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It can take a Tensor(or LoDTensor) or a list of Tensor(or LoDTensor) as its inputs(see Args in detail). It creates a variable called weight for each input Tensor, which represents a fully connected weight matrix from each input unit to each output unit. The fully connected layer multiplies each input Tensor with its corresponding weight to produce an output Tensor with shape :math:`[M, size]` , where M is batch size. If a list of Tensor is given, the results of multiple output Tensors with shape :math:`[M, size]` will be summed up. If :attr:`bias_attr` is not None, a bias variable will be created and added to the output. Finally, if :attr:`act` is not None, it will be applied to the output as well. When the input is a single Tensor(or LoDTensor): .. math:: Out = Act({XW + b}) When the input is a list of Tensor(or LoDTensor): .. math:: Out = Act({\sum_{i=0}^{N-1}X_iW_i + b}) In the above equation: * :math:`N`: Number of the input. N equals to len(input) if input is list of Variable. * :math:`X_i`: The i-th input tensor. * :math:`W_i`: The i-th weights matrix corresponding i-th input tensor. * :math:`b`: The bias parameter created by this layer (if needed). * :math:`Act`: The activation function. * :math:`Out`: The output Tensor. .. code-block:: text Case 1: Given a single Tensor data_1, and num_flatten_dims = 2: data_1.data = [[[0.1, 0.2], [0.3, 0.4]]] data_1.shape = (1, 2, 2) # 1 is batch_size out = fluid.layers.fc(input=data_1, size=1, num_flatten_dims=2) Then output is: out.data = [[0.83234344], [0.34936576]] out.shape = (1, 2, 1) Case 2: Given a list of Tensor: data_1.data = [[[0.1, 0.2], [0.3, 0.4]]] data_1.shape = (1, 2, 2) # 1 is batch_size data_2 = [[[0.1, 0.2, 0.3]]] data_2.shape = (1, 1, 3) out = fluid.layers.fc(input=[data_1, data_2], size=2) Then: out.data = [[0.18669507, 0.1893476]] out.shape = (1, 2) Args: input (Variable|list of Variable): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` or a list of Tensor(or LoDTensor). The dimensions of the input Tensor is at least 2 and the data type should be float32 or float64. size(int): The number of output units in this layer, which also means the feature size of output Tensor(or LoDTensor). num_flatten_dims (int): The fc layer can accept an input Tensor with more than two dimensions. If this happens, the multidimensional tensor will first be flattened into a 2-D matrix. The parameter :attr:`num_flatten_dims` determines how the input Tensor is flattened: the first :attr:`num_flatten_dims` (inclusive, index starts from 1) dimensions will be flatten to form the first dimension of the final matrix (height of the matrix), and the rest :math:`rank(X) - num\_flatten\_dims` dimensions are flattened to form the second dimension of the final matrix (width of the matrix). For example, assuming that X is a 5-dimensional Tensor with a shape [2, 3, 4, 5, 6], and :attr:`num_flatten_dims` = 3. Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30]. Default: 1. param_attr (ParamAttr): To specify the weight parameter property. Default: None, which means the default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . bias_attr (ParamAttr): To specify the bias parameter property. Default: None, which means the default bias parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . act (str): Activation to be applied to the output of this layer, such as tanh, softmax, sigmoid, relu. For more information, please refer to :ref:`api_guide_activations_en` . Default: None. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Variable: Tensor or LoDTensor calculated by fc layer. The data type is same with input. Raises: ValueError: If dimensions of the input Tensor is less than 2. Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() # when input is single tensor data = fluid.data(name="data", shape=[-1, 32], dtype="float32") fc = fluid.layers.fc(input=data, size=1000, act="tanh") # when input are multiple tensors data_1 = fluid.data(name="data_1", shape=[-1, 32], dtype="float32") data_2 = fluid.data(name="data_2", shape=[-1, 36], dtype="float32") fc = fluid.layers.fc(input=[data_1, data_2], size=1000, act="tanh") r+inputinput[])float16uint16float32float64rrcS||SNr)abrrruzfc..NFattrrdtypeis_biasrXYOutx_num_col_dimsy_num_col_dimstypeinputsoutputsattrsrrrr) dim_start)r+)rlocalsr'listtupler isinstance enumeratestr input_dtyper(Ziter_inputs_and_paramsrlenr!create_parameter"create_variable_for_type_inference append_opappendappend_bias_opappend_activation)rrZnum_flatten_dims param_attr bias_attrrnamehelperiZinput_xrZ mul_resultsZ input_var input_shape param_shapewtmppre_biasZpre_activationrrrr+sXv       r+z2.0.0zpaddle.nn.functional.embedding)sinceZ update_torc Cstdit}t|ddgdt|dgdd|r"d}td|r&d nd}|j|j||dd } ||} |d ur=d n |d krC|n|d |}|j d|| dd| i||||dd| S)a^ :api_attr: Static Graph **WARING:** This OP will be deprecated in a future release. This OP requires the last dimension of Tensor shape must be equal to 1. It is recommended to use fluid. :ref:`api_fluid_embedding` . The operator is used to lookup embeddings vector of ids provided by :attr:`input` . It automatically constructs a 2D embedding matrix based on the input :attr:`size` (vocab_size, emb_size) and :attr:`dtype` . This OP requires the last dimension of Tensor shape must be equal to 1. The shape of output Tensor is generated by replacing the last dimension of the input Tensor shape with emb_size. **Note:** The id in :attr:`input` must satisfy :math:`0 =< id < size[0]` , otherwise the program will throw an exception and exit. .. code-block:: text Case 1: input is a Tensor. padding_idx = -1 input.data = [[[1], [3]], [[2], [4]], [[4], [127]]] input.shape = [3, 2, 1] Given size = [128, 16] output is a Tensor: out.shape = [3, 2, 16] out.data = [[[0.129435295, 0.244512452, ..., 0.436322452], [0.345421456, 0.524563927, ..., 0.144534654]], [[0.345249859, 0.124939536, ..., 0.194353745], [0.945345345, 0.435394634, ..., 0.435345365]], [[0.945345345, 0.435394634, ..., 0.435345365], [0.0, 0.0, ..., 0.0 ]]] # padding data The input padding_idx is less than 0, it is automatically converted to padding_idx = -1 + 128 = 127 It will pad all-zero data when ids is 127. Case 2: input is a LoDTensor with 1-level LoD. padding_idx = 0 input.lod = [[2, 3]] input.data = [[1], [3], [2], [4], [0]] input.shape = [5, 1] Given size = [128, 16] output is a LoDTensor: out.lod = [[2, 3]] out.shape = [5, 16] out.data = [[0.129435295, 0.244512452, ..., 0.436322452], [0.345421456, 0.524563927, ..., 0.144534654], [0.345249859, 0.124939536, ..., 0.194353745], [0.945345345, 0.435394634, ..., 0.435345365], [0.0, 0.0, ..., 0.0 ]] # padding data It will pad all-zero data when ids is 0. Args: input(Variable): A Tensor or LoDTensor with type int64, which contains the id information. The last dimension of Tensor shape must be equal to 1. The value of the input id should satisfy :math:`0<= id < size[0]` . size(tuple|list): The shape of lookup table parameter. It should have two elements which indicates the size of the dictionary of embeddings and the size of each embedding vector respectively. is_sparse(bool): The flag indicating whether to use sparse update. This parameter only affects the performance of the backwards gradient update. It is recommended to set True because sparse update is faster. But some optimizer does not support sparse update, such as :ref:`api_fluid_optimizer_AdadeltaOptimizer` , :ref:`api_fluid_optimizer_AdamaxOptimizer` , :ref:`api_fluid_optimizer_DecayedAdagradOptimizer` , :ref:`api_fluid_optimizer_FtrlOptimizer` , :ref:`api_fluid_optimizer_LambOptimizer` and :ref:`api_fluid_optimizer_LarsMomentumOptimizer` . In these case, is_sparse must be False. Default: False. is_distributed(bool): Whether to store the embedding matrix in a distributed manner. Only used in multi-machine distributed CPU training. Default: False. padding_idx(int|long|None): padding_idx needs to be in the interval [-vocab_size, vocab_size). If :math:`padding\_idx < 0`, the :math:`padding\_idx` will automatically be converted to :math:`vocab\_size + padding\_idx` . It will output all-zero padding data whenever lookup encounters :math:`padding\_idx` in id. And the padding data will not be updated while training. If set None, it makes no effect to output. Default: None. param_attr(ParamAttr): To specify the weight parameter property. Default: None, which means the default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . In addition, user-defined or pre-trained word vectors can be loaded with the :attr:`param_attr` parameter. The local word vector needs to be transformed into numpy format, and the shape of local word vector should be consistent with :attr:`size` . Then :ref:`api_fluid_initializer_NumpyArrayInitializer` is used to load custom or pre-trained word vectors. See code example 2 for details. dtype(str|core.VarDesc.VarType): It refers to the data type of output Tensor. It must be float32 or float64. Default: float32. Returns: Variable: Embedding Tensor or LoDTensor mapped by input. The data type is the same as :attr:`dtype` . Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle paddle.enable_static() data = fluid.data(name='x', shape=[None, 1], dtype='int64') # example 1 emb_1 = fluid.embedding(input=data, size=[128, 64]) # example 2: load custom or pre-trained word vectors weight_data = np.random.random(size=(128, 100)) # word vectors with numpy format w_param_attrs = fluid.ParamAttr( name="emb_weight", learning_rate=0.5, initializer=fluid.initializer.NumpyArrayInitializer(weight_data), trainable=True) emb_2 = fluid.layers.embedding(input=data, size=(128, 100), param_attr=w_param_attrs, dtype='float32') r,rint64zfluid.layers.embeddingr)rrrrFzWis_distributed is go out of use, `fluid.contrib.layers.sparse_embedding` is your neededTrNrrZ lookup_tableIdsWr) is_sparseis_distributedremote_prefetch padding_idxr)r,) rrr&r(warningswarnrrrr) rrrrrrrr rr rrrrr,sDw      r,Tc  Ct|fit} | } | |g} dd| D} ||||||| dd} | j||g|ddd\}}| jd| |dd | i| d t| d krJ| d S| S) a **Pull Fleet Sparse Layer** This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in Fleet lookup table. The result of this lookup is the embedding of each ID in the :attr:`input`. Args: input(Variable|list of Variable): Input is a Tensor Variable, which contains the IDs information. size(int): The embedding size parameter, which indicates the size of each embedding vector respectively. table_id(int): the fleet table id of this embedding. accessor_class(str): the pslib accessor of the table, default is DownpourCtrAccessor. ctr_label_name(str): the layer name of click. padding_id(int): the padding id during lookup, default is 0. dtype(str): The dtype refers to the data type of output tensor. Only supports float32 now. scale_sparse_grad(bool): whether to scale sparse gradient with batch size. default is True. Returns: Variable|list of Variable: The tensor variable storing the embeddings of the \ supplied inputs. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1) emb = fluid.layers.nn._pull_sparse( input=data, size=11, table_id=0, accessor_class="DownpourCtrAccessor") cSg|]}|jqSrr.0r rrr _z _pull_sparse..TZ EmbeddingDimZTableIdZ AccessorClassZ CtrLabelNameZ PaddingIdZScaleSparseGradZ InputNamesrFrrrr persistableZ pull_sparserrrrrrrmultiple_inputrcreate_or_get_global_variablerrrrZtable_idZaccessor_classrZctr_label_nameZ padding_idrZscale_sparse_gradr routsZ input_namesrr rrrr _pull_sparse2:*    r+c  Cr) a **Pull Fleet Sparse Layer** This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in Fleet lookup table. The result of this lookup is the embedding of each ID in the :attr:`input`. Args: input(Variable|list of Variable): Input is a Tensor Variable, which contains the IDs information. size(int): The embedding size parameter, which indicates the size of each embedding vector respectively. table_id(int): the pslib table id of this embedding. accessor_class(str): the fleet accessor of the table, default is DownpourCtrAccessor. ctr_label_name(str): the layer name of click. padding_id(int): the padding id during lookup, default is 0. dtype(str): The dtype refers to the data type of output tensor. Only supports float32 now. scale_sparse_grad(bool): whether to scale sparse gradient with batch size. default is True. Returns: Variable|list of Variable: The tensor variable storing the embeddings of the \ supplied inputs. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1) emb = fluid.layers.nn._pull_sparse_v2( input=data, size=11, table_id=0, accessor_class="DownpourCtrAccessor") cSrrrrrrrr!r"z#_pull_sparse_v2..Tr#Fr$Zpull_sparse_v2rrrrrr&r)rrr_pull_sparse_v2}r,r-cstditdkrtd}fddtt|D}jj|dgdd}j d||d d |i|||d d t|d krQ|dS|S)a **Pull GpuPS Sparse Layer** This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in GpuPS lookup table. The result of this lookup is the embedding of each ID in the :attr:`input`. Args: input(Variable|list of Variable): Input is a Tensor Variable, which contains the IDs information. size(int|list of int): The embedding size parameter of each input, which indicates the size of each embedding vector respectively. dtype(str): The dtype refers to the data type of output tensor. Only supports float32 now. Returns: Variable|list of Variable: The tensor variable storing the embeddings of the \ supplied inputs, whose size are indicated by size respectively. Examples: .. code-block:: python import paddle.fluid as fluid slots = [] data_1 = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1) slots.append(data_1) data_2 = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1) slots.append(data_2) embs = fluid.layers.pull_gpups_sparse(input=slots, size=[11, 35]) pull_gpups_sparserz?GpuPS only support float type embedding now, and your type is: cg|]}qSrrrrr rrr!z&_pull_gpups_sparse..rFrrrrrrrrN)r. rr ValueErrorrr'rangerrrrrrrrrrr*r rr1r_pull_gpups_sparses<#   r8cstditdkrtd}fddtt|D}jj|gdd}j d||dd |i|||d d t|d krO|d S|S)a **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]) pull_box_sparserz?BoxPS only support float type embedding now, and your type is: cr/rr0rr1rrr!0r2z$_pull_box_sparse..Frrrr3rrrN)r9r4r7rr1r_pull_box_sparse s<   r:c Cst|dddgdt|ddgdtdit}|r |jdn|jd}|j|j|d|g|d }|j|d }|j|d }|j|d } |j|d } |g||gd } |rc|g| d <|jd| |g|g| | d d| S)a :api_attr: Static Graph Linear Chain CRF. ${comment} Args: input(${emission_type}): ${emission_comment} label(${label_type}): ${label_comment} Length(${length_type}): ${length_comment} param_attr(ParamAttr): The attribute of the learnable parameter for transition parameter. Returns: output(${emission_exps_type}): ${emission_exps_comment} output(${transition_exps_type}): ${transition_exps_comment} output(${log_likelihood_type}): ${log_likelihood_comment} Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle paddle.enable_static() #define net structure, using LodTensor train_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): input_data = fluid.data(name='input_data', shape=[-1,10], dtype='float32') label = fluid.data(name='label', shape=[-1,1], dtype='int') emission= fluid.layers.fc(input=input_data, size=10, act="tanh") crf_cost = fluid.layers.linear_chain_crf( input=emission, label=label, param_attr=fluid.ParamAttr( name='crfw', learning_rate=0.01)) use_cuda = False place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_program) #define data, using LoDTensor a = fluid.create_lod_tensor(np.random.rand(12,10).astype('float32'), [[3,3,4,2]], place) b = fluid.create_lod_tensor(np.array([[1],[1],[2],[3],[1],[1],[1],[3],[1],[1],[1],[1]]),[[3,3,4,2]] , place) feed1 = {'input_data':a,'label':b} loss= exe.run(train_program,feed=feed1, fetch_list=[crf_cost]) print(loss) #define net structure, using padding train_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): input_data2 = fluid.data(name='input_data2', shape=[-1,10,10], dtype='float32') label2 = fluid.data(name='label2', shape=[-1,10,1], dtype='int') label_length = fluid.data(name='length', shape=[-1,1], dtype='int') emission2= fluid.layers.fc(input=input_data2, size=10, act="tanh", num_flatten_dims=2) crf_cost2 = fluid.layers.linear_chain_crf( input=emission2, label=label2, length=label_length, param_attr=fluid.ParamAttr( name='crfw', learning_rate=0.01)) use_cuda = False place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_program) #define data, using padding cc=np.random.rand(4,10,10).astype('float32') dd=np.random.rand(4,10,1).astype('int64') ll=np.array([[3],[3],[4],[2]]) feed2 = {'input_data2':cc,'label2':dd,'length':ll} loss2= exe.run(train_program,feed=feed2, fetch_list=[crf_cost2]) print(loss2) #[array([[ 7.8902354], # [ 7.3602567], # [ 10.004011], # [ 5.86721 ]], dtype=float32)] #you can use find_var to get transition parameter. transition=np.array(fluid.global_scope().find_var('crfw').get_tensor()) print(transition) rrrr-labelrrrrrrrZEmissionZ TransitionLabelLength)AlphaZ EmissionExpsZTransitionExpsZ LogLikelihoodrrrN)r-) r&rrrrrrrr) rr;rlengthr r transitionalphaZ emission_expsZtransition_expsZlog_likelihoodZ this_inputsrrrr-HsJ Z   r-cCstt|dddgdtd it}||j}|jtjjj d}|g||d}|r-||d<|j d|d|gid |S) a  :api_attr: Static Graph ${comment} Args: input(Tensor): ${emission_comment} param_attr (ParamAttr|None): To specify the weight parameter attribute. Default: None, which means the default weight parameter property is used. See usage for details in :ref:`api_paddle_fluid_param_attr_ParamAttr` . label(${label_type}, optional): ${label_comment} length(${length_type}, optional): ${length_comment} Returns: Tensor: ${viterbi_path_comment} Examples: .. code-block:: python import paddle paddle.enable_static() # LoDTensor-based example num_labels = 10 feature = paddle.static.data(name='word_emb', shape=[-1, 784], dtype='float32', lod_level=1) label = paddle.static.data(name='label', shape=[-1, 1], dtype='int64', lod_level=1) emission = paddle.static.nn.fc(feature, size=num_labels) crf_cost = paddle.fluid.layers.linear_chain_crf(input=emission, label=label, param_attr=paddle.ParamAttr(name="crfw")) crf_decode = paddle.static.nn.crf_decoding(input=emission, param_attr=paddle.ParamAttr(name="crfw")) # Common tensor example num_labels, max_len = 10, 20 feature = paddle.static.data(name='word_emb_pad', shape=[-1, max_len, 784], dtype='float32') label = paddle.static.data(name='label_pad', shape=[-1, max_len, 1], dtype='int64') length = paddle.static.data(name='length', shape=[-1, 1], dtype='int64') emission = paddle.static.nn.fc(feature, size=num_labels, num_flatten_dims=2) crf_cost = paddle.fluid.layers.linear_chain_crf(input=emission, label=label, length=length, param_attr=paddle.ParamAttr(name="crfw_pad")) crf_decode = paddle.static.nn.crf_decoding(input=emission, length=length, param_attr=paddle.ParamAttr(name="crfw_pad")) rrrr.r=r>r@Z ViterbiPathrBN)r.) r&rrZ get_parameterrrr"VarDescVarTypeINT64r)rrr;rCr rDZ viterbi_pathrrrrr.s 3 r.cCst|ddgdt|ddgdtd it}|j|jd}|j|jd}|j|jd}|jd|g|gd|g|g|gdd|S) a ${comment} Args: X (Tensor): ${x_comment}. Y (Tensor): ${y_comment}. Returns: A Tensor representing the output of cosine(X, Y). Examples: .. code-block:: python import paddle x = paddle.rand(shape=[3, 7], dtype='float32') y = paddle.rand(shape=[1, 7], dtype='float32') out = paddle.fluid.layers.cos_sim(x, y) print(out) rrr/rr=r)rZXNormZYNormrBN)r/r&rrrrr)rrr rZxnormZynormrrrr/s  r/zpaddle.nn.functional.dropoutdowngrade_in_inferc s*t|tttfs tdt|ttfr|dkr|StrR|dus$|dkr.tjdkr.tj}|dur7tj }t |d|d|d|dud|durI|ndd \}}|Sfd d }t dit } t|d gd d | j|jd}| jtjjjdd}|| j|||} | jd d|gi|g|gd| d|S)a_ Computes dropout. Drop or keep each element of `x` independently. Dropout is a regularization technique for reducing overfitting by preventing neuron co-adaption during training. The dropout operator randomly sets (according to the given dropout probability) the outputs of some units to zero, while others are remain unchanged. dropout op can be removed from the program to make the program more efficient. Args: x (Variable): The input tensor variable. The data type is float16 or float32 or float64. dropout_prob (float): Probability of setting units to zero. is_test (bool): A flag indicating whether it is in test phrase or not. Default None, in dynamic graph, it use global tracer mode; in static graph, it means False. seed (int): A Python integer used to create random seeds. If this parameter is set to None, a random seed is used. NOTE: If an integer seed is given, always the same output units will be dropped. DO NOT use a fixed seed in training.Default: None. name (str|None): A name for this layer(optional). If set None, the layer will be named automatically. dropout_implementation(string): ['downgrade_in_infer'(default)|'upscale_in_train'] 1. downgrade_in_infer(default), downgrade the outcome at inference - train: out = input * mask - inference: out = input * (1.0 - dropout_prob) (mask is a tensor same shape with input, value is 0 or 1 ratio of 0 is dropout_prob) 2. upscale_in_train, upscale the outcome at training time - train: out = input * mask / ( 1.0 - dropout_prob ) - inference: out = input (mask is a tensor same shape with input, value is 0 or 1 ratio of 0 is dropout_prob) Returns: A Variable holding Tensor representing the dropout, has same shape and data type with `x`. Examples: .. code-block:: python import paddle import paddle.fluid as fluid paddle.enable_static() x = fluid.data(name="data", shape=[None, 32, 32], dtype="float32") dropped = fluid.layers.dropout(x, dropout_prob=0.5) z?dropout_prob argument should be a number(int|float) or VariablerN dropout_probis_testfix_seedseeddropout_implementationcsj|dus|dkr|jdkr|j}t|tr#|jdgks#td|j|||du|dur.|ndd}|S)NrrzjRequired dropout_prob.shape == [1] if type(dropout_prob) is Variable, but received dropout_prob.shape = {})rKrLrMrNrO) random_seedrr r TypeErrorformat)progrKrLrNrrOrr get_attrsszdropout..get_attrsrErrrrr=Tr stop_gradientr)rMaskr)rE)rfloatintr rQr rrPr Z _train_moder*rErrr&rrr"rFrGZUINT8 main_programr) rrKrLrNrrOrmaskrUr rrrTrrE2sP>     rEc Cstd it}t|ddgdt|ddgd|jdd}|jdd}|jdd} |jdd} |jdd} |jdd} |g|gd} |durL|g| d <|jd| |g|g| g| g| g| gd |||pcgd d ||| | | | fS)a 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 (Tensor): A Tensor representing the predicted labels from the network. Its shape would be `[N, M, 1]`, where `N` stands for batch size, `M` for sequence length. The data type should be int64. label (Tensor): A Tensor representing the ground-truth labels. It should 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(Tensor, optional): A 1D Tensor containing the length of each sequence when ``input`` and ``label`` are Tensor. 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=[None, 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.data( name='label', shape=[None, 1], lod_level=1, dtype='int64') 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=int((label_dict_len - 1) / 2)) r0rrr;rr=)Z Inferencer?NZ SeqLength)Z PrecisionZRecallzF1-ScoreZNumInferChunksZNumLabelChunksZNumCorrectChunks)num_chunk_types chunk_schemeexcluded_chunk_typesr)r0)rrr&rr)rr;r_r^r`Z seq_lengthr  precisionZrecallZf1_scoreZnum_infer_chunksZnum_label_chunksZnum_correct_chunksZ this_inputrrrr0s>q        r0zpaddle.nn.functional.softmaxc Cstr t||Strt|d|d|Sd|gi}||d}td it}t|dgdd|}| |}|j dd|id|i|d |S) a This operator implements the softmax layer. The calculation process is as follows: 1. The dimension :attr:`axis` of the ``input`` will be permuted to the last. 2. Then the input tensor will be logically flattened to a 2-D matrix. The matrix's second dimension(row length) is the same as the dimension :attr:`axis` of the input tensor, and the first dimension(column length) is the product of all other dimensions of the input tensor. For each row of the matrix, the softmax operator squashes the K-dimensional(K is the width of the matrix, which is also the size of the input tensor's dimension :attr:`axis`) vector of arbitrary real values to a K-dimensional vector of real values in the range [0, 1] that add up to 1. 3. After the softmax operation is completed, the inverse operations of steps 1 and 2 are performed to restore the two-dimensional matrix to the same dimension as the ``input``. It computes the exponential of the given dimension and the sum of exponential values of all the other dimensions in the K-dimensional vector input. Then the ratio of the exponential of the given dimension and the sum of exponential values of all the other dimensions is the output of the softmax operator. For each row :math:`i` and each column :math:`j` in the matrix, we have: .. math:: Out[i, j] = \\frac{\\exp(X[i, j])}{\\sum_j(exp(X[i, j])} Example: .. code-block:: text Case 1: Input: X.shape = [2, 3, 4] X.data = [[[2.0, 3.0, 4.0, 5.0], [3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 8.0, 9.0]], [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [6.0, 7.0, 8.0, 9.0]]] Attrs: axis = -1 Output: Out.shape = [2, 3, 4] Out.data = [[[0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.07232949, 0.19661193, 0.19661193, 0.53444665]], [[0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.0320586 , 0.08714432, 0.23688282, 0.64391426]]] Case 2: Input: X.shape = [2, 3, 4] X.data = [[[2.0, 3.0, 4.0, 5.0], [3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 8.0, 9.0]], [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [6.0, 7.0, 8.0, 9.0]]] Attrs: axis = 1 Output: Out.shape = [2, 3, 4] Out.data = [[[0.00657326, 0.00657326, 0.01714783, 0.01714783], [0.01786798, 0.01786798, 0.04661262, 0.04661262], [0.97555875, 0.97555875, 0.93623955, 0.93623955]], [[0.00490169, 0.00490169, 0.00490169, 0.00490169], [0.26762315, 0.26762315, 0.26762315, 0.26762315], [0.72747516, 0.72747516, 0.72747516, 0.72747516]]] Args: input (Tensor): The input tensor. A multi-dimension ``Tensor`` with type float32 or float64. use_cudnn (bool, optional): Use cudnn kernel or not, it is valid only when the cudnn \ library is installed. To improve performance, set use_cudnn to True by default. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Default: None. will be named automatically. Default: None. axis (int, optional): The index of dimension to perform softmax calculations, it should be in range :math:`[-1, rank - 1]`, while :math:`rank` is the rank of input tensor. Default: -1. -1 means the last dimension. Returns: Tensor: ``Tensor`` indicates the output of softmax. The data type and shape are the same as ``input`` . Examples: .. code-block:: python import paddle import paddle.nn.functional as F x = paddle.to_tensor([[[2.0, 3.0, 4.0, 5.0], [3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 8.0, 9.0]], [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [6.0, 7.0, 8.0, 9.0]]], dtype='float32') y = F.softmax(x, axis=1) print(y) # [[[0.00657326, 0.00657326, 0.01714783, 0.01714783], # [0.01786798, 0.01786798, 0.04661262, 0.04661262], # [0.97555870, 0.97555870, 0.93623954, 0.93623954]], # [[0.00490169, 0.00490169, 0.00490169, 0.00490169], # [0.26762316, 0.26762316, 0.26762316, 0.26762316], # [0.72747517, 0.72747517, 0.72747517, 0.72747517]]] r use_cudnnr)rrbr3zinput/xrVrrN)r3) rr)r3r r*rrr&rrr) rrbrrrrr rZ softmax_outrrrr3@s(r     r3NCHWc st|dgddt|jdkrtdt|j|jdt| ts,tdt| | dvr8td t| | d k} | rC|jd n|jdd krYtd t|jtf|dusaJd|durh}n|d krstd||d krtd|j||}d}|kr|d kr| sd}|kr|d krt rd}t r|kr|krd}nd}t |fit }| }tddt|dd}t|dd}dd}d}t|tr |}|dvrtdt||dkrd}d d g}n |dkr d}d d g}||| }|t|g}fdd }|j|j|||d!}||}trAtjd"d"rAd} |j|||d#d$|i||||| dd|| d% d&| d'krg|j|ddd(}n|j|d dd(}||S))a :api_attr: Static Graph The convolution2D layer calculates the output based on the input, filter and strides, paddings, dilations, groups parameters. Input and Output are in NCHW or NHWC format, where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature. Filter is in MCHW format, where M is the number of output image channels, C is the number of input image channels, H is the height of the filter, and W is the width of the filter. If the groups is greater than 1, C will equal the number of input image channels divided by the groups. Please refer to UFLDL's `convolution `_ for more details. If bias attribution and activation type are provided, bias is added to the output of the convolution, and the corresponding activation function is applied to the final result. For each input :math:`X`, the equation is: .. math:: Out = \sigma (W \\ast X + b) Where: * :math:`X`: Input value, a tensor with NCHW or NHWC format. * :math:`W`: Filter value, a tensor with MCHW format. * :math:`\\ast`: Convolution operation. * :math:`b`: Bias value, a 2-D tensor with shape [M, 1]. * :math:`\\sigma`: Activation function. * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different. Example: - Input: Input shape: :math:`(N, C_{in}, H_{in}, W_{in})` Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)` - Output: Output shape: :math:`(N, C_{out}, H_{out}, W_{out})` Where .. math:: H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\ W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1 Args: input (Tensor): The input is 4-D Tensor with shape [N, C, H, W], the data type of input is float16 or float32 or float64. num_filters(int): The number of filter. It is as same as the output image channel. filter_size (int|tuple): The filter size. If filter_size is a tuple, it must contain two integers, (filter_size_height, filter_size_width). Otherwise, filter_size_height = filter_size_width =\ filter_size. stride (int|tuple): The stride size. It means the stride in convolution. If stride is a tuple, it must contain two integers, (stride_height, stride_width). Otherwise, stride_height = stride_width = stride. Default: stride = 1. padding (string|int|list|tuple): The padding size. It means the number of zero-paddings on both sides for each dimension.If `padding` is a string, either 'VALID' or 'SAME' which is the padding algorithm. If padding size is a tuple or list, it could be in three forms: `[pad_height, pad_width]` or `[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `data_format` is `"NCHW"`, `padding` can be in the form `[[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`. when `data_format` is `"NHWC"`, `pool_padding` can be in the form `[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`. Default: padding = 0. dilation (int|tuple): The dilation size. It means the spacing between the kernel points. If dilation is a tuple, it must contain two integers, (dilation_height, dilation_width). Otherwise, dilation_height = dilation_width = dilation. Default: dilation = 1. groups (int): The groups number of the Conv2d Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only connected to the second half of the input channels. Default: groups=1. param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights of conv2d. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as param_attr. If the Initializer of the param_attr is not set, the parameter is initialized with :math:`Normal(0.0, std)`, and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None. bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d. If it is set to False, no bias will be added to the output units. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as bias_attr. If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None. use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn library is installed. Default: True act (str): Activation type, if it is set to None, activation is not appended. Default: None name(str|None): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. Returns: A Tensor representing the conv2d, whose data type is the same with input. If act is None, the tensor storing the convolution result, and if act is not None, the tensor storing convolution and non-linearity activation result. Raises: ValueError: If the type of `use_cudnn` is not bool. ValueError: If `data_format` is not "NCHW" or "NHWC". ValueError: If the channel dimmention of the input is less than or equal to zero. ValueError: If `padding` is a string, but not "SAME" or "VALID". ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 or the element corresponding to the input's channel is not 0. ShapeError: If the input is not 4-D Tensor. ShapeError: If the input's dimension size and filter's dimension size not equal. ShapeError: If the dimension size of input minus the size of `stride` is not 2. ShapeError: If the number of input channels is not equal to filter's channels * groups. ShapeError: If the number of output channels is not be divided by groups. Examples: .. code-block:: python import paddle paddle.enable_static() data = paddle.static.data(name='data', shape=[None, 3, 32, 32], dtype='float32') conv2d = paddle.static.nn.conv2d(input=data, num_filters=2, filter_size=3, act="relu") print(conv2d.shape) # [-1, 2, 30, 30] rrVr1'Input size should be 4, but received {}rGAttr(use_cudnn) should be True or False. Received Attr(use_cudnn): %s. rcNHWCMAttr(data_format) should be 'NCHW' or 'NHWC'. Received Attr(data_format): %s.rhr#rHThe channel dimmention of the input(%s) should be defined. Received: %s.F$param_attr should not be False here.NRthe groups of input must be greater than 0, but received the groups of input is {}zthe channel of input must be divisible by groups,received: the channel of input is {}, the shape of input is {}, the groups is {}Zdepthwise_conv2dr filter_sizestridedilationcSdd}||rt|dkr||dr>|dkr>|dddgkr(|dddgks0tdt||dd}d d |D}n/||drm|d krm|dddgkrX|d ddgks`tdt||dd }d d |D}t|dd}t|dr|d|dg}|St|dd}|S)NcSt|ts t|tr dSdSNTFrrrelerrris_list_or_tuplez9conv2d.._update_padding..is_list_or_tuplerdrrcrINon-zero padding(%s) in the batch or channel dimensions is not supported.rcSg|] }|D]}|qqSrrr a_listrurrrr!z3conv2d.._update_padding..rhr#cSryrrrzrrrr!r|paddingrr5rrconvert_to_list_is_symmetric_paddingr} data_formatrvrrr_update_paddings4     zconv2d.._update_paddingEXPLICITSAMEVALID8Unknown padding: '%s'. It can only be 'SAME' or 'VALID'.rrcBdd}|dkrtd|d|d}td|dSNrrwInvalid filter number, excepted number is larger than 0, but received {}, please check the input shape and filter size.@?r5rRrZfilter_elem_numstdrmZ num_channelsrr_get_default_param_initializer  z.conv2d.._get_default_param_initializerrrrdefault_initializerZFLAGS_conv2d_disable_cudnnInputFilterOutput) stridespaddings dilationsgroupsrbrZfuse_relu_before_depthwise_convpadding_algorithmrrrcrZdim_end)r&rrr5rRrboolrr"is_compiled_with_rocmZis_compiled_with_npurrrrrupperr[rrrZis_compiled_with_cudapaddleZfluidZ get_flagsrrr)r num_filtersrmrnr}rorrrrbrrr channel_lastnum_filter_channelsl_typer rrr filter_shaper filter_paramrpre_actrrrr1s               r1NCDHWc  sHd} |dus Jdt| fit}|}t| ts$tdt| | dvr0tdt| | dk}t|jdkrCtd |j|rJ|jd n|jd d kr`td t|jtf|durg}n!|d krrtd ||d krtdtt|f|}t ddt |dd}t |dd}dd}d}t|tr| }|dvrtdt||dkrd}gd}n |dkrd}gd}||| }|j}||g}fdd}|j |j|||d}||}|j| ||d d!|i||||| d|| d"d#| d$kr|j|d d%d&}n|j|d dd&}||S)'a :api_attr: Static Graph 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 (Tensor): 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 dimension. If `padding` is a string, either 'VALID' or 'SAME' which is the padding algorithm. If padding size is a tuple or list, it could be in three forms: `[pad_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, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. Returns: A 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. Raises: ValueError: If the type of `use_cudnn` is not bool. ValueError: If `data_format` is not "NCDHW" or "NDHWC". ValueError: If the channel dimmention of the input is less than or equal to zero. ValueError: If `padding` is a string, but not "SAME" or "VALID". ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 or the element corresponding to the input's channel is not 0. ShapeError: If the input is not 5-D Tensor. ShapeError: If the input's dimension size and filter's dimension size not equal. ShapeError: If the dimension size of input minus the size of `stride` is not 2. ShapeError: If the number of input channels is not equal to filter's channels * groups. ShapeError: If the number of output channels is not be divided by groups. Examples: .. code-block:: python import paddle import numpy as np paddle.enable_static() data = paddle.static.data(name='data', shape=[None, 3, 12, 32, 32], dtype='float32') param_attr = paddle.framework.ParamAttr(name='conv3d.weight', initializer=paddle.nn.initializer.XavierNormal(), learning_rate=0.001) res = paddle.static.nn.conv3d(input=data, num_filters=2, filter_size=3, act="relu", param_attr=param_attr) place = paddle.CPUPlace() exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) x = np.random.rand(1, 3, 12, 32, 32).astype("float32") output = exe.run(feed={"data": x}, fetch_list=[res]) print(output) r2FrkrfrNDHWCzOAttr(data_format) should be 'NCDHW' or 'NDHWC'. Received Attr(data_format): %s.rBInput should be 5D tensor, but received input with the shape of {}rdrrrjNzBthe groups of conv3d should be greater than 0. Received groups: {}zmThe number of input channels must be divisible by Attr(groups). Received: number of channels(%s), groups(%s).r#rmrnrocShdd}||rt|dkr||dr>|dkr>|dddgkr(|dddgks0tdt||dd}d d |D}n/||drm|d krm|dddgkrX|d ddgks`tdt||dd }d d |D}t|dd}t|dr|d|d|d g}|S||rt|dkrt|dd}t|dr|d|d|d g}|St|dd}|S)NcSrqrrrsrtrrrrvrwz9conv3d.._update_padding..is_list_or_tuplerrrrrxrcSryrrrzrrrr!r|z3conv3d.._update_padding..rrdcSryrrrzrrrr!r|r}r#r~rrrrrs>      zconv3d.._update_paddingrrrrrrrrcsJddd}|dkrtd|d|d}td|dS)Nrrrrrrrrrrrrrs  z.conv3d.._get_default_param_initializerrrr)rrrrrbrrrrrrr)rrrrrr5rrrrRrrrrrrrrr)rrrmrnr}rorrrrbrrrrr rrrrrr rrrrrrrrr2s  "        r2maxc Cs|dvr tdt||dur|dkrtdt|t|ts(tdt|| dvr4tdt| t|d d }t|d d }d d } d} t|trz|}|dvr]tdt||dkrpd} ddg}|dkrotdn |dkrzd} ddg}| || }trt |||||| | ||d| | Sd} t | fit }| }||}|j| d|id|i|||||| ||d| | d d|S)a ${comment} Args: input (Variable): The input tensor of pooling operator which is a 4-D tensor with shape [N, C, H, W]. The format of input tensor is `"NCHW"` or `"NHWC"`, where `N` is batch size, `C` is the number of channels, `H` is the height of the feature, and `W` is the width of the feature. The data type if float32 or float64. pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain two integers, (pool_size_Height, pool_size_Width). Otherwise, the pool kernel size will be a square of an int. pool_type: ${pooling_type_comment} pool_stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list, it must contain two integers, (pool_stride_Height, pool_stride_Width). Otherwise, the pool stride size will be a square of an int. pool_padding (string|int|list|tuple): The pool padding. If `pool_padding` is a string, either 'VALID' or 'SAME' which is the padding algorithm. If pool padding size is a tuple or list, it could be in three forms: `[pad_height, pad_width]` or `[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `data_format` is `"NCHW"`, `pool_padding` can be in the form `[[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`. when `data_format` is `"NHWC"`, `pool_padding` can be in the form `[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`. Otherwise, the pool padding size will be a square of an int. global_pooling (bool): ${global_pooling_comment} use_cudnn (bool): ${use_cudnn_comment} ceil_mode (bool): ${ceil_mode_comment} name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. exclusive (bool): Whether to exclude padding points in average pooling mode, default is `true`. data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. Returns: Variable: The output tensor of pooling result. The data type is same as input tensor. Raises: ValueError: If `pool_type` is not "max" nor "avg". ValueError: If `global_pooling` is False and `pool_size` is -1. TypeError: If `use_cudnn` is not a bool value. ValueError: If `data_format` is not "NCHW" or "NHWC". ValueError: If `pool_padding` is a string, but not "SAME" or "VALID". ValueError: If `pool_padding` is "VALID", but `ceil_mode` is True. ValueError: If `pool_padding` is a list or tuple, but the elements in the batch or channel dimensions are non-zero. ShapeError: If the input is not a 4-D or 5-D Tensor. ShapeError: If the dimension of input minus the size of `pool_stride` is not 2. ShapeError: If the size of `pool_size` and `pool_stride` is not equal. ShapeError: If the output's shape calculated is not greater than 0. Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32') # max pool2d pool2d = fluid.layers.pool2d( input = data, pool_size = 2, pool_type = "max", pool_stride = 1, global_pooling=False) # average pool2d pool2d = fluid.layers.pool2d( input = data, pool_size = 2, pool_type = "avg", pool_stride = 1, global_pooling=False) # global average pool2d pool2d = fluid.layers.pool2d( input = data, pool_size = 2, pool_type = "avg", pool_stride = 1, global_pooling=True) # Attr(pool_padding) is a list with 4 elements, Attr(data_format) is "NCHW". out_1 = fluid.layers.pool2d( input = data, pool_size = 3, pool_type = "avg", pool_stride = 1, pool_padding = [1, 2, 1, 0], data_format = "NCHW") # Attr(pool_padding) is a string, Attr(data_format) is "NCHW". out_2 = fluid.layers.pool2d( input = data, pool_size = 3, pool_type = "avg", pool_stride = 1, pool_padding = "VALID", data_format = "NCHW") ravg=Unknown Attr(pool_type): '%s'. It can only be 'max' or 'avg'.FrzpWhen Attr(global_pooling) is False, Attr(pool_size) must be passed and be a valid value. Received pool_size: %s.zFAttr(use_cudnn) should be True or False. Received Attr(use_cudnn): %s.rgrir pool_size pool_stridecSrp)NcSrqrrrsrtrrrrvrwz8pool2d..update_padding..is_list_or_tuplerdrrcrNNon-zero pool_padding(%s) in the batch or channel dimensions is not supported.rcSryrrrzrrrr!r|z2pool2d..update_padding..rhr#cSryrrrzrrrr!r|r}r~rrrrupdate_paddings4     zpool2d..update_paddingrrCUnknown Attr(pool_padding): '%s'. It can only be 'SAME' or 'VALID'.rrz\When Attr(pool_padding) is "VALID", Attr(ceil_mode) must be False. Received ceil_mode: True.rr4rr pooling_typeksizeglobal_poolingrrrrb ceil_moder exclusiverr)r5rrrrQrrrrr)r4rrrrrrr pool_typerZ pool_paddingrrbrrrrrrop_typer rpool_outrrrr4sw     r4c Cs\|dvr tdt||dur|dkrtdt|t|ts(tdt|| dvr4tdt| t|d d }t|d d }d d } d} t|trz|}|dvr]tdt||dkrpd} gd}|dkrotdn |dkrzd} gd}| || }d} t| fit }| }| |}|j | d|id|i|||||| ||d| | d d|S)a ${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 import paddle paddle.enable_static() 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") rrFrzvWhen Attr(global_pooling) is False, Attr(pool_size) must be passed and be a valid value. Received Attr(pool_size): %s.rfrzNAttr(data_format) should be 'NCDHW' or 'NDHWC'. Received Attr(data_format): %sr#rrcSr)NcSst|ttfr dSdSrrrsrtrrrrv sz8pool3d..update_padding..is_list_or_tuplerrrrrrcSryrrrzrrrr! r|z2pool3d..update_padding..rrdcSryrrrzrrrr! r|rr}r#r~rrrrr s>       zpool3d..update_paddingrrrrrzVWhen Attr(pool_padding) is "VALID", ceil_mode must be False. Received ceil_mode: True.rr5rrrr) r5rrrrQrrrrrrrrrrrrr5s} #   r5)r)rc Ct|dgddt|dtdt|dtttfdt|dtd|dvr,tdt||d kr6|r6td t |d d}|d krDd }nd}t |fit }| }| |}d|i} |d kri| |} | | d<|j|d|i| ||ddd|r~|| fS|S)a+ 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 (Tensor): 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: Tensor: 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 dimensions # 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 paddle.enable_static() data = paddle.rand(shape=[1,3,32,32]) pool_out = paddle.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 dimensions # 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 data = paddle.rand(shape=[1,3,32,32]) pool_out = paddle.fluid.layers.adaptive_pool2d( input=data, pool_size=[3, 3], pool_type='max') rrrrint32rr6rr require_indexr7Unknown pool_type: '%s'. It can only be 'max' or 'avg'.r?invalid setting 'require_index' true when 'pool_type' is 'avg'.rrZmax_pool2d_with_indexr4rrYrTrrZadaptiverr&r'rr[rrrr5rrrrrrr rrrrrrr rrrr]rrrr6 sHf     r6c Cr)a 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 (Tensor): 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: Tensor: 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 dimensions # 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 paddle.enable_static() data = paddle.rand(shape=[1,3,32,32,32]) pool_out = paddle.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 dimensions # 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 data = paddle.rand(shape=[1,3,32,32,32]) pool_out = paddle.fluid.layers.adaptive_pool3d( input=data, pool_size=[3, 3, 3], pool_type='max') rrr7rrrrrrrr#rZmax_pool3d_with_indexr5rrYrTrrrrrrrr7o sHt     r7?h㈵>c%Cs|dusJdtd#it}t|dgdd|}|tjjjkr)tjjj}|j }|dkr5|d}n|dkr>|d }nt d ||g}|j |j ||t d d }|j |j||d d}|j t| t dd| d||d}d |_|j t| t d d| d||d}d |_|}|}trd}d}tjj}t||rd }nd }d}|rd|d|d|d|ddddd| f}nd|d|d|ddddd| f }|rtj||||||||g|R\}}}}}}ntj|||||d||g|R\}}}}}}tj||ddS|j|d d}|j|d d} d}!|s|j|d d}!|r|n||}|||||||d}"|||dd| d}#t|tr:||"d<n||#d<||||| d }$|!durO|!|$d!<|jd|"|$|#d"||S)$a :api_attr: Static Graph **Batch Normalization Layer** Can be used as a normalizer function for convolution or fully_connected operations. The required data format for this layer is one of the following: 1. NHWC `[batch, in_height, in_width, in_channels]` 2. NCHW `[batch, in_channels, in_height, in_width]` Refer to `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift `_ for more details. :math:`input` is the input features over a mini-batch. .. math:: \\mu_{\\beta} &\\gets \\frac{1}{m} \\sum_{i=1}^{m} x_i \\qquad &//\\ \ mini-batch\ mean \\\\ \\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\ \\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\ \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\ \\sigma_{\\beta}^{2} + \\epsilon}} \\qquad &//\ normalize \\\\ y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift moving\_mean = moving\_mean * momentum + mini-batch\_mean * (1. - momentum) \\\\ moving\_var = moving\_var * momentum + mini-batch\_var * (1. - momentum) moving_mean is global mean and moving_var is global variance. When use_global_stats = True, the :math:`\\mu_{\\beta}` and :math:`\\sigma_{\\beta}^{2}` are not the statistics of one mini-batch. They are global (or running) statistics. (It usually got from the pre-trained model.) The training and testing (or inference) have the same behavior: .. math:: \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\ \\sigma_{\\beta}^{2} + \\epsilon}} \\\\ y_i &\\gets \\gamma \\hat{x_i} + \\beta Note: if build_strategy.sync_batch_norm=True, the batch_norm in network will use sync_batch_norm automatically. `is_test = True` can only be used in test program and inference program, `is_test` CANNOT be set to True in train program, if you want to use global status from pre_train model in train program, please set `use_global_stats = True`. Args: input(Tensor): The rank of input Tensor can be 2, 3, 4, 5. The data type is float16 or float32 or float64. act(string, Default None): Activation type, linear|relu|prelu|... is_test (bool, Default False): A flag indicating whether it is in test phrase or not. momentum(float|Tensor, Default 0.9): The value used for the moving_mean and moving_var computation. This should be a float number or a Tensor with shape [1] and data type as float32. The updated formula is: :math:`moving\_mean = moving\_mean * momentum + new\_mean * (1. - momentum)` :math:`moving\_var = moving\_var * momentum + new\_var * (1. - momentum)` Default is 0.9. epsilon(float, Default 1e-05): A value added to the denominator for numerical stability. Default is 1e-5. param_attr(ParamAttr|None): The parameter attribute for Parameter `scale` of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm will create ParamAttr as param_attr, the name of scale can be set in ParamAttr. If the Initializer of the param_attr is not set, the parameter is initialized with Xavier. Default: None. bias_attr(ParamAttr|None): The parameter attribute for the bias of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None. data_layout (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. in_place(bool, Default False): Make the input and output of batch norm reuse memory. name(str|None): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. moving_mean_name(str, Default None): The name of moving_mean which store the global Mean. If it is set to None, batch_norm will save global mean with a random name, otherwise, batch_norm will save global mean with the string. moving_variance_name(str, Default None): The name of the moving_variance which store the global Variance. If it is set to None, batch_norm will save global variance with a random name, otherwise, batch_norm will save global variance with the string. do_model_average_for_mean_and_var(bool, Default True): Whether parameter mean and variance should do model average when model average is enabled. use_global_stats(bool, Default False): Whether to use global mean and variance. In inference or test mode, set use_global_stats to true or is_test to true, and the behavior is equivalent. In train mode, when setting use_global_stats True, the global mean and variance are also used during train period. Returns: A Tensor which is the result after applying batch normalization on the input, has same shape and data type with input. Examples: .. code-block:: python import paddle paddle.enable_static() x = paddle.static.data(name='x', shape=[3, 7, 3, 7], dtype='float32') hidden1 = paddle.static.nn.fc(x=x, size=200) print(hidden1.shape) # [3, 200] hidden2 = paddle.static.nn.batch_norm(input=hidden1) print(hidden2.shape) # [3, 200] Fz,bias_attr should not be False in batch_norm.r8rrVrcrrhrunsupported data layout:?rTrrr initializer trainableZdo_model_averager<rmomentumepsilonrL data_layoutrfuse_with_reluuse_global_statsN)rrrW)rScaleBiasMeanVarianceMeanOut VarianceOut)rrLrrrrMomemtumTensorrrr SavedMean SavedVariance ReserveSpacer)r8)rrr&rr"rFrGFP16FP32rr5rrrrrrXreagerTensorrr*r8rrrr rr)%rrrLrrrrrin_placermoving_mean_namemoving_variance_name!do_model_average_for_mean_and_varrr rr  channel_numr rbiasrvariancemean_out variance_outinputs_has_MomemtumTensorattrs_has_momentumtmp_tensor_typeZattrs_batch_norm_outr saved_meansaved_variance reserve_spacerrrrrrr8 s                r8rc%Cs|dvsJd|dusJdtd'it}t|dddgd|}|j}|d kr1|d }n|d kr:|d }ntd ||g}|j|j||tdd}|j|j ||dd}|jt | tdd| d||d}d|_ |jt | tdd| d||d}d|_ |}|}|}t rd}d}t jj}t||rd}nd}d}|rd|d|d|d|ddddd| d|d| f}nd|d|d|ddddd| d|d| f}|rtj||||||||g|R\}}}}}}|Stj|||||d||g|R\}}}}}}|S|j|dd }|j|dd } |j|dd }!|||||d!}"|||dd| || d"}#t|tr*||"d#<n||#d<||||| d$}$|!dur?|!|$d%<|jd|"|$|#d&|S)(as **In-place Activation Batch Normalization Layer** This layer calculates batch normalization and activation with in-place memory. For batch normalization calculations, see `fluid.layers.batch_norm`. For in-place activation batch normalization, see `In-Place Activated BatchNorm for Memory-Optimized Training of DNNs `_ `inplace_abn` only support activation type as `None`, `identity`, `leaky_relu`, `elu` currently. `inplace_abn` only support data type as `float32`, `float64` currently. Note: if build_strategy.sync_batch_norm=True, the batch_norm in network will use sync_batch_norm automatically. `is_test = True` can only be used in test program and inference program, `is_test` CANNOT be set to True in train program, if you want to use global status from pre_train model in train program, please set `use_global_stats = True`. Args: input(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|Variable, Default 0.9): The value used for the moving_mean and moving_var computation. This should be a float number or a Variable with shape [1] and data type as float32. The updated formula is: :math:`moving\_mean = moving\_mean * momentum + new\_mean * (1. - momentum)` :math:`moving\_var = moving\_var * momentum + new\_var * (1. - momentum)` Default is 0.9. epsilon(float, Default 1e-05): A value added to the denominator for numerical stability. Default is 1e-5. param_attr(ParamAttr|None): The parameter attribute for Parameter `scale` of inplace_abn. If it is set to None or one attribute of ParamAttr, inplace_abn 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 inplace_abn. If it is set to None or one attribute of ParamAttr, inplace_abn will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None. data_layout (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. 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, inplace_abn will save global mean with a random name, otherwise, inplace_abn 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, inplace_abn, will save global variance with a random name, otherwise, inplace_abn 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. act_alpha(float, Default 1.0): when activation is in ['elu', 'identity', 'leaky_relu'], inplace activative batch normalization will be used, and alpha parameter for activation can be given by this parameter. Returns: A Variable holding Tensor which is the result after applying batch normalization and activation 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.inplace_abn(input=hidden1) hidden3 = fluid.layers.inplace_abn(input=hidden2, act='leaky_relu', act_alpha=0.2) )Nidentityr{rszOinplace_abn only support act as None, 'identity', 'leaky_relu', 'elu' currentlyFz-bias_attr should not be False in inplace_abn.r9rrrrcrrhrrrrTrrrr<rrrrLrrrr activationrENrW)rrrrr)rrLrrrrrrErrrr)r9)rrr&rrr5rrrrrrXrr"rrrr*Z inplace_abn_rr r)%rrrLrrrrrrrrrrZ act_alphar rr rr rrrrrrrrrrZattrs__rrrrrrrrrrr9 s \           r9cCsRt|dddgd|dur|dusJdtdit}|}|tjjjkr-tjjj}|j }t |j dks>t |j dkrIt d t |j ||d }|g} |dkrq|dkrq|j |j| |td d } |j |j| |d tdd} |j|d d} |j|d d} ||}d|i}|dkr|dkr| |d<| |d<|jd||| | dd|id|S)ak :api_attr: Static Graph **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(Tensor): The rank of input tensor 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|bool, optional): 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. If the param_attr is set to False, instance_norm will not create param_attr. Default: None. bias_attr(ParamAttr|None|bool, optional): 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. If the bias_attr is set to False, instance_norm will not create bias_attr. Default: None. name(string, Default None): A name for this layer(optional). If set None, the layer will be named automatically. Returns: A 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 paddle.enable_static() x = paddle.static.data(name='x', shape=[3, 7, 3, 7], dtype='float32') hidden1 = paddle.static.nn.fc(x, size=200) hidden2 = paddle.static.nn.instance_norm(hidden1) rrrr:FzOparam_attr and bias_attr must be set to Fasle at the same time in instance_normrrzGexpected 2D or 3D or 4D or 5D input (got {}D input, input shape is: {})rrrTrrrrrrrWrrr)rrrrrN)r:)r&rrrr"rFrGrrrrr5rRrrrrrr)rrrrrr rr rr rrrrZinstance_norm_outrrrrr: sd D   r:P?c" CsBtd#it}|}|j}|dkr|d}n|dkr!|d}ntd||g}d}d}d}d }d}|rMt|trM|d d}|d d}|d d}| r[|d d }|dd}|dkrad}| r|jt |dt t |ddd||j d}|jt |dt t |ddd||j d}|jt |dt t |ddd||j d}|jt |dt t |ddd||j d}|jt |dt t |ddd||j d}|j |dd}|j |dd}|r|n|j |d}||||d} ||| | d}!| dkr| |!d<| r| |!d <| r || d <|| d<|jd| ||||||d!|!d"||S)$a :api_attr: Static Graph **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(Tensor): The input Tensor. 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 (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. in_place(bool, Default False): Make the input and output of batch norm reuse memory. name(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. slot_dim(int): The embedding dimension of one slot. Slot is a set of one specific feature. In pslib mode, we distinguish feature ids by slot and pull their embeddings from parameter server (pslib). The first place of the embedding is the historical show number (occurence time of this feature id with a label 0). If the input of this op is concated by slot-wise embeddings, and the show number is zero when this slot is new or empty, the normalization result may be impractical. To avoid this, we add slot_dim to locate the show number and judge if the show number is zero. If so, we choose to skip normalization on this embedding. sync_stats(bool, Default False): When running with multiple GPU cards, using allreduce to sync the summary messages. summary_decay_rate(float, Default 0.9999999): The decay rate when updating summary. enable_scale_and_shift(bool, Default False): do scale&shift after normalization. Returns: Tensor: A tensor which is the result after applying data normalization on the input. Examples: .. code-block:: python import paddle paddle.enable_static() x = paddle.randn(shape=[32,100]) hidden2 = paddle.static.nn.data_norm(input=x) r;rcrrhrrg@rr batch_size batch_sumZ batch_squarescale_wrNdnz.scale_w)valueT)rrrr<z.biasz .batch_sizez .batch_sumz.batch_square_sumrWr=)r BatchSizeBatchSumBatchSquareSum)rr sync_statssummary_decay_raterslot_dimenable_scale_and_shift)rZMeansZScalesrrrr)r;)rrrrr5rdictgetrrrrZrcreate_variablerr)"rrrrrrrrrrr rrr r rr rr Zbatch_size_defaultZbatch_sum_defaultZbatch_square_sum_defaultZscale_w_defaultZ bias_defaultrrrrZbatch_square_sumZmeansscalesZ data_norm_outrrrrrr;| sP              r;c Cs6tdus Jdtdit} t|dddgd| } d|i} |j} tdd | |d g} |rL|d us;Jd | j| j| | t d d}|| d<n|rSt d|rl|d us]Jd| j| j | | dd}|| d<n|rst d| j | dd}| j | dd}| | }| jd| |||d||dd| |S)a :api_attr: Static Graph **Layer Normalization Layer** The API implements the function of the Layer Normalization Layer and can be applied to mini-batch input data. Refer to `Layer Normalization `_ The formula is as follows: .. math:: \\mu & = \\frac{1}{H}\\sum_{i=1}^{H} x_i \\sigma & = \\sqrt{\\frac{1}{H}\sum_{i=1}^{H}{(x_i - \\mu)^2} + \\epsilon} y & = f(\\frac{g}{\\sigma}(x - \\mu) + b) - :math:`x`: the vector representation of the summed inputs to the neurons in that layer. - :math:`H`: the number of hidden units in a layers - :math:`\\epsilon`: the small value added to the variance to prevent division by zero. - :math:`g`: the trainable scale parameter. - :math:`b`: the trainable bias parameter. Args: input(Tensor): A multi-dimension ``Tensor`` , and the data type is float32 or float64. scale(bool, optional): Whether to learn the adaptive gain :math:`g` after normalization. Default: True. shift(bool, optional): Whether to learn the adaptive bias :math:`b` after normalization. Default: True. begin_norm_axis(int, optional): The normalization will be performed along dimensions from :attr:`begin_norm_axis` to :attr:`rank(input)`. Default: 1. epsilon(float, optional): The small value added to the variance to prevent division by zero. Default: 1e-05. param_attr(ParamAttr, optional): The parameter attribute for the learnable gain :math:`g`. If :attr:`scale` is False, :attr:`param_attr` is omitted. If :attr:`scale` is True and :attr:`param_attr` is None, a default :code:`ParamAttr` would be added as scale. The :attr:`param_attr` is initialized as 1 if it is added. Default: None. bias_attr(ParamAttr, optional): The parameter attribute for the learnable bias :math:`b`. If :attr:`shift` is False, :attr:`bias_attr` is omitted. If :attr:`shift` is True and :attr:`param_attr` is None, a default :code:`ParamAttr` would be added as bias. The :attr:`bias_attr` is initialized as 0 if it is added. Default: None. act(str, optional): Activation to be applied to the output of layer normalization. Default: None. name(str): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor: ``Tensor`` indicating the normalized result, the data type is the same as ``input`` , and the return dimension is the same as ``input`` . Examples: .. code-block:: python import paddle paddle.enable_static() x = paddle.static.data(name='x', shape=[8, 32, 32], dtype='float32') output = paddle.static.nn.layer_norm(input=x, begin_norm_axis=1) print(output.shape) # [8, 32, 32] Tz;please use LayerNorm instead of layer_norm in dygraph mode!rOrrrrcSrrrrrrrrrrzlayer_norm..NFz0param_attr should not be False when using scale.rrrz0param_attr is only available with scale is True.z/bias_attr should not be False when using shift.rrz/bias_attr is only available with shift is True.rWrrr)rbegin_norm_axisr)rO)r rrr&rrr!rrrrrrrrr)rrshiftrrrrrrr rrr r rrrZlayer_norm_outrrrrO5sfH       rOcCs,tdit}|} t|dddgdd|i} |j} t| dkr+tdt| |dkr;|d kr;td |d |dkrC| d n| d } | g} |r\|j|j| | t dd}|| d<|rl|j|j | | dd}|| d<|j | dd}|j | dd}|j | d}|j d| |||d|||dd| |S)a :api_attr: Static Graph **Group Normalization Layer** Refer to `Group Normalization `_ . Parameters: input(Tensor): Tensor with dimension greater than 1, 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 normalization. data_layout(str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, *]`. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor: A Tensor has same data type and data format with `input`. Examples: .. code-block:: python import paddle paddle.enable_static() data = paddle.static.data(name='data', shape=[2, 8, 32, 32], dtype='float32') x = paddle.static.nn.group_norm(input=data, groups=4) print(x.shape) # [2, 8, 32, 32] rPrrrrrzZThe dimensions of Op(fluid.layers.group_norm)'s input should be more than 1. But received rcrhzLParam(data_layout) of Op(fluid.layers.group_norm) got wrong value: received ! but only NCHW or NHWC supported.rrrrrTrrrWr=r)rrrrN)rP)rrrr&rrr5rrrrr rr)rrrrrrrrr rrr rr rrrrZgroup_norm_outrrrrPsd3     rP-q=cCs@tdit}t|dddgdt|dtdt|dtdt|dtd|j}|j}|dks6Jd |t |ksCJd |||}t ||} |j t|g|td d d } d| _|j t| g|td d d } d| _tr}t|| | |||Sd|i} | | d<| | d<|j|d} |jd| d| i|||dd| S)a :api_attr: Static Graph **Spectral Normalization Layer** This operation calculates the spectral normalization value of weight parameters of fc, conv1d, conv2d, conv3d layers which should be 2-D, 3-D, 4-D, 5-D Parameters. Output tensor will be in same shape with input tensor. Calculations are showed as follows. Step 1: Generate vector U in shape of [H], and V in shape of [W]. While H is the :attr:`dim` th dimension of the input weights, and W is the product result of remaining dimensions. Step 2: :attr:`power_iters` should be a positive integer, do following calculations with U and V for :attr:`power_iters` rounds. Calculations as follows: .. math:: \mathbf{v} := \\frac{\mathbf{W}^{T} \mathbf{u}}{\|\mathbf{W}^{T} \mathbf{u}\|_2} \mathbf{u} := \\frac{\mathbf{W}^{T} \mathbf{v}}{\|\mathbf{W}^{T} \mathbf{v}\|_2} Step 3: Calculate :math:`\sigma(\mathbf{W})` and normalize weight values. .. math:: \sigma(\mathbf{W}) = \mathbf{u}^{T} \mathbf{W} \mathbf{v} \mathbf{W} = \\frac{\mathbf{W}}{\sigma(\mathbf{W})} Refer to `Spectral Normalization `_ . Args: weight(Tensor): ${weight_comment} dim(int): ${dim_comment} power_iters(int): ${power_iters_comment} eps(float): ${eps_comment} name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. Returns: Tensor: A tensor of weight parameters after spectral normalization. The data type and shape is same as input tensor. Examples: .. code-block:: python import paddle paddle.enable_static() weight = paddle.static.data(name='weight', shape=[2, 8, 32, 32], dtype='float32') x = paddle.static.nn.spectral_norm(weight=weight, dim=1, power_iters=2) print(x.shape) # [2, 8, 32, 32] rQweightrrdim power_itersepsrz,Any dimension of input cannot be equal to 0.zMThe input `dim` should be less than the rank of `weight`, but received dim={}rrrTWeightUVr=r)rrrrN)rQ)rrr&r'r[rZrrZnumelrrRnpprodrrrrXrr)rQr r)rrrrrr rr hr uvrrrrrrQsV?   rQc Cs2|dusJdt|jdkrtdt|j| dvr%td| d| dkr.|jd n|jd }d }||krA||krA| sAd }t|fit}t|tsStd t |dd}t |dd}t| t sjtddd}d}t|t r| }|dvrtdt ||dkrd}gd}n |dkrd}gd}||| }|durg}n`t|ttfrt |rt |}nNt |dd}nFt|trt |dd}n9t|trt|jdddgd t|jd kr|jdd ks|jddkr|jdd kr||g}ntdtd |dur|gurtd!ts)t|ts$t |r(td"nt |}t|d kr>t |ddd}| dkrH|jdn|jd }| dkrW|jd#n|jd}|d|d |d|d|d d |dd }|d |d |d |d|d#d |d d }||g}nt |dd$}t|dkrt |dr|d|dg}|durd }n |dkrtd%||||g|}|j|j||jd&}|j|jd'}|j||g|gd(d)|i||||||| | d*d+| dkr |j|d dd,}n|j|d#dd,}||}|S)-a#$ :api_attr: Static Graph 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(Tensor): 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(str|int|list|tuple, optional): The padding size. It means the number of zero-paddings on both sides for each dimension. If `padding` is a string, either 'VALID' or 'SAME' which is the padding algorithm. If `padding` 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): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. Returns: A Tensor representing the conv2d_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 storing the transposed convolution result, and if act is not None, the tensor storing transposed convolution and non-linearity activation result. Raises: ValueError: If the type of `use_cudnn` is not bool. ValueError: If `data_format` is not "NCHW" or "NHWC". ValueError: If `padding` is a string, but not "SAME" or "VALID". ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 or the element corresponding to the input's channel is not 0. ValueError: If `output_size` and filter_size are None at the same time. ShapeError: If the input is not 4-D Tensor. ShapeError: If the input's dimension size and filter's dimension size not equal. ShapeError: If the dimension size of input minus the size of `stride` is not 2. ShapeError: If the number of input channels is not equal to filter's channels. ShapeError: If the size of `output_size` is not equal to that of `stride`. Examples: .. code-block:: python import paddle paddle.enable_static() data = paddle.static.data(name='data', shape=[None, 3, 32, 32], dtype='float32') conv2d_transpose = paddle.static.nn.conv2d_transpose(input=data, num_filters=2, filter_size=3) print(conv2d_transpose.shape) # [-1, 2, 34, 34] Fz3param_attr should not be False in conv2d_transpose.rdrergzQAttr(data_format) of Op(fluid.layers.conv2d_transpose) got wrong value: received rrcrrr<Zdepthwise_conv2d_transposez*Input of conv2d_transpose must be Variablerrnro!use_cudnn should be True or FalsecSsdd}||rvt|dkrv||dr>|dkr>|dddgkr(|dddgks0tdt||dd}d d |D}n/||drm|d krm|dddgkrX|d ddgks`tdt||dd }d d |D}t|dd}|St|dd}|d|d|d|dg}|S)NcSrqrrrsrtrrrrvQrwzCconv2d_transpose.._update_padding..is_list_or_tuplerdrrcrrxrcSryrrrzrrrr!]r|z=conv2d_transpose.._update_padding..rhr#cSryrrrzrrrr!dr|r}rr5rrrrrrrrOs2    z)conv2d_transpose.._update_paddingrrrrrrrrrN output_sizerrrz-output_size must contain one or two integers.z|dkr>|dddgkr(|dddgks0tdt||dd}d d |D}n/||drm|d krm|dddgkrX|d ddgks`tdt||dd }d d |D}t|dd}|S||rt|dkrt|dd}|St|dd}|d|d|d|d|d|dg}|S)NcSrqrrrsrtrrrrvrwzCconv3d_transpose.._update_padding..is_list_or_tuplerrrrrxrcSryrrrzrrrr!r|z=conv3d_transpose.._update_padding..rrdcSryrrrzrrrr!r|rr}r#r"rrrrrs<     z)conv3d_transpose.._update_paddingrrrr)rrrrrrrNr%rrdrzconv3d_transpose.filter_sizerr$z2output_size should be int, list[int] or tuple[int]zLthe groups of conv3d_transpose should be greater than 0. Received groups: {}zaAttr(num_filters) must be divisible by groups,Received: Attr(num_filters) is {}, the groups is {}r&rcrrhr=rrr'rr)r5rrrr rQrrrRrrrrrr[rrrrrrrrrr)rrr$rmr}rnrorrrrbrrrrr r+rrZd_inr,r-Z filter_size_dr.r/rr0rrrrrrr=s2  #             r=c Cs~|dur t|ts |g}trC|dks |gks t|t|jkr"dnd}|dkr.|gkr.|ndg}|r;t|gd|St||d|Strs|dksW|gksWt|t|jkrYdnd}|dkre|gkre|ndg}t |d|d|d|S|dkr}|gkr}|ndg||dks|gkst|t|jkrdndd}t |d gd d t dit }|j |d }|jd d |id|i|d|S)ab Computes the sum of tensor elements over the given dimension. Args: input (Variable): The input variable which is a Tensor, the data type is float32, float64, int32, int64. dim (list|int, optional): The dimensions along which the sum is performed. If :attr:`None`, sum all elements of :attr:`input` and return a Tensor variable with a single element, otherwise must be in the range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`. keep_dim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the :attr:`input` unless :attr:`keep_dim` is true, default value is False. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: Variable: Tensor, results of summation operation on the specified dim of input tensor, it's data type is the same as input's Tensor. Raises: TypeError, if out data type is different with the input data type. Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the corresponding output tensor. x = fluid.data(name='x', shape=[2, 4], dtype='float32') fluid.layers.reduce_sum(x) # [3.5] fluid.layers.reduce_sum(x, dim=0) # [0.3, 0.5, 1.1, 1.6] fluid.layers.reduce_sum(x, dim=-1) # [1.9, 1.6] fluid.layers.reduce_sum(x, dim=1, keep_dim=True) # [[1.9], [1.6]] # y is a Tensor variable with shape [2, 2, 2] and elements as below: # [[[1, 2], [3, 4]], # [[5, 6], [7, 8]]] # Each example is followed by the corresponding output tensor. y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32') fluid.layers.reduce_sum(y, dim=[1, 2]) # [10, 26] fluid.layers.reduce_sum(y, dim=[0, 1]) # [16, 20] NTFrrkeep_dimrCrr1rCrrr>r=rrr)r>)rrrrrr)rrr*r>r&rrrrr)rrr1rrCrr rrrrr>sP4  " r>z paddle.meancCstj||||dS)a 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 import paddle.fluid as fluid paddle.enable_static() # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the corresponding output tensor. x = fluid.data(name='x', shape=[2, 4], dtype='float32') fluid.layers.reduce_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 corresponding 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] )rrZkeepdimr)rr)rrr1rrrrr?xs6r?c Ctd it}|j|d}|durt|ts|g}tr.t||dkr*||Sg|S|j dd|id|i|dkrA|gkrA|ndg||dksU|gksUt |t |j krWdndd d |S) a, 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 import paddle paddle.enable_static() # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the corresponding output tensor. x = fluid.data(name='x', shape=[2, 4], dtype='float32') fluid.layers.reduce_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 corresponding 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] r@r=NrrrTFr2r)r@) rrrrrrrr)rrrrrrr1rr rrrrr@s$0 r@c Cr3) a- 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 import paddle paddle.enable_static() # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the corresponding output tensor. x = fluid.data(name='x', shape=[2, 4], dtype='float32') fluid.layers.reduce_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 corresponding 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] rAr=NrrrTFr2r)rA) rrrrrrrr)minrrrr4rrrrAs$1 rAc Cs2|dur%t|ts%t|trt|}nt|tr|g}n tdt|trOt ||dkr5|gkr5|ndg||dksI|gksIt |t |j krLdSdSt dit }t|dgdd|j|d }|jdd |id |i|dkr{|gkr{|ndg||dks|gkst |t |j krdndd d |S)ai 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 (int|list|tuple, optional): The dimensions along which the product is performed. If :attr:`None`, multiply 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 import paddle paddle.enable_static() # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the corresponding output tensor. x = fluid.data(name='x', shape=[2, 4], dtype='float32') fluid.layers.reduce_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 corresponding 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] Nz= 0rnumsectionszfThe type of 'num_or_sections' in split must be int, list or tuple in imperative mode, but received %s.cSsg|]}tqSr)r)r nrrrr!nr"zsplit..rrrrrrrrFnum_or_sectionsrrrrcsg}d}t|D]=\}}t|trd|_||qt|ts!J|dkr1|dks/Jd||}d}tdgd|d|d||q|S)NrTzbOnly one value of 'num_or_section' in split can be -1. But received num_or_section[%d] is also -1.rr force_cpur)rrr rXrr[rr)Zone_listZ tensor_list unk_dim_idxidxdim_sizetemp_outr rr_get_SectionsTensorLists.     z&split.._get_SectionsTensorListTZ AxisTensorrz$num_or_sections must be more than 1.zThe input's size along the split dimension must be evenly divisible by Attr(num_or_sections). But %d is not evenly divisible by %d. z.ZSectionsTensorListcg|] }jdqSr=rrrrCrrr!rr)rF)r rr numpyitemrrr[rrrr(rrQrrr)Zsplit_with_numrFrr6r*r&r'r(rrrrXmapr) rr<rrr8rindexrJrr rrDr*rrCrrFs4                 rFcCst|jdkr d}tr7tr!t||durdn||d\}}|Str5t|d|dur.dn|d|\}}|St|ddd t dit }|j |j d }|j |j d }|j d d|i||d |durddn||d d|S)a This op normalizes `x` along dimension `axis` using an L2 norm. For a 1-D tensor (`dim` is fixed to 0), this layer computes .. math:: y = \\frac{x}{ \sqrt{\sum {x^2} + epsion }} For `x` with more dimensions, this layer independently normalizes each 1-D slice along dimension `axis`. Args: x(Variable|list): The input tensor could be N-D tensor, and the input data type could be float16, float32 or float64. axis(int): The axis on which to apply normalization. If `axis < 0`, \ the dimension to normalization is rank(X) + axis. -1 is the last dimension. epsilon(float): The epsilon value is used to avoid division by zero, \ the default value is 1e-12. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: Variable: The output has the same shape and data type with `x`. Examples: .. code-block:: python :name: code-example1 import paddle X = paddle.randn(shape=[3, 5], dtype='float64') out = paddle.fluid.layers.l2_normalize(X, axis=-1) print(out) # [[ 0.21558504 0.56360189 0.47466096 0.46269539 -0.44326736] # [-0.70602414 -0.52745777 0.37771788 -0.2804768 -0.04449922] # [-0.33972208 -0.43014923 0.31772556 0.76617881 -0.10761525]] rrNFrrrrVnormrHr=)rZNorm)rrr)rH)rrr rr)rMrr*r&rrrrr)rrrrrrr rMrrrrHs2)  rHz paddle.matmulc strt|jd}t|||dddt| |Sfdd}t|d}|||td it} | j|jd}| j d||d d |i|d |S)a Applies matrix multiplication to two tensors. Currently, the input tensors' rank can be any, but when the rank of any inputs is bigger than 3, this two inputs' rank should be equal. The actual behavior depends on the shapes of :math:`x`, :math:`y` and the flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically: - If a transpose flag is specified, the last two dimensions of the tensor are transposed. If the tensor is rank-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]` in nontransposed form and as :math:`[D, 1]` in transposed form, whereas for :math:`y` it is the opposite: It is treated as :math:`[D, 1]` in nontransposed form and as :math:`[1, D]` in transposed form. - After transpose, the two tensors are 2-D or n-D and matrix multiplication performs in the following way. - If both are 2-D, they are multiplied like conventional matrices. - If either is n-D, it is treated as a stack of matrices residing in the last two dimensions and a batched matrix multiply supporting broadcast applies on the two tensors. Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and nontransposed, the prepended or appended dimension :math:`1` will be removed after matrix multiplication. Args: x (Variable): The input variable which is a Tensor or LoDTensor. y (Variable): The input variable which is a Tensor or LoDTensor. transpose_x (bool): Whether to transpose :math:`x` before multiplication. transpose_y (bool): Whether to transpose :math:`y` before multiplication. alpha (float): The scale of output. Default 1.0. name(str|None): A name for this layer(optional). If set None, the layer will be named automatically. Returns: Variable: The product Tensor (or LoDTensor) variable. Examples: .. code-block:: python # Examples to clarify shapes of the inputs and output # x: [B, ..., M, K], y: [B, ..., K, N] # fluid.layers.matmul(x, y) # out: [B, ..., M, N] # x: [B, M, K], y: [B, K, N] # fluid.layers.matmul(x, y) # out: [B, M, N] # x: [B, M, K], y: [K, N] # fluid.layers.matmul(x, y) # out: [B, M, N] # x: [M, K], y: [K, N] # fluid.layers.matmul(x, y) # out: [M, N] # x: [B, M, K], y: [K] # fluid.layers.matmul(x, y) # out: [B, M] # x: [K], y: [K] # fluid.layers.matmul(x, y) # out: [1] # x: [M], y: [N] # fluid.layers.matmul(x, y, True, True) # out: [M, N] import paddle import paddle.fluid as fluid paddle.enable_static() x = fluid.layers.data(name='x', shape=[2, 3], dtype='float32') y = fluid.layers.data(name='y', shape=[3, 2], dtype='float32') out = fluid.layers.matmul(x, y, True, True) r= transpose_X transpose_YrEc sV||d}|D] \}}t||gddq t|j}t|j}t|dkr,dg|}t|dkr7|dg}rF|d|d|d<|d<rU|d|d|d<|d<|d|dkrq|ddksq|ddksqJd||ft|dkrt|dkrt|ddD]#\}}|d ks||d krq|||krtd ||||fqdSdSdS) NrrVrIrrzAfter 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 rrzWhen 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. )itemsr&rrrrr5) rrZ var_namesrvalx_shapeZy_shaper Zdim_x transpose_x transpose_yrr __check_inputUsF         zmatmul..__check_input)rNrOrErIrrrN)rI) r rrr*rIrZrrrr) rrrUrVrErrrWrr rrTrrIs,K  & rIc Cstr$t|tr|dn|}t|d|\}}d|_d|_||fSd|gi}i}t|tr6|g|d<nd|i}td it }|j |j d} |j dd}|j d|| g|gd |d d| _d|_| |fS) ai :alias_main: paddle.topk :alias: paddle.topk,paddle.tensor.topk,paddle.tensor.search.topk :old_api: paddle.fluid.layers.topk This OP is used to find values and indices of the k largest entries for the last dimension. If the input is a 1-D Tensor, finds the k largest entries and outputs their values and indices. If the input is a Tensor with higher rank, this operator computes the top k entries along the last dimension. .. code-block:: text Case 1: Input: input.shape = [3, 4] input.data = [[5, 4, 2, 3], [9, 7, 10, 25], [6, 2, 10, 1]] k = 2 Output: The first output: values.shape = [3, 2] values.data = [[5, 4], [10, 25], [6, 10]] The second output: indices.shape = [3, 2] indices.data = [[0, 1], [2, 3], [0, 2]] Args: input(Variable): The input tensor. Support data types: float32, float64. k(int | Variable): The number of top elements to look for along the last dimension of input tensor. name (str, optional): Please refer to :ref:`api_guide_Name`, Default None. Returns: Values (Variable): Input tensor's k largest elements along each last dimensional slice. The dimension is: :math:`input.shape[:-1]+[k]`. Indices (Variable): Indices of k largest elements alone the last dimension of input. The dimension is same as values. Raises: ValueError: If :math:`k < 1` or :math:`k > last dimension of input`. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.layers as layers # set batch size=None input = fluid.data(name="input", shape=[None, 13, 11], dtype='float32') top5_values, top5_indices = layers.topk(input, k=5) # top5_values.shape[None, 13, 5], top5_indices.shape=[None, 13, 5] # 1D Tensor input1 = fluid.data(name="input1", shape=[None, 13], dtype='float32') top5_values, top5_indices = layers.topk(input1, k=5) #top5_values.shape=[None, 5], top5_indices.shape=[None, 5] # k=Variable input2 = fluid.data(name="input2", shape=[None, 13, 11], dtype='float32') vk = fluid.data(name="vk", shape=[None, 1], dtype='int32') # save k in vk.data[0] vk_values, vk_indices = layers.topk(input2, k=vk) #vk_values.shape=[None, 13, k], vk_indices.shape=[None, 13, k] rkTrKtop_kr=r)rZIndicesrN)rZ) r rr rIrJr*rZrXrrrrr) rrXrZ_krindicesrrr valuesrrrrJs0G    rJc Cst|dddgdtdit}t|dd\}}|jdd}|d ur7|jd d |gid |gid |dd|S|jdd} t|dg} |jd | g|gd|g| gdd ||dd|| fS)a This op is used to decode sequences by greedy policy by the following steps: 1. Get the indexes of maximum value for each row in input. a.k.a. numpy.argmax(input, axis=0). 2. For each sequence in result of step1, merge repeated tokens between two blanks and delete all blanks. This op is implemented in two modes: lod and padding, either of them can be used. The input can be either LoDTensor or Tensor, corresponding to lod and padding mode respectively. A simple example as below: .. code-block:: text Given: (1) for lod mode: input.data = [[0.6, 0.1, 0.3, 0.1], [0.3, 0.2, 0.4, 0.1], [0.1, 0.5, 0.1, 0.3], [0.5, 0.1, 0.3, 0.1], [0.5, 0.1, 0.3, 0.1], [0.2, 0.2, 0.2, 0.4], [0.2, 0.2, 0.1, 0.5], [0.5, 0.1, 0.3, 0.1]] input.lod = [[4, 4]] Computation: step1: Apply argmax to first input sequence which is input.data[0:4]. Then we get: [[0], [2], [1], [0]] step2: merge repeated tokens and remove blank which is 0. Then we get first output sequence: [[2], [1]] Finally: output.data = [[2], [1], [3]] output.lod = [[2, 1]] (2) for padding mode: input.data = [[[0.6, 0.1, 0.3, 0.1], [0.3, 0.2, 0.4, 0.1], [0.1, 0.5, 0.1, 0.3], [0.5, 0.1, 0.3, 0.1]], [[0.5, 0.1, 0.3, 0.1], [0.2, 0.2, 0.2, 0.4], [0.2, 0.2, 0.1, 0.5], [0.5, 0.1, 0.3, 0.1]]] input_length.data = [[4], [4]] input.shape = [2, 4, 4] step1: Apply argmax to first input sequence which is input.data[0:4]. Then we get: [[0], [2], [1], [0]], for input.data[4:8] is [[0], [3], [3], [0]], shape is [2,4,1] step2: Change the argmax result to use padding mode, then argmax result is [[0, 2, 1, 0], [0, 3, 3, 0]], shape is [2, 4], lod is [], input_length is [[4], [4]] step3: Apply ctc_align to padding argmax result, padding_value is 0 Finally: output.data = [[2, 1, 0, 0], [3, 0, 0, 0]] output_length.data = [[2], [1]] Parameters: input(Variable): the probabilities of variable-length sequences. When in lod mode, it is a 2-D LoDTensor with LoD information. It's shape is [Lp, num_classes + 1] where Lp is the sum of all input sequences' length and num_classes is the true number of classes. When in padding mode, it is a 3-D Tensor with padding, It's shape is [batch_size, N, num_classes + 1]. (not including the blank label). The data type can be float32 or float64. blank(int): the blank label index of Connectionist Temporal Classification (CTC) loss, which is in the half-opened interval [0, num_classes + 1). input_length(Variable, optional): 2-D LoDTensor, shape is [batch_size, 1], data type is int64. It is used for padding mode. In lod mode, input_length is None. padding_value(int): padding value. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: For lod mode, returns the result of CTC greedy decoder, 2-D LoDTensor, shape is [Lp, 1], \ data type is int64. 'Lp' is the sum of all output sequences' length. If all the sequences \ in result were empty, the result LoDTensor will be [-1] with empty \ LoD [[]]. For padding mode, returns a tuple of (output, output_length), which was described as below: output, 2-D Tensor, shape is [batch_size, N], data type is int64. output_length, 2-D Tensor, shape is [batch_size, 1], data type is int64. It is the length of \ each sequence of output for padding mode. Return type: For lod mode: Variable For padding mode: tuple of two Variables (output, output_length). Examples: .. code-block:: python # for lod mode import paddle.fluid as fluid x = fluid.data(name='x', shape=[None, 8], dtype='float32', lod_level=1) cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0) # for padding mode x_pad = fluid.data(name='x_pad', shape=[10, 4, 8], dtype='float32') x_pad_len = fluid.data(name='x_pad_len', shape=[10, 1], dtype='int64') out, out_len = fluid.layers.ctc_greedy_decoder(input=x_pad, blank=0, input_length=x_pad_len) rrrrGr)rXrr=NZ ctc_alignrrT)merge_repeatedblankrr)rZ InputLength)rZ OutputLength)r]r^ padding_value)rG)r&rrrJrrrV) rr^Z input_lengthr_rr rZ topk_indicesZctc_outZ ctc_out_lenZ ctc_inputrrrrGs>    rGc Cstr t||Strt|d|\}}|St|dgddt|dtt fdt |t r2t|}t |t |j krHt dt |j t |ft|D]\}}|t |j kret d|||t |j fqLtd it}||j}||j}|jdd |gi|g|gd d|id |S)a 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 (Tensor): The input Tensor. It is a N-D Tensor of data types bool, float32, float64, int32. perm (list|tuple): Permute the input according to the data of perm. name (str): The name of this layer. It is optional. Returns: Tensor: A transposed n-D Tensor, with data type being bool, 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 import paddle x = paddle.randn([2, 3, 4]) x_transposed = paddle.transpose(x, perm=[1, 0, 2]) print(x_transposed.shape) # [3L, 2L, 4L] rrrrrrrr complex64 complex128rKpermzInput(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.zEach 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. transpose2rrZXShaperN)rK)rr)rKrr*rdr&r'rrrrrr5rrrrrr) rrcrrrr@rr rSrrrrKsH2     rKc CstrJdt|ddgdt|tr||g}t|tr!||g}t|tr*||g}t|dkr>||d||dd|i}|||d }|r[t|trS||g}||d <||d <tdit} | j| d } | j d|d | i|d| S)aq :api_attr: Static Graph Extracts image patches from the input tensor to form a tensor of shape {input.batch_size * output_height * output_width, filter_size_height * filter_size_width * input.channels}. This op use filter to scan images and convert these images to sequences. After expanding, the number of time step are output_height * output_width for an image, in which output_height and output_width are calculated by below equation: .. math:: output\_height = 1 + \ (padding\_up + padding\_down + input\_height - filter\_size\_height + stride\_height - 1) / stride\_height \\\\ output\_width = 1 + \ (padding\_left + padding\_right + input\_width - filter\_size\_width + stride\_width - 1) / stride\_width And the dimension of each time step is filter_size_height * filter_size_width * input.channels. Parameters: input (Variable): The input should be a 4-D Tensor in :math:`NCHW` format. The data type is float32. filter_size(int32 | List[int32]): The filter size. If filter_size is a List, it must contain two integers, :math:`[filter\_size\_height, filter\_size\_width]` . Otherwise, the filter size will be a square :math:`[filter\_size, filter\_size]` . Default is 1. stride(int32 | List[int32]): The stride size. If stride is a List, it must contain two integers, :math:`[stride\_height, stride\_width]` . Otherwise, the stride size will be a square :math:`[stride\_size, stride\_size]` . Default is 1. padding(int32 | List[int32]): The padding size. If padding is a List, it can contain four integers like :math:`[padding\_up, padding\_left, padding\_down, padding\_right]` to indicate paddings of four direction. Or it can contain two integers :math:`[padding\_height, padding\_width]` which means padding_up = padding_down = padding_height and padding_left = padding_right = padding_width. Otherwise, a scalar padding means padding_up = padding_down = padding_left = padding_right = padding. Default is 0. input_image_size(Variable, optional): the input contains image real size.It's dim is :math:`[batchsize, 2]` . It is just for batch inference when not None. Default is None. out_stride(int32 | List[int32]): The scaling of image through CNN. It is valid only when input_image_size is not None. If out_stride is List, it must contain two integers, :math:`[out\_stride\_height, out\_stride\_W]` . Otherwise, the out_stride_height = out_stride_width = out_stride. Default is 1. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: The output is a 2-D LoDTensor with shape {input.batch\_size * output\_height * output\_width, \ filter\_size\_height * filter\_size\_width * input.channels}. The data type is float32. Return Type: Variable Examples: .. code-block:: text Given: x = [[[[ 6. 2. 1.] [ 8. 3. 5.] [ 0. 2. 6.]] [[ 2. 4. 4.] [ 6. 3. 0.] [ 6. 4. 7.]]] [[[ 6. 7. 1.] [ 5. 7. 9.] [ 2. 4. 8.]] [[ 1. 2. 1.] [ 1. 3. 5.] [ 9. 0. 8.]]]] x.dims = {2, 2, 3, 3} And: filter = [2, 2] stride = [1, 1] padding = [0, 0] Then: output.data = [[ 6. 2. 8. 3. 2. 4. 6. 3.] [ 2. 1. 3. 5. 4. 4. 3. 0.] [ 8. 3. 0. 2. 6. 3. 6. 4.] [ 3. 5. 2. 6. 3. 0. 4. 7.] [ 6. 7. 5. 7. 1. 2. 1. 3.] [ 7. 1. 7. 9. 2. 1. 3. 5.] [ 5. 7. 2. 4. 1. 3. 9. 0.] [ 7. 9. 4. 8. 3. 5. 0. 8.]] output.dims = {8, 8} output.lod = [[4, 4]] Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32') output = fluid.layers.im2sequence( input=data, stride=[1, 1], filter_size=[2, 2]) z4sequence layer is not supported in dygraph mode yet.rrrLrrrr)Zkernelsrrr out_strider=rrN)rL) r r&rr[rrrrrrr) rrmrnr}Zinput_image_sizerfrrrr rrrrrLs8x      rLc Cstd it}t|ddgd|}|d|jdg}|j|j||d}||}|jd|g|gdd|gid | |S) a :api_attr: Static Graph ${comment} Args: input (${x_type}): ${x_comment}. future_context_size (int): Future context size. Please note, the shape of convolution kernel is [future_context_size + 1, D]. param_attr (ParamAttr): Attributes of parameters, including name, initializer etc. act (str): Non-linear activation to be applied to output variable. Returns: ${out_comment}. Examples: .. code-block:: python # for LodTensor inputs import paddle paddle.enable_static() x = paddle.static.data(name='x', shape=[9, 16], dtype='float32', lod_level=1) out = paddle.static.nn.row_conv(input=x, future_context_size=2) # for Tensor inputs x = paddle.static.data(name='x', shape=[9, 4, 16], dtype='float32') out = paddle.static.nn.row_conv(input=x, future_context_size=2) rMrrrrr<)rrrrBN)rM) rrr&rrrrrrr) rZfuture_context_sizerrr rrrrrrrrMs    rMcCstr t||Strt||Stdit}t|dtdt |dkr+t dt |D]\}}t |dt |dgddq/t |dd d gd||d j}|jd||d d |gid|S)a Based on the given index parameter, the OP selects a specific row from each input Tensor to construct the output Tensor. If the input of this OP contains :math:`m` Tensors, where :math:`I_{i}` means the i-th input Tensor, :math:`i` between :math:`[0,m)` . And :math:`O` means the output, where :math:`O[i]` means the i-th row of the output, then the output satisfies that :math:`O[i] = I_{index[i]}[i]` . For Example: .. code-block:: text Given: inputs = [[[0,0,3,4], [0,1,3,4], [0,2,4,4], [0,3,3,4]], [[1,0,3,4], [1,1,7,8], [1,2,4,2], [1,3,3,4]], [[2,0,3,4], [2,1,7,8], [2,2,4,2], [2,3,3,4]], [[3,0,3,4], [3,1,7,8], [3,2,4,2], [3,3,3,4]]] index = [[3],[0],[1],[2]] out = [[3,0,3,4], # out[0] = inputs[index[0]][0] = inputs[3][0] = [3,0,3,4] [0,1,3,4], # out[1] = inputs[index[1]][1] = inputs[0][1] = [0,1,3,4] [1,2,4,2], # out[2] = inputs[index[2]][2] = inputs[1][2] = [1,2,4,2] [2,3,3,4]] # out[3] = inputs[index[3]][3] = inputs[2][3] = [2,3,3,4] Args: inputs (list): The input Tensor list. The list elements are N-D Tensors of data types float32, float64, int32, int64. All input Tensor shapes should be the same and rank must be at least 2. index (Tensor): Used to select some rows in the input Tensor to construct an index of the output Tensor. It is a 2-D Tensor with data type int32 or int64 and shape [M, 1], where M is the number of input Tensors. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Output of multiplex OP, with data type being float32, float64, int32, int64. Examples: .. code-block:: python import paddle import numpy as np img1 = np.array([[1, 2], [3, 4]]).astype(np.float32) img2 = np.array([[5, 6], [7, 8]]).astype(np.float32) inputs = [paddle.to_tensor(img1), paddle.to_tensor(img2)] index = paddle.to_tensor(np.array([[1], [0]]).astype(np.int32)) res = paddle.multiplex(inputs, index) print(res) # [array([[5., 6.], [3., 4.]], dtype=float32)] rNrrz8inputs should be a list object with at least 2 elements.rrr6rLrrr)rrrrBN)rN)rr*rNrr)rrr'rrr5rr&rrrr)rrLrr idrrrrrrNs05   rNcCst|dddgdt|dddgdtd it}|j|jd}|j|jd}|jd||||d||dd |d ur<|nd id |S)a This layer computes the smooth L1 loss for Variable :attr:`x` and :attr:`y`. It takes the first dimension of :attr:`x` and :attr:`y` as batch size. For each instance, it computes the smooth L1 loss element by element first and then sums all the losses. So the shape of output Variable is [batch_size, 1]. Args: x (Variable): A tensor with rank at least 2. The input value of smooth L1 loss op with shape [batch_size, dim1, ..., dimN]. A LoDTensor or Tensor with type float32. y (Variable): A tensor with rank at least 2. The target value of smooth L1 loss op with same shape as :attr:`x`. A LoDTensor or Tensor with type float32. inside_weight (Variable|None): A tensor with rank at least 2. This input is optional and should have same shape with :attr:`x`. If provided, the result of (:attr:`x` - :attr:`y`) will be multiplied by this tensor element by element. A Tensor with type float32. outside_weight (Variable|None): A tensor with rank at least 2. This input is optional and should have same shape with :attr:`x`. If provided, the out smooth L1 loss will be multiplied by this tensor element by element. A Tensor with type float32. sigma (float|None): Hyper parameter of smooth L1 loss layer. A float scalar with default value 1.0. Returns: Variable: The output smooth L1 loss with shape [batch_size, 1]. A Tensor with type float32. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle paddle.enable_static() data = fluid.data(name="x", shape=[-1, 3], dtype="float32") label = fluid.data(name="y", shape=[-1, 3], dtype="float32") result = fluid.layers.smooth_l1(data,label) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) x = np.random.rand(3,3).astype("float32") y = np.random.rand(3,3).astype("float32") output= exe.run(feed={"x":x, "y":y}, fetch_list=[result]) print(output) #[array([[0.08220536], # [0.36652038], # [0.20541131]], dtype=float32)] rrrsmooth_l1_lossrr=)rrZ InsideWeightZ OutsideWeight)DiffrsigmaNrr)rhrI)rrZ inside_weightZoutside_weightrjr diffZlossrrrrR s"8 rRzpaddle.nn.functional.one_hotcCstr(t|tr|}|jdksJd|d}t|d|d|}d|_|St dit }t |dd d gdt |dt jtfd|jd d }t|tsXd |i}||d}n d|_||d}d|i}|jd||d|idd|_|S)ai **WARING:** This OP requires the last dimension of Tensor shape must be equal to 1. This OP will be deprecated in a future release. It is recommended to use fluid. :ref:`api_fluid_one_hot` . The operator converts each id in the input to an one-hot vector with a :attr:`depth` length. The value in the vector dimension corresponding to the id is 1, and the value in the remaining dimension is 0. The shape of output Tensor or LoDTensor is generated by adding :attr:`depth` dimension behind the last dimension of the input shape. .. code-block:: text Example 1 (allow_out_of_range=False): input: X.shape = [4, 1] X.data = [[1], [1], [3], [0]] depth = 4 output: Out.shape = [4, 4] Out.data = [[0., 1., 0., 0.], [0., 1., 0., 0.], [0., 0., 0., 1.], [1., 0., 0., 0.]] Example 2 (allow_out_of_range=True): input: X.shape = [4, 1] X.data = [[1], [1], [5], [0]] depth = 4 allow_out_of_range = True output: Out.shape = [4, 4] Out.data = [[0., 1., 0., 0.], [0., 1., 0., 0.], [0., 0., 0., 0.], # This id is 5, which goes beyond depth, so set it all-zeros data. [1., 0., 0., 0.]] Example 3 (allow_out_of_range=False): input: X.shape = [4, 1] X.data = [[1], [1], [5], [0]] depth = 4 allow_out_of_range = False output: Throw an exception for Illegal value The second dimension in X is 5, which is greater than depth. Allow_out_of_range =False means that does not allow the word id to exceed depth, so it throws an exception. Args: input(Variable): Tensor or LoDTensor with shape :math:`[N_1, N_2, ..., N_k, 1]` , which contains at least one dimension and the last dimension must be 1. The data type is int32 or int64. depth(scalar): An integer defining the :attr:`depth` of the one hot dimension. If input is word id, depth is generally the dictionary size. allow_out_of_range(bool): A bool value indicating whether the input indices could be out of range :math:`[0, depth)` . When input indices are out of range, exceptions :code:`Illegal value` is raised if :attr:`allow_out_of_range` is False, or zero-filling representations is created if it is set True. Default: False. Returns: Variable: The one-hot representations of input. A Tensor or LoDTensor with type float32. Examples: .. code-block:: python import paddle import paddle.fluid as fluid paddle.enable_static() # Correspond to the first example above, where label.shape is [4, 1] and one_hot_label.shape is [4, 4]. label = fluid.data(name="label", shape=[4, 1], dtype="int64") one_hot_label = fluid.layers.one_hot(input=label, depth=4) )rz,depth of type Variable should have shape [1]rdepthallow_out_of_rangeTrSrrrrr=r)rlrm)rZ depth_tensorrrrrrN)rS)r rr rIrrJr*rSrXrrr&r'six integer_typesrr)rrlrmrr Z one_hot_outrrrrrrSZs:T        rScCstd}|dur d}|j|ddgddd\}}|r?|j|t|dddd |jjd d |gid |gid t|idd|_|S)a' :api_attr: Static Graph Create an auto-increase variable. which will be automatically increased by 1 in every iteration. By default, the first return of this counter is 1, and the step size is 1. Args: counter_name(str, optional): The counter name. Default '@STEP_COUNTER@'. begin(int, optional): The first return value of this counter. Default 1. step(int, optional): The step size. Default 1. Returns: Variable: The auto-increased Variable with data type int64. Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() global_step = fluid.layers.autoincreased_step_counter( counter_name='@LR_DECAY_COUNTER@', begin=0, step=1) Zglobal_step_counterNz@STEP_COUNTER@rrT)rrrr%Zbelong_to_optimizer)rr>)r incrementrrstepr) rr(Zset_variable_initializerrr\Z global_blockZ _prepend_oprZrX)Z counter_namebeginrrr counterZ is_new_varrrrrTs0   rTcs8trAtjj}|rtdt|ttfr#dd|D}t |}nt||r2d|_ t |}n t d t|t||Strt}|rMtdt|ttfrfdd|D}tdd|\}}nt||rwd|_ t|\}}n t d t|t||Std gd d t|dtttfd t|d ttdfd tdit} fdd} di} i} t|trd|_ || d<n4t|ttfrt|dksJdt|| || d<t|rt|| d<n t|trd|_ || d<|rn| jjd}| jjd} | jd | | || dd| |S)aI :alias_main: paddle.reshape :alias: paddle.reshape,paddle.tensor.reshape,paddle.tensor.manipulation.reshape 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 guarantee 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 index 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(Tensor): An N-D Tensor. The data type is ``float32``, ``float64``, ``int32`` or ``int64``. shape(list|tuple|Tensor): 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 Tensor, 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(Tensor)``. \ 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: Tensor: A reshaped Tensor with the same data type 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. Examples: .. code-block:: python import paddle import paddle.fluid as fluid paddle.enable_static() # example 1: # attr shape is a list which doesn't contain Tensors. 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]) # the shape of reshaped_1 is [2,4,3,2]. # example 2: # attr shape is a list which contains Tensors. 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]. # example 3: data_3 = fluid.data( name="data_3", shape=[2,4,6], dtype='float32') reshaped_3 = fluid.layers.reshape(x=data_3, shape=[6,8]) # the shape of reshaped_3 is [6,8]. zRInplace on reshape is not allowed and will be discarded in dygraph mode currently.cS(g|]}t|tr|dn|qSrrr rIrJr rJrrrr!fzreshape..TzEshape must be an instance of `list`, `tuple` or `Variable`, got '{}.'cSrurvrwrxrrrr!}ryNrr)rrrint16rrrrrU actual_shapereshape2csd}g}t|D]J\}}t|tr|dq|||dkr-|dks*Jd||}q|dkrD|tjksCJd|tjfq|dksRJd|t|fq|S)NraOnly one dimension value of 'shape' in reshape can be -1. But received shape[%d] is also -1. # N = x.shape()[2] # N is an int. (NOT recommend under @to_static) N = paddle.shape(x)[2] # N is a Tensor. (Recommend) z = paddle.reshape([N, -1, 4]) # z.shape is [-1, -1, 4] If your target shape in Reshape represents dynamic shape, please turn it into a Tensor under @to_static. See above example for details.rz}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.zzEach dimension value of 'shape' in reshape must not be negative except one unknown dimension. But received shape[%d] = %s.)rrr rrrr)Z list_shaper?Z attrs_shapedim_idxrArrrget_attr_shapes2        zreshape..get_attr_shaperShaperz>The size of 'shape' in reshape can't be zero, but received %s. ShapeTensorr=rern)r|)!rr"rrrrrrrr)rUrXr5rRrrrrr r*r|r&r'rrrrr(r)rrrr)rrr{rZinplacerrrrr rrrrSrr~rrUs`        "       rUcCstr t||Strt|d|\}}|Std it}t|dgddt |dt t t fdi}t |t rAd|_||d<nt |t t frYt|rUt||d<n||d<|j|jd}|j|jd}|jdd |i|||d d |S)a 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. Supported data type: float32, float64, bool, 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] axesrVr) rrrrint8rrrarb axis/axesTr=squeeze2rrernN)rV)rr)rVrr*rrrr&r'rrr rrXrr(r)rrr)rrrrrr rrSrrrrVs85     rVc CsJtr:t|tr |g}nt|tr|}nt|ttfr&dd|D}tr4t |d|\}}|St ||St |dttttfdt|dgddtdit}d |i}i}t|trd|g}t|trqd |_||d <nt|ttfrt|rt||d <n||d<|j|jd }|j|jd }|jd||||dd|S)ak Insert single-dimensional entries to the shape of a Tensor. Takes one required argument axes, a list of dimensions that will be inserted. Dimension indices in axes are as seen in the output tensor. For example: .. code-block:: text Given a tensor such that tensor with shape [3, 4, 5], then Unsqueezed tensor with axes=[0, 4] has shape [1, 3, 4, 5, 1]. Args: input (Variable): The input Tensor to be unsqueezed. Supported data type: float32, float64, bool, int8, int32, int64. axes (int|list|tuple|Variable): Indicates the dimensions to be inserted. The data type is ``int32`` . If ``axes`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``axes`` is an Variable, it should be an 1-D Tensor . name (str|None): Name for this layer. Returns: Variable: Unsqueezed Tensor, with the same data type as input. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[5, 10]) y = fluid.layers.unsqueeze(input=x, axes=[1]) cSrurvrwrxrrrr!Oryzunsqueeze..rrrWr) rrrrrrzrrrarb unsqueeze2rTZ AxesTensorZAxesTensorListr=rernN)r)r rr[r rItolistrrrr*rr)rWr'r&rrrXrr(r)rrr) rrrrrr rrrSrrrrW,sN          rWcCst|dgddtdit}|j|jd}|dur2t|dtd|jd||dd|id |S|durG|jdd |id |id|id |Std )a Set LoD of :attr:`x` to a new one specified by :attr:`y` or :attr:`target_lod`. When :attr:`y` provided, :attr:`y.lod` would be considered as target LoD first, otherwise :attr:`y.data` would be considered as target LoD. If :attr:`y` is not provided, target LoD should be specified by :attr:`target_lod`. If target LoD is specified by :attr:`y.data` or :attr:`target_lod`, only one level LoD is supported. .. code-block:: text * Example 1: Given a 1-level LoDTensor x: x.lod = [[ 2, 3, 1 ]] x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]] x.dims = [6, 1] target_lod: [4, 2] then we get a 1-level LoDTensor: out.lod = [[4, 2]] out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]] out.dims = [6, 1] * Example 2: Given a 1-level LoDTensor x: x.lod = [[2, 3, 1]] x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]] x.dims = [6, 1] y is a Tensor: y.data = [[2, 4]] y.dims = [1, 3] then we get a 1-level LoDTensor: out.lod = [[2, 4]] out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]] out.dims = [6, 1] * Example 3: Given a 1-level LoDTensor x: x.lod = [[2, 3, 1]] x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]] x.dims = [6, 1] y is a 2-level LoDTensor: y.lod = [[2, 2], [2, 2, 1, 1]] y.data = [[1.1], [2.1], [3.1], [4.1], [5.1], [6.1]] y.dims = [6, 1] then we get a 2-level LoDTensor: out.lod = [[2, 2], [2, 2, 1, 1]] out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]] out.dims = [6, 1] Args: x (Variable): Input variable which could be a Tensor or LoDTensor. The data type should be int32, int64, float32 or float64. y (Variable, optional): If provided, output's LoD would be derived from :attr:`y`. If y's lod level>0, the data type can be any type. If y's lod level=0, the data type should be int32. target_lod (list|tuple, optional): One level LoD which should be considered as target LoD when :attr:`y` not provided. Returns: Variable: Output variable with LoD specified by this layer. Raises: ValueError: If :attr:`y` and :attr:`target_lod` are both None. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[10]) y = fluid.layers.data(name='y', shape=[10, 20], lod_level=2) out = fluid.layers.lod_reset(x=x, y=y) rr6rXr=NrrrrBr target_lodrnz)y and target_lod should not be both none.)rX) r&rrrrr'r rr5)rrrr rrrrrXs, Q rXcCszddlm}Wn ddlm}Y|durtdt||s)t|ts)tdt|dgddtdit}|j |j d }d |i}d d i}t|trS||d <n||d<|j d||d|id|S)a 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. The data type should be int32, int64, float32 or float64. level (list|tuple|Variable, optional): The LoD level to be appended into LoD of x. If level is variable and its lod level>0, the data type can be any type. If level is variable and its lod level=0, the data type should be int32. 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]) r)IterableNzInput(x) can't be None.z-Input(level) must be list, tuple or Variable.rr6rYr=rrTrrrXrrn)rY) collections.abcr collectionsr5rr r&rrrrr)rlevelrr rrrrrrrYs0'   rYr-C6??c Cstdit}t|ddgd|}|j} t| } | dkr%td| |dvr1td|d|j|d d } ||} |jdd |i| | d |||||d d| S)a :alias_main: paddle.nn.functional.lrn :alias: paddle.nn.functional.lrn,paddle.nn.functional.norm.lrn :old_api: paddle.fluid.layers.lrn 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): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. Returns: Variable: 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 rZrrrdz=Input's dimension size of Op(lrn) must be 4, but received %d.rgz7Attr(data_format) of Op(lrn) got wrong value: received rTrWr)rZMidOut)r:rXrEbetarrN)rZ) rrr&rrrr5rr) rr:rXrErrrr rr dimsZmid_outZlrn_outrrrrZ.sH< rZrcCst|dgddt|dtttfdt|trt|}td it}|jdd}| |}|j dd|id|i||dd |S) ah :alias_main: paddle.nn.functional.pad :alias: paddle.nn.functional.pad,paddle.nn.functional.common.pad :old_api: paddle.fluid.layers.pad This op will pad a tensor with a constant value given by :attr:`pad_value`, and the padded shape is specified by :attr:`paddings`. Specifically, the number of values padded before the elements of :attr:`x` in dimension :attr:`i` is indicated by :attr:`paddings[2*i]`, and the number of values padded after the elements of :attr:`x` in dimension :attr:`i` is indicated by :attr:`paddings[2*i+1]`. See below for an example. .. code-block:: text Given: x = [[1, 2], [3, 4]] paddings = [0, 1, 1, 2] pad_value = 0 Return: out = [[0, 1, 2, 0, 0] [0, 3, 4, 0, 0] [0, 0, 0, 0, 0]] Args: x (Variable): Tensor, data type is float32. paddings (list): A list of integers. Its elements specify the padded width before and after each dimension in turn. The length of :attr:`paddings` must be equal to :math:`rank(x) \\times 2`. pad_value (float): The constant value used to pad. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: The padded tensor, with the same data type and rank as :attr:`x` Return Type: Variable Examples: .. code-block:: python # x is a rank 2 tensor variable import paddle.fluid as fluid x = fluid.data(name='data', shape=[300, 300], dtype='float32') out = fluid.layers.pad(x=x, paddings=[0, 1, 1, 2], pad_value=0.) r)rrrrrrarbr[ pad_valueZinput_param_namerr)rrrN)r[) r&r'rZr[r rrrrrr)rrrrr rrrrrr[s" 7   r[cCsnt|dtdt|dgddtd it}|jdd}||}|jd||dd|idt|id |S) a 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 = 0. Return: Out = [[[[35, 36, 37], [ 0, 0, 0]], [[38, 39, 40], [ 0, 0, 0]], [[41, 42, 43], [ 0, 0, 0]]], [[[ 0, 0, 0], [ 0, 0, 0]], [[ 0, 0, 0], [ 0, 0, 0]], [[ 0, 0, 0], [ 0, 0, 0]]]] Out.shape = [2, 3, 2, 3] Args: x (Variable): Tensor, its shape specifies 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] rr\rr6rrrrrN)r\) r'r r&rrrrrrZ)rrrrr rrrrrr\sO    r\皙?cCstr t||t|S|dks|dkrtdtr%t||dt|St|dddgdtdit }d |_ | |}|j d|rH||d nd |id |idt|id |S)a :alias_main: paddle.nn.functional.label_smooth :alias: paddle.nn.functional.label_smooth,paddle.nn.functional.common.label_smooth :old_api: paddle.fluid.layers.label_smooth 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. The dtype can be "float32" and "float64". 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="int32") one_hot_label = layers.one_hot(input=label, depth=10) smooth_label = layers.label_smooth( label=one_hot_label, epsilon=0.1, dtype="float32") rrz-The value of epsilon must be between 0 and 1.rr;rrr]T)rZ PriorDistrrrN)r]) rr)r]rZr5r r*r&rrrXrr)r;Z prior_distrrrr Z smooth_labelrrrr]:s2>    r]c Cstr|dus Jdt|||d|d|d| \}}||fSt|ddgdt|d dgdtdit} | } | | }| jd d }||d } |durR|| d <| jd| ||d|||dd|S)a 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 rois_num (Tensor): The number of RoIs in each image. 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. 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 import paddle paddle.enable_static() 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) rois_num_data = np.array([2]).astype('int32') x = fluid.data(name='input', shape=[None,1,4,4], dtype=DATATYPE) rois = fluid.data(name='roi', shape=[None,4], dtype=DATATYPE) rois_num = fluid.data(name='rois_num', shape=[None], dtype='int32') pool_out = fluid.layers.roi_pool( input=x, rois=rois, pooled_height=1, pooled_width=1, spatial_scale=1.0, rois_num=rois_num) exe = fluid.Executor(place) out, = exe.run(feed={'input':input_data ,'roi':roi_data, 'rois_num': rois_num_data}, fetch_list=[pool_out.name]) print(out) #array([[[[11.]]], [[[16.]]]], dtype=float32) print(np.array(out).shape) # (2, 1, 1, 1) N,rois_num should not be None in dygraph mode. pooled_height pooled_width spatial_scalerrr^roisrr=rROIsRoisNum)rZArgmax)rrrr)r^) r r*r^r&rrrrr) rrrrrrois_numrrZargmaxesr rrrrrr^s:H    r^c Cstr|dus Jdt|||||||dStr3|dus"Jdt|||d|d|d|d| }|St|dd d gd t|d d d gd tdit} | } | | }||d } |durc|| d<| j d | d|i||||dd|S)ak ${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 rois_num (Tensor): The number of RoIs in each image. 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. Returns: Variable: Output: ${out_comment}. Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() x = fluid.data( name='data', shape=[None, 256, 32, 32], dtype='float32') rois = fluid.data( name='rois', shape=[None, 4], dtype='float32') rois_num = fluid.data(name='rois_num', shape=[None], dtype='int32') align_out = fluid.layers.roi_align(input=x, rois=rois, pooled_height=7, pooled_width=7, spatial_scale=0.5, sampling_ratio=-1, rois_num=rois_num) NrFrrrsampling_ratiorrrr_rrrr)rrrrr)r_) rr)r_rr*r&rrrrr) rrrrrrrrZ align_outr rrrrrr_sJ7     r_cCstjjj||||dS)a 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 (Tensor): Tensor, rank>=2, shape is :math:`[N_1, N_2, ..., N_k, D]`, where :math:`N_1` is the batch_size, :math:`D` is the number of categories. It is usually the output predictions of sigmoid activation. The data type can be float32 or float64. label (Tensor): Tensor, the groud truth with the same rank as input, shape is :math:`[N_1, N_2, ..., N_k, 1]`. where :math:`N_1` is the batch_size. The data type can be int32 or int64. 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: Tensor, which shape is [1], data type is the same as `input` . Example: .. code-block:: python import paddle import paddle.nn.functional as F x = paddle.randn((3,224,224,2)) label = paddle.randint(high=2, shape=(3,224,224,1)) predictions = F.softmax(x) loss = F.dice_loss(input=predictions, label=label) )rr)rnn functionalr`rr;rrrrrr`Ys *r`BILINEARc Cs(dddddd} |}|| vrtd| |} |dkr't|jdkr'td |d vr6t|jd kr6td |d krEt|jdkrEtdt|tsNtd|dkrZ|dkrZtd|durf|durftdtd| fit } | } t|jdkr|dvrtd|dt|jd kr|dvrtd|dt|jdkr|dvrtd|ddd} |d ks|d!ks|d"krd }|d#ks|d$ks|d%krd#}d&|i}d'd'd'| |||d(}|dur3t|t rt sd)|_ ||d*<n}t r t|t rt|}nt|}t|D]\}}t|t r|d||<q | |s)td+d,}t|D]\}}t|t r=d)}q/|dksFJd-q/|rg}g}|D]9}t|t rhd)|_ |||d'qQt|tspJ| d.}tdgd.|d)|d/||||qQ||d0<t|jdkrt|dkrtd1|r|d|d2<nDttt|}|d|d2<n6t|jd krt|d3krtd4|r|d|d5<|d|d2<nttt|}|d|d5<|d|d2<t|jdkr2t|dkrtd6|r|d|d7<|d|d5<|d3|d2<nWttt|}|d|d7<|d|d5<|d3|d2<n=t rBt|t rB|}n.t|t rPd)|_ ||d8<n t|ts\t|trl|dkretd9t||d:<ntd;t|t rtd<d)|_ ||d*<n |durtd=t rg}|D]\}}||||qt|}| dkrtj||g|R}|S| dkrtj||g|R}|S| dkrtj||g|R}|S| dkrtj||g|R}|S| d>krtj ||g|R}|S| | }| j!d| |d?|i|d@|S)Aa/ This op resizes a batch of images. The input must be a 3-D Tensor of the shape (num_batches, channels, in_w) or a 4-D Tensor of the shape (num_batches, channels, in_h, in_w) or (num_batches, in_h, in_w, channels), or a 5-D Tensor of the shape (num_batches, channels, in_d, in_h, in_w) or (num_batches, in_d, in_h, in_w, channels), and the resizing only applies on the three dimensions(depth, height and width). **Warning:** the parameter :attr:`actual_shape` will be deprecated in the future and only use :attr:`out_shape` instead. Supporting resample methods: 'LINEAR' : Linear interpolation 'BILINEAR' : Bilinear interpolation 'TRILINEAR' : Trilinear interpolation 'NEAREST' : Nearest neighbor interpolation 'BICUBIC' : Bicubic interpolation Linear interpolation is the method of using a line connecting two known quantities to determine the value of an unknown quantity between the two known quantities. Nearest neighbor interpolation is to perform nearest neighbor interpolation in both the 3rd dimension(in height direction) and the 4th dimension(in width direction) on input tensor. Bilinear interpolation is an extension of linear interpolation for interpolating functions of two variables (e.g. H-direction and W-direction in this op) on a rectilinear 2D grid. The key idea is to perform linear interpolation first in one direction, and then again in the other direction. Trilinear interpolation is an extension of linear interpolation for interpolating functions of three variables (e.g. D-direction, H-direction and W-direction in this op) on a rectilinear 3D grid. The linear interpolation is performed on three directions. Bicubic interpolation is an extension of cubic interpolation for interpolating data points on a two-dimensional regular grid. The interpolated surface is smoother than corresponding surfaces obtained by bilinear interpolation or nearest-neighbor interpolation. Align_corners and align_mode are optional parameters,the calculation method of interpolation can be selected by them. Example: .. code-block:: text For scale: if align_corners = True && out_size > 1 : scale_factor = (in_size-1.0)/(out_size-1.0) else: scale_factor = float(in_size/out_size) Nearest neighbor interpolation: if: align_corners = False input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = floor (H_{in} * scale_{factor}) W_out = floor (W_{in} * scale_{factor}) else: align_corners = True input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = round(H_{in} * scale_{factor}) W_out = round(W_{in} * scale_{factor}) linear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,W_in) output: (N,C,W_out) where: W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,W_in) output: (N,C,H_out,W_out) where: W_out = W_{in} * scale_{factor} Bilinear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = (H_{in}+0.5) * scale_{factor} - 0.5 W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = H_{in} * scale_{factor} W_out = W_{in} * scale_{factor} Trilinear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,D_in,H_in,W_in) output: (N,C,D_out,H_out,W_out) where: D_out = (D_{in}+0.5) * scale_{factor} - 0.5 H_out = (H_{in}+0.5) * scale_{factor} - 0.5 W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,D_in,H_in,W_in) output: (N,C,D_out,H_out,W_out) where: D_out = D_{in} * scale_{factor} Trilinear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,D_in,H_in,W_in) output: (N,C,D_out,H_out,W_out) where: D_out = (D_{in}+0.5) * scale_{factor} - 0.5 H_out = (H_{in}+0.5) * scale_{factor} - 0.5 W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,D_in,H_in,W_in) output: (N,C,D_out,H_out,W_out) where: D_out = D_{in} * scale_{factor} H_out = H_{in} * scale_{factor} W_out = W_{in} * scale_{factor} For details of linear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Linear_interpolation. For details of nearest neighbor interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation. For details of bilinear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Bilinear_interpolation. For details of trilinear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Trilinear_interpolation. For details of bicubic interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Bicubic_interpolation Parameters: input (Variable): 3-D, 4-D or 5-D Tensor, its data type is float32, float64, or uint8, its data format is specified by :attr:`data_format`. out_shape (list|tuple|Variable|None): Output shape of image resize layer, the shape is (out_w, ) when input is a 3-D Tensor, the shape is (out_h, out_w) when input is a 4-D Tensor and is (out_d, out_h, out_w) when input is a 5-D Tensor. Default: None. If a list, each element can be an integer or a Tensor Variable of shape: [1]. If a Tensor Variable, its dimensions size should be a 1. scale(float|Variable|None): The multiplier for the input height or width. At least one of :attr:`out_shape` or :attr:`scale` must be set. And :attr:`out_shape` has a higher priority than :attr:`scale`. Default: None. name(str|None): A name for this layer(optional). If set None, the layer will be named automatically. resample(str): The resample method. It supports 'LINEAR', 'BICUBIC', 'BILINEAR', 'TRILINEAR' and 'NEAREST' currently. Default: 'BILINEAR' actual_shape(Variable): An optional input to specify output shape dynamically. If provided, image resize according to this given shape rather than :attr:`out_shape` and :attr:`scale` specifying shape. That is to say actual_shape has the highest priority. It is recommended to use :attr:`out_shape` if you want to specify output shape dynamically, because :attr:`actual_shape` will be deprecated. When using actual_shape to specify output shape, one of :attr:`out_shape` and :attr:`scale` should also be set, otherwise errors would be occurred in graph constructing stage. Default: None align_corners(bool) : An optional bool, If True, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Default: True align_mode(int) : An optional for linear/bilinear/trilinear interpolation. Refer to the fomula in the the example code above, it can be '0' for src_idx = scale*(dst_indx+0.5)-0.5 , can be '1' for src_idx = scale*dst_index. data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from:`NCW`, `NWC`, `"NCHW"`, `"NHWC"`, `"NCDHW"`, `"NDHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`. Returns: A 3-D Tensor of the shape (num_batches, channels, out_w) or (num_batches, out_w, channels), A 4-D Tensor of the shape (num_batches, channels, out_h, out_w) or (num_batches, out_h, out_w, channels), or 5-D Tensor of the shape (num_batches, channels, out_d, out_h, out_w) or (num_batches, out_d, out_h, out_w, channels). Raises: TypeError: out_shape should be a list or tuple or Variable. TypeError: actual_shape should either be Variable or None. ValueError: The 'resample' of image_resize can only be 'LINEAR', 'BILINEAR', 'TRILINEAR', 'BICUBIC' or 'NEAREST' currently. ValueError: 'LINEAR' only support 3-D tensor. ValueError: 'BICUBIC', 'BILINEAR' and 'NEAREST' only support 4-D tensor. ValueError: 'TRILINEAR' only support 5-D tensor. ValueError: One of out_shape and scale must not be None. ValueError: out_shape length should be 1 for input 3-D tensor. ValueError: out_shape length should be 2 for input 4-D tensor. ValueError: out_shape length should be 3 for input 5-D tensor. ValueError: scale should be greater than zero. TypeError: align_corners should be a bool value ValueError: align_mode can only be '0' or '1' ValueError: data_format can only be 'NCW', 'NWC', 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'. Examples: .. code-block:: python #declarative mode import paddle import paddle.fluid as fluid import numpy as np paddle.enable_static() input = fluid.data(name="input", shape=[None,3,6,10]) #1 output = fluid.layers.image_resize(input=input,out_shape=[12,12]) #2 #x = np.array([2]).astype("int32") #dim1 = fluid.data(name="dim1", shape=[1], dtype="int32") #fluid.layers.assign(input=x, output=dim1) #output = fluid.layers.image_resize(input=input,out_shape=[12,dim1]) #3 #x = np.array([3,12]).astype("int32") #shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32") #fluid.layers.assign(input=x, output=shape_tensor) #output = fluid.layers.image_resize(input=input,out_shape=shape_tensor) #4 #x = np.array([0.5]).astype("float32") #scale_tensor = fluid.data(name="scale", shape=[1], dtype="float32") #fluid.layers.assign(x,scale_tensor) #output = fluid.layers.image_resize(input=input,scale=scale_tensor) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) input_data = np.random.rand(2,3,6,10).astype("float32") output_data = exe.run(fluid.default_main_program(), feed={"input":input_data}, fetch_list=[output], return_numpy=True) print(output_data[0].shape) #1 # (2, 3, 12, 12) #2 # (2, 3, 12, 2) #3 # (2, 3, 3, 12) #4 # (2, 3, 3, 5) #imperative mode import paddle.fluid.dygraph as dg with dg.guard(place) as g: input = dg.to_variable(input_data) output = fluid.layers.image_resize(input=input, out_shape=[12,12]) print(output.shape) # [2L, 3L, 12L, 12L] ZlinearZbilinearZ trilinearZnearest)LINEARr TRILINEARNEARESTrzdThe 'resample' of image_resize can only be 'LINEAR', 'BILINEAR', 'TRILINEAR' or 'NEAREST' currently.rr#z'LINER only support 3-D tensor.)rrrdz1'BILINEAR' and 'NEAREST' only support 4-D tensor.rrz#'TRILINEAR'only support 5-D tensor.z)Attr align_corners should be a bool valuerrzalign_mode can only be 0 or 1Nz,One of out_shape and scale must not be None.z {}_interp)NCWNWCz)Got wrong value for param `data_format`: z: received but only `NCW` or `NWC` supported for 3-D input.rgz< received but only `NCHW` or `NHWC` supported for 4-D input.rz> received but only `NCDHW` or `NDHWC` supported for 5-D input.cSst|tp t|tSrrs)datarrr_is_list_or_turple_sz)image_resize.._is_list_or_turple_rcrrrhrrrr)out_dout_hout_wZ interp_method align_corners align_moderTZOutSizez0out_shape should be a list or tuple or Variable.Fz>Each dimension size given in out_shape must be greater than 0.rr=Z SizeTensorz2out_shape length should be 1 for input 3-D tensor.rrz2out_shape length should be 2 for input 4-D tensor.rz2out_shape length should be 3 for input 5-D tensor.rrz(Attr(scale) should be greater than zero.rz4Attr(scale)'s type should be float, int or Variable.zactual_shape will be deprecated, it is recommended to use out_shape instead of actual_shape to specify output shape dynamically.z/actual_shape should either be Variable or None.Zbicubicrr)"rr5rrrrrQrrRrrr r rXrrIrrr[rrrKrZrrrQrr* linear_interpbilinear_interptrilinear_interpnearest_interpZbicubic_interpr)r out_shaperrresampler{rrrZresample_methodsZ resample_typer rrrrrr rZ contain_varr}rAZnew_size_tensorZ size_listrB attr_listrXr Zdy_attrrrrrrasj8                                  rarrc Ct||||d|||| S)af This op resizes the input by performing linear 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. Align_corners and align_mode are optional parameters,the calculation method of interpolation can be selected by them. Example: .. code-block:: text For scale: if align_corners = True && out_size > 1 : scale_factor = (in_size-1.0)/(out_size-1.0) else: scale_factor = float(in_size/out_size) Linear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,W_in) output: (N,C,W_out) where: W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,W_in) output: (N,C,W_out) where: W_out = W_{in} * scale_{factor} Parameters: input(Variable): 3-D Tensor(NCW), 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 linear layer, the shape is (out_w,). Default: None. If a list, each element can be an integer or a Tensor Variable with shape: [1]. If a Tensor Variable, its dimension size should be 1. scale(float|Variable|None): The multiplier for the input height or width. At least one of :attr:`out_shape` or :attr:`scale` must be set. And :attr:`out_shape` has a higher priority than :attr:`scale`. Default: None. actual_shape(Variable): An optional input to specify output shape dynamically. If provided, image resize according to this given shape rather than :attr:`out_shape` and :attr:`scale` specifying shape. That is to say actual_shape has the highest priority. It is recommended to use :attr:`out_shape` if you want to specify output shape dynamically, because :attr:`actual_shape` will be deprecated. When using actual_shape to specify output shape, one of :attr:`out_shape` and :attr:`scale` should also be set, otherwise errors would be occurred in graph constructing stage. Default: None align_corners(bool): ${align_corners_comment} align_mode(bool): ${align_mode_comment} data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCW"`, `"NWC"`. The default is `"NCW"`. When it is `"NCW"`, the data is stored in the order of: `[batch_size, input_channels, input_width]`. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: Variable: 3-D tensor(NCW or NWC). Examples: .. code-block:: python #declarative mode import paddle.fluid as fluid import numpy as np input = fluid.data(name="input", shape=[None,3,100]) output = fluid.layers.resize_linear(input=input,out_shape=[50,]) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) input_data = np.random.rand(1,3,100).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, 3, 50) #imperative mode import paddle.fluid.dygraph as dg with dg.guard(place) as g: input = dg.to_variable(input_data) output = fluid.layers.resize_linear(input=input, out_shape=[50,]) print(output.shape) # [1L, 3L, 50L] rrarrrrr{rrrrrrrcs{rcrc Cr)a This op resizes the input by performing bilinear interpolation based on given output shape which specified by actual_shape, out_shape and scale in priority order. **Warning:** the parameter :attr:`actual_shape` will be deprecated in the future and only use :attr:`out_shape` instead. Bilinear interpolation is an extension of linear interpolation for interpolating functions of two variables (e.g. H-direction and W-direction in this op) on a rectilinear 2D grid. The key idea is to perform linear interpolation first in one direction, and then again in the other direction. For details of bilinear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Bilinear_interpolation Align_corners and align_mode are optional parameters,the calculation method of interpolation can be selected by them. Example: .. code-block:: text For scale: if align_corners = True && out_size > 1 : scale_factor = (in_size-1.0)/(out_size-1.0) else: scale_factor = float(in_size/out_size) Bilinear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = (H_{in}+0.5) * scale_{factor} - 0.5 W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = H_{in} * scale_{factor} W_out = W_{in} * scale_{factor} Parameters: input(Variable): 4-D Tensor(NCHW), its data type is float32, float64, or uint8, its data format is specified by :attr:`data_format`. out_shape(list|tuple|Variable|None): Output shape of resize bilinear layer, the shape is (out_h, out_w).Default: None. If a list, each element can be an integer or a Tensor Variable with shape: [1]. If a Tensor Variable, its dimension size should be 1. scale(float|Variable|None): The multiplier for the input height or width. At least one of :attr:`out_shape` or :attr:`scale` must be set. And :attr:`out_shape` has a higher priority than :attr:`scale`. Default: None. actual_shape(Variable): An optional input to specify output shape dynamically. If provided, image resize according to this given shape rather than :attr:`out_shape` and :attr:`scale` specifying shape. That is to say actual_shape has the highest priority. It is recommended to use :attr:`out_shape` if you want to specify output shape dynamically, because :attr:`actual_shape` will be deprecated. When using actual_shape to specify output shape, one of :attr:`out_shape` and :attr:`scale` should also be set, otherwise errors would be occurred in graph constructing stage. Default: None align_corners(bool): ${align_corners_comment} align_mode(bool): ${align_mode_comment} data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: Variable: 4-D tensor(NCHW or NHWC). Examples: .. code-block:: python #declarative mode import paddle.fluid as fluid import numpy as np import paddle paddle.enable_static() input = fluid.data(name="input", shape=[None,3,6,10]) #1 output = fluid.layers.resize_bilinear(input=input,out_shape=[12,12]) #2 #x = np.array([2]).astype("int32") #dim1 = fluid.data(name="dim1", shape=[1], dtype="int32") #fluid.layers.assign(input=x, output=dim1) #output = fluid.layers.resize_bilinear(input=input,out_shape=[12,dim1]) #3 #x = np.array([3,12]).astype("int32") #shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32") #fluid.layers.assign(input=x, output=shape_tensor) #output = fluid.layers.resize_bilinear(input=input,out_shape=shape_tensor) #4 #x = np.array([0.5]).astype("float32") #scale_tensor = fluid.data(name="scale", shape=[1], dtype="float32") #fluid.layers.assign(x,scale_tensor) #output = fluid.layers.resize_bilinear(input=input,scale=scale_tensor) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) input_data = np.random.rand(2,3,6,10).astype("float32") output_data = exe.run(fluid.default_main_program(), feed={"input":input_data}, fetch_list=[output], return_numpy=True) print(output_data[0].shape) #1 # (2, 3, 12, 12) #2 # (2, 3, 12, 2) #3 # (2, 3, 3, 12) #4 # (2, 3, 3, 5) #imperative mode import paddle.fluid.dygraph as dg with dg.guard(place) as g: input = dg.to_variable(input_data) output = fluid.layers.resize_bilinear(input=input, out_shape=[12,12]) print(output.shape) # [2L, 3L, 12L, 12L] rrrrrrrd s#rdrc Cr)a This op resizes the input by performing trilinear interpolation based on given output shape which specified by actual_shape, out_shape and scale in priority order. **Warning:** the parameter :attr:`actual_shape` will be deprecated in the future and only use :attr:`out_shape` instead. Trilinear interpolation is an extension of linear interpolation for interpolating functions of three variables (e.g. D-direction, H-direction and W-direction in this op) on a rectilinear 3D grid. The linear interpolation is performed on three directions. For details of trilinear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Trilinear_interpolation Align_corners and align_mode are optional parameters,the calculation method of interpolation can be selected by them. Example: .. code-block:: text For scale: if align_corners = True && out_size > 1 : scale_factor = (in_size-1.0)/(out_size-1.0) else: scale_factor = float(in_size/out_size) Bilinear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,D_in,H_in,W_in) output: (N,C,D_out,H_out,W_out) where: D_out = (D_{in}+0.5) * scale_{factor} - 0.5 H_out = (H_{in}+0.5) * scale_{factor} - 0.5 W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,D_in,H_in,W_in) output: (N,C,D_out,H_out,W_out) where: D_out = D_{in} * scale_{factor} H_out = H_{in} * scale_{factor} W_out = W_{in} * scale_{factor} Parameters: input(${x_type}): 5-D Tensor, its data type is float32, float64, or uint8, its data format is specified by :attr:`data_format`. out_shape(list|tuple|Variable|None): The output shape of resized tensor, the shape is (out_d, out_h, out_w). Default: None. Every element should be an integer or a Tensor Variable with shape: [1] if it is a list. If it is a Tensor Variable, its dimension size should be 1. scale(float|Variable|None): The multiplier for the input depth, height or width. At least one of :attr:`out_shape` or :attr:`scale` must be set. And :attr:`out_shape` has a higher priority than :attr:`scale`. Default: None. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` actual_shape(Variable): An optional input to specify output shape dynamically. If provided, image resize according to this given shape rather than :attr:`out_shape` and :attr:`scale` specifying shape. That is to say actual_shape has the highest priority. It is recommended to use :attr:`out_shape` if you want to specify output shape dynamically, because :attr:`actual_shape` will be deprecated. When using actual_shape to specify output shape, one of :attr:`out_shape` and :attr:`scale` should also be set, otherwise errors would be occurred in graph constructing stage. Default: None align_corners(bool): ${align_corners_comment} align_mode(bool): ${align_mode_comment} data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCDHW"`, `"NDHWC"`. The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`. Returns: Variable: A 5-D Tensor(NCDHW or NDHWC) Examples: .. code-block:: python #declarative mode import paddle.fluid as fluid import paddle import numpy as np paddle.enable_static() input = fluid.data(name="input", shape=[None,3,6,8,10]) #1 output = fluid.layers.resize_trilinear(input=input,out_shape=[12,12,12]) #2 #x = np.array([2]).astype("int32") #dim1 = fluid.data(name="dim1", shape=[1], dtype="int32") #fluid.layers.assign(input=x, output=dim1) #output = fluid.layers.resize_trilinear(input=input,out_shape=[12,dim1,4]) #3 #x = np.array([3,12,12]).astype("int32") #shape_tensor = fluid.data(name="shape_tensor", shape=[3], dtype="int32") #fluid.layers.assign(input=x, output=shape_tensor) #output = fluid.layers.resize_trilinear(input=input,out_shape=shape_tensor) #4 #x = np.array([0.5]).astype("float32") #scale_tensor = fluid.data(name="scale", shape=[1], dtype="float32") #fluid.layers.assign(x,scale_tensor) #output = fluid.layers.resize_trilinear(input=input,scale=scale_tensor) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) input_data = np.random.rand(2,3,6,8,10).astype("float32") output_data = exe.run(fluid.default_main_program(), feed={"input":input_data}, fetch_list=[output], return_numpy=True) print(output_data[0].shape) #1 # (2, 3, 12, 12, 12) #2 # (2, 3, 12, 2, 4) #3 # (2, 3, 3, 12, 12) #4 # (2, 3, 3, 4, 5) #imperative mode import paddle.fluid.dygraph as dg with dg.guard(place) as g: input = dg.to_variable(input_data) output = fluid.layers.resize_trilinear(input=input, out_shape=[12,12,12]) print(output.shape) # [2L, 3L, 12L, 12L, 12L] rrrrrrre s %rerc Cst||||d||d|d S)aU This op resizes the input by performing nearest neighbor interpolation in both the height direction and the width direction based on given output shape which is specified by actual_shape, out_shape and scale in priority order. **Warning:** the parameter :attr:`actual_shape` will be deprecated in the future and only use :attr:`out_shape` instead. Example: .. code-block:: text For scale: if align_corners = True && out_size > 1 : scale_factor = (in_size-1.0)/(out_size-1.0) else: scale_factor = float(in_size/out_size) Nearest neighbor interpolation: if: align_corners = False input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = floor(H_{in} * scale_{factor}) W_out = floor(W_{in} * scale_{factor}) else: align_corners = True input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = round(H_{in} * scale_{factor}) W_out = round(W_{in} * scale_{factor}) For details of nearest neighbor interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation Parameters: input(${x_type}): 4-D Tensor, its data type is float32, float64, or uint8, its data format is specified by :attr:`data_format`. out_shape(list|tuple|Variable|None): The output shape of resized tensor, the shape is (out_h, out_w). Default: None. Every element should be an integer or a tensor Variable with shape: [1] if it is a list. If it is a tensor Variable, its dimension size should be 1. scale(float|Variable|None): The multiplier for the input height or width. At least one of :attr:`out_shape` or :attr:`scale` must be set. And :attr:`out_shape` has a higher priority than :attr:`scale`. Default: None. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` actual_shape(Variable): An optional input to specify output shape dynamically. If provided, image resize according to this given shape rather than :attr:`out_shape` and :attr:`scale` specifying shape. That is to say actual_shape has the highest priority. It is recommended to use :attr:`out_shape` if you want to specify output shape dynamically, because :attr:`actual_shape` will be deprecated. When using actual_shape to specify output shape, one of :attr:`out_shape` and :attr:`scale` should also be set, otherwise errors would be occurred in graph constructing stage. Default: None align_corners(bool): ${align_corners_comment} data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. Returns: Variable: 4-D tensor(NCHW or NHWC). Examples: .. code-block:: python #declarative mode import paddle.fluid as fluid import numpy as np import paddle paddle.enable_static() input = fluid.data(name="input", shape=[None,3,6,10]) #1 output = fluid.layers.resize_nearest(input=input,out_shape=[12,12]) #2 #x = np.array([2]).astype("int32") #dim1 = fluid.data(name="dim1", shape=[1], dtype="int32") #fluid.layers.assign(input=x, output=dim1) #output = fluid.layers.resize_nearest(input=input,out_shape=[12,dim1]) #3 #x = np.array([3,12]).astype("int32") #shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32") #fluid.layers.assign(input=x, output=shape_tensor) #output = fluid.layers.resize_nearest(input=input,out_shape=shape_tensor) #4 #x = np.array([0.5]).astype("float32") #scale_tensor = fluid.data(name="scale", shape=[1], dtype="float32") #fluid.layers.assign(x,scale_tensor) #output = fluid.layers.resize_nearest(input=input,scale=scale_tensor) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) input_data = np.random.rand(2,3,6,10).astype("float32") output_data = exe.run(fluid.default_main_program(), feed={"input":input_data}, fetch_list=[output], return_numpy=True) print(output_data[0].shape) #1 # (2, 3, 12, 12) #2 # (2, 3, 12, 2) #3 # (2, 3, 3, 12) #4 # (2, 3, 3, 5) #imperative mode import paddle.fluid.dygraph as dg with dg.guard(place) as g: input = dg.to_variable(input_data) output = fluid.layers.resize_nearest(input=input, out_shape=[12,12]) print(output.shape) # [2L, 3L, 12L, 12L] rr)rrr)rrrrr{rrrrrrfN!srfcCs|j}t|dkr td|dd}|t|}d|}t|}|||<tt||t|t||d||<t|||dS)a 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) rdz@The rank of input must be 4 (num_batches, channels, in_h, in_w).rrr)rrr) rrr5rLr5rr[rZra)rZ out_short_lenrZin_shapeZhwZ short_idxZlong_idxrrrrrb!s$   rbz paddle.gathercCstr t||dd|St|dgddt|dddgdtd it}|}||}|jd||d d |id|id |S) a 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 (Tensor): 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 (Tensor): 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 (Tensor): The output is a tensor with the same rank as input. Examples: .. code-block:: python import paddle import paddle.fluid as fluid paddle.enable_static() 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) N overwriter)rrrrruint8rgrLrrrIndexrr)rg) r r*rgr&rrrrr)rrLrr rrrrrrg"s$5 rgzpaddle.gather_ndcCstr t||Strt||St|dgddt|dddgdtd it}|}| |}|j d||dd |id |S) a **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 (Tensor): The input Tensor which it's data type should be bool, float32, float64, int32, int64. index (Tensor): The index input with rank > 1, index.shape[-1] <= input.rank. Its dtype should be int32, 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: output (Tensor): A tensor with the shape index.shape[:-1] + input.shape[index.shape[-1]:] Examples: .. code-block:: python import paddle import paddle.fluid as fluid paddle.enable_static() 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) r)rrrrzrrZ gather_nprLrrrhrrrBN)rh) rr)rhrr*r&rrrrr)rrLrr routputrrrrh`"s&K   rhzpaddle.scattercCsHtdit}|}||}|jd|||dd|id|id|S)a :alias_main: paddle.scatter :alias: paddle.scatter,paddle.tensor.scatter,paddle.tensor.manipulation.scatter :old_api: paddle.fluid.layers.scatter **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 should 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 paddle import numpy as np import paddle.fluid as fluid paddle.enable_static() 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)] ri)rrUpdatesrrrnN)ri)rrrrr)rrLupdatesrrr rrrrrri"sN ricCstr t|||Strttd}||||S|j|jkr"tdtd it }|j dd}| |}|j d|||dd|id|S) ap **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 import paddle paddle.enable_static() 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) rjz)ref and updates must have same data type.refr)rrrrrBN)rj) rr)rjrrr*rr5rrrrr)rrLrrrr rrrrrrj#s$D     rjcCstt||j|||S)aA **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(paddle.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 (Tensor): The index input with ndim > 1 and index.shape[-1] <= len(shape). Its dtype should be int32 or int64 as it is used as indexes. updates (Tensor): The updated value of scatter_nd op. Its dtype should be 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 Tensor name. If set None, the layer will be named automatically. Returns: output (Tensor): The output is a tensor with the same type as :attr:`updates` . Examples: .. code-block:: python import paddle import numpy as np index_data = np.array([[1, 1], [0, 1], [1, 3]]).astype(np.int64) index = paddle.to_tensor(index_data) updates = paddle.rand(shape=[3, 9, 10], dtype='float32') shape = [3, 5, 9, 10] output = paddle.scatter_nd(index, updates, shape) )rjrr)rLrrrrrrrkw#s(rkcCstdit}t|dgddt|dttfd|j}||}|dur-tj dd}d|i}t |t rF||d<|j td d d d }n t |tsOtd |jd||d||d|d|S)a0 ${comment} Args: x(${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: ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid img = fluid.data("img", [None, 3, 256, 256]) # cropped_img is [-1, 3, 224, 224] cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224]) # cropped_img2 shape: [-1, 2, 224, 224] # cropped_img2 = fluid.layers.random_crop(img, shape=[2, 224, 224]) # cropped_img3 shape: [-1, 3, 128, 224] # cropped_img3 = fluid.layers.random_crop(img, shape=[128, 224]) rlr)rrrrzrrNiiZ startup_seedZrandom_crop_seedrT)rrr%z$'seed' must be a Variable or an int.)rZSeed)rZSeedOutr)rl)rrr&r'rr rrrrandomrandintrr[r r generater5r)rrrNr rrZop_attrsrrrrl#s<    rlcCs~trt|Strt|St|dddgdd|gi}td it}|jdd}| |}|j dd|id|id|S) a Calculates the natural log of the given input tensor, element-wise. .. math:: Out = \\ln(x) Args: x (Tensor): Input 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: Tensor: The natural log of the input Tensor computed element-wise. Examples: .. code-block:: python import paddle x = [[2,3,4], [7,8,9]] x = paddle.to_tensor(x, dtype='float32') res = paddle.log(x) # [[0.693147, 1.09861, 1.38629], [1.94591, 2.07944, 2.19722]] rrrrprrrrBN)rp) rr)rprr*r&rrrrrrrrr rrrrrrp#s     rpzpaddle.nn.functional.relucCstrt|Strt|St|dgddd|gi}td it}|jdd}| |}|j dd| did|id|S) a ${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: ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np in1 = np.array([[-1,0],[1,2.6]]) with fluid.dygraph.guard(): x1 = fluid.dygraph.to_variable(in1) out1 = fluid.layers.relu(x1) print(out1.numpy()) # [[0. 0. ] # [1. 2.6]] rrVrnrrrrBN)rn) rr)rnrr*r&rrrrrrrrrrrn$s      rnzpaddle.nn.functional.selucCszt|dddgdtd it}|jdd}||}i}|dur&||d<|dur.||d<|jdd |id |i|d |S) a 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 import paddle.fluid as fluid import numpy as np paddle.enable_static() 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)] rrrrorNrrErrr)ro)r&rrrrr)rrrErr rrrrrrro5$s7  rocCstr t||d|Std it}t|dddgdt|dddgd|jdd}|jdd}|jdd}|jd||d |||d d|id |||fS)aN Mean Intersection-Over-Union is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes. IOU is defined as follows: .. math:: IOU = \\frac{true\_positive}{(true\_positive + false\_positive + false\_negative)}. The predictions are accumulated in a confusion matrix and mean-IOU is then calculated from it. Parameters: input (Tensor): A n-D Tensor of prediction results for semantic labels with type int32 or int64. label (Tensor): A Tensor of ground truth labels with type int32 or int64. Its shape should be the same as input. num_classes (int32): The possible number of labels. Returns: Three Tensors. - mean_iou(Tensor) : A 1-D Tensor representing the mean intersection-over-union with shape [1]. \ Data type is float32. - out_wrong(Tensor) : A 1-D Tensor with shape [num_classes]. Data type is int32. \ The wrong numbers of each class. - out_correct(Tensor): A 1-D Tensor with shape [num_classes]. Data type is int32. The correct numbers of each class. Examples: .. code-block:: python import paddle iou_shape = [64, 32, 32] num_classes = 5 predict = paddle.randint(low=0, high=255, shape=iou_shape, dtype='int64') label = paddle.randint(low=0, high=255, shape=iou_shape, dtype='int64') mean_iou, out_wrong, out_correct = paddle.metric.mean_iou(predict, label, num_classes) num_classesrm PredictionsrrLabelsrr=)rr)Z OutMeanIouZOutWrongZ OutCorrectrN)rm)r r*rmrrr&rr)rr;rr Z out_mean_iouZ out_wrongZ out_correctrrrrm~$s*+     rmcCst|ddgdt|dtttfdtd it}|dur&dgt|j}| |j }d|i}i}t |tr<||d<n||d<t |trJ||d <n||d <|j d|d |it|dkr]dn|d |S)a* 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, optional): 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, optional): 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: Tensor, The cropped Tensor, which has the same rank and data type with `x`. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid as fluid import paddle paddle.enable_static() 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]) rrrqrNrrrOffsetsoffsetsrr)rq) r&r'rrr rrrrrrrr)rrrrr riptsrrrrrq$s(J     rqcCsXtdit}t|dgddt|dtttfdt|dttttdfd|dur4dgt|j }| |j }d|i}i}d d }d d } t |tr_d |_ ||d<dgt|j |d<nTt|rg} g} |D]2} t | trd | _ | | | dqj| | | d} tdgd| d | d| | | | qj| |d<| |d<n |D]}| |q||d<t |trd |_ ||d<nVt|rg}g}|D]2}t |trd |_ |||dq||| d} tdgd|d | d|| ||q||d<||d<n|D]}||q ||d<|jd|d|it|dkr&dn|d|S)a 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 (Tensor): 1-D to 6-D Tensor, the data type is float32, float64, int32 or int64. shape (list|tuple|Tensor): 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 Tensor, it should 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 Tensor, it should 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: Tensor: The cropped Tensor has same data type with `x`. Examples: .. code-block:: python :name: code-example1 import paddle x = paddle.to_tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # x.shape = [3, 3] # x = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] # shape can be a 1-D Tensor or list or tuple. shape = paddle.to_tensor([2, 2], dtype='int32') # shape = [2, 2] # shape = (2, 2) out = paddle.crop(x, shape) # out.shape = [2, 2] # out = [[1,2], [4,5]] # offsets can be a 1-D Tensor or list or tuple. offsets = paddle.to_tensor([0, 1], dtype='int32') # offsets = [1, 0] # offsets = (1, 1) out = paddle.crop(x, shape, offsets) # out.shape = [2, 2] # if offsets = [0, 0], out = [[1,2], [4,5]] # if offsets = [0, 1], out = [[2,3], [5,6]] # if offsets = [1, 0], out = [[4,5], [7,8]] # if offsets = [1, 1], out = [[5,6], [8,9]] rrrr6rrNrrcSsNt|ts tdt||dkrtdt||dkr%tdt|dS)NzIAttr(shape)'s dtype of Op(crop_tensor) should be int32, but received: %s.rzDAttr(shape) of Op(crop_tensor) should not be zero, but received: %s.rzgWhen the element in Attr(shape) of Op(crop_tensor) is negative, only -1 is supported, but received: %s.rr[rQrr5r)Z shape_valrrr_attr_shape_check%s& z&crop_tensor.._attr_shape_checkcSs6t|ts tdt||dkrtdt|dS)NzKAttr(offsets)'s dtype of Op(crop_tensor) should be int32, but received: %s.rzVAttr(offsets) of Op(crop_tensor) should be greater or equal to zero, but received: %s.r)Z offset_valrrr_attr_offsets_check%s z(crop_tensor.._attr_offsets_checkTrrrrr=Z OffsetsTensorrrrr)rr)rrr&r'rrr rrrrrrrXrr(rrr)rrrrr rrrrrZnew_offsets_tensorZ offsets_attrrrBoffsetZnew_shape_tensorZ shape_attrrArrrrr%%sX                       rrcCstd}t|dddgdt|ts t|ts t|ts tdt|ts)td||j}d|i}i}t|trG||d<t|d d gdn||d <t rSd |d <|j d|d|it |dkrbdn|d|S)a :alias_main: paddle.nn.functional.affine_grid :alias: paddle.nn.functional.affine_grid,paddle.nn.functional.vision.affine_grid :old_api: paddle.fluid.layers.affine_grid 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]) rthetarrz2The out_shape should be a list, tuple or Variable.zThe theta should be a Variable.ThetaZ OutputShaperrZ output_shapeFrbrrNr) rr&rrrr r5rrr"rrr)rrrr rrrrrrr%s6-     rr#constantc Cstrt|tr|n|}t|d|d|d|d| St|dgdd|||d}d |gi}t|tr@|g|d <g|d<n||d<tdit } |d vsTJd | j dd } | | } | j d|d| i|d| S)a Pad 2-d images according 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 (Tensor): 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 (Tensor | 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: Tensor, a 4-D Tensor padded according to paddings and mode and data type is same as input. Examples: .. code-block:: text Input = [[[[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 numpy as np import paddle import paddle.nn.functional as F # example 1 x_shape = (1, 1, 3, 4) x = np.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape) + 1 tensor_x = paddle.to_tensor(x) y = paddle.fluid.layers.pad2d(tensor_x, paddings=[1, 2, 2, 1], pad_value=1, mode='constant') print(y.numpy()) # [[[[ 1. 1. 1. 1. 1. 1. 1.] # [ 1. 1. 1. 2. 3. 4. 1.] # [ 1. 1. 5. 6. 7. 8. 1.] # [ 1. 1. 9. 10. 11. 12. 1.] # [ 1. 1. 1. 1. 1. 1. 1.] # [ 1. 1. 1. 1. 1. 1. 1.]]]] # example 2 x_shape = (1, 1, 2, 3) x = np.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape) + 1 tensor_x = paddle.to_tensor(x) y = paddle.fluid.layers.pad2d(tensor_x, paddings=[1, 1, 1, 1], mode='reflect') print(y.numpy()) # [[[[5. 4. 5. 6. 5.] # [2. 1. 2. 3. 2.] # [5. 4. 5. 6. 5.] # [2. 1. 2. 3. 2.]]]] moderrrrrr)rrrrZPaddings)Zreflectedgerz.mode should be one of constant, reflect, edge.rrrN)r) r rr rIrr*rr&rrrrr) rrrrrrZ _paddingsrrr rrrrrr,&s@\         rzpaddle.nn.functional.elucCsRtd it}t|dgdd|j|jd}|jdd|id|id|id|S) a :alias_main: paddle.nn.functional.elu :alias: paddle.nn.functional.elu,paddle.nn.functional.activation.elu :old_api: paddle.fluid.layers.elu ${comment} Args: x(${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 ]] rsrrVr=rrrErN)rs)rrr&rrr)rrErr rrrrrs&srszpaddle.nn.functional.relu6@cCsZt|dgddtd it}|j|jd}|jdd|id|i|tddd |S) a ${comment} Args: x(${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}): ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np in1 = np.array([[-1,0],[2.5,7.8]]) with fluid.dygraph.guard(): x1 = fluid.dygraph.to_variable(in1) out1 = fluid.layers.relu6(x=x1, threshold=6.0) print(out1.numpy()) # [[0. 0. ] # [2.5 6. ]] rrVrtr=rrFLAGS_use_mkldnn) thresholdrrN)rt)r&rrrrrrrrrr rrrrrt&srtcCst|dgddtd it}d|i}i}t|tr,t|ddgdd|_||d<n||d<|j|jd }|jd|d |i|d |S)al This is Pow Activation Operator. :math:`out = x^{factor}` Args: x(Variable): A ``Tensor`` or ``LoDTensor`` . The data type is ``float32`` or ``float64``. factor(float32|Variable, optional): A scalar with type ``float32`` or a ``Tensor`` with shape [1] and type ``float32``. The exponential factor of Pow. Default 1.0. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Variable: A ``Tensor`` or ``LoDTensor``. The data type is same as ``x``. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name="x", shape=[32,32], dtype="float32") # example 1: argument factor is float y_1 = fluid.layers.pow(x, factor=2.0) # y_1 is x^{2.0} # example 2: argument factor is Variable factor_tensor = fluid.layers.fill_constant([1], "float32", 3.0) y_2 = fluid.layers.pow(x, factor=factor_tensor) # y_2 is x^{3.0} r)rrrrrrurfactorrTZ FactorTensorr=rrN)ru) r&rrrr rXrrr)rrrr rrrrrrru's$   ruq= ףp?jMSt?cCltr t|d|d|St|dgddtd it}|j|jd}|jdd|id|i||d d |S) aQ stanh activation. .. math:: out = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}} Parameters: x (Tensor): The input Tensor with data type float32, float64. scale_a (float, optional): The scale factor a of the input. Default is 0.67. scale_b (float, optional): The scale factor b of the output. Default is 1.7159. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python import paddle x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0]) out = paddle.stanh(x, scale_a=0.67, scale_b=1.72) # [1.00616539, 1.49927628, 1.65933108, 1.70390463] scale_ascale_brrVrvr=rr)rrrN)rv) r r*rvr&rrrrr)rrrrr rrrrrv5'srv皙?rcCr) a ${comment} Parameters: x (${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 import paddle paddle.enable_static() 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]] sloperrrVrwr=rr)rrrN)rw) r r*rwr&rrrrr)rrrrr rrrrrwc's rwcCRt|dgddtd it}|j|jd}|jdd|id|id|id|S) a :alias_main: paddle.nn.functional.swish :alias: paddle.nn.functional.swish,paddle.nn.functional.activation.swish :old_api: paddle.fluid.layers.swish 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) rrVrxr=rrrrN)rxrI)rrrr rrrrrx'sIrxzpaddle.static.nn.preluc CsZt|dgddtdit}|dvrtddg}|dkr\gd}||vr/td ||dd kr7d nd }t|jd ksDJd|d krRddd|jdg}n%d|jdddg}n|dkrwt|jdkskJddgt|jdd}|jdd}|j |j ||dt dd} t rt || ||S||} |jd|| d||dd| id| S)a prelu activation. .. math:: prelu(x) = 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. Parameters: x (Tensor): The input Tensor or LoDTensor with data type float32. mode (str): The mode for weight sharing. param_attr (ParamAttr|None, optional): 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, optional): Name for the operation (optional, default is None). \ For more information, please refer to :ref:`api_guide_Name`. data_format(str, optional): Data format that specifies the layout of input. It may be "NC", "NCL", "NCHW", "NCDHW", "NLC", "NHWC" or "NDHWC". Default: "NCHW". Returns: Tensor: A tensor with the same shape and data type as x. Examples: .. code-block:: python import paddle x = paddle.to_tensor([-1., 2., 3.]) param = paddle.ParamAttr(initializer=paddle.nn.initializer.Constant(0.2)) out = paddle.static.nn.prelu(x, 'all', param) # [-0.2, 2., 3.] rrVry)r7channelelementz,mode should be one of all, channel, element.rr)ZNCNCLrcrZNLCrhrz^data_format must be one of 'NC', 'NCL', 'NCHW', 'NCDHW', 'NLC', 'NHWC', 'NDHWC' but receive {}CrcrhrzZThe size of input shape should be equal or larger than 2 in prelu() when mode is 'channel'rrzZThe size of input shape should be equal or larger than 1 in prelu() when mode is 'element'NrF?r)rrA)rrrrn)ry)r&rrr5rRrrrrrrrrr)ryrr) rrrrrr Z alpha_shapeZtrue_data_formatrrErrrrry'sh/   ry8@cCr) a ${comment} Args: x(${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 paddle import numpy as np paddle.enable_static() 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.]] t_mint_maxrrVrzr=rr)rrrN)rz) r r*rzr&rrrrr)rrrrr rrrrrzM(srzzpaddle.nn.functional.leaky_relu{Gz?cCstjj|||S)aT ${comment} Args: x(${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 x = paddle.to_tensor([[-1, 2], [3, -4]], dtype='float32') y = paddle.fluid.layers.leaky_relu(x, alpha=0.1) print(y) # [[-0.1, 2], [3, -0.4]] )rrrr{)rrErrrrr{|(sr{D@cCr) a 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 import numpy as np import paddle paddle.enable_static() 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)] rrVr|r=rrrrN)r|rIrrrrr|(s $r|cCst|dgddtrt|d|dStd it}t|ts&tdt|t r6|t |j ks6|dkr:td| |j }| |j }|jdd |i||d d|id |S)a9 **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, uint8. 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 import paddle.fluid as fluid paddle.enable_static() 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] r)rrrrrrr}rrz The input x should be a Variablez3The axis should be a int, and in range [0, rank(x)]flatten2rrerN)r})r&r r*rrrrr r5r[rrrrr)rrrr rrSrrrr}(s*@     r}cCsT|durdn|}trt||Strt|d|St|tsDt|tsDt|tr8|j t j j jkr8|g}n tddddt |ftdit}||dj}|dj t j j jkrt|dksnJd t||jd d }|D] }t|dgd dqv|jd d|di|g|gd|ddd|S|jdd|id|id|id|S)ab 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 (list(Variable)|tuple(Variable)): Input :code:`x` can be a :code:`list` or :code:`tuple` of Tensors, 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}]`. Supported data types: float32, float64, int32, int64. axis (int, optional): The axis along which all inputs are stacked. ``axis`` range is ``[-(R+1), R+1)``, where ``R`` is the number of dimensions of the first input tensor ``x[0]``. If ``axis < 0``, ``axis = axis+R+1``. The default value of axis is 0. name (str, optional): Please refer to :ref:`api_guide_Name`, Default None. 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] Nrrz2The type of '%s' in %s must be %s, but received %srr~z*list[Tensor], tuple[Tensor] or TensorArrayrzpIf the elements of 'x' in stack are Variable(LoDTensorArray), number of the elements must be 1, but received %s.rr=rrr)rZOutIndexT)rZ use_stackrr)r~)rr)r~rr*rrrr descrr"rFrGZLOD_TENSOR_ARRAYrQrrrrrr&r)rrrr rZ out_indexr rrrr~!)sXO   r~rc Csjtdit}|j|jd}|jtjd}|j|jd}|jd|||d|||d||dd||gS) a **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. out_val_if_empty(Int64): If the output after filter is empty, this value will be set to Output tensor. 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) rr=)ZInsZIns_tagZ Filter_tag)rZ LossWeightZIndexMap)is_lodout_val_if_emptyrN)r)rrrrrrr) ZinsZins_tagZ filter_tagrrr rZ loss_weightmmaprrrr)s$6cCstr|dkr |j|}|dkrgSt||dt|d|Std it}|dur>|dus5|j|dkr9td|j|}g}t|D] }| | |j qD|j dd|gid|i||d d |S) a :alias_main: paddle.unstack :alias: paddle.unstack,paddle.tensor.unstack,paddle.tensor.manipulation.unstack :old_api: paddle.fluid.layers.unstack **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 (Tensor): 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(Tensor): 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 x = paddle.ones(name='x', shape=[2, 3, 5], dtype='float32') # create a tensor with shape=[2, 3, 5] y = paddle.unstack(x, axis=1) # unstack with second axis, which results 3 tensors with shape=[2, 5] Nrrr8rzunknown unstack numberrr)rr8r)r) r rr*rr[rrr5r6rrrr)rrr8r r*rrrrr)s,#   rz paddle.expandc CsLtr0d}d}t|ttfrdd|D}|d|f7}n t|tr&|}d|_tj||g|RSd|gi}i}t|dgd d t |dtttfd t |j d krZ|jdkrZt d t dd |it}dd}t|trud|_||d<nt|ttfr|||d<t|rt||d<|jdd}||} |jd |d| i|d| S)a :alias_main: paddle.expand :alias: paddle.expand,paddle.tensor.expand,paddle.tensor.manipulation.expand :old_api: paddle.fluid.layers.expand 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]. rNcSrurvrwrxrrrr!q*ryzexpand.. expand_timesTrrr;rrz?expand op bool date type must set the stop_gradient to be FalsercSsJg}t|D]\}}t|tr|dq|||dks"Jdq|S)Nrrz8Each element given in expand_times must not be negative.)rrr r)Zlist_expand_timesZattrs_expand_timesr@timesrrrget_attr_expand_times*s    z%expand..get_attr_expand_timesZ ExpandTimesexpand_times_tensorrrr)r)r rrrr rXr*rr&r'r%rr5rrrr(r)rrr) rrrrrrr rrrrrrr0*sT=         rzpaddle.expand_ascCstr t||St|dgddt|dgddtd d|it}|jdd}||}||d}|jd|d|id |S) aD :alias_main: paddle.expand_as :alias: paddle.expand_as,paddle.tensor.expand_as,paddle.tensor.manipulation.expand_as :old_api: paddle.fluid.layers.expand_as 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 import paddle.fluid as fluid import numpy as np paddle.enable_static() 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) r)rrrrrr target_tensorrr)rrrrBN)r) r r*rr&rrrrr)rrrr rrrrrrr*s D    r)convert_np_dtype_to_dtype_z1.8.0zpaddle.uniformc Cs~t|dddt|dttfdt|dddtd it}||} t|} |j dd|id| i||||||| dd| S) a@ 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 import paddle.fluid as fluid paddle.enable_static() # 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] rrrrrrrr)r input_dim_idxoutput_dim_idxr5rrNrrN)r) r&r'rrr(rrrrr) rrrrrr5rrNr rc_dtyperrrr*s.H  rz paddle.normalc Cst|tjjs t|}tr$t|}t}t |t |t ||||St r>t|}t d|dt |dt |d|d| St|dtttfdt|dddgdi}||||d d }tj|||dd tdit} | |} | jd |d | i|d| S)a This OP returns a Tensor filled with random values sampled from a Gaussian distribution, with ``shape`` and ``dtype``. Args: shape(list|tuple|Tensor): The shape of the output Tensor. If ``shape`` is a list or tuple, the elements of it should be integers or Tensors (with the shape [1], and the data type int32 or int64). If ``shape`` is a Tensor, it should be a 1-D Tensor(with the data type int32 or int64). mean(float|int, optional): Mean of the output tensor, default is 0.0. std(float|int, optional): Standard deviation of the output tensor, default is 1.0. seed(int, optional): ${seed_comment} dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of the output Tensor. Supported data types: float32, float64. Default is float32. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor filled with random values sampled from a Gaussian distribution, with ``shape`` and ``dtype``. Examples: .. code-block:: python import paddle import paddle.fluid as fluid paddle.enable_static() # example 1: # attr shape is a list which doesn't contain Tensor. result_1 = fluid.layers.gaussian_random(shape=[3, 4]) # [[-0.31261674, 1.8736548, -0.6274357, 0.96988016], # [-0.12294637, 0.9554768, 1.5690808, -1.2894802 ], # [-0.60082096, -0.61138713, 1.5345167, -0.21834975]] # example 2: # attr shape is a list which contains Tensor. dim_1 = fluid.layers.fill_constant([1], "int64", 2) dim_2 = fluid.layers.fill_constant([1], "int32", 3) result_2 = fluid.layers.gaussian_random(shape=[dim_1, dim_2]) # [[ 0.51398206, -0.3389769, 0.23597084], # [ 1.0388143, -1.2015356, -1.0499583 ]] # example 3: # attr shape is a Tensor, the data type must be int64 or int32. var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64") result_3 = fluid.layers.gaussian_random(var_shape) # if var_shape's value is [2, 3] # result_3 is: # [[-0.12310527, 0.8187662, 1.923219 ] # [ 0.70721835, 0.5210541, -0.03214082]] .. code-block:: python # declarative mode # required: skiptest import numpy as np from paddle import fluid x = fluid.layers.gaussian_random((2, 3), std=2., seed=10) place = fluid.CPUPlace() exe = fluid.Executor(place) start = fluid.default_startup_program() main = fluid.default_main_program() exe.run(start) x_np, = exe.run(main, feed={}, fetch_list=[x]) x_np # array([[2.3060477, 2.676496 , 3.9911983], # [0.9990833, 2.8675377, 2.2279181]], dtype=float32) .. code-block:: python # imperative mode import numpy as np from paddle import fluid import paddle.fluid.dygraph as dg place = fluid.CPUPlace() with dg.guard(place) as g: x = fluid.layers.gaussian_random((2, 4), mean=2., dtype="float32", seed=10) x_np = x.numpy() x_np # array([[2.3060477 , 2.676496 , 3.9911983 , 0.9990833 ], # [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32) rrrrNrzgaussian_random/randnrrF)rrrNrrrrrrrrrN)r)rr"rFrGrrrr*rr)rrZrr*r'rrr r(get_shape_tensor_inputsrrrr) rrrrNrrZplacerrr rrrrr^+sJd     rcCs@tdit}||}|jdd|id|i|||dd|S)a$ 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) rrr)r5rrNrN)r)rrrr)rr5rrNrr rrrrr+s  rc Cstd it}t|dtdt|dttfdt|dgdd||} t|} |j dd|id| i||||||| d d | S) aG ${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 import paddle.fluid as fluid paddle.enable_static() 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) rrz,fluid.layers.gaussian_random_batch_size_likerr)rrr[rr)rrrrrrNrrN)r) rrr'r rrr(rrr) rrrrrrrNrr rrrrrr,s2'    rcCs t|S)a ${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: ${out_comment} Examples: .. code-block:: python import paddle.fluid as fluid input0 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=5) input1 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=3) sum = fluid.layers.sum([input0, input1]) # You can print out 'sum' via executor. out = fluid.layers.Print(sum, message="the sum of input0 and input1: ") exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_main_program()) # The printed result is: # 1570701754 the sum of input0 and input1: The place is:CPUPlace # Tensor[sum_0.tmp_0] # shape: [2,3,] # dtype: l # data: 8,8,8,8,8,8, # the sum of input0 and input1 is 2-D Tensor with shape [2,3]. # dtype is the corresponding C++ data type, which may vary in different environments. # Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t, # so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux, # and '__int64' on Windows. They both represent 64-bit integer variables. )rZadd_nr~rrrrU,s Erc s0trd}d}d}t|ttfrKt|}t|dkrtdtt|D]%}||dkr;td||t|j||<q$t t|jd||||<q$n td t |tddtt|D}t j jt|ttfrvfd d |D}nt|r|} d d | D}t|ttfrfd d |D}|d |f7}nt|r|} dd | D}t|||||gStrd}d}d}t|ttfrt|}t|dkrtdtt|D]%}||dkrtd||t|j||<qt t|jd||||<qn td t |tddtt|D}tt|ttfr4fdd |D}|d|f7}nt|rL|}d|_tddtt|D}t|ttfrdfdd |D}|d |f7}nt|r||}d|_tddtt|D}tj|||ddd|d|g |RSt|tttfstdt|tttfstdtd(it} d|i} d|i}tddtt|D}t|trd|_|| d<tddtt|D}nBt|ttfr!g|d<t|rt|| d<t|D]\}} t| tr|dd d ||<q|d| qn||d<t|tr,zslice..c(g|]}t|r|dn|qSrvrrIrJrxrrrr!, zslice..cSg|]}|qSrrr rurrrr!-crrvrrxrrrr! -s  endscSrrrrrrrr!-rcsrrrrrrrr)-rcrrvrrxrrrr!.-rstartsTcsrrNrrrrrr7-rcrrvrrxrrrr!:-rcsrr rrrrrrC-rr infer_flagsz7Input starts must be an Variable, python list or tuple.z5Input ends must be an Variable, python list or tuple.rrcsrrrrrrrrT-r StartsTensorcsrr rrrrrrZ-rStartsTensorListr EndsTensorcsrr rrrrrrl-rEndsTensorListrr=rrn)r) rrrrrr5r6rrr5rRrr"rrrIr)rrr rXr*rrrr(r)rrrrr)rrrrrZ starts_tensorZ ends_tensorr r Ztensor_tr rrrrrrr,s I                       rzpaddle.strided_slicec s tr t|||||Stditt|dgddt|dttfdt|dttt fdt|dttt fdt|dttt fddd }||d||d||d||dfd d }d |i}d|i}td dt t |D} t rd |i}||||| d}nt |t rd|_||d<n.check_list_elements_dtypecsdg}|D]+}t|trd|_||qt|tsJd}tdgd|d|d||q|S)NTrrr=)rr rXrr[rr)Zold_listZnew_list_tensorrrBrCrrget_new_list_tensor.s    z*strided_slice..get_new_list_tensorrcsrrrrrrrr.rz strided_slice..)rrrrr Tr r rr rZ StridesTensorZStridesTensorListr r=rrnN)r)rr)rrrr&r'rrr r6rr rrXrr(rrrrr) rrrrrrrrrr r rrrrCrr-se                       rcCstr t|}d|_|Strt|}d|_|St|dgddtd it}|j dd}|j dd|id|idd |S) a5 :alias_main: paddle.shape :alias: paddle.shape,paddle.tensor.shape,paddle.tensor.attribute.shape :old_api: paddle.fluid.layers.shape **Shape Layer** Get the shape of the input. .. code-block:: text Case1: Given N-D Tensor: input = [ [1, 2, 3, 4], [5, 6, 7, 8] ] Then: input.shape = [2, 4] Case2: Given SelectedRows: input.rows = [0, 4, 19] input.height = 20 input.value = [ [1, 2], [3, 4], [5, 6] ] # inner tensor Then: input.shape = [3, 2] Args: input (Variable): The input can be N-D Tensor or SelectedRows with data type bool, float16, float32, float64, int32, int64. If input variable is type of SelectedRows, returns the shape of it's inner tensor. Returns: Variable (Tensor): The shape of the input variable. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle paddle.enable_static() inputs = fluid.data(name="x", shape=[3, 100, 100], dtype="float32") output = fluid.layers.shape(inputs) exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) img = np.ones((3, 100, 100)).astype(np.float32) res = exe.run(fluid.default_main_program(), feed={'x':img}, fetch_list=[output]) print(res) # [array([ 3, 100, 100], dtype=int32)] Trr`rrr=rr)rrrrXN)r) rr)rrXrr*r&rrrr)rrr rrrr_.s&5    rcCs,t|dtdt|j}tt|d}|S)a The OP returns the number of dimensions for a tensor, which is a 0-D int32 Tensor. Args: input (Tensor): The input N-D tensor with shape of :math:`[N_1, N_2, ..., N_k]`, the data type is arbitrary. Returns: Tensor, the output data type is int32.: The 0-D tensor with the dimensions of the input Tensor. Examples: .. code-block:: python import paddle input = paddle.rand((3, 100, 100)) rank = paddle.rank(input) print(rank) # 3 rr)r'r rrrrarray)rZndimsrrrrr.s rz paddle.numelcCsjtrt|Strt|St|dgddtd it}|jdd}|j dd|id|id|S) a **Size Layer** Returns the number of elements for a tensor, which is a int64 Tensor with shape [1]. Args: input (Tensor): The input Tensor, it's data type can be bool, float16, float32, float64, int32, int64. Returns: Tensor: The number of elements for the input Tensor. Raises: TypeError: ``input`` must be a Tensor and the data type of ``input`` must be one of bool, float16, float32, float64, int32, int64. Examples: .. code-block:: python import paddle import paddle.fluid.layers as layers paddle.enable_static() input = layers.data( name="input", shape=[3, 100], dtype="float32", append_batch_size=False) rank = layers.size(input) # 300 rr;rrr=rrrBN)r) rr)rrr*r&rrrr)rr rrrrr.s   rcCs|j}|jdd}|jdd}|dusJd||dus'Jd|t|dgd|t|dgd||jdd}|jdd }|jd d}|j|jd }|j|||d d |i||dd||S)Nrrzx cannot be None in {}zy cannot be None in {})rrrrrrrrrFrr=rr)rrr) Z layer_typekwargsr rRr&rrrr)r rrrrrrrrrr_elementwise_op.s6   rc Cstrt||t||}t|Str7t|tr!| dn|}t |dt|dt|d|}t|St |dgddd|gi}t||d} t|trW|g|d <nt|| d<t dit} | j|jd }| jd|d |i| d | |S)av 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(Tensor): Input N-D Tensor of scale operator. Data type can be float32, float64, int8, int16, int32, int64, uint8. scale(float|Tensor): The scale factor of the input, it should be a float number or a Tensor with shape [1] and data type as float32. 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: Tensor: Output tensor of scale operator, with shape and data type same as input. Examples: .. code-block:: python # scale as a float32 number import paddle data = paddle.randn(shape=[2,3], dtype='float32') res = paddle.scale(data, scale=2.0, bias=1.0) .. code-block:: python # scale with parameter scale as a Tensor import paddle data = paddle.randn(shape=[2, 3], dtype='float32') factor = paddle.to_tensor([2], dtype='float32') res = paddle.scale(data, scale=factor, bias=1.0) rrrbias_after_scaler) rrrrrrzrrrr)rrZ ScaleTensorr=rrN)r)rr)rrZrrr rr rIrJr*r&rrrrrr) rrrrrrrZ_scalerrr rrrr/s8/        rcCs4trt||||dtddSttditS)a Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = fluid.layers.elementwise_add(x, y) # z = x + y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # [3., 8., 6.] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[3,4], dtype='float32') z = fluid.layers.elementwise_add(x, y, axis=1) # z = x + y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'), "y": np.random.randint(1, 5, size=[5]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[5], dtype='float32') z = fluid.layers.elementwise_add(x, y, axis=3) # z = x + y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] rr)rrrrN)r)r rrrrrrrrrrrrrrc/sPrz paddle.dividecC,tr t||||ddSttditS)a Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = fluid.layers.elementwise_div(x, y) # z = x / y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # [2., 0.6, 2.] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[3,4], dtype='float32') z = fluid.layers.elementwise_div(x, y, axis=1) # z = x / y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'), "y": np.random.randint(1, 5, size=[5]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[5], dtype='float32') z = fluid.layers.elementwise_div(x, y, axis=3) # z = x / y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] rrrrN)rr rrrrrrrrr/RrcCr)a Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = fluid.layers.elementwise_sub(x, y) # z = x - y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # [1., -2., 2.] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[3,4], dtype='float32') z = fluid.layers.elementwise_sub(x, y, axis=1) # z = x - y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'), "y": np.random.randint(1, 5, size=[5]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[5], dtype='float32') z = fluid.layers.elementwise_sub(x, y, axis=3) # z = x - y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] rrN)rrrrrrr0sQrzpaddle.multiplycCr)a Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = fluid.layers.elementwise_mul(x, y) # z = x * y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # [2., 15., 8.] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[3,4], dtype='float32') z = fluid.layers.elementwise_mul(x, y, axis=1) # z = x * y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'), "y": np.random.randint(1, 5, size=[5]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[5], dtype='float32') z = fluid.layers.elementwise_mul(x, y, axis=3) # z = x * y place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) print(z_value) # z.shape=[2,3,4,5] rrN)rrrrrrrv0rrcCr)aj :alias_main: paddle.elementwise_max :alias: paddle.elementwise_max,paddle.tensor.elementwise_max,paddle.tensor.math.elementwise_max :old_api: paddle.fluid.layers.elementwise_max Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = fluid.layers.elementwise_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 import paddle def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[3,4], dtype='float32') z = fluid.layers.elementwise_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.]]]] rrN)rrrrrrr0s9rcCr)aj :alias_main: paddle.elementwise_min :alias: paddle.elementwise_min,paddle.tensor.elementwise_min,paddle.tensor.math.elementwise_min :old_api: paddle.fluid.layers.elementwise_min Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = fluid.layers.elementwise_min(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 import paddle def gen_data(): return { "x": np.ones((2, 3, 4, 5)).astype('float32'), "y": np.zeros((3, 4)).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') y = fluid.data(name="y", shape=[3,4], dtype='float32') z = fluid.layers.elementwise_min(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.]]]] rrN)rrrrrrr1s7rcCr)a Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } paddle.enable_static() x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = fluid.layers.elementwise_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] rrN)rrrrrrrV1szpaddle.remaindercCr)a Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.array([10, 15, 8]).astype('int32'), "y": np.array([3, 6, 5]).astype('int32') } paddle.enable_static() 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] rrN)rrrrrrr{1rzpaddle.floor_dividecCr)a Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle def gen_data(): return { "x": np.array([10, 15, 8]).astype('int32'), "y": np.array([3, 7, 5]).astype('int32') } paddle.enable_static() 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] rrN)rrrrrrr1rr)zaxis (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. zact (string, optional): Activation applied to the output. Default is None. Details: :ref:`api_guide_activations_en` zname (string, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name` >rZmkldnn_data_typeZScale_xZ use_quantizerZ Scale_outZScale_yZ x_data_formatZ y_data_format)additional_args_linesZskip_attrs_set z Warning: z instead.z'and will be removed in future versions.ccs|]}|VqdSrrrrrrr1rrrz8act (basestring|None): Activation applied to the output.z+name (basestring|None): Name of the output.)raC 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) cCstrtt|}|r|||S||St|dgd||dur)t|dgd||dur4t|dt|t|fit}|rP|j|jkrPt d||j|jf|dur[|j |jd}|rl|j |||dd|id |S|j |d |id|id |S) Nr)rrrzrrrrrrz^(InvalidArgument) The DataType of %s Op's Variable must be consistent, but received %s and %s.r=rrrBr) r rr*r&r'r rrrr5rr)rrrrr binary_oprr rrr _logical_op!2sJ    r!cC&tr t||Std||||ddS)ao ``logical_and`` operator computes element-wise logical AND on ``x`` and ``y``, and returns ``out``. ``out`` is N-dim boolean ``Tensor``. Each element of ``out`` is calculated by .. math:: out = x \&\& y .. note:: ``paddle.logical_and`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting`. Args: x (Tensor): the input tensor, it's data type should be one of bool, int8, int16, in32, in64, float32, float64. y (Tensor): the input tensor, it's data type should be one of bool, int8, int16, in32, in64, float32, float64. out(Tensor): The ``Tensor`` that specifies the output of the operator, which can be any ``Tensor`` that has been created in the program. The default value is None, and a new ``Tensor`` will be created to save the output. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor. A location into which the result is stored. It's dimension equals with ``x``. Examples: .. code-block:: python import paddle x = paddle.to_tensor([True]) y = paddle.to_tensor([True, False, True, False]) res = paddle.logical_and(x, y) print(res) # [True False True False] rTrrrrrr )rr)rr!rrrrrrrrK2s rcCr")a# ``logical_or`` operator computes element-wise logical OR on ``x`` and ``y``, and returns ``out``. ``out`` is N-dim boolean ``Tensor``. Each element of ``out`` is calculated by .. math:: out = x || y .. note:: ``paddle.logical_or`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting`. Args: x (Tensor): the input tensor, it's data type should be one of bool, int8, int16, in32, in64, float32, float64. y (Tensor): the input tensor, it's data type should be one of bool, int8, int16, in32, in64, float32, float64. out(Tensor): The ``Variable`` that specifies the output of the operator, which can be any ``Tensor`` that has been created in the program. The default value is None, and a new ``Tensor`` will be created to save the output. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor. A location into which the result is stored. It's dimension equals with ``x``. Examples: .. code-block:: python import paddle import numpy as np x_data = np.array([True, False], dtype=np.bool_).reshape(2, 1) y_data = np.array([True, False, True, False], dtype=np.bool_).reshape(2, 2) x = paddle.to_tensor(x_data) y = paddle.to_tensor(y_data) res = paddle.logical_or(x, y) print(res) # [[ True True] [ True False]] rTr#)rr)rr!r$rrrrv2s# rcCr")a; ``logical_xor`` operator computes element-wise logical XOR on ``x`` and ``y``, and returns ``out``. ``out`` is N-dim boolean ``Tensor``. Each element of ``out`` is calculated by .. math:: out = (x || y) \&\& !(x \&\& y) .. note:: ``paddle.logical_xor`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting`. Args: x (Tensor): the input tensor, it's data type should be one of bool, int8, int16, in32, in64, float32, float64. y (Tensor): the input tensor, it's data type should be one of bool, int8, int16, in32, in64, float32, float64. out(Tensor): The ``Tensor`` that specifies the output of the operator, which can be any ``Tensor`` that has been created in the program. The default value is None, and a new ``Tensor`` will be created to save the output. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor. A location into which the result is stored. It's dimension equals with ``x``. Examples: .. code-block:: python import paddle import numpy as np x_data = np.array([True, False], dtype=np.bool_).reshape([2, 1]) y_data = np.array([True, False, True, False], dtype=np.bool_).reshape([2, 2]) x = paddle.to_tensor(x_data) y = paddle.to_tensor(y_data) res = paddle.logical_xor(x, y) print(res) # [[False, True], [ True, False]] rTr#)rr)rr!r$rrrr2s# rcCs$trt|Std|d||ddS)a ``logical_not`` operator computes element-wise logical NOT on ``x``, and returns ``out``. ``out`` is N-dim boolean ``Variable``. Each element of ``out`` is calculated by .. math:: out = !x Args: x(Tensor): Operand of logical_not operator. Must be a Tensor of type bool, int8, int16, in32, in64, float32, or float64. out(Tensor): The ``Tensor`` that specifies the output of the operator, which can be any ``Tensor`` that has been created in the program. The default value is None, and a new ``Tensor` will be created to save the output. name(str|None): The default value is None. Normally there is no need for users to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: ${out_comment} Examples: .. code-block:: python import paddle x = paddle.to_tensor([True, False, True, False]) res = paddle.logical_not(x) print(res) # [False True False True] rNFr#)rr)rr!)rrrrrrr2s rcCsztd it}t|dgdd|dur td|jdg}|j|j||j dd}|j dd |i||d d |id |S)a :old_api: paddle.fluid.layers.clip ${comment} Args: x(${x_type}): ${x_comment} min(float): ${min_comment} max(float): ${max_comment} name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: ${out_comment} Return Type: ${out_type} Examples: .. code-block:: python import paddle.fluid as fluid input = fluid.data( name='data', shape=[1], dtype='float32') reward = fluid.layers.clip(x=input, min=-1.0, max=1.0) rrrVN.rFrrrr%r)r5rrrn)r) rrr&r generate_with_ignorable_keyjoinrr rrr)rr5rrr rrrrr2s&rcCstr t||Strt|d|Std it}t|dddgdt|dt d|dur:t d |j dg}|j|j||jd d }|jdd|id|id |id |S)a ${comment} Args: x(${x_type}): ${x_comment} max_norm(${max_norm_type}): ${max_norm_comment} name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. Returns: Tensor: out(${out_type}): ${out_comment} Examples: .. code-block:: python import paddle import paddle.fluid as fluid input = paddle.to_tensor([[2.0, 2.0], [2.0, 2.0]], dtype='float32') reward = fluid.layers.clip_by_norm(x=input, max_norm=1.0) # [[0.5, 0.5], [0.5, 0.5]] max_normrrrrNr%rFr&rrn)r)rr)rr r*rrr&r'rZr r'r(rr rrr)rr)rr rrrrr,3s, rcCsntrt|Strt|Std it}t|dgdd|j |j d}|j dd|iid|id|S) a ${comment} Args: x(${x_type}): ${x_comment} name(basestring|None): Name of the output. Returns: out(${out_type}): ${out_comment} Examples: .. code-block:: python import paddle import paddle.fluid as fluid paddle.enable_static() input = fluid.layers.data( name='data', shape=[2, 3], dtype='float32') mean = paddle.mean(input) rrrVr=rrrnN)r) rr*rrr)Zmean_allrrr&rrrrrr rrrrrc3s  rcCsLtrt|Stdit}|j|jd}|jdd|iid|id|S)a ${comment} Args: x(${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) rr=rrrnN)r)r r*rrrrrrr*rrrr3s rc Cstr t||d|d|S|g|gd}||d}td it}t|dgddt|dgdd|j|jd }|jd||d|d |id |S)a Mul Operator. This operator is used to perform matrix multiplication for input $x$ and $y$. The equation is: .. math:: Out = x * y Both the input $x$ and $y$ can carry the LoD (Level of Details) information, or not. But the output only shares the LoD information with input $x$. Args: x (Variable): The first input Tensor/LoDTensor of mul_op. y (Variable): The second input Tensor/LoDTensor of mul_op. x_num_col_dims (int, optional): The mul_op can take tensors with more than two dimensions as its inputs. If the input $x$ is a tensor with more than two dimensions, $x$ will be flattened into a two-dimensional matrix first. The flattening rule is: the first `num_col_dims` will be flattened to form the first dimension of the final matrix (the height of the matrix), and the rest `rank(x) - num_col_dims` dimensions are flattened to form the second dimension of the final matrix (the width of the matrix). As a result, height of the flattened matrix is equal to the product of $x$'s first `x_num_col_dims` dimensions' sizes, and width of the flattened matrix is equal to the product of $x$'s last `rank(x) - num_col_dims` dimensions' size. For example, suppose $x$ is a 6-dimensional tensor with the shape [2, 3, 4, 5, 6], and `x_num_col_dims` = 3. Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30]. Default is 1. y_num_col_dims (int, optional): The mul_op can take tensors with more than two dimensions as its inputs. If the input $y$ is a tensor with more than two dimensions, $y$ will be flattened into a two-dimensional matrix first. The attribute `y_num_col_dims` determines how $y$ is flattened. See comments of `x_num_col_dims` for more details. Default is 1. name (str, optional): Name of the output. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default is None. Returns: Variable(Tensor/LoDTensor): The output Tensor/LoDTensor of mul op. Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() dataX = fluid.layers.data(name="dataX", append_batch_size = False, shape=[2, 5], dtype="float32") dataY = fluid.layers.data(name="dataY", append_batch_size = False, shape=[5, 3], dtype="float32") output = fluid.layers.mul(dataX, dataY, x_num_col_dims = 1, y_num_col_dims = 1) rrrrrrrVrr=rrnN)r) r r*rrrr&rrr) rrrrrrrr rrrrr3s$#  rzpaddle.nn.functional.maxoutcCstjjjditS)aY ${comment} Args: x(${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. ValueError: If the number of input channels can not be divisible by `groups`. Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() input = fluid.data( name='data', shape=[None, 256, 32, 32], dtype='float32') out = fluid.layers.maxout(input, groups=2) Nr)rrrrr)rrrrrrrr3s"rcCsdtd it}t|tstdt|dgdd|j|jd}|jdd|id|id|id |S) aH 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 reorganize 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: Tensor, The output, which should be 4 dims Tensor or LodTensor, with the shape \ [batch, channel * blocksize * blocksize, height/blocksize, width/blocksize] Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import numpy as np import paddle paddle.enable_static() 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)] rzblocksize must be a python Intrrr=r blocksizerrnN)r) rrrr[r5r&rrr)rr+rr rrrrr 4sT rcCstd it}t|dddgdt|dttdfdt|dttdfd|j|jd}|jd|||d d |id |id | |S)a 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, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. 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 import paddle.fluid as fluid import paddle paddle.enable_static() 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] rrrrrNrr=)rrrrrrn)r) rrr&r'r rrrrr)rrrrrrr rrrrrq4sB rcCstdit}t|dddgdt|dtdt|dtd|dkr/|dkr/|d kr/td t|d kr9td |j|j d }|j dd|id|i||dd|S)a 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 import paddle paddle.enable_static() data = fluid.data( name='data', shape=[-1, 3, 2, 2], dtype='float32') fluid.layers.similarity_focus(input=data, axis=1, indexes=[0]) rrrrrindexesrrr#zaxis must be 1, 2 or 3.rzindexes can not be empty.r=rr)rr,rN)r) rrr&r'r[rr5rrrr)rrr,rr rrrrr4s&]  rcCstt|dddgdt|dtdt|dtdtdit}|j|dd}|jdd |id |i||d d |S)aX This OP hash the input to an integer less than the hash_size. The hash algorithm we used was xxHash - Extremely fast hash algorithm (https://github.com/Cyan4973/xxHash/tree/v0.6.5) Args: input(Variable): A **Two-Dimensional** LoDTensor with type int32, int64. **Only support LoDTensor**. num_hash(int, optional): The times of hash, default is 1. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Variable: A LoDTensor with the same data type as input. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np import paddle paddle.enable_static() place = fluid.core.CPUPlace() x = fluid.data(name="x", shape=[2,2], dtype="int32", lod_level=1) res = fluid.layers.hash(name="res", input=x, hash_size=1000, num_hash=4) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) in1 = np.array([[1,2],[3,4]]).astype("int32") print(in1) x_i = fluid.create_lod_tensor(in1, [[0, 2]], place) res = exe.run(fluid.default_main_program(), feed={'x':x_i}, fetch_list=[res], return_numpy=False) print(np.array(res[0])) # [[[722] # [407] # [337] # [395]] # [[603] # [590] # [386] # [901]]] rrrr hash_sizenum_hashT)rXrr)r.Zmod_byrN)r)r&r'r[rrrrr)rr-r.rr rrrrr75s/ rcCstdit}t|dddgdt|dddgdt|ts#tdSt|ts,tdS||j}||d}t r?d d ini}|j d|d |i|d |S)a~ This operation samples input X by using bilinear interpolation based on flow field grid, which is usually generated by :code:`affine_grid` . The grid of shape [N, H, W, 2] is the concatenation of (x, y) coordinates with shape [N, H, W] each, where x is indexing the 4th dimension (in width dimension) of input data x and y is indexing the 3rd dimension (in height dimension), finally results is the bilinear interpolation value of 4 nearest corner points. The output tensor shape will be [N, C, H, W]. .. code-block:: text Step 1: Get (x, y) grid coordinates and scale to [0, H-1/W-1]. .. code-block:: text grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1) grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1) Step 2: Indices input data X with grid (x, y) in each [H, W] area, and bilinear interpolate point value by 4 nearest points. wn ------- y_n ------- en | | | | d_n | | | | x_w --d_w-- grid--d_e-- x_e | | | | d_s | | | | ws ------- y_s ------- wn x_w = floor(x) // west side x coord x_e = x_w + 1 // east side x coord y_n = floor(y) // north side y coord y_s = y_s + 1 // south side y coord d_w = grid_x - x_w // distance to west side d_e = x_e - grid_x // distance to east side d_n = grid_y - y_n // distance to north side d_s = y_s - grid_y // distance to south side wn = X[:, :, y_n, x_w] // north-west point value en = X[:, :, y_n, x_e] // north-east point value ws = X[:, :, y_s, x_w] // south-east point value es = X[:, :, y_s, x_w] // north-east point value output = wn * d_e * d_s + en * d_w * d_s + ws * d_e * d_n + es * d_w * d_n Args: x(Variable): The input tensor, which is a 4-D tensor with shape [N, C, H, W], N is the batch size, C is the channel number, H and W is the feature height and width. The data type is float32 or float64. grid(Variable): Input grid tensor of shape [N, H, W, 2]. The data type is float32 or float64. name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. Returns: Variable: Output of shape [N, C, H, W] data samples input X using bilnear interpolation based on input grid. The data type is same as input tensor. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid as fluid import paddle paddle.enable_static() # use with affine_grid x = fluid.data(name='x', shape=[None, 10, 32, 32], dtype='float32') theta = fluid.layers.data(name='theta', shape=[2, 3], dtype='float32') grid = fluid.layers.affine_grid(theta=theta, out_shape=[3, 10, 32, 32]) out = fluid.layers.grid_sampler(x=x, grid=grid) rrrrgridzThe x should be a VariablezThe grid should be a Variable)rZGridrbFrrN)r) rrr&rr r5rrr"rr)rr/rr rrrrrrrv5s$W     rcCstjj||||S)a **Negative Log Loss Layer** This layer accepts input predictions and target label and returns the negative log loss. .. math:: Out = -label * \log{(input + \epsilon)} - (1 - label) * \log{(1 - input + \epsilon)} Args: input (Tensor|list): A 2-D tensor with shape [N x 1], where N is the batch size. This input is a probability computed by the previous operator. Data type float32. label (Tensor|list): The ground truth which is a 2-D tensor with shape [N x 1], where N is the batch size. Data type float32. epsilon (float, optional): A small number for numerical stability. Default 1e-4. name(str|None): For detailed information, please refer to :ref:`api_guide_Name` . Usually name is no need to set and None by default. Returns: Tensor, which shape is [N x 1], data type is float32. Examples: .. code-block:: python import paddle import paddle.nn.functional as F label = paddle.randn((10,1)) prob = paddle.randn((10,1)) cost = F.log_loss(input=prob, label=label) )rrrrrrrrr5s%rcCsrtr t|d|d|Std it}t|dddgd|}|j|d}|jdd|id |i||d d |S)a 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 tensor = paddle.randn([16, 32, 64]) position_tensor = paddle.fluid.layers.add_position_encoding( input=tensor, alpha=1.0, beta=1.0) rErrrrrr=rr)rErrN)r) r r*rrrr&rrr)rrErrr rrrrrr 6s$0   rcCstd it}|d}||jd|jdg} |j|j| |dd} |j|d} ||| d} |jrCd|g} |j|j| |dd}|| d <|jd| d | id | | S)a" :api_attr: Static Graph **Bilinear Tensor Product Layer** This layer performs bilinear tensor product on two inputs. For example: .. math:: out_{i} = x * W_{i} * {y^\mathrm{T}}, i=0,1,...,size-1 In this formula: - :math:`x`: the first input contains M elements, shape is [batch_size, M]. - :math:`y`: the second input contains N elements, shape is [batch_size, N]. - :math:`W_{i}`: the i-th learned weight, shape is [M, N]. - :math:`out_{i}`: the i-th element of out, shape is [batch_size, size]. - :math:`y^\mathrm{T}`: the transpose of :math:`y_{2}`. Args: x (Variable): 2-D input tensor with shape [batch_size, M]. Data type is float32 or float64. y (Variable): 2-D input tensor with shape [batch_size, N]. Data type should be same as **x**. size (int): The dimension of this layer. act (str|None): Activation to be applied to the output of this layer. Default None. name(str|None): For detailed information, please refer to :ref:`api_guide_Name` . Usually name is no need to set and None by default. param_attr (ParamAttr|None): To specify the weight parameter attribute. Default: None, which means the default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . bias_attr (ParamAttr|None): To specify the bias parameter attribute. Default: None, which means the default bias parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . Returns: Variable: A 2-D Tensor of shape [batch_size, size]. Data type is the same as input **x**. Examples: .. code-block:: python import paddle paddle.enable_static() layer1 = paddle.static.data("t1", shape=[-1, 5], dtype="float32") layer2 = paddle.static.data("t2", shape=[-1, 4], dtype="float32") tensor = paddle.static.nn.bilinear_tensor_product(x=layer1, y=layer2, size=1000) rrrFrr=)rrrTrrrBN)r) rrrrrrrrrr)rrrrrrrr rr r rrZ bias_sizerrrrrQ6s.4    rcCsbt|dtd|jtjjjkrtdtd it }|j |j d}|j dd|id|iid|S) a This operator gets tensor data from input with SelectedRows type, and outputs a LoDTensor. .. code-block:: text input x is SelectedRows: x.rows = [0, 5, 5, 4, 19] x.height = 20 x.value = [[1, 1] [2, 2] [2, 2] [3, 3] [6, 6]] Output is LoDTensor: out.shape = [5, 2] out.data = [[1, 1], [2, 2], [2, 2], [3, 3], [6, 6]] Args: x(SelectedRows): Input with SelectedRows type. The data type is float32, float64, int32 or int64. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Variable: LoDTensor transformed from SelectedRows. The data type is same with input. Examples: .. code-block:: python import paddle.fluid as fluid b = fluid.default_main_program().global_block() input = b.create_var(name="X", dtype="float32", persistable=True, type=fluid.core.VarDesc.VarType.SELECTED_ROWS) out = fluid.layers.get_tensor_from_selected_rows(input) rrzGThe type of 'x' in get_tensor_from_selected_rows must be SELECTED_ROWS.r=rrrN)r) r'r rr"rFrGZ SELECTED_ROWSrQrrrrrr*rrrr6s%rcCsRtd it}|j|jd}t|tstd|jdd|id|id|id|S) aj 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 with 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 counts 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 import paddle.fluid as fluid paddle.enable_static() input = fluid.data(name='input', shape=[None,4,2,2], dtype='float32') out = fluid.layers.shuffle_channel(x=input, group=2) rr=zgroup must be int typerrgrouprN)r)rrrrrr[rQr)rr0rr rrrrr6s; rrcCstjj|||||S)a **Temporal Shift Operator** ${comment} Args: x(Tensor): ${x_comment} seg_num(int): ${seg_num_comment} shift_ratio(float): ${shift_ratio_comment} name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. data_format(str, optional): Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Default: "NCHW". Returns: out(Tensor): The temporal shifting result is a tensor with the same shape and same data type as the input. Raises: TypeError: seg_num must be int type. Examples: .. code-block:: python import paddle import paddle.nn.functional as F input = paddle.randn([6, 4, 2, 2]) out = F.temporal_shift(x=input, seg_num=2, shift_ratio=0.2) )rrrr)rZseg_numZ shift_ratiorrrrrr7s"rc@sDeZdZgZddZeddZeddZeddZ d d Z d S) PyFuncRegistrycCs|dust|s td||_t|j}t|ddkr-|ddur-|ddur-d|_n|d|_t||_ t j |dS)Nzfunc must be a Python functionrrr) callablerQ_funcinspect getargspecr _named_argsr"Z,_append_python_callable_object_and_return_id_idr1_register_funcsr)selffuncargsrrr__init__E7s (  zPyFuncRegistry.__init__cCs |j|jSr)r8r3)clsr@rrrregistered_funcb7s zPyFuncRegistry.registered_funccCs t|jSr)rr8)r=rrrregistered_func_numf7s z"PyFuncRegistry.registered_func_numcCs|jSr)r7)r9rrrrgj7szPyFuncRegistry.idc Gs|jdur |}n!t}d}|jD] }||||<|d7}q|j||di|}t|ttfs5|f}g}|D].}|dusEt|tjrK||q9t|t j sVt |}t}| |t ||q9t|S)Nrr)r6r3r rrrr"Z LoDTensorrrndarrayrsetZCPUPlace) r9r;Zfunc_retrr@argretZeach_rettensorrrr__call__n7s*         zPyFuncRegistry.__call__N) __name__ __module__ __qualname__r8r< classmethodr>r?propertyrgrErrrrr1B7s    r1c Cstdit}t|dttttdfd|durg}nt|tr%|g}nt|tr/t|}n t|tttfs;tdt|dttttdfd|durOg}nt|trX|g}nt|trbt|}n t|trj|}ntdt |j }|dur|t |j nd}|D] } t | j dkrt d qt} |dur|durt|tr|g}d d |D} | d d |Dt| } t} |D]} | j| vrt d | j| | jq|jdd|id|i||t| dd|S)a :api_attr: Static Graph This OP is used to register customized Python OP to Paddle. The design principe of py_func is that Tensor and numpy array can be converted to each other easily. So you can use Python and numpy API to register a python OP. The forward function of the registered OP is ``func`` and the backward function of that is ``backward_func``. Paddle will call ``func`` at forward runtime and call ``backward_func`` at backward runtime(if ``backward_func`` is not None). ``x`` is the input of ``func``, whose type must be Tensor; ``out`` is the output of ``func``, whose type can be either Tensor or numpy array. The input of the backward function ``backward_func`` is ``x``, ``out`` and the gradient of ``out``. If ``out`` have no gradient, the relevant input of ``backward_func`` is None. If ``x`` do not have a gradient, the user should return None in ``backward_func``. The data type and shape of ``out`` should also be set correctly before this API is called, and the data type and shape of the gradient of ``out`` and ``x`` will be inferred automatically. This API can also be used to debug the neural network by setting the ``func`` as a function that only print variables. Args: func (callable): The forward function of the registered OP. When the network is running, the forward output ``out`` will be calculated according to this function and the forward input ``x``. In ``func`` , it's suggested that we actively convert Tensor into a numpy array, so that we can use Python and numpy API arbitrarily. If not, some operations of numpy may not be compatible. x (Tensor|tuple(Tensor)|list[Tensor]): The input of the forward function ``func``. It can be Tensor|tuple(Tensor)|list[Tensor]. In addition, Multiple Tensor should be passed in the form of tuple(Tensor) or list[Tensor]. out (T|tuple(T)|list[T]): The output of the forward function ``func``, it can be T|tuple(T)|list[T], where T can be either Tensor or numpy array. Since Paddle cannot automatically infer the shape and type of ``out``, you must create ``out`` in advance. backward_func (callable, optional): The backward function of the registered OP. Its default value is None, which means there is no reverse calculation. If it is not None, ``backward_func`` is called to calculate the gradient of ``x`` when the network is at backward runtime. skip_vars_in_backward_input (Tensor, optional): It's used to limit the input list of ``backward_func``, and it can be Tensor|tuple(Tensor)|list[Tensor]. It must belong to either ``x`` or ``out``. The default value is None, which means that no tensors need to be removed from ``x`` and ``out``. If it is not None, these tensors will not be the input of ``backward_func``. This parameter is only useful when ``backward_func`` is not None. Returns: Tensor|tuple(Tensor)|list[Tensor]: The output ``out`` of the forward function ``func``. Examples: .. code-block:: python # example 1: import paddle import six import numpy as np paddle.enable_static() # Creates a forward function, Tensor can be input directly without # being converted into numpy array. def tanh(x): return np.tanh(x) # Skip x in backward function and return the gradient of x # Tensor must be actively converted to numpy array, otherwise, # operations such as +/- can't be used. def tanh_grad(y, dy): return np.array(dy) * (1 - np.square(np.array(y))) # Creates a forward function for debugging running networks(print value) def debug_func(x): print(x) def create_tmp_var(name, dtype, shape): return paddle.static.default_main_program().current_block().create_var( name=name, dtype=dtype, shape=shape) def simple_net(img, label): hidden = img for idx in six.moves.range(4): hidden = paddle.static.nn.fc(hidden, size=200) new_hidden = create_tmp_var(name='hidden_{}'.format(idx), dtype=hidden.dtype, shape=hidden.shape) # User-defined forward and backward hidden = paddle.static.py_func(func=tanh, x=hidden, out=new_hidden, backward_func=tanh_grad, skip_vars_in_backward_input=hidden) # User-defined debug functions that print out the input Tensor paddle.static.py_func(func=debug_func, x=hidden, out=None) prediction = paddle.static.nn.fc(hidden, size=10, activation='softmax') ce_loss = paddle.nn.loss.CrossEntropyLoss() return ce_loss(prediction, label) x = paddle.static.data(name='x', shape=[1,4], dtype='float32') y = paddle.static.data(name='y', shape=[1,10], dtype='int64') res = simple_net(x, y) exe = paddle.static.Executor(paddle.CPUPlace()) exe.run(paddle.static.default_startup_program()) input1 = np.random.random(size=[1,4]).astype('float32') input2 = np.random.randint(1, 10, size=[1,10], dtype='int64') out = exe.run(paddle.static.default_main_program(), feed={'x':input1, 'y':input2}, fetch_list=[res.name]) print(out) .. code-block:: python # example 2: # This example shows how to turn Tensor into numpy array and # use numpy API to register an Python OP import paddle import numpy as np paddle.enable_static() def element_wise_add(x, y): # Tensor must be actively converted to numpy array, otherwise, # numpy.shape can't be used. x = np.array(x) y = np.array(y) if x.shape != y.shape: raise AssertionError("the shape of inputs must be the same!") result = np.zeros(x.shape, dtype='int32') for i in range(len(x)): for j in range(len(x[0])): result[i][j] = x[i][j] + y[i][j] return result def create_tmp_var(name, dtype, shape): return paddle.static.default_main_program().current_block().create_var( name=name, dtype=dtype, shape=shape) def py_func_demo(): start_program = paddle.static.default_startup_program() main_program = paddle.static.default_main_program() # Input of the forward function x = paddle.static.data(name='x', shape=[2,3], dtype='int32') y = paddle.static.data(name='y', shape=[2,3], dtype='int32') # Output of the forward function, name/dtype/shape must be specified output = create_tmp_var('output','int32', [3,1]) # Multiple Variable should be passed in the form of tuple(Variale) or list[Variale] paddle.static.py_func(func=element_wise_add, x=[x,y], out=output) exe=paddle.static.Executor(paddle.CPUPlace()) exe.run(start_program) # Feed numpy array to main_program input1 = np.random.randint(1, 10, size=[2,3], dtype='int32') input2 = np.random.randint(1, 10, size=[2,3], dtype='int32') out = exe.run(main_program, feed={'x':input1, 'y':input2}, fetch_list=[output.name]) print("{0} + {1} = {2}".format(input1, input2, out)) py_func_demo() # Reference output: # [[5, 9, 9] + [[7, 8, 4] = [array([[12, 17, 13] # [7, 5, 2]] [1, 3, 3]] [8, 8, 5]], dtype=int32)] rrNz5Input must be Variable/list(Variable)/tuple(Variable)rz6Output must be Variable/list(Variable)/tuple(Variable)rrz@Output shapes of py_func op should be provided by users manuallycSrrrr r rrrr!c8r"zpy_func..cSrrrrKrrrr!d8r"z6Variable {} is not found in forward inputs and outputs)Zforward_callable_idZbackward_callable_idbackward_skip_varsr)r)rrr'rrr rrrQr1rgrrr5rAextendrrRrr) r:rrZ backward_funcZskip_vars_in_backward_inputr Zout_listZ fwd_func_idZ bwd_func_idZeach_outrLZ fwd_in_outr rrrr7sx2          rc Cstd it}t|tstdt|tstdt|ts#tdt|ts,td|}||} |jd||dd| i||||dd | S) a ${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 import paddle paddle.enable_static() 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) rz output_channels must be int type spatial_scale must be float typepooled_height must be int typepooled_width must be int typerr)output_channelsrrrrN)r) rrrr[rQrZrrr) rrrQrrrrr rrrrrr~8s.+      rc Cst|ddgdt|ddgdtdit}t|ts!tdt|ts*tdt|ts3td|}||} ||d} |d urI|| d <|j d| d | i|||d d | S)a The precise roi pooling implementation for paddle. Reference: https://arxiv.org/pdf/1807.11590.pdf Args: input (Variable):The input of precise roi pooliing.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 or Tensor of shape (num_rois, 4), the lod level is 1 when it is LoDTensor. The LoD include the rois's batch index information. If rois is Tensor, its batch index information should be provided by batch_index. 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. batch_roi_nums (Variable): The number of roi for each image in batch. It should be 1-D Tensor, with shape [N] and dtype int64, where N is the batch size. Default: None. Be note: The lod of input should be empty when batch_roi_nums has values; name (str, default None): The name of this operation. Returns: Variable(Tensor):The shape of the returned Tensor is (N, C, pooled_height, pooled_width), with value type float32,float16. N, C denote batch_size and channels of input respectively. Examples: .. code-block:: python ## prroi_pool without batch_roi_num import paddle.fluid as fluid x = fluid.data(name='x', shape=[None, 490, 28, 28], dtype='float32') rois = fluid.data(name='rois', shape=[None, 4], lod_level=1, dtype='float32') pool_out = fluid.layers.prroi_pool(x, rois, 1.0, 7, 7) ## prroi_pool with batch_roi_num batchsize=4 x2 = fluid.data(name='x2', shape=[batchsize, 490, 28, 28], dtype='float32') rois2 = fluid.data(name='rois2', shape=[batchsize, 4], dtype='float32') batch_rois_num = fluid.data(name='rois_nums', shape=[batchsize], dtype='int64') pool_out2 = fluid.layers.prroi_pool(x2, rois2, 1.0, 7, 7, batch_roi_nums=batch_rois_num) rrrrrNrOrPrNZ BatchRoINumsr)rrrr)r) r&rrrrZrQr[rrr) rrrrrZbatch_roi_numsrr rrZ inputs_oprrrr8s.7     rcCsdt|dddgdtd it}|j|jd}t|ts!td|jdd|id|id |id |S) a 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) rrrrr=zupscale factor must be int typerrupscale_factorrN)r) r&rrrrrr[rQr)rrRr rrrrr9s- rcCsft|dddgdt|dddgdtd it}|j|jddd}|jd||d d |id |S)a **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) rrrrrrr=ZfsprrrBN)r)r&rrrrr)rrr rrrrrQ9s+ rcCsZtd it}|j|jd}t|dgdd|jd|g|gdd|gid|id|S) a **continuous_value_model layers** Now, this OP is used in CTR project to remove or dispose show and click value in :attr:`input`. :attr:`input` is an embedding vector including show and click value, whose shape is :math:`[N, D]` (N is batch size. D is `2 + embedding dim` ). Show and click at first two dims of embedding vector D. If :attr:`use_cvm` is True, it will calculate :math:`log(show)` and :math:`log(click)` , and output shape is :math:`[N, D]` . If :attr:`use_cvm` is False, it will remove show and click from :attr:`input` , and output shape is :math:`[N, D - 2]` . :attr:`cvm` is show_click info, whose shape is :math:`[N, 2]` . Args: input (Variable): The input variable. A 2-D LoDTensor with shape :math:`[N, D]` , where N is the batch size, D is `2 + the embedding dim` . `lod level = 1` . A Tensor with type float32, float64. cvm (Variable): Show and click variable. A 2-D Tensor with shape :math:`[N, 2]` , where N is the batch size, 2 is show and click. A Tensor with type float32, float64. use_cvm (bool): Use show_click or not. if use, the output dim is the same as input. if not use, the output dim is `input dim - 2` (remove show and click) Returns: Variable: A 2-D LodTensor with shape :math:`[N, M]` . if :attr:`use_cvm` = True, M is equal to input dim D. if False, M is equal to `D - 2`. \ A Tensor with same type as input. Examples: .. code-block:: python import paddle.fluid as fluid input = fluid.data(name="input", shape=[64, 1], dtype="int64") label = fluid.data(name="label", shape=[64, 1], dtype="int64") embed = fluid.layers.embedding( input=input, size=[100, 11], dtype='float32') ones = fluid.layers.fill_constant_batch_size_like(input=label, shape=[-1, 1], dtype="int64", value=1) show_clk = fluid.layers.cast(fluid.layers.concat([ones, label], axis=1), dtype='float32') show_clk.stop_gradient = True input_with_cvm = fluid.layers.continuous_value_model(embed, show_clk, True) cvmr=rrV)rZCVMruse_cvmrN)rS)rrr rr&r)rrSrTr rrrrr9s+ rcCs`trt|Strt|Stdit}|jtj j j d}|j dd|id|gid|S)a 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) # [[]] where_indexr= ConditionrrBN)rU) rr)rUrr*rrrr"rFrGrHr) conditionr rrrrr9s"  rz paddle.signcCsztd it}t|dttjfdt|tjrt|}t|j dgdd|j |j d}|j dd|gid|gid|S) a 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')) rrrVr=rrrBN)r) rrr'r rr@rrr(rrr)rr rrrrr9s rrcCsjt|dgddtd it}|j|jd}||}|jdd|idt|i|g|gdd||fS) a4 Return a unique tensor for `x` and an index tensor pointing to this unique tensor. Args: x(Tensor): A 1-D input tensor, it's data type should be float32, float64, int32, int64. dtype(np.dtype|str, optional): The type of index tensor: int32, int64. Default: int32. 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.layers.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] rr6rr=rr)rrrnN)r)r&rrrrrr)rrr rrLrrrr:s   rcCst|dgdd|dks|dkstd|dus t|jdkr$td tdit}|j|jd }||}||}|j dd |id t |i|g|g|gd d|||fS)a This OP return a unique tensor for `x` , and count tensor that the count of unique result in raw input, \ and an index tensor pointing to this unique tensor. **NOTICE**: This op 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. Default 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 unique 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,) rr6rrrz9Op unique_with_counts, index dtype must be int32 or int64NrzCOp unique_with_counts, x must not be null and size of dim must be 1r=rr)rrZCountrn)r) r&rQrrr5rrrrrr)rrr rrLcountrrrr::s0     rc st|dddgdt|dddgdt|dttdfd|jd| d us*Jd tdit}|}t|ts?t d t|tsHt d |durO}n|d krYt d|}t ddt |dd}t |dd}t |dd}|j}|t |g}fdd}|j|j|||d}||}| r|jd||||dd|i||||| | ddn|jd|||dd|i||||| | dd|j|ddd}|S)a :api_attr: Static Graph **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 devisable 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 import paddle paddle.enable_static() 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) rrrrrr]NrFrkz)Input of deformable_conv must be Variablez0Input Offset of deformable_conv must be Variablerz)num_channels must be divisible by groups.rrmrnr}rocrrrrrrrr&;rz7deformable_conv.._get_default_param_initializerr)rrOffsetrYr)rrrrdeformable_groups im2col_steprZdeformable_conv_v1)rrrYr)r)r&r'r rrrrrrrQr5rrr[rrrrr)rrr]rrmrnr}rorrZr[rrZ modulatedrr rrr rrrrrrrrru:s        rcCstjj||||||S)a- This op returns a col buffer of sliding local blocks of input x, also known as im2col for batched 2D image tensors. For each block under the convolution filter, all element will be rearranged as a column. While the convolution filter sliding over the input feature map, a series of such columns will be formed. For each input :math:`x` with shape [N, C, H, W], the output shape [N, Cout, Lout] can be calculated as following. .. math:: dkernel[0] &= dilations[0] \times (kernel\_sizes[0] - 1) + 1 dkernel[1] &= dilations[1] \times (kernel\_sizes[1] - 1) + 1 hout &= \frac{H + paddings[0] + paddings[2] - dkernel[0]}{strides[0]} + 1 wout &= \frac{W + paddings[1] + paddings[3] - dkernel[1]}{strides[1]} + 1 Cout &= C \times kernel\_sizes[0] \times kernel\_sizes[1] Lout &= hout \times wout Parameters: x(Tensor): 4-D Tensor, input tensor of format [N, C, H, W], data type can be float32 or float64 kernel_sizes(int|list): The size of convolution kernel, should be [k_h, k_w] or an integer k treated as [k, k]. strides(int|list): The strides, should be [stride_h, stride_w] or an integer stride treated as [sride, stride]. For default, strides will be [1, 1]. paddings(int|list): The paddings of each dimension, should be [padding_top, padding_left, padding_bottom, padding_right] or [padding_h, padding_w] or an integer padding. If [padding_h, padding_w] was given, it will expanded to [padding_h, padding_w, padding_h, padding_w]. If an integer padding was given, [padding, padding, padding, padding] will be used. For default, paddings will be [0, 0, 0, 0] dilations(int|list): the dilations of convolution kernel, should be [dilation_h, dilation_w], or an integer dilation treated as [dilation, dilation]. For default, it will be [1, 1]. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: The tensor corresponding to the sliding local blocks. The output shape is [N, Cout, Lout] as decriabled above. Cout is the total number of values within each block, and Lout is the total number of such blocks. The data type of output is the same as the input :math:`x` Return Type: Tensor Examples: .. code-block:: python import paddle import paddle.nn.functional as F x = paddle.randn((100,3,224,224)) y = F.unfold(x, [3, 3], 1, 1, 1) )rrrr)rZ kernel_sizesrrrrrrrr_;sFrc Cs t|dddgdt|dddgdt|dddgdt|dttfd|dur1t|d ttfd|jd } | d kr=| }n| ||}|durO|}|}||g}t|d d }t|d d}tdit}| }| |}|j dd}|j d |||d||d|||||||| | d d|S)ar Deformable ROI Pooling Layer Performs deformable region-of-interest pooling on inputs. As described in `Deformable Convolutional Networks `_, it will get offset for each bin after roi pooling so that pooling at correct region. Batch_size will change to the number of region bounding boxes after deformable_roi_pooling. The operation has three steps: 1. Dividing each region proposal into equal-sized sections with the pooled_width and pooled_height. 2. Add offset to pixel in ROI to get new location and the new value which are computed directly through bilinear interpolation with four nearest pixel. 3. Sample several points in each bin to get average values as output. Args: input (Variable):The input of deformable roi pooling and it is tensor which value type is float32. The shape of input is [N, C, H, W]. Where N is batch size, C is number of input channels, H is height of the feature, and W is the width of the feature. rois (Variable): ROIs (Regions of Interest) with type float32 to pool over. It should be a 2-D LoDTensor of shape (num_rois, 4), and the lod level is 1. Given as [[x1, y1, x2, y2], ...], (x1, y1) is the top left coordinates, and (x2, y2) is the bottom right coordinates, which value type is float32. trans (Variable): Offset of features on ROIs while pooling which value type is float32. The format is [N, C, H, W], where N is number of ROIs, C is number of channels, which indicate the offset distance in the x and y directions, H is pooled height, and W is pooled width. no_trans (bool): Whether to add offset to get new value or not while roi pooling, which value with type bool is True or False. If value is True, no offset will be added in operation. Default: False. spatial_scale (float): Ratio of input feature map height (or width) to raw image height (or width), which value type is float32. Equals the reciprocal of total stride in convolutional layers, Default: 1.0. group_size (list|tuple): The number of groups which input channels are divided and the input is list or tuple, which value type is int32. (eg.number of input channels is k1 * k2 * (C + 1), which k1 and k2 are group width and height and C+1 is number of output channels.) eg.(4, 6), which 4 is height of group and 6 is width of group. Default: [1, 1]. pooled_height (int): The pooled output height which value type is int32. Default: 1. pooled_width (int): The pooled output width which value type is int32. Default: 1. part_size (list|tuple): The height and width of offset which values in list or tuple is int32, eg.(4, 6), which height is 4 and width is 6, and values always equal to pooled_height \ and pooled_width. Default: if None, default value is [pooled_height, pooled_width]. sample_per_part (int): The number of samples in each bin which value type is int32. If value is bigger, it will consume more performance. Default: 1. trans_std (float): Coefficient of offset which value type is float32. It controls weight of offset. Default: 0.1. position_sensitive (bool): Whether to choose deformable psroi pooling mode or not, and value type is bool(True or False). If value is False, input dimension equals to output dimension. \ If value is True, input dimension should be output dimension * pooled_height * pooled_width. Default: False. name (str|None): Name of layer. Default: None. Returns: Variable: Output of deformable roi pooling is that, if position sensitive is False, input dimension equals to output dimension. If position sensitive is True,\ input dimension should be the result of output dimension divided by pooled height and pooled width. Examples: .. code-block:: python # position_sensitive=True import paddle.fluid as fluid input = fluid.data(name="input", shape=[2, 192, 64, 64], dtype='float32') rois = fluid.data(name="rois", shape=[-1, 4], dtype='float32', lod_level=1) trans = fluid.data(name="trans", shape=[2, 384, 64, 64], dtype='float32') x = fluid.layers.deformable_roi_pooling(input=input, rois=rois, trans=trans, no_trans=False, spatial_scale=1.0, group_size=(1, 1), pooled_height=8, pooled_width=8, part_size=(8, 8), sample_per_part=4, trans_std=0.1, position_sensitive=True) # position_sensitive=False import paddle.fluid as fluid input = fluid.data(name="input", shape=[2, 192, 64, 64], dtype='float32') rois = fluid.data(name="rois", shape=[-1, 4], dtype='float32', lod_level=1) trans = fluid.data(name="trans", shape=[2, 384, 64, 64], dtype='float32') x = fluid.layers.deformable_roi_pooling(input=input, rois=rois, trans=trans, no_trans=False, spatial_scale=1.0, group_size=(1, 1), pooled_height=8, pooled_width=8, part_size=(8, 8), sample_per_part=4, trans_std=0.1, position_sensitive=False) rrrrrtrans group_sizeN part_sizerFrdeformable_psroi_poolingrr=)rrZTrans)rZTopCount) no_transrZ output_dimr]rrr^sample_per_part trans_stdr)r_) r&r'rrrrrrrrrr)rrr\r`rr]rrr^rarbZposition_sensitiverZinput_channelsrQZ part_heightZ part_widthr rrZ top_countrrrr;sd u        rzpaddle.shard_indexc Cstr t|||||St|dddgdd}t|fit}|dks(||kr0td||f|j|jd}|j |d|gid |i||||d d d |S) a Reset the values of `input` according to the shard it beloning to. Every value in `input` must be a non-negative integer, and the parameter `index_num` represents the integer above the maximum value of `input`. Thus, all values in `input` must be in the range [0, index_num) and each value can be regarded as the offset to the beginning of the range. The range is further split into multiple shards. Specifically, we first compute the `shard_size` according to the following formula, which represents the number of integers each shard can hold. So for the i'th shard, it can hold values in the range [i*shard_size, (i+1)*shard_size). :: shard_size = (index_num + nshards - 1) // nshards For each value `v` in `input`, we reset it to a new value according to the following formula: :: v = v - shard_id * shard_size if shard_id * shard_size <= v < (shard_id+1) * shard_size else ignore_value That is, the value `v` is set to the new offset within the range represented by the shard `shard_id` if it in the range. Otherwise, we reset it to be `ignore_value`. Args: input (Tensor): Input tensor with data type int64 or int32. It's last dimension must be 1. index_num (int): An integer represents the integer above the maximum value of `input`. nshards (int): The number of shards. shard_id (int): The index of the current shard. ignore_value (int): An integer value out of sharded index range. Returns: Tensor. Examples: .. code-block:: python import paddle label = paddle.to_tensor([[16], [1]], "int64") shard_label = paddle.shard_index(input=label, index_num=20, nshards=2, shard_id=0) print(shard_label) # [[-1], [1]] rrrrrz%The shard_id(%d) should be in [0, %d)r=rr) index_numnshardsshard_id ignore_valueT)rrrrrX) rr)rr&rrr5rrr)rrcrdrerfrr rrrrrR<s./  r@c Csrtrt|d|d|d|St|dgddtd it}|j|jd}|jdd|id |i|||d d |S)a This operator implements the hard_swish activation function. Hard_swish is proposed in MobileNetV3, and performs better in computational stability and efficiency compared to swish function. For more details please refer to: https://arxiv.org/pdf/1905.02244.pdf The formula is as follows: .. math:: out = \\frac{x * (min(max(0, x+offset), threshold))}{scale} In the above equation: ``threshold`` and ``scale`` should be positive, ``offset`` can be positive or negative. It is recommended to use default parameters. Args: x (Variable): Input feature, multi-dimensional Tensor. The data type should be float32 or float64. threshold (float, optional): The threshold in Relu function. Default: 6.0 scale (float, optional): The scale factor. Default: 6.0 offset (float, optional): The offset factor. Default: 3.0 name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: Variable: The output tensor with the same shape and data type as input. Examples: .. code-block:: python import paddle.fluid as fluid import paddle import numpy as np paddle.enable_static() DATATYPE='float32' x_data = np.array([i for i in range(1,5)]).reshape([1,1,4]).astype(DATATYPE) x = fluid.data(name="x", shape=[None,1,4], dtype=DATATYPE) y = fluid.layers.hard_swish(x) place = fluid.CPUPlace() #place = fluid.CUDAPlace(0) exe = fluid.Executor(place) out, = exe.run(feed={'x':x_data}, fetch_list=[y.name]) print(out) # [[0.66666667, 1.66666667,3., 4.]] rrrrrVrr=rr)rrrrN)r) r r*rr&rrrrr)rrrrrr rrrrr<s$2  rcCstr t||Strt|d|St|dddgdt|dttfd|dks0Jd |t d it }|j |j d}|jdd |id |id|id |S)a This operator implements the mish activation function. Refer to `Mish: A Self Regularized Non-Monotonic Neural Activation Function `_ The formula is as follows if :attr:`threshold` is :code:`None` or negative: .. math:: out = x * \\tanh(\\ln(1 + e^{x})) The formula is as follows if :attr:`threshold` is set as positive value: .. math:: out = \\begin{cases} x \\ast \\tanh(x), \\text{if } x > \\text{threshold} \\\\ x \\ast \\tanh(e^{x}), \\text{if } x < -\\text{threshold} \\\\ x \\ast \\tanh(\\ln(1 + e^{x})), \\text{otherwise} \\end{cases} Args: x (Variable): Input feature, multi-dimensional Tensor. The data type should be float16, float32 or float64. threshold (float|None): threshold for softplus in Mish operator. Approximate value of softplus will be used if absolute value of input is greater than :attr:threshold and :attr:threshold is set as positive value. For none or negative threshold, approximate value is not used. Default 20. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: Variable: The output tensor with the same shape and data type as input. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np DATATYPE='float32' x_data = np.array([i for i in range(1,5)]).reshape([1,1,4]).astype(DATATYPE) x = fluid.data(name="x", shape=[None,1,4], dtype=DATATYPE) y = fluid.layers.mish(x) place = fluid.CPUPlace() # place = fluid.CUDAPlace(0) exe = fluid.Executor(place) out, = exe.run(feed={'x':x_data}, fetch_list=[y.name]) print(out) # [[0.66666667, 1.66666667, 3., 4.]] rrrrrrz6threshold of mish should be greater than 0, but got {}r=rrrN)r)rr)rrr*r&r'rZr[rRrrrrrrrrrr<s"<  rcCstjj||S)aj To be used after beam search. After beam search, we get selected ids at each time step and the corresponding parents in the search tree. Both ids and parents have the layout :attr:`[max_time, batch_size, beam_size]`. Then :attr:`gather_tree` is used to backtrace from the last time step and generate the full sequences by collecting selected ids. Here is an example: .. code-block:: text Given: ids = [[[2 2] [6 1]] [[3 9] [6 1]] [[0 1] [9 0]]] parents = [[[0 0] [1 1]] [[1 0] [1 0]] [[0 0] [0 1]]] Then: gather_tree(ids, parents) = [[[2 2] [1 6]] [[3 3] [6 1]] [[0 1] [9 0]]] Args: ids(Tensor): A Tensor with shape :attr:`[length, batch_size, beam_size]` and data type :attr:`int32` or :attr:`int64`. It contains the selected ids of all time steps. parents(Tensor): A Tensor with the same shape and data type as :attr:`ids`, It contains the parents corresponding to selected ids when searching among beams. Returns: A Tensor with the same shape and data type as :attr:`ids`. \ It contains the full sequences. The sequences are collected from \ :attr:`ids` by backtracing according to :attr:`parents`. Examples: .. code-block:: python import paddle ids = paddle.to_tensor([[[2, 2], [6, 1]], [[3, 9], [6, 1]], [[0, 1], [9, 0]]]) parents = paddle.to_tensor([[[0, 0], [1, 1]], [[1, 0], [1, 0]], [[0, 0], [0, 1]]]) final_sequences = paddle.nn.functional.gather_tree(ids, parents) # [[[2, 2], [1, 6]], [[3, 3], [6, 1]], [[0, 1], [9, 0]]] )rrrr)Zidsparentsrrrr/=s=rc Cs(t|tjjs t|}tr"t|}t ||t |t ||t St rrHzunbind..rrrrN)r)rrr'r rr(rr[rQrrZgenericZasscalarrrr6r)rrrr Zaxis_r8r*rrCrr=s0     r)rNFN)rNNNN)FFNNr)r,rrrT)rFF)NN)NNNrJ)TNr) rrrNNNTNNrc) rrrNNNTNNr) rrrrFTFNTrc) rrrrFTFNTr)rFN) NFrrNNrcFNNNTF) NFrrNNrcNNNTFr)rNNN) NrNrcFNNNTrFrF)TTrrNNNN)rNNNrcN)rrrN) NNrrrNNNTNNrc) NNrrrNNNTNNr)NFNr )rN)FFrNr)NrN)rrrNrN)NNN)F)Nrr)NNFN)rrrrNrc)rN)NrrN)rrrNN)rrrrNN)rN)NNNrNTrrc)NNNNTrr)NNNNTrrc)NNNNTrr)NNNNTrc)r)T)NT)rN)rN)rrN)rrN)NrcN)rrN)rN)rNr)rNrv)rrrrrr)rrrrN)rrrr)rrrrrr)rrTNN)rNN)NNT)rrN)Nr)NNrcNN)rN)NNNN)rNrc)rrrNN)r) rrrNNNNNTN)rrrN)r)rrrgN)rhN)rrrrN)__doc__ __future__rosr4rrIrrorZ layer_helperrZpaddle.fluid.frameworkrrrrrZ frameworkr r r r r rrrrrrrrrrZlayer_function_generatorrrrrDrrrrrrr functoolsr!r"r$Z data_feederr%r&r'r(Z paddle.utilsr)r*__all__rrr+r,r+r-r8r:r-r.r/rEr0r3r1r2r4r5r6r7r8r9r:r;rOrPrQr<r=r>r?r@rArBrCrDrFrHrIrJrGrKrLrMrNrRrSrTrUrVrWrXrYrZr[r\r]r^r_r`rarcrdrerfrbrgrhrirjrkrlrprnrormrqrrrrrsrtrurvrwrxryrzr{r|r}r~rrrrrrrrrrrrrrrrrrrrrrrrrrr:instanceZ get_op_protorFZop_protor splitlinesZdoc_listrr@rR startswithendswithinsertpopr(r!rrrrrrrrrrrrrrrrrrrrrobjectr1rr>r?rrrrrrrrrrrrrrrrrrrrrrs    4            ' O M D >|B  )v   7 $]d  d 9}et N  J Z 8 H H T D ? ( E g  *[ 0 N N s 0 W X UhF _ Ma Xi \1 {~&( $ % H _ [ [+ ; ) -H C d ;N  (+4-+ Si .0Z M ? tT _ (>G i Y L  + P \[ [[CA % &'    "  # '*+-.%4 6(! 8#fSs?m(HO2I%JmE P <4 9 2!(@kM *GE N @u