# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import layers __all__ = [ "simple_img_conv_pool", "sequence_conv_pool", "glu", "dot_product_attention", ] def simple_img_conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act, param_attr=None, pool_type='max', use_cudnn=True): conv_out = layers.conv2d( input=input, num_filters=num_filters, filter_size=filter_size, param_attr=param_attr, act=act, use_cudnn=use_cudnn) pool_out = layers.pool2d( input=conv_out, pool_size=pool_size, pool_type=pool_type, pool_stride=pool_stride, use_cudnn=use_cudnn) return pool_out def img_conv_group(input, conv_num_filter, pool_size, conv_padding=1, conv_filter_size=3, conv_act=None, param_attr=None, conv_with_batchnorm=False, conv_batchnorm_drop_rate=0.0, pool_stride=1, pool_type=None, use_cudnn=True): """ Image Convolution Group, Used for vgg net. """ tmp = input assert isinstance(conv_num_filter, list) or \ isinstance(conv_num_filter, tuple) def __extend_list__(obj): if not hasattr(obj, '__len__'): return [obj] * len(conv_num_filter) else: return obj conv_padding = __extend_list__(conv_padding) conv_filter_size = __extend_list__(conv_filter_size) param_attr = __extend_list__(param_attr) conv_with_batchnorm = __extend_list__(conv_with_batchnorm) conv_batchnorm_drop_rate = __extend_list__(conv_batchnorm_drop_rate) for i in xrange(len(conv_num_filter)): local_conv_act = conv_act if conv_with_batchnorm[i]: local_conv_act = None tmp = layers.conv2d( input=tmp, num_filters=conv_num_filter[i], filter_size=conv_filter_size[i], padding=conv_padding[i], param_attr=param_attr[i], act=local_conv_act, use_cudnn=use_cudnn) if conv_with_batchnorm[i]: tmp = layers.batch_norm(input=tmp, act=conv_act) drop_rate = conv_batchnorm_drop_rate[i] if abs(drop_rate) > 1e-5: tmp = layers.dropout(x=tmp, dropout_prob=drop_rate) pool_out = layers.pool2d( input=tmp, pool_size=pool_size, pool_type=pool_type, pool_stride=pool_stride, use_cudnn=use_cudnn) return pool_out def sequence_conv_pool(input, num_filters, filter_size, param_attr=None, act="sigmoid", pool_type="max"): conv_out = layers.sequence_conv( input=input, num_filters=num_filters, filter_size=filter_size, param_attr=param_attr, act=act) pool_out = layers.sequence_pool(input=conv_out, pool_type=pool_type) return pool_out def glu(input, dim=-1): """ The gated linear unit composed by split, sigmoid activation and elementwise multiplication. Specifically, Split the input into two equal sized parts :math:`a` and :math:`b` along the given dimension and then compute as following: .. math:: {GLU}(a, b)= a \otimes \sigma(b) Refer to `Language Modeling with Gated Convolutional Networks `_. Args: input (Variable): The input variable which is a Tensor or LoDTensor. dim (int): The dimension along which to split. If :math:`dim < 0`, the dimension to split along is :math:`rank(input) + dim`. Returns: Variable: The Tensor variable with half the size of input. Examples: .. code-block:: python # x is a Tensor variable with shape [3, 6, 9] fluid.nets.glu(input=x, dim=1) # shape of output: [3, 3, 9] """ a, b = layers.split(input, num_or_sections=2, dim=dim) act_b = layers.sigmoid(x=b) out = layers.elementwise_mul(x=a, y=act_b) return out def dot_product_attention(querys, keys, values): """ The dot-product attention. Attention mechanism can be seen as mapping a query and a set of key-value pairs to an output. The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function (dot-product here) of the query with the corresponding key. The dot-product attention can be implemented through (batch) matrix multipication as follows: .. math:: Attention(Q, K, V)= softmax(QK^\mathrm{T})V Refer to `Attention Is All You Need `_. Note that batch data containing sequences with different lengths is not supported by this because of the (batch) matrix multipication. Args: query (Variable): The input variable which is a Tensor or LoDTensor. key (Variable): The input variable which is a Tensor or LoDTensor. value (Variable): The input variable which is a Tensor or LoDTensor. Returns: tuple: The Tensor variables representing the output and attention scores. Examples: .. code-block:: python # Suppose q, k, v are tensor variables with the following shape: # q: [3, 5, 9], k: [3, 6, 9], v: [3, 6, 10] out, attn_scores = fluid.nets.dot_product_attention(q, k, v) out.shape # [3, 5, 10] attn_scores.shape # [3, 5, 6] """ assert keys.shape[-2] == values.shape[ -2], 'The shapes of keys and values mismatch.' assert querys.shape[-1] == keys.shape[ -1], 'The shapes of querys and keys mismatch.' product = layers.matmul(x=querys, y=keys, transpose_y=True) attn_scores = layers.reshape( x=layers.reshape( x=product, shape=[-1, product.shape[-1]], act='softmax'), shape=product.shape) out = layers.matmul(attn_scores, values) return out, attn_scores