# 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. """ from __future__ import print_function import numpy as np import warnings import six import os import inspect from ..layer_helper import LayerHelper from ..initializer import Normal, Constant, NumpyArrayInitializer from ..framework import Variable, OpProtoHolder, in_dygraph_mode from ..dygraph import base from ..param_attr import ParamAttr from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_ from .tensor import concat, assign, fill_constant, zeros from . import utils from .. import unique_name from functools import reduce from .. import core from ..dygraph import layers from ..data_feeder import convert_dtype, check_type_and_dtype, check_type, check_dtype __all__ = [ 'fc', 'embedding', 'linear_chain_crf', 'crf_decoding', 'cos_sim', 'chunk_eval', 'conv2d', 'conv3d', 'softmax', 'pool2d', 'pool3d', 'adaptive_pool2d', 'adaptive_pool3d', 'batch_norm', 'instance_norm', 'data_norm', 'conv2d_transpose', 'conv3d_transpose', 'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min', 'reduce_prod', 'reduce_all', 'reduce_any', 'dropout', 'split', 'ctc_greedy_decoder', 'l2_normalize', 'matmul', 'topk', 'transpose', 'im2sequence', 'row_conv', 'multiplex', 'layer_norm', 'group_norm', 'spectral_norm', 'smooth_l1', 'one_hot', 'autoincreased_step_counter', 'reshape', 'squeeze', 'unsqueeze', 'lod_reset', 'lod_append', 'lrn', 'pad', 'pad_constant_like', 'label_smooth', 'roi_pool', 'roi_align', 'dice_loss', 'image_resize', 'image_resize_short', 'resize_bilinear', 'resize_trilinear', 'resize_nearest', 'gather', 'gather_nd', 'scatter', 'scatter_nd_add', 'scatter_nd', 'random_crop', 'mean_iou', 'relu', 'selu', 'log', 'crop', 'crop_tensor', 'elu', 'relu6', 'pow', 'stanh', 'hard_sigmoid', 'swish', 'prelu', 'brelu', 'leaky_relu', 'soft_relu', 'flatten', 'stack', 'pad2d', 'unstack', 'unique', 'unique_with_counts', 'expand', 'expand_as', 'scale', 'elementwise_add', 'elementwise_div', 'elementwise_sub', 'elementwise_mul', 'elementwise_max', 'elementwise_min', 'elementwise_pow', 'elementwise_mod', 'elementwise_floordiv', 'uniform_random_batch_size_like', 'gaussian_random', 'sampling_id', 'gaussian_random_batch_size_like', 'sum', 'slice', 'strided_slice', 'shape', 'rank', 'size', 'logical_and', 'logical_or', 'logical_xor', 'logical_not', 'clip', 'clip_by_norm', 'mean', 'mul', 'maxout', 'space_to_depth', 'affine_grid', 'affine_channel', 'similarity_focus', 'hash', 'grid_sampler', 'log_loss', 'add_position_encoding', 'bilinear_tensor_product', 'merge_selected_rows', 'get_tensor_from_selected_rows', 'shuffle_channel', 'temporal_shift', 'py_func', 'psroi_pool', 'prroi_pool', 'pixel_shuffle', 'fsp_matrix', 'continuous_value_model', 'where', 'sign', 'deformable_conv', 'unfold', 'deformable_roi_pooling', 'filter_by_instag', 'shard_index', 'hard_swish', 'gather_tree', 'uniform_random', ] def fc(input, size, num_flatten_dims=1, param_attr=None, bias_attr=None, act=None, name=None): """ **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 ouput 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 # 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', 'float32', 'float64'], 'fc') mul_results = [] for input_var, param_attr in helper.iter_inputs_and_params(): input_shape = input_var.shape 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) def embedding(input, size, is_sparse=False, is_distributed=False, padding_idx=None, param_attr=None, dtype='float32'): """ **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 shoud 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 data = fluid.data(name='x', shape=[None, 1], dtype='int64') # exampel 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_type_and_dtype(input, 'input', Variable, ['int64'], 'fluid.layers.embedding') check_dtype(dtype, 'dtype', ['float16', 'float32', 'float64'], 'fluid.layers.embedding') remote_prefetch = is_sparse and (not is_distributed) if remote_prefetch: assert is_sparse is True and is_distributed is 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_box_sparse(input, size, dtype='float32'): """ **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)) ] helper.append_op( type='pull_box_sparse', inputs={'Ids': inputs}, outputs={'Out': outs}, attrs={'size': size}) if len(outs) == 1: return outs[0] return outs @templatedoc() def linear_chain_crf(input, label, param_attr=None, length=None): """ 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 #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) """ 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): """ ${comment} Args: input(${emission_type}): ${emission_comment} param_attr (ParamAttr|None): To specify the weight parameter attribute. Default: None, which means the default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . label(${label_type}, optional): ${label_comment} length(${length_type}, optional): ${length_comment} Returns: Variable: ${viterbi_path_comment} Examples: .. code-block:: python import paddle.fluid as fluid # LoDTensor-based example num_labels = 10 feature = fluid.data(name='word_emb', shape=[-1, 784], dtype='float32', lod_level=1) label = fluid.data(name='label', shape=[-1, 1], dtype='int64', lod_level=1) emission = fluid.layers.fc(input=feature, size=num_labels) crf_cost = fluid.layers.linear_chain_crf(input=emission, label=label, param_attr=fluid.ParamAttr(name="crfw")) crf_decode = fluid.layers.crf_decoding(input=emission, param_attr=fluid.ParamAttr(name="crfw")) # Common tensor example num_labels, max_len = 10, 20 feature = fluid.data(name='word_emb_pad', shape=[-1, max_len, 784], dtype='float32') label = fluid.data(name='label_pad', shape=[-1, max_len, 1], dtype='int64') length = fluid.data(name='length', shape=[-1, 1], dtype='int64') emission = fluid.layers.fc(input=feature, size=num_labels, num_flatten_dims=2) crf_cost = fluid.layers.linear_chain_crf(input=emission, label=label, length=length, param_attr=fluid.ParamAttr(name="crfw_pad")) crf_decode = fluid.layers.crf_decoding(input=emission, length=length, param_attr=fluid.ParamAttr(name="crfw_pad")) """ helper = LayerHelper('crf_decoding', **locals()) transition = helper.get_parameter(param_attr.name) viterbi_path = helper.create_variable_for_type_inference( dtype=helper.input_dtype()) 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 @templatedoc() def cos_sim(X, Y): """ ${comment} Args: X (Variable): ${x_comment}. Y (Variable): ${y_comment}. Returns: A Variable holding LoDTensor representing the output of cosine(X, Y). Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name='x', shape=[3, 7], dtype='float32') y = fluid.data(name='y', shape=[1, 7], dtype='float32') out = fluid.layers.cos_sim(x, y) """ helper = LayerHelper('cos_sim', **locals()) out = helper.create_variable_for_type_inference(dtype=X.dtype) xnorm = helper.create_variable_for_type_inference(dtype=X.dtype) ynorm = helper.create_variable_for_type_inference(dtype=X.dtype) helper.append_op( type='cos_sim', inputs={'X': [X], 'Y': [Y]}, outputs={'Out': [out], 'XNorm': [xnorm], 'YNorm': [ynorm]}) return out def dropout(x, dropout_prob, is_test=False, 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. 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.fluid as fluid x = fluid.data(name="data", shape=[None, 32, 32], dtype="float32") droped = fluid.layers.dropout(x, dropout_prob=0.5) """ helper = LayerHelper('dropout', **locals()) check_type_and_dtype(x, 'x', Variable, ['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) if (seed is None or seed == 0) and helper.main_program.random_seed != 0: seed = helper.main_program.random_seed helper.append_op( type='dropout', inputs={'X': [x]}, outputs={'Out': [out], 'Mask': [mask]}, 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 out @templatedoc() def chunk_eval(input, label, chunk_scheme, num_chunk_types, excluded_chunk_types=None, seq_length=None): """ This operator computes the precision, recall and F1-score for chunk detection. It is often used in sequence tagging tasks, such as Named Entity Recognition(NER). For some basics of chunking, please refer to `Chunking with Support Vector Machines `_ . This operator supports IOB, IOE, IOBES and IO (also known as plain) tagging schemes. Here is a NER example for the usage of these tagging schemes: .. code-block:: python ====== ====== ====== ===== == ============ ===== ===== ===== == ========= Li Ming works at Agricultural Bank of China in Beijing. ====== ====== ====== ===== == ============ ===== ===== ===== == ========= IO I-PER I-PER O O I-ORG I-ORG I-ORG I-ORG O I-LOC IOB B-PER I-PER O O B-ORG I-ORG I-ORG I-ORG O B-LOC IOE I-PER E-PER O O I-ORG I-ORG I-ORG E-ORG O E-LOC IOBES B-PER E-PER O O I-ORG I-ORG I-ORG E-ORG O S-LOC ====== ====== ====== ===== == ============ ===== ===== ===== == ========= There are three chunk types(named entity types) including PER(person), ORG(organization) and LOC(location), and we can see that the labels have the form `-` . Since the implementation of this operator actually uses label ids rather than label strings, to make it work, there should be a way to map label ids to tag types and chunk types. This operator uses the following way to do mapping: .. code-block:: python tag_type = label % num_tag_type chunk_type = label / num_tag_type where `num_tag_type` is the num of tag types in the tagging scheme, `num_chunk_type` is the num of chunk types, and `tag_type` get its value from the following table. .. code-block:: python Scheme Begin Inside End Single plain 0 - - - IOB 0 1 - - IOE - 0 1 - IOBES 0 1 2 3 Accordingly, in the above NER example, if the tagging scheme is IOB and chunk types are ORG, PER and LOC, then the label ids would be as follows: .. code-block:: python B-ORG 0 I-ORG 1 B-PER 2 I-PER 3 B-LOC 4 I-LOC 5 O 6 With which we can map each label id to the corresponding tag type and chunk type correctly. Args: input (Variable): A Tensor or LoDTensor, representing the predicted labels from the network. When it is a Tensor, its shape would be `[N, M, 1]`, where `N` stands for batch size, `M` for sequence length; When it is a LoDTensor, its shape would be `[N, 1]` where `N` stands for the total sequence lengths in this mini-batch. The data type should be int64. label (Variable): A Tensor or LoDTensor representing the ground-truth labels. It shoud have the same shape, lod and data type as ``input`` . chunk_scheme (str): Indicate the tagging schemes used here. The value must be IOB, IOE, IOBES or plain. num_chunk_types (int): The number of chunk types. excluded_chunk_types (list, optional): Indicate the chunk types shouldn't be taken into account. It should be a list of chunk type ids(integer). Default None. seq_length(Variable, optional): A 1D Tensor containing the length of each sequence when ``input`` and ``label`` are Tensor. It needn't be provided if ``input`` and ``label`` are LoDTensor. Default None. Returns: tuple: A tuple including precision, recall, F1-score, chunk number detected, \ chunk number in ground-truth, chunk number correctly detected. Each \ is a Tensor with shape `[1]`. The data type of precision, recall and \ F1-score all is float32, and the others' data type all is int64. Examples: .. code-block:: python import paddle.fluid as fluid dict_size = 10000 label_dict_len = 7 sequence = fluid.data( name='id', shape=[-1, 1], lod_level=1, dtype='int64') embedding = fluid.embedding( input=sequence, size=[dict_size, 512]) hidden = fluid.layers.fc(input=embedding, size=512) label = fluid.layers.data( name='label', shape=[1], lod_level=1, dtype='int32') crf = fluid.layers.linear_chain_crf( input=hidden, label=label, param_attr=fluid.ParamAttr(name="crfw")) crf_decode = fluid.layers.crf_decoding( input=hidden, param_attr=fluid.ParamAttr(name="crfw")) fluid.layers.chunk_eval( input=crf_decode, label=label, chunk_scheme="IOB", num_chunk_types=(label_dict_len - 1) / 2) """ helper = LayerHelper("chunk_eval", **locals()) # prepare output precision = helper.create_variable_for_type_inference(dtype="float32") recall = helper.create_variable_for_type_inference(dtype="float32") f1_score = helper.create_variable_for_type_inference(dtype="float32") num_infer_chunks = helper.create_variable_for_type_inference(dtype="int64") num_label_chunks = helper.create_variable_for_type_inference(dtype="int64") num_correct_chunks = helper.create_variable_for_type_inference( dtype="int64") this_input = {"Inference": [input], "Label": [label]} if seq_length: this_input["SeqLength"] = [seq_length] helper.append_op( type="chunk_eval", inputs=this_input, outputs={ "Precision": [precision], "Recall": [recall], "F1-Score": [f1_score], "NumInferChunks": [num_infer_chunks], "NumLabelChunks": [num_label_chunks], "NumCorrectChunks": [num_correct_chunks] }, attrs={ "num_chunk_types": num_chunk_types, "chunk_scheme": chunk_scheme, "excluded_chunk_types": excluded_chunk_types or [] }) return (precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks) def softmax(input, use_cudnn=False, name=None, axis=-1): """ 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 (Variable): The input variable. 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 numerical stablity, set use_cudnn to \ False 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 variable. Default: -1. -1 means the last dimension. Returns: Variable: ``Tensor`` indicates the output of softmax. The data type and shape are the same as ``input`` . Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np data = fluid.data(name="input", shape=[-1, 3],dtype="float32") result = fluid.layers.softmax(data,axis=1) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) x = np.random.rand(3, 3).astype("float32") output= exe.run(feed={"input": x}, fetch_list=[result[0]]) print(output) """ helper = LayerHelper('softmax', **locals()) check_type_and_dtype(input, 'input', Variable, ['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={"axis": axis, "use_cudnn": use_cudnn}) 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"): """ 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 (Variable): 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 dimention.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): 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: A Variable holding Tensor representing the conv2d, whose data type is the same with input. If act is None, the tensor variable storing the convolution result, and if act is not None, the tensor variable storing convolution and non-linearity activation result. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32') conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu") """ check_type_and_dtype(input, 'input', Variable, ['float16', 'float32', 'float64'], 'conv2d') 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." l_type = 'conv2d' if (num_channels == groups and num_filters % num_channels == 0 and not use_cudnn): l_type = 'depthwise_conv2d' helper = LayerHelper(l_type, **locals()) dtype = helper.input_dtype() if groups is None: num_filter_channels = num_channels 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 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 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) 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) def conv3d(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="NCDHW"): """ The convolution3D layer calculates the output based on the input, filter and strides, paddings, dilations, groups parameters. Input(Input) and Output(Output) are in NCDHW or NDHWC format. Where N is batch size C is the number of channels, D is the depth of the feature, H is the height of the feature, and W is the width of the feature. Convlution3D is similar with Convlution2D but adds one dimension(depth). 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) In the above equation: * :math:`X`: Input value, a tensor with NCDHW or NDHWC format. * :math:`W`: Filter value, a tensor with MCDHW 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}, D_{in}, H_{in}, W_{in})` Filter shape: :math:`(C_{out}, C_{in}, D_f, H_f, W_f)` - Output: Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` Where .. math:: D_{out}&= \\frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{strides[0]} + 1 \\\\ H_{out}&= \\frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{strides[1]} + 1 \\\\ W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1 Args: input (Variable): The input is 5-D Tensor with shape [N, C, D, 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 three integers, (filter_size_depth, filter_size_height, filter_size_width). Otherwise, filter_size_depth = 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 three integers, (stride_depth, stride_height, stride_width). Otherwise, stride_depth = 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 dimention. 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_depth, pad_height, pad_width]` or `[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `data_format` is `"NCDHW"`, `pool_padding` can be in the form `[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`. when `data_format` is `"NDHWC"`, `pool_padding` can be in the form `[[0,0], [pad_depth_front, pad_depth_back], [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 three integers, (dilation_depth, dilation_height, dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation. Default: dilation = 1. groups (int): The groups number of the Conv3d 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 conv3d. If it is set to None or one attribute of ParamAttr, conv3d will create ParamAttr as param_attr. If it is set to None, 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 conv3d. 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, conv3d 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): The data format of the input and output data. 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: A Variable holding Tensor representing the conv3d, whose data type is the same with input. If act is None, the tensor variable storing the convolution result, and if act is not None, the tensor variable storing convolution and non-linearity activation result. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.data(name='data', shape=[None, 3, 12, 32, 32], dtype='float32') conv3d = fluid.layers.conv3d(input=data, num_filters=2, filter_size=3, act="relu") """ l_type = 'conv3d' assert param_attr is not False, "param_attr should not be False here." helper = LayerHelper(l_type, **locals()) dtype = helper.input_dtype() 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 ["NCDHW", "NDHWC"]: raise ValueError( "Attr(data_format) should be 'NCDHW' or 'NDHWC'. Received " "Attr(data_format): %s." % str(data_format)) channel_last = (data_format == "NDHWC") num_channels = input.shape[4] 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))) if groups is None: num_filter_channels = num_channels else: if num_channels % groups != 0: raise ValueError( "The number of input channels must be divisible by Attr(groups). " "Received: number of channels(%s), groups(%s)." % (str(num_channels), str(groups))) num_filter_channels = num_channels // groups filter_size = utils.convert_to_list(filter_size, 3, 'filter_size') stride = utils.convert_to_list(stride, 3, 'stride') dilation = utils.convert_to_list(dilation, 3, 'dilation') 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) == 5: if is_list_or_tuple(padding[0]) and (data_format == "NCDHW"): 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[2:5] padding = [ele for a_list in padding for ele in a_list] elif is_list_or_tuple(padding[0]) and (data_format == "NDHWC"): if not (padding[0] == [0, 0] and padding[4] == [0, 0]): raise ValueError( "Non-zero padding(%s) in the batch or channel dimensions " "is not supported." % str(padding)) padding = padding[1:4] padding = [ele for a_list in padding for ele in a_list] padding = utils.convert_to_list(padding, 6, 'padding') if utils._is_symmetric_padding(padding, 3): padding = [padding[0], padding[2], padding[4]] elif is_list_or_tuple(padding) and len(padding) == 6: padding = utils.convert_to_list(padding, 6, 'padding') if utils._is_symmetric_padding(padding, 3): padding = [padding[0], padding[2], padding[4]] else: padding = utils.convert_to_list(padding, 3, '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, 0] elif padding == "SAME": padding_algorithm = "SAME" padding = [0, 0, 0] padding = _update_padding(padding, data_format) input_shape = input.shape filter_shape = [num_filters, num_filter_channels] + filter_size def _get_default_param_initializer(): filter_elem_num = filter_size[0] * filter_size[1] * filter_size[ 2] * num_channels 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) 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, "padding_algorithm": padding_algorithm, "data_format": data_format, }) if data_format == 'NCDHW': 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=4, dim_end=5) 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"`, `"NDHW"`. 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 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) 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 @templatedoc() def pool3d(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="NCDHW"): """ ${comment} Args: input (Variable): The input tensor of pooling operator, which is a 5-D tensor with shape [N, C, D, H, W]. The format of input tensor is `"NCDHW"` or `"NDHWC"`, where `N` is batch size, `C` is the number of channels, `D` is the depth of the feature, `H` is the height of the feature, and `W` is the width of the feature. pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain three integers, (pool_size_Depth, pool_size_Height, pool_size_Width). Otherwise, the pool kernel size will be the cube of an int. pool_type (string): ${pooling_type_comment} pool_stride (string|int|list|tuple)): The pool padding. If `pool_padding` is a string, either 'VALID' or 'SAME' which is the padding algorithm. If pool stride size is a tuple or list, it must contain three integers, `[stride_Depth, stride_Height, stride_Width]`. Otherwise, the pool stride size will be a cube of an int. pool_padding (int|list|tuple): The pool padding size. If pool padding size is a tuple or list, it could be in three forms: `[pad_depth, pad_height, pad_width]` or `[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `data_format` is `"NCDHW"`, `pool_padding` can be in the form `[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`. when `data_format` is `"NDHWC"`, `pool_padding` can be in the form `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`. 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: `"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: 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 "NCDHW" or "NDHWC". 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 data = fluid.data(name='data', shape=[None, 3, 32, 32, 32], dtype='float32') # max pool3d pool3d = fluid.layers.pool3d( input = data, pool_size = 2, pool_type = "max", pool_stride = 1, global_pooling=False) # average pool3d pool3d = fluid.layers.pool3d( input = data, pool_size = 2, pool_type = "avg", pool_stride = 1, global_pooling=False) # global average pool3d pool3d = fluid.layers.pool3d( input = data, pool_size = 2, pool_type = "avg", pool_stride = 1, global_pooling=True) # example 1: # Attr(pool_padding) is a list with 6 elements, Attr(data_format) is "NCDHW". out_1 = fluid.layers.pool3d( input = data, pool_size = 2, pool_type = "avg", pool_stride = 1, pool_padding = [1, 2, 1, 0, 1, 2], global_pooling = False, data_format = "NCDHW") # example 2: # Attr(pool_padding) is a string, Attr(data_format) is "NCDHW". out_2 = fluid.layers.pool3d( input = data, pool_size = 3, pool_type = "avg", pool_stride = 1, pool_padding = "VALID", global_pooling = False, data_format = "NCDHW") """ 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 Attr(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 ["NCDHW", "NDHWC"]: raise ValueError( "Attr(data_format) should be 'NCDHW' or 'NDHWC'. Received " "Attr(data_format): %s" % str(data_format)) pool_size = utils.convert_to_list(pool_size, 3, 'pool_size') pool_stride = utils.convert_to_list(pool_stride, 3, 'pool_stride') def update_padding(padding, data_format): def is_list_or_tuple(ele): if isinstance(ele, (list, tuple)): return True return False if is_list_or_tuple(padding) and len(padding) == 5: if is_list_or_tuple(padding[0]) and (data_format == "NCDHW"): 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[2:5] padding = [ele for a_list in padding for ele in a_list] elif is_list_or_tuple(padding[0]) and (data_format == "NDHWC"): if not (padding[0] == [0, 0] and padding[4] == [0, 0]): raise ValueError( "Non-zero pool_padding(%s) in the batch or channel dimensions " "is not supported." % str(padding)) padding = padding[1:4] padding = [ele for a_list in padding for ele in a_list] padding = utils.convert_to_list(padding, 6, 'padding') if utils._is_symmetric_padding(padding, 3): padding = [padding[0], padding[2], padding[4]] elif is_list_or_tuple(padding) and len(padding) == 6: padding = utils.convert_to_list(padding, 6, 'padding') if utils._is_symmetric_padding(padding, 3): padding = [padding[0], padding[2], padding[4]] else: padding = utils.convert_to_list(padding, 3, '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, 0] if ceil_mode != False: raise ValueError( "When Attr(pool_padding) is \"VALID\", ceil_mode must be False. " "Received ceil_mode: True.") elif pool_padding == "SAME": padding_algorithm = "SAME" pool_padding = [0, 0, 0] pool_padding = update_padding(pool_padding, data_format) op_type = "pool3d" 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 @templatedoc(op_type="pool2d") def adaptive_pool2d(input, pool_size, pool_type="max", require_index=False, name=None): """ This operation calculates the output based on the input, pool_size, pool_type parameters. Input(X) and output(Out) are in NCHW 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. Parameters(pool_size) should contain two elements which represent height and width, respectively. Also the H and W dimensions of output(Out) is same as Parameter(pool_size). The output tensor shape will be [N, C, pool_size[0], pool_size[1]] For average adaptive pool2d: .. math:: hstart &= floor(i * H_{in} / H_{out}) hend &= ceil((i + 1) * H_{in} / H_{out}) wstart &= floor(j * W_{in} / W_{out}) wend &= ceil((j + 1) * W_{in} / W_{out}) Output(i ,j) &= \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)} 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, 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 is 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). pool_type: ${pooling_type_comment} require_index (bool): If true, the index of max pooling point will be returned along with outputs. It cannot be set in average pooling type. Default False. 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: The output tensor of adaptive pooling result. The data type is same as input tensor. Raises: ValueError: 'pool_type' is not 'max' nor 'avg'. ValueError: invalid setting 'require_index' true when 'pool_type' is 'avg'. ValueError: 'pool_size' should be a list or tuple with length as 2. Examples: .. code-block:: python # average adaptive pool2d # suppose input data in shape of [N, C, H, W], `pool_size` is [m, n], # output shape is [N, C, m, n], adaptive pool divide H and W dimentions # of input data into m * n grids averagely and performs poolings in each # grid to get output. # adaptive average pool performs calculations as follow: # # for i in range(m): # for j in range(n): # hstart = floor(i * H / m) # hend = ceil((i + 1) * H / m) # wstart = floor(i * W / n) # wend = ceil((i + 1) * W / n) # output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend]) # import paddle.fluid as fluid data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32') pool_out = fluid.layers.adaptive_pool2d( input=data, pool_size=[3, 3], pool_type='avg') # max adaptive pool2d # suppose input data in shape of [N, C, H, W], `pool_size` is [m, n], # output shape is [N, C, m, n], adaptive pool divide H and W dimentions # of input data into m * n grids averagely and performs poolings in each # grid to get output. # adaptive average pool performs calculations as follow: # # for i in range(m): # for j in range(n): # hstart = floor(i * H / m) # hend = ceil((i + 1) * H / m) # wstart = floor(i * W / n) # wend = ceil((i + 1) * W / n) # output[:, :, i, j] = max(input[:, :, hstart: hend, wstart: wend]) # import paddle.fluid as fluid data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32') pool_out = fluid.layers.adaptive_pool2d( input=data, pool_size=[3, 3], pool_type='max') """ if pool_type not in ["max", "avg"]: raise ValueError( "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.", str(pool_type)) if pool_type == "avg" and require_index: raise ValueError( "invalid setting 'require_index' true when 'pool_type' is 'avg'.") pool_size = utils.convert_to_list(pool_size, 2, 'pool_size') if pool_type == "max": l_type = 'max_pool2d_with_index' else: l_type = "pool2d" helper = LayerHelper(l_type, **locals()) dtype = helper.input_dtype() pool_out = helper.create_variable_for_type_inference(dtype) outputs = {"Out": pool_out} if pool_type == "max": mask = helper.create_variable_for_type_inference(dtype) outputs["Mask"] = mask helper.append_op( type=l_type, inputs={"X": input}, outputs=outputs, attrs={ "pooling_type": pool_type, "ksize": pool_size, "adaptive": True, }) return (pool_out, mask) if require_index else pool_out @templatedoc(op_type="pool3d") def adaptive_pool3d(input, pool_size, pool_type="max", require_index=False, name=None): """ This operation calculates the output based on the input, pool_size, pool_type parameters. Input(X) and output(Out) are in NCDHW format, where N is batch size, C is the number of channels, D is the depth of the feature, H is the height of the feature, and W is the width of the feature. Parameters(pool_size) should contain three elements which represent height and width, respectively. Also the D, H and W dimensions of output(Out) is same as Parameter(pool_size). The output tensor shape will be [N, C, pool_size[0], pool_size[1], pool_size[2]] For average adaptive pool3d: .. math:: dstart &= floor(i * D_{in} / D_{out}) dend &= ceil((i + 1) * D_{in} / D_{out}) hstart &= floor(j * H_{in} / H_{out}) hend &= ceil((j + 1) * H_{in} / H_{out}) wstart &= floor(k * W_{in} / W_{out}) wend &= ceil((k + 1) * W_{in} / W_{out}) Output(i ,j, k) &= \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)} Args: input (Variable): The input tensor of pooling operator, which is a 5-D tensor with shape [N, C, D, H, W]. The format of input tensor is NCDHW, where N is batch size, C is the number of channels, D is the depth of the feature, H is the height of the feature, and W is the width of the feature. The data type is float32 or float64. pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain three integers, (Depth, Height, Width). pool_type: ${pooling_type_comment} require_index (bool): If true, the index of max pooling point will be returned along with outputs. It cannot be set in average pooling type. Default False. 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: The output tensor of adaptive pooling result. The data type is same as input tensor. Raises: ValueError: 'pool_type' is not 'max' nor 'avg'. ValueError: invalid setting 'require_index' true when 'pool_type' is 'avg'. ValueError: 'pool_size' should be a list or tuple with length as 2. Examples: .. code-block:: python # average adaptive pool3d # suppose input data in shape of [N, C, D, H, W], `pool_size` is [l, m, n], # output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimentions # of input data into l * m * n grids averagely and performs poolings in each # grid to get output. # adaptive average pool performs calculations as follow: # # for i in range(l): # for j in range(m): # for k in range(n): # dstart = floor(i * D / l) # dend = ceil((i + 1) * D / l) # hstart = floor(j * H / m) # hend = ceil((j + 1) * H / m) # wstart = floor(k * W / n) # wend = ceil((k + 1) * W / n) # output[:, :, i, j, k] = # avg(input[:, :, dstart:dend, hstart: hend, wstart: wend]) # import paddle.fluid as fluid data = fluid.data( name='data', shape=[None, 3, 32, 32, 32], dtype='float32') pool_out = fluid.layers.adaptive_pool3d( input=data, pool_size=[3, 3, 3], pool_type='avg') # max adaptive pool3d # suppose input data in shape of [N, C, D, H, W], `pool_size` is [l, m, n], # output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimentions # of input data into l * m * n grids averagely and performs poolings in each # grid to get output. # adaptive average pool performs calculations as follow: # # for i in range(l): # for j in range(m): # for k in range(n): # dstart = floor(i * D / l) # dend = ceil((i + 1) * D / l) # hstart = floor(j * H / m) # hend = ceil((j + 1) * H / m) # wstart = floor(k * W / n) # wend = ceil((k + 1) * W / n) # output[:, :, i, j, k] = # avg(input[:, :, dstart:dend, hstart: hend, wstart: wend]) # import paddle.fluid as fluid data = fluid.data( name='data', shape=[None, 3, 32, 32, 32], dtype='float32') pool_out = fluid.layers.adaptive_pool3d( input=data, pool_size=[3, 3, 3], pool_type='max') """ if pool_type not in ["max", "avg"]: raise ValueError( "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.", str(pool_type)) if pool_type == "avg" and require_index: raise ValueError( "invalid setting 'require_index' true when 'pool_type' is 'avg'.") pool_size = utils.convert_to_list(pool_size, 3, 'pool_size') if pool_type == "max": l_type = 'max_pool3d_with_index' else: l_type = "pool3d" helper = LayerHelper(l_type, **locals()) dtype = helper.input_dtype() pool_out = helper.create_variable_for_type_inference(dtype) outputs = {"Out": pool_out} if pool_type == "max": mask = helper.create_variable_for_type_inference(dtype) outputs["Mask"] = mask helper.append_op( type=l_type, inputs={"X": input}, outputs=outputs, attrs={ "pooling_type": pool_type, "ksize": pool_size, "adaptive": True, }) return (pool_out, mask) if require_index else 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): """ **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. Args: input(variable): The rank of input variable 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, Default 0.9): The value used for the moving_mean and moving_var computation. 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, default NCHW): the data_layout of input, is NCHW or NHWC. 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 Variable holding 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.fluid as fluid x = fluid.data(name='x', shape=[3, 7, 3, 7], dtype='float32') hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w') hidden2 = fluid.layers.batch_norm(input=hidden1) """ assert bias_attr is not False, "bias_attr should not be False in batch_norm." helper = LayerHelper('batch_norm', **locals()) check_type_and_dtype(input, 'input', Variable, ['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 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) batch_norm_out = input if in_place else helper.create_variable_for_type_inference( dtype) helper.append_op( type="batch_norm", inputs={ "X": input, "Scale": scale, "Bias": bias, "Mean": mean, "Variance": variance }, outputs={ "Y": batch_norm_out, "MeanOut": mean_out, "VarianceOut": variance_out, "SavedMean": saved_mean, "SavedVariance": saved_variance }, 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 }) return helper.append_activation(batch_norm_out) def instance_norm(input, epsilon=1e-05, param_attr=None, bias_attr=None, name=None): """ **Instance 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: DataLayout: NCHW `[batch, in_channels, in_height, in_width]` Refer to `Instance Normalization: The Missing Ingredient for Fast Stylization `_ for more details. :math:`input` is the input features over a mini-batch. .. math:: \\mu_{\\beta} &\\gets \\frac{1}{HW} \\sum_{i=1}^{HW} x_i \\qquad &//\\ \\ mean\ of\ one\ feature\ map\ in\ mini-batch \\\\ \\sigma_{\\beta}^{2} &\\gets \\frac{1}{HW} \\sum_{i=1}^{HW}(x_i - \\ \\mu_{\\beta})^2 \\qquad &//\ variance\ of\ one\ feature\ map\ in\ mini-batch \\\\ \\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 Note: `H` means height of feature map, `W` means width of feature map. Args: input(variable): The rank of input variable can be 2, 3, 4, 5. The data type is float32 or float64. 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 instance_norm. If it is set to None or one attribute of ParamAttr, instance_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 instance_norm. If it is set to None or one attribute of ParamAttr, instance_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. name(string, Default None): A name for this layer(optional). If set None, the layer will be named automatically. Returns: A Variable holding Tensor which is the result after applying instance normalization on the input, has same shape and data type with input. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name='x', shape=[3, 7, 3, 7], dtype='float32') hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w') hidden2 = fluid.layers.instance_norm(input=hidden1) """ assert bias_attr is not False, "bias_attr should not be False in instance_norm." helper = LayerHelper('instance_norm', **locals()) dtype = helper.input_dtype() # use fp32 for in parameter if dtype == core.VarDesc.VarType.FP16: dtype = core.VarDesc.VarType.FP32 input_shape = input.shape channel_num = input_shape[1] 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, default_initializer=Constant(0.0)) # create output 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) instance_norm_out = helper.create_variable_for_type_inference(dtype) helper.append_op( type="instance_norm", inputs={ "X": input, "Scale": scale, "Bias": bias, }, outputs={ "Y": instance_norm_out, "SavedMean": saved_mean, "SavedVariance": saved_variance }, attrs={"epsilon": epsilon, }) return instance_norm_out def data_norm(input, act=None, epsilon=1e-05, param_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): """ **Data Normalization Layer** This op can be used as a normalizer function for conv2d and 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]` :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 Args: input(variable): The input variable which is a LoDTensor. act(string, Default None): Activation type, linear|relu|prelu|... epsilon(float, Default 1e-05): param_attr(ParamAttr): The parameter attribute for Parameter `scale`. data_layout(string, default NCHW): NCHW|NHWC in_place(bool, Default False): Make the input and output of batch norm reuse memory. name(string, Default None): A name for this layer(optional). If set None, the layer will be named automatically. moving_mean_name(string, Default None): The name of moving_mean which store the global Mean. moving_variance_name(string, Default None): The name of the moving_variance which store the global Variance. do_model_average_for_mean_and_var(bool, Default True): Whether parameter mean and variance should do model average when model average is enabled. Returns: Variable: A tensor variable which is the result after applying data normalization on the input. Examples: .. code-block:: python import paddle.fluid as fluid hidden1 = fluid.data(name="hidden1", shape=[64, 200]) hidden2 = fluid.layers.data_norm(name="hidden2", input=hidden1) """ helper = LayerHelper('data_norm', **locals()) dtype = helper.input_dtype() 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] batch_size_default = 1e4 batch_sum_default = 0.0 batch_square_sum_default = 1e4 if param_attr and isinstance(param_attr, dict): batch_size_default = param_attr.get("batch_size", 1e4) batch_sum_default = param_attr.get("batch_sum", 0.0) batch_square_sum_default = param_attr.get("batch_square", 1e4) # create parameter batch_size = helper.create_parameter( attr=ParamAttr( name=name + '.batch_size', initializer=Constant(value=float(batch_size_default)), trainable=True), shape=param_shape, dtype=input.dtype) batch_sum = helper.create_parameter( attr=ParamAttr( name=name + '.batch_sum', initializer=Constant(value=float(batch_sum_default)), trainable=True), shape=param_shape, dtype=input.dtype) batch_square_sum = helper.create_parameter( attr=ParamAttr( name=name + '.batch_square_sum', initializer=Constant(value=float(batch_square_sum_default)), trainable=True), shape=param_shape, dtype=input.dtype) means = helper.create_variable(dtype=dtype, stop_gradient=True) scales = helper.create_variable(dtype=dtype, stop_gradient=True) data_norm_out = input if in_place else helper.create_variable(dtype=dtype) helper.append_op( type="data_norm", inputs={ "X": input, "BatchSize": batch_size, "BatchSum": batch_sum, "BatchSquareSum": batch_square_sum }, outputs={"Y": data_norm_out, "Means": means, "Scales": scales}, attrs={"epsilon": epsilon}) return helper.append_activation(data_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): """ **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(Variable): 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 normalizaiton. 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: Variable: ``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.fluid as fluid import numpy as np x = fluid.data(name='x', shape=[-1, 32, 32], dtype='float32') hidden1 = fluid.layers.layer_norm(input=x, begin_norm_axis=1) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) np_x = np.random.random(size=(8, 3, 32, 32)).astype('float32') output = exe.run(feed={"x": np_x}, fetch_list = [hidden1]) print(output) """ assert in_dygraph_mode( ) is not True, "please use FC instead of fc in dygraph mode!" helper = LayerHelper('layer_norm', **locals()) 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 avaliable 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 avaliable 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 group_norm(input, groups, epsilon=1e-05, param_attr=None, bias_attr=None, act=None, data_layout='NCHW', name=None): """ **Group Normalization Layer** Refer to `Group Normalization `_ . Parameters: input(Variable): 4-D Tensor, the data type is float32 or float64. groups(int): The number of groups that divided from channels, the data type is int32. epsilon(float, optional): The small value added to the variance to prevent division by zero, the data type is float32. Default: 1e-05. param_attr(ParamAttr|bool, optional): ParamAttr object that specifies weight parameter attribute. If a bool type, only False is supported, which means there is no weight parameter. Default: None, the default weight parameter attribute is used. For more information, please refer to :ref:`api_guide_ParamAttr` . bias_attr(ParamAttr|bool, optional): ParamAttr object that specifies bias parameter attribute. If a bool type, only False is supported, which means there is no bias parameter. Default: None, the default bias parameter attribute is used. For more information, please refer to :ref:`api_guide_ParamAttr` . act(str, optional): Activation to be applied to the output of group normalizaiton. data_layout(str, optional): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, channels, height, width]`. Default: "NCHW". 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 4-D Tensor has same data type and data format with `input`. Raises: ValueError: If `data_layout` is neither 'NCHW' nor 'NHWC'. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.data(name='data', shape=[None, 8, 32, 32], dtype='float32') x = fluid.layers.group_norm(input=data, groups=4) """ helper = LayerHelper('group_norm', **locals()) dtype = helper.input_dtype() # create intput and parameters inputs = {'X': input} input_shape = input.shape if data_layout != 'NCHW' and data_layout != 'NHWC': raise ValueError( "Param(data_layout) of Op(fluid.layers.group_norm) got wrong value: received " + data_layout + " but only NCHW or NHWC supported.") channel_num = input_shape[1] if data_layout == 'NCHW' else input_shape[-1] param_shape = [channel_num] if param_attr: scale = helper.create_parameter( attr=helper.param_attr, shape=param_shape, dtype=dtype, default_initializer=Constant(1.0)) inputs['Scale'] = scale if bias_attr: bias = helper.create_parameter( attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True) inputs['Bias'] = bias # create output mean_out = helper.create_variable(dtype=dtype, stop_gradient=True) variance_out = helper.create_variable(dtype=dtype, stop_gradient=True) group_norm_out = helper.create_variable(dtype=dtype) helper.append_op( type="group_norm", inputs=inputs, outputs={ "Y": group_norm_out, "Mean": mean_out, "Variance": variance_out, }, attrs={ "epsilon": epsilon, "groups": groups, "data_layout": data_layout }) return helper.append_activation(group_norm_out) @templatedoc() def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None): """ **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` shoule be a positive interger, 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(${weight_type}): ${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: Variable: A tensor variable of weight parameters after spectral normalization. The data type and shape is same as input tensor. Examples: .. code-block:: python import paddle.fluid as fluid weight = fluid.data(name='weight', shape=[2, 8, 32, 32], dtype='float32') x = fluid.layers.spectral_norm(weight=weight, dim=1, power_iters=2) """ helper = LayerHelper('spectral_norm', **locals()) dtype = weight.dtype # create intput and parameters inputs = {'Weight': weight} input_shape = weight.shape h = input_shape[dim] w = np.prod(input_shape) // h u = helper.create_parameter( attr=ParamAttr(), shape=[h], dtype=dtype, default_initializer=Normal(0., 1.)) u.stop_gradient = True inputs['U'] = u v = helper.create_parameter( attr=ParamAttr(), shape=[w], dtype=dtype, default_initializer=Normal(0., 1.)) inputs['V'] = v v.stop_gradient = True # 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 conv2d_transpose(input, num_filters, output_size=None, filter_size=None, padding=0, stride=1, dilation=1, groups=None, param_attr=None, bias_attr=None, use_cudnn=True, act=None, name=None, data_format='NCHW'): """ The convolution2D transpose layer calculates the output based on the input, filter, and dilations, strides, paddings. Input(Input) and output(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. Parameters(dilations, strides, paddings) are two elements. These two elements represent height and width, respectively. The details of convolution transpose layer, please refer to the following explanation and references `therein `_. 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 4-D Tensor with NCHW or NHWC format. * :math:`W`: Filter value, a 4-D 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, a 4-D Tensor with data format 'NCHW' or 'NHWC', 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_{in}, C_{out}, H_f, W_f)` - Output: Output shape: :math:`(N, C_{out}, H_{out}, W_{out})` Where .. math:: H^\prime_{out} &= (H_{in} - 1) * strides[0] - pad_height_top - pad_height_bottom + dilations[0] * (H_f - 1) + 1 \\\\ W^\prime_{out} &= (W_{in} - 1) * strides[1] - pad_width_left - pad_width_right + dilations[1] * (W_f - 1) + 1 \\\\ H_{out} &\in [ H^\prime_{out}, H^\prime_{out} + strides[0] ] \\\\ W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[1] ] Note: The conv2d_transpose can be seen as the backward of the conv2d. For conv2d, when stride > 1, conv2d maps multiple input shape to the same output shape, so for conv2d_transpose, when stride > 1, input shape maps multiple output shape. If output_size is None, :math:`H_{out} = H^\prime_{out}, W_{out} = W^\prime_{out}`; else, the :math:`H_{out}` of the output size must between :math:`H^\prime_{out}` and :math:`H^\prime_{out} + strides[0]`, and the :math:`W_{out}` of the output size must between :math:`W^\prime_{out}` and :math:`W^\prime_{out} + strides[1]`, conv2d_transpose can compute the kernel size automatically. Args: input(Variable): 4-D Tensor with [N, C, H, W] or [N, H, W, C] format, its data type is float32 or float64. num_filters(int): The number of the filter. It is as same as the output image channel. output_size(int|tuple, optional): The output image size. If output size is a tuple, it must contain two integers, (image_height, image_width). None if use filter_size, padding, and stride to calculate output_size. If output_size and filter_size are specified at the same time, They should follow the formula above. Default: None. output_size and filter_size should not be None at the same time. filter_size(int|tuple, optional): 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. None if use output size to calculate filter_size. Default: None. filter_size and output_size should not be None at the same time. stride(int|tuple, optional): The stride size. It means the stride in transposed 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(int|list|str|tuple, optional): The padding size. The padding argument effectively adds `dilation * (kernel - 1)` amount of zero-padding on both sides of input. If `padding` is a string, either 'VALID' or 'SAME' supported, which is the padding algorithm. If `padding` 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'`, `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, optional): 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. filter_size(int|tuple, optional): 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. None if use output size to calculate filter_size. Default: None. groups(int, optional): The groups number of the Conv2d transpose layer. Inspired by grouped convolution in Alex Krizhevsky's Deep CNN paper, in which 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, optional): The parameter attribute for learnable parameters/weights of conv2d_transpose. If it is set to None or one attribute of ParamAttr, conv2d_transpose will create ParamAttr as param_attr. If the Initializer of the param_attr is not set, the parameter is initialized with Xavier. Default: None. bias_attr (ParamAttr|bool, optional): The parameter attribute for the bias of conv2d_transpose. 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_transpose 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, optional): Use cudnn kernel or not, it is valid only when the cudnn library is installed. Default: True. act (str, optional): Activation type, if it is set to None, activation is not appended. Default: None. 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): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. Default: 'NCHW'. Returns: A Variable holding Tensor representing the conv2d_transpose, whose data type is the same with input and shape is (num_batches, channels, out_h, out_w) or (num_batches, out_h, out_w, channels). If act is None, the tensor variable storing the transposed convolution result, and if act is not None, the tensor variable storing transposed convolution and non-linearity activation result. Raises: ValueError: If the shapes of output, input, filter_size, stride, padding and groups mismatch. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32') conv2d_transpose = fluid.layers.conv2d_transpose(input=data, num_filters=2, filter_size=3) """ assert param_attr is not False, "param_attr should not be False in conv2d_transpose." if data_format not in ['NCHW', 'NHWC']: raise ValueError( "Attr(data_format) of Op(fluid.layers.conv2d_transpose) got wrong value: received " + data_format + " but only NCHW or NHWC supported.") input_channel = input.shape[1] if data_format == 'NCHW' else input.shape[-1] op_type = 'conv2d_transpose' if (input_channel == groups and num_filters == input_channel and not use_cudnn): op_type = 'depthwise_conv2d_transpose' helper = LayerHelper(op_type, **locals()) if not isinstance(input, Variable): raise TypeError("Input of conv2d_transpose must be Variable") stride = utils.convert_to_list(stride, 2, 'stride') dilation = utils.convert_to_list(dilation, 2, 'dilation') if not isinstance(use_cudnn, bool): raise ValueError("use_cudnn should be True or False") 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') else: padding = utils.convert_to_list(padding, 2, 'padding') padding = [padding[0], padding[0], padding[1], padding[1]] 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, 0, 0] elif padding == "SAME": padding_algorithm = "SAME" padding = [0, 0, 0, 0] padding = _update_padding(padding, data_format) if filter_size is None: if output_size is None: raise ValueError("output_size must be set when filter_size is None") if isinstance(output_size, int): output_size = [output_size, output_size] h_in = input.shape[2] if data_format == 'NCHW' else input.shape[1] w_in = input.shape[3] if data_format == 'NCHW' else input.shape[2] filter_size_h = (output_size[0] - (h_in - 1) * stride[0] + padding[0] + padding[1] - 1) // dilation[0] + 1 filter_size_w = (output_size[1] - (w_in - 1) * stride[1] + padding[2] + padding[3] - 1) // dilation[1] + 1 filter_size = [filter_size_h, filter_size_w] else: filter_size = utils.convert_to_list(filter_size, 2, 'conv2d_transpose.filter_size') if len(padding) == 4 and utils._is_symmetric_padding(padding, 2): padding = [padding[0], padding[2]] if output_size is None: output_size = [] elif isinstance(output_size, list) or isinstance(output_size, int): output_size = utils.convert_to_list(output_size, 2, 'output_size') else: raise ValueError("output_size should be list or int") groups = 1 if groups is None else groups filter_shape = [input_channel, num_filters // groups] + filter_size img_filter = helper.create_parameter( dtype=input.dtype, shape=filter_shape, attr=helper.param_attr) pre_bias = helper.create_variable_for_type_inference(dtype=input.dtype) helper.append_op( type=op_type, inputs={'Input': [input], 'Filter': [img_filter]}, outputs={'Output': pre_bias}, attrs={ 'output_size': output_size, 'strides': stride, 'paddings': padding, 'padding_algorithm': padding_algorithm, 'dilations': dilation, 'groups': groups, 'use_cudnn': use_cudnn, '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) out = helper.append_activation(pre_act) return out def conv3d_transpose(input, num_filters, output_size=None, filter_size=None, padding=0, stride=1, dilation=1, groups=None, param_attr=None, bias_attr=None, use_cudnn=True, act=None, name=None, data_format='NCDHW'): """ The convolution3D transpose layer calculates the output based on the input, filter, and dilations, strides, paddings. Input(Input) and output(Output) are in NCDHW or NDHWC format. Where N is batch size, C is the number of channels, D is the depth of the feature, H is the height of the feature, and W is the width of the feature. Parameters(dilations, strides, paddings) are two elements. These two elements represent height and width, respectively. The details of convolution transpose layer, please refer to the following explanation and references `therein `_. 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) In the above equation: * :math:`X`: Input value, a Tensor with NCDHW or NDHWC format. * :math:`W`: Filter value, a Tensor with MCDHW 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}, D_{in}, H_{in}, W_{in})` Filter shape: :math:`(C_{in}, C_{out}, D_f, H_f, W_f)` - Output: Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` Where .. math:: D^\prime_{out} &= (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\ H^\prime_{out} &= (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\ W^\prime_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1 \\\\ D_{out} &\in [ D^\prime_{out}, D^\prime_{out} + strides[0] ] \\\\ H_{out} &\in [ H^\prime_{out}, H^\prime_{out} + strides[1] ] \\\\ W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[2] ] Note: The conv3d_transpose can be seen as the backward of the conv3d. For conv3d, when stride > 1, conv3d maps multiple input shape to the same output shape, so for conv3d_transpose, when stride > 1, input shape maps multiple output shape. If output_size is None, :math:`H_{out} = H^\prime_{out}, :math:`H_{out} = \ H^\prime_{out}, W_{out} = W^\prime_{out}`; else, the :math:`D_{out}` of the output size must between :math:`D^\prime_{out}` and :math:`D^\prime_{out} + strides[0]`, the :math:`H_{out}` of the output size must between :math:`H^\prime_{out}` and :math:`H^\prime_{out} + strides[1]`, and the :math:`W_{out}` of the output size must between :math:`W^\prime_{out}` and :math:`W^\prime_{out} + strides[2]`, conv3d_transpose can compute the kernel size automatically. Args: input(Variable): The input is 5-D Tensor with shape [N, C, D, H, W] or [N, D, H, W, C], the data type of input is float32 or float64. num_filters(int): The number of the filter. It is as same as the output image channel. output_size(int|tuple, optional): The output image size. If output size is a tuple, it must contain three integers, (image_depth, image_height, image_width). This parameter only works when filter_size is None. If output_size and filter_size are specified at the same time, They should follow the formula above. Default: None. Output_size and filter_size should not be None at the same time. filter_size(int|tuple, optional): The filter size. If filter_size is a tuple, it must contain three integers, (filter_size_depth, filter_size_height, filter_size_width). Otherwise, filter_size_depth = filter_size_height = \ filter_size_width = filter_size. None if use output size to calculate filter_size. Default: None. filter_size and output_size should not be None at the same time. padding(int|list|str|tuple, optional): The padding size. The padding argument effectively adds `dilation * (kernel - 1)` amount of zero-padding on both sides of input. If `padding` is a string, either 'VALID' or 'SAME' supported, which is the padding algorithm. If `padding` is a tuple or list, it could be in three forms: `[pad_depth, pad_height, pad_width]` or `[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `data_format` is `'NCDHW'`, `padding` can be in the form `[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`. when `data_format` is `'NDHWC'`, `padding` can be in the form `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`. Default: padding = 0. stride(int|tuple, optional): The stride size. It means the stride in transposed convolution. If stride is a tuple, it must contain three integers, (stride_depth, stride_height, stride_width). Otherwise, stride_depth = stride_height = stride_width = stride. Default: stride = 1. dilation(int|tuple, optional): The dilation size. It means the spacing between the kernel points. If dilation is a tuple, it must contain three integers, (dilation_depth, dilation_height, dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation. Default: dilation = 1. groups(int, optional): The groups number of the Conv3d transpose layer. Inspired by grouped convolution in Alex Krizhevsky's Deep CNN paper, in which 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, optional): The parameter attribute for learnable parameters/weights of conv3d_transpose. If it is set to None or one attribute of ParamAttr, conv3d_transpose will create ParamAttr as param_attr. If the Initializer of the param_attr is not set, the parameter is initialized with Xavier. Default: None. bias_attr (ParamAttr|bool, optional): The parameter attribute for the bias of conv3d_transpose. 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, conv3d_transpose 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, optional): Use cudnn kernel or not, it is valid only when the cudnn library is installed. Default: True act (str, optional): Activation type, if it is set to None, activation is not appended. Default: None. 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):The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. Default: 'NCDHW'. Returns: A Variable holding Tensor representing the conv3d_transpose, whose data type is the same with input and shape is (num_batches, channels, out_d, out_h, out_w) or (num_batches, out_d, out_h, out_w, channels). If act is None, the tensor variable storing the transposed convolution result, and if act is not None, the tensor variable storing transposed convolution and non-linearity activation result. Raises: ValueError: If the shapes of output, input, filter_size, stride, padding and groups mismatch. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.data(name='data', shape=[None, 3, 12, 32, 32], dtype='float32') conv3d_transpose = fluid.layers.conv3d_transpose(input=data, num_filters=2, filter_size=3) """ assert param_attr is not False, "param_attr should not be False in conv3d_transpose." if data_format not in ['NCDHW', 'NDHWC']: raise ValueError( "Param(data_format) of Op(fluid.layers.conv3d_transpose) got wrong value: received " + data_format + " but only NCDHW or NDHWC supported.") l_type = "conv3d_transpose" helper = LayerHelper(l_type, **locals()) if not isinstance(input, Variable): raise TypeError("Input of conv3d_transpose must be Variable") input_channel = input.shape[1] if data_format == 'NCDHW' else input.shape[ -1] stride = utils.convert_to_list(stride, 3, 'stride') dilation = utils.convert_to_list(dilation, 3, 'dilation') if not isinstance(use_cudnn, bool): raise ValueError("use_cudnn should be True or False") 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) == 5: if is_list_or_tuple(padding[0]) and (data_format == "NCDHW"): 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[2:5] padding = [ele for a_list in padding for ele in a_list] elif is_list_or_tuple(padding[0]) and (data_format == "NDHWC"): if not (padding[0] == [0, 0] and padding[4] == [0, 0]): raise ValueError( "Non-zero padding(%s) in the batch or channel dimensions " "is not supported." % str(padding)) padding = padding[1:4] padding = [ele for a_list in padding for ele in a_list] padding = utils.convert_to_list(padding, 6, 'padding') elif is_list_or_tuple(padding) and len(padding) == 6: padding = utils.convert_to_list(padding, 6, 'padding') else: padding = utils.convert_to_list(padding, 3, 'padding') padding = [ padding[0], padding[0], padding[1], padding[1], padding[2], padding[2] ] 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, 0, 0, 0, 0] elif padding == "SAME": padding_algorithm = "SAME" padding = [0, 0, 0, 0, 0, 0] padding = _update_padding(padding, data_format) if filter_size is None: if output_size is None: raise ValueError("output_size must be set when filter_size is None") if isinstance(output_size, int): output_size = [output_size, output_size] d_in = input.shape[2] if data_format == 'NCDHW' else input.shape[1] h_in = input.shape[3] if data_format == 'NCDHW' else input.shape[2] w_in = input.shape[4] if data_format == 'NCDHW' else input.shape[3] filter_size_d = (output_size[0] - (d_in - 1) * stride[0] + padding[0] + padding[1] - 1) // dilation[0] + 1 filter_size_h = (output_size[1] - (h_in - 1) * stride[1] + padding[2] + padding[3] - 1) // dilation[1] + 1 filter_size_w = (output_size[2] - (w_in - 1) * stride[2] + padding[4] + padding[5] - 1) // dilation[2] + 1 filter_size = [filter_size_d, filter_size_h, filter_size_w] else: filter_size = utils.convert_to_list(filter_size, 3, 'conv3d_transpose.filter_size') if len(padding) == 6 and utils._is_symmetric_padding(padding, 3): padding = [padding[0], padding[2], padding[4]] groups = 1 if groups is None else groups filter_shape = [input_channel, num_filters // groups] + filter_size img_filter = helper.create_parameter( dtype=input.dtype, shape=filter_shape, attr=helper.param_attr) if data_format == 'NCDHW': data_format = 'NCHW' if data_format == 'NDHWC': data_format = 'NHWC' pre_bias = helper.create_variable_for_type_inference(dtype=input.dtype) helper.append_op( type=l_type, inputs={'Input': [input], 'Filter': [img_filter]}, outputs={'Output': pre_bias}, attrs={ 'strides': stride, 'paddings': padding, 'padding_algorithm': padding_algorithm, 'dilations': dilation, 'groups': groups, 'use_cudnn': use_cudnn, '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=4, dim_end=5) out = helper.append_activation(pre_act) 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 # 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] """ helper = LayerHelper('reduce_sum', **locals()) check_type_and_dtype(input, 'input', Variable, ['float32', 'float64', 'int32', 'int64'], 'reduce_sum') 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_sum', inputs={'X': input}, outputs={'Out': out}, attrs={ 'dim': dim if dim != None else [0], 'keep_dim': keep_dim, 'reduce_all': True if dim == None else False }) return out def reduce_mean(input, dim=None, keep_dim=False, name=None): """ Computes the mean of the input tensor's elements along 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 dimension along which the mean is computed. If `None`, compute the mean over all elements of :attr:`input` and return a variable with a single element, otherwise it must be in the range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank(input) + 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 average 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 # 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 correspending output tensor. x = fluid.data(name='x', shape=[2, 4], dtype='float32') fluid.layers.reduce_mean(x) # [0.4375] fluid.layers.reduce_mean(x, dim=0) # [0.15, 0.25, 0.55, 0.8] fluid.layers.reduce_mean(x, dim=-1) # [0.475, 0.4] fluid.layers.reduce_mean(x, dim=1, keep_dim=True) # [[0.475], [0.4]] # y is a Tensor variable with shape [2, 2, 2] and elements as below: # [[[1.0, 2.0], [3.0, 4.0]], # [[5.0, 6.0], [7.0, 8.0]]] # Each example is followed by the correspending output tensor. y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32') fluid.layers.reduce_mean(y, dim=[1, 2]) # [2.5, 6.5] fluid.layers.reduce_mean(y, dim=[0, 1]) # [4.0, 5.0] """ helper = LayerHelper('reduce_mean', **locals()) check_type_and_dtype(input, 'input', Variable, ['float32', 'float64', 'int32', 'int64'], 'reduce_mean') 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_mean', inputs={'X': input}, outputs={'Out': out}, attrs={ 'dim': dim if dim != None else [0], 'keep_dim': keep_dim, 'reduce_all': True if dim == None else False }) return out def reduce_max(input, dim=None, keep_dim=False, name=None): """ Computes the maximum 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 dimension along which the maximum is computed. If :attr:`None`, compute the maximum 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]`. 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 maximum on the specified dim of input tensor, it's data type is the same as input's Tensor. Examples: .. code-block:: python import paddle.fluid as fluid # 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 correspending output tensor. x = fluid.data(name='x', shape=[2, 4], dtype='float32') fluid.layers.reduce_max(x) # [0.9] fluid.layers.reduce_max(x, dim=0) # [0.2, 0.3, 0.6, 0.9] fluid.layers.reduce_max(x, dim=-1) # [0.9, 0.7] fluid.layers.reduce_max(x, dim=1, keep_dim=True) # [[0.9], [0.7]] # y is a Tensor variable with shape [2, 2, 2] and elements as below: # [[[1.0, 2.0], [3.0, 4.0]], # [[5.0, 6.0], [7.0, 8.0]]] # Each example is followed by the correspending output tensor. y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32') fluid.layers.reduce_max(y, dim=[1, 2]) # [4.0, 8.0] fluid.layers.reduce_max(y, dim=[0, 1]) # [7.0, 8.0] """ helper = LayerHelper('reduce_max', **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_max', inputs={'X': input}, outputs={'Out': out}, attrs={ 'dim': dim if dim != None else [0], 'keep_dim': keep_dim, 'reduce_all': True if dim == None else False }) return out def reduce_min(input, dim=None, keep_dim=False, name=None): """ Computes the minimum 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 minimum is computed. If :attr:`None`, compute the minimum 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]`. 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, result of minimum on the specified dim of input tensor, it's data type is the same as input's Tensor. Examples: .. code-block:: python import paddle.fluid as fluid # 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 correspending output tensor. x = fluid.data(name='x', shape=[2, 4], dtype='float32') fluid.layers.reduce_min(x) # [0.1] fluid.layers.reduce_min(x, dim=0) # [0.1, 0.2, 0.5, 0.7] fluid.layers.reduce_min(x, dim=-1) # [0.2, 0.1] fluid.layers.reduce_min(x, dim=1, keep_dim=True) # [[0.2], [0.1]] # y is a Tensor variable with shape [2, 2, 2] and elements as below: # [[[1.0, 2.0], [3.0, 4.0]], # [[5.0, 6.0], [7.0, 8.0]]] # Each example is followed by the correspending output tensor. y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32') fluid.layers.reduce_min(y, dim=[1, 2]) # [1.0, 5.0] fluid.layers.reduce_min(y, dim=[0, 1]) # [1.0, 2.0] """ helper = LayerHelper('reduce_min', **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_min', inputs={'X': input}, outputs={'Out': out}, attrs={ 'dim': dim if dim != None else [0], 'keep_dim': keep_dim, 'reduce_all': True if dim == None else False }) return out def reduce_prod(input, dim=None, keep_dim=False, name=None): """ Computes the product 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 product is performed. If :attr:`None`, multipy 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, result of product on the specified dim of input tensor, it's data type is the same as input's Tensor. Examples: .. code-block:: python import paddle.fluid as fluid # 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 correspending output tensor. x = fluid.data(name='x', shape=[2, 4], dtype='float32') fluid.layers.reduce_prod(x) # [0.0002268] fluid.layers.reduce_prod(x, dim=0) # [0.02, 0.06, 0.3, 0.63] fluid.layers.reduce_prod(x, dim=-1) # [0.027, 0.0084] fluid.layers.reduce_prod(x, dim=1, keep_dim=True) # [[0.027], [0.0084]] # y is a Tensor variable with shape [2, 2, 2] and elements as below: # [[[1.0, 2.0], [3.0, 4.0]], # [[5.0, 6.0], [7.0, 8.0]]] # Each example is followed by the correspending output tensor. y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32') fluid.layers.reduce_prod(y, dim=[1, 2]) # [24.0, 1680.0] fluid.layers.reduce_prod(y, dim=[0, 1]) # [105.0, 384.0] """ helper = LayerHelper('reduce_prod', **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_prod', inputs={'X': input}, outputs={'Out': out}, attrs={ 'dim': dim if dim != None else [0], 'keep_dim': keep_dim, 'reduce_all': True if dim == None else False }) 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 (Variable): The input variable which is a Tensor or LoDTensor, the input 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: Variable, the output data type is bool. : The reduced tensor variable with ``logical and`` in given dims. Examples: .. code-block:: python 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 = layers.assign(np.array([[1, 0], [1, 1]], dtype='int32')) x = layers.cast(x, 'bool') out = layers.reduce_all(x) # False out = layers.reduce_all(x, dim=0) # [True, False] out = layers.reduce_all(x, dim=-1) # [False, True] # keep_dim=False, x.shape=(2,2), out.shape=(2,) out = layers.reduce_all(x, dim=1, keep_dim=True) # [[False], [True]] # keep_dim=True, x.shape=(2,2), out.shape=(2,1) """ helper = LayerHelper('reduce_all', **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_all', inputs={'X': input}, outputs={'Out': out}, attrs={ 'dim': dim if dim != None else [0], 'keep_dim': keep_dim, 'reduce_all': True if dim == None 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 (Variable): The input variable which is a Tensor or LoDTensor, the input 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 Returns: Variable, the output data type is bool. : The reduced tensor variable with ``logical or`` in given dims. Examples: .. code-block:: python 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 = layers.assign(np.array([[1, 0], [0, 0]], dtype='int32')) x = layers.cast(x, 'bool') out = layers.reduce_any(x) # True out = layers.reduce_any(x, dim=0) # [True, False] out = layers.reduce_any(x, dim=-1) # [True, False] # keep_dim=False, x.shape=(2,2), out.shape=(2,) out = layers.reduce_any(x, dim=1, keep_dim=True) # [[True], [False]] # keep_dim=True, x.shape=(2,2), out.shape=(2,1) """ 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 != None else [0], 'keep_dim': keep_dim, 'reduce_all': True if dim == None else False }) return out def split(input, num_or_sections, dim=-1, name=None): """ Split the input tensor into multiple sub-Tensors. Args: input (Variable): The input variable which is an N-D Tensor or LoDTensor, data type being float32, float64, int32 or int64. num_or_sections (int|list|tuple): If :attr:`num_or_sections` is an integer, then the integer indicates the number of equal sized sub-Tensors that the Tensor will be divided into. If :attr:`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' :attr:`dim` dimension orderly. The length of the list mustn't be larger than the Tensor's size of :attr:`dim` . dim (int32|Varible, optional): A scalar with type ``int32`` or a ``Tensor`` with shape [1] and type ``int32``. The dimension along which to split. 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(Variable): The list of segmented Tensor variables. Raises: TypeError: num_or_sections is not int, list or tuple. TypeError: dim is not int or Variable. Example: .. code-block:: python import paddle.fluid as fluid # input is a variable which shape is [3, 9, 5] input = fluid.data( name="input", shape=[3, 9, 5], dtype="float32") x0, x1, x2 = fluid.layers.split(input, num_or_sections=3, dim=1) # x0.shape [3, 3, 5] # x1.shape [3, 3, 5] # x2.shape [3, 3, 5] x0, x1, x2 = fluid.layers.split(input, num_or_sections=[2, 3, 4], dim=1) # x0.shape [3, 2, 5] # x1.shape [3, 3, 5] # x2.shape [3, 4, 5] x0, x1, x2 = fluid.layers.split(input, num_or_sections=[2, 3, -1], dim=1) # x0.shape [3, 2, 5] # x1.shape [3, 3, 5] # x2.shape [3, 4, 5] """ if not isinstance(num_or_sections, (int, list, tuple)): raise TypeError( "The type of 'num_or_sections' in split must be int, list or " "tuple, but received %s." % (type(num_or_sections))) if not isinstance(dim, (int, Variable)): raise TypeError( "The type of 'dim' in split must be int or Variable, but " "received %s." % (type(dim))) 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: 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)) contain_var = not all(not isinstance(ele, Variable) for ele in num_or_sections) if contain_var: 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): """ 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 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 # declarative mode import paddle.fluid as fluid import numpy as np input = fluid.data(name="input", shape=[2,3]) output = fluid.layers.l2_normalize(x=input,axis=0) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) input_data = np.random.rand(2,3).astype("float32") print(input_data) # [[0.5171216 0.12704141 0.56018186] # [0.93251234 0.5382788 0.81709313]] output_data = exe.run(fluid.default_main_program(), feed={"input":input_data}, fetch_list=[output], return_numpy=True) print(output_data) # [array([[0.48496857, 0.22970329, 0.56545246], # [0.8745316 , 0.9732607 , 0.82478094]], dtype=float32)] # imperative mode import paddle.fluid.dygraph as dg with dg.guard(place) as g: input = dg.to_variable(input_data) output = fluid.layers.l2_normalize(x=input, axis=-1) print(output.numpy()) # [[0.66907585 0.16437206 0.7247892 ] # [0.6899054 0.3982376 0.6045142 ]] """ if len(x.shape) == 1: axis = 0 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 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.fluid as fluid 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) """ def __check_input(x, y): var_names = {'x': x, 'y': y} for name, val in var_names.items(): check_type_and_dtype(val, name, Variable, ['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)) __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={ 'transpose_X': transpose_x, 'transpose_Y': transpose_y, 'alpha': float(alpha), }) return out def topk(input, k, name=None): """ 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] """ helper = LayerHelper("top_k", **locals()) values = helper.create_variable_for_type_inference(dtype=input.dtype) indices = helper.create_variable_for_type_inference(dtype="int64") inputs = {"X": [input]} attrs = None if isinstance(k, Variable): inputs['K'] = k else: attrs = {'k': k} 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): """ 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 describled 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) """ 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 = 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 transpose(x, perm, name=None): """ Permute the data dimensions of `input` according to `perm`. The `i`-th dimension of the returned tensor will correspond to the perm[i]-th dimension of `input`. Args: x (Variable): The input Tensor. It is a N-D Tensor of data types float32, float64, int32. perm (list): Permute the input accoring to the data of perm. name (str): The name of this layer. It is optional. Returns: Variable: A transposed n-D Tensor, with data type being float32, float64, int32, int64. For Example: .. code-block:: text x = [[[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]] [[13 14 15 16] [17 18 19 20] [21 22 23 24]]] shape(x) = [2,3,4] # Example 1 perm0 = [1,0,2] y_perm0 = [[[ 1 2 3 4] [13 14 15 16]] [[ 5 6 7 8] [17 18 19 20]] [[ 9 10 11 12] [21 22 23 24]]] shape(y_perm0) = [3,2,4] # Example 2 perm1 = [2,1,0] y_perm1 = [[[ 1 13] [ 5 17] [ 9 21]] [[ 2 14] [ 6 18] [10 22]] [[ 3 15] [ 7 19] [11 23]] [[ 4 16] [ 8 20] [12 24]]] shape(y_perm1) = [4,3,2] Examples: .. code-block:: python # use append_batch_size=False to avoid prepending extra # batch size in shape import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[2, 3, 4], dtype='float32', append_batch_size=False) x_transposed = fluid.layers.transpose(x, perm=[1, 0, 2]) print x_transposed.shape #(3L, 2L, 4L) """ check_type_and_dtype(x, 'x', Variable, ['float16', 'float32', 'float64', 'int32', 'int64'], 'transpose') check_type(perm, 'perm', list, 'transpose') if len(perm) != len(x.shape): raise ValueError( "Input(perm) is the permutation of dimensions of Input(x), " "its length should be equal to dimensions of Input(x), " "but received dimension of Input(x) is %s, " "the length of Input(perm) is %s." % (len(x.shape), len(perm))) for idx, dim in enumerate(perm): if dim >= len(x.shape): raise ValueError( "Each element in Input(perm) should be less than Input(x)'s dimension, " "but %d-th element in Input(perm) is %d which exceeds Input(x)'s " "dimension %d." % (idx, perm[idx], len(x.shape))) helper = LayerHelper('transpose', **locals()) out = helper.create_variable_for_type_inference(x.dtype) x_shape = helper.create_variable_for_type_inference(x.dtype) helper.append_op( type='transpose2', inputs={'X': [x]}, outputs={'Out': [out], 'XShape': [x_shape]}, attrs={'axis': perm}) return out def im2sequence(input, filter_size=1, stride=1, padding=0, input_image_size=None, out_stride=1, name=None): """ 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 intergers, :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 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 in_dygraph_mode(), ( "sequence layer is not supported in dygraph mode yet.") 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): """ ${comment} Args: input (${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: >>> # for LodTensor inputs >>> import paddle.fluid as fluid >>> x = fluid.data(name='x', shape=[9, 16], >>> dtype='float32', lod_level=1) >>> out = fluid.layers.row_conv(input=x, future_context_size=2) >>> # for Tensor inputs >>> x = fluid.data(name='x', shape=[9, 4, 16], dtype='float32') >>> out = fluid.layers.row_conv(input=x, future_context_size=2) """ helper = LayerHelper('row_conv', **locals()) 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): """ 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 (Variable): 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. Returns: Variable(Tensor): Output of multiplex OP, with data type being float32, float64, int32, int64. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np x1 = fluid.data(name='x1', shape=[None, 2], dtype='float32') x2 = fluid.data(name='x2', shape=[None, 2], dtype='float32') index = fluid.data(name='index', shape=[None, 1], dtype='int32') out = fluid.layers.multiplex(inputs=[x1, x2], index=index) exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) img1 = np.array([[1, 2], [3, 4]]).astype(np.float32) img2 = np.array([[5, 6], [7, 8]]).astype(np.float32) index = np.array([[1], [0]]).astype(np.int32) res = exe.run(fluid.default_main_program(), feed={'x1':img1, 'x2':img2, 'index':index}, fetch_list=[out]) print(res) # [array([[5., 6.], [3., 4.]], dtype=float32)] """ helper = LayerHelper('multiplex', **locals()) if not isinstance(inputs, list) and len(inputs) < 2: raise ValueError("inputs should be a list object and contains at least " "2 elements.") 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 ouput 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 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)] """ 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 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.fluid as fluid # 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) """ helper = LayerHelper("one_hot", **locals()) one_hot_out = helper.create_variable_for_type_inference(dtype='float32') if in_dygraph_mode(): inputs = {'X': input} attrs = {'depth': depth} else: if not isinstance(depth, Variable): # user attribute inputs = {'X': input} attrs = {'depth': depth} else: depth.stop_gradient = True inputs = {'X': input, 'depth_tensor': depth} attrs = {} 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): """ 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 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 reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None): """ This operator changes the shape of ``x`` without changing its data. The target shape can be given by ``shape`` or ``actual_shape``. When ``shape`` and ``actual_shape`` are set at the same time, ``actual_shape`` has a higher priority than ``shape`` but at this time ``shape`` can only be an integer list or tuple, and ``shape`` still should be set correctly to gurantee shape inference in compile-time. Some tricks exist when specifying the target shape. 1. -1 means the value of this dimension is inferred from the total element number of x and remaining dimensions. Thus one and only one dimension can be set -1. 2. 0 means the actual dimension value is going to be copied from the corresponding dimension of x. The indice of 0s in shape can not exceed the dimension of x. Here are some examples to explain it. 1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [6, 8], the reshape operator will transform x into a 2-D tensor with shape [6, 8] and leaving x's data unchanged. 2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape specified is [2, 3, -1, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this case, one dimension of the target shape is set to -1, the value of this dimension is inferred from the total element number of x and remaining dimensions. 3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case, besides -1, 0 means the actual dimension value is going to be copied from the corresponding dimension of x. **Note**: The parameter ``actual_shape`` will be deprecated in the future and only use ``shape`` instead to represent the target shape. Args: x(Variable): A ``Tensor`` or ``LoDTensor`` . The data type is ``float32``, ``float64``, ``int32`` or ``int64``. shape(list|tuple|Variable): Define the target shape. At most one dimension of the target shape can be -1. The data type is ``int32`` . If ``shape`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``shape`` is an Variable, it should be an 1-D Tensor . actual_shape(variable, optional): An 1-D ``Tensor`` or ``LoDTensor`` . The data type is ``int32`` . If provided, reshape according to this given shape rather than ``shape`` specifying shape. That is to say ``actual_shape`` has a higher priority than ``shape(list|tuple)`` but not ``shape(Variable)``. \ This argument ``actual_shape`` will be removed in a future version. \ Instructions for updating: ``actual_shape`` will be removed in future versions and replaced by ``shape``. act (str, optional): The non-linear activation to be applied to the reshaped input. Default None. inplace(bool, optional): If ``inplace`` is True, the input and output of ``layers.reshape`` are the same variable. Otherwise, the input and output of ``layers.reshape`` are different variable. Default False. Note that if ``x`` is more than one OPs' input, ``inplace`` must be 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: A ``Tensor`` or ``LoDTensor``. The data type is same as ``x``. It is a new tensor variable if ``inplace`` is ``False``, otherwise it is ``x``. If ``act`` is None, return the reshaped tensor variable, otherwise return the activated tensor variable. Raises: TypeError: If actual_shape is neither Variable nor None. ValueError: If more than one elements of ``shape`` is -1. ValueError: If the element of ``shape`` is 0, the corresponding dimension should be less than or equal to the dimension of ``x``. ValueError: If the elements in ``shape`` is negative except -1. Examples: .. code-block:: python import paddle.fluid as fluid # example 1: # attr shape is a list which doesn't contain tensor Variable. data_1 = fluid.data( name='data_1', shape=[2, 4, 6], dtype='float32') reshaped_1 = fluid.layers.reshape( x=data_1, shape=[-1, 0, 3, 2], inplace=True) # the shape of reshaped_1 is [2,4,3,2]. # example 2: # attr shape is a list which contains tensor Variable. data_2 = fluid.layers.fill_constant([2,25], "int32", 3) dim = fluid.layers.fill_constant([1], "int32", 5) reshaped_2 = fluid.layers.reshape(data_2, shape=[dim, 10]) # the shape of reshaped_2 is [5,10]. """ check_type_and_dtype(x, 'x', Variable, ['float16', 'float32', 'float64', 'int32', 'int64'], 'reshape') check_type(shape, 'shape', (list, tuple, Variable), 'reshape') check_type(actual_shape, 'actual_shape', (Variable, type(None)), 'reshape') helper = LayerHelper("reshape2", **locals()) inputs = {"X": x} attrs = {} def contain_var(one_list): for ele in one_list: if isinstance(ele, Variable): return True return False def get_new_shape_tensor(list_shape): new_shape_tensor = [] for dim in list_shape: if isinstance(dim, Variable): dim.stop_gradient = True new_shape_tensor.append(dim) 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_shape_tensor.append(temp_out) return new_shape_tensor def get_attr_shape(list_shape): unk_dim_idx = -1 attrs_shape = [] for dim_idx, dim_size in enumerate(list_shape): if isinstance(dim_size, Variable): attrs_shape.append(-1) else: attrs_shape.append(dim_size) if dim_size == -1: assert unk_dim_idx == -1, ( "Only one dimension value of 'shape' in reshape can " "be -1. But received shape[%d] is also -1." % dim_idx) unk_dim_idx = dim_idx elif dim_size == 0: assert dim_idx < len(x.shape), ( "The index of 0 in `shape` must be less than " "the input tensor X's dimensions. " "But received shape[%d] = 0, X's dimensions = %d." % (dim_idx, len(x.shape))) else: assert dim_size > 0, ( "Each dimension value of 'shape' in reshape must not " "be negtive except one unknown dimension. " "But received shape[%d] = %s." % (dim_idx, str(dim_size))) return attrs_shape if in_dygraph_mode(): inputs = {'X': x} attrs = {'shape': shape} else: if isinstance(shape, Variable): shape.stop_gradient = True inputs["Shape"] = shape elif isinstance(shape, (list, tuple)): assert len(shape) > 0, ( "The size of 'shape' in reshape can't be zero, " "but received %s." % len(shape)) attrs["shape"] = get_attr_shape(shape) if contain_var(shape): inputs['ShapeTensor'] = get_new_shape_tensor(shape) elif isinstance(actual_shape, Variable): actual_shape.stop_gradient = True inputs["Shape"] = actual_shape out = x if inplace else helper.create_variable_for_type_inference( dtype=x.dtype) x_shape = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type="reshape2", inputs=inputs, attrs=attrs, outputs={"Out": out, "XShape": x_shape}) return helper.append_activation(out) def squeeze(input, axes, name=None): """ This OP will squeeze single-dimensional entries of input tensor's shape. If axes is provided, will remove the dims by axes, the dims selected by axes should be one. If not provide axes, all dims equal to one will be deleted. .. code-block:: text Case1: Input: X.shape = (1, 3, 1, 5) axes = [0] Output: Out.shape = (3, 1, 5) Case2: Input: X.shape = (1, 3, 1, 5) axes = [] Output: Out.shape = (3, 5) Case3: Input: X.shape = [1,3,1,5] axes = [-2] Output: Out.shape = [1,3,5] Args: input (Variable): The input Tensor. Support data type: float32, float64, int8, int32, int64. axes (list): One integer or List of integers, indicating the dimensions to be squeezed. Axes range is :math:`[-rank(input), rank(input))`. If axes is negative, :math:`axes=axes+rank(input)`. name (str, optional): Please refer to :ref:`api_guide_Name`, Default None. Returns: Variable: Output squeezed Tensor. Data type is same as input Tensor. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.layers as layers # set batch size=None x = fluid.data(name='x', shape=[None, 5, 1, 10]) y = layers.squeeze(input=x, axes=[2]) # y.shape=[None, 5, 10] """ helper = LayerHelper("squeeze", **locals()) check_type_and_dtype(input, 'input', Variable, ['float32', 'float64', 'int8', 'int32', 'int64'], 'squeeze') check_type(axes, 'axes', list, 'squeeze') 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="squeeze2", inputs={"X": input}, attrs={"axes": axes}, outputs={"Out": out, "XShape": x_shape}) return out 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. It is a N-D Tensor of data types float32, float64, int32. 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: Output unsqueezed Tensor, with data type being float32, float64, int32, int64. 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 not isinstance(axes, (int, list, tuple, Variable)): raise TypeError( "The type of 'axes' in unsqueeze must be int, list, tuple or Variable, but " "received %s." % (type(axes))) helper = LayerHelper("unsqueeze2", **locals()) inputs = {"X": input} attrs = {} def _to_Variable_list(one_list): Variable_list = [] for ele in one_list: if isinstance(ele, Variable): ele.stop_gradient = True Variable_list.append(ele) else: assert (isinstance(ele, int)) temp_out = helper.create_variable_for_type_inference('int32') fill_constant([1], 'int32', ele, force_cpu=True, out=temp_out) Variable_list.append(temp_out) return Variable_list if isinstance(axes, int): axes = [axes] if isinstance(axes, Variable): axes.stop_gradient = True inputs["AxesTensor"] = axes elif isinstance(axes, (list, tuple)): contain_var = not all(not isinstance(ele, Variable) for ele in axes) if contain_var: inputs["AxesTensorList"] = _to_Variable_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. y (Variable|None): If provided, output's LoD would be derived from :attr:`y`. target_lod (list|tuple|None): 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) """ helper = LayerHelper("lod_reset", **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) if y is not None: 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 lod_append(x, level): """ Append level to LoD of :attr:`x`. .. 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] level: [1, 1, 1, 1, 1, 1, 1] then we get a 2-level LoDTensor: x.lod = [[ 2, 3, 1 ], [1, 1, 1, 1, 1, 1]] x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]] x.dims = [6, 1] Args: x (Variable): Input variable which could be a tensor or LoDTensor. level (list|tuple|Variable): The LoD level to be appended into LoD of x. Returns: Variable: Output variable with new LoD level. Raises: ValueError: If :attr:`y` is None or and :attr:`level` is not Iterator. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[6, 10], lod_level=1) out = fluid.layers.lod_append(x, [1,1,1,1,1,1]) """ from collections import Iterable if x is None: raise ValueError("Input(x) can't be None.") if (not isinstance(level, Iterable)) and (not isinstance(level, Variable)): raise ValueError("Input(level) must be list, tuple or Variable.") helper = LayerHelper("lod_append", **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) inputs = {'X': x} attrs = {'append': True} if isinstance(level, Variable): inputs['Y'] = level else: attrs['target_lod'] = level helper.append_op( type="lod_reset", inputs=inputs, attrs=attrs, outputs={'Out': out}) return out def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None, data_format='NCHW'): """ This operator implements the Local Response Normalization Layer. This layer performs a type of "lateral inhibition" by normalizing over local input regions. For more information, please refer to `ImageNet Classification with Deep Convolutional Neural Networks `_ The formula is as follows: .. math:: Output(i, x, y) = Input(i, x, y) / \\left(k + \\alpha \\sum\\limits^{\\min(C-1, i + n/2)}_{j = \\max(0, i - n/2)}(Input(j, x, y))^2\\right)^{\\beta} In the above equation: - :math:`n` : The number of channels to sum over. - :math:`k` : The offset (avoid being divided by 0). - :math:`\\alpha` : The scaling parameter. - :math:`\\beta` : The exponent parameter. Args: input (Variable): Input feature, 4D-Tensor with the shape of [N,C,H,W] or [N, H, W, C], where N is the batch size, C is the input channel, H is Height, W is weight. The data type is float32. The rank of this tensor must be 4, otherwise it will raise ValueError. n (int, optional): The number of channels to sum over. Default: 5 k (float, optional): An offset, positive. Default: 1.0 alpha (float, optional): The scaling parameter, positive. Default:1e-4 beta (float, optional): The exponent, positive. Default:0.75 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` data_format(str, optional): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. Default: 'NCHW'. Returns: Variable: A tensor variable storing the transformation result with the same shape and data type as input. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.data( name="data", shape=[None, 3, 112, 112], dtype="float32") lrn = fluid.layers.lrn(input=data) print(lrn.shape) # [-1, 3, 112, 112] print(lrn.dtype) # float32 """ helper = LayerHelper('lrn', **locals()) dtype = helper.input_dtype() input_shape = input.shape dims = len(input_shape) if dims != 4: raise ValueError( "Input's dimension size of Op(lrn) must be 4, but received %d." % (dims)) if data_format not in ['NCHW', 'NHWC']: raise ValueError( "Attr(data_format) of Op(lrn) got wrong value: received " + data_format + " but only NCHW or NHWC supported.") mid_out = helper.create_variable_for_type_inference( dtype=dtype, stop_gradient=True) lrn_out = helper.create_variable_for_type_inference(dtype) helper.append_op( type="lrn", inputs={"X": input}, outputs={ "Out": lrn_out, "MidOut": mid_out, }, attrs={ "n": n, "k": k, "alpha": alpha, "beta": beta, "data_format": data_format }) return lrn_out def pad(x, paddings, pad_value=0., name=None): """ 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 with shape [100, 224]. # out will be a tensor of shape [101, 227] import paddle.fluid as fluid x = fluid.data(name='data', shape=[100, 224], dtype='float32') out = fluid.layers.pad( x=x, paddings=[0, 1, 1, 2], pad_value=0.) """ helper = LayerHelper('pad', input=x, **locals()) dtype = helper.input_dtype() out = helper.create_variable_for_type_inference(dtype) helper.append_op( type='pad', inputs={'X': x}, outputs={'Out': out}, attrs={'paddings': paddings, 'pad_value': float(pad_value)}) return out def pad_constant_like(x, y, pad_value=0., name=None): """ Pad :attr:`y` with :attr:`pad_value`, the number of values padded to the edges of each axis is specified by the difference of the shape of :attr:`x` and :attr:`y` . ((0, shape_x_0 - shape_y_0), ... (0, shape_x_n - shape_y_n)) specify padding widths for each axis. The input should be a k-D tensor(k > 0 and k < 7). See below for an example. .. code-block:: text Given: X = [[[[ 0, 1, 2], [ 3, 4, 5]], [[ 6, 7, 8], [ 9, 10, 11]], [[12, 13, 14], [15, 16, 17]]], [[[18, 19, 20], [21, 22, 23]], [[24, 25, 26], [27, 28, 29]], [[30, 31, 32], [33, 34, 35]]]] X.shape = (2, 3, 2, 3) Y = [[[[35, 36, 37]], [[38, 39, 40]], [[41, 42, 43]]]] Y.shape = (1, 3, 1, 3) And pad_value = -1, Return: Out = [[[[35, 36, 37], [-1, -1, -1]], [[38, 39, 40], [-1, -1, -1]], [[41, 42, 43], [-1, -1, -1]]], [[[-1, -1, -1], [-1, -1, -1]], [[-1, -1, -1], [-1, -1, -1]], [[-1, -1, -1], [-1, -1, -1]]]] Out.shape = (2, 3, 2, 3) Args: x (Variable): Tensor, its shape spicifies the shape of output. y (Variable): Tensor, its rank is the same with :attr:`x`, and for each dimension :math:`i` , :math:`y\_shape[i] <= x\_shape[i]` . The data type can be float32 or float64. 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 shape as :attr:`x` and the same data type as :attr:`y` Return Type: Variable Examples: .. code-block:: python # x is a rank 4 tensor variable, x.shape = (2, 3, 2, 3) # y is a rank 4 tensor variable, y.shape = (1, 3, 1, 3) import paddle.fluid as fluid x = fluid.data(name='x', shape=[2,3,2,3], dtype='float32') y = fluid.data(name='y', shape=[1,3,1,3], dtype='float32') out = fluid.layers.pad_constant_like(x=x, y=y, pad_value=0.) # out is a rank 4 tensor variable, and out.shape = [2, 3 ,2 , 3] """ helper = LayerHelper('pad_constant_like', input=x, **locals()) dtype = helper.input_dtype() out = helper.create_variable_for_type_inference(dtype) helper.append_op( type='pad_constant_like', inputs={'X': x, 'Y': y}, outputs={'Out': out}, attrs={'pad_value': float(pad_value)}) return out def label_smooth(label, prior_dist=None, epsilon=0.1, dtype="float32", name=None): """ Label smoothing is a mechanism to regularize the classifier layer and is called label-smoothing regularization (LSR). Label smoothing is proposed to encourage the model to be less confident, since optimizing the log-likelihood of the correct label directly may cause overfitting and reduce the ability of the model to adapt. Label smoothing replaces the ground-truth label :math:`y` with the weighted sum of itself and some fixed distribution :math:`\mu`. For class :math:`k`, i.e. .. math:: \\tilde{y_k} = (1 - \epsilon) * y_k + \epsilon * \mu_k, where :math:`1 - \epsilon` and :math:`\epsilon` are the weights respectively, and :math:`\\tilde{y}_k` is the smoothed label. Usually uniform distribution is used for :math:`\mu`. See more details about label smoothing in https://arxiv.org/abs/1512.00567. Parameters: label(Variable): The input variable containing the label data. The label data should use one-hot representation. It's a multidimensional tensor with a shape of :math:`[N_1, ..., Depth]`, where Depth is class number. prior_dist(Variable, optional): The prior distribution to be used to smooth labels. If not provided, an uniform distribution is used. It's a multidimensional tensor with a shape of :math:`[1, class\_num]` . The default value is None. epsilon(float, optional): The weight used to mix up the original ground-truth distribution and the fixed distribution. The default value is 0.1. dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type can be set as 'float32', 'float64'. The default value 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: Variable: The tensor variable containing the smoothed labels. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.layers as layers label = layers.data(name="label", shape=[1], dtype="float32") one_hot_label = layers.one_hot(input=label, depth=10) smooth_label = layers.label_smooth( label=one_hot_label, epsilon=0.1, dtype="float32") """ if epsilon > 1. or epsilon < 0.: raise ValueError("The value of epsilon must be between 0 and 1.") helper = LayerHelper("label_smooth", **locals()) label.stop_gradient = True smooth_label = helper.create_variable_for_type_inference(dtype) helper.append_op( type="label_smooth", inputs={"X": label, "PriorDist": prior_dist} if prior_dist else {"X": label}, outputs={"Out": smooth_label}, attrs={"epsilon": float(epsilon)}) return smooth_label @templatedoc() def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0): """ This operator implements the roi_pooling layer. Region of interest pooling (also known as RoI pooling) is to perform max pooling on inputs of nonuniform sizes to obtain fixed-size feature maps (e.g. 7*7). The operator has three steps: 1. Dividing each region proposal into equal-sized sections with the pooled_width and pooled_height; 2. Finding the largest value in each section; 3. Copying these max values to the output buffer. For more information, please refer to https://stackoverflow.com/questions/43430056/what-is-roi-layer-in-fast-rcnn Args: input (Variable): Input feature, 4D-Tensor with the shape of [N,C,H,W], where N is the batch size, C is the input channel, H is Height, W is weight. The data type is float32 or float64. rois (Variable): ROIs (Regions of Interest) to pool over. 2D-LoDTensor with the shape of [num_rois,4], 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. pooled_height (int, optional): The pooled output height, data type is int32. Default: 1 pooled_width (int, optional): The pooled output height, data type is int32. Default: 1 spatial_scale (float, optional): Multiplicative spatial scale factor to translate ROI coords from their input scale to the scale used when pooling. Default: 1.0 Returns: Variable: The pooled feature, 4D-Tensor with the shape of [num_rois, C, pooled_height, pooled_width]. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np DATATYPE='float32' place = fluid.CPUPlace() #place = fluid.CUDAPlace(0) input_data = np.array([i for i in range(1,17)]).reshape(1,1,4,4).astype(DATATYPE) roi_data =fluid.create_lod_tensor(np.array([[1., 1., 2., 2.], [1.5, 1.5, 3., 3.]]).astype(DATATYPE),[[2]], place) x = fluid.data(name='input', shape=[None,1,4,4], dtype=DATATYPE) rois = fluid.data(name='roi', shape=[None,4], dtype=DATATYPE) pool_out = fluid.layers.roi_pool( input=x, rois=rois, pooled_height=1, pooled_width=1, spatial_scale=1.0) exe = fluid.Executor(place) out, = exe.run(feed={'input':input_data ,'roi':roi_data}, fetch_list=[pool_out.name]) print(out) #array([[[[11.]]], [[[16.]]]], dtype=float32) print(np.array(out).shape) # (2, 1, 1, 1) """ helper = LayerHelper('roi_pool', **locals()) dtype = helper.input_dtype() pool_out = helper.create_variable_for_type_inference(dtype) argmaxes = helper.create_variable_for_type_inference(dtype='int32') helper.append_op( type="roi_pool", inputs={"X": input, "ROIs": rois}, outputs={"Out": pool_out, "Argmax": argmaxes}, attrs={ "pooled_height": pooled_height, "pooled_width": pooled_width, "spatial_scale": spatial_scale }) return pool_out @templatedoc() def roi_align(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0, sampling_ratio=-1, name=None): """ ${comment} Args: input (Variable): ${x_comment} rois (Variable): ROIs (Regions of Interest) to pool over.It should be a 2-D LoDTensor of shape (num_rois, 4), the lod level is 1. The data type is float32 or float64. Given as [[x1, y1, x2, y2], ...], (x1, y1) is the top left coordinates, and (x2, y2) is the bottom right coordinates. pooled_height (int32, optional): ${pooled_height_comment} Default: 1 pooled_width (int32, optional): ${pooled_width_comment} Default: 1 spatial_scale (float32, optional): ${spatial_scale_comment} Default: 1.0 sampling_ratio(int32, optional): ${sampling_ratio_comment} Default: -1 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: ${out_comment}. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data( name='data', shape=[None, 256, 32, 32], dtype='float32') rois = fluid.data( name='rois', shape=[None, 4], dtype='float32') align_out = fluid.layers.roi_align(input=x, rois=rois, pooled_height=7, pooled_width=7, spatial_scale=0.5, sampling_ratio=-1) """ helper = LayerHelper('roi_align', **locals()) dtype = helper.input_dtype() align_out = helper.create_variable_for_type_inference(dtype) helper.append_op( type="roi_align", inputs={"X": input, "ROIs": rois}, outputs={"Out": align_out}, attrs={ "pooled_height": pooled_height, "pooled_width": pooled_width, "spatial_scale": spatial_scale, "sampling_ratio": sampling_ratio }) return align_out def dice_loss(input, label, epsilon=0.00001, name=None): """ Dice loss for comparing the similarity between the input predictions and the label. This implementation is for binary classification, where the input is sigmoid predictions of each pixel, usually used for segmentation task. The dice loss can be defined as the following equation: .. math:: dice\_loss &= 1 - \\frac{2 * intersection\_area}{total\_area} \\\\ &= \\frac{(total\_area - intersection\_area) - intersection\_area}{total\_area} \\\\ &= \\frac{(union\_area - intersection\_area)}{total\_area} Parameters: input (Variable): Tensor, rank>=2, shape is :math:`[N_1, N_2, ..., N_D]`, where :math:`N_1` is the batch_size, :math:`N_D` is 1. It is usually the output predictions of sigmoid activation. The data type can be float32 or float64. label (Variable): Tensor, the groud truth with the same rank as input, shape is :math:`[N_1, N_2, ..., N_D]`. where :math:`N_1` is the batch_size, :math:`N_D` is 1. The data type can be float32 or float64. epsilon (float): The epsilon will be added to the numerator and denominator. If both input and label are empty, it makes sure dice is 1. Default: 0.00001 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 dice loss with shape [1], data type is the same as `input` . Return Type: Varaible Example: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name='data', shape = [3, 224, 224, 1], dtype='float32') label = fluid.data(name='label', shape=[3, 224, 224, 1], dtype='float32') predictions = fluid.layers.sigmoid(x) loss = fluid.layers.dice_loss(input=predictions, label=label) """ label = one_hot(label, depth=input.shape[-1]) reduce_dim = list(range(1, len(input.shape))) inse = reduce_sum(input * label, dim=reduce_dim) dice_denominator = reduce_sum( input, dim=reduce_dim) + reduce_sum( label, dim=reduce_dim) dice_score = 1 - inse * 2 / (dice_denominator + epsilon) return reduce_mean(dice_score) 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 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, hight and width). **Warning:** the parameter :attr:`actual_shape` will be deprecated in the future and only use :attr:`out_shape` instead. Supporting resample methods: 'BILINEAR' : Bilinear interpolation 'TRILINEAR' : Trilinear interpolation 'NEAREST' : Nearest neighbor interpolation Nearest neighbor interpolation is to perform nearest neighbor interpolation in both the 3rd dimention(in height direction) and the 4th dimention(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. Align_corners and align_mode are optinal 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}) 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} H_out = H_{in} * scale_{factor} W_out = W_{in} * scale_{factor} 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. Parameters: input (Variable): 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_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 '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 occured 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 bilinear interpolation. 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): NCHW(num_batches, channels, height, width) or NHWC(num_batches, height, width, channels) for 4-D Tensor, NCDHW(num_batches, channels, depth, height, width) or NDHWC(num_batches, depth, height, width, channels) for 5-D Tensor. Default: 'NCHW'. Returns: 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 'BILINEAR', 'TRILINEAR' or 'NEAREST' currently. ValueError: '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 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 shoule be a bool value ValueError: align_mode can only be '0' or '1' ValueError: data_format can only be 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'. Examples: .. code-block:: python #declarative mode import paddle.fluid as fluid import numpy as np 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 = { 'BILINEAR': 'bilinear', 'TRILINEAR': 'trilinear', 'NEAREST': 'nearest', } if resample not in resample_methods: raise ValueError( "The 'resample' of image_resize can only be 'BILINEAR', 'TRILINEAR' " "or 'NEAREST' currently.") resample_type = resample_methods[resample] if resample in ['BILINEAR', 'NEAREST'] and len(input.shape) != 4: raise ValueError("'BILINEAR' and 'NEAREST' only support 4-D tensor.") if 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) == 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': data_layout = 'NCHW' if data_format == 'NHWC' or data_format == 'NDHWC': 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): out_shape.stop_gradient = True inputs['OutSize'] = out_shape else: 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) == 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 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.") 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 optinal 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 occured in graph constructing stage. Default: None align_corners(bool): ${align_corners_comment} align_mode(bool): ${align_mode_comment} data_format(str, optional): NCHW(num_batches, channels, height, width) or NHWC(num_batches, height, width, channels). Default: 'NCHW'. 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 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 optinal 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 occured in graph constructing stage. Default: None align_corners(bool): ${align_corners_comment} align_mode(bool): ${align_mode_comment} data_format(str, optional): NCDHW(num_batches, channels, depth, height, width) or NDHWC(num_batches, depth, height, width, channels). Default: 'NCDHW'. Returns: Variable: A 5-D Tensor(NCDHW or NDHWC) Examples: .. code-block:: python #declarative mode import paddle.fluid as fluid import numpy as np 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 occured in graph constructing stage. Default: None align_corners(bool): ${align_corners_comment} data_format(str, optional): NCHW(num_batches, channels, height, width) or NHWC(num_batches, height, width, channels). Default: 'NCHW'. Returns: Variable: 4-D tensor(NCHW or NHWC). Examples: .. code-block:: python #declarative mode import paddle.fluid as fluid import numpy as np 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) def image_resize_short(input, out_short_len, resample='BILINEAR'): """ This op resizes a batch of images. The short edge of input images will be resized to the given 'out_short_len'. The long edge of input images will be resized proportionately to make images' length-width ratio constant. Parameters: input (Variable): 4-D tensor(NCHW), The input tensor of image resize layer. out_short_len(int): The length of output images' short edge. resample (str): resample method, default: BILINEAR. Returns: Variable: 4-D tensor(NCHW). Examples: .. code-block:: python import paddle.fluid as fluid input = fluid.data(name="input", shape=[None,3,6,9], dtype="float32") out = fluid.layers.image_resize_short(input, out_short_len=3) """ in_shape = input.shape if len(in_shape) != 4: raise ValueError( "The rank of input must be 4 (num_batches, channels, in_h, in_w).") hw = in_shape[2:4] short_idx = hw.index(min(hw)) long_idx = 1 - short_idx out_shape = list(hw) out_shape[short_idx] = out_short_len out_shape[long_idx] = int( float(out_shape[long_idx]) * (float(out_short_len) / float(hw[ short_idx])) + 0.5) return image_resize(input=input, out_shape=out_shape, resample=resample) def gather(input, index, overwrite=True): """ **Gather Layer** Output is obtained by gathering entries of the outer-most dimension of X indexed by `index` and concatenate them together. .. math:: Out = X[Index] .. code-block:: text Given: X = [[1, 2], [3, 4], [5, 6]] Index = [1, 2] Then: Out = [[3, 4], [5, 6]] Args: input (Variable): The source input tensor with rank>=1. Supported data type is int32, int64, float32, float64 and uint8 (only for CPU), float16 (only for GPU). index (Variable): The index input tensor with rank=1. Data type is int32 or int64. overwrite (bool, optional): The mode that updating the grad when has same index. If True, use the overwrite mode to update the grad of the same index, if False, use the accumulate mode to update the grad of the same index. Default value is True. Returns: output (Variable): The output is a tensor with the same rank as input. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name='x', shape=[-1, 5], dtype='float32') index = fluid.data(name='index', shape=[-1, 1], dtype='int32') output = fluid.layers.gather(x, index) """ helper = LayerHelper('gather', **locals()) dtype = helper.input_dtype() out = helper.create_variable_for_type_inference(dtype) helper.append_op( type="gather", inputs={"X": input, "Index": index}, outputs={"Out": out}, attrs={'overwrite': overwrite}) return out def gather_nd(input, index, name=None): """ **Gather Nd Layer** This function is actually a high-dimensional extension of :code:`gather` and supports for simultaneous indexing by multiple axes. :attr:`index` is a K-dimensional integer tensor, which is regarded as a (K-1)-dimensional tensor of :attr:`index` into :attr:`input`, where each element defines a slice of params: .. math:: output[(i_0, ..., i_{K-2})] = input[index[(i_0, ..., i_{K-2})]] Obviously, :code:`index.shape[-1] <= input.rank` . And, the output tensor has shape :code:`index.shape[:-1] + input.shape[index.shape[-1]:]` . .. code-block:: text Given: input = [[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]] input.shape = (2, 3, 4) * Case 1: index = [[1]] gather_nd(input, index) = [input[1, :, :]] = [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]] * Case 2: index = [[0,2]] gather_nd(input, index) = [input[0, 2, :]] = [8, 9, 10, 11] * Case 3: index = [[1, 2, 3]] gather_nd(input, index) = [input[1, 2, 3]] = [23] Args: input (Variable): The source input. Its dtype should be int32, int64, float32, float64. index (Variable): The index input with rank > 1, index.shape[-1] <= input.rank. Its dtype should be int32, int64. name (str|None): A name for this layer(optional). If set None, the layer will be named automatically. Returns: output (Variable): A tensor with the shape index.shape[:-1] + input.shape[index.shape[-1]:] Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name='x', shape=[3, 4, 5], dtype='float32') index = fluid.data(name='index', shape=[2, 2], dtype='int32') output = fluid.layers.gather_nd(x, index) """ helper = LayerHelper('gather_nd', **locals()) dtype = helper.input_dtype() if name is None: output = helper.create_variable_for_type_inference(dtype) else: output = helper.create_variable( name=name, dtype=dtype, persistable=False) helper.append_op( type="gather_nd", inputs={"X": input, "Index": index}, outputs={"Out": output}) return output def scatter(input, index, updates, name=None, overwrite=True): """ **Scatter Layer** Output is obtained by updating the input on selected indices based on updates. .. code-block:: python import numpy as np #input: input = np.array([[1, 1], [2, 2], [3, 3]]) index = np.array([2, 1, 0, 1]) # shape of updates should be the same as input # shape of updates with dim > 1 should be the same as input updates = np.array([[1, 1], [2, 2], [3, 3], [4, 4]]) overwrite = False # calculation: if not overwrite: for i in range(len(index)): input[index[i]] = np.zeros((2)) for i in range(len(index)): if (overwrite): input[index[i]] = updates[i] else: input[index[i]] += updates[i] # output: out = np.array([[3, 3], [6, 6], [1, 1]]) out.shape # [3, 2] Args: input (Variable): The input N-D Tensor with rank>=1. Data type can be float32. index (Variable): The index 1-D Tensor. Data type can be int32, int64. The length of index cannot exceed updates's length, and the value in index cannot exceed input's length. updates (Variable): update input with updates parameter based on index. shape should be the same as input, and dim value with dim > 1 shoule be the same as input. 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` . overwrite (bool): The mode that updating the output when there are same indices. If True, use the overwrite mode to update the output of the same index, if False, use the accumulate mode to update the output of the same index. Default value is True. Returns: Variable(Tensor|LoDTensor): The output is a Tensor with the same shape as input. Examples: .. code-block:: python import numpy as np import paddle.fluid as fluid input = fluid.layers.data(name='data', shape=[3, 2], dtype='float32', append_batch_size=False) index = fluid.layers.data(name='index', shape=[4], dtype='int64', append_batch_size=False) updates = fluid.layers.data(name='update', shape=[4, 2], dtype='float32', append_batch_size=False) output = fluid.layers.scatter(input, index, updates, overwrite=False) exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) in_data = np.array([[1, 1], [2, 2], [3, 3]]).astype(np.float32) index_data = np.array([2, 1, 0, 1]).astype(np.int64) update_data = np.array([[1, 1], [2, 2], [3, 3], [4, 4]]).astype(np.float32) res = exe.run(fluid.default_main_program(), feed={'data':in_data, "index":index_data, "update":update_data}, fetch_list=[output]) print(res) # [array([[3., 3.], # [6., 6.], # [1., 1.]], dtype=float32)] """ helper = LayerHelper('scatter', **locals()) dtype = helper.input_dtype() out = helper.create_variable_for_type_inference(dtype) helper.append_op( type="scatter", inputs={"X": input, "Ids": index, "Updates": updates}, attrs={'overwrite': overwrite}, outputs={"Out": out}) return out def scatter_nd_add(ref, index, updates, name=None): """ **Scatter_nd_add Layer** Output is obtained by applying sparse addition to a single value or slice in a Variable. :attr:`ref` is a Tensor with rank :math:`R` and :attr:`index` is a Tensor with rank :math:`K` . Thus, :attr:`index` has shape :math:`[i_0, i_1, ..., i_{K-2}, Q]` where :math:`Q \leq R` . :attr:`updates` is a Tensor with rank :math:`K - 1 + R - Q` and its shape is :math:`index.shape[:-1] + ref.shape[index.shape[-1]:]` . According to the :math:`[i_0, i_1, ..., i_{K-2}]` of :attr:`index` , add the corresponding :attr:`updates` slice to the :attr:`ref` slice which is obtained by the last one dimension of :attr:`index` . .. code-block:: text Given: * Case 1: ref = [0, 1, 2, 3, 4, 5] index = [[1], [2], [3], [1]] updates = [9, 10, 11, 12] we get: output = [0, 22, 12, 14, 4, 5] * Case 2: ref = [[65, 17], [-14, -25]] index = [[], []] updates = [[[-1, -2], [1, 2]], [[3, 4], [-3, -4]]] ref.shape = (2, 2) index.shape = (2, 0) updates.shape = (2, 2, 2) we get: output = [[67, 19], [-16, -27]] Args: ref (Variable): The ref input. Its dtype should be int32, int64, float32, float64. index (Variable): The index input with rank > 1 and index.shape[-1] <= ref.rank. Its dtype should be int32 or int64 as it is used as indexes. updates (Variable): The updated value of scatter_nd_add op, and it must have the same dtype as ref. It must have the shape index.shape[:-1] + ref.shape[index.shape[-1]:]. name (str|None): The output variable name. If set None, the layer will be named automatically. Returns: output (Variable): The output is a tensor with the same shape and dtype as ref. Examples: .. code-block:: python import paddle.fluid as fluid ref = fluid.data(name='ref', shape=[3, 5, 9, 10], dtype='float32') index = fluid.data(name='index', shape=[3, 2], dtype='int32') updates = fluid.data(name='update', shape=[3, 9, 10], dtype='float32') output = fluid.layers.scatter_nd_add(ref, index, updates) """ if ref.dtype != updates.dtype: raise ValueError("ref and updates must have same data type.") helper = LayerHelper('scatter_nd_add', **locals()) dtype = helper.input_dtype() if name is None: output = helper.create_variable_for_type_inference(dtype) else: output = helper.create_variable( name=name, dtype=dtype, persistable=False) helper.append_op( type="scatter_nd_add", inputs={"X": ref, "Index": index, "Updates": updates}, outputs={"Out": output}) return output def scatter_nd(index, updates, shape, name=None): """ **Scatter_nd Layer** Output is obtained by scattering the :attr:`updates` in a new tensor according to :attr:`index` . This op is similar to :code:`scatter_nd_add`, except the tensor of :attr:`shape` is zero-initialized. Correspondingly, :code:`scatter_nd(index, updates, shape)` is equal to :code:`scatter_nd_add(fluid.layers.zeros(shape, updates.dtype), index, updates)` . If :attr:`index` has repeated elements, then the corresponding updates are accumulated. Because of the numerical approximation issues, the different order of repeated elements in :attr:`index` may cause different results. The specific calculation method can be seen :code:`scatter_nd_add` . This op is the inverse of the :code:`gather_nd` op. Args: index (Variable): The index input with rank > 1 and index.shape[-1] <= len(shape). Its dtype should be int32 or int64 as it is used as indexes. updates (Variable): The updated value of scatter_nd op. Its dtype should be int32, int64, float32, float64. It must have the shape index.shape[:-1] + shape[index.shape[-1]:] shape(tuple|list): Shape of output tensor. name (str|None): The output variable name. If set None, the layer will be named automatically. Returns: output (Variable): The output is a tensor with the same type as :attr:`updates` . Examples: .. code-block:: python import paddle.fluid as fluid index = fluid.data(name='index', shape=[3, 2], dtype='int64') updates = fluid.data(name='update', shape=[3, 9, 10], dtype='float32') shape = [3, 5, 9, 10] output = fluid.layers.scatter_nd(index, updates, shape) """ return scatter_nd_add(zeros(shape, updates.dtype), index, updates, name) @templatedoc() def random_crop(x, shape, seed=None): """ ${comment} Args: x(${x_type}): ${x_comment} shape(${shape_type}): ${shape_comment} seed(int|${seed_type}|None): ${seed_comment} By default, the seed will get from `random.randint(-65536, 65535)`. Returns: ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid img = fluid.data("img", [None, 3, 256, 256]) # cropped_img is [-1, 3, 224, 224] cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224]) # cropped_img2 shape: [-1, 2, 224, 224] # cropped_img2 = fluid.layers.random_crop(img, shape=[2, 224, 224]) # cropped_img3 shape: [-1, 3, 128, 224] # cropped_img3 = fluid.layers.random_crop(img, shape=[128, 224]) """ helper = LayerHelper("random_crop", **locals()) dtype = x.dtype out = helper.create_variable_for_type_inference(dtype) if seed is None: seed = np.random.randint(-65536, 65536) op_attrs = {"shape": shape} if isinstance(seed, int): op_attrs["startup_seed"] = seed seed = helper.create_variable( name=unique_name.generate("random_crop_seed"), dtype="int64", persistable=True) elif not isinstance(seed, Variable): raise ValueError("'seed' must be a Variable or an int.") helper.append_op( type="random_crop", inputs={"X": x, "Seed": seed}, outputs={"Out": out, "SeedOut": seed}, attrs=op_attrs) return out def log(x, name=None): """ Calculates the natural log of the given input tensor, element-wise. .. math:: Out = \\ln(x) Args: x (Variable): Input LoDTensor or Tensor. Must be one of the following types: float32, float64. name (str|None): 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 natural log of the input LoDTensor or Tensor computed element-wise. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np # Graph Organizing x = fluid.layers.data(name="x", shape=[1], dtype="float32") res = fluid.layers.log(x) # Create an executor using CPU as an example exe = fluid.Executor(fluid.CPUPlace()) # Execute x_i = np.array([[1], [2]]).astype(np.float32) res_val, = exe.run(fluid.default_main_program(), feed={'x':x_i}, fetch_list=[res]) print(res_val) # [[0.], [0.6931472]] """ helper = LayerHelper('log', **locals()) dtype = helper.input_dtype(input_param_name='x') out = helper.create_variable_for_type_inference(dtype) helper.append_op(type="log", inputs={"X": x}, outputs={"Out": out}) return out @templatedoc() 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]] """ 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 def selu(x, scale=None, alpha=None, name=None): """ Selu Operator. The equation is: .. math:: selu= \\lambda* \\begin{cases} x &\\quad \\text{ if } x>0 \n \\alpha * e^x - \\alpha &\\quad \\text{ if } x<=0 \\end{cases} The input `X` can carry the LoD (Level of Details) information, or not. And the output shares the LoD information with input `X`. Args: x (Variable): The input N-D Tensor. scale(float, optional): lambda in selu activation function, the default value is 1.0507009873554804934193349852946. For more information about this value, please refer to: https://arxiv.org/abs/1706.02515. alpha(float, optional): alpha in selu activation function, the default value is 1.6732632423543772848170429916717. For more information about this value, please refer to: https://arxiv.org/abs/1706.02515. 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|LoDTensor): The output Tensor or LoDTensor with the same shape and LoD information as input. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np inputs = fluid.layers.data(name="x", shape=[2, 2], dtype="float32") output = fluid.layers.selu(inputs) exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) img = np.array([[0, 1],[2, 3]]).astype(np.float32) res = exe.run(fluid.default_main_program(), feed={'x':img}, fetch_list=[output]) print(res) # [array([[0. , 1.050701],[2.101402, 3.152103]], dtype=float32)] """ helper = LayerHelper('selu', **locals()) dtype = helper.input_dtype(input_param_name='x') out = helper.create_variable_for_type_inference(dtype) attrs = {} if scale is not None: attrs["scale"] = scale if alpha is not None: attrs["alpha"] = alpha helper.append_op( type="selu", inputs={"X": x}, outputs={"Out": out}, attrs=attrs) return out def mean_iou(input, label, num_classes): """ Mean Intersection-Over-Union is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes. IOU is defined as follows: .. math:: IOU = \\frac{true\_positive}{(true\_positive + false\_positive + false\_negative)}. The predictions are accumulated in a confusion matrix and mean-IOU is then calculated from it. Parameters: input (Variable): A n-D Tensor of prediction results for semantic labels with type int32 or int64. label (Variable): A Tensor of ground truth labels with type int32 or int64. Its shape should be the same as input. num_classes (int32): The possible number of labels. Returns: Three Variables. - mean_iou(Variable) : A 1-D Tensor representing the mean intersection-over-union with shape [1]. \ Data type is float32. - out_wrong(Variable) : A 1-D Tensor with shape [num_classes]. Data type is int32. \ The wrong numbers of each class. - out_correct(Variable): A 1-D Tensor with shape [num_classes]. Data type is int32. The correct numbers of each class. Examples: .. code-block:: python import paddle.fluid as fluid iou_shape = [None, 32, 32] num_classes = 5 predict = fluid.data(name='predict', shape=iou_shape, dtype='int64') label = fluid.data(name='label', shape=iou_shape, dtype='int64') mean_iou, out_wrong, out_correct = fluid.layers.mean_iou(predict, label, num_classes) """ helper = LayerHelper('mean_iou', **locals()) dtype = helper.input_dtype() out_mean_iou = helper.create_variable_for_type_inference(dtype='float32') out_wrong = helper.create_variable_for_type_inference(dtype='int32') out_correct = helper.create_variable_for_type_inference(dtype='int32') helper.append_op( type="mean_iou", inputs={"Predictions": input, "Labels": label}, outputs={ "OutMeanIou": out_mean_iou, "OutWrong": out_wrong, "OutCorrect": out_correct }, attrs={"num_classes": num_classes}) return out_mean_iou, out_wrong, out_correct def crop(x, shape=None, offsets=None, name=None): """ Crop input into output, as specified by offsets and shape. **Warning:** THIS OP IS DEPRECATED. It will be removed in the future version. Instructions for updating: Use :ref:`api_fluid_layers_crop_tensor` instead. .. code-block:: text * Case 1: Given X = [[0, 1, 2, 0, 0] [0, 3, 4, 0, 0] [0, 0, 0, 0, 0]], and shape = [2, 2], offsets = [0, 1], output is: Out = [[1, 2], [3, 4]]. * Case 2: Given X = [[0, 1, 2, 5, 0] [0, 3, 4, 6, 0] [0, 0, 0, 0, 0]], and shape is tensor shape = [[0, 0, 0] [0, 0, 0]] and offsets = [0, 1], output is: Out = [[1, 2, 5], [3, 4, 6]]. Parameters: x (Variable): Tensor, data type can be float32 or float64. shape (Variable|list/tuple of integers): The output shape is specified by `shape`, which can be a Tensor or a list/tuple of integers. If it is a Tensor, it's rank must be the same as `x` , only it's shape will be used, and the value of it will be ignored. This way is suitable for the case that the output shape may be changed each iteration. If it is a list/tuple of integers, it's length must be the same as the rank of `x` offsets (Variable|list/tuple of integers|None): Specifies the cropping offsets at each dimension. It can be a Tensor or a list/tuple of integers. If it is a Tensor, it's rank must be the same as `x`. This way is suitable for the case that the offsets may be changed each iteration. If it is a list/tuple of integers, it's length must be the same as the rank of `x`. If None, the offsets are 0 at each dimension. name(str, optional): For detailed information, please refer to :ref:`api_guide_Name` . Usually name is no need to set and None by default. Returns: The cropped Tensor, which has the same rank and data type with `x` Return Type: Variable Raises: ValueError: If shape is not a list, tuple or Variable. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name="x", shape=[3, 3, 5], dtype="float32") y = fluid.data(name="y", shape=[2, 2, 3], dtype="float32") crop = fluid.layers.crop(x, shape=y) # or z = fluid.data(name="z", shape=[3, 3, 5], dtype="float32") crop = fluid.layers.crop(z, shape=[2, 2, 3]) """ helper = LayerHelper('crop', **locals()) if not (isinstance(shape, list) or isinstance(shape, tuple) or \ isinstance(shape, Variable)): raise ValueError("The shape should be a list, tuple or Variable.") if offsets is None: offsets = [0] * len(x.shape) out = helper.create_variable_for_type_inference(x.dtype) ipts = {'X': x} attrs = {} if isinstance(shape, Variable): ipts['Y'] = shape else: attrs['shape'] = shape if isinstance(offsets, Variable): ipts['Offsets'] = offsets else: attrs['offsets'] = offsets helper.append_op( type='crop', inputs=ipts, outputs={'Out': out}, attrs=None if len(attrs) == 0 else attrs) return out def crop_tensor(x, shape=None, offsets=None, name=None): """ Crop input into output, as specified by offsets and shape. .. code-block:: text * Case 1 (input is a 2-D Tensor): Input: X.shape = [3, 5] X.data = [[0, 1, 2, 0, 0], [0, 3, 4, 0, 0], [0, 0, 0, 0, 0]] Parameters: shape = [2, 2] offsets = [0, 1] Output: Out.shape = [2, 2] Out.data = [[1, 2], [3, 4]] * Case 2 (input is a 3-D Tensor): Input: X.shape = [2, 3, 4] X.data = [[[0, 1, 2, 3], [0, 5, 6, 7], [0, 0, 0, 0]], [[0, 3, 4, 5], [0, 6, 7, 8], [0, 0, 0, 0]]] Parameters: shape = [2, 2, -1] offsets = [0, 0, 1] Output: Out.shape = [2, 2, 3] Out.data = [[[1, 2, 3], [5, 6, 7]], [[3, 4, 5], [6, 7, 8]]] Parameters: x (Variable): 1-D to 6-D Tensor, the data type is float32, float64, int32 or int64. shape (list|tuple|Variable): The output shape is specified by `shape`. Its data type is int32. If a list/tuple, it's length must be the same as the dimension size of `x`. If a Variable, it shoule be a 1-D Tensor. When it is a list, each element can be an integer or a Tensor of shape: [1]. If Variable contained, it is suitable for the case that the shape may be changed each iteration. offsets (list|tuple|Variable, optional): Specifies the cropping offsets at each dimension. Its data type is int32. If a list/tuple, it's length must be the same as the dimension size of `x`. If a Variable, it shoule be a 1-D Tensor. When it is a list, each element can be an integer or a Tensor of shape: [1]. If Variable contained, it is suitable for the case that the offsets may be changed each iteration. Default: None, the offsets are 0 at each dimension. 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 cropped Tensor has same data type with `x`. Raises: TypeError: If the data type of `x` is not in: float32, float64, int32, int64. TypeError: If `shape` is not a list, tuple or Variable. TypeError: If the data type of `shape` is not int32. TypeError: If `offsets` is not None and not a list, tuple or Variable. TypeError: If the data type of `offsets` is not int32. ValueError: If the element in `offsets` is less than zero. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name="x", shape=[None, 3, 5], dtype="float32") # x.shape = [-1, 3, 5], where -1 indicates batch size, and it will get the exact value in runtime. # shape is a 1-D Tensor crop_shape = fluid.data(name="crop_shape", shape=[3], dtype="int32") crop0 = fluid.layers.crop_tensor(x, shape=crop_shape) # crop0.shape = [-1, -1, -1], it means crop0.shape[0] = x.shape[0] in runtime. # or shape is a list in which each element is a constant crop1 = fluid.layers.crop_tensor(x, shape=[-1, -1, 3], offsets=[0, 1, 0]) # crop1.shape = [-1, 2, 3] # or shape is a list in which each element is a constant or Variable y = fluid.data(name="y", shape=[3, 8, 8], dtype="float32") dim1 = fluid.data(name="dim1", shape=[1], dtype="int32") crop2 = fluid.layers.crop_tensor(y, shape=[3, dim1, 4]) # crop2.shape = [3, -1, 4] # offsets is a 1-D Tensor crop_offsets = fluid.data(name="crop_offsets", shape=[3], dtype="int32") crop3 = fluid.layers.crop_tensor(x, shape=[-1, 2, 3], offsets=crop_offsets) # crop3.shape = [-1, 2, 3] # offsets is a list in which each element is a constant or Variable offsets_var = fluid.data(name="dim1", shape=[1], dtype="int32") crop4 = fluid.layers.crop_tensor(x, shape=[-1, 2, 3], offsets=[0, 1, offsets_var]) # crop4.shape = [-1, 2, 3] """ helper = LayerHelper('crop_tensor', **locals()) check_type_and_dtype(x, 'x', Variable, ['float32', 'float64', 'int32', 'int64'], 'crop_tensor') check_type(shape, 'shape', (list, tuple, Variable), 'crop_tensor') check_type(offsets, 'offsets', (list, tuple, Variable, type(None)), 'crop_tensor') if offsets is None: offsets = [0] * len(x.shape) out = helper.create_variable_for_type_inference(x.dtype) ipts = {'X': x} attrs = {} def _contain_var(input_list): for ele in input_list: if isinstance(ele, Variable): return True return False def _attr_shape_check(shape_val): if not isinstance(shape_val, int): raise TypeError( "Attr(shape)'s dtype of Op(crop_tensor) should be int32, but received: %s." % type(shape_val)) if shape_val == 0: raise ValueError( "Attr(shape) of Op(crop_tensor) should not be zero, but received: %s." % str(shape_val)) if shape_val < -1: raise ValueError( "When the element in Attr(shape) of Op(crop_tensor) is negative, only -1 is supported, but received: %s." % str(shape_val)) def _attr_offsets_check(offset_val): if not isinstance(offset_val, int): raise TypeError( "Attr(offsets)'s dtype of Op(crop_tensor) should be int32, but received: %s." % type(offset_val)) if offset_val < 0: raise ValueError( "Attr(offsets) of Op(crop_tensor) should be greater or equal to zero, but received: %s." % str(offset_val)) if isinstance(offsets, Variable): offsets.stop_gradient = True ipts['Offsets'] = offsets attrs['offsets'] = [-1] * len(x.shape) elif _contain_var(offsets): new_offsets_tensor = [] offsets_attr = [] for dim in offsets: if isinstance(dim, Variable): dim.stop_gradient = True new_offsets_tensor.append(dim) offsets_attr.append(-1) else: _attr_offsets_check(dim) temp_out = helper.create_variable_for_type_inference('int32') fill_constant([1], 'int32', dim, force_cpu=True, out=temp_out) new_offsets_tensor.append(temp_out) offsets_attr.append(dim) ipts['OffsetsTensor'] = new_offsets_tensor attrs['offsets'] = offsets_attr else: for offset in offsets: _attr_offsets_check(offset) attrs['offsets'] = offsets if isinstance(shape, Variable): shape.stop_gradient = True ipts['Shape'] = shape elif _contain_var(shape): new_shape_tensor = [] shape_attr = [] for dim_size in shape: if isinstance(dim_size, Variable): dim_size.stop_gradient = True new_shape_tensor.append(dim_size) shape_attr.append(0) else: _attr_shape_check(dim_size) temp_out = helper.create_variable_for_type_inference('int32') fill_constant( [1], 'int32', dim_size, force_cpu=True, out=temp_out) new_shape_tensor.append(temp_out) shape_attr.append(dim_size) ipts['ShapeTensor'] = new_shape_tensor attrs['shape'] = shape_attr else: for dim_size in shape: _attr_shape_check(dim_size) attrs['shape'] = shape helper.append_op( type='crop_tensor', inputs=ipts, outputs={'Out': out}, attrs=None if len(attrs) == 0 else attrs) return out def affine_grid(theta, out_shape, name=None): """ It generates a grid of (x,y) coordinates using the parameters of the affine transformation that correspond to a set of points where the input feature map should be sampled to produce the transformed output feature map. Args: theta (Variable) - A Tensor with shape [N, 2, 3]. It contains a batch of affine transform parameters. The data type can be float32 or float64. out_shape (Variable | list | tuple): The shape of target output with format [batch_size, channel, height, width]. ``out_shape`` can be a Tensor or a list or tuple. The data type must be int32. name(str|None): 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 Tensor with shape [batch_size, H, W, 2] while 'H' and 'W' are the height and width of feature map in affine transformation. The data type is the same as `theta`. Raises: ValueError: If the type of arguments is not supported. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np place = fluid.CPUPlace() theta = fluid.data(name="x", shape=[None, 2, 3], dtype="float32") out_shape = fluid.data(name="y", shape=[4], dtype="int32") grid_0 = fluid.layers.affine_grid(theta, out_shape) grid_1 = fluid.layers.affine_grid(theta, [5, 3, 28, 28]) batch_size=2 exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) output= exe.run(feed={"x": np.random.rand(batch_size,2,3).astype("float32"), "y": np.array([5, 3, 28, 28]).astype("int32")}, fetch_list=[grid_0.name, grid_1.name]) print(output[0]) print(output[1]) """ helper = LayerHelper('affine_grid') if not (isinstance(out_shape, list) or isinstance(out_shape, tuple) or \ isinstance(out_shape, Variable)): raise ValueError("The out_shape should be a list, tuple or Variable.") if not isinstance(theta, Variable): raise ValueError("The theta should be a Variable.") out = helper.create_variable_for_type_inference(theta.dtype) ipts = {'Theta': theta} attrs = {} if isinstance(out_shape, Variable): ipts['OutputShape'] = out_shape else: attrs['output_shape'] = out_shape helper.append_op( type='affine_grid', inputs=ipts, outputs={'Output': out}, attrs=None if len(attrs) == 0 else attrs) return out def pad2d(input, paddings=[0, 0, 0, 0], mode='constant', pad_value=0.0, data_format="NCHW", name=None): """ Pad 2-d images accordding to 'paddings' and 'mode'. If mode is 'reflect', paddings[0] and paddings[1] must be no greater than height-1. And the width dimension has the same condition. Parameters: input (Variable): The input image with [N, C, H, W] format or [N, H, W, C] format, which is a 4-D Tensor with data type float32. paddings (Variable | List[int32]): The padding size. If padding is a List, it must contain four integers, (padding_top, padding_bottom, padding_left, padding_right). Otherwise, it is a 1-D Tensor with shape [4]. Data type is int32. Default is [0, 0, 0, 0]. mode (str): Three modes: 'constant' (default), 'reflect', 'edge' . When in 'constant' mode, this op uses a constant value to pad the input tensor. When in 'reflect' mode, uses reflection of the input boundaries to pad the input tensor. When in 'edge' mode, uses input boundaries to pad the input tensor. Default is 'constant' pad_value (float32): The value to fill the padded areas in 'constant' mode . Default is 0.0 data_format (str): An string from: "NHWC", "NCHW". Specify the data format of the input data. Default is "NCHW" 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: a 4-D Tensor padded accordding to paddings and mode and data type is same as input. Return Type: Variable Examples: .. code-block:: text Given that X is a channel of image from input: X = [[1, 2, 3], [4, 5, 6]] Case 0: paddings = [0, 1, 2, 3], mode = 'constant' pad_value = 0 Out = [[0, 0, 1, 2, 3, 0, 0, 0] [0, 0, 4, 5, 6, 0, 0, 0] [0, 0, 0, 0, 0, 0, 0, 0]] Case 1: paddings = [0, 1, 2, 1], mode = 'reflect' Out = [[3, 2, 1, 2, 3, 2] [6, 5, 4, 5, 6, 5] [3, 2, 1, 2, 3, 2]] Case 2: paddings = [0, 1, 2, 1], mode = 'edge' Out = [[1, 1, 1, 2, 3, 3] [4, 4, 4, 5, 6, 6] [4, 4, 4, 5, 6, 6]] Code Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32') result = fluid.layers.pad2d(input=data, paddings=[1, 2, 3, 4], mode='reflect') """ helper = LayerHelper('pad2d', **locals()) assert mode in ['reflect', 'edge', 'constant' ], "mode should be one of constant, reflect, edge." dtype = helper.input_dtype(input_param_name='input') out = helper.create_variable_for_type_inference(dtype) inputs = {'X': input} attrs = {'mode': mode, 'pad_value': pad_value, 'data_format': data_format} if isinstance(paddings, Variable): inputs['Paddings'] = paddings attrs['paddings'] = [] else: attrs['paddings'] = paddings helper.append_op( type='pad2d', inputs=inputs, outputs={"Out": out}, attrs=attrs) return out @templatedoc() def elu(x, alpha=1.0, name=None): """ ${comment} Args: x(${x_type}): ${x_comment} alpha(${alpha_type}|1.0): ${alpha_comment} name(str|None): 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_type}: ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np input_elu = np.array([[-1,6],[1,15.6]]) with fluid.dygraph.guard(): x = fluid.dygraph.to_variable(input_elu) y = fluid.layers.elu(x, alpha=0.2) print(y.numpy()) # [[-0.12642411 6. ] # [ 1. 15.6 ]] """ helper = LayerHelper('elu', **locals()) check_type_and_dtype(x, 'x', Variable, ['float16', 'float32', 'float64'], 'elu') out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='elu', inputs={'X': x}, outputs={'Out': out}, attrs={'alpha': alpha}) return out @templatedoc() def relu6(x, threshold=6.0, name=None): """ ${comment} Args: x(${x_type}): ${x_comment} threshold(float, optional): ${threshold_comment} name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: output(${out_type}): ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np in1 = np.array([[-1,0],[2.5,7.8]]) with fluid.dygraph.guard(): x1 = fluid.dygraph.to_variable(in1) out1 = fluid.layers.relu6(x=x1, threshold=6.0) print(out1.numpy()) # [[0. 0. ] # [2.5 6. ]] """ helper = LayerHelper('relu6', **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='relu6', inputs={'X': x}, outputs={'Out': out}, attrs={'threshold': threshold}) return out @templatedoc() def pow(x, factor=1.0, name=None): """ This is Pow Activation Operator. :math:`out = x^{factor}` Args: x(Variable): A ``Tensor`` or ``LoDTensor`` . The data type is ``float32`` or ``float64``. factor(float32|Variable, optional): A scalar with type ``float32`` or a ``Tensor`` with shape [1] and type ``float32``. The exponential factor of Pow. Default 1.0. 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: A ``Tensor`` or ``LoDTensor``. The data type is same as ``x``. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name="x", shape=[32,32], dtype="float32") # example 1: argument factor is float y_1 = fluid.layers.pow(x, factor=2.0) # y_1 is x^{2.0} # example 2: argument factor is Variable factor_tensor = fluid.layers.fill_constant([1], "float32", 3.0) y_2 = fluid.layers.pow(x, factor=factor_tensor) # y_2 is x^{3.0} """ helper = LayerHelper('pow', **locals()) inputs = {'X': x} attrs = {} if isinstance(factor, Variable): factor.stop_gradient = True inputs['FactorTensor'] = factor else: attrs['factor'] = factor out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='pow', inputs=inputs, outputs={'Out': out}, attrs=attrs) return out @templatedoc() def stanh(x, scale_a=0.67, scale_b=1.7159, name=None): """ ${comment} Args: x(${x_type}): ${x_comment} scale_a(${scale_a_type}|2.0 / 3.0): ${scale_a_comment} scale_b(${scale_b_type}|1.7159): ${scale_b_comment} name(str|None): A name for this layer(optional). If set None, the layer will be named automatically. Returns: output(${out_type}): ${out_comment}. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np data = fluid.data(name="input", shape=[-1, 3]) result = fluid.layers.stanh(data,scale_a=0.67, scale_b=1.72) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) x = np.random.random(size=(3, 3)).astype('float32') output= exe.run(feed={"input": x}, fetch_list=[result]) print(output) #[array([[0.626466 , 0.89842904, 0.7501062 ], # [0.25147712, 0.7484996 , 0.22902708], # [0.62705994, 0.23110689, 0.56902856]], dtype=float32)] """ helper = LayerHelper('stanh', **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='stanh', inputs={'X': x}, outputs={'Out': out}, attrs={'scale_a': scale_a, 'scale_b': scale_b}) return out @templatedoc() def hard_sigmoid(x, slope=0.2, offset=0.5, name=None): """ ${comment} Parameters: x (${x_type}): ${x_comment} slope (float, optional): ${slope_comment} offset (float, optional): ${offset_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_type}: ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.layers.fill_constant(shape=[3, 2], value=0.5, dtype='float32') # [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]] result = fluid.layers.hard_sigmoid(data) # [[0.6, 0.6], [0.6, 0.6], [0.6, 0.6]] """ helper = LayerHelper('hard_sigmoid', **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='hard_sigmoid', inputs={'X': x}, outputs={'Out': out}, attrs={'slope': slope, 'offset': offset}) return out @templatedoc() def swish(x, beta=1.0, name=None): """ Elementwise swish activation function. See `Searching for Activation Functions `_ for more details. Equation: .. math:: out = \\frac{x}{1 + e^{- beta * x}} Args: x(Variable): Tensor or LoDTensor, dtype: float32 or float64, the input of swish activation. beta(float): Constant beta of swish operator, default 1.0. 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: Output of the swish activation, Tensor or LoDTensor, with the same dtype and shape with the input x. Examples: .. code-block:: python # declarative mode import numpy as np from paddle import fluid x = fluid.data(name="x", shape=(-1, 3), dtype="float32") y = fluid.layers.swish(x, beta=2.0) place = fluid.CPUPlace() exe = fluid.Executor(place) start = fluid.default_startup_program() main = fluid.default_main_program() data = np.random.randn(2, 3).astype("float32") exe.run(start) y_np, = exe.run(main, feed={"x": data}, fetch_list=[y]) data # array([[-1.1239197 , 1.3391294 , 0.03921051], # [ 1.1970421 , 0.02440812, 1.2055548 ]], dtype=float32) y_np # array([[-0.2756806 , 1.0610548 , 0.01998957], # [ 0.9193261 , 0.01235299, 0.9276883 ]], dtype=float32) .. code-block:: python # imperative mode import numpy as np from paddle import fluid import paddle.fluid.dygraph as dg data = np.random.randn(2, 3).astype("float32") place = fluid.CPUPlace() with dg.guard(place) as g: x = dg.to_variable(data) y = fluid.layers.swish(x) y_np = y.numpy() data # array([[-0.0816701 , 1.1603649 , -0.88325626], # [ 0.7522361 , 1.0978601 , 0.12987892]], dtype=float32) y_np # array([[-0.03916847, 0.8835007 , -0.25835553], # [ 0.51126915, 0.82324016, 0.06915068]], dtype=float32) """ helper = LayerHelper('swish', **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='swish', inputs={'X': x}, outputs={'Out': out}, attrs={'slope': beta}) return out def prelu(x, mode, param_attr=None, name=None): """ Equation: .. math:: y = \max(0, x) + \\alpha * \min(0, x) There are three modes for the activation: .. code-block:: text all: All elements share same alpha. channel: Elements in same channel share same alpha. element: All elements do not share alpha. Each element has its own alpha. Args: x (Variable): The input Tensor or LoDTensor with data type float32. mode (str): The mode for weight sharing. param_attr(ParamAttr|None): The parameter attribute for the learnable weight (alpha), it can be create by ParamAttr. None by default. For detailed information, please refer to :ref:`api_fluid_ParamAttr`. 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: Variable: output(Variable): The tensor or LoDTensor with the same shape as input. The data type is float32. Examples: .. code-block:: python import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr x = fluid.data(name="x", shape=[None,5,10,10], dtype="float32") mode = 'channel' output = fluid.layers.prelu( x,mode,param_attr=ParamAttr(name='alpha')) """ helper = LayerHelper('prelu', **locals()) if mode not in ['all', 'channel', 'element']: raise ValueError('mode should be one of all, channel, element.') alpha_shape = [1] if mode == 'channel': alpha_shape = [1, x.shape[1], 1, 1] elif mode == 'element': alpha_shape = x.shape[1:] dtype = helper.input_dtype(input_param_name='x') alpha = helper.create_parameter( attr=helper.param_attr, shape=alpha_shape, dtype='float32', is_bias=False, default_initializer=Constant(0.25)) out = helper.create_variable_for_type_inference(dtype) helper.append_op( type="prelu", inputs={"X": x, 'Alpha': alpha}, attrs={"mode": mode}, outputs={"Out": out}) return out @templatedoc() def brelu(x, t_min=0.0, t_max=24.0, name=None): """ ${comment} Args: x(${x_type}): ${x_comment} t_min(${t_min_type}|0.0): ${t_min_comment} t_max(${t_max_type}|24.0): ${t_max_comment} name(str|None): 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_type}: ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np input_brelu = np.array([[-1,6],[1,15.6]]) with fluid.dygraph.guard(): x = fluid.dygraph.to_variable(input_brelu) y = fluid.layers.brelu(x, t_min=1.0, t_max=10.0) print(y.numpy()) #[[ 1. 6.] #[ 1. 10.]] """ helper = LayerHelper('brelu', **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='brelu', inputs={'X': x}, outputs={'Out': out}, attrs={'t_min': t_min, 't_max': t_max}) return out @templatedoc() def leaky_relu(x, alpha=0.02, name=None): """ ${comment} Args: x(${x_type}): ${x_comment} alpha(${alpha_type}|0.02): ${alpha_comment} name(str|None): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: output(${out_type}): ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np # Graph Organizing x = fluid.layers.data(name="x", shape=[2], dtype="float32") res = fluid.layers.leaky_relu(x, alpha=0.1) # Create an executor using CPU as an example exe = fluid.Executor(fluid.CPUPlace()) # Execute x_i = np.array([[-1, 2], [3, -4]]).astype(np.float32) res_val, = exe.run(fluid.default_main_program(), feed={'x':x_i}, fetch_list=[res]) print(res_val) # [[-0.1, 2], [3, -0.4]] """ helper = LayerHelper('leaky_relu', **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='leaky_relu', inputs={'X': x}, outputs={'Out': out}, attrs={'alpha': alpha}) return out def soft_relu(x, threshold=40.0, name=None): """ SoftRelu Activation Operator. $out = \ln(1 + \exp(\max(\min(x, threshold), -threshold)))$ Args: x(Variable): Input of soft_relu operator. Data type can be float32, float64. threshold(float, optional): The threshold value of soft_relu, default value being 40.0. 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(Tensor|LoDTensor)): Output of soft_relu operator, shape and LoD same as input. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np inputs = fluid.layers.data(name="x", shape=[2, 2], dtype="float32") output = fluid.layers.soft_relu(inputs, threshold=20.0) exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) img = np.array([[0, 1],[2, 3]]).astype(np.float32) res = exe.run(fluid.default_main_program(), feed={'x':img}, fetch_list=[output]) print(res) # [array([[0.6931472, 1.3132616], [2.126928 , 3.0485873]], dtype=float32)] """ helper = LayerHelper('soft_relu', **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='soft_relu', inputs={'X': x}, outputs={'Out': out}, attrs={'threshold': threshold}) return out def flatten(x, axis=1, name=None): """ **Flatten op** Flatten the input tensor into a 2D matrix. For Example: .. code-block:: text Case 1: Given X.shape = (3, 100, 100, 4) and axis = 2 We get: Out.shape = (3 * 100, 4 * 100) Case 2: Given X.shape = (3, 100, 100, 4) and axis = 0 We get: Out.shape = (1, 3 * 100 * 100 * 4) Args: x (Variable): A tensor of rank >= axis. A tensor with type float32, float64, int8, int32, int64. axis (int): Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. Default: 1. name(str, Optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Variable: A 2D tensor with the contents of the input tensor, with input \ dimensions up to axis flattened to the outer dimension of \ the output and remaining input dimensions flattened into the \ inner dimension of the output. A Tensor with type same as input x. Raises: ValueError: If x is not a variable. ValueError: If axis is not in range [0, rank(x)]. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name="x", shape=[4, 4, 3], dtype="float32") # x shape is [4, 4, 3] out = fluid.layers.flatten(x=x, axis=2) # out shape is [16, 3] """ helper = LayerHelper('flatten', **locals()) if not (isinstance(x, Variable)): raise ValueError("The input x should be a Variable") if not (isinstance(axis, int)) or axis > len(x.shape) or axis < 0: raise ValueError("The axis should be a int, and in range [0, rank(x)]") out = helper.create_variable_for_type_inference(x.dtype) x_shape = helper.create_variable_for_type_inference(x.dtype) helper.append_op( type='flatten2', inputs={"X": x}, outputs={'Out': out, 'XShape': x_shape}, attrs={"axis": axis}) return out def stack(x, axis=0): """ This OP stacks all the inputs :code:`x` along axis. .. code-block:: text Case 1: Input: x[0].shape = [1, 2] x[0].data = [ [1.0 , 2.0 ] ] x[1].shape = [1, 2] x[1].data = [ [3.0 , 4.0 ] ] x[2].shape = [1, 2] x[2].data = [ [5.0 , 6.0 ] ] Attrs: axis = 0 Output: Out.dims = [3, 1, 2] Out.data =[ [ [1.0, 2.0] ], [ [3.0, 4.0] ], [ [5.0, 6.0] ] ] Case 2: Input: x[0].shape = [1, 2] x[0].data = [ [1.0 , 2.0 ] ] x[1].shape = [1, 2] x[1].data = [ [3.0 , 4.0 ] ] x[2].shape = [1, 2] x[2].data = [ [5.0 , 6.0 ] ] Attrs: axis = 1 or axis = -2 Output: Out.shape = [1, 3, 2] Out.data =[ [ [1.0, 2.0] [3.0, 4.0] [5.0, 6.0] ] ] Args: x (Variable|list(Variable)): Input :code:`x` can be a single Tensor, a :code:`list` of Tensors. If :code:`x` is a :code:`list`, the shapes of all these Tensors must be the same. Supposing input is N dims Tensors :math:`[d_0, d_1, ..., d_{n-1}]`, the output is N+1 dims Tensor :math:`[d_0, d_1, d_{axis-1}, len(x), d_{axis}, ..., d_{n-1}]`. Support data types: float32, float64, int32, int64. axis (int, optional): The axis along which all inputs are stacked. ``axis`` range is :math:`[-(R+1), R+1)`. R is the first tensor of inputs. If ``axis`` < 0, :math:`axis=axis+rank(x[0])+1`. The default value of axis is 0. Returns: Variable: The stacked Tensor, has same data type with input Tensors. Output dim is :math:`rank(x[0])+1`. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.layers as layers # set batch size=None x1 = fluid.data(name='x1', shape=[None, 1, 2], dtype='int32') x2 = fluid.data(name='x2', shape=[None, 1, 2], dtype='int32') # stack Tensor list data = layers.stack([x1,x2]) # stack according to axis 0, data.shape=[2, None, 1, 2] data = layers.stack([x1,x2], axis=1) # stack according to axis 1, data.shape=[None, 2, 1, 2] # stack single Tensor data = layers.stack(x1) # stack according to axis 0, data.shape=[1, None, 1, 2] """ helper = LayerHelper('stack', **locals()) axis = 0 if axis is None else axis if not isinstance(x, list) and not isinstance(x, tuple): x = [x] out = helper.create_variable_for_type_inference(x[0].dtype) helper.append_op( type='stack', inputs={'X': x}, outputs={'Y': out}, attrs={'axis': axis}) return out @templatedoc(op_type="filter_by_instag") def filter_by_instag(ins, ins_tag, filter_tag, is_lod): """ **Filter By Instag Layer** This function filter a batch of ins by instag, There are multiple ins, and every ins belongs to some tags. We can specify some tags we want. So the ins which belongs to that tags remains in the output, and others removed. For example, one batch has 4 ins. Every ins has its tag list. | Ins | Ins_Tag | |:-----:|:------:| | 0 | 0, 1 | | 1 | 1, 3 | | 2 | 0, 3 | | 3 | 2, 6 | And Lod is [1,1,1,1] And the filter tags [1] From the definition above, ins which has tag 1 can pass the filter So Ins 0 and Ins 1 can pass and be seen in the output, Ins 2 and 3 cannot pass because they do not has tag 1. Actually, if is_lod is false, it is normal tensor that equals to lod_tensor with all 1, similar to the example above. Args: ins (Variable): Input Variable (LoDTensor), usually it is 2D tensor And first dimension can have lod info or not. ins_tag (Variable): Input Variable (LoDTensor), usually it is 1D list And split them by lod info filter_tag (Variable): Input Variable (1D Tensor/List), usually it is list that holds the tags. is_lod (Bool): Boolean value to indicate ins is lod tensor or not. Returns: Variable: filtered ins (LoDTensor) and loss weight (Tensor) Examples: .. code-block:: python import paddle.fluid.layers as layers ins = layers.data(name='Ins', shape=[-1,32], lod_level=0, dtype='float64') ins_tag = layers.data(name='Ins_tag', shape=[-1,16], lod_level=0, dtype='int64') filter_tag = layers.data(name='Filter_tag', shape=[-1,16], dtype='int64') out, loss_weight = layers.filter_by_instag(ins, ins_tag, filter_tag, True) """ helper = LayerHelper('filter_by_instag', **locals()) out = helper.create_variable_for_type_inference(dtype=ins.dtype) loss_weight = helper.create_variable_for_type_inference(dtype=np.float64) mmap = helper.create_variable_for_type_inference(dtype=ins_tag.dtype) helper.append_op( type='filter_by_instag', inputs={'Ins': ins, 'Ins_tag': ins_tag, 'Filter_tag': filter_tag}, outputs={'Out': out, 'LossWeight': loss_weight, 'IndexMap': mmap}, attrs={'is_lod': is_lod}) return [out, loss_weight] def unstack(x, axis=0, num=None): """ **UnStack Layer** This layer unstacks input Tensor :code:`x` into several Tensors along :code:`axis`. If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x)`. If :code:`num` is None, it would be inferred from :code:`x.shape[axis]`, and if :code:`x.shape[axis]` <= 0 or is unknown, :code:`ValueError` is raised. Args: x (Variable): Input Tensor. It is a N-D Tensors of data types float32, float64, int32, int64. axis (int): The axis along which the input is unstacked. num (int|None): The number of output variables. Returns: list(Variable): The unstacked Tensors list. The list elements are N-D Tensors of data types float32, float64, int32, int64. Raises: ValueError: If x.shape[axis] <= 0 or axis is not in range [-D, D). Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[2, 3, 5], dtype='float32') # create a tensor with shape=[2, 3, 5] y = fluid.layers.unstack(x, axis=1) # unstack with second axis, which results 3 tensors with shape=[2, 5] """ helper = LayerHelper('unstack', **locals()) if num is None: if axis is None or x.shape[axis] <= 0: raise ValueError('unknown unstack number') else: num = x.shape[axis] outs = [] for _ in range(num): outs.append(helper.create_variable_for_type_inference(x.dtype)) helper.append_op( type='unstack', inputs={'X': [x]}, outputs={'Y': outs}, attrs={'axis': axis, 'num': num}) return outs def expand(x, expand_times, name=None): """ This operation tiles ``x`` multiple times according to the parameter ``expand_times``. The times number for each dimension of ``x`` is set by the parameter ``expand_times``. The rank of ``x`` should be less than or equal to 6. Please note that size of ``expand_times`` must be the same with X's rank. Following is a using case: .. code-block:: text Input(X) is a 3-D tensor with shape [2, 3, 1]: [ [[1], [2], [3]], [[4], [5], [6]] ] Attr(expand_times): [1, 2, 2] Output(Out) is a 3-D tensor with shape [2, 6, 2]: [ [[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]], [[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]] ] Args: x (Variable): A ``Tensor`` or ``LoDTensor`` with dimension in [1, 6]. The data type is ``bool``, ``float32``, ``float64`` or ``int32`` . expand_times (list|tuple|Variable): The data type is ``int32`` . If ``expand_times`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``expand_times`` is an Variable, it should be an 1-D Tensor. Expand times number for each dimension of ``x`` . 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 ``Tensor`` or ``LoDTensor``. The data type is same as ``x``. After expanding, size of each dimension of output is equal to the size of the corresponding dimension of ``x`` multiplying the corresponding value given by ``expand_times`` . Raises: TypeError: The type of ``expand_times`` must be list, tuple or Variable. ValueError: The elements of ``expand_times`` cannot be negative. Examples: .. code-block:: python import paddle.fluid as fluid # example 1: data_1 = fluid.layers.fill_constant(shape=[2, 3, 1], dtype='int32', value=0) expanded_1 = fluid.layers.expand(data_1, expand_times=[1, 2, 2]) # the shape of expanded_1 is [2, 6, 2]. # example 2: data_2 = fluid.layers.fill_constant(shape=[12, 14], dtype="int32", value=3) expand_times = fluid.layers.fill_constant(shape=[2], dtype="int32", value=4) expanded_2 = fluid.layers.expand(data_2, expand_times=expand_times) # the shape of expanded_2 is [48, 56]. """ check_type_and_dtype(x, 'x', Variable, ['bool', 'float32', 'float64', 'int32', 'int64'], 'expand') check_type(expand_times, 'expand_times', (list, tuple, Variable), 'expand') if convert_dtype(x.dtype) == 'bool' and x.stop_gradient == True: raise ValueError( "expand op bool date type must set the stop_gradient to be False") helper = LayerHelper('expand', input=x, **locals()) inputs = {"X": x} attrs = {} def contain_var(expand_times): for ele in expand_times: if isinstance(ele, Variable): return True return False def get_attr_expand_times(list_expand_times): attrs_expand_times = [] for idx, times in enumerate(list_expand_times): if isinstance(times, Variable): attrs_expand_times.append(-1) else: attrs_expand_times.append(times) assert times > 0, ( "Each element given in expand_times must not be negtive.") return attrs_expand_times def get_new_expand_times_tensor(list_expand_times): new_expand_times_tensor = [] for ele in list_expand_times: if isinstance(ele, Variable): ele.stop_gradient = True new_expand_times_tensor.append(ele) else: assert (isinstance(ele, int)) temp_out = helper.create_variable_for_type_inference('int32') fill_constant([1], 'int32', ele, force_cpu=True, out=temp_out) new_expand_times_tensor.append(temp_out) return new_expand_times_tensor if in_dygraph_mode(): inputs = {'X': x} attrs = {'expand_times': expand_times} else: if isinstance(expand_times, Variable): expand_times.stop_gradient = True inputs['ExpandTimes'] = expand_times elif isinstance(expand_times, (list, tuple)): attrs['expand_times'] = get_attr_expand_times(expand_times) if contain_var(expand_times): inputs['expand_times_tensor'] = get_new_expand_times_tensor( expand_times) dtype = helper.input_dtype(input_param_name='x') out = helper.create_variable_for_type_inference(dtype) helper.append_op( type='expand', inputs=inputs, outputs={'Out': out}, attrs=attrs) return out def expand_as(x, target_tensor, name=None): """ expand_as operator tiles to the input by given expand tensor. You should set expand tensor for each dimension by providing tensor 'target_tensor'. The rank of X should be in [1, 6]. Please note that size of 'target_tensor' must be the same with X's rank. Following is a using case: .. code-block:: text Input(X) is a 3-D tensor with shape [2, 3, 1]: [ [[1], [2], [3]], [[4], [5], [6]] ] target_tensor's shape: [2, 6, 2] Output(Out) is a 3-D tensor with shape [2, 6, 2]: [ [[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]], [[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]] ] Args: x (Variable): A Tensor with dtype float64, float32, int32. A tensor with rank in [1, 6]. target_tensor (Variable): A Tensor with dtype float64, float32, int32. target_tensor for expanding to Input(X). Only use target_tensor'shape. Returns: Variable: A Tensor with dtype float64, float32, int32. After expanding, size of each dimension of Output(Out) is equal to the size of the corresponding dimension of target_tensor multiplying the corresponding value given by target_tensor. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np data = fluid.layers.data(name="data", shape=[-1,10], dtype='float64') target_tensor = fluid.layers.data( name="target_tensor", shape=[-1,20], dtype='float64') result = fluid.layers.expand_as(x=data, target_tensor=target_tensor) use_cuda = False place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) x = np.random.rand(3,10) y = np.random.rand(3,20) output= exe.run(feed={"data":x,"target_tensor":y},fetch_list=[result.name]) print(output[0].shape) #(3,20) """ helper = LayerHelper('expand_as', input=x, **locals()) dtype = helper.input_dtype(input_param_name='x') out = helper.create_variable_for_type_inference(dtype) inputs = {'X': x, 'target_tensor': target_tensor} helper.append_op(type='expand_as', inputs=inputs, outputs={'Out': out}) return out from paddle.fluid.framework import convert_np_dtype_to_dtype_ @templatedoc() def uniform_random_batch_size_like(input, shape, dtype='float32', input_dim_idx=0, output_dim_idx=0, min=-1.0, max=1.0, seed=0): """ This OP initializes a variable with random values sampled from a uniform distribution in the range [min, max). The input_dim_idx used to get the input dimension value which will be used to resize the output dimension. .. code-block:: text *Case 1: Given: input =[[0.946741 , 0.1357001 , 0.38086128]] # input.shape=[1,3] shape=[2,4] result.shape[output_dim_idx] = input.shape[input_dim_idx], output_dim_idx = 0, input_dim_idx = 0, result.shape[0] = input.shape[0], then: result=[[ 0.3443427 , -0.23056602, 0.3477049 , 0.06139076]] # result.shape=[1,4] *Case 2: Given: input =[[0.946741 , 0.1357001 , 0.38086128]] # input.shape=[1,3] shape=[2,4] input_dim_idx=1 output_dim_idx=1 result.shape[output_dim_idx] = input.shape[input_dim_idx], output_dim_idx = 1, input_dim_idx = 1, result.shape[1] = input.shape[1], then: result=[[-0.23133647, -0.84195036, 0.21441269], [-0.08774924, 0.25605237, -0.09403259]] # result.shape=[2,3] Args: input (Variable): A Tensor. Supported data types: float32, float64. shape (tuple|list): A python list or python tuple. The shape of the output Tensor, the data type is int. input_dim_idx (int, optional): An index used to get the input dimension value which will be used to resize the output dimension. Default 0. output_dim_idx (int, optional): An index used to indicate the specific dimension that will be replaced by corresponding input dimension value. Default 0. min (float, optional): The lower bound on the range of random values to generate, the min is included in the range. Default -1.0. max (float, optional): The upper bound on the range of random values to generate, the max is excluded in the range. Default 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. dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type of output Tensor. Supported data types: float32, float64. Default float32. Returns: Variable: A Tensor of the specified shape filled with uniform_random values. The shape of the Tensor is determined by the shape parameter and the specified dimension of the input Tensor. Examples: .. code-block:: python import paddle.fluid as fluid # example 1: input = fluid.data(name="input", shape=[1, 3], dtype='float32') out_1 = fluid.layers.uniform_random_batch_size_like(input, [2, 4]) # out_1.shape=[1, 4] # example 2: out_2 = fluid.layers.uniform_random_batch_size_like(input, [2, 4], input_dim_idx=1, output_dim_idx=1) # out_2.shape=[2, 3] """ helper = LayerHelper('uniform_random_batch_size_like', **locals()) out = helper.create_variable_for_type_inference(dtype) c_dtype = convert_np_dtype_to_dtype_(dtype) helper.append_op( type='uniform_random_batch_size_like', inputs={'Input': input}, outputs={'Out': out}, attrs={ 'shape': shape, 'input_dim_idx': input_dim_idx, 'output_dim_idx': output_dim_idx, 'min': min, 'max': max, 'seed': seed, 'dtype': c_dtype }) return out @templatedoc() def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'): """ Generate a random tensor whose data is drawn from a Gaussian distribution. Args: shape (Tuple[int] | List[int]): Shape of the generated random tensor. mean (float): Mean of the random tensor, defaults to 0.0. std (float): Standard deviation of the random tensor, defaults to 1.0. seed (int): ${seed_comment} dtype(np.dtype | core.VarDesc.VarType | str): Output data type, float32 or float64. Returns: Variable: Random tensor whose data is drawn from a Gaussian distribution, dtype: flaot32 or float64 as specified. Examples: .. code-block:: python # declarative mode 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) """ helper = LayerHelper('gaussian_random', **locals()) out = helper.create_variable_for_type_inference(dtype) c_dtype = convert_np_dtype_to_dtype_(dtype) helper.append_op( type='gaussian_random', outputs={'Out': out}, attrs={ 'shape': shape, 'mean': mean, 'std': std, 'seed': seed, 'dtype': c_dtype, 'use_mkldnn': False }) 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 @templatedoc() def gaussian_random_batch_size_like(input, shape, input_dim_idx=0, output_dim_idx=0, mean=0.0, std=1.0, seed=0, dtype='float32'): """ ${comment} Args: input (Variable): ${input_comment} shape (tuple|list): ${shape_comment} input_dim_idx (int): ${input_dim_idx_comment} output_dim_idx (int): ${output_dim_idx_comment} mean (float): ${mean_comment} std (float): ${std_comment} seed (int): ${seed_comment} dtype(np.dtype|core.VarDesc.VarType|str): The type of output data, float32 or float_64. Returns: out (Variable): ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid input = fluid.data(name="input", shape=[13, 11], dtype='float32') out = fluid.layers.gaussian_random_batch_size_like( input, shape=[-1, 11], mean=1.0, std=2.0) """ helper = LayerHelper('gaussian_random_batch_size_like', **locals()) out = helper.create_variable_for_type_inference(dtype) c_dtype = convert_np_dtype_to_dtype_(dtype) helper.append_op( type='gaussian_random_batch_size_like', inputs={'Input': input}, outputs={'Out': out}, attrs={ 'shape': shape, 'input_dim_idx': input_dim_idx, 'output_dim_idx': output_dim_idx, 'mean': mean, 'std': std, 'seed': seed, 'dtype': c_dtype }) return out @templatedoc() def sum(x): """ ${comment} Case 1: :: Input: Input. Shape = [2, 3] Input = [[1, 2, 3], [4, 5, 6]] Output: The output. Shape = [2, 3] Output = [[1, 2, 3], [4, 5, 6]] Case 2: :: Input: First input: Input1. Shape = [2, 3] Input1 = [[1, 2, 3], [4, 5, 6]] The second input: Input2. Shape = [2, 3] Input2 = [[7, 8, 9], [10, 11, 12]] Output: The output. Shape = [2, 3] Output = [[8, 10, 12], [14, 16, 18]] Args: x (Variable|list(Variable)): ${x_comment} Returns: Variable: ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid input0 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=5) input1 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=3) sum = fluid.layers.sum([input0, input1]) # You can print out 'sum' via executor. out = fluid.layers.Print(sum, message="the sum of input0 and input1: ") exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_main_program()) # The printed result is: # 1570701754 the sum of input0 and input1: The place is:CPUPlace # Tensor[sum_0.tmp_0] # shape: [2,3,] # dtype: l # data: 8,8,8,8,8,8, # the sum of input0 and input1 is 2-D Tensor with shape [2,3]. # dtype is the corresponding C++ data type, which may vary in different environments. # Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t, # so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux, # and '__int64' on Windows. They both represent 64-bit integer variables. """ helper = LayerHelper('sum', **locals()) out = helper.create_variable_for_type_inference( dtype=helper.input_dtype('x')) helper.append_op( type='sum', inputs={'X': x}, outputs={'Out': out}, attrs={'use_mkldnn': False}) return out @templatedoc() def slice(input, axes, starts, ends): """ This operator produces a slice of ``input`` along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html Slice uses ``axes``, ``starts`` and ``ends`` attributes to specify the start and end dimension for each axis in the list of axes and Slice uses this information to slice the input data tensor. If a negative value is passed to ``starts`` or ``ends`` such as :math:`-i`, it represents the reverse position of the axis :math:`i-1` (here 0 is the initial position). If the value passed to ``starts`` or ``ends`` is greater than n (the number of elements in this dimension), it represents n. For slicing to the end of a dimension with unknown size, it is recommended to pass in INT_MAX. The size of ``axes`` must be equal to ``starts`` and ``ends``. Following examples will explain how slice works: .. code-block:: text Case1: Given: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] Then: result = [ [5, 6, 7], ] Case2: Given: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [0, 1] ends = [-1, 1000] # -1 denotes the reverse 0th position of dimension 0. Then: result = [ [2, 3, 4], ] # result = data[0:1, 1:4] Args: input (Variable): A ``Tensor`` or ``LoDTensor`` . The data type is ``float16``, ``float32``, ``float64``, ``int32`` or ``int64``. axes (list|tuple): The data type is ``int32`` . Axes that `starts` and `ends` apply to. It's optional. If it is not provides, it will be treated as :math:`[0,1,...,len(starts)-1]`. starts (list|tuple|Variable): The data type is ``int32`` . If ``starts`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``starts`` is an Variable, it should be an 1-D Tensor. It represents starting indices of corresponding axis in ``axes``. ends (list|tuple|Variable): The data type is ``int32`` . If ``ends`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``ends`` is an Variable, it should be an 1-D Tensor . It represents ending indices of corresponding axis in ``axes``. Returns: Variable: A ``Tensor`` or ``LoDTensor``. The data type is same as ``input``. Raises: TypeError: The type of ``starts`` must be list, tuple or Variable. TypeError: The type of ``ends`` must be list, tuple or Variable. Examples: .. code-block:: python import paddle.fluid as fluid input = fluid.data( name="input", shape=[4, 5, 6], dtype='float32') # example 1: # attr starts is a list which doesn't contain tensor Variable. axes = [0, 1, 2] starts = [-3, 0, 2] ends = [3, 2, 4] sliced_1 = fluid.layers.slice(input, axes=axes, starts=starts, ends=ends) # sliced_1 is input[0:3, 0:2, 2:4]. # example 2: # attr starts is a list which contain tensor Variable. minus_3 = fluid.layers.fill_constant([1], "int32", -3) sliced_2 = fluid.layers.slice(input, axes=axes, starts=[minus_3, 0, 2], ends=ends) # sliced_2 is input[0:3, 0:2, 2:4]. """ if not isinstance(starts, (list, tuple, Variable)): raise ValueError( "Input starts must be an Variable, python list or tuple.") if not isinstance(ends, (list, tuple, Variable)): raise ValueError( "Input ends must be an Variable, python list or tuple.") helper = LayerHelper('slice', **locals()) def contain_var(one_list): for ele in one_list: if isinstance(ele, Variable): return True return False def get_new_list_tensor(old_list): new_list_tensor = [] for dim in old_list: if isinstance(dim, Variable): dim.stop_gradient = True new_list_tensor.append(dim) 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_list_tensor.append(temp_out) return new_list_tensor inputs = {'Input': input} attrs = {'axes': axes} infer_flags = list(1 for i in range(len(axes))) if in_dygraph_mode(): inputs = {'Input': input} attrs = { 'axes': axes, 'starts': starts, 'ends': ends, 'infer_flags': infer_flags } else: # starts if isinstance(starts, Variable): starts.stop_gradient = True inputs['StartsTensor'] = starts infer_flags = list(-1 for i in range(len(axes))) elif isinstance(starts, (list, tuple)): attrs['starts'] = [] if not contain_var(starts): attrs['starts'] = starts else: inputs['StartsTensorList'] = get_new_list_tensor(starts) for i, dim in enumerate(starts): if isinstance(dim, Variable): attrs['starts'].append(-1) infer_flags[i] = -1 else: attrs['starts'].append(dim) # ends if isinstance(ends, Variable): ends.stop_gradient = True inputs['EndsTensor'] = ends infer_flags = list(-1 for i in range(len(axes))) elif isinstance(ends, (list, tuple)): attrs['ends'] = [] if not contain_var(ends): attrs['ends'] = ends else: inputs['EndsTensorList'] = get_new_list_tensor(ends) for i, dim in enumerate(ends): if isinstance(dim, Variable): attrs['ends'].append(-1) infer_flags[i] = -1 else: attrs['ends'].append(dim) # infer_flags attrs['infer_flags'] = infer_flags out = helper.create_variable_for_type_inference( dtype=helper.input_dtype('input')) helper.append_op( type='slice', inputs=inputs, attrs=attrs, outputs={'Out': out}) return out @templatedoc() def strided_slice(input, axes, starts, ends, strides): """ This operator produces a slice of ``input`` along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html Slice uses ``axes``, ``starts`` and ``ends`` attributes to specify the start and end dimension for each axis in the list of axes and Slice uses this information to slice the input data tensor. If a negative value is passed to ``starts`` or ``ends`` such as :math:`-i`, it represents the reverse position of the axis :math:`i-1` th(here 0 is the initial position). The ``strides`` represents steps of slicing and if the ``strides`` is negative, slice operation is in the opposite direction. If the value passed to ``starts`` or ``ends`` is greater than n (the number of elements in this dimension), it represents n. For slicing to the end of a dimension with unknown size, it is recommended to pass in INT_MAX. The size of ``axes`` must be equal to ``starts`` , ``ends`` and ``strides``. Following examples will explain how strided_slice works: .. code-block:: text Case1: Given: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] strides = [1, 1] Then: result = [ [5, 6, 7], ] Case2: Given: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [0, 1] ends = [2, 0] strides = [1, -1] Then: result = [ [8, 7, 6], ] Case3: Given: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [-1, 1000] ends = [-1, 1000] strides = [1, 3] Then: result = [ [2], ] Args: input (Variable): An N-D ``Tensor`` or ``LoDTensor`` . The data type is ``float32``, ``float64``, ``int32`` or ``int64``. axes (list|tuple): The data type is ``int32`` . Axes that `starts` and `ends` apply to. It's optional. If it is not provides, it will be treated as :math:`[0,1,...,len(starts)-1]`. starts (list|tuple|Variable): The data type is ``int32`` . If ``starts`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``starts`` is an Variable, it should be an 1-D Tensor. It represents starting indices of corresponding axis in ``axes``. ends (list|tuple|Variable): The data type is ``int32`` . If ``ends`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``ends`` is an Variable, it should be an 1-D Tensor . It represents ending indices of corresponding axis in ``axes``. strides (list|tuple|Variable): The data type is ``int32`` . If ``strides`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``strides`` is an Variable, it should be an 1-D Tensor . It represents slice step of corresponding axis in ``axes``. Returns: Variable: A ``Tensor`` or ``LoDTensor`` with the same dimension as ``input``. The data type is same as ``input``. Raises: TypeError: The type of ``starts`` must be list, tuple or Variable. TypeError: The type of ``ends`` must be list, tuple or Variable. TypeError: The type of ``strides`` must be list, tuple or Variable. Examples: .. code-block:: python import paddle.fluid as fluid input = fluid.data( name="input", shape=[3, 4, 5, 6], dtype='float32') # example 1: # attr starts is a list which doesn't contain tensor Variable. axes = [0, 1, 2] starts = [-3, 0, 2] ends = [3, 2, 4] strides_1 = [1, 1, 1] strides_2 = [1, 1, 2] sliced_1 = fluid.layers.strided_slice(input, axes=axes, starts=starts, ends=ends, strides=strides_1) # sliced_1 is input[:, 0:3:1, 0:2:1, 2:4:1]. # example 2: # attr starts is a list which contain tensor Variable. minus_3 = fluid.layers.fill_constant([1], "int32", -3) sliced_2 = fluid.layers.strided_slice(input, axes=axes, starts=[minus_3, 0, 2], ends=ends, strides=strides_2) # sliced_2 is input[:, 0:3:1, 0:2:1, 2:4:2]. """ if not isinstance(starts, (list, tuple, Variable)): raise ValueError( "Input starts must be an Variable, python list or tuple.") if not isinstance(ends, (list, tuple, Variable)): raise ValueError( "Input ends must be an Variable, python list or tuple.") if not isinstance(strides, (list, tuple, Variable)): raise ValueError( "Input strides must be an Variable, python list or tuple.") helper = LayerHelper('strided_slice', **locals()) def contain_var(one_list): for ele in one_list: if isinstance(ele, Variable): return True return False def get_new_list_tensor(old_list): new_list_tensor = [] for dim in old_list: if isinstance(dim, Variable): dim.stop_gradient = True new_list_tensor.append(dim) 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_list_tensor.append(temp_out) return new_list_tensor inputs = {'Input': input} attrs = {'axes': axes} infer_flags = list(1 for i in range(len(axes))) if in_dygraph_mode(): inputs = {'Input': input} attrs = { 'axes': axes, 'starts': starts, 'ends': ends, 'strides': strides, 'infer_flags': infer_flags } else: # starts if isinstance(starts, Variable): starts.stop_gradient = True inputs['StartsTensor'] = starts elif isinstance(starts, (list, tuple)): attrs['starts'] = [] if not contain_var(starts): attrs['starts'] = starts else: inputs['StartsTensorList'] = get_new_list_tensor(starts) for i, dim in enumerate(starts): if isinstance(dim, Variable): attrs['starts'].append(-1) infer_flags[i] = -1 else: attrs['starts'].append(dim) # ends if isinstance(ends, Variable): ends.stop_gradient = True inputs['EndsTensor'] = ends elif isinstance(ends, (list, tuple)): attrs['ends'] = [] if not contain_var(ends): attrs['ends'] = ends else: inputs['EndsTensorList'] = get_new_list_tensor(ends) for i, dim in enumerate(ends): if isinstance(dim, Variable): attrs['ends'].append(-1) infer_flags[i] = -1 else: attrs['ends'].append(dim) # strides if isinstance(strides, Variable): strides.stop_gradient = True inputs['StridesTensor'] = strides elif isinstance(strides, (list, tuple)): attrs['strides'] = [] if not contain_var(strides): attrs['strides'] = strides else: inputs['StridesTensorList'] = get_new_list_tensor(strides) for i, dim in enumerate(strides): if isinstance(dim, Variable): attrs['strides'].append(-1) infer_flags[i] = -1 else: attrs['strides'].append(dim) attrs['infer_flags'] = infer_flags out = helper.create_variable_for_type_inference( dtype=helper.input_dtype('input')) helper.append_op( type='strided_slice', inputs=inputs, attrs=attrs, outputs={'Out': out}) return out def shape(input): """ **Shape Layer** Get the shape of the input. Args: input (Variable): The input N-D Tensor. Datatype can be float32, float64, int32, int64. Returns: Variable (Tensor): The shape of the input variable. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np inputs = fluid.layers.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)] """ helper = LayerHelper('shape', **locals()) out = helper.create_variable_for_type_inference(dtype='int32') helper.append_op( type='shape', inputs={'Input': input}, outputs={'Out': out}) return out def rank(input): """ The OP returns the number of dimensions for a tensor, which is a 0-D int32 Tensor. Args: input (Variable): The input N-D tensor with shape of :math:`[N_1, N_2, ..., N_k]`, the data type is arbitrary. Returns: Variable, the output data type is int32.: The 0-D tensor with the dimensions of the input variable. Examples: .. code-block:: python import paddle.fluid as fluid input = fluid.data(name="input", shape=[3, 100, 100], dtype="float32") rank = fluid.layers.rank(input) # rank=(3,) """ ndims = len(input.shape) out = assign(np.array(ndims, 'int32')) return out def size(input): """ **Size Layer** Returns the number of elements for a tensor, which is a int64 Tensor with shape [1]. Args: input (Variable): The input variable. Returns: Variable: The number of elements for the input variable. Examples: .. code-block:: python import paddle.fluid.layers as layers input = layers.data( name="input", shape=[3, 100], dtype="float32", append_batch_size=False) rank = layers.size(input) # 300 """ helper = LayerHelper('size', **locals()) out = helper.create_variable_for_type_inference(dtype='int64') helper.append_op(type='size', inputs={'Input': input}, outputs={'Out': out}) return out def _elementwise_op(helper): op_type = helper.layer_type x = helper.kwargs.get('x', None) y = helper.kwargs.get('y', None) if in_dygraph_mode(): x = base.to_variable(x) y = base.to_variable(y) 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_type_and_dtype(x, 'x', Variable, ['float16', 'float32', 'float64', 'int32', 'int64'], op_type) check_type_and_dtype(y, 'y', Variable, ['float16', '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) if name is None: out = helper.create_variable_for_type_inference(dtype=x.dtype) else: out = helper.create_variable( name=name, dtype=x.dtype, persistable=False) 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 scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None): """ Scale operator. Putting scale and bias to the input Tensor as following: ``bias_after_scale`` is True: .. math:: Out=scale*X+bias ``bias_after_scale`` is False: .. math:: Out=scale*(X+bias) Args: x(Variable): Input N-D Tensor of scale operator. Data type can be float32, float64, int8, int16, int32, int64, uint8. scale(float): The scale factor of the input. bias(float): The bias to be put on the input. bias_after_scale(bool): Apply bias addition after or before scaling. It is useful for numeric stability in some circumstances. act(str, optional): Activation applied to the output such as tanh, softmax, sigmoid, relu. 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|LoDTensor): Output tensor of scale operator, with shape and data type same as input. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np inputs = fluid.layers.data(name="x", shape=[2, 3], dtype='float32') output = fluid.layers.scale(inputs, scale = 2.0, bias = 1.0) exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32) res = exe.run(fluid.default_main_program(), feed={'x':img}, fetch_list=[output]) print(res) # [array([[ 3., 5., 7.], [ 9., 11., 13.]], dtype=float32)] """ helper = LayerHelper('scale', **locals()) if name is None: out = helper.create_variable_for_type_inference(dtype=x.dtype) else: out = helper.create_variable( name=name, dtype=x.dtype, persistable=False) helper.append_op( type='scale', inputs={'X': x}, outputs={'Out': out}, attrs={ 'scale': float(scale), 'bias': float(bias), 'bias_after_scale': bias_after_scale }) 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 def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } 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) 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 def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } 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) 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 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') } 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) 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] """ return _elementwise_op(LayerHelper('elementwise_add', **locals())) def elementwise_div(x, y, axis=-1, act=None, name=None): """ Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } 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) 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 def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } 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) 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 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') } 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) 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] """ 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 def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } 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) 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 def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } 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) 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 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') } 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) 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] """ return _elementwise_op(LayerHelper('elementwise_sub', **locals())) def elementwise_mul(x, y, axis=-1, act=None, name=None): """ Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } 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) 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 def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } 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) 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 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') } 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) 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] """ return _elementwise_op(LayerHelper('elementwise_mul', **locals())) def elementwise_max(x, y, axis=-1, act=None, name=None): """ Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = fluid.layers.elementwise_max(x, y) place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) #[2, 5, 4] .. code-block:: python import paddle.fluid as fluid import numpy as np def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } 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_max(x, y, axis=1) place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value)#[[[[1., 1., 1., 1., 1.] .... [1., 1., 1., 1., 1.]]]] """ return _elementwise_op(LayerHelper('elementwise_max', **locals())) def elementwise_min(x, y, axis=-1, act=None, name=None): """ Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = fluid.layers.elementwise_max(x, y) place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) #[1, 3, 2] .. code-block:: python import paddle.fluid as fluid import numpy as np def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } 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_max(x, y, axis=1) place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value)#[[[[0., 0., 0., 0., 0.] .... [0., 0., 0., 0., 0.]]]] """ return _elementwise_op(LayerHelper('elementwise_min', **locals())) def elementwise_pow(x, y, axis=-1, act=None, name=None): """ Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = fluid.layers.elementwise_pow(x, y) place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) #[2, 243, 16] """ return _elementwise_op(LayerHelper('elementwise_pow', **locals())) def elementwise_mod(x, y, axis=-1, act=None, name=None): """ Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np def gen_data(): return { "x": np.array([10, 15, 8]).astype('int32'), "y": np.array([3, 6, 5]).astype('int32') } x = fluid.data(name="x", shape=[3], dtype='int32') y = fluid.data(name="y", shape=[3], dtype='int32') z = fluid.layers.elementwise_mod(x, y) place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) #[1, 3, 3] """ return _elementwise_op(LayerHelper('elementwise_mod', **locals())) def elementwise_floordiv(x, y, axis=-1, act=None, name=None): """ Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np def gen_data(): return { "x": np.array([10, 15, 8]).astype('int32'), "y": np.array([3, 7, 5]).astype('int32') } x = fluid.data(name="x", shape=[3], dtype='int32') y = fluid.data(name="y", shape=[3], dtype='int32') z = fluid.layers.elementwise_floordiv(x, y) place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) #[3, 2, 1] """ return _elementwise_op(LayerHelper('elementwise_floordiv', **locals())) for func in [ elementwise_add, elementwise_div, elementwise_sub, elementwise_mul, elementwise_max, elementwise_pow, elementwise_min, elementwise_mod, elementwise_floordiv, ]: op_proto = OpProtoHolder.instance().get_op_proto(func.__name__) 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" }) + """\n""" + str(func.__doc__) 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): helper = LayerHelper(op_name, **locals()) if binary_op: assert x.dtype == y.dtype if out is None: if name is None: out = helper.create_variable_for_type_inference(dtype=x.dtype) else: out = helper.create_variable( name=name, dtype=x.dtype, persistable=False) 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 logical_and(x, y, out=None, name=None): """ logical_and Operator It operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean LoDTensor or Tensor. Each element of Out is calculated by .. math:: Out = X \land Y Args: x(${x_type}): ${x_comment} y(${y_type}): ${y_comment} out(LoDTensor or Tensor): The LoDTensor or Tensor that specifies the output of the operator, which can be any Variable that has been created in the program. The default value is None, and a new Variable will be created to save the output. name(str|None): 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_type}: ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np # Graph organizing x = fluid.layers.data(name='x', shape=[2], dtype='bool') y = fluid.layers.data(name='y', shape=[2], dtype='bool') res = fluid.layers.logical_and(x=x, y=y) # The comment lists another available method. # res = fluid.layers.fill_constant(shape=[2], dtype='bool', value=0) # fluid.layers.logical_and(x=x, y=y, out=res) # Create an executor using CPU as an example exe = fluid.Executor(fluid.CPUPlace()) # Execute x_i = np.array([[1, 0], [0, 1]]).astype(np.bool) y_i = np.array([[1, 1], [0, 0]]).astype(np.bool) res_val, = exe.run(fluid.default_main_program(), feed={'x':x_i, 'y':y_i}, fetch_list=[res]) print(res_val) # [[True, False], [False, False]] """ return _logical_op( op_name="logical_and", x=x, y=y, name=name, out=out, binary_op=True) @templatedoc() def logical_or(x, y, out=None, name=None): """ logical_or Operator It operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean LoDTensor or Tensor. Each element of Out is calculated by .. math:: Out = X \lor Y Args: x(${x_type}): ${x_comment} y(${y_type}): ${y_comment} out(LoDTensor or Tensor): The LoDTensor or Tensor that specifies the output of the operator, which can be any Variable that has been created in the program. The default value is None, and a new Variable will be created to save the output. name(str|None): 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_type}: ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np # Graph organizing x = fluid.layers.data(name='x', shape=[2], dtype='bool') y = fluid.layers.data(name='y', shape=[2], dtype='bool') res = fluid.layers.logical_or(x=x, y=y) # The comment lists another available method. # res = fluid.layers.fill_constant(shape=[2], dtype='bool', value=0) # fluid.layers.logical_or(x=x, y=y, out=res) # Create an executor using CPU as an example exe = fluid.Executor(fluid.CPUPlace()) # Execute x_i = np.array([[1, 0], [0, 1]]).astype(np.bool) y_i = np.array([[1, 1], [0, 0]]).astype(np.bool) res_val, = exe.run(fluid.default_main_program(), feed={'x':x_i, 'y':y_i}, fetch_list=[res]) print(res_val) # [[True, True], [False, True]] """ return _logical_op( op_name="logical_or", x=x, y=y, name=name, out=out, binary_op=True) @templatedoc() def logical_xor(x, y, out=None, name=None): """ logical_xor Operator It operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean LoDTensor or Tensor. Each element of Out is calculated by .. math:: Out = (X \lor Y) \land \lnot (X \land Y) Args: x(${x_type}): ${x_comment} y(${y_type}): ${y_comment} out(LoDTensor or Tensor): The LoDTensor or Tensor that specifies the output of the operator, which can be any Variable that has been created in the program. The default value is None, and a new Variable will be created to save the output. name(str|None): 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_type}: ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np # Graph organizing x = fluid.layers.data(name='x', shape=[2], dtype='bool') y = fluid.layers.data(name='y', shape=[2], dtype='bool') res = fluid.layers.logical_xor(x=x, y=y) # The comment lists another available method. # res = fluid.layers.fill_constant(shape=[2], dtype='bool', value=0) # fluid.layers.logical_xor(x=x, y=y, out=res) # Create an executor using CPU as an example exe = fluid.Executor(fluid.CPUPlace()) # Execute x_i = np.array([[1, 0], [0, 1]]).astype(np.bool) y_i = np.array([[1, 1], [0, 0]]).astype(np.bool) res_val, = exe.run(fluid.default_main_program(), feed={'x':x_i, 'y':y_i}, fetch_list=[res]) print(res_val) # [[False, True], [False, True]] """ return _logical_op( op_name="logical_xor", x=x, y=y, name=name, out=out, binary_op=True) @templatedoc() def logical_not(x, out=None, name=None): """ logical_not Operator It operates element-wise on X, and returns the Out. X and Out are N-dim boolean LoDTensor or Tensor. Each element of Out is calculated by .. math:: Out = \lnot X Args: x(${x_type}): ${x_comment} out(LoDTensor/Tensor): The LoDTensor/Tensor that specifies the output of the operator, which can be any Variable that has been created in the program. The default value is None, and a new Variable will be created to save the output. name(str|None): 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_type}: ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np # Graph organizing x = fluid.layers.data(name='x', shape=[2], dtype='bool') res = fluid.layers.logical_not(x) # The comment lists another availble method. # res = fluid.layers.fill_constant(shape=[2], dtype='bool', value=0) # fluid.layers.logical_not(x, out=res) # Create an executor using CPU as an example exe = fluid.Executor(fluid.CPUPlace()) # Execute x_i = np.array([[1, 0]]).astype(np.bool) res_val, = exe.run(fluid.default_main_program(), feed={'x':x_i}, fetch_list=[res]) print(res_val) # [[False, True]] """ return _logical_op( op_name="logical_not", x=x, y=None, name=name, out=out, binary_op=False) @templatedoc() def clip(x, min, max, name=None): """ ${comment} Args: x(${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()) 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}): ${max_norm_comment} name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. Returns: Variable: out(${out_type}): ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid input = fluid.data( name='data', shape=[None, 1], dtype='float32') reward = fluid.layers.clip_by_norm(x=input, max_norm=1.0) """ helper = LayerHelper("clip_by_norm", **locals()) 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 @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.fluid as fluid input = fluid.layers.data( name='data', shape=[2, 3], dtype='float32') mean = fluid.layers.mean(input) """ helper = LayerHelper("mean", **locals()) check_type_and_dtype(x, 'x', Variable, ['float16', 'float32', 'float64'], 'mean') if name is None: out = helper.create_variable_for_type_inference(dtype=x.dtype) else: out = helper.create_variable( name=name, dtype=x.dtype, persistable=False) 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) """ 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 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) """ helper = LayerHelper("mul", **locals()) check_type_and_dtype(x, 'x', Variable, ['float16', 'float32', 'float64'], 'mul') check_type_and_dtype(y, 'y', Variable, ['float16', 'float32', 'float64'], 'mul') if name is None: out = helper.create_variable_for_type_inference(dtype=x.dtype) else: out = helper.create_variable( name=name, dtype=x.dtype, persistable=False) helper.append_op( type="mul", inputs={"X": x, "Y": y}, attrs={ "x_num_col_dims": x_num_col_dims, "y_num_col_dims": y_num_col_dims }, outputs={"Out": out}) return out @templatedoc() def maxout(x, groups, name=None, axis=1): """ ${comment} Args: x(${x_type}): ${x_comment} groups(int): ${groups_comment} axis(int, optional): ${axis_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: Variable: ${out_comment} Raises: ValueError: If `axis` is not 1, -1 or 3. Examples: .. code-block:: python import paddle.fluid as fluid input = fluid.data( name='data', shape=[None, 256, 32, 32], dtype='float32') out = fluid.layers.maxout(input, groups=2) """ helper = LayerHelper("maxout", **locals()) if axis not in [1, -1, 3]: raise ValueError( "Attr(axis) should be 1 when data format is NCHW, -1 or 3 when data format is NHWC. Received " "Attr(axis): %s." % str(axis)) if axis == -1: axis = 3 if name is None: out = helper.create_variable_for_type_inference(dtype=x.dtype) else: out = helper.create_variable( name=name, dtype=x.dtype, persistable=False) helper.append_op( type="maxout", inputs={"X": x}, attrs={"groups": groups, "axis": axis}, outputs={"Out": out}) return out def space_to_depth(x, blocksize, name=None): """ Gives a blocksize to space_to_depth the input LoDtensor with Layout: [batch, channel, height, width] This op rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of \ theinput LoDtensor where values from the height and width dimensions are moved to the channel \ dimension. The attr blocksize indicates the input block size. space_to_depth will reorgnize the elements of input with shape[batch, channel, height, width] \ according to blocksize to construct output with shape \ [batch, channel * blocksize * blocksize, height/blocksize, width/blocksize]: - Non-overlapping blocks of size block_size x block size are rearranged into depth at each location. - The Y, X coordinates within each block of the input become the high order component of the output channel index - channel should be divisible by square of blocksize - height, width should be divsible by blocksize This OP is useful for resizing the activations between convolutions \ (but keeping all data) .. code-block:: text Given the input x with the shape [1, 1, 4, 4]: x.data = [[[[1, 2, 5, 6], [3, 4, 7, 8], [9, 10, 13, 14], [11, 12, 15, 16]]]] blocksize = 2 then get the output with the shape [1, 4, 2, 2]: out.data = [[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]], [[13, 14], [15, 16]]]] Args: x (Variable): The input, which should be 4 dims Tensor or LodTensor, with the shape \ [batch, channel, height, width] blocksize (int): The blocksize to select the element on each feature map should be > 2 name(str, optional): For detailed information, please refer \ to :ref:`api_guide_Name`. Usually name is no need to set and \ None by default. Returns: The output, which should be 4 dims Tensor or LodTensor, with the shape \ [batch, channel * blocksize * blocksize, height/blocksize, width/blocksize] Return Type: Variable Raises: TypeError: blocksize type must be int64. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np data = fluid.data( name='data', shape=[1, 4, 2, 2], dtype='float32') space_to_depthed = fluid.layers.space_to_depth( x=data, blocksize=2) exe = fluid.Executor(fluid.CPUPlace()) data_np = np.arange(0,16).reshape((1,4,2,2)).astype('float32') print(data_np) #array([[[[ 0., 1.], [ 2., 3.]], # [[ 4., 5.], [ 6., 7.]], # [[ 8., 9.], [10., 11.]], # [[12., 13.], [14., 15.]]]], dtype=float32) out_main = exe.run(fluid.default_main_program(), feed={'data': data_np}, fetch_list=[space_to_depthed]) print(out_main) #[array([[[[ 0.]], [[ 4.]], [[ 1.]], [[ 5.]], # [[ 8.]], [[12.]], [[ 9.]], [[13.]], # [[ 2.]], [[ 6.]], [[ 3.]], [[ 7.]], # [[10.]], [[14.]], [[11.]], [[15.]]]], dtype=float32)] """ helper = LayerHelper("space_to_depth", **locals()) if not (isinstance(blocksize, int)): raise ValueError("blocksize must be a python Int") if name is None: out = helper.create_variable_for_type_inference( dtype=x.dtype) #fix create else: out = helper.create_variable( name=name, dtype=x.dtype, persistable=False) helper.append_op( type="space_to_depth", inputs={"X": x}, attrs={"blocksize": blocksize}, outputs={"Out": out}) return out def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None, act=None): """ Applies a separate affine transformation to each channel of the input. Useful for replacing spatial batch norm with its equivalent fixed transformation. The input also can be 2D tensor and applies a affine transformation in second dimension. Args: x (Variable): Feature map input can be a 4D tensor with order NCHW or NHWC. It also can be a 2D tensor and the affine transformation is applied in the second dimension.The data type is float32 or float64. scale (Variable): 1D input of shape (C), the c-th element is the scale factor of the affine transformation for the c-th channel of the input.The data type is float32 or float64. bias (Variable): 1D input of shape (C), the c-th element is the bias of the affine transformation for the c-th channel of the input. The data type is float32 or float64. data_layout (str, default NCHW): NCHW or NHWC. If input is 2D tensor, you can ignore data_layout. name (str, default None): The name of this layer. For more information, please refer to :ref:`api_guide_Name` . act (str, default None): Activation to be applied to the output of this layer. Returns: Variable: A tensor which has the same shape, data layout and data type with x. Examples: .. code-block:: python import numpy as np import paddle.fluid as fluid use_gpu = False place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) data = fluid.data(name='data', shape=[None, 1, 2, 2], dtype='float32') input_scale = fluid.layers.create_parameter(shape=[1], dtype="float32", default_initializer=fluid.initializer.Constant(2.0)) input_bias = fluid.layers.create_parameter(shape=[1],dtype="float32", default_initializer=fluid.initializer.Constant(0.5)) out = fluid.layers.affine_channel(data,scale=input_scale, bias=input_bias) exe.run(fluid.default_startup_program()) test_program = fluid.default_main_program().clone(for_test=True) [out_array] = exe.run(test_program, fetch_list=out, feed={'data': np.ones([1,1,2,2]).astype('float32')}) # out_array is [[[[2.5, 2.5], # [2.5, 2.5]]]] with shape: [1, 1, 2, 2] """ helper = LayerHelper("affine_channel", **locals()) if name is None: out = helper.create_variable_for_type_inference(dtype=x.dtype) else: out = helper.create_variable( name=name, dtype=x.dtype, persistable=False) helper.append_op( type="affine_channel", inputs={"X": x, 'Scale': scale, 'Bias': bias}, attrs={"data_layout": data_layout}, outputs={"Out": out}) return helper.append_activation(out) def similarity_focus(input, axis, indexes, name=None): """ SimilarityFocus Operator Generate a similarity focus mask with the same shape of input using the following method: 1. Extract the 3-D tensor(here the first dimension is BatchSize) corresponding to the axis according to the indexes. For example, if axis=1 and indexes=[a], it will get the matrix T=X[:, a, :, :]. In this case, if the shape of input X is (BatchSize, A, B, C), the shape of tensor T is (BatchSize, B, C). 2. For each index, find the largest numbers in the tensor T, so that the same row and same column has at most one number(what it means is that if the largest number has been found in the i-th row and the j-th column, then the numbers in the i-th row or j-th column will be skipped. And then the next largest number will be selected from the remaining numbers. Obviously there will be min(B, C) numbers), and mark the corresponding position of the 3-D similarity focus mask as 1, otherwise as 0. Do elementwise-or for each index. 3. Broadcast the 3-D similarity focus mask to the same shape of input X. Refer to `Similarity Focus Layer `_ .. code-block:: text * Example : Given a 4-D tensor x with the shape (BatchSize, C, A, B), where C is the number of channels and the shape of feature map is (A, B): x.shape = (2, 3, 2, 2) x.data = [[[[0.8, 0.1], [0.4, 0.5]], [[0.9, 0.7], [0.9, 0.9]], [[0.8, 0.9], [0.1, 0.2]]], [[[0.2, 0.5], [0.3, 0.4]], [[0.9, 0.7], [0.8, 0.4]], [[0.0, 0.2], [0.4, 0.7]]]] Given axis: 1 (the axis of the channel) Given indexes: [0] then we get a 4-D tensor out with the same shape of input x: out.shape = (2, 3, 2, 2) out.data = [[[[1.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [0.0, 1.0]]], [[[0.0, 1.0], [1.0, 0.0]], [[0.0, 1.0], [1.0, 0.0]], [[0.0, 1.0], [1.0, 0.0]]]] Args: input(Variable): The input tensor variable(default float). It should be a 4-D tensor with shape [BatchSize, A, B, C]. Data type is float32 or float64. axis(int): Indicating the dimension to be selected. It can only be 1, 2 or 3. indexes(list): Indicating the indexes of the selected dimension. Returns: Variable: A tensor variable with the same shape and same type \ as the input. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.data( name='data', shape=[-1, 3, 2, 2], dtype='float32') fluid.layers.similarity_focus(input=data, axis=1, indexes=[0]) """ helper = LayerHelper('similarity_focus', **locals()) # check attrs if isinstance(axis, int) is False: raise TypeError("axis must be int type.") if isinstance(indexes, list) is False: raise TypeError("indexes must be list type.") if axis != 1 and axis != 2 and axis != 3: raise ValueError("axis must be 1, 2 or 3.") if len(indexes) == 0: raise ValueError("indexes can not be empty.") if name is None: out = helper.create_variable_for_type_inference(dtype=input.dtype) else: out = helper.create_variable( name=name, dtype=input.dtype, persistable=False) helper.append_op( type='similarity_focus', inputs={'X': input}, outputs={'Out': out}, attrs={"axis": axis, "indexes": indexes}) 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 place = fluid.core.CPUPlace() x = fluid.data(name="x", shape=[1], 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.core.LoDTensor() x_i.set(in1,place) x_i.set_recursive_sequence_lengths([[0,2]]) 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]]] """ 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 gennerated 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 indexng the 3rd dimention (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 # 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()) 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} helper.append_op(type='grid_sampler', inputs=ipts, outputs={'Output': out}) return out def log_loss(input, label, epsilon=1e-4, name=None): """ **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 (Variable|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 (Variable|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: Variable: A 2-D tensor with shape [N x 1], the negative log loss. Examples: .. code-block:: python import paddle.fluid as fluid label = fluid.data(name='label', shape=[-1, 1], dtype='int64') prob = fluid.data(name='prob', shape=[-1, 10], dtype='float32') cost = fluid.layers.log_loss(input=prob, label=label) """ helper = LayerHelper('log_loss', **locals()) if name is None: loss = helper.create_variable_for_type_inference(dtype=input.dtype) else: loss = helper.create_variable( name=name, dtype=input.dtype, persistable=False) helper.append_op( type='log_loss', inputs={'Predicted': [input], 'Labels': [label]}, outputs={'Loss': [loss]}, attrs={'epsilon': epsilon}) return loss def add_position_encoding(input, alpha, beta, name=None): """ This operator performs weighted sum of input feature at each position (position in the sequence) and the corresponding position encoding. For more details of position encoding, please refer to `Attention Is All You Need `_ . The formula is as follows: .. math:: PE(pos, 2i) &= \\sin{(pos / 10000^{2i / P})} \\\\ PE(pos, 2i + 1) &= \\cos{(pos / 10000^{2i / P})} \\\\ Out(:, pos, i) &= \\alpha * input(:, pos, i) + \\beta * PE(pos, i) Where: - :math:`PE(pos, 2i)` : the value at even index `2i` for encoding of position `pos`. - :math:`PE(pos, 2i + 1)` : the value at odd index `2i+1` for encoding of position `pos` Args: input(Variable): A Tensor or LoDTensor (lod level is 1). If it is a Tensor, the shape should be `[N, M, P]`, where `N` stands for batch size, `M` for sequence length, `P` for the size of feature dimension. If it is a LoDTensor, the shape should be `[N, P]`, where `N` stands for the total sequence lengths in this mini-batch, `P` for the size of feature. The data type should be float32 or float64. alpha(float): Indicate the weight coefficient for `input` when performing weighted sum. beta(float): Indicate the weight coefficient for position encoding when performing weighted sum. 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: A Tensor or LoDTensor. It has the same shape, data type and lod as `input`. Examples: .. code-block:: python import paddle.fluid as fluid tensor = fluid.data( name='tensor', shape=[None, 64, 512], dtype='float32') position_tensor = fluid.layers.add_position_encoding( input=tensor, alpha=1.0, beta=1.0) """ helper = LayerHelper('add_position_encoding', **locals()) dtype = helper.input_dtype() if name is None: out = helper.create_variable_for_type_inference(dtype=dtype) else: out = helper.create_variable(name=name, dtype=dtype, persistable=False) helper.append_op( type="add_position_encoding", inputs={"X": input}, outputs={"Out": out}, attrs={"alpha": alpha, "beta": beta}) return out def bilinear_tensor_product(x, y, size, act=None, name=None, param_attr=None, bias_attr=None): """ **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.fluid as fluid layer1 = fluid.data("t1", shape=[-1, 5], dtype="float32") layer2 = fluid.data("t2", shape=[-1, 4], dtype="float32") tensor = fluid.layers.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) if name is None: out = helper.create_variable_for_type_inference(dtype=dtype) else: out = helper.create_variable(name=name, dtype=dtype, persistable=False) 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]] Ouput 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) """ 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 def shuffle_channel(x, group, name=None): """ This operator shuffles the channels of input x. It divide the input channels in each group into :attr:`group` subgroups, and obtain a new order by selecting element from every subgroup one by one. Please refer to the paper https://arxiv.org/pdf/1707.01083.pdf .. code-block:: text Given a 4-D tensor input with the shape (N, C, H, W): input.shape = (1, 4, 2, 2) input.data =[[[[0.1, 0.2], [0.2, 0.3]], [[0.3, 0.4], [0.4, 0.5]], [[0.5, 0.6], [0.6, 0.7]], [[0.7, 0.8], [0.8, 0.9]]]] Given group: 2 then we get a 4-D tensor out whth the same shape of input: out.shape = (1, 4, 2, 2) out.data = [[[[0.1, 0.2], [0.2, 0.3]], [[0.5, 0.6], [0.6, 0.7]], [[0.3, 0.4], [0.4, 0.5]], [[0.7, 0.8], [0.8, 0.9]]]] Args: x(Variable): The input tensor variable. It should be a 4-D tensor with shape [N, C, H, W] group(int): Indicating the conuts of subgroups, It should divide the number of channels. Returns: out(Variable): the channels shuffling result is a tensor variable with the same shape and same type as the input. Raises: ValueError: If group is not an int type variable. Examples: .. code-block:: python import paddle.fluid as fluid input = fluid.data(name='input', shape=[None,4,2,2], dtype='float32') out = fluid.layers.shuffle_channel(x=input, group=2) """ helper = LayerHelper("shuffle_channel", **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) if not isinstance(group, int): raise TypeError("group must be int type") helper.append_op( type="shuffle_channel", inputs={"X": x}, outputs={"Out": out}, attrs={"group": group}) return out @templatedoc() def temporal_shift(x, seg_num, shift_ratio=0.25, name=None): """ **Temporal Shift Operator** ${comment} Args: x(Variable): ${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. Returns: out(Variable): The temporal shifting result is a tensor variable 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.fluid as fluid input = fluid.data(name='input', shape=[None,4,2,2], dtype='float32') out = fluid.layers.temporal_shift(x=input, seg_num=2, shift_ratio=0.2) """ helper = LayerHelper("temporal_shift", **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) if not isinstance(seg_num, int): raise TypeError("seg_num must be int type.") helper.append_op( type="temporal_shift", inputs={"X": x}, outputs={"Out": out}, attrs={"seg_num": seg_num, "shift_ratio": shift_ratio}) return out class PyFuncRegistry(object): _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.getargspec(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) @templatedoc() def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None): """ This API is used to register customized OP to Fluid. 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 LoDTensor; ``out`` is the output of ``func``, whose type can be either LoDTensor or NumPy array. The input of the backward function ``backward_func`` is ``x``, ``out`` and the gradient of ``out``. If some variables of ``out`` have no gradient, the relevant input variable of ``backward_func`` is None. If some variables of ``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``. x (Variable): The input of the forward function ``func``, its type can be Variable | tuple[Variable] | list[Variale], in which Variable is LoDTensor. out (Variable): The output of the forward function ``func``, its type can be Variable | tuple[Variable] | list[Variale], in which Variable can be either LoDTensor or NumPy array. Since Paddle cannot automatically infer the shape and data type of ``out``, ``out`` must be created 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 (Variable, optional): It's used to limit the input variable list of ``backward_func``, and it can be single Variable, tuple[Variable] or list[Variable]. It must belong to either ``x`` or ``out``. The default value is None, which means that no variables need to be removed from ``x`` and ``out``. If it is not None, these variables will not be the input of ``backward_func``. This parameter is only useful when ``backward_func`` is not None. Returns: Variable: The output ``out`` of the forward function ``func``. Examples: .. code-block:: python import paddle.fluid as fluid import six def create_tmp_var(name, dtype, shape): return fluid.default_main_program().current_block().create_var( name=name, dtype=dtype, shape=shape) # Tanh activation function provided by Paddle C++ op # Here, tanh is used as an example to show how to use py_func def tanh(x): return np.tanh(x) # Skip forward input x def tanh_grad(y, dy): return np.array(dy) * (1 - np.square(np.array(y))) def debug_func(x): print(x) def simple_net(img, label): hidden = img for idx in six.moves.range(4): hidden = fluid.layers.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 = fluid.layers.py_func(func=tanh, x=hidden, out=new_hidden, backward_func=tanh_grad, skip_vars_in_backward_input=hidden) # User-defined debugging layer, which can print out variable details fluid.layers.py_func(func=debug_func, x=hidden, out=None) prediction = fluid.layers.fc(hidden, size=10, act='softmax') loss = fluid.layers.cross_entropy(input=prediction, label=label) return fluid.layers.mean(loss) """ helper = LayerHelper('py_func', **locals()) if x is None: x = [] elif isinstance(x, Variable): x = [x] elif not isinstance(x, (list, tuple)): raise TypeError('Input must be Variable/list(Variable)/tuple(Variable)') if out is None: out_list = [] elif isinstance(out, Variable): out_list = [out] elif isinstance(out, (list, tuple)): 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 @templatedoc() def psroi_pool(input, rois, output_channels, spatial_scale, pooled_height, pooled_width, name=None): """ ${comment} Parameters: input (Variable): ${x_comment} rois (Variable): LoDTensor, ROIs (Regions of Interest) to pool over.It should be a 2-D LoDTensor of shape (num_rois, 4), 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. The data type is the same as `input` output_channels (int): ${output_channels_comment} spatial_scale (float): ${spatial_scale_comment} Default: 1.0 pooled_height (int): ${pooled_height_comment} Default: 1 pooled_width (int): ${pooled_width_comment} Default: 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: ${out_comment}. Return Type: Variable Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name='x', shape=[100, 490, 28, 28], dtype='float32') rois = fluid.data(name='rois', shape=[None, 4], lod_level=1, dtype='float32') pool_out = fluid.layers.psroi_pool(x, rois, 10, 1.0, 7, 7) """ helper = LayerHelper('psroi_pool', **locals()) # check attrs if not isinstance(output_channels, int): raise TypeError("output_channels must be int type") if not isinstance(spatial_scale, float): raise TypeError("spatial_scale must be float type") if not isinstance(pooled_height, int): raise TypeError("pooled_height must be int type") if not isinstance(pooled_width, int): raise TypeError("pooled_width must be int type") dtype = helper.input_dtype() out = helper.create_variable_for_type_inference(dtype) helper.append_op( type='psroi_pool', inputs={'X': input, 'ROIs': rois}, outputs={'Out': out}, attrs={ 'output_channels': output_channels, 'spatial_scale': spatial_scale, 'pooled_height': pooled_height, 'pooled_width': pooled_width }) return out @templatedoc() def prroi_pool(input, rois, spatial_scale=1.0, pooled_height=1, pooled_width=1, name=None): """ The precise roi pooling implementation for paddle?https://arxiv.org/pdf/1807.11590.pdf Args: input (Variable):The input of Deformable PSROIPooling.The shape of input tensor 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) to pool over.It should be a 2-D LoDTensor of shape (num_rois, 4), 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. spatial_scale (float): Ratio of input feature map height (or width) to raw image height (or width). Equals the reciprocal of total stride in convolutional layers, Default: 1.0. pooled_height (integer): The pooled output height. Default: 1. pooled_width (integer): The pooled output width. Default: 1. name (str, default None): The name of this operation. Returns: Variable(Tensor): The shape of the returned Tensor is (num_rois, output_channels, pooled_h, pooled_w), with value type float32,float16.. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[490, 28, 28], dtype='float32') rois = fluid.layers.data(name='rois', shape=[4], lod_level=1, dtype='float32') pool_out = fluid.layers.prroi_pool(x, rois, 1.0, 7, 7) """ helper = LayerHelper('prroi_pool', **locals()) # check attrs if not isinstance(spatial_scale, float): raise TypeError("spatial_scale must be float type") if not isinstance(pooled_height, int): raise TypeError("pooled_height must be int type") if not isinstance(pooled_width, int): raise TypeError("pooled_width must be int type") dtype = helper.input_dtype() out = helper.create_variable_for_type_inference(dtype) helper.append_op( type='prroi_pool', inputs={'X': input, 'ROIs': rois}, outputs={'Out': out}, attrs={ 'spatial_scale': spatial_scale, 'pooled_height': pooled_height, 'pooled_width': pooled_width }) return out def pixel_shuffle(x, upscale_factor): """ This op rearranges elements in a tensor of shape [N, C, H, W] to a tensor of shape [N, C/r**2, H*r, W*r]. This is useful for implementing efficient sub-pixel convolution with a stride of 1/r. Please refer to the paper: `Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network `_ . by Shi et. al (2016) for more details. Parameters: x(Variable): 4-D tensor, the data type should be float32 or float64. upscale_factor(int): factor to increase spatial resolution. Returns: Out(Variable): Reshaped tensor according to the new dimension. Raises: ValueError: If the square of upscale_factor cannot divide the channels of input. Examples: .. code-block:: python # declarative mode import paddle.fluid as fluid import numpy as np input = fluid.data(name="input", shape=[2,9,4,4]) output = fluid.layers.pixel_shuffle(x=input, upscale_factor=3) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) input_data = np.random.rand(2,9,4,4).astype("float32") output_data = exe.run(fluid.default_main_program(), feed={"input":input_data}, fetch_list=[output], return_numpy=True) # print(output.shape) # (2L, 1L, 12L, 12L) """ helper = LayerHelper("pixel_shuffle", **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) if not isinstance(upscale_factor, int): raise TypeError("upscale factor must be int type") helper.append_op( type="pixel_shuffle", inputs={"X": x}, outputs={"Out": out}, attrs={"upscale_factor": upscale_factor}) return out def fsp_matrix(x, y): """ **FSP matrix op** This op is used to calculate the flow of solution procedure (FSP) matrix of two 4-D Tensor feature maps. Given feature map x with shape [x_channel, h, w] and feature map y with shape [y_channel, h, w], we can get the fsp matrix of x and y in two steps: 1. reshape x into matrix with shape [x_channel, h * w] and reshape and transpose y into matrix with shape [h * w, y_channel]. 2. multiply x and y to get fsp matrix with shape [x_channel, y_channel]. The output is a batch of fsp matrices. Args: x (Variable): A 4-D Tensor feature map with shape [batch_size, x_channel, height, width]. A Tensor with type float32, float64. y (Variable): A 4-D Tensor feature map with shape [batch_size, y_channel, height, width]. The y_channel can be different with the x_channel of Input(X) while the other dimensions must be the same with Input(X)'s. A Tensor with type float32, float64. Returns: fsp matrix (Variable): The output of FSP op with shape [batch_size, x_channel, y_channel]. The x_channel is the channel of x and the y_channel is the channel of y. A Tensor with type float32, float64. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.data(name='data', shape=[None, 3, 32, 32]) feature_map_0 = fluid.layers.conv2d(data, num_filters=2, filter_size=3) feature_map_1 = fluid.layers.conv2d(feature_map_0, num_filters=2, filter_size=1) loss = fluid.layers.fsp_matrix(feature_map_0, feature_map_1) """ helper = LayerHelper('fsp_matrix', **locals()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype( input_param_name='x')) helper.append_op(type='fsp', inputs={'X': x, 'Y': y}, outputs={'Out': out}) return out def continuous_value_model(input, cvm, use_cvm=True): """ **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 caculate :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) helper.append_op( type='cvm', inputs={'X': [input], 'CVM': [cvm]}, outputs={'Y': [out]}, attrs={"use_cvm": use_cvm}) return out def where(condition): """ Return an int64 tensor with rank 2, specifying the coordinate of true element in `condition`. Args: condition(Variable): A bool tensor with rank at least 1, the data type is bool. Returns: Variable, the output data type is int64. : The tensor variable storing a 2-D tensor, which involves all coordinate. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.layers as layers import numpy as np # condition is a tensor [True, False, True] condition = layers.assign(np.array([1, 0, 1], dtype='int32')) condition = layers.cast(condition, 'bool') out = layers.where(condition) # [[0], [2]] # condition is a tensor [[True, False], [False, True]] condition = layers.assign(np.array([[1, 0], [0, 1]], dtype='int32')) condition = layers.cast(condition, 'bool') out = layers.where(condition) # [[0, 0], [1, 1]] # condition is a tensor [False, False, False] condition = layers.assign(np.array([0, 0, 0], dtype='int32')) condition = layers.cast(condition, 'bool') out = layers.where(condition) # [[]] """ helper = LayerHelper("where", **locals()) out = helper.create_variable_for_type_inference( dtype=core.VarDesc.VarType.INT64) helper.append_op( type='where', inputs={'Condition': condition}, outputs={'Out': [out]}) return out def sign(x): """ This OP returns sign of every element in `x`: 1 for positive, -1 for negative and 0 for zero. Args: x(Variable|numpy.ndarray): The input variable could be N-D tensor or N-D numpy array, \ the input data type is float32 or float64. Returns: Variable, the output data type is the same as input data type. : The output sign tensor with identical shape to input :attr:`x`. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np # [1.0, 0.0, -1.0] data = fluid.layers.sign(np.array([3.0, 0.0, -2.0], dtype='float32')) """ helper = LayerHelper("sign", **locals()) check_type(x, 'x', (Variable, np.ndarray), 'sign') if isinstance(x, np.ndarray): x = assign(x) check_dtype(x.dtype, 'x', ['float16', 'float32', 'float64'], 'sign') out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op(type='sign', inputs={'X': [x]}, outputs={'Out': [out]}) return out def unique(x, dtype='int32'): """ **unique** Return a unique tensor for `x` and an index tensor pointing to this unique tensor. Args: x(Variable): A 1-D input tensor. dtype(np.dtype|core.VarDesc.VarType|str): The type of index tensor: int32, int64. Returns: tuple: (out, index). `out` is the unique tensor for `x`, with identical dtype to `x`, and \ `index` is an index tensor pointing to `out`, by which user can recover the original `x` tensor. Examples: .. code-block:: python import numpy as np import paddle.fluid as fluid x = fluid.assign(np.array([2, 3, 3, 1, 5, 3], dtype='int32')) out, index = fluid.layers.unique(x) # out is [2, 3, 1, 5]; index is [0, 1, 1, 2, 3, 1] """ helper = LayerHelper("unique", **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) index = helper.create_variable_for_type_inference(dtype) helper.append_op( type='unique', inputs={'X': x}, attrs={'dtype': convert_np_dtype_to_dtype_(dtype)}, outputs={'Out': [out], 'Index': [index]}) return out, index def unique_with_counts(x, dtype='int32'): """ This OP return a unique tensor for `x` , and count tensor that the count of unqiue result in raw input, \ and an index tensor pointing to this unique tensor. **NOTICE**: This op just be supported in device of CPU, and support the variable type of Tensor only. Args: x(Variable): A 1-D input tensor with input shape of :math:`[N]` , the input data type is float32, float64, int32, int64. dtype(np.dtype|core.VarDesc.VarType|str): The type of count and index tensor, it could be int32, int64. Defalut value is int32. Returns: tuple, the variable type in tuple is Tensor, the output :attr:`out` data type is the same as input :attr:`x`, \ and data type of output :attr:`index` and :attr:`count` will be int32 or int64.: The :attr:`out` is unique tensor for input :attr:`x`,\ the data shape is :math:`[K]`, the `K` may be different to the `N` in shape of :attr:`x`. :attr:`index` is an index tensor pointing\ to :attr:`out`, the data shape is :math:`[N]` , the data shape is the same as input :attr:`x`. :attr:`count` is count of unqiue element in\ the :attr:`x`, the data shape is :math:`[K]`, the data shape is the same as output :attr:`out`. Examples: .. code-block:: python import numpy as np import paddle.fluid as fluid x = fluid.layers.assign(np.array([2, 3, 3, 1, 5, 3], dtype='int32')) out, index, count = fluid.layers.unique_with_counts(x) # out is [2, 3, 1, 5]; index is [0, 1, 1, 2, 3, 1] # count is [1, 3, 1, 1] # x.shape=(6,) out.shape=(4,), index.shape=(6,), count.shape=(4,) """ if not (dtype == 'int32' or dtype == 'int64'): raise TypeError( "Op unique_with_counts, index dtype must be int32 or int64") if x is None or len(x.shape) != 1: raise ValueError( "Op unique_with_counts, x must not be null and size of dim must be 1" ) helper = LayerHelper("unique_with_counts", **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) index = helper.create_variable_for_type_inference(dtype) count = helper.create_variable_for_type_inference(dtype) helper.append_op( type='unique_with_counts', inputs={'X': x}, attrs={'dtype': convert_np_dtype_to_dtype_(dtype)}, outputs={'Out': [out], 'Index': [index], 'Count': [count]}) return out, index, count def deformable_conv(input, offset, mask, num_filters, filter_size, stride=1, padding=0, dilation=1, groups=None, deformable_groups=None, im2col_step=None, param_attr=None, bias_attr=None, modulated=True, name=None): """ **Deformable Convolution op** Compute 2-D deformable convolution on 4-D input. Given input image x, output feature map y, the deformable convolution operation can be expressed as follow: Deformable Convolution v2: .. math:: y(p) = \sum_{k=1}^{K}{w_k * x(p + p_k + \Delta p_k) * \Delta m_k} Deformable Convolution v1: .. math:: y(p) = \sum_{k=1}^{K}{w_k * x(p + p_k + \Delta p_k)} Where :math:`\Delta p_k` and :math:`\Delta m_k` are the learnable offset and modulation scalar for the k-th location, Which :math:`\Delta m_k` is one in deformable convolution v1. Please refer to `Deformable ConvNets v2: More Deformable, Better Results `_ and `Deformable Convolutional Networks `_. Example: - Input: Input shape: :math:`(N, C_{in}, H_{in}, W_{in})` Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)` Offset shape: :math:`(N, 2 * deformable\_groups * H_f * H_w, H_{in}, W_{in})` Mask shape: :math:`(N, deformable\_groups * H_f * H_w, H_{in}, W_{in})` - 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 (Variable): The input image with [N, C, H, W] format. A Tensor with type float32, float64. offset (Variable): The input coordinate offset of deformable convolution layer. A Tensor with type float32, float64. Mask (Variable, Optional): The input mask of deformable convolution layer. A Tensor with type float32, float64. It should be None when you use deformable convolution v1. 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_H, filter_size_W). Otherwise, the filter will be a square. stride (int|tuple): The stride size. If stride is a tuple, it must contain two integers, (stride_H, stride_W). Otherwise, the stride_H = stride_W = stride. Default: stride = 1. padding (int|tuple): The padding size. If padding is a tuple, it must contain two integers, (padding_H, padding_W). Otherwise, the padding_H = padding_W = padding. Default: padding = 0. dilation (int|tuple): The dilation size. If dilation is a tuple, it must contain two integers, (dilation_H, dilation_W). Otherwise, the dilation_H = dilation_W = dilation. Default: dilation = 1. groups (int): The groups number of the deformable conv 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. deformable_groups (int): The number of deformable group partitions. Default: deformable_groups = 1. im2col_step (int): Maximum number of images per im2col computation; The total batch size should be divisable by this value or smaller than this value; if you face out of memory problem, you can try to use a smaller value here. Default: im2col_step = 64. param_attr (ParamAttr, Optional): The parameter attribute for learnable parameters/weights of deformable conv. If it is set to None or one attribute of ParamAttr, deformable conv 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, Optional): The parameter attribute for the bias of deformable conv layer. 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. modulated (bool): Make sure which version should be used between v1 and v2, where v2 is \ used while True. Default: True. name(str, Optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Variable: The tensor variable storing the deformable convolution \ result. A Tensor with type float32, float64. Raises: ValueError: If the shapes of input, filter_size, stride, padding and groups mismatch. Examples: .. code-block:: python #deformable conv v2: import paddle.fluid as fluid C_in, H_in, W_in = 3, 32, 32 filter_size, deformable_groups = 3, 1 data = fluid.data(name='data', shape=[None, C_in, H_in, W_in], dtype='float32') offset = fluid.data(name='offset', shape=[None, 2*deformable_groups*filter_size**2, H_in, W_in], dtype='float32') mask = fluid.data(name='mask', shape=[None, deformable_groups*filter_size**2, H_in, W_in], dtype='float32') out = fluid.layers.deformable_conv(input=data, offset=offset, mask=mask, num_filters=2, filter_size=filter_size, padding=1, modulated=True) #deformable conv v1: import paddle.fluid as fluid C_in, H_in, W_in = 3, 32, 32 filter_size, deformable_groups = 3, 1 data = fluid.data(name='data', shape=[None, C_in, H_in, W_in], dtype='float32') offset = fluid.data(name='offset', shape=[None, 2*deformable_groups*filter_size**2, H_in, W_in], dtype='float32') out = fluid.layers.deformable_conv(input=data, offset=offset, mask=None, num_filters=2, filter_size=filter_size, padding=1, modulated=False) """ num_channels = input.shape[1] assert param_attr is not False, "param_attr should not be False here." helper = LayerHelper('deformable_conv', **locals()) dtype = helper.input_dtype() if not isinstance(input, Variable): raise TypeError("Input of deformable_conv must be Variable") if not isinstance(offset, Variable): raise TypeError("Input Offset of deformable_conv must be Variable") if groups is None: num_filter_channels = num_channels else: if num_channels % groups != 0: raise ValueError("num_channels must be divisible by groups.") num_filter_channels = num_channels // groups filter_size = utils.convert_to_list(filter_size, 2, 'filter_size') stride = utils.convert_to_list(stride, 2, 'stride') padding = utils.convert_to_list(padding, 2, 'padding') dilation = utils.convert_to_list(dilation, 2, 'dilation') input_shape = input.shape 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 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 modulated: helper.append_op( type='deformable_conv', inputs={ 'Input': input, 'Filter': filter_param, 'Offset': offset, 'Mask': mask, }, outputs={"Output": pre_bias}, attrs={ 'strides': stride, 'paddings': padding, 'dilations': dilation, 'groups': groups, 'deformable_groups': deformable_groups, 'im2col_step': im2col_step, }) else: helper.append_op( type='deformable_conv_v1', inputs={ 'Input': input, 'Filter': filter_param, 'Offset': offset, }, outputs={"Output": pre_bias}, attrs={ 'strides': stride, 'paddings': padding, 'dilations': dilation, 'groups': groups, 'deformable_groups': deformable_groups, 'im2col_step': im2col_step, }) output = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2) return output def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None): """ 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 silding 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(Varaible): 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, shold be [dilation_h, dilation_w], or an integer dialtion 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 variable corresponding to the sliding local blocks. The output shape is [N, Cout, Lout] as decribled 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: Variable Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name = 'data', shape = [100, 3, 224, 224], dtype = 'float32') y = fluid.layers.unfold(x, [3, 3], 1, 1, 1) """ helper = LayerHelper("unfold", **locals()) assert len(x.shape) == 4, \ "input should be the format of [N, C, H, W]" if isinstance(kernel_sizes, int): kernel_sizes = [kernel_sizes, kernel_sizes] else: assert isinstance(kernel_sizes, list) and (len(kernel_sizes) == 2), \ "kernel_sizes should either be an integer or a list of two integers" if isinstance(strides, int): strides = [strides, strides] else: assert isinstance(strides, list) and (len(strides) == 2), \ "strides should either be an integer or a list of two integers" if isinstance(dilations, int): dilations = [dilations, dilations] else: assert isinstance(dilations, list) and (len(dilations) == 2), \ "dilations should either be an integer or a list of two integers" if isinstance(paddings, int): paddings = [paddings] * 4 elif isinstance(paddings, list): if len(paddings) == 2: paddings = paddings * 2 elif len(paddings) == 4: pass else: raise ValueError( "paddings should either be an integer or a list of 2 or 4 integers" ) else: raise ValueError( "Unexpected type of paddings, it should be either an integer or a list" "of 2 or 4 integers") out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type="unfold", inputs={"X": x}, outputs={"Y": out}, attrs={ "kernel_sizes": kernel_sizes, "strides": strides, "paddings": paddings, "dilations": dilations }) return out 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): """ 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 chanels.) 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 shoule 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) """ 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 def shard_index(input, index_num, nshards, shard_id, ignore_value=-1): """ This operator recomputes the `input` indices according to the offset of the shard. The length of the indices is evenly divided into N shards, and if the `shard_id` matches the shard with the input index inside, the index is recomputed on the basis of the shard offset, elsewise it is set to `ignore_value`. The detail is as follows: :: shard_size = (index_num + nshards - 1) // nshards y = x % shard_size if x // shard_size == shard_id else ignore_value NOTE: If the length of indices cannot be evely divided by the shard number, the size of the last shard will be less than the calculated `shard_size` Examples: :: Input: X.shape = [4, 1] X.data = [[1], [6], [12], [19]] index_num = 20 nshards = 2 ignore_value = -1 if shard_id == 0, we get: Out.shape = [4, 1] Out.data = [[1], [6], [-1], [-1]] if shard_id == 1, we get: Out.shape = [4, 1] Out.data = [[-1], [-1], [2], [9]] Args: - **input** (Variable): Input indices, last dimension must be 1. - **index_num** (scalar): An interger defining the range of the index. - **nshards** (scalar): The number of shards - **shard_id** (scalar): The index of the current shard - **ignore_value** (scalar): An ingeter value out of sharded index range Returns: Variable: The sharded index of input. Examples: .. code-block:: python import paddle.fluid as fluid batch_size = 32 label = fluid.data(name="label", shape=[batch_size, 1], dtype="int64") shard_label = fluid.layers.shard_index(input=label, index_num=20, nshards=2, shard_id=0) """ op_type = 'shard_index' helper = LayerHelper(op_type, **locals()) if index_num % nshards != 0: raise ValueError( 'The index_num(%d) cannot be evenly divided by nshards(%d)' % (index_num, nshards)) 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): """ 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 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.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.]] """ 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 def gather_tree(ids, parents): """ To be used after beam search. After beam search, we get selected ids at each time step and the corresponding parents in the search tree. Both ids and parents have the layout :attr:`[max_time, batch_size, beam_size]`. Then :attr:`gather_tree` is used to backtrace from the last time step and generate the full sequences by collecting selected ids. Here is an example: .. code-block:: text Given: ids = [[[2 2] [6 1]] [[3 9] [6 1]] [[0 1] [9 0]]] parents = [[[0 0] [1 1]] [[1 0] [1 0]] [[0 0] [0 1]]] Then: gather_tree(ids, parents) = [[[2 2] [1 6]] [[3 3] [6 1]] [[0 1] [9 0]]] Args: ids(Variable): A Tensor with shape :attr:`[length, batch_size, beam_size]` and data type :attr:`int32` or :attr:`int64`. It contains the selected ids of all time steps. parents(Variable): A Tensor with the same shape and data type as :attr:`ids`, It contains the parents corresponding to selected ids when searching among beams. Returns: Variable: A Tensor with the same shape and data type as :attr:`ids`. \ It contains the full sequences. The sequences are collected from \ :attr:`ids` by backtracing according to :attr:`parents`. Examples: .. code-block:: python import paddle.fluid as fluid ids = fluid.layers.data(name='ids', shape=[5, 2, 2], dtype='int64', append_batch_size=False) parents = fluid.layers.data(name='parents', shape=[5, 2, 2], dtype='int64', append_batch_size=False) final_sequences = fluid.layers.gather_tree(ids, parents) """ helper = LayerHelper('gather_tree', **locals()) out = helper.create_variable_for_type_inference(dtype=ids.dtype) helper.append_op( type="gather_tree", inputs={"Ids": ids, "Parents": parents}, outputs={"Out": out}) return out @templatedoc() def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0): """ This OP initializes a variable with random values sampled from a uniform distribution in the range [min, max). Examples: :: Input: shape = [1, 2] Output: result=[[0.8505902, 0.8397286]] Args: shape (list|tuple|Variable): The shape of the output Tensor, if the shape is a list or tuple, its elements can be an integer or a Tensor with the shape [1], and the type of the Tensor must be int32 or int64. If the shape is a Variable, it is a 1-D Tensor, and the type of the Tensor must be int32 or int64. dtype(np.dtype|core.VarDesc.VarType|str, optional): The type of the output Tensor. Supported data types: float32, float64. Default: float32. min (float, optional): The lower bound on the range of random values to generate, the min is included in the range. Default -1.0. max (float, optional): The upper bound on the range of random values to generate, the max is excluded in the range. Default 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 0. Returns: Variable: A Tensor of the specified shape filled with uniform_random values. Raises: TypeError: The shape type should be list or tupple or variable. Examples: .. code-block:: python import paddle.fluid as fluid # example 1: # attr shape is a list which doesn't contain tensor Variable. result_1 = fluid.layers.uniform_random(shape=[3, 4]) # example 2: # attr shape is a list which contains tensor Variable. dim_1 = fluid.layers.fill_constant([1],"int64",3) dim_2 = fluid.layers.fill_constant([1],"int32",5) result_2 = fluid.layers.uniform_random(shape=[dim_1, dim_2]) # example 3: # attr shape is a Variable, 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) var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32") result_4 = fluid.layers.uniform_random(var_shape_int32) """ check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random') if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) check_dtype(dtype, 'dtype', ['float32', 'float64'], 'uniform_random') def contain_var(one_list): for ele in one_list: if isinstance(ele, Variable): return True return False def get_new_shape_tensor(list_shape): new_shape_tensor = [] for dim in list_shape: if isinstance(dim, Variable): dim.stop_gradient = True new_shape_tensor.append(dim) else: assert (isinstance(dim, int)) temp_out = helper.create_variable_for_type_inference('int64') fill_constant([1], 'int64', dim, force_cpu=True, out=temp_out) new_shape_tensor.append(temp_out) return new_shape_tensor def get_attr_shape(list_shape): unk_dim_idx = -1 attrs_shape = [] for dim_idx, dim_size in enumerate(list_shape): if isinstance(dim_size, Variable): attrs_shape.append(-1) else: attrs_shape.append(dim_size) assert dim_size > 0, ( "Each dimension size given in shape must not be negtive " "except one unknown dimension.") return attrs_shape helper = LayerHelper("uniform_random", **locals()) inputs = dict() attrs = {'seed': seed, 'min': min, 'max': max} if in_dygraph_mode(): attrs['shape'] = shape else: if isinstance(shape, Variable): shape.stop_gradient = True inputs["ShapeTensor"] = shape elif isinstance(shape, (list, tuple)): assert len(shape) > 0, ( "The size of argument(shape) can't be zero.") attrs["shape"] = get_attr_shape(shape) if contain_var(shape): inputs['ShapeTensorList'] = get_new_shape_tensor(shape) out = helper.create_variable_for_type_inference(dtype) helper.append_op( type="uniform_random", inputs=inputs, attrs=attrs, outputs={"Out": out}) return helper.append_activation(out)