16', 'float32', 'float64'], 'batch_norm' ) dtype = helper.input_dtype() # use fp32 for bn parameter if dtype == core.VarDesc.VarType.FP16: dtype = core.VarDesc.VarType.FP32 input_shape = input.shape if data_layout == 'NCHW': channel_num = input_shape[1] else: if data_layout == 'NHWC': channel_num = input_shape[-1] else: raise ValueError("unsupported data layout:" + data_layout) param_shape = [channel_num] # create parameter scale = helper.create_parameter( attr=helper.param_attr, shape=param_shape, dtype=dtype, default_initializer=Constant(1.0), ) bias = helper.create_parameter( attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True ) mean = helper.create_parameter( attr=ParamAttr( name=moving_mean_name, initializer=Constant(0.0), trainable=False, do_model_average=do_model_average_for_mean_and_var, ), shape=param_shape, dtype=dtype, ) mean.stop_gradient = True variance = helper.create_parameter( attr=ParamAttr( name=moving_variance_name, initializer=Constant(1.0), trainable=False, do_model_average=do_model_average_for_mean_and_var, ), shape=param_shape, dtype=dtype, ) variance.stop_gradient = True # create output # mean and mean_out share the same memory mean_out = mean # variance and variance_out share the same memory variance_out = variance if in_dygraph_mode(): inputs_has_MomemtumTensor = False attrs_has_momentum = False tmp_tensor_type = core.eager.Tensor if isinstance(momentum, tmp_tensor_type): inputs_has_MomemtumTensor = True else: attrs_has_momentum = True attrs_ = () if attrs_has_momentum: attrs_ = ( 'momentum', momentum, 'epsilon', epsilon, 'is_test', is_test, 'data_layout', data_layout, 'use_mkldnn', False, 'fuse_with_relu', False, 'use_global_stats', use_global_stats, ) else: attrs_ = ( 'epsilon', epsilon, 'is_test', is_test, 'data_layout', data_layout, 'use_mkldnn', False, 'fuse_with_relu', False, 'use_global_stats', use_global_stats, ) if inputs_has_MomemtumTensor: batch_norm_out, _, _, _, _, _ = _legacy_C_ops.batch_norm( input, scale, bias, mean, variance, momentum, mean_out, variance_out, *attrs_, ) else: batch_norm_out, _, _, _, _, _ = _legacy_C_ops.batch_norm( input, scale, bias, mean, variance, None, mean_out, variance_out, *attrs_, ) return dygraph_utils._append_activation_in_dygraph( batch_norm_out, act=act, use_mkldnn=False ) saved_mean = helper.create_variable_for_type_inference( dtype=dtype, stop_gradient=True ) saved_variance = helper.create_variable_for_type_inference( dtype=dtype, stop_gradient=True ) reserve_space = None if not is_test: reserve_space = helper.create_variable_for_type_inference( dtype=helper.input_dtype(), stop_gradient=True ) batch_norm_out = ( input if in_place else helper.create_variable_for_type_inference(dtype) ) inputs = { "X": input, "Scale": scale, "Bias": bias, "Mean": mean, "Variance": variance, "MeanOut": mean_out, "VarianceOut": variance_out, } attrs = { "epsilon": epsilon, "is_test": is_test, "data_layout": data_layout, "use_mkldnn": False, "fuse_with_relu": False, "use_global_stats": use_global_stats, } if isinstance(momentum, Variable): inputs['MomemtumTensor'] = momentum else: attrs['momentum'] = momentum outputs = { "Y": batch_norm_out, "MeanOut": mean_out, "VarianceOut": variance_out, "SavedMean": saved_mean, "SavedVariance": saved_variance, } if reserve_space is not None: outputs["ReserveSpace"] = reserve_space helper.append_op( type="batch_norm", inputs=inputs, outputs=outputs, attrs=attrs ) return helper.append_activation(batch_norm_out) @templatedoc() def layer_norm( input, scale=True, shift=True, begin_norm_axis=1, epsilon=1e-05, param_attr=None, bias_attr=None, act=None, name=None, ): r""" :api_attr: Static Graph **Layer Normalization Layer** The API implements the function of the Layer Normalization Layer and can be applied to mini-batch input data. Refer to `Layer Normalization `_ The formula is as follows: .. math:: \\mu & = \\frac{1}{H}\\sum_{i=1}^{H} x_i \\sigma & = \\sqrt{\\frac{1}{H}\sum_{i=1}^{H}{(x_i - \\mu)^2} + \\epsilon} y & = f(\\frac{g}{\\sigma}(x - \\mu) + b) - :math:`x`: the vector representation of the summed inputs to the neurons in that layer. - :math:`H`: the number of hidden units in a layers - :math:`\\epsilon`: the small value added to the variance to prevent division by zero. - :math:`g`: the trainable scale parameter. - :math:`b`: the trainable bias parameter. Args: input(Tensor): A multi-dimension ``Tensor`` , and the data type is float32 or float64. scale(bool, optional): Whether to learn the adaptive gain :math:`g` after normalization. Default: True. shift(bool, optional): Whether to learn the adaptive bias :math:`b` after normalization. Default: True. begin_norm_axis(int, optional): The normalization will be performed along dimensions from :attr:`begin_norm_axis` to :attr:`rank(input)`. Default: 1. epsilon(float, optional): The small value added to the variance to prevent division by zero. Default: 1e-05. param_attr(ParamAttr, optional): The parameter attribute for the learnable gain :math:`g`. If :attr:`scale` is False, :attr:`param_attr` is omitted. If :attr:`scale` is True and :attr:`param_attr` is None, a default :code:`ParamAttr` would be added as scale. The :attr:`param_attr` is initialized as 1 if it is added. Default: None. bias_attr(ParamAttr, optional): The parameter attribute for the learnable bias :math:`b`. If :attr:`shift` is False, :attr:`bias_attr` is omitted. If :attr:`shift` is True and :attr:`param_attr` is None, a default :code:`ParamAttr` would be added as bias. The :attr:`bias_attr` is initialized as 0 if it is added. Default: None. act(str, optional): Activation to be applied to the output of layer normalization. Default: None. name(str): 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: Tensor: ``Tensor`` indicating the normalized result, the data type is the same as ``input`` , and the return dimension is the same as ``input`` . Examples: .. code-block:: python import paddle paddle.enable_static() x = paddle.static.data(name='x', shape=[8, 32, 32], dtype='float32') output = paddle.static.nn.layer_norm(input=x, begin_norm_axis=1) print(output.shape) # [8, 32, 32] """ assert ( _non_static_mode() is not True ), "please use LayerNorm instead of layer_norm in dygraph mode!" helper = LayerHelper('layer_norm', **locals()) check_variable_and_dtype( input, 'input', ['float32', 'float64'], 'layer_norm' ) dtype = helper.input_dtype() # create intput and parameters inputs = {'X': input} input_shape = input.shape param_shape = [reduce(lambda x, y: x * y, input_shape[begin_norm_axis:])] if scale: assert ( param_attr is not False ), "param_attr should not be False when using scale." scale = helper.create_parameter( attr=helper.param_attr, shape=param_shape, dtype=dtype, default_initializer=Constant(1.0), ) inputs['Scale'] = scale else: if param_attr: warnings.warn("param_attr is only available with scale is True.") if shift: assert ( bias_attr is not False ), "bias_attr should not be False when using shift." bias = helper.create_parameter( attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True ) inputs['Bias'] = bias else: if bias_attr: warnings.warn("bias_attr is only available with shift is True.") # create output mean_out = helper.create_variable_for_type_inference( dtype=dtype, stop_gradient=True ) variance_out = helper.create_variable_for_type_inference( dtype=dtype, stop_gradient=True ) layer_norm_out = helper.create_variable_for_type_inference(dtype) helper.append_op( type="layer_norm", inputs=inputs, outputs={ "Y": layer_norm_out, "Mean": mean_out, "Variance": variance_out, }, attrs={"epsilon": epsilon, "begin_norm_axis": begin_norm_axis}, ) return helper.append_activation(layer_norm_out) @templatedoc() def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None): r""" :api_attr: Static Graph **Spectral Normalization Layer** This operation calculates the spectral normalization value of weight parameters of fc, conv1d, conv2d, conv3d layers which should be 2-D, 3-D, 4-D, 5-D Parameters. Output tensor will be in same shape with input tensor. Calculations are showed as follows. Step 1: Generate vector U in shape of [H], and V in shape of [W]. While H is the :attr:`dim` th dimension of the input weights, and W is the product result of remaining dimensions. Step 2: :attr:`power_iters` should be a positive integer, do following calculations with U and V for :attr:`power_iters` rounds. Calculations as follows: .. math:: \mathbf{v} := \\frac{\mathbf{W}^{T} \mathbf{u}}{\|\mathbf{W}^{T} \mathbf{u}\|_2} \mathbf{u} := \\frac{\mathbf{W}^{T} \mathbf{v}}{\|\mathbf{W}^{T} \mathbf{v}\|_2} Step 3: Calculate :math:`\sigma(\mathbf{W})` and normalize weight values. .. math:: \sigma(\mathbf{W}) = \mathbf{u}^{T} \mathbf{W} \mathbf{v} \mathbf{W} = \\frac{\mathbf{W}}{\sigma(\mathbf{W})} Refer to `Spectral Normalization `_ . Args: weight(Tensor): ${weight_comment} dim(int): ${dim_comment} power_iters(int): ${power_iters_comment} eps(float): ${eps_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. Returns: Tensor: A tensor of weight parameters after spectral normalization. The data type and shape is same as input tensor. Examples: .. code-block:: python import paddle paddle.enable_static() weight = paddle.static.data(name='weight', shape=[2, 8, 32, 32], dtype='float32') x = paddle.static.nn.spectral_norm(weight=weight, dim=1, power_iters=2) print(x.shape) # [2, 8, 32, 32] """ helper = LayerHelper('spectral_norm', **locals()) check_variable_and_dtype( weight, 'weight', ['float32', 'float64'], 'spectral_norm' ) check_type(dim, 'dim', int, 'spectral_norm') check_type(power_iters, 'power_iters', int, 'spectral_norm') check_type(eps, 'eps', float, 'spectral_norm') dtype = weight.dtype # create intput and parameters input_shape = weight.shape assert weight.numel() > 0, "Any dimension of input cannot be equal to 0." assert dim < len(input_shape), ( "The input `dim` should be less than the " "rank of `weight`, but received dim=" "{}".format(dim) ) h = input_shape[dim] w = np.prod(input_shape) // h u = helper.create_parameter( attr=ParamAttr(), shape=[h], dtype=dtype, default_initializer=Normal(0.0, 1.0), ) u.stop_gradient = True v = helper.create_parameter( attr=ParamAttr(), shape=[w], dtype=dtype, default_initializer=Normal(0.0, 1.0), ) v.stop_gradient = True if in_dygraph_mode(): return _C_ops.spectral_norm(weight, u, v, dim, power_iters, eps) inputs = {'Weight': weight} inputs['U'] = u inputs['V'] = v # create output out = helper.create_variable(dtype=dtype) helper.append_op( type="spectral_norm", inputs=inputs, outputs={ "Out": out, }, attrs={ "dim": dim, "power_iters": power_iters, "eps": eps, }, ) return out def reduce_sum(input, dim=None, keep_dim=False, name=None): """ Computes the sum of tensor elements over the given dimension. Args: input (Variable): The input variable which is a Tensor, the data type is float32, float64, int32, int64. dim (list|int, optional): The dimensions along which the sum is performed. If :attr:`None`, sum all elements of :attr:`input` and return a Tensor variable with a single element, otherwise must be in the range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`. keep_dim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the :attr:`input` unless :attr:`keep_dim` is true, default value is False. 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: Variable: Tensor, results of summation operation on the specified dim of input tensor, it's data type is the same as input's Tensor. Raises: TypeError, if out data type is different with the input data type. Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the corresponding output tensor. x = fluid.data(name='x', shape=[2, 4], dtype='float32') fluid.layers.reduce_sum(x) # [3.5] fluid.layers.reduce_sum(x, dim=0) # [0.3, 0.5, 1.1, 1.6] fluid.layers.reduce_sum(x, dim=-1) # [1.9, 1.6] fluid.layers.reduce_sum(x, dim=1, keep_dim=True) # [[1.9], [1.6]] # y is a Tensor variable with shape [2, 2, 2] and elements as below: # [[[1, 2], [3, 4]], # [[5, 6], [7, 8]]] # Each example is followed by the corresponding output tensor. y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32') fluid.layers.reduce_sum(y, dim=[1, 2]) # [10, 26] fluid.layers.reduce_sum(y, dim=[0, 1]) # [16, 20] """ reduce_all, dim = _get_reduce_dim(dim, input) if in_dygraph_mode(): return _C_ops.sum(input, dim, None, keep_dim) elif _in_legacy_dygraph(): return _legacy_C_ops.reduce_sum( input, 'dim', dim, 'keep_dim', keep_dim, 'reduce_all', reduce_all ) attrs = {'dim': dim, 'keep_dim': keep_dim, 'reduce_all': reduce_all} check_variable_and_dtype( input, 'input', ['float16', 'float32', 'float64', 'int32', 'int64'], 'reduce_sum', ) helper = LayerHelper('reduce_sum', **locals()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) helper.append_op( type='reduce_sum', inputs={'X': input}, outputs={'Out': out}, attrs=attrs, ) return out def reduce_all(input, dim=None, keep_dim=False, name=None): """ This OP computes the ``logical and`` of tensor elements over the given dimension, and output the result. Args: input (Tensor): the input tensor, it's data type should be `bool`. dim (list|int|optional): The dimension along which the logical and is computed. If :attr:`None`, compute the logical and over all elements of :attr:`input` and return a Tensor variable with a single element, otherwise must be in the range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`. The default value is None. keep_dim (bool): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the :attr:`input` unless :attr:`keep_dim` is true. The default value is False. name(str|None): A name for this layer(optional). If set None, the layer will be named automatically. The default value is None. Returns: Tensor, the output data type is bool. : The reduced tensor variable with ``logical and`` in given dims. Examples: .. code-block:: python import paddle import paddle.fluid as fluid import paddle.fluid.layers as layers import numpy as np # x is a bool Tensor variable with following elements: # [[True, False] # [True, True]] x = fluid.layers.assign(np.array([[1, 0], [1, 1]], dtype='int32')) x = fluid.layers.cast(x, 'bool') out = fluid.layers.reduce_all(x) # False out = fluid.layers.reduce_all(x, dim=0) # [True, False] out = fluid.layers.reduce_all(x, dim=-1) # [False, True] # keep_dim=False, x.shape=(2,2), out.shape=(2,) out = fluid.layers.reduce_all(x, dim=1, keep_dim=True) # [[False], [True]] # keep_dim=True, x.shape=(2,2), out.shape=(2,1) """ if dim is not None and not isinstance(dim, list): dim = [dim] if in_dygraph_mode(): return _C_ops.all(input, dim if dim is not None else [], keep_dim) check_variable_and_dtype(input, 'input', ('bool'), 'reduce_all') helper = LayerHelper('reduce_all', **locals()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) helper.append_op( type='reduce_all', inputs={'X': input}, outputs={'Out': out}, attrs={ 'dim': dim if dim is not None and dim != [] else [0], 'keep_dim': keep_dim, 'reduce_all': True if dim is None or dim == [] or len(dim) == len(input.shape) else False, }, ) return out def reduce_any(input, dim=None, keep_dim=False, name=None): """ This OP computes the ``logical or`` of tensor elements over the given dimension, and output the result. Args: input (Tensor): the input tensor, it's data type should be `bool`. dim (list|int|optional): The dimension along which the logical and is computed. If :attr:`None`, compute the logical and over all elements of :attr:`input` and return a Tensor variable with a single element, otherwise must be in the range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`. The default value is None. keep_dim (bool): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the :attr:`input` unless :attr:`keep_dim` is true. The default value is False. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the output data type is bool. : The reduced tensor variable with ``logical or`` in given dims. Examples: .. code-block:: python import paddle import paddle.fluid as fluid import paddle.fluid.layers as layers import numpy as np # x is a bool Tensor variable with following elements: # [[True, False] # [False, False]] x = fluid.layers.assign(np.array([[1, 0], [0, 0]], dtype='int32')) x = fluid.layers.cast(x, 'bool') out = fluid.layers.reduce_any(x) # True out = fluid.layers.reduce_any(x, dim=0) # [True, False] out = fluid.layers.reduce_any(x, dim=-1) # [True, False] # keep_dim=False, x.shape=(2,2), out.shape=(2,) out = fluid.layers.reduce_any(x, dim=1, keep_dim=True) # [[True], [False]] # keep_dim=True, x.shape=(2,2), out.shape=(2,1) """ check_variable_and_dtype(input, 'input', ('bool'), 'reduce_any') helper = LayerHelper('reduce_any', **locals()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) if dim is not None and not isinstance(dim, list): dim = [dim] helper.append_op( type='reduce_any', inputs={'X': input}, outputs={'Out': out}, attrs={ 'dim': dim if dim is not None and dim != [] else [0], 'keep_dim': keep_dim, 'reduce_all': True if dim is None or dim == [] or len(dim) == len(input.shape) else False, }, ) return out def split(input, num_or_sections, dim=-1, name=None): """ Split the input tensor into multiple sub-Tensors. Args: input (Tensor): A N-D Tensor. The data type is bool, float16, float32, float64, int32 or int64. num_or_sections (int|list|tuple): If ``num_or_sections`` is int, then the ``num_or_sections`` indicates the number of equal sized sub-Tensors that the ``input`` will be divided into. If ``num_or_sections`` is a list or tuple, the length of it indicates the number of sub-Tensors and the elements in it indicate the sizes of sub-Tensors' dimension orderly. The length of the list mustn't be larger than the ``input`` 's size of specified dim. dim (int|Tensor, optional): The dimension along which to split, it can be a scalar with type ``int`` or a ``Tensor`` with shape [1] and data type ``int32`` or ``int64``. If :math:`dim < 0`, the dimension to split along is :math:`rank(input) + dim`. Default is -1. 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: list(Tensor): The list of segmented Tensors. Example: .. code-block:: python import paddle.fluid as fluid # input is a Tensor which shape is [3, 9, 5] input = fluid.data( name="input", shape=[3, 9, 5], dtype="float32") out0, out1, out2 = fluid.layers.split(input, num_or_sections=3, dim=1) # out0.shape [3, 3, 5] # out1.shape [3, 3, 5] # out2.shape [3, 3, 5] out0, out1, out2 = fluid.layers.split(input, num_or_sections=[2, 3, 4], dim=1) # out0.shape [3, 2, 5] # out1.shape [3, 3, 5] # out2.shape [3, 4, 5] out0, out1, out2 = fluid.layers.split(input, num_or_sections=[2, 3, -1], dim=1) # out0.shape [3, 2, 5] # out1.shape [3, 3, 5] # out2.shape [3, 4, 5] # dim is negative, the real dim is (rank(input) + axis) which real # value is 1. out0, out1, out2 = fluid.layers.split(input, num_or_sections=3, dim=-2) # out0.shape [3, 3, 5] # out1.shape [3, 3, 5] # out2.shape [3, 3, 5] """ if _non_static_mode(): num = None attrs = () if isinstance(dim, Variable): dim = dim.numpy() dim = dim.item(0) assert len(input.shape) + dim >= 0, "(rank(x) + axis) must >= 0" dim = (len(input.shape) + dim) if dim < 0 else dim attrs += ('axis', dim) if isinstance(num_or_sections, int): num = num_or_sections attrs += ('num', num_or_sections) elif isinstance(num_or_sections, (list, tuple)): num = len(num_or_sections) if utils._contain_var(num_or_sections): for index, item in enumerate(num_or_sections): if isinstance(item, Variable): num_or_sections[index] = num_or_sections[index].numpy()[ 0 ] attrs += ('sections', list(num_or_sections)) else: attrs += ('sections', list(num_or_sections)) else: raise TypeError( "The type of 'num_or_sections' in split must be int, list or tuple in imperative mode, but " "received %s." % (type(num_or_sections)) ) if in_dygraph_mode(): if isinstance(num_or_sections, int): return _C_ops.split_with_num(input, num_or_sections, dim) else: return _C_ops.split(input, num_or_sections, dim) elif _in_legacy_dygraph(): out = [_varbase_creator() for n in range(num)] _legacy_C_ops.split(input, out, *attrs) return out check_variable_and_dtype( input, 'input', ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'], 'split', ) check_type(num_or_sections, 'num_or_sections', (list, int, tuple), 'split') check_type(dim, 'dim', (int, Variable), 'split') if isinstance(dim, Variable): check_dtype(dim.dtype, 'dim', ['int32', 'int64'], 'split') helper = LayerHelper('split', **locals()) input_shape = input.shape inputs = {'X': input} attrs = {'num': num_or_sections if isinstance(num_or_sections, int) else 0} def _get_SectionsTensorList(one_list): tensor_list = [] unk_dim_idx = -1 for idx, dim_size in enumerate(one_list): if isinstance(dim_size, Variable): dim_size.stop_gradient = True tensor_list.append(dim_size) else: assert isinstance(dim_size, int) if dim_size == -1: assert unk_dim_idx == -1, ( "Only one value of 'num_or_section' in split can " "be -1. But received num_or_section[%d] is also -1." % idx ) unk_dim_idx = idx temp_out = helper.create_variable_for_type_inference('int32') fill_constant( [1], 'int32', dim_size, force_cpu=True, out=temp_out ) tensor_list.append(temp_out) return tensor_list if isinstance(dim, Variable): dim.stop_gradient = True inputs['AxisTensor'] = dim else: assert len(input.shape) + dim >= 0, "(rank(x) + axis) must >= 0" dim = (len(input_shape) + dim) if dim < 0 else dim attrs['axis'] = dim if isinstance(num_or_sections, int): assert num_or_sections > 1, 'num_or_sections must be more than 1.' if isinstance(dim, int) and input_shape[dim] > 0: assert input_shape[dim] % num_or_sections == 0, ( "The input's size along the split dimension " "must be evenly divisible by Attr(num_or_sections). " "But %d is not evenly divisible by %d. " % (num_or_sections, input_shape[dim]) ) num = num_or_sections else: if isinstance(dim, int) and input_shape[dim] > 0: assert ( len(num_or_sections) <= input_shape[dim] ), 'len(num_or_sections) must not be more than input.shape[dim].' num = len(num_or_sections) attrs['sections'] = list( map( lambda ele: -1 if isinstance(ele, Variable) else ele, num_or_sections, ) ) if utils._contain_var(num_or_sections): inputs['SectionsTensorList'] = _get_SectionsTensorList( num_or_sections ) outs = [ helper.create_variable_for_type_inference(dtype=helper.input_dtype()) for i in range(num) ] helper.append_op( type='split', inputs=inputs, outputs={'Out': outs}, attrs=attrs ) return outs def l2_normalize(x, axis, epsilon=1e-12, name=None): r""" This op normalizes `x` along dimension `axis` using an L2 norm. For a 1-D tensor (`dim` is fixed to 0), this layer computes .. math:: y = \\frac{x}{ \sqrt{\sum {x^2} + epsion }} For `x` with more dimensions, this layer independently normalizes each 1-D slice along dimension `axis`. Args: x(Variable|list): The input tensor could be N-D tensor, and the input data type could be float16, float32 or float64. axis(int): The axis on which to apply normalization. If `axis < 0`, \ the dimension to normalization is rank(X) + axis. -1 is the last dimension. epsilon(float): The epsilon value is used to avoid division by zero, \ the default value is 1e-12. 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: Variable: The output has the same shape and data type with `x`. Examples: .. code-block:: python :name: code-example1 import paddle X = paddle.randn(shape=[3, 5], dtype='float64') out = paddle.fluid.layers.l2_normalize(X, axis=-1) print(out) # [[ 0.21558504 0.56360189 0.47466096 0.46269539 -0.44326736] # [-0.70602414 -0.52745777 0.37771788 -0.2804768 -0.04449922] # [-0.33972208 -0.43014923 0.31772556 0.76617881 -0.10761525]] """ if len(x.shape) == 1: axis = 0 if _non_static_mode(): if in_dygraph_mode(): out, _ = _C_ops.norm(x, 1 if axis is None else axis, epsilon, False) elif _in_legacy_dygraph(): _, out = _legacy_C_ops.norm( x, 'axis', 1 if axis is None else axis, 'epsilon', epsilon ) return out check_variable_and_dtype(x, "X", ("float16", "float32", "float64"), "norm") helper = LayerHelper("l2_normalize", **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) norm = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type="norm", inputs={"X": x}, outputs={"Out": out, "Norm": norm}, attrs={ "axis": 1 if axis is None else axis, "epsilon": epsilon, }, ) return out @deprecated(since="2.0.0", update_to="paddle.matmul") def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None): """ Applies matrix multiplication to two tensors. Currently, the input tensors' rank can be any, but when the rank of any inputs is bigger than 3, this two inputs' rank should be equal. The actual behavior depends on the shapes of :math:`x`, :math:`y` and the flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically: - If a transpose flag is specified, the last two dimensions of the tensor are transposed. If the tensor is rank-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]` in nontransposed form and as :math:`[D, 1]` in transposed form, whereas for :math:`y` it is the opposite: It is treated as :math:`[D, 1]` in nontransposed form and as :math:`[1, D]` in transposed form. - After transpose, the two tensors are 2-D or n-D and matrix multiplication performs in the following way. - If both are 2-D, they are multiplied like conventional matrices. - If either is n-D, it is treated as a stack of matrices residing in the last two dimensions and a batched matrix multiply supporting broadcast applies on the two tensors. Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and nontransposed, the prepended or appended dimension :math:`1` will be removed after matrix multiplication. Args: x (Variable): The input variable which is a Tensor or LoDTensor. y (Variable): The input variable which is a Tensor or LoDTensor. transpose_x (bool): Whether to transpose :math:`x` before multiplication. transpose_y (bool): Whether to transpose :math:`y` before multiplication. alpha (float): The scale of output. Default 1.0. name(str|None): A name for this layer(optional). If set None, the layer will be named automatically. Returns: Variable: The product Tensor (or LoDTensor) variable. Examples: .. code-block:: python # Examples to clarify shapes of the inputs and output # x: [B, ..., M, K], y: [B, ..., K, N] # fluid.layers.matmul(x, y) # out: [B, ..., M, N] # x: [B, M, K], y: [B, K, N] # fluid.layers.matmul(x, y) # out: [B, M, N] # x: [B, M, K], y: [K, N] # fluid.layers.matmul(x, y) # out: [B, M, N] # x: [M, K], y: [K, N] # fluid.layers.matmul(x, y) # out: [M, N] # x: [B, M, K], y: [K] # fluid.layers.matmul(x, y) # out: [B, M] # x: [K], y: [K] # fluid.layers.matmul(x, y) # out: [1] # x: [M], y: [N] # fluid.layers.matmul(x, y, True, True) # out: [M, N] import paddle import paddle.fluid as fluid paddle.enable_static() x = fluid.layers.data(name='x', shape=[2, 3], dtype='float32') y = fluid.layers.data(name='y', shape=[3, 2], dtype='float32') out = fluid.layers.matmul(x, y, True, True) """ if _non_static_mode(): out = _varbase_creator(dtype=x.dtype) _legacy_C_ops.matmul( x, y, out, 'transpose_X', transpose_x, 'transpose_Y', transpose_y, 'alpha', float(alpha), ) return out def __check_input(x, y): var_names = {'x': x, 'y': y} for name, val in var_names.items(): check_variable_and_dtype( val, name, ['float16', 'float32', 'float64'], 'matmul' ) x_shape = list(x.shape) y_shape = list(y.shape) if len(x_shape) == 1: x_shape = [1] + x_shape if len(y_shape) == 1: y_shape = y_shape + [1] # check the inner 2 dimensions if transpose_x: x_shape[-2], x_shape[-1] = x_shape[-1], x_shape[-2] if transpose_y: y_shape[-2], y_shape[-1] = y_shape[-1], y_shape[-2] if x_shape[-1] != y_shape[-2]: assert (x_shape[-1] == -1) or (y_shape[-2] == -1), ( "After performing an optional transpose, Input X's width should be " "equal to Y's width for multiplication " "prerequisites. But received X's shape: %s, Y's shape: %s\n" % (x_shape, y_shape) ) if len(y_shape) > 2 and len(x_shape) > 2: for i, dim_x in enumerate(x_shape[:-2]): # don't check neg shape if dim_x < 0 or y_shape[i] < 0: continue if dim_x != y_shape[i]: raise ValueError( "When the matrix is larger than 2 dimensions, the higher " "dimensional values of the two matrices need to be equal. " "But received x_shape[%d] != y_shape[%d]. X's shape: %s, " "Y's shape: %s.\n" % (i, i, x_shape, y_shape) ) attrs = { 'transpose_X': transpose_x, 'transpose_Y': transpose_y, 'alpha': float(alpha), } __check_input(x, y) helper = LayerHelper('matmul', **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='matmul', inputs={'X': x, 'Y': y}, outputs={'Out': out}, attrs=attrs, ) return out def topk(input, k, name=None): """ :alias_main: paddle.topk :alias: paddle.topk,paddle.tensor.topk,paddle.tensor.search.topk :old_api: paddle.fluid.layers.topk This OP is used to find values and indices of the k largest entries for the last dimension. If the input is a 1-D Tensor, finds the k largest entries and outputs their values and indices. If the input is a Tensor with higher rank, this operator computes the top k entries along the last dimension. .. code-block:: text Case 1: Input: input.shape = [3, 4] input.data = [[5, 4, 2, 3], [9, 7, 10, 25], [6, 2, 10, 1]] k = 2 Output: The first output: values.shape = [3, 2] values.data = [[5, 4], [10, 25], [6, 10]] The second output: indices.shape = [3, 2] indices.data = [[0, 1], [2, 3], [0, 2]] Args: input(Variable): The input tensor. Support data types: float32, float64. k(int | Variable): The number of top elements to look for along the last dimension of input tensor. name (str, optional): Please refer to :ref:`api_guide_Name`, Default None. Returns: Values (Variable): Input tensor's k largest elements along each last dimensional slice. The dimension is: :math:`input.shape[:-1]+[k]`. Indices (Variable): Indices of k largest elements alone the last dimension of input. The dimension is same as values. Raises: ValueError: If :math:`k < 1` or :math:`k > last dimension of input`. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.layers as layers # set batch size=None input = fluid.data(name="input", shape=[None, 13, 11], dtype='float32') top5_values, top5_indices = layers.topk(input, k=5) # top5_values.shape[None, 13, 5], top5_indices.shape=[None, 13, 5] # 1D Tensor input1 = fluid.data(name="input1", shape=[None, 13], dtype='float32') top5_values, top5_indices = layers.topk(input1, k=5) #top5_values.shape=[None, 5], top5_indices.shape=[None, 5] # k=Variable input2 = fluid.data(name="input2", shape=[None, 13, 11], dtype='float32') vk = fluid.data(name="vk", shape=[None, 1], dtype='int32') # save k in vk.data[0] vk_values, vk_indices = layers.topk(input2, k=vk) #vk_values.shape=[None, 13, k], vk_indices.shape=[None, 13, k] """ if _non_static_mode(): _k = k.numpy().item(0) if isinstance(k, Variable) else k out, indices = _legacy_C_ops.top_k(input, 'k', _k) out.stop_gradient = True indices.stop_gradient = True return out, indices inputs = {"X": [input]} attrs = {} if isinstance(k, Variable): inputs['K'] = [k] else: attrs = {'k': k} helper = LayerHelper("top_k", **locals()) values = helper.create_variable_for_type_inference(dtype=input.dtype) indices = helper.create_variable_for_type_inference(dtype="int64") helper.append_op( type="top_k", inputs=inputs, outputs={"Out": [values], "Indices": [indices]}, attrs=attrs, ) values.stop_gradient = True indices.stop_gradient = True return values, indices def ctc_greedy_decoder( input, blank, input_length=None, padding_value=0, name=None ): r""" This op is used to decode sequences by greedy policy by the following steps: 1. Get the indexes of maximum value for each row in input. a.k.a. numpy.argmax(input, axis=0). 2. For each sequence in result of step1, merge repeated tokens between two blanks and delete all blanks. This op is implemented in two modes: lod and padding, either of them can be used. The input can be either LoDTensor or Tensor, corresponding to lod and padding mode respectively. A simple example as below: .. code-block:: text Given: (1) for lod mode: input.data = [[0.6, 0.1, 0.3, 0.1], [0.3, 0.2, 0.4, 0.1], [0.1, 0.5, 0.1, 0.3], [0.5, 0.1, 0.3, 0.1], [0.5, 0.1, 0.3, 0.1], [0.2, 0.2, 0.2, 0.4], [0.2, 0.2, 0.1, 0.5], [0.5, 0.1, 0.3, 0.1]] input.lod = [[4, 4]] Computation: step1: Apply argmax to first input sequence which is input.data[0:4]. Then we get: [[0], [2], [1], [0]] step2: merge repeated tokens and remove blank which is 0. Then we get first output sequence: [[2], [1]] Finally: output.data = [[2], [1], [3]] output.lod = [[2, 1]] (2) for padding mode: input.data = [[[0.6, 0.1, 0.3, 0.1], [0.3, 0.2, 0.4, 0.1], [0.1, 0.5, 0.1, 0.3], [0.5, 0.1, 0.3, 0.1]], [[0.5, 0.1, 0.3, 0.1], [0.2, 0.2, 0.2, 0.4], [0.2, 0.2, 0.1, 0.5], [0.5, 0.1, 0.3, 0.1]]] input_length.data = [[4], [4]] input.shape = [2, 4, 4] step1: Apply argmax to first input sequence which is input.data[0:4]. Then we get: [[0], [2], [1], [0]], for input.data[4:8] is [[0], [3], [3], [0]], shape is [2,4,1] step2: Change the argmax result to use padding mode, then argmax result is [[0, 2, 1, 0], [0, 3, 3, 0]], shape is [2, 4], lod is [], input_length is [[4], [4]] step3: Apply ctc_align to padding argmax result, padding_value is 0 Finally: output.data = [[2, 1, 0, 0], [3, 0, 0, 0]] output_length.data = [[2], [1]] Parameters: input(Variable): the probabilities of variable-length sequences. When in lod mode, it is a 2-D LoDTensor with LoD information. It's shape is [Lp, num_classes + 1] where Lp is the sum of all input sequences' length and num_classes is the true number of classes. When in padding mode, it is a 3-D Tensor with padding, It's shape is [batch_size, N, num_classes + 1]. (not including the blank label). The data type can be float32 or float64. blank(int): the blank label index of Connectionist Temporal Classification (CTC) loss, which is in the half-opened interval [0, num_classes + 1). input_length(Variable, optional): 2-D LoDTensor, shape is [batch_size, 1], data type is int64. It is used for padding mode. In lod mode, input_length is None. padding_value(int): padding value. 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: For lod mode, returns the result of CTC greedy decoder, 2-D LoDTensor, shape is [Lp, 1], \ data type is int64. 'Lp' is the sum of all output sequences' length. If all the sequences \ in result were empty, the result LoDTensor will be [-1] with empty \ LoD [[]]. For padding mode, returns a tuple of (output, output_length), which was described as below: output, 2-D Tensor, shape is [batch_size, N], data type is int64. output_length, 2-D Tensor, shape is [batch_size, 1], data type is int64. It is the length of \ each sequence of output for padding mode. Return type: For lod mode: Variable For padding mode: tuple of two Variables (output, output_length). Examples: .. code-block:: python # for lod mode import paddle.fluid as fluid x = fluid.data(name='x', shape=[None, 8], dtype='float32', lod_level=1) cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0) # for padding mode x_pad = fluid.data(name='x_pad', shape=[10, 4, 8], dtype='float32') x_pad_len = fluid.data(name='x_pad_len', shape=[10, 1], dtype='int64') out, out_len = fluid.layers.ctc_greedy_decoder(input=x_pad, blank=0, input_length=x_pad_len) """ check_variable_and_dtype( input, 'input', ['float32', 'float64'], 'ctc_greedy_decoder' ) helper = LayerHelper("ctc_greedy_decoder", **locals()) _, topk_indices = topk(input, k=1) # ctc align op ctc_out = helper.create_variable_for_type_inference(dtype="int64") if input_length is None: helper.append_op( type="ctc_align", inputs={"Input": [topk_indices]}, outputs={"Output": [ctc_out]}, attrs={"merge_repeated": True, "blank": blank}, ) return ctc_out else: ctc_out_len = helper.create_variable_for_type_inference(dtype="int64") ctc_input = paddle.squeeze(topk_indices, [2]) helper.append_op( type="ctc_align", inputs={"Input": [ctc_input], "InputLength": [input_length]}, outputs={"Output": [ctc_out], "OutputLength": [ctc_out_len]}, attrs={ "merge_repeated": True, "blank": blank, "padding_value": padding_value, }, ) return ctc_out, ctc_out_len def im2sequence( input, filter_size=1, stride=1, padding=0, input_image_size=None, out_stride=1, name=None, ): r""" :api_attr: Static Graph Extracts image patches from the input tensor to form a tensor of shape {input.batch_size * output_height * output_width, filter_size_height * filter_size_width * input.channels}. This op use filter to scan images and convert these images to sequences. After expanding, the number of time step are output_height * output_width for an image, in which output_height and output_width are calculated by below equation: .. math:: output\_height = 1 + \ (padding\_up + padding\_down + input\_height - filter\_size\_height + stride\_height - 1) / stride\_height \\\\ output\_width = 1 + \ (padding\_left + padding\_right + input\_width - filter\_size\_width + stride\_width - 1) / stride\_width And the dimension of each time step is filter_size_height * filter_size_width * input.channels. Parameters: input (Variable): The input should be a 4-D Tensor in :math:`NCHW` format. The data type is float32. filter_size(int32 | List[int32]): The filter size. If filter_size is a List, it must contain two integers, :math:`[filter\_size\_height, filter\_size\_width]` . Otherwise, the filter size will be a square :math:`[filter\_size, filter\_size]` . Default is 1. stride(int32 | List[int32]): The stride size. If stride is a List, it must contain two integers, :math:`[stride\_height, stride\_width]` . Otherwise, the stride size will be a square :math:`[stride\_size, stride\_size]` . Default is 1. padding(int32 | List[int32]): The padding size. If padding is a List, it can contain four integers like :math:`[padding\_up, padding\_left, padding\_down, padding\_right]` to indicate paddings of four direction. Or it can contain two integers :math:`[padding\_height, padding\_width]` which means padding_up = padding_down = padding_height and padding_left = padding_right = padding_width. Otherwise, a scalar padding means padding_up = padding_down = padding_left = padding_right = padding. Default is 0. input_image_size(Variable, optional): the input contains image real size.It's dim is :math:`[batchsize, 2]` . It is just for batch inference when not None. Default is None. out_stride(int32 | List[int32]): The scaling of image through CNN. It is valid only when input_image_size is not None. If out_stride is List, it must contain two integers, :math:`[out\_stride\_height, out\_stride\_W]` . Otherwise, the out_stride_height = out_stride_width = out_stride. Default is 1. 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: The output is a 2-D LoDTensor with shape {input.batch\_size * output\_height * output\_width, \ filter\_size\_height * filter\_size\_width * input.channels}. The data type is float32. Return Type: Variable Examples: .. code-block:: text Given: x = [[[[ 6. 2. 1.] [ 8. 3. 5.] [ 0. 2. 6.]] [[ 2. 4. 4.] [ 6. 3. 0.] [ 6. 4. 7.]]] [[[ 6. 7. 1.] [ 5. 7. 9.] [ 2. 4. 8.]] [[ 1. 2. 1.] [ 1. 3. 5.] [ 9. 0. 8.]]]] x.dims = {2, 2, 3, 3} And: filter = [2, 2] stride = [1, 1] padding = [0, 0] Then: output.data = [[ 6. 2. 8. 3. 2. 4. 6. 3.] [ 2. 1. 3. 5. 4. 4. 3. 0.] [ 8. 3. 0. 2. 6. 3. 6. 4.] [ 3. 5. 2. 6. 3. 0. 4. 7.] [ 6. 7. 5. 7. 1. 2. 1. 3.] [ 7. 1. 7. 9. 2. 1. 3. 5.] [ 5. 7. 2. 4. 1. 3. 9. 0.] [ 7. 9. 4. 8. 3. 5. 0. 8.]] output.dims = {8, 8} output.lod = [[4, 4]] Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32') output = fluid.layers.im2sequence( input=data, stride=[1, 1], filter_size=[2, 2]) """ assert ( not _non_static_mode() ), "sequence layer is not supported in dygraph mode yet." check_variable_and_dtype(input, 'input', ['float32'], 'im2sequence') if isinstance(filter_size, int): filter_size = [filter_size, filter_size] if isinstance(stride, int): stride = [stride, stride] if isinstance(padding, int): padding = [padding, padding] if len(padding) == 2: padding.append(padding[0]) padding.append(padding[1]) inputs = {"X": input} attrs = {"kernels": filter_size, "strides": stride, "paddings": padding} if input_image_size: if isinstance(out_stride, int): out_stride = [out_stride, out_stride] inputs["Y"] = input_image_size attrs["out_stride"] = out_stride helper = LayerHelper('im2sequence', **locals()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) helper.append_op( type='im2sequence', inputs=inputs, outputs={'Out': out}, attrs=attrs ) return out @templatedoc() def row_conv(input, future_context_size, param_attr=None, act=None): """ :api_attr: Static Graph ${comment} Args: input (${x_type}): ${x_comment}. future_context_size (int): Future context size. Please note, the shape of convolution kernel is [future_context_size + 1, D]. param_attr (ParamAttr): Attributes of parameters, including name, initializer etc. act (str): Non-linear activation to be applied to output variable. Returns: ${out_comment}. Examples: .. code-block:: python # for LodTensor inputs import paddle paddle.enable_static() x = paddle.static.data(name='x', shape=[9, 16], dtype='float32', lod_level=1) out = paddle.static.nn.row_conv(input=x, future_context_size=2) # for Tensor inputs x = paddle.static.data(name='x', shape=[9, 4, 16], dtype='float32') out = paddle.static.nn.row_conv(input=x, future_context_size=2) """ helper = LayerHelper('row_conv', **locals()) check_variable_and_dtype(input, 'input', ['float32'], 'row_conv') dtype = helper.input_dtype() filter_shape = [future_context_size + 1, input.shape[-1]] filter_param = helper.create_parameter( attr=helper.param_attr, shape=filter_shape, dtype=dtype ) out = helper.create_variable_for_type_inference(dtype) helper.append_op( type='row_conv', inputs={'X': [input], 'Filter': [filter_param]}, outputs={'Out': [out]}, ) return helper.append_activation(out) @templatedoc() def multiplex(inputs, index, name=None): """ Based on the given index parameter, the OP selects a specific row from each input Tensor to construct the output Tensor. If the input of this OP contains :math:`m` Tensors, where :math:`I_{i}` means the i-th input Tensor, :math:`i` between :math:`[0,m)` . And :math:`O` means the output, where :math:`O[i]` means the i-th row of the output, then the output satisfies that :math:`O[i] = I_{index[i]}[i]` . For Example: .. code-block:: text Given: inputs = [[[0,0,3,4], [0,1,3,4], [0,2,4,4], [0,3,3,4]], [[1,0,3,4], [1,1,7,8], [1,2,4,2], [1,3,3,4]], [[2,0,3,4], [2,1,7,8], [2,2,4,2], [2,3,3,4]], [[3,0,3,4], [3,1,7,8], [3,2,4,2], [3,3,3,4]]] index = [[3],[0],[1],[2]] out = [[3,0,3,4], # out[0] = inputs[index[0]][0] = inputs[3][0] = [3,0,3,4] [0,1,3,4], # out[1] = inputs[index[1]][1] = inputs[0][1] = [0,1,3,4] [1,2,4,2], # out[2] = inputs[index[2]][2] = inputs[1][2] = [1,2,4,2] [2,3,3,4]] # out[3] = inputs[index[3]][3] = inputs[2][3] = [2,3,3,4] Args: inputs (list): The input Tensor list. The list elements are N-D Tensors of data types float32, float64, int32, int64. All input Tensor shapes should be the same and rank must be at least 2. index (Tensor): Used to select some rows in the input Tensor to construct an index of the output Tensor. It is a 2-D Tensor with data type int32 or int64 and shape [M, 1], where M is the number of input Tensors. 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: Tensor: Output of multiplex OP, with data type being float32, float64, int32, int64. Examples: .. code-block:: python import paddle import numpy as np img1 = np.array([[1, 2], [3, 4]]).astype(np.float32) img2 = np.array([[5, 6], [7, 8]]).astype(np.float32) inputs = [paddle.to_tensor(img1), paddle.to_tensor(img2)] index = paddle.to_tensor(np.array([[1], [0]]).astype(np.int32)) res = paddle.multiplex(inputs, index) print(res) # [array([[5., 6.], [3., 4.]], dtype=float32)] """ if _in_legacy_dygraph(): return _legacy_C_ops.multiplex(index, inputs) if in_dygraph_mode(): return _C_ops.multiplex(inputs, index) helper = LayerHelper('multiplex', **locals()) check_type(inputs, 'inputs', (list), 'multiplex') if len(inputs) < 2: raise ValueError( "inputs should be a list object with at least 2 elements." ) for id, x in enumerate(inputs): check_variable_and_dtype( x, 'input[' + str(id) + ']', ['float32', 'float64', 'int32', 'int64'], 'multiplex', ) check_variable_and_dtype(index, "index", ['int32', 'int64'], 'multiplex') out = helper.create_variable_for_type_inference(inputs[0].dtype) helper.append_op( type='multiplex', inputs={'X': inputs, 'Ids': index}, outputs={'Out': [out]}, ) return out def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None): """ This layer computes the smooth L1 loss for Variable :attr:`x` and :attr:`y`. It takes the first dimension of :attr:`x` and :attr:`y` as batch size. For each instance, it computes the smooth L1 loss element by element first and then sums all the losses. So the shape of output Variable is [batch_size, 1]. Args: x (Variable): A tensor with rank at least 2. The input value of smooth L1 loss op with shape [batch_size, dim1, ..., dimN]. A LoDTensor or Tensor with type float32. y (Variable): A tensor with rank at least 2. The target value of smooth L1 loss op with same shape as :attr:`x`. A LoDTensor or Tensor with type float32. inside_weight (Variable|None): A tensor with rank at least 2. This input is optional and should have same shape with :attr:`x`. If provided, the result of (:attr:`x` - :attr:`y`) will be multiplied by this tensor element by element. A Tensor with type float32. outside_weight (Variable|None): A tensor with rank at least 2. This input is optional and should have same shape with :attr:`x`. If provided, the out smooth L1 loss will be multiplied by this tensor element by element. A Tensor with type float32. sigma (float|None): Hyper parameter of smooth L1 loss layer. A float scalar with default value 1.0. Returns: Variable: The output smooth L1 loss with shape [batch_size, 1]. A Tensor with type float32. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle paddle.enable_static() data = fluid.data(name="x", shape=[-1, 3], dtype="float32") label = fluid.data(name="y", shape=[-1, 3], dtype="float32") result = fluid.layers.smooth_l1(data,label) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) x = np.random.rand(3,3).astype("float32") y = np.random.rand(3,3).astype("float32") output= exe.run(feed={"x":x, "y":y}, fetch_list=[result]) print(output) #[array([[0.08220536], # [0.36652038], # [0.20541131]], dtype=float32)] """ check_variable_and_dtype(x, 'X', ['float32', 'float64'], 'smooth_l1_loss') check_variable_and_dtype(y, 'Y', ['float32', 'float64'], 'smooth_l1_loss') helper = LayerHelper('smooth_l1_loss', **locals()) diff = helper.create_variable_for_type_inference(dtype=x.dtype) loss = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='smooth_l1_loss', inputs={ 'X': x, 'Y': y, 'InsideWeight': inside_weight, 'OutsideWeight': outside_weight, }, outputs={'Diff': diff, 'Out': loss}, attrs={'sigma': sigma if sigma is not None else 1.0}, ) return loss @deprecated(since='2.0.0', update_to='paddle.nn.functional.one_hot') def one_hot(input, depth, allow_out_of_range=False): """ **WARING:** This OP requires the last dimension of Tensor shape must be equal to 1. This OP will be deprecated in a future release. It is recommended to use fluid. :ref:`api_fluid_one_hot` . The operator converts each id in the input to an one-hot vector with a :attr:`depth` length. The value in the vector dimension corresponding to the id is 1, and the value in the remaining dimension is 0. The shape of output Tensor or LoDTensor is generated by adding :attr:`depth` dimension behind the last dimension of the input shape. .. code-block:: text Example 1 (allow_out_of_range=False): input: X.shape = [4, 1] X.data = [[1], [1], [3], [0]] depth = 4 output: Out.shape = [4, 4] Out.data = [[0., 1., 0., 0.], [0., 1., 0., 0.], [0., 0., 0., 1.], [1., 0., 0., 0.]] Example 2 (allow_out_of_range=True): input: X.shape = [4, 1] X.data = [[1], [1], [5], [0]] depth = 4 allow_out_of_range = True output: Out.shape = [4, 4] Out.data = [[0., 1., 0., 0.], [0., 1., 0., 0.], [0., 0., 0., 0.], # This id is 5, which goes beyond depth, so set it all-zeros data. [1., 0., 0., 0.]] Example 3 (allow_out_of_range=False): input: X.shape = [4, 1] X.data = [[1], [1], [5], [0]] depth = 4 allow_out_of_range = False output: Throw an exception for Illegal value The second dimension in X is 5, which is greater than depth. Allow_out_of_range =False means that does not allow the word id to exceed depth, so it throws an exception. Args: input(Variable): Tensor or LoDTensor with shape :math:`[N_1, N_2, ..., N_k, 1]` , which contains at least one dimension and the last dimension must be 1. The data type is int32 or int64. depth(scalar): An integer defining the :attr:`depth` of the one hot dimension. If input is word id, depth is generally the dictionary size. allow_out_of_range(bool): A bool value indicating whether the input indices could be out of range :math:`[0, depth)` . When input indices are out of range, exceptions :code:`Illegal value` is raised if :attr:`allow_out_of_range` is False, or zero-filling representations is created if it is set True. Default: False. Returns: Variable: The one-hot representations of input. A Tensor or LoDTensor with type float32. Examples: .. code-block:: python import paddle import paddle.fluid as fluid paddle.enable_static() # Correspond to the first example above, where label.shape is [4, 1] and one_hot_label.shape is [4, 4]. label = fluid.data(name="label", shape=[4, 1], dtype="int64") one_hot_label = fluid.layers.one_hot(input=label, depth=4) """ if _non_static_mode(): if isinstance(depth, Variable): depth = depth.numpy() assert depth.shape == ( 1, ), "depth of type Variable should have shape [1]" depth = depth.item(0) out = _legacy_C_ops.one_hot( input, 'depth', depth, 'allow_out_of_range', allow_out_of_range ) out.stop_gradient = True return out helper = LayerHelper("one_hot", **locals()) check_variable_and_dtype(input, 'input', ['int32', 'int64'], 'one_hot') check_type(depth, 'depth', (int, Variable), 'one_hot') one_hot_out = helper.create_variable_for_type_inference(dtype='float32') if not isinstance(depth, Variable): # user attribute inputs = {'X': input} attrs = {'depth': depth, 'allow_out_of_range': allow_out_of_range} else: depth.stop_gradient = True inputs = {'X': input, 'depth_tensor': depth} attrs = {'allow_out_of_range': allow_out_of_range} helper.append_op( type="one_hot", inputs=inputs, attrs=attrs, outputs={'Out': one_hot_out} ) one_hot_out.stop_gradient = True return one_hot_out def autoincreased_step_counter(counter_name=None, begin=1, step=1): """ :api_attr: Static Graph Create an auto-increase variable. which will be automatically increased by 1 in every iteration. By default, the first return of this counter is 1, and the step size is 1. Args: counter_name(str, optional): The counter name. Default '@STEP_COUNTER@'. begin(int, optional): The first return value of this counter. Default 1. step(int, optional): The step size. Default 1. Returns: Variable: The auto-increased Variable with data type int64. Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() global_step = fluid.layers.autoincreased_step_counter( counter_name='@LR_DECAY_COUNTER@', begin=0, step=1) """ helper = LayerHelper('global_step_counter') if counter_name is None: counter_name = '@STEP_COUNTER@' counter, is_new_var = helper.create_or_get_global_variable( name=counter_name, dtype='int64', shape=[1], persistable=True, belong_to_optimizer=True, ) if is_new_var: helper.set_variable_initializer( counter, initializer=Constant(value=begin - 1, force_cpu=True) ) helper.main_program.global_block()._prepend_op( type='increment', inputs={'X': [counter]}, outputs={'Out': [counter]}, attrs={'step': float(step)}, ) counter.stop_gradient = True return counter def unsqueeze(input, axes, name=None): """ Insert single-dimensional entries to the shape of a Tensor. Takes one required argument axes, a list of dimensions that will be inserted. Dimension indices in axes are as seen in the output tensor. For example: .. code-block:: text Given a tensor such that tensor with shape [3, 4, 5], then Unsqueezed tensor with axes=[0, 4] has shape [1, 3, 4, 5, 1]. Args: input (Variable): The input Tensor to be unsqueezed. Supported data type: float32, float64, bool, int8, int32, int64. axes (int|list|tuple|Variable): Indicates the dimensions to be inserted. The data type is ``int32`` . If ``axes`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``axes`` is an Variable, it should be an 1-D Tensor . name (str|None): Name for this layer. Returns: Variable: Unsqueezed Tensor, with the same data type as input. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[5, 10]) y = fluid.layers.unsqueeze(input=x, axes=[1]) """ if _non_static_mode(): if isinstance(axes, int): axes = [axes] elif isinstance(axes, Variable): axes = axes.numpy().tolist() elif isinstance(axes, (list, tuple)): axes = [ item.numpy().item(0) if isinstance(item, Variable) else item for item in axes ] if _in_legacy_dygraph(): out, _ = _legacy_C_ops.unsqueeze2(input, 'axes', axes) return out return _C_ops.unsqueeze(input, axes) check_type(axes, 'axis/axes', (int, list, tuple, Variable), 'unsqueeze') check_variable_and_dtype( input, 'input', [ 'float16', 'float32', 'float64', 'bool', 'int8', 'int16', 'int32', 'int64', 'complex64', 'complex128', ], 'unsqueeze', ) helper = LayerHelper("unsqueeze2", **locals()) inputs = {"X": input} attrs = {} if isinstance(axes, int): axes = [axes] if isinstance(axes, Variable): axes.stop_gradient = True inputs["AxesTensor"] = axes elif isinstance(axes, (list, tuple)): if utils._contain_var(axes): inputs["AxesTensorList"] = utils._convert_to_tensor_list(axes) else: attrs["axes"] = axes out = helper.create_variable_for_type_inference(dtype=input.dtype) x_shape = helper.create_variable_for_type_inference(dtype=input.dtype) helper.append_op( type="unsqueeze2", inputs=inputs, attrs=attrs, outputs={"Out": out, "XShape": x_shape}, ) return out def lod_reset(x, y=None, target_lod=None): """ Set LoD of :attr:`x` to a new one specified by :attr:`y` or :attr:`target_lod`. When :attr:`y` provided, :attr:`y.lod` would be considered as target LoD first, otherwise :attr:`y.data` would be considered as target LoD. If :attr:`y` is not provided, target LoD should be specified by :attr:`target_lod`. If target LoD is specified by :attr:`y.data` or :attr:`target_lod`, only one level LoD is supported. .. code-block:: text * Example 1: Given a 1-level LoDTensor x: x.lod = [[ 2, 3, 1 ]] x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]] x.dims = [6, 1] target_lod: [4, 2] then we get a 1-level LoDTensor: out.lod = [[4, 2]] out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]] out.dims = [6, 1] * Example 2: Given a 1-level LoDTensor x: x.lod = [[2, 3, 1]] x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]] x.dims = [6, 1] y is a Tensor: y.data = [[2, 4]] y.dims = [1, 3] then we get a 1-level LoDTensor: out.lod = [[2, 4]] out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]] out.dims = [6, 1] * Example 3: Given a 1-level LoDTensor x: x.lod = [[2, 3, 1]] x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]] x.dims = [6, 1] y is a 2-level LoDTensor: y.lod = [[2, 2], [2, 2, 1, 1]] y.data = [[1.1], [2.1], [3.1], [4.1], [5.1], [6.1]] y.dims = [6, 1] then we get a 2-level LoDTensor: out.lod = [[2, 2], [2, 2, 1, 1]] out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]] out.dims = [6, 1] Args: x (Variable): Input variable which could be a Tensor or LoDTensor. The data type should be int32, int64, float32 or float64. y (Variable, optional): If provided, output's LoD would be derived from :attr:`y`. If y's lod level>0, the data type can be any type. If y's lod level=0, the data type should be int32. target_lod (list|tuple, optional): One level LoD which should be considered as target LoD when :attr:`y` not provided. Returns: Variable: Output variable with LoD specified by this layer. Raises: ValueError: If :attr:`y` and :attr:`target_lod` are both None. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[10]) y = fluid.layers.data(name='y', shape=[10, 20], lod_level=2) out = fluid.layers.lod_reset(x=x, y=y) """ check_variable_and_dtype( x, 'x', ['float32', 'float64', 'int32', 'int64'], 'lod_reset' ) helper = LayerHelper("lod_reset", **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) if y is not None: check_type(y, 'y', (Variable), 'lod_reset') # TODO: check y.lod_level = 0 dtype helper.append_op( type="lod_reset", inputs={'X': x, 'Y': y}, outputs={'Out': out} ) elif target_lod is not None: helper.append_op( type="lod_reset", inputs={'X': x}, attrs={'target_lod': target_lod}, outputs={'Out': out}, ) else: raise ValueError("y and target_lod should not be both none.") return out def pad(x, paddings, pad_value=0.0, name=None): r""" :alias_main: paddle.nn.functional.pad :alias: paddle.nn.functional.pad,paddle.nn.functional.common.pad :old_api: paddle.fluid.layers.pad This op will pad a tensor with a constant value given by :attr:`pad_value`, and the padded shape is specified by :attr:`paddings`. Specifically, the number of values padded before the elements of :attr:`x` in dimension :attr:`i` is indicated by :attr:`paddings[2*i]`, and the number of values padded after the elements of :attr:`x` in dimension :attr:`i` is indicated by :attr:`paddings[2*i+1]`. See below for an example. .. code-block:: text Given: x = [[1, 2], [3, 4]] paddings = [0, 1, 1, 2] pad_value = 0 Return: out = [[0, 1, 2, 0, 0] [0, 3, 4, 0, 0] [0, 0, 0, 0, 0]] Args: x (Variable): Tensor, data type is float32. paddings (list): A list of integers. Its elements specify the padded width before and after each dimension in turn. The length of :attr:`paddings` must be equal to :math:`rank(x) \\times 2`. pad_value (float): The constant value used to pad. 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: The padded tensor, with the same data type and rank as :attr:`x` Return Type: Variable Examples: .. code-block:: python # x is a rank 2 tensor variable import paddle.fluid as fluid x = fluid.data(name='data', shape=[300, 300], dtype='float32') out = fluid.layers.pad(x=x, paddings=[0, 1, 1, 2], pad_value=0.) """ check_variable_and_dtype( x, 'x', [ 'float16', 'float32', 'float64', 'int32', 'int64', 'complex64', 'complex128', ], "pad", ) check_type(pad_value, 'pad_value', (float, int, Variable), 'pad') if isinstance(pad_value, int): pad_value = float(pad_value) helper = LayerHelper('pad', **locals()) dtype = helper.input_dtype(input_param_name='x') out = helper.create_variable_for_type_inference(dtype) helper.append_op( type='pad', inputs={'X': x}, outputs={'Out': out}, attrs={'paddings': paddings, 'pad_value': pad_value}, ) return out def image_resize( input, out_shape=None, scale=None, name=None, resample='BILINEAR', actual_shape=None, align_corners=True, align_mode=1, data_format='NCHW', ): """ This op resizes a batch of images. The input must be a 3-D Tensor of the shape (num_batches, channels, in_w) or a 4-D Tensor of the shape (num_batches, channels, in_h, in_w) or (num_batches, in_h, in_w, channels), or a 5-D Tensor of the shape (num_batches, channels, in_d, in_h, in_w) or (num_batches, in_d, in_h, in_w, channels), and the resizing only applies on the three dimensions(depth, height and width). **Warning:** the parameter :attr:`actual_shape` will be deprecated in the future and only use :attr:`out_shape` instead. Supporting resample methods: 'LINEAR' : Linear interpolation 'BILINEAR' : Bilinear interpolation 'TRILINEAR' : Trilinear interpolation 'NEAREST' : Nearest neighbor interpolation 'BICUBIC' : Bicubic interpolation Linear interpolation is the method of using a line connecting two known quantities to determine the value of an unknown quantity between the two known quantities. Nearest neighbor interpolation is to perform nearest neighbor interpolation in both the 3rd dimension(in height direction) and the 4th dimension(in width direction) on input tensor. Bilinear interpolation is an extension of linear interpolation for interpolating functions of two variables (e.g. H-direction and W-direction in this op) on a rectilinear 2D grid. The key idea is to perform linear interpolation first in one direction, and then again in the other direction. Trilinear interpolation is an extension of linear interpolation for interpolating functions of three variables (e.g. D-direction, H-direction and W-direction in this op) on a rectilinear 3D grid. The linear interpolation is performed on three directions. Bicubic interpolation is an extension of cubic interpolation for interpolating data points on a two-dimensional regular grid. The interpolated surface is smoother than corresponding surfaces obtained by bilinear interpolation or nearest-neighbor interpolation. Align_corners and align_mode are optional parameters,the calculation method of interpolation can be selected by them. Example: .. code-block:: text For scale: if align_corners = True && out_size > 1 : scale_factor = (in_size-1.0)/(out_size-1.0) else: scale_factor = float(in_size/out_size) Nearest neighbor interpolation: if: align_corners = False input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = floor (H_{in} * scale_{factor}) W_out = floor (W_{in} * scale_{factor}) else: align_corners = True input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = round(H_{in} * scale_{factor}) W_out = round(W_{in} * scale_{factor}) linear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,W_in) output: (N,C,W_out) where: W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,W_in) output: (N,C,H_out,W_out) where: W_out = W_{in} * scale_{factor} Bilinear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = (H_{in}+0.5) * scale_{factor} - 0.5 W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = H_{in} * scale_{factor} W_out = W_{in} * scale_{factor} Trilinear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,D_in,H_in,W_in) output: (N,C,D_out,H_out,W_out) where: D_out = (D_{in}+0.5) * scale_{factor} - 0.5 H_out = (H_{in}+0.5) * scale_{factor} - 0.5 W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,D_in,H_in,W_in) output: (N,C,D_out,H_out,W_out) where: D_out = D_{in} * scale_{factor} Trilinear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,D_in,H_in,W_in) output: (N,C,D_out,H_out,W_out) where: D_out = (D_{in}+0.5) * scale_{factor} - 0.5 H_out = (H_{in}+0.5) * scale_{factor} - 0.5 W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,D_in,H_in,W_in) output: (N,C,D_out,H_out,W_out) where: D_out = D_{in} * scale_{factor} H_out = H_{in} * scale_{factor} W_out = W_{in} * scale_{factor} For details of linear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Linear_interpolation. For details of nearest neighbor interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation. For details of bilinear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Bilinear_interpolation. For details of trilinear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Trilinear_interpolation. For details of bicubic interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Bicubic_interpolation Parameters: input (Variable): 3-D, 4-D or 5-D Tensor, its data type is float32, float64, or uint8, its data format is specified by :attr:`data_format`. out_shape (list|tuple|Variable|None): Output shape of image resize layer, the shape is (out_w, ) when input is a 3-D Tensor, the shape is (out_h, out_w) when input is a 4-D Tensor and is (out_d, out_h, out_w) when input is a 5-D Tensor. Default: None. If a list, each element can be an integer or a Tensor Variable of shape: [1]. If a Tensor Variable, its dimensions size should be a 1. scale(float|Variable|None): The multiplier for the input height or width. At least one of :attr:`out_shape` or :attr:`scale` must be set. And :attr:`out_shape` has a higher priority than :attr:`scale`. Default: None. name(str|None): A name for this layer(optional). If set None, the layer will be named automatically. resample(str): The resample method. It supports 'LINEAR', 'BICUBIC', 'BILINEAR', 'TRILINEAR' and 'NEAREST' currently. Default: 'BILINEAR' actual_shape(Variable): An optional input to specify output shape dynamically. If provided, image resize according to this given shape rather than :attr:`out_shape` and :attr:`scale` specifying shape. That is to say actual_shape has the highest priority. It is recommended to use :attr:`out_shape` if you want to specify output shape dynamically, because :attr:`actual_shape` will be deprecated. When using actual_shape to specify output shape, one of :attr:`out_shape` and :attr:`scale` should also be set, otherwise errors would be occurred in graph constructing stage. Default: None align_corners(bool) : An optional bool, If True, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Default: True align_mode(int) : An optional for linear/bilinear/trilinear interpolation. Refer to the fomula in the the example code above, it can be \'0\' for src_idx = scale*(dst_indx+0.5)-0.5 , can be \'1\' for src_idx = scale*dst_index. data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from:`NCW`, `NWC`, `"NCHW"`, `"NHWC"`, `"NCDHW"`, `"NDHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`. Returns: A 3-D Tensor of the shape (num_batches, channels, out_w) or (num_batches, out_w, channels), A 4-D Tensor of the shape (num_batches, channels, out_h, out_w) or (num_batches, out_h, out_w, channels), or 5-D Tensor of the shape (num_batches, channels, out_d, out_h, out_w) or (num_batches, out_d, out_h, out_w, channels). Raises: TypeError: out_shape should be a list or tuple or Variable. TypeError: actual_shape should either be Variable or None. ValueError: The 'resample' of image_resize can only be 'LINEAR', 'BILINEAR', 'TRILINEAR', 'BICUBIC' or 'NEAREST' currently. ValueError: 'LINEAR' only support 3-D tensor. ValueError: 'BICUBIC', 'BILINEAR' and 'NEAREST' only support 4-D tensor. ValueError: 'TRILINEAR' only support 5-D tensor. ValueError: One of out_shape and scale must not be None. ValueError: out_shape length should be 1 for input 3-D tensor. ValueError: out_shape length should be 2 for input 4-D tensor. ValueError: out_shape length should be 3 for input 5-D tensor. ValueError: scale should be greater than zero. TypeError: align_corners should be a bool value ValueError: align_mode can only be '0' or '1' ValueError: data_format can only be 'NCW', 'NWC', 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'. Examples: .. code-block:: python #declarative mode import paddle import paddle.fluid as fluid import numpy as np paddle.enable_static() input = fluid.data(name="input", shape=[None,3,6,10]) #1 output = fluid.layers.image_resize(input=input,out_shape=[12,12]) #2 #x = np.array([2]).astype("int32") #dim1 = fluid.data(name="dim1", shape=[1], dtype="int32") #fluid.layers.assign(input=x, output=dim1) #output = fluid.layers.image_resize(input=input,out_shape=[12,dim1]) #3 #x = np.array([3,12]).astype("int32") #shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32") #fluid.layers.assign(input=x, output=shape_tensor) #output = fluid.layers.image_resize(input=input,out_shape=shape_tensor) #4 #x = np.array([0.5]).astype("float32") #scale_tensor = fluid.data(name="scale", shape=[1], dtype="float32") #fluid.layers.assign(x,scale_tensor) #output = fluid.layers.image_resize(input=input,scale=scale_tensor) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) input_data = np.random.rand(2,3,6,10).astype("float32") output_data = exe.run(fluid.default_main_program(), feed={"input":input_data}, fetch_list=[output], return_numpy=True) print(output_data[0].shape) #1 # (2, 3, 12, 12) #2 # (2, 3, 12, 2) #3 # (2, 3, 3, 12) #4 # (2, 3, 3, 5) #imperative mode import paddle.fluid.dygraph as dg with dg.guard(place) as g: input = dg.to_variable(input_data) output = fluid.layers.image_resize(input=input, out_shape=[12,12]) print(output.shape) # [2L, 3L, 12L, 12L] """ resample_methods = { 'LINEAR': 'linear', 'BILINEAR': 'bilinear', 'TRILINEAR': 'trilinear', 'NEAREST': 'nearest', 'LINEAR': 'linear', } resample = resample.upper() if resample not in resample_methods: raise ValueError( "The 'resample' of image_resize can only be 'LINEAR', 'BILINEAR', 'TRILINEAR' " "or 'NEAREST' currently." ) resample_type = resample_methods[resample] if resample == 'LINEAR' and len(input.shape) != 3: raise ValueError("'LINER only support 3-D tensor.") elif resample in ['BILINEAR', 'NEAREST'] and len(input.shape) != 4: raise ValueError("'BILINEAR' and 'NEAREST' only support 4-D tensor.") elif resample == 'TRILINEAR' and len(input.shape) != 5: raise ValueError("'TRILINEAR'only support 5-D tensor.") if not isinstance(align_corners, bool): raise TypeError("Attr align_corners should be a bool value") if align_mode != 0 and align_mode != 1: raise ValueError("align_mode can only be 0 or 1") if out_shape is None and scale is None: raise ValueError("One of out_shape and scale must not be None.") helper = LayerHelper('{}_interp'.format(resample_type), **locals()) dtype = helper.input_dtype() if len(input.shape) == 3 and data_format not in ['NCW', 'NWC']: raise ValueError( "Got wrong value for param `data_format`: " + data_format + " received but only `NCW` or `NWC` supported for 3-D input." ) elif len(input.shape) == 4 and data_format not in ['NCHW', 'NHWC']: raise ValueError( "Got wrong value for param `data_format`: " + data_format + " received but only `NCHW` or `NHWC` supported for 4-D input." ) elif len(input.shape) == 5 and data_format not in ['NCDHW', 'NDHWC']: raise ValueError( "Got wrong value for param `data_format`: " + data_format + " received but only `NCDHW` or `NDHWC` supported for 5-D input." ) def _is_list_or_turple_(data): return isinstance(data, list) or isinstance(data, tuple) if data_format == 'NCHW' or data_format == 'NCDHW' or data_format == 'NCW': data_layout = 'NCHW' if data_format == 'NHWC' or data_format == 'NDHWC' or data_format == 'NWC': data_layout = 'NHWC' inputs = {"X": input} attrs = { "out_d": -1, "out_h": -1, "out_w": -1, "interp_method": resample_type, "align_corners": align_corners, "align_mode": align_mode, "data_layout": data_layout, } if out_shape is not None: if isinstance(out_shape, Variable) and not _non_static_mode(): out_shape.stop_gradient = True inputs['OutSize'] = out_shape else: if _non_static_mode(): if isinstance(out_shape, Variable): out_shape = list(out_shape.numpy()) else: out_shape = list(out_shape) for i, dim in enumerate(out_shape): if isinstance(dim, Variable): out_shape[i] = dim.numpy()[0] if not (_is_list_or_turple_(out_shape)): raise TypeError( "out_shape should be a list or tuple or Variable." ) # Validate the shape contain_var = False for dim_idx, dim_size in enumerate(out_shape): if isinstance(dim_size, Variable): contain_var = True continue assert ( dim_size > 0 ), "Each dimension size given in out_shape must be greater than 0." if contain_var: new_size_tensor = [] size_list = [] for dim in out_shape: if isinstance(dim, Variable): dim.stop_gradient = True new_size_tensor.append(dim) size_list.append(-1) else: assert isinstance(dim, int) temp_out = helper.create_variable_for_type_inference( 'int32' ) fill_constant( [1], 'int32', dim, force_cpu=True, out=temp_out ) new_size_tensor.append(temp_out) size_list.append(dim) inputs['SizeTensor'] = new_size_tensor if len(input.shape) == 3: if len(out_shape) != 1: raise ValueError( "out_shape length should be 1 for " "input 3-D tensor." ) if contain_var: attrs['out_w'] = size_list[0] else: out_shape = list(map(int, out_shape)) attrs['out_w'] = out_shape[0] elif len(input.shape) == 4: if len(out_shape) != 2: raise ValueError( "out_shape length should be 2 for " "input 4-D tensor." ) if contain_var: attrs['out_h'] = size_list[0] attrs['out_w'] = size_list[1] else: out_shape = list(map(int, out_shape)) attrs['out_h'] = out_shape[0] attrs['out_w'] = out_shape[1] if len(input.shape) == 5: if len(out_shape) != 3: raise ValueError( "out_shape length should be 3 for " "input 5-D tensor." ) if contain_var: attrs['out_d'] = size_list[0] attrs['out_h'] = size_list[1] attrs['out_w'] = size_list[2] else: out_shape = list(map(int, out_shape)) attrs['out_d'] = out_shape[0] attrs['out_h'] = out_shape[1] attrs['out_w'] = out_shape[2] else: if _non_static_mode() and isinstance(scale, Variable): scale = scale.numpy() elif isinstance(scale, Variable): scale.stop_gradient = True inputs["Scale"] = scale elif isinstance(scale, float) or isinstance(scale, int): if scale <= 0: raise ValueError("Attr(scale) should be greater than zero.") attrs['scale'] = float(scale) else: raise TypeError( "Attr(scale)'s type should be float, int or Variable." ) if isinstance(actual_shape, Variable): warnings.warn( "actual_shape will be deprecated, it is recommended to use " "out_shape instead of actual_shape to specify output shape dynamically." ) actual_shape.stop_gradient = True inputs["OutSize"] = actual_shape elif actual_shape is not None: raise TypeError("actual_shape should either be Variable or None.") if _non_static_mode(): attr_list = [] for k, v in attrs.items(): attr_list.append(k) attr_list.append(v) dy_attr = tuple(attr_list) if resample_type == "linear": out = _legacy_C_ops.linear_interp(input, actual_shape, *dy_attr) elif resample_type == "bilinear": out = _legacy_C_ops.bilinear_interp(input, actual_shape, *dy_attr) elif resample_type == "trilinear": out = _legacy_C_ops.trilinear_interp(input, actual_shape, *dy_attr) elif resample_type == "nearest": out = _legacy_C_ops.nearest_interp(input, actual_shape, *dy_attr) elif resample_type == "bicubic": out = _legacy_C_ops.bicubic_interp(input, actual_shape, *dy_attr) return out out = helper.create_variable_for_type_inference(dtype) helper.append_op( type='{}_interp'.format(resample_type), inputs=inputs, outputs={"Out": out}, attrs=attrs, ) return out @templatedoc(op_type="bilinear_interp") def resize_bilinear( input, out_shape=None, scale=None, name=None, actual_shape=None, align_corners=True, align_mode=1, data_format='NCHW', ): """ This op resizes the input by performing bilinear interpolation based on given output shape which specified by actual_shape, out_shape and scale in priority order. **Warning:** the parameter :attr:`actual_shape` will be deprecated in the future and only use :attr:`out_shape` instead. Bilinear interpolation is an extension of linear interpolation for interpolating functions of two variables (e.g. H-direction and W-direction in this op) on a rectilinear 2D grid. The key idea is to perform linear interpolation first in one direction, and then again in the other direction. For details of bilinear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Bilinear_interpolation Align_corners and align_mode are optional parameters,the calculation method of interpolation can be selected by them. Example: .. code-block:: text For scale: if align_corners = True && out_size > 1 : scale_factor = (in_size-1.0)/(out_size-1.0) else: scale_factor = float(in_size/out_size) Bilinear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = (H_{in}+0.5) * scale_{factor} - 0.5 W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = H_{in} * scale_{factor} W_out = W_{in} * scale_{factor} Parameters: input(Variable): 4-D Tensor(NCHW), its data type is float32, float64, or uint8, its data format is specified by :attr:`data_format`. out_shape(list|tuple|Variable|None): Output shape of resize bilinear layer, the shape is (out_h, out_w).Default: None. If a list, each element can be an integer or a Tensor Variable with shape: [1]. If a Tensor Variable, its dimension size should be 1. scale(float|Variable|None): The multiplier for the input height or width. At least one of :attr:`out_shape` or :attr:`scale` must be set. And :attr:`out_shape` has a higher priority than :attr:`scale`. Default: None. actual_shape(Variable): An optional input to specify output shape dynamically. If provided, image resize according to this given shape rather than :attr:`out_shape` and :attr:`scale` specifying shape. That is to say actual_shape has the highest priority. It is recommended to use :attr:`out_shape` if you want to specify output shape dynamically, because :attr:`actual_shape` will be deprecated. When using actual_shape to specify output shape, one of :attr:`out_shape` and :attr:`scale` should also be set, otherwise errors would be occurred in graph constructing stage. Default: None align_corners(bool): ${align_corners_comment} align_mode(bool): ${align_mode_comment} data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. 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: Variable: 4-D tensor(NCHW or NHWC). Examples: .. code-block:: python #declarative mode import paddle.fluid as fluid import numpy as np import paddle paddle.enable_static() input = fluid.data(name="input", shape=[None,3,6,10]) #1 output = fluid.layers.resize_bilinear(input=input,out_shape=[12,12]) #2 #x = np.array([2]).astype("int32") #dim1 = fluid.data(name="dim1", shape=[1], dtype="int32") #fluid.layers.assign(input=x, output=dim1) #output = fluid.layers.resize_bilinear(input=input,out_shape=[12,dim1]) #3 #x = np.array([3,12]).astype("int32") #shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32") #fluid.layers.assign(input=x, output=shape_tensor) #output = fluid.layers.resize_bilinear(input=input,out_shape=shape_tensor) #4 #x = np.array([0.5]).astype("float32") #scale_tensor = fluid.data(name="scale", shape=[1], dtype="float32") #fluid.layers.assign(x,scale_tensor) #output = fluid.layers.resize_bilinear(input=input,scale=scale_tensor) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) input_data = np.random.rand(2,3,6,10).astype("float32") output_data = exe.run(fluid.default_main_program(), feed={"input":input_data}, fetch_list=[output], return_numpy=True) print(output_data[0].shape) #1 # (2, 3, 12, 12) #2 # (2, 3, 12, 2) #3 # (2, 3, 3, 12) #4 # (2, 3, 3, 5) #imperative mode import paddle.fluid.dygraph as dg with dg.guard(place) as g: input = dg.to_variable(input_data) output = fluid.layers.resize_bilinear(input=input, out_shape=[12,12]) print(output.shape) # [2L, 3L, 12L, 12L] """ return image_resize( input, out_shape, scale, name, 'BILINEAR', actual_shape, align_corners, align_mode, data_format, ) @templatedoc(op_type="trilinear_interp") def resize_trilinear( input, out_shape=None, scale=None, name=None, actual_shape=None, align_corners=True, align_mode=1, data_format='NCDHW', ): """ This op resizes the input by performing trilinear interpolation based on given output shape which specified by actual_shape, out_shape and scale in priority order. **Warning:** the parameter :attr:`actual_shape` will be deprecated in the future and only use :attr:`out_shape` instead. Trilinear interpolation is an extension of linear interpolation for interpolating functions of three variables (e.g. D-direction, H-direction and W-direction in this op) on a rectilinear 3D grid. The linear interpolation is performed on three directions. For details of trilinear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Trilinear_interpolation Align_corners and align_mode are optional parameters,the calculation method of interpolation can be selected by them. Example: .. code-block:: text For scale: if align_corners = True && out_size > 1 : scale_factor = (in_size-1.0)/(out_size-1.0) else: scale_factor = float(in_size/out_size) Bilinear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,D_in,H_in,W_in) output: (N,C,D_out,H_out,W_out) where: D_out = (D_{in}+0.5) * scale_{factor} - 0.5 H_out = (H_{in}+0.5) * scale_{factor} - 0.5 W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,D_in,H_in,W_in) output: (N,C,D_out,H_out,W_out) where: D_out = D_{in} * scale_{factor} H_out = H_{in} * scale_{factor} W_out = W_{in} * scale_{factor} Parameters: input(${x_type}): 5-D Tensor, its data type is float32, float64, or uint8, its data format is specified by :attr:`data_format`. out_shape(list|tuple|Variable|None): The output shape of resized tensor, the shape is (out_d, out_h, out_w). Default: None. Every element should be an integer or a Tensor Variable with shape: [1] if it is a list. If it is a Tensor Variable, its dimension size should be 1. scale(float|Variable|None): The multiplier for the input depth, height or width. At least one of :attr:`out_shape` or :attr:`scale` must be set. And :attr:`out_shape` has a higher priority than :attr:`scale`. Default: None. 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` actual_shape(Variable): An optional input to specify output shape dynamically. If provided, image resize according to this given shape rather than :attr:`out_shape` and :attr:`scale` specifying shape. That is to say actual_shape has the highest priority. It is recommended to use :attr:`out_shape` if you want to specify output shape dynamically, because :attr:`actual_shape` will be deprecated. When using actual_shape to specify output shape, one of :attr:`out_shape` and :attr:`scale` should also be set, otherwise errors would be occurred in graph constructing stage. Default: None align_corners(bool): ${align_corners_comment} align_mode(bool): ${align_mode_comment} data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCDHW"`, `"NDHWC"`. The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`. Returns: Variable: A 5-D Tensor(NCDHW or NDHWC) Examples: .. code-block:: python #declarative mode import paddle.fluid as fluid import paddle import numpy as np paddle.enable_static() input = fluid.data(name="input", shape=[None,3,6,8,10]) #1 output = fluid.layers.resize_trilinear(input=input,out_shape=[12,12,12]) #2 #x = np.array([2]).astype("int32") #dim1 = fluid.data(name="dim1", shape=[1], dtype="int32") #fluid.layers.assign(input=x, output=dim1) #output = fluid.layers.resize_trilinear(input=input,out_shape=[12,dim1,4]) #3 #x = np.array([3,12,12]).astype("int32") #shape_tensor = fluid.data(name="shape_tensor", shape=[3], dtype="int32") #fluid.layers.assign(input=x, output=shape_tensor) #output = fluid.layers.resize_trilinear(input=input,out_shape=shape_tensor) #4 #x = np.array([0.5]).astype("float32") #scale_tensor = fluid.data(name="scale", shape=[1], dtype="float32") #fluid.layers.assign(x,scale_tensor) #output = fluid.layers.resize_trilinear(input=input,scale=scale_tensor) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) input_data = np.random.rand(2,3,6,8,10).astype("float32") output_data = exe.run(fluid.default_main_program(), feed={"input":input_data}, fetch_list=[output], return_numpy=True) print(output_data[0].shape) #1 # (2, 3, 12, 12, 12) #2 # (2, 3, 12, 2, 4) #3 # (2, 3, 3, 12, 12) #4 # (2, 3, 3, 4, 5) #imperative mode import paddle.fluid.dygraph as dg with dg.guard(place) as g: input = dg.to_variable(input_data) output = fluid.layers.resize_trilinear(input=input, out_shape=[12,12,12]) print(output.shape) # [2L, 3L, 12L, 12L, 12L] """ return image_resize( input, out_shape, scale, name, 'TRILINEAR', actual_shape, align_corners, align_mode, data_format, ) @templatedoc(op_type="nearest_interp") def resize_nearest( input, out_shape=None, scale=None, name=None, actual_shape=None, align_corners=True, data_format='NCHW', ): """ This op resizes the input by performing nearest neighbor interpolation in both the height direction and the width direction based on given output shape which is specified by actual_shape, out_shape and scale in priority order. **Warning:** the parameter :attr:`actual_shape` will be deprecated in the future and only use :attr:`out_shape` instead. Example: .. code-block:: text For scale: if align_corners = True && out_size > 1 : scale_factor = (in_size-1.0)/(out_size-1.0) else: scale_factor = float(in_size/out_size) Nearest neighbor interpolation: if: align_corners = False input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = floor(H_{in} * scale_{factor}) W_out = floor(W_{in} * scale_{factor}) else: align_corners = True input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = round(H_{in} * scale_{factor}) W_out = round(W_{in} * scale_{factor}) For details of nearest neighbor interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation Parameters: input(${x_type}): 4-D Tensor, its data type is float32, float64, or uint8, its data format is specified by :attr:`data_format`. out_shape(list|tuple|Variable|None): The output shape of resized tensor, the shape is (out_h, out_w). Default: None. Every element should be an integer or a tensor Variable with shape: [1] if it is a list. If it is a tensor Variable, its dimension size should be 1. scale(float|Variable|None): The multiplier for the input height or width. At least one of :attr:`out_shape` or :attr:`scale` must be set. And :attr:`out_shape` has a higher priority than :attr:`scale`. Default: None. 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` actual_shape(Variable): An optional input to specify output shape dynamically. If provided, image resize according to this given shape rather than :attr:`out_shape` and :attr:`scale` specifying shape. That is to say actual_shape has the highest priority. It is recommended to use :attr:`out_shape` if you want to specify output shape dynamically, because :attr:`actual_shape` will be deprecated. When using actual_shape to specify output shape, one of :attr:`out_shape` and :attr:`scale` should also be set, otherwise errors would be occurred in graph constructing stage. Default: None align_corners(bool): ${align_corners_comment} data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. Returns: Variable: 4-D tensor(NCHW or NHWC). Examples: .. code-block:: python #declarative mode import paddle.fluid as fluid import numpy as np import paddle paddle.enable_static() input = fluid.data(name="input", shape=[None,3,6,10]) #1 output = fluid.layers.resize_nearest(input=input,out_shape=[12,12]) #2 #x = np.array([2]).astype("int32") #dim1 = fluid.data(name="dim1", shape=[1], dtype="int32") #fluid.layers.assign(input=x, output=dim1) #output = fluid.layers.resize_nearest(input=input,out_shape=[12,dim1]) #3 #x = np.array([3,12]).astype("int32") #shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32") #fluid.layers.assign(input=x, output=shape_tensor) #output = fluid.layers.resize_nearest(input=input,out_shape=shape_tensor) #4 #x = np.array([0.5]).astype("float32") #scale_tensor = fluid.data(name="scale", shape=[1], dtype="float32") #fluid.layers.assign(x,scale_tensor) #output = fluid.layers.resize_nearest(input=input,scale=scale_tensor) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) input_data = np.random.rand(2,3,6,10).astype("float32") output_data = exe.run(fluid.default_main_program(), feed={"input":input_data}, fetch_list=[output], return_numpy=True) print(output_data[0].shape) #1 # (2, 3, 12, 12) #2 # (2, 3, 12, 2) #3 # (2, 3, 3, 12) #4 # (2, 3, 3, 5) #imperative mode import paddle.fluid.dygraph as dg with dg.guard(place) as g: input = dg.to_variable(input_data) output = fluid.layers.resize_nearest(input=input, out_shape=[12,12]) print(output.shape) # [2L, 3L, 12L, 12L] """ return image_resize( input, out_shape, scale, name, 'NEAREST', actual_shape, align_corners, align_mode=1, data_format=data_format, ) @deprecated(since="2.0.0", update_to="paddle.nn.functional.relu") def relu(x, name=None): """ ${comment} Args: x(Variable): ${x_comment} 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: Variable: ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np in1 = np.array([[-1,0],[1,2.6]]) with fluid.dygraph.guard(): x1 = fluid.dygraph.to_variable(in1) out1 = fluid.layers.relu(x1) print(out1.numpy()) # [[0. 0. ] # [1. 2.6]]""" if in_dygraph_mode(): return _C_ops.relu(x) if _in_legacy_dygraph(): return _legacy_C_ops.relu(x) check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'relu') inputs = {'X': [x]} helper = LayerHelper('relu', **locals()) dtype = helper.input_dtype(input_param_name='x') out = helper.create_variable_for_type_inference(dtype) helper.append_op( type="relu", inputs={"X": helper.input('x')}, outputs={"Out": out} ) return out from paddle.fluid.framework import convert_np_dtype_to_dtype_ @deprecated(since="2.0.0", update_to="paddle.normal") @templatedoc() def gaussian_random( shape, mean=0.0, std=1.0, seed=0, dtype='float32', name=None ): """ This OP returns a Tensor filled with random values sampled from a Gaussian distribution, with ``shape`` and ``dtype``. Args: shape(list|tuple|Tensor): The shape of the output Tensor. If ``shape`` is a list or tuple, the elements of it should be integers or Tensors (with the shape [1], and the data type int32 or int64). If ``shape`` is a Tensor, it should be a 1-D Tensor(with the data type int32 or int64). mean(float|int, optional): Mean of the output tensor, default is 0.0. std(float|int, optional): Standard deviation of the output tensor, default is 1.0. seed(int, optional): ${seed_comment} dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of the output Tensor. Supported data types: float32, float64. Default is float32. 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: Tensor: A Tensor filled with random values sampled from a Gaussian distribution, with ``shape`` and ``dtype``. Examples: .. code-block:: python import paddle import paddle.fluid as fluid paddle.enable_static() # example 1: # attr shape is a list which doesn't contain Tensor. result_1 = fluid.layers.gaussian_random(shape=[3, 4]) # [[-0.31261674, 1.8736548, -0.6274357, 0.96988016], # [-0.12294637, 0.9554768, 1.5690808, -1.2894802 ], # [-0.60082096, -0.61138713, 1.5345167, -0.21834975]] # example 2: # attr shape is a list which contains Tensor. dim_1 = fluid.layers.fill_constant([1], "int64", 2) dim_2 = fluid.layers.fill_constant([1], "int32", 3) result_2 = fluid.layers.gaussian_random(shape=[dim_1, dim_2]) # [[ 0.51398206, -0.3389769, 0.23597084], # [ 1.0388143, -1.2015356, -1.0499583 ]] # example 3: # attr shape is a Tensor, the data type must be int64 or int32. var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64") result_3 = fluid.layers.gaussian_random(var_shape) # if var_shape's value is [2, 3] # result_3 is: # [[-0.12310527, 0.8187662, 1.923219 ] # [ 0.70721835, 0.5210541, -0.03214082]] .. code-block:: python # declarative mode # required: skiptest import numpy as np from paddle import fluid x = fluid.layers.gaussian_random((2, 3), std=2., seed=10) place = fluid.CPUPlace() exe = fluid.Executor(place) start = fluid.default_startup_program() main = fluid.default_main_program() exe.run(start) x_np, = exe.run(main, feed={}, fetch_list=[x]) x_np # array([[2.3060477, 2.676496 , 3.9911983], # [0.9990833, 2.8675377, 2.2279181]], dtype=float32) .. code-block:: python # imperative mode import numpy as np from paddle import fluid import paddle.fluid.dygraph as dg place = fluid.CPUPlace() with dg.guard(place) as g: x = fluid.layers.gaussian_random((2, 4), mean=2., dtype="float32", seed=10) x_np = x.numpy() x_np # array([[2.3060477 , 2.676496 , 3.9911983 , 0.9990833 ], # [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32) """ if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) if in_dygraph_mode(): shape = utils.convert_shape_to_list(shape) place = _current_expected_place() return _C_ops.gaussian( shape, float(mean), float(std), seed, dtype, place ) if _in_legacy_dygraph(): shape = utils.convert_shape_to_list(shape) return _legacy_C_ops.gaussian_random( 'shape', shape, 'mean', float(mean), 'std', float(std), 'seed', seed, 'dtype', dtype, ) check_type(shape, 'shape', (list, tuple, Variable), 'gaussian_random/randn') check_dtype(dtype, 'dtype', ['float32', 'float64'], 'gaussian_random/randn') inputs = {} attrs = { 'mean': mean, 'std': std, 'seed': seed, 'dtype': dtype, 'use_mkldnn': False, } utils.get_shape_tensor_inputs( inputs=inputs, attrs=attrs, shape=shape, op_type='gaussian_random/randn' ) helper = LayerHelper('gaussian_random', **locals()) out = helper.create_variable_for_type_inference(dtype) helper.append_op( type='gaussian_random', inputs=inputs, outputs={'Out': out}, attrs=attrs ) return out @templatedoc() def sampling_id(x, min=0.0, max=1.0, seed=0, dtype='float32'): """ This op is used for sampling id from multinomial distribution from the input, sampling one id for one sample. Parameters: x (Variable): 2-D tensor, [batch_size, input_feature_dimensions] min (Float): minimum , default 0.0. max (Float): maximum, default 1.0. seed (Float): Random seed, default 0. if seed is not 0, will generate same number every time. dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc Returns: Variable: sampling tensor. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data( name="X", shape=[13, 11], dtype='float32') out = fluid.layers.sampling_id(x) """ helper = LayerHelper('sampling_id', **locals()) out = helper.create_variable_for_type_inference(dtype) helper.append_op( type='sampling_id', inputs={'X': x}, outputs={'Out': out}, attrs={'min': min, 'max': max, 'seed': seed}, ) return out def shape(input): """ :alias_main: paddle.shape :alias: paddle.shape,paddle.tensor.shape,paddle.tensor.attribute.shape :old_api: paddle.fluid.layers.shape **Shape Layer** Get the shape of the input. .. code-block:: text Case1: Given N-D Tensor: input = [ [1, 2, 3, 4], [5, 6, 7, 8] ] Then: input.shape = [2, 4] Case2: Given SelectedRows: input.rows = [0, 4, 19] input.height = 20 input.value = [ [1, 2], [3, 4], [5, 6] ] # inner tensor Then: input.shape = [3, 2] Args: input (Variable): The input can be N-D Tensor or SelectedRows with data type bool, float16, float32, float64, int32, int64. If input variable is type of SelectedRows, returns the shape of it's inner tensor. Returns: Variable (Tensor): The shape of the input variable. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle paddle.enable_static() inputs = fluid.data(name="x", shape=[3, 100, 100], dtype="float32") output = fluid.layers.shape(inputs) exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) img = np.ones((3, 100, 100)).astype(np.float32) res = exe.run(fluid.default_main_program(), feed={'x':img}, fetch_list=[output]) print(res) # [array([ 3, 100, 100], dtype=int32)] """ if in_dygraph_mode(): out = _C_ops.shape(input) out.stop_gradient = True return out if _in_legacy_dygraph(): out = _legacy_C_ops.shape(input) out.stop_gradient = True return out check_variable_and_dtype( input, 'input', [ 'bool', 'float16', 'float32', 'float64', 'int32', 'int64', 'complex64', 'complex128', ], 'shape', ) helper = LayerHelper('shape', **locals()) out = helper.create_variable_for_type_inference(dtype='int32') helper.append_op( type='shape', inputs={'Input': input}, outputs={'Out': out}, stop_gradient=True, ) return out def _elementwise_op(helper): op_type = helper.layer_type x = helper.kwargs.get('x', None) y = helper.kwargs.get('y', None) assert x is not None, 'x cannot be None in {}'.format(op_type) assert y is not None, 'y cannot be None in {}'.format(op_type) check_variable_and_dtype( x, 'x', ['float16', 'uint16', 'float32', 'float64', 'int32', 'int64'], op_type, ) check_variable_and_dtype( y, 'y', ['float16', 'uint16', 'float32', 'float64', 'int32', 'int64'], op_type, ) axis = helper.kwargs.get('axis', -1) use_mkldnn = helper.kwargs.get('use_mkldnn', False) name = helper.kwargs.get('name', None) out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type=op_type, inputs={'X': x, 'Y': y}, outputs={'Out': out}, attrs={'axis': axis, 'use_mkldnn': use_mkldnn}, ) return helper.append_activation(out) def elementwise_add(x, y, axis=-1, act=None, name=None): """ Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = fluid.layers.elementwise_add(x, y) # z = x + y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # [3., 8., 6.] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[3,4], dtype='float32') z = fluid.layers.elementwise_add(x, y, axis=1) # z = x + y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'), "y": np.random.randint(1, 5, size=[5]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[5], dtype='float32') z = fluid.layers.elementwise_add(x, y, axis=3) # z = x + y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] """ if _non_static_mode(): return _elementwise_op_in_dygraph( x, y, axis=axis, act=act, op_name='elementwise_add', use_mkldnn=_global_flags()["FLAGS_use_mkldnn"], ) return _elementwise_op(LayerHelper('elementwise_add', **locals())) @deprecated(since="2.0.0", update_to="paddle.divide") def elementwise_div(x, y, axis=-1, act=None, name=None): """ Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = fluid.layers.elementwise_div(x, y) # z = x / y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # [2., 0.6, 2.] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[3,4], dtype='float32') z = fluid.layers.elementwise_div(x, y, axis=1) # z = x / y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'), "y": np.random.randint(1, 5, size=[5]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[5], dtype='float32') z = fluid.layers.elementwise_div(x, y, axis=3) # z = x / y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] """ if _non_static_mode(): return _elementwise_op_in_dygraph( x, y, axis=axis, act=act, op_name='elementwise_div' ) return _elementwise_op(LayerHelper('elementwise_div', **locals())) def elementwise_sub(x, y, axis=-1, act=None, name=None): """ Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = fluid.layers.elementwise_sub(x, y) # z = x - y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # [1., -2., 2.] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[3,4], dtype='float32') z = fluid.layers.elementwise_sub(x, y, axis=1) # z = x - y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'), "y": np.random.randint(1, 5, size=[5]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[5], dtype='float32') z = fluid.layers.elementwise_sub(x, y, axis=3) # z = x - y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] """ if _non_static_mode(): return _elementwise_op_in_dygraph( x, y, axis=axis, act=act, op_name='elementwise_sub' ) return _elementwise_op(LayerHelper('elementwise_sub', **locals())) @deprecated(since="2.0.0", update_to="paddle.multiply") def elementwise_mul(x, y, axis=-1, act=None, name=None): """ Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = fluid.layers.elementwise_mul(x, y) # z = x * y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # [2., 15., 8.] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[3,4], dtype='float32') z = fluid.layers.elementwise_mul(x, y, axis=1) # z = x * y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'), "y": np.random.randint(1, 5, size=[5]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[5], dtype='float32') z = fluid.layers.elementwise_mul(x, y, axis=3) # z = x * y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] """ if _non_static_mode(): return _elementwise_op_in_dygraph( x, y, axis=axis, act=act, op_name='elementwise_mul' ) return _elementwise_op(LayerHelper('elementwise_mul', **locals())) for func in [ elementwise_add, elementwise_div, elementwise_sub, elementwise_mul, ]: op_proto = OpProtoHolder.instance().get_op_proto(func.__name__) # insert the c++ doc string on top of python doc string func.__doc__ = ( _generate_doc_string_( op_proto, additional_args_lines=[ "axis (int32, optional): If X.dimension != Y.dimension, \ Y.dimension must be a subsequence of x.dimension. \ And axis is the start dimension index for broadcasting Y onto X. ", "act (string, optional): Activation applied to the output. \ Default is None. Details: :ref:`api_guide_activations_en` ", "name (string, optional): Name of the output. \ Default is None. It's used to print debug info for developers. Details: \ :ref:`api_guide_Name` ", ], skip_attrs_set={ "x_data_format", "y_data_format", "axis", "use_quantizer", "mkldnn_data_type", "Scale_x", "Scale_y", "Scale_out", }, ) + """\n""" + str(func.__doc__) ) doc_list = func.__doc__.splitlines() for idx, val in enumerate(doc_list): if ( val.startswith("Warning: ") and val.endswith(" instead.") and "and will be removed in future versions." in val ): doc_list.insert(0, doc_list.pop(idx)) func.__doc__ = "\n" + "\n".join(i for i in doc_list) break for func in []: op_proto = OpProtoHolder.instance().get_op_proto(func.__name__) func.__doc__ = _generate_doc_string_( op_proto, additional_args_lines=[ "act (basestring|None): Activation applied to the output.", "name (basestring|None): Name of the output.", ], ) func.__doc__ = ( func.__doc__ + """ Examples: .. code-block:: python import paddle.fluid as fluid # example 1: shape(x) = (2, 3, 4, 5), shape(y) = (2, 3, 4, 5) x0 = fluid.layers.data(name="x0", shape=[2, 3, 4, 5], dtype='float32') y0 = fluid.layers.data(name="y0", shape=[2, 3, 4, 5], dtype='float32') z0 = fluid.layers.%s(x0, y0) # example 2: shape(X) = (2, 3, 4, 5), shape(Y) = (5) x1 = fluid.layers.data(name="x1", shape=[2, 3, 4, 5], dtype='float32') y1 = fluid.layers.data(name="y1", shape=[5], dtype='float32') z1 = fluid.layers.%s(x1, y1) # example 3: shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5), with axis=-1(default) or axis=2 x2 = fluid.layers.data(name="x2", shape=[2, 3, 4, 5], dtype='float32') y2 = fluid.layers.data(name="y2", shape=[4, 5], dtype='float32') z2 = fluid.layers.%s(x2, y2, axis=2) # example 4: shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 x3 = fluid.layers.data(name="x3", shape=[2, 3, 4, 5], dtype='float32') y3 = fluid.layers.data(name="y3", shape=[3, 4], dtype='float32') z3 = fluid.layers.%s(x3, y3, axis=1) # example 5: shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0 x4 = fluid.layers.data(name="x4", shape=[2, 3, 4, 5], dtype='float32') y4 = fluid.layers.data(name="y4", shape=[2], dtype='float32') z4 = fluid.layers.%s(x4, y4, axis=0) # example 6: shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0 x5 = fluid.layers.data(name="x5", shape=[2, 3, 4, 5], dtype='float32') y5 = fluid.layers.data(name="y5", shape=[2], dtype='float32') z5 = fluid.layers.%s(x5, y5, axis=0) """ % ( func.__name__, func.__name__, func.__name__, func.__name__, func.__name__, func.__name__, ) ) def _logical_op(op_name, x, y, out=None, name=None, binary_op=True): if _non_static_mode(): op = getattr(_legacy_C_ops, op_name) if binary_op: return op(x, y) else: return op(x) check_variable_and_dtype( x, "x", ["bool", "int8", "int16", "int32", "int64", "float32", "float64"], op_name, ) if y is not None: check_variable_and_dtype( y, "y", ["bool", "int8", "int16", "int32", "int64", "float32", "float64"], op_name, ) if out is not None: check_type(out, "out", Variable, op_name) helper = LayerHelper(op_name, **locals()) if binary_op and x.dtype != y.dtype: raise ValueError( "(InvalidArgument) The DataType of %s Op's Variable must be consistent, but received %s and %s." % (op_name, x.dtype, y.dtype) ) if out is None: out = helper.create_variable_for_type_inference(dtype=x.dtype) if binary_op: helper.append_op( type=op_name, inputs={"X": x, "Y": y}, outputs={"Out": out} ) else: helper.append_op(type=op_name, inputs={"X": x}, outputs={"Out": out}) return out @templatedoc() def clip(x, min, max, name=None): """ :old_api: paddle.fluid.layers.clip ${comment} Args: x(${x_type}): ${x_comment} min(float): ${min_comment} max(float): ${max_comment} 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: ${out_comment} Return Type: ${out_type} Examples: .. code-block:: python import paddle.fluid as fluid input = fluid.data( name='data', shape=[1], dtype='float32') reward = fluid.layers.clip(x=input, min=-1.0, max=1.0) """ helper = LayerHelper("clip", **locals()) check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'clip') if name is None: name = unique_name.generate_with_ignorable_key( ".".join([helper.name, 'tmp']) ) out = helper.create_variable( type=x.type, name=name, dtype=x.dtype, persistable=False ) helper.append_op( type="clip", inputs={"X": x}, attrs={"min": min, "max": max}, outputs={"Out": out}, ) return out @templatedoc() def clip_by_norm(x, max_norm, name=None): """ ${comment} Args: x(${x_type}): ${x_comment} max_norm(${max_norm_type}): ${max_norm_comment} name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. Returns: Tensor: out(${out_type}): ${out_comment} Examples: .. code-block:: python import paddle import paddle.fluid as fluid input = paddle.to_tensor([[2.0, 2.0], [2.0, 2.0]], dtype='float32') reward = fluid.layers.clip_by_norm(x=input, max_norm=1.0) # [[0.5, 0.5], [0.5, 0.5]] """ if in_dygraph_mode(): return _C_ops.clip_by_norm(x, max_norm) if _non_static_mode(): return _legacy_C_ops.clip_by_norm(x, 'max_norm', max_norm) helper = LayerHelper("clip_by_norm", **locals()) check_variable_and_dtype(x, 'X', ['float32', 'float16'], 'clip_by_norm') check_type(max_norm, 'max_norm', (float), 'clip_by_norm') if name is None: name = unique_name.generate_with_ignorable_key( ".".join([helper.name, 'tmp']) ) out = helper.create_variable( type=x.type, name=name, dtype=x.dtype, persistable=False ) helper.append_op( type="clip_by_norm", inputs={"X": x}, attrs={"max_norm": max_norm}, outputs={"Out": out}, ) return out @deprecated(since="2.0.0", update_to="paddle.mean") @templatedoc() def mean(x, name=None): """ ${comment} Args: x(${x_type}): ${x_comment} name(basestring|None): Name of the output. Returns: out(${out_type}): ${out_comment} Examples: .. code-block:: python import paddle import paddle.fluid as fluid paddle.enable_static() input = fluid.layers.data( name='data', shape=[2, 3], dtype='float32') mean = paddle.mean(input) """ if _in_legacy_dygraph(): return _legacy_C_ops.mean(x) if in_dygraph_mode(): return _C_ops.mean_all(x) helper = LayerHelper("mean", **locals()) check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'mean') out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type="mean", inputs={"X": x}, attrs={}, outputs={"Out": out} ) return out @templatedoc() def merge_selected_rows(x, name=None): """ ${comment} Args: x(${x_type}): ${x_comment} name(basestring|None): Name of the output. Returns: out(${out_type}): ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid b = fluid.default_main_program().global_block() var = b.create_var( name="X", dtype="float32", persistable=True, type=fluid.core.VarDesc.VarType.SELECTED_ROWS) y = fluid.layers.merge_selected_rows(var) """ if in_dygraph_mode(): return _C_ops.merge_selected_rows(x) if _non_static_mode(): return _legacy_C_ops.merge_selected_rows(x) helper = LayerHelper("merge_selected_rows", **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type="merge_selected_rows", inputs={"X": x}, attrs={}, outputs={"Out": out}, ) return out def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None): """ Mul Operator. This operator is used to perform matrix multiplication for input $x$ and $y$. The equation is: .. math:: Out = x * y Both the input $x$ and $y$ can carry the LoD (Level of Details) information, or not. But the output only shares the LoD information with input $x$. Args: x (Variable): The first input Tensor/LoDTensor of mul_op. y (Variable): The second input Tensor/LoDTensor of mul_op. x_num_col_dims (int, optional): The mul_op can take tensors with more than two dimensions as its inputs. If the input $x$ is a tensor with more than two dimensions, $x$ will be flattened into a two-dimensional matrix first. The flattening rule is: the first `num_col_dims` will be flattened to form the first dimension of the final matrix (the height of the matrix), and the rest `rank(x) - num_col_dims` dimensions are flattened to form the second dimension of the final matrix (the width of the matrix). As a result, height of the flattened matrix is equal to the product of $x$'s first `x_num_col_dims` dimensions' sizes, and width of the flattened matrix is equal to the product of $x$'s last `rank(x) - num_col_dims` dimensions' size. For example, suppose $x$ is a 6-dimensional tensor with the shape [2, 3, 4, 5, 6], and `x_num_col_dims` = 3. Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30]. Default is 1. y_num_col_dims (int, optional): The mul_op can take tensors with more than two dimensions as its inputs. If the input $y$ is a tensor with more than two dimensions, $y$ will be flattened into a two-dimensional matrix first. The attribute `y_num_col_dims` determines how $y$ is flattened. See comments of `x_num_col_dims` for more details. Default is 1. name (str, optional): Name of the output. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default is None. Returns: Variable(Tensor/LoDTensor): The output Tensor/LoDTensor of mul op. Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() dataX = fluid.layers.data(name="dataX", append_batch_size = False, shape=[2, 5], dtype="float32") dataY = fluid.layers.data(name="dataY", append_batch_size = False, shape=[5, 3], dtype="float32") output = fluid.layers.mul(dataX, dataY, x_num_col_dims = 1, y_num_col_dims = 1) """ if _non_static_mode(): return _legacy_C_ops.mul( x, y, 'x_num_col_dims', x_num_col_dims, 'y_num_col_dims', y_num_col_dims, ) inputs = {"X": [x], "Y": [y]} attrs = {"x_num_col_dims": x_num_col_dims, "y_num_col_dims": y_num_col_dims} helper = LayerHelper("mul", **locals()) check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'mul') check_variable_and_dtype(y, 'y', ['float16', 'float32', 'float64'], 'mul') out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type="mul", inputs={"X": x, "Y": y}, attrs=attrs, outputs={"Out": out} ) return out def hash(input, hash_size, num_hash=1, name=None): """ This OP hash the input to an integer less than the hash_size. The hash algorithm we used was xxHash - Extremely fast hash algorithm (https://github.com/Cyan4973/xxHash/tree/v0.6.5) Args: input(Variable): A **Two-Dimensional** LoDTensor with type int32, int64. **Only support LoDTensor**. num_hash(int, optional): The times of hash, default is 1. 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: Variable: A LoDTensor with the same data type as input. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle paddle.enable_static() place = fluid.core.CPUPlace() x = fluid.data(name="x", shape=[2,2], dtype="int32", lod_level=1) res = fluid.layers.hash(name="res", input=x, hash_size=1000, num_hash=4) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) in1 = np.array([[1,2],[3,4]]).astype("int32") print(in1) x_i = fluid.create_lod_tensor(in1, [[0, 2]], place) res = exe.run(fluid.default_main_program(), feed={'x':x_i}, fetch_list=[res], return_numpy=False) print(np.array(res[0])) # [[[722] # [407] # [337] # [395]] # [[603] # [590] # [386] # [901]]] """ check_variable_and_dtype(input, 'input', ['int32', 'int64'], 'hash') check_type(hash_size, 'hash_size', int, 'hash') check_type(num_hash, 'num_hash', int, 'hash') helper = LayerHelper('hash', **locals()) out = helper.create_variable_for_type_inference( helper.input_dtype(), stop_gradient=True ) helper.append_op( type='hash', inputs={'X': input}, outputs={'Out': out}, attrs={'num_hash': num_hash, 'mod_by': hash_size}, ) return out @templatedoc() def grid_sampler(x, grid, name=None): """ This operation samples input X by using bilinear interpolation based on flow field grid, which is usually generated by :code:`affine_grid` . The grid of shape [N, H, W, 2] is the concatenation of (x, y) coordinates with shape [N, H, W] each, where x is indexing the 4th dimension (in width dimension) of input data x and y is indexing the 3rd dimension (in height dimension), finally results is the bilinear interpolation value of 4 nearest corner points. The output tensor shape will be [N, C, H, W]. .. code-block:: text Step 1: Get (x, y) grid coordinates and scale to [0, H-1/W-1]. .. code-block:: text grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1) grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1) Step 2: Indices input data X with grid (x, y) in each [H, W] area, and bilinear interpolate point value by 4 nearest points. wn ------- y_n ------- en | | | | d_n | | | | x_w --d_w-- grid--d_e-- x_e | | | | d_s | | | | ws ------- y_s ------- wn x_w = floor(x) // west side x coord x_e = x_w + 1 // east side x coord y_n = floor(y) // north side y coord y_s = y_s + 1 // south side y coord d_w = grid_x - x_w // distance to west side d_e = x_e - grid_x // distance to east side d_n = grid_y - y_n // distance to north side d_s = y_s - grid_y // distance to south side wn = X[:, :, y_n, x_w] // north-west point value en = X[:, :, y_n, x_e] // north-east point value ws = X[:, :, y_s, x_w] // south-east point value es = X[:, :, y_s, x_w] // north-east point value output = wn * d_e * d_s + en * d_w * d_s + ws * d_e * d_n + es * d_w * d_n Args: x(Variable): The input tensor, which is a 4-D tensor with shape [N, C, H, W], N is the batch size, C is the channel number, H and W is the feature height and width. The data type is float32 or float64. grid(Variable): Input grid tensor of shape [N, H, W, 2]. The data type is float32 or float64. 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: Variable: Output of shape [N, C, H, W] data samples input X using bilnear interpolation based on input grid. The data type is same as input tensor. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid as fluid import paddle paddle.enable_static() # use with affine_grid x = fluid.data(name='x', shape=[None, 10, 32, 32], dtype='float32') theta = fluid.layers.data(name='theta', shape=[2, 3], dtype='float32') grid = fluid.layers.affine_grid(theta=theta, out_shape=[3, 10, 32, 32]) out = fluid.layers.grid_sampler(x=x, grid=grid) """ helper = LayerHelper("grid_sampler", **locals()) check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'grid_sampler') check_variable_and_dtype( grid, 'grid', ['float32', 'float64'], 'grid_sampler' ) if not isinstance(x, Variable): return ValueError("The x should be a Variable") if not isinstance(grid, Variable): return ValueError("The grid should be a Variable") out = helper.create_variable_for_type_inference(x.dtype) ipts = {'X': x, 'Grid': grid} attrs = {'use_cudnn': False} if core.is_compiled_with_rocm() else {} helper.append_op( type='grid_sampler', inputs=ipts, outputs={'Output': out}, attrs=attrs ) return out def log_loss(input, label, epsilon=1e-4, name=None): r""" **Negative Log Loss Layer** This layer accepts input predictions and target label and returns the negative log loss. .. math:: Out = -label * \log{(input + \epsilon)} - (1 - label) * \log{(1 - input + \epsilon)} Args: input (Tensor|list): A 2-D tensor with shape [N x 1], where N is the batch size. This input is a probability computed by the previous operator. Data type float32. label (Tensor|list): The ground truth which is a 2-D tensor with shape [N x 1], where N is the batch size. Data type float32. epsilon (float, optional): A small number for numerical stability. Default 1e-4. name(str|None): For detailed information, please refer to :ref:`api_guide_Name` . Usually name is no need to set and None by default. Returns: Tensor, which shape is [N x 1], data type is float32. Examples: .. code-block:: python import paddle import paddle.nn.functional as F label = paddle.randn((10,1)) prob = paddle.randn((10,1)) cost = F.log_loss(input=prob, label=label) """ return paddle.nn.functional.log_loss(input, label, epsilon, name) def bilinear_tensor_product( x, y, size, act=None, name=None, param_attr=None, bias_attr=None ): r""" :api_attr: Static Graph **Bilinear Tensor Product Layer** This layer performs bilinear tensor product on two inputs. For example: .. math:: out_{i} = x * W_{i} * {y^\mathrm{T}}, i=0,1,...,size-1 In this formula: - :math:`x`: the first input contains M elements, shape is [batch_size, M]. - :math:`y`: the second input contains N elements, shape is [batch_size, N]. - :math:`W_{i}`: the i-th learned weight, shape is [M, N]. - :math:`out_{i}`: the i-th element of out, shape is [batch_size, size]. - :math:`y^\mathrm{T}`: the transpose of :math:`y_{2}`. Args: x (Variable): 2-D input tensor with shape [batch_size, M]. Data type is float32 or float64. y (Variable): 2-D input tensor with shape [batch_size, N]. Data type should be same as **x**. size (int): The dimension of this layer. act (str|None): Activation to be applied to the output of this layer. Default None. name(str|None): For detailed information, please refer to :ref:`api_guide_Name` . Usually name is no need to set and None by default. param_attr (ParamAttr|None): To specify the weight parameter attribute. Default: None, which means the default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . bias_attr (ParamAttr|None): To specify the bias parameter attribute. Default: None, which means the default bias parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . Returns: Variable: A 2-D Tensor of shape [batch_size, size]. Data type is the same as input **x**. Examples: .. code-block:: python import paddle paddle.enable_static() layer1 = paddle.static.data("t1", shape=[-1, 5], dtype="float32") layer2 = paddle.static.data("t2", shape=[-1, 4], dtype="float32") tensor = paddle.static.nn.bilinear_tensor_product(x=layer1, y=layer2, size=1000) """ helper = LayerHelper('bilinear_tensor_product', **locals()) dtype = helper.input_dtype('x') param_shape = [size, x.shape[1], y.shape[1]] w = helper.create_parameter( attr=helper.param_attr, shape=param_shape, dtype=dtype, is_bias=False ) out = helper.create_variable_for_type_inference(dtype=dtype) inputs = {"X": x, "Y": y, "Weight": w} if helper.bias_attr: bias_size = [1, size] bias = helper.create_parameter( attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True ) inputs["Bias"] = bias helper.append_op( type="bilinear_tensor_product", inputs=inputs, outputs={"Out": out} ) # add activation return helper.append_activation(out) @templatedoc() def get_tensor_from_selected_rows(x, name=None): """ This operator gets tensor data from input with SelectedRows type, and outputs a LoDTensor. .. code-block:: text input x is SelectedRows: x.rows = [0, 5, 5, 4, 19] x.height = 20 x.value = [[1, 1] [2, 2] [2, 2] [3, 3] [6, 6]] Output is LoDTensor: out.shape = [5, 2] out.data = [[1, 1], [2, 2], [2, 2], [3, 3], [6, 6]] Args: x(SelectedRows): Input with SelectedRows type. The data type is float32, float64, int32 or int64. 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: Variable: LoDTensor transformed from SelectedRows. The data type is same with input. Examples: .. code-block:: python import paddle.fluid as fluid b = fluid.default_main_program().global_block() input = b.create_var(name="X", dtype="float32", persistable=True, type=fluid.core.VarDesc.VarType.SELECTED_ROWS) out = fluid.layers.get_tensor_from_selected_rows(input) """ check_type(x, 'x', Variable, 'get_tensor_from_selected_rows') if x.type != core.VarDesc.VarType.SELECTED_ROWS: raise TypeError( "The type of 'x' in get_tensor_from_selected_rows must be SELECTED_ROWS." ) helper = LayerHelper('get_tensor_from_selected_rows', **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='get_tensor_from_selected_rows', inputs={'X': x}, outputs={'Out': out}, attrs={}, ) 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) """ return paddle.nn.functional.temporal_shift( x, seg_num, shift_ratio, name, data_format ) class PyFuncRegistry: _register_funcs = [] def __init__(self, func): if func is None or not callable(func): raise TypeError('func must be a Python function') self._func = func # find named args using reflection args = inspect.getfullargspec(self._func) if len(args[0]) == 0 and args[1] is None and args[2] is None: # Function with no inputs self._named_args = None else: self._named_args = args[0] self._id = core._append_python_callable_object_and_return_id(self) ''' Why record self here? 1. For debug usage. Users can call :code:`py_func.registered_func(idx)` method to find the registered function corresponding to :code:`idx`. 2. For increasing reference count of self. It seems that to release Python object whose reference count is 1 would cause segmentation fault error in C++ side. May be lack of Python GC in C++ side? ''' PyFuncRegistry._register_funcs.append(self) @classmethod def registered_func(cls, idx): return cls._register_funcs[idx]._func @classmethod def registered_func_num(cls): return len(cls._register_funcs) @property def id(self): return self._id def __call__(self, *args): if self._named_args is None: func_ret = self._func() else: kwargs = dict() idx = 0 for arg in self._named_args: kwargs[arg] = args[idx] idx += 1 func_ret = self._func(*args[idx:], **kwargs) if not isinstance(func_ret, (list, tuple)): func_ret = (func_ret,) ret = [] for each_ret in func_ret: if each_ret is None or isinstance(each_ret, core.LoDTensor): ret.append(each_ret) continue if not isinstance(each_ret, np.ndarray): each_ret = np.array(each_ret) tensor = core.LoDTensor() tensor.set(each_ret, core.CPUPlace()) ret.append(tensor) return tuple(ret) @static_only @templatedoc() def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None): """ :api_attr: Static Graph This OP is used to register customized Python OP to Paddle. The design principe of py_func is that Tensor and numpy array can be converted to each other easily. So you can use Python and numpy API to register a python OP. The forward function of the registered OP is ``func`` and the backward function of that is ``backward_func``. Paddle will call ``func`` at forward runtime and call ``backward_func`` at backward runtime(if ``backward_func`` is not None). ``x`` is the input of ``func``, whose type must be Tensor; ``out`` is the output of ``func``, whose type can be either Tensor or numpy array. The input of the backward function ``backward_func`` is ``x``, ``out`` and the gradient of ``out``. If ``out`` have no gradient, the relevant input of ``backward_func`` is None. If ``x`` do not have a gradient, the user should return None in ``backward_func``. The data type and shape of ``out`` should also be set correctly before this API is called, and the data type and shape of the gradient of ``out`` and ``x`` will be inferred automatically. This API can also be used to debug the neural network by setting the ``func`` as a function that only print variables. Args: func (callable): The forward function of the registered OP. When the network is running, the forward output ``out`` will be calculated according to this function and the forward input ``x``. In ``func`` , it's suggested that we actively convert Tensor into a numpy array, so that we can use Python and numpy API arbitrarily. If not, some operations of numpy may not be compatible. x (Tensor|tuple(Tensor)|list[Tensor]): The input of the forward function ``func``. It can be Tensor|tuple(Tensor)|list[Tensor]. In addition, Multiple Tensor should be passed in the form of tuple(Tensor) or list[Tensor]. out (T|tuple(T)|list[T]): The output of the forward function ``func``, it can be T|tuple(T)|list[T], where T can be either Tensor or numpy array. Since Paddle cannot automatically infer the shape and type of ``out``, you must create ``out`` in advance. backward_func (callable, optional): The backward function of the registered OP. Its default value is None, which means there is no reverse calculation. If it is not None, ``backward_func`` is called to calculate the gradient of ``x`` when the network is at backward runtime. skip_vars_in_backward_input (Tensor, optional): It's used to limit the input list of ``backward_func``, and it can be Tensor|tuple(Tensor)|list[Tensor]. It must belong to either ``x`` or ``out``. The default value is None, which means that no tensors need to be removed from ``x`` and ``out``. If it is not None, these tensors will not be the input of ``backward_func``. This parameter is only useful when ``backward_func`` is not None. Returns: Tensor|tuple(Tensor)|list[Tensor]: The output ``out`` of the forward function ``func``. Examples: .. code-block:: python # example 1: import paddle import numpy as np paddle.enable_static() # Creates a forward function, Tensor can be input directly without # being converted into numpy array. def tanh(x): return np.tanh(x) # Skip x in backward function and return the gradient of x # Tensor must be actively converted to numpy array, otherwise, # operations such as +/- can't be used. def tanh_grad(y, dy): return np.array(dy) * (1 - np.square(np.array(y))) # Creates a forward function for debugging running networks(print value) def debug_func(x): print(x) def create_tmp_var(name, dtype, shape): return paddle.static.default_main_program().current_block().create_var( name=name, dtype=dtype, shape=shape) def simple_net(img, label): hidden = img for idx in range(4): hidden = paddle.static.nn.fc(hidden, size=200) new_hidden = create_tmp_var(name='hidden_{}'.format(idx), dtype=hidden.dtype, shape=hidden.shape) # User-defined forward and backward hidden = paddle.static.py_func(func=tanh, x=hidden, out=new_hidden, backward_func=tanh_grad, skip_vars_in_backward_input=hidden) # User-defined debug functions that print out the input Tensor paddle.static.py_func(func=debug_func, x=hidden, out=None) prediction = paddle.static.nn.fc(hidden, size=10, activation='softmax') ce_loss = paddle.nn.loss.CrossEntropyLoss() return ce_loss(prediction, label) x = paddle.static.data(name='x', shape=[1,4], dtype='float32') y = paddle.static.data(name='y', shape=[1], dtype='int64') res = simple_net(x, y) exe = paddle.static.Executor(paddle.CPUPlace()) exe.run(paddle.static.default_startup_program()) input1 = np.random.random(size=[1,4]).astype('float32') input2 = np.random.randint(1, 10, size=[1], dtype='int64') out = exe.run(paddle.static.default_main_program(), feed={'x':input1, 'y':input2}, fetch_list=[res.name]) print(out) .. code-block:: python # example 2: # This example shows how to turn Tensor into numpy array and # use numpy API to register an Python OP import paddle import numpy as np paddle.enable_static() def element_wise_add(x, y): # Tensor must be actively converted to numpy array, otherwise, # numpy.shape can't be used. x = np.array(x) y = np.array(y) if x.shape != y.shape: raise AssertionError("the shape of inputs must be the same!") result = np.zeros(x.shape, dtype='int32') for i in range(len(x)): for j in range(len(x[0])): result[i][j] = x[i][j] + y[i][j] return result def create_tmp_var(name, dtype, shape): return paddle.static.default_main_program().current_block().create_var( name=name, dtype=dtype, shape=shape) def py_func_demo(): start_program = paddle.static.default_startup_program() main_program = paddle.static.default_main_program() # Input of the forward function x = paddle.static.data(name='x', shape=[2,3], dtype='int32') y = paddle.static.data(name='y', shape=[2,3], dtype='int32') # Output of the forward function, name/dtype/shape must be specified output = create_tmp_var('output','int32', [3,1]) # Multiple Variable should be passed in the form of tuple(Variale) or list[Variale] paddle.static.py_func(func=element_wise_add, x=[x,y], out=output) exe=paddle.static.Executor(paddle.CPUPlace()) exe.run(start_program) # Feed numpy array to main_program input1 = np.random.randint(1, 10, size=[2,3], dtype='int32') input2 = np.random.randint(1, 10, size=[2,3], dtype='int32') out = exe.run(main_program, feed={'x':input1, 'y':input2}, fetch_list=[output.name]) print("{0} + {1} = {2}".format(input1, input2, out)) py_func_demo() # Reference output: # [[5, 9, 9] + [[7, 8, 4] = [array([[12, 17, 13] # [7, 5, 2]] [1, 3, 3]] [8, 8, 5]], dtype=int32)] """ helper = LayerHelper('py_func', **locals()) check_type(x, 'X', (list, tuple, Variable, type(None)), 'py_func') if x is None: x = [] elif isinstance(x, Variable): x = [x] elif isinstance(x, tuple): x = list(x) elif not isinstance(x, (list, tuple, Variable)): raise TypeError('Input must be Variable/list(Variable)/tuple(Variable)') check_type(out, 'Out', (list, tuple, Variable, type(None)), 'py_func') if out is None: out_list = [] elif isinstance(out, Variable): out_list = [out] elif isinstance(out, tuple): out_list = list(out) elif isinstance(out, list): out_list = out else: raise TypeError( 'Output must be Variable/list(Variable)/tuple(Variable)' ) fwd_func_id = PyFuncRegistry(func).id bwd_func_id = ( PyFuncRegistry(backward_func).id if backward_func is not None else -1 ) for each_out in out_list: if len(each_out.shape) == 0: raise ValueError( 'Output shapes of py_func op should be provided by users manually' ) backward_skip_vars = set() if backward_func is not None and skip_vars_in_backward_input is not None: if isinstance(skip_vars_in_backward_input, Variable): skip_vars_in_backward_input = [skip_vars_in_backward_input] fwd_in_out = [v.name for v in x] fwd_in_out.extend([v.name for v in out_list]) fwd_in_out = set(fwd_in_out) backward_skip_vars = set() for v in skip_vars_in_backward_input: if not v.name in fwd_in_out: raise ValueError( 'Variable {} is not found in forward inputs and outputs'.format( v.name ) ) backward_skip_vars.add(v.name) helper.append_op( type='py_func', inputs={'X': x}, outputs={'Out': out_list}, attrs={ 'forward_callable_id': fwd_func_id, 'backward_callable_id': bwd_func_id, 'backward_skip_vars': list(backward_skip_vars), }, ) return out # For debug usage py_func.registered_func = PyFuncRegistry.registered_func py_func.registered_func_num = PyFuncRegistry.registered_func_num def continuous_value_model(input, cvm, use_cvm=True): r""" **continuous_value_model layers** Now, this OP is used in CTR project to remove or dispose show and click value in :attr:`input`. :attr:`input` is an embedding vector including show and click value, whose shape is :math:`[N, D]` (N is batch size. D is `2 + embedding dim` ). Show and click at first two dims of embedding vector D. If :attr:`use_cvm` is True, it will calculate :math:`log(show)` and :math:`log(click)` , and output shape is :math:`[N, D]` . If :attr:`use_cvm` is False, it will remove show and click from :attr:`input` , and output shape is :math:`[N, D - 2]` . :attr:`cvm` is show_click info, whose shape is :math:`[N, 2]` . Args: input (Variable): The input variable. A 2-D LoDTensor with shape :math:`[N, D]` , where N is the batch size, D is `2 + the embedding dim` . `lod level = 1` . A Tensor with type float32, float64. cvm (Variable): Show and click variable. A 2-D Tensor with shape :math:`[N, 2]` , where N is the batch size, 2 is show and click. A Tensor with type float32, float64. use_cvm (bool): Use show_click or not. if use, the output dim is the same as input. if not use, the output dim is `input dim - 2` (remove show and click) Returns: Variable: A 2-D LodTensor with shape :math:`[N, M]` . if :attr:`use_cvm` = True, M is equal to input dim D. if False, M is equal to `D - 2`. \ A Tensor with same type as input. Examples: .. code-block:: python import paddle.fluid as fluid input = fluid.data(name="input", shape=[64, 1], dtype="int64") label = fluid.data(name="label", shape=[64, 1], dtype="int64") embed = fluid.layers.embedding( input=input, size=[100, 11], dtype='float32') ones = fluid.layers.fill_constant_batch_size_like(input=label, shape=[-1, 1], dtype="int64", value=1) show_clk = fluid.layers.cast(fluid.layers.concat([ones, label], axis=1), dtype='float32') show_clk.stop_gradient = True input_with_cvm = fluid.layers.continuous_value_model(embed, show_clk, True) """ helper = LayerHelper('cvm', **locals()) out = helper.create_variable(dtype=input.dtype) check_variable_and_dtype( input, 'input', ['float16', 'float32', 'float64'], 'cvm' ) helper.append_op( type='cvm', inputs={'X': [input], 'CVM': [cvm]}, outputs={'Y': [out]}, attrs={"use_cvm": use_cvm}, ) return out def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None): r""" This op returns a col buffer of sliding local blocks of input x, also known as im2col for batched 2D image tensors. For each block under the convolution filter, all element will be rearranged as a column. While the convolution filter sliding over the input feature map, a series of such columns will be formed. For each input :math:`x` with shape [N, C, H, W], the output shape [N, Cout, Lout] can be calculated as following. .. math:: dkernel[0] &= dilations[0] \times (kernel\_sizes[0] - 1) + 1 dkernel[1] &= dilations[1] \times (kernel\_sizes[1] - 1) + 1 hout &= \frac{H + paddings[0] + paddings[2] - dkernel[0]}{strides[0]} + 1 wout &= \frac{W + paddings[1] + paddings[3] - dkernel[1]}{strides[1]} + 1 Cout &= C \times kernel\_sizes[0] \times kernel\_sizes[1] Lout &= hout \times wout Parameters: x(Tensor): 4-D Tensor, input tensor of format [N, C, H, W], data type can be float32 or float64 kernel_sizes(int|list): The size of convolution kernel, should be [k_h, k_w] or an integer k treated as [k, k]. strides(int|list): The strides, should be [stride_h, stride_w] or an integer stride treated as [sride, stride]. For default, strides will be [1, 1]. paddings(int|list): The paddings of each dimension, should be [padding_top, padding_left, padding_bottom, padding_right] or [padding_h, padding_w] or an integer padding. If [padding_h, padding_w] was given, it will expanded to [padding_h, padding_w, padding_h, padding_w]. If an integer padding was given, [padding, padding, padding, padding] will be used. For default, paddings will be [0, 0, 0, 0] dilations(int|list): the dilations of convolution kernel, should be [dilation_h, dilation_w], or an integer dilation treated as [dilation, dilation]. For default, it will be [1, 1]. 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: The tensor corresponding to the sliding local blocks. The output shape is [N, Cout, Lout] as decriabled above. Cout is the total number of values within each block, and Lout is the total number of such blocks. The data type of output is the same as the input :math:`x` Return Type: Tensor Examples: .. code-block:: python import paddle import paddle.nn.functional as F x = paddle.randn((100,3,224,224)) y = F.unfold(x, [3, 3], 1, 1, 1) """ return paddle.nn.functional.unfold( x, kernel_sizes, strides, paddings, dilations, name ) def deformable_roi_pooling( input, rois, trans, no_trans=False, spatial_scale=1.0, group_size=[1, 1], pooled_height=1, pooled_width=1, part_size=None, sample_per_part=1, trans_std=0.1, position_sensitive=False, name=None, ): r""" Deformable ROI Pooling Layer Performs deformable region-of-interest pooling on inputs. As described in `Deformable Convolutional Networks `_, it will get offset for each bin after roi pooling so that pooling at correct region. Batch_size will change to the number of region bounding boxes after deformable_roi_pooling. The operation has three steps: 1. Dividing each region proposal into equal-sized sections with the pooled_width and pooled_height. 2. Add offset to pixel in ROI to get new location and the new value which are computed directly through bilinear interpolation with four nearest pixel. 3. Sample several points in each bin to get average values as output. Args: input (Variable):The input of deformable roi pooling and it is tensor which value type is float32. The shape of input is [N, C, H, W]. Where N is batch size, C is number of input channels, H is height of the feature, and W is the width of the feature. rois (Variable): ROIs (Regions of Interest) with type float32 to pool over. It should be a 2-D LoDTensor of shape (num_rois, 4), and the lod level is 1. Given as [[x1, y1, x2, y2], ...], (x1, y1) is the top left coordinates, and (x2, y2) is the bottom right coordinates, which value type is float32. trans (Variable): Offset of features on ROIs while pooling which value type is float32. The format is [N, C, H, W], where N is number of ROIs, C is number of channels, which indicate the offset distance in the x and y directions, H is pooled height, and W is pooled width. no_trans (bool): Whether to add offset to get new value or not while roi pooling, which value with type bool is True or False. If value is True, no offset will be added in operation. Default: False. spatial_scale (float): Ratio of input feature map height (or width) to raw image height (or width), which value type is float32. Equals the reciprocal of total stride in convolutional layers, Default: 1.0. group_size (list|tuple): The number of groups which input channels are divided and the input is list or tuple, which value type is int32. (eg.number of input channels is k1 * k2 * (C + 1), which k1 and k2 are group width and height and C+1 is number of output channels.) eg.(4, 6), which 4 is height of group and 6 is width of group. Default: [1, 1]. pooled_height (int): The pooled output height which value type is int32. Default: 1. pooled_width (int): The pooled output width which value type is int32. Default: 1. part_size (list|tuple): The height and width of offset which values in list or tuple is int32, eg.(4, 6), which height is 4 and width is 6, and values always equal to pooled_height \ and pooled_width. Default: if None, default value is [pooled_height, pooled_width]. sample_per_part (int): The number of samples in each bin which value type is int32. If value is bigger, it will consume more performance. Default: 1. trans_std (float): Coefficient of offset which value type is float32. It controls weight of offset. Default: 0.1. position_sensitive (bool): Whether to choose deformable psroi pooling mode or not, and value type is bool(True or False). If value is False, input dimension equals to output dimension. \ If value is True, input dimension should be output dimension * pooled_height * pooled_width. Default: False. name (str|None): Name of layer. Default: None. Returns: Variable: Output of deformable roi pooling is that, if position sensitive is False, input dimension equals to output dimension. If position sensitive is True,\ input dimension should be the result of output dimension divided by pooled height and pooled width. Examples: .. code-block:: python # position_sensitive=True import paddle.fluid as fluid input = fluid.data(name="input", shape=[2, 192, 64, 64], dtype='float32') rois = fluid.data(name="rois", shape=[-1, 4], dtype='float32', lod_level=1) trans = fluid.data(name="trans", shape=[2, 384, 64, 64], dtype='float32') x = fluid.layers.deformable_roi_pooling(input=input, rois=rois, trans=trans, no_trans=False, spatial_scale=1.0, group_size=(1, 1), pooled_height=8, pooled_width=8, part_size=(8, 8), sample_per_part=4, trans_std=0.1, position_sensitive=True) # position_sensitive=False import paddle.fluid as fluid input = fluid.data(name="input", shape=[2, 192, 64, 64], dtype='float32') rois = fluid.data(name="rois", shape=[-1, 4], dtype='float32', lod_level=1) trans = fluid.data(name="trans", shape=[2, 384, 64, 64], dtype='float32') x = fluid.layers.deformable_roi_pooling(input=input, rois=rois, trans=trans, no_trans=False, spatial_scale=1.0, group_size=(1, 1), pooled_height=8, pooled_width=8, part_size=(8, 8), sample_per_part=4, trans_std=0.1, position_sensitive=False) """ check_variable_and_dtype( input, 'input', ['float32', 'float64'], 'deformable_roi_pooling' ) check_variable_and_dtype( rois, 'rois', ['float32', 'float64'], 'deformable_roi_pooling' ) check_variable_and_dtype( trans, 'trans', ['float32', 'float64'], 'deformable_roi_pooling' ) check_type( group_size, 'group_size', (list, tuple), 'deformable_roi_pooling' ) if part_size is not None: check_type( part_size, 'part_size', (list, tuple), 'deformable_roi_pooling' ) input_channels = input.shape[1] if position_sensitive == False: output_channels = input_channels else: output_channels = input_channels / pooled_height / pooled_width if part_size is None: part_height = pooled_height part_width = pooled_width part_size = [part_height, part_width] part_size = utils.convert_to_list(part_size, 2, 'part_size') group_size = utils.convert_to_list(group_size, 2, 'group_size') helper = LayerHelper('deformable_psroi_pooling', **locals()) dtype = helper.input_dtype() output = helper.create_variable_for_type_inference(dtype) top_count = helper.create_variable_for_type_inference(dtype='int32') helper.append_op( type="deformable_psroi_pooling", inputs={"Input": input, "ROIs": rois, "Trans": trans}, outputs={"Output": output, "TopCount": top_count}, attrs={ "no_trans": no_trans, "spatial_scale": spatial_scale, "output_dim": output_channels, "group_size": group_size, "pooled_height": pooled_height, "pooled_width": pooled_width, "part_size": part_size, "sample_per_part": sample_per_part, "trans_std": trans_std, }, ) return output @deprecated(since="2.0.0", update_to="paddle.shard_index") def shard_index(input, index_num, nshards, shard_id, ignore_value=-1): """ Reset the values of `input` according to the shard it beloning to. Every value in `input` must be a non-negative integer, and the parameter `index_num` represents the integer above the maximum value of `input`. Thus, all values in `input` must be in the range [0, index_num) and each value can be regarded as the offset to the beginning of the range. The range is further split into multiple shards. Specifically, we first compute the `shard_size` according to the following formula, which represents the number of integers each shard can hold. So for the i'th shard, it can hold values in the range [i*shard_size, (i+1)*shard_size). :: shard_size = (index_num + nshards - 1) // nshards For each value `v` in `input`, we reset it to a new value according to the following formula: :: v = v - shard_id * shard_size if shard_id * shard_size <= v < (shard_id+1) * shard_size else ignore_value That is, the value `v` is set to the new offset within the range represented by the shard `shard_id` if it in the range. Otherwise, we reset it to be `ignore_value`. Args: input (Tensor): Input tensor with data type int64 or int32. It's last dimension must be 1. index_num (int): An integer represents the integer above the maximum value of `input`. nshards (int): The number of shards. shard_id (int): The index of the current shard. ignore_value (int): An integer value out of sharded index range. Returns: Tensor. Examples: .. code-block:: python import paddle label = paddle.to_tensor([[16], [1]], "int64") shard_label = paddle.shard_index(input=label, index_num=20, nshards=2, shard_id=0) print(shard_label) # [[-1], [1]] """ if in_dygraph_mode(): return _C_ops.shard_index( input, index_num, nshards, shard_id, ignore_value ) check_variable_and_dtype(input, 'input', ['int64', 'int32'], 'shard_index') op_type = 'shard_index' helper = LayerHelper(op_type, **locals()) if shard_id < 0 or shard_id >= nshards: raise ValueError( 'The shard_id(%d) should be in [0, %d)' % (shard_id, nshards) ) out = helper.create_variable_for_type_inference(dtype=input.dtype) helper.append_op( type=op_type, inputs={'X': [input]}, outputs={'Out': out}, attrs={ 'index_num': index_num, 'nshards': nshards, 'shard_id': shard_id, 'ignore_value': ignore_value, }, stop_gradient=True, ) return out @templatedoc() def hard_swish(x, threshold=6.0, scale=6.0, offset=3.0, name=None): r""" This operator implements the hard_swish activation function. Hard_swish is proposed in MobileNetV3, and performs better in computational stability and efficiency compared to swish function. For more details please refer to: https://arxiv.org/pdf/1905.02244.pdf The formula is as follows: .. math:: out = \\frac{x * (min(max(0, x+offset), threshold))}{scale} In the above equation: ``threshold`` and ``scale`` should be positive, ``offset`` can be positive or negative. It is recommended to use default parameters. Args: x (Variable): Input feature, multi-dimensional Tensor. The data type should be float32 or float64. threshold (float, optional): The threshold in Relu function. Default: 6.0 scale (float, optional): The scale factor. Default: 6.0 offset (float, optional): The offset factor. Default: 3.0 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` Returns: Variable: The output tensor with the same shape and data type as input. Examples: .. code-block:: python import paddle.fluid as fluid import paddle import numpy as np paddle.enable_static() DATATYPE='float32' x_data = np.array([i for i in range(1,5)]).reshape([1,1,4]).astype(DATATYPE) x = fluid.data(name="x", shape=[None,1,4], dtype=DATATYPE) y = fluid.layers.hard_swish(x) place = fluid.CPUPlace() #place = fluid.CUDAPlace(0) exe = fluid.Executor(place) out, = exe.run(feed={'x':x_data}, fetch_list=[y.name]) print(out) # [[0.66666667, 1.66666667,3., 4.]] """ if _non_static_mode(): return _legacy_C_ops.hard_swish( x, 'threshold', threshold, 'scale', scale, 'offset', offset ) check_variable_and_dtype( x, 'x', ['float16', 'float32', 'float64'], 'hard_swish' ) helper = LayerHelper('hard_swish', **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='hard_swish', inputs={'X': x}, outputs={'Out': out}, attrs={'threshold': threshold, 'scale': scale, 'offset': offset}, ) return out @templatedoc() def mish(x, threshold=20, name=None): r""" This operator implements the mish activation function. Refer to `Mish: A Self Regularized Non-Monotonic Neural Activation Function `_ The formula is as follows if :attr:`threshold` is :code:`None` or negative: .. math:: out = x * \\tanh(\\ln(1 + e^{x})) The formula is as follows if :attr:`threshold` is set as positive value: .. math:: out = \\begin{cases} x \\ast \\tanh(x), \\text{if } x > \\text{threshold} \\\\ x \\ast \\tanh(e^{x}), \\text{if } x < -\\text{threshold} \\\\ x \\ast \\tanh(\\ln(1 + e^{x})), \\text{otherwise} \\end{cases} Args: x (Variable): Input feature, multi-dimensional Tensor. The data type should be float16, float32 or float64. threshold (float|None): threshold for softplus in Mish operator. Approximate value of softplus will be used if absolute value of input is greater than :attr:threshold and :attr:threshold is set as positive value. For none or negative threshold, approximate value is not used. Default 20. 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: Variable: The output tensor with the same shape and data type as input. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np DATATYPE='float32' x_data = np.array([i for i in range(1,5)]).reshape([1,1,4]).astype(DATATYPE) x = fluid.data(name="x", shape=[None,1,4], dtype=DATATYPE) y = fluid.layers.mish(x) place = fluid.CPUPlace() # place = fluid.CUDAPlace(0) exe = fluid.Executor(place) out, = exe.run(feed={'x':x_data}, fetch_list=[y.name]) print(out) # [[0.66666667, 1.66666667, 3., 4.]] """ if in_dygraph_mode(): return _C_ops.mish(x, threshold) if _in_legacy_dygraph(): return _legacy_C_ops.mish(x, 'threshold', threshold) check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'mish') check_type(threshold, 'threshold', (float, int), 'mish') assert ( threshold > 0 ), "threshold of mish should be greater than 0, " "but got {}".format( threshold ) helper = LayerHelper('mish', **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='mish', inputs={'X': x}, outputs={'Out': out}, attrs={'threshold': threshold}, ) return out @deprecated(since="2.0.0", update_to="paddle.uniform") @templatedoc() def uniform_random( shape, dtype='float32', min=-1.0, max=1.0, seed=0, name=None ): """ This OP returns a Tensor filled with random values sampled from a uniform distribution in the range [``min``, ``max``), with ``shape`` and ``dtype``. Examples: :: Input: shape = [1, 2] Output: result=[[0.8505902, 0.8397286]] Args: shape(list|tuple|Tensor): The shape of the output Tensor. If ``shape`` is a list or tuple, the elements of it should be integers or Tensors (with the shape [1], and the data type int32 or int64). If ``shape`` is a Tensor, it should be a 1-D Tensor(with the data type int32 or int64). dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of the output Tensor. Supported data types: float32, float64. Default is float32. min(float|int, optional): The lower bound on the range of random values to generate, ``min`` is included in the range. Default is -1.0. max(float|int, optional): The upper bound on the range of random values to generate, ``max`` is excluded in the range. Default is 1.0. seed(int, optional): Random seed used for generating samples. 0 means use a seed generated by the system. Note that if seed is not 0, this operator will always generate the same random numbers every time. 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`. Returns: Tensor: A Tensor filled with random values sampled from a uniform distribution in the range [``min``, ``max``), with ``shape`` and ``dtype``. Raises: TypeError: If ``shape`` is not list, tuple, Tensor. TypeError: If ``dtype`` is not float32, float64. Examples: .. code-block:: python import paddle import paddle.fluid as fluid paddle.enable_static() # example 1: # attr shape is a list which doesn't contain Tensor. result_1 = fluid.layers.uniform_random(shape=[3, 4]) # [[ 0.84524226, 0.6921872, 0.56528175, 0.71690357], # [-0.34646994, -0.45116323, -0.09902662, -0.11397249], # [ 0.433519, 0.39483607, -0.8660099, 0.83664286]] # example 2: # attr shape is a list which contains Tensor. dim_1 = fluid.layers.fill_constant([1], "int64", 2) dim_2 = fluid.layers.fill_constant([1], "int32", 3) result_2 = fluid.layers.uniform_random(shape=[dim_1, dim_2]) # [[-0.9951253, 0.30757582, 0.9899647 ], # [ 0.5864527, 0.6607096, -0.8886161 ]] # example 3: # attr shape is a Tensor, the data type must be int64 or int32. var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64") result_3 = fluid.layers.uniform_random(var_shape) # if var_shape's value is [2, 3] # result_3 is: # [[-0.8517412, -0.4006908, 0.2551912 ], # [ 0.3364414, 0.36278176, -0.16085452]] """ if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) if in_dygraph_mode(): shape = utils.convert_shape_to_list(shape) return _C_ops.uniform( shape, dtype, float(min), float(max), seed, _current_expected_place(), ) elif _in_legacy_dygraph(): shape = utils.convert_shape_to_list(shape) return _legacy_C_ops.uniform_random( 'shape', shape, 'min', float(min), 'max', float(max), 'seed', seed, 'dtype', dtype, ) check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random/rand') check_dtype( dtype, 'dtype', ('float32', 'float64', 'uint16'), 'uniform_random/rand' ) check_type(min, 'min', (float, int, Variable), 'uniform_random/rand') check_type(max, 'max', (float, int, Variable), 'uniform_random/rand') inputs = dict() attrs = {'seed': seed, 'min': min, 'max': max, 'dtype': dtype} utils.get_shape_tensor_inputs( inputs=inputs, attrs=attrs, shape=shape, op_type='uniform_random/rand' ) helper = LayerHelper("uniform_random", **locals()) out = helper.create_variable_for_type_inference(dtype) helper.append_op( type="uniform_random", inputs=inputs, attrs=attrs, outputs={"Out": out} ) utils.try_set_static_shape_tensor(out, shape) return out def unbind(input, axis=0): """ Removes a tensor dimension, then split the input tensor into multiple sub-Tensors. Args: input (Variable): The input variable which is an N-D Tensor, data type being float32, float64, int32 or int64. axis (int32|int64, optional): A scalar with type ``int32|int64`` shape [1]. The dimension along which to unbind. If :math:`axis < 0`, the dimension to unbind along is :math:`rank(input) + axis`. Default is 0. Returns: list(Variable): The list of segmented Tensor variables. Example: .. code-block:: python import paddle # input is a variable which shape is [3, 4, 5] input = paddle.fluid.data( name="input", shape=[3, 4, 5], dtype="float32") [x0, x1, x2] = paddle.tensor.unbind(input, axis=0) # x0.shape [4, 5] # x1.shape [4, 5] # x2.shape [4, 5] [x0, x1, x2, x3] = paddle.tensor.unbind(input, axis=1) # x0.shape [3, 5] # x1.shape [3, 5] # x2.shape [3, 5] # x3.shape [3, 5] """ helper = LayerHelper("unbind", **locals()) check_type(input, 'input', (Variable), 'unbind') dtype = helper.input_dtype() check_dtype( dtype, 'unbind', ['float32', 'float64', 'int32', 'int64'], 'unbind' ) if not isinstance(axis, (int)): raise TypeError( "The type of 'axis' must be int, but received %s." % (type(axis)) ) if isinstance(axis, np.generic): axis = np.asscalar(axis) input_shape = input.shape axis_ = axis if axis >= 0 else len(input_shape) + axis num = input_shape[axis_] outs = [ helper.create_variable_for_type_inference(dtype=helper.input_dtype()) for i in range(num) ] helper.append_op( type="unbind", inputs={"X": input}, outputs={"Out": outs}, attrs={"axis": axis}, ) return outs