nn.py

# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import print_function

from six.moves import reduce

from .. import core
from ..layers import utils
from . import layers
from ..framework import Variable, OpProtoHolder
from ..param_attr import ParamAttr
from ..initializer import Normal, Constant
__all__ = ['Conv2D', 'Pool2D', 'FC', 'BatchNorm', 'Embedding']


class Conv2D(layers.Layer):
    def __init__(self,
                 name_scope,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=None,
                 use_cudnn=True,
                 act=None,
                 param_attr=None,
                 bias_attr=None,
                 dtype=core.VarDesc.VarType.FP32):
        assert param_attr is not False, "param_attr should not be False here."
        super(Conv2D, self).__init__(name_scope)
        self._groups = groups
        self._stride = utils.convert_to_list(stride, 2, 'stride')
        self._padding = utils.convert_to_list(padding, 2, 'padding')
        self._dilation = utils.convert_to_list(dilation, 2, 'dilation')
        self._act = act
        if not isinstance(use_cudnn, bool):
            raise ValueError("use_cudnn should be True or False")
        self._use_cudnn = use_cudnn
        self._num_channels = num_channels
        if (self._num_channels == self._groups and
                num_filters % self._num_channels == 0 and not self._use_cudnn):
            self._l_type = 'depthwise_conv2d'
        else:
            self._l_type = 'conv2d'

        if groups is None:
            num_filter_channels = num_channels
        else:
            if num_channels % groups != 0:
                raise ValueError("num_channels must be divisible by groups.")
            num_filter_channels = num_channels // groups
        filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
        filter_shape = [num_filters, int(num_filter_channels)] + filter_size

        def _get_default_param_initializer():
            filter_elem_num = filter_size[0] * filter_size[1] * num_channels
            std = (2.0 / filter_elem_num)**0.5
            return Normal(0.0, std, 0)

        self._filter_param = self.create_parameter(
            attr=param_attr,
            shape=filter_shape,
            dtype=self._dtype,
            default_initializer=_get_default_param_initializer())

        if self._use_cudnn:
            self.create_variable(
                name="kCUDNNFwdAlgoCache",
                persistable=True,
                type=core.VarDesc.VarType.RAW)
            self.create_variable(
                name="kCUDNNBwdDataAlgoCache",
                persistable=True,
                type=core.VarDesc.VarType.RAW)
            self.create_variable(
                name="kCUDNNBwdFilterAlgoCache",
                persistable=True,
                type=core.VarDesc.VarType.RAW)

        self._bias_param = self.create_parameter(
            attr=bias_attr,
            shape=[num_filters],
            dtype=self._dtype,
            is_bias=True)

    def forward(self, input):
        pre_bias = self._helper.create_variable_for_type_inference(
            dtype=self._dtype)

        self._helper.append_op(
            type=self._l_type,
            inputs={
                'Input': input,
                'Filter': self._filter_param,
            },
            outputs={"Output": pre_bias},
            attrs={
                'strides': self._stride,
                'paddings': self._padding,
                'dilations': self._dilation,
                'groups': self._groups if self._groups else 1,
                'use_cudnn': self._use_cudnn,
                'use_mkldnn': False,
            })

        pre_act = self._helper.create_variable_for_type_inference(
            dtype=self._dtype)

        self._helper.append_op(
            type='elementwise_add',
            inputs={'X': [pre_bias],
                    'Y': [self._bias_param]},
            outputs={'Out': [pre_act]},
            attrs={'axis': 1})

        # Currently, we don't support inplace in imperative mode
        return self._helper.append_activation(pre_act, act=self._act)


class Pool2D(layers.Layer):
    def __init__(self,
                 name_scope,
                 pool_size=-1,
                 pool_type="max",
                 pool_stride=1,
                 pool_padding=0,
                 global_pooling=False,
                 use_cudnn=True,
                 ceil_mode=False,
                 exclusive=True,
                 dtype=core.VarDesc.VarType.FP32):
        if pool_type not in ["max", "avg"]:
            raise ValueError(
                "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
                str(pool_type))

        if global_pooling is False and pool_size == -1:
            raise ValueError(
                "When the global_pooling is False, pool_size must be passed "
                "and be a valid value. Received pool_size: " + str(pool_size))

        if not isinstance(use_cudnn, bool):
            raise ValueError("use_cudnn should be True or False")

        super(Pool2D, self).__init__(name_scope, dtype=dtype)

        self._pool_type = pool_type
        self._pool_size = utils.convert_to_list(pool_size, 2, 'pool_size')
        self._pool_padding = utils.convert_to_list(pool_padding, 2,
                                                   'pool_padding')
        self._pool_stride = utils.convert_to_list(pool_stride, 2, 'pool_stride')
        self._global_pooling = global_pooling
        self._use_cudnn = use_cudnn
        self._ceil_mode = ceil_mode
        self._exclusive = exclusive
        self._l_type = 'pool2d'

    def forward(self, input):
        pool_out = self._helper.create_variable_for_type_inference(self._dtype)

        self._helper.append_op(
            type=self._l_type,
            inputs={"X": input},
            outputs={"Out": pool_out},
            attrs={
                "pooling_type": self._pool_type,
                "ksize": self._pool_size,
                "global_pooling": self._global_pooling,
                "strides": self._pool_stride,
                "paddings": self._pool_padding,
                "use_cudnn": self._use_cudnn,
                "ceil_mode": self._ceil_mode,
                "use_mkldnn": False,
                "exclusive": self._exclusive,
            })
        return pool_out


class FC(layers.Layer):
    def __init__(self,
                 name_scope,
                 size,
                 param_attr=None,
                 bias_attr=None,
                 num_flatten_dims=1,
                 dtype=core.VarDesc.VarType.FP32,
                 act=None):
        super(FC, self).__init__(name_scope)

        self._size = size
        self._num_flatten_dims = num_flatten_dims
        self._dtype = dtype
        self._param_attr = param_attr
        self._bias_attr = bias_attr
        self._act = act

    def _build_once(self, input):
        input_shape = input.shape
        param_shape = [
            reduce(lambda a, b: a * b, input_shape[self._num_flatten_dims:], 1)
        ] + [self._size]
        self._w = self.create_parameter(
            attr=self._param_attr,
            shape=param_shape,
            dtype=self._dtype,
            is_bias=False)

        if self._bias_attr:
            size = list([self._size])
            self._b = self.create_parameter(
                attr=self._bias_attr,
                shape=size,
                dtype=self._dtype,
                is_bias=True)
        else:
            self._b = None

    def forward(self, input):
        tmp = self._helper.create_variable_for_type_inference(self._dtype)
        self._helper.append_op(
            type="mul",
            inputs={"X": input,
                    "Y": self._w},
            outputs={"Out": tmp},
            attrs={
                "x_num_col_dims": self._num_flatten_dims,
                "y_num_col_dims": 1
            })

        pre_bias = self._helper.create_variable_for_type_inference(self._dtype)
        self._helper.append_op(
            type="sum",
            inputs={"X": [tmp]},
            outputs={"Out": pre_bias},
            attrs={"use_mkldnn": False})

        if self._b:
            pre_activation = self._helper.create_variable_for_type_inference(
                dtype=self._dtype)
            self._helper.append_op(
                type='elementwise_add',
                inputs={'X': [pre_bias],
                        'Y': [self._b]},
                outputs={'Out': [pre_activation]},
                attrs={'axis': self._num_flatten_dims})
        else:
            pre_activation = pre_bias
        # Currently, we don't support inplace in imperative mode
        return self._helper.append_activation(pre_activation, act=self._act)


class BatchNorm(layers.Layer):
    def __init__(self,
                 name_scope,
                 num_channels,
                 act=None,
                 is_test=False,
                 momentum=0.9,
                 epsilon=1e-05,
                 param_attr=None,
                 bias_attr=None,
                 dtype=core.VarDesc.VarType.FP32,
                 data_layout='NCHW',
                 in_place=False,
                 moving_mean_name=None,
                 moving_variance_name=None,
                 do_model_average_for_mean_and_var=False,
                 fuse_with_relu=False,
                 use_global_stats=False):
        super(BatchNorm, self).__init__(name_scope)
        self._param_attr = param_attr
        self._param_attr = bias_attr
        self._act = act

        assert bias_attr is not False, "bias_attr should not be False in batch_norm."

        if dtype == core.VarDesc.VarType.FP16:
            self._dtype = core.VarDesc.VarType.FP32
        else:
            self._dtype = dtype

        param_shape = [num_channels]

        # create parameter
        self._scale = self.create_parameter(
            attr=self._param_attr,
            shape=param_shape,
            dtype=self._dtype,
            default_initializer=Constant(1.0))
        if use_global_stats and self._param_attr.learning_rate == 0.:
            self._scale._stop_gradient = True

        self._bias = self.create_parameter(
            attr=self._param_attr,
            shape=param_shape,
            dtype=self._dtype,
            is_bias=True)
        if use_global_stats and self._param_attr.learning_rate == 0.:
            self._bias._stop_gradient = True

        self._mean = self.create_parameter(
            attr=ParamAttr(
                name=moving_mean_name,
                initializer=Constant(0.0),
                trainable=False,
                do_model_average=do_model_average_for_mean_and_var),
            shape=param_shape,
            dtype=self._dtype)
        self._mean._stop_gradient = True

        self._variance = self.create_parameter(
            attr=ParamAttr(
                name=moving_variance_name,
                initializer=Constant(1.0),
                trainable=False,
                do_model_average=do_model_average_for_mean_and_var),
            shape=param_shape,
            dtype=self._dtype)
        self._variance._stop_gradient = True

        self._in_place = in_place
        self._momentum = momentum
        self._epsilon = epsilon
        self._is_test = is_test
        self._fuse_with_relu = fuse_with_relu
        self._use_global_stats = use_global_stats

    def _build_once(self, input):
        pass

    def forward(self, input):
        # create output
        # mean and mean_out share the same memory
        mean_out = self._mean
        # variance and variance out share the same memory
        variance_out = self._variance

        saved_mean = self._helper.create_variable_for_type_inference(
            dtype=self._dtype, stop_gradient=True)
        saved_variance = self._helper.create_variable_for_type_inference(
            dtype=self._dtype, stop_gradient=True)
        batch_norm_out = input if self._in_place else self._helper.create_variable_for_type_inference(
            self._dtype)

        self._helper.append_op(
            type="batch_norm",
            inputs={
                "X": input,
                "Scale": self._scale,
                "Bias": self._bias,
                "Mean": self._mean,
                "Variance": self._variance
            },
            outputs={
                "Y": batch_norm_out,
                "MeanOut": mean_out,
                "VarianceOut": variance_out,
                "SavedMean": saved_mean,
                "SavedVariance": saved_variance
            },
            attrs={
                "momentum": self._momentum,
                "epsilon": self._epsilon,
                "is_test": self._is_test,
                "use_mkldnn": False,
                "fuse_with_relu": self._fuse_with_relu,
                "use_global_stats": self._use_global_stats
            })

        # Currently, we don't support inplace in imperative mode
        return self._helper.append_activation(batch_norm_out, self._act)


class Embedding(layers.Layer):
    """
    **Embedding Layer**

    This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
    a lookup table. The result of this lookup is the embedding of each ID in the
    :attr:`input`.

    All the input variables are passed in as local variables to the LayerHelper
    constructor.

    Args:
        name_scope: See base class.
        size(tuple|list): The shape of the look up table parameter. It should
            have two elements which indicate 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.
        is_distributed(bool): Whether to run lookup table from remote parameter server.
        padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup.
            Otherwise the given :attr:`padding_idx` indicates padding the output
            with zeros whenever lookup encounters it in :attr:`input`. If
            :math:`padding_idx < 0`, the :attr:`padding_idx` to use in lookup is
            :math:`size[0] + dim`.
        param_attr(ParamAttr): Parameters for this layer
        dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc

    Returns:
        Variable: The tensor variable storing the embeddings of the \
                  supplied inputs.

    Examples:
        .. code-block:: python

          dict_size = len(dataset.ids)
          input = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
          embedding = fluid.imperative.Embedding(size=[dict_size, 16])
          fc = embedding(input)
    """

    def __init__(self,
                 name_scope,
                 size,
                 is_sparse=False,
                 is_distributed=False,
                 padding_idx=None,
                 param_attr=None,
                 dtype='float32'):

        super(Embedding, self).__init__(name_scope)
        self._size = size
        self._is_sparse = is_sparse
        self._is_distributed = is_distributed

        self._padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (
            size[0] + padding_idx)

        self._param_attr = param_attr
        self._dtype = dtype
        self._remote_prefetch = self._is_sparse and (not self._is_distributed)
        if self._remote_prefetch:
            assert self._is_sparse is True and self._is_distributed is False

        self._w = self.create_parameter(
            attr=self._param_attr,
            shape=self._size,
            dtype=self._dtype,
            is_bias=False)

    def forward(self, input):
        out = self._helper.create_variable_for_type_inference(self._dtype)
        self._helper.append_op(
            type='lookup_table',
            inputs={'Ids': input,
                    'W': self._w},
            outputs={'Out': out},
            attrs={
                'is_sparse': self._is_sparse,
                'is_distributed': self._is_distributed,
                'remote_prefetch': self._remote_prefetch,
                'padding_idx': self._padding_idx
            })

        return out