提交 2183d017 编写于 作者: C chengduoZH

Add pool3d and conv3d_trans Python API

上级 3ab32532
...@@ -39,13 +39,16 @@ __all__ = [ ...@@ -39,13 +39,16 @@ __all__ = [
'chunk_eval', 'chunk_eval',
'sequence_conv', 'sequence_conv',
'conv2d', 'conv2d',
'conv3d',
'sequence_pool', 'sequence_pool',
'sequence_softmax', 'sequence_softmax',
'softmax', 'softmax',
'pool2d', 'pool2d',
'pool3d',
'batch_norm', 'batch_norm',
'beam_search_decode', 'beam_search_decode',
'conv2d_transpose', 'conv2d_transpose',
'conv3d_transpose',
'sequence_expand', 'sequence_expand',
'lstm_unit', 'lstm_unit',
'reduce_sum', 'reduce_sum',
...@@ -1385,13 +1388,12 @@ def conv3d(input, ...@@ -1385,13 +1388,12 @@ def conv3d(input,
The convolution3D layer calculates the output based on the input, filter The convolution3D layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input(Input) and and strides, paddings, dilations, groups parameters. Input(Input) and
Output(Output) are in NCHW format. Where N is batch size, C is the number of Output(Output) are in NCDHW 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. channels, D is the depth of the feature, H is the height of the feature,
The details of convolution layer, please refer UFLDL's `convolution, and W is the width of the feature. Convlution3D is similar with Convlution2D
<http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_ . but adds one dimension(depth). If bias attribution and activation type are
If bias attribution and activation type are provided, bias is added to the provided, bias is added to the output of the convolution, and the
output of the convolution, and the corresponding activation function is corresponding activation function is applied to the final result.
applied to the final result.
For each input :math:`X`, the equation is: For each input :math:`X`, the equation is:
...@@ -1401,8 +1403,8 @@ def conv3d(input, ...@@ -1401,8 +1403,8 @@ def conv3d(input,
In the above equation: In the above equation:
* :math:`X`: Input value, a tensor with NCHW format. * :math:`X`: Input value, a tensor with NCDHW format.
* :math:`W`: Filter value, a tensor with MCHW format. * :math:`W`: Filter value, a tensor with MCDHW format.
* :math:`\\ast`: Convolution operation. * :math:`\\ast`: Convolution operation.
* :math:`b`: Bias value, a 2-D tensor with shape [M, 1]. * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
* :math:`\\sigma`: Activation function. * :math:`\\sigma`: Activation function.
...@@ -1433,16 +1435,16 @@ def conv3d(input, ...@@ -1433,16 +1435,16 @@ def conv3d(input,
num_filters(int): The number of filter. It is as same as the output num_filters(int): The number of filter. It is as same as the output
image channel. image channel.
filter_size (int|tuple|None): The filter size. If filter_size is a tuple, filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_D, filter_size_H, filter_size_W). it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
Otherwise, the filter will be a square. Otherwise, the filter will be a square.
stride (int|tuple): The stride size. If stride is a tuple, it must stride (int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_D, stride_H, stride_W). Otherwise, the contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
stride_D = stride_H = stride_W = stride. Default: stride = 1. stride_D = stride_H = stride_W = stride. Default: stride = 1.
padding (int|tuple): The padding size. If padding is a tuple, it must padding (int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_D, padding_H, padding_W). Otherwise, the contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
padding_D = padding_H = padding_W = padding. Default: padding = 0. padding_D = padding_H = padding_W = padding. Default: padding = 0.
dilation (int|tuple): The dilation size. If dilation is a tuple, it must dilation (int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_D, dilation_H, dilation_W). Otherwise, the contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1. dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
groups (int): The groups number of the Conv3d Layer. According to grouped groups (int): The groups number of the Conv3d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2, convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
...@@ -1528,7 +1530,7 @@ def conv3d(input, ...@@ -1528,7 +1530,7 @@ def conv3d(input,
'use_mkldnn': use_mkldnn 'use_mkldnn': use_mkldnn
}) })
pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=3) pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
return helper.append_activation(pre_act) return helper.append_activation(pre_act)
...@@ -1720,12 +1722,84 @@ def pool2d(input, ...@@ -1720,12 +1722,84 @@ def pool2d(input,
if not isinstance(use_cudnn, bool): if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False") raise ValueError("use_cudnn should be True or False")
helper = LayerHelper('pool2d', **locals()) l_type = 'conv2d'
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
helper.append_op(
type=l_type,
inputs={"X": input},
outputs={"Out": pool_out},
attrs={
"pooling_type": pool_type,
"ksize": pool_size,
"global_pooling": global_pooling,
"strides": pool_stride,
"paddings": pool_padding,
"use_cudnn": use_cudnn,
"ceil_mode": ceil_mode,
"use_mkldnn": use_mkldnn
})
return pool_out
def pool3d(input,
pool_size=-1,
pool_type="max",
pool_stride=1,
pool_padding=0,
global_pooling=False,
use_cudnn=True,
ceil_mode=False,
use_mkldnn=False,
name=None):
"""
This function adds the operator for pooling in 3-dimensions, using the
pooling configurations mentioned in input parameters.
Args:
input (Variable): ${input_comment}
pool_size (int): ${ksize_comment}
pool_type (str): ${pooling_type_comment}
pool_stride (int): stride of the pooling layer.
pool_padding (int): padding size.
global_pooling (bool): ${global_pooling_comment}
use_cudnn (bool): ${use_cudnn_comment}
ceil_mode (bool): ${ceil_mode_comment}
use_mkldnn (bool): ${use_mkldnn_comment}
name (str): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: output of pool3d layer.
"""
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))
pool_size = utils.convert_to_list(pool_size, 3, 'pool_size')
pool_padding = utils.convert_to_list(pool_padding, 3, 'pool_padding')
pool_stride = utils.convert_to_list(pool_stride, 3, 'pool_stride')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
l_type = "pool3d"
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype() dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype) pool_out = helper.create_tmp_variable(dtype)
helper.append_op( helper.append_op(
type="pool2d", type=l_type,
inputs={"X": input}, inputs={"X": input},
outputs={"Out": pool_out}, outputs={"Out": pool_out},
attrs={ attrs={
...@@ -2146,6 +2220,173 @@ def conv2d_transpose(input, ...@@ -2146,6 +2220,173 @@ def conv2d_transpose(input,
return out return out
def conv3d_transpose(input,
num_filters,
output_size=None,
filter_size=None,
padding=0,
stride=1,
dilation=1,
groups=None,
param_attr=None,
bias_attr=None,
use_cudnn=True,
act=None,
name=None):
"""
**Convlution3D transpose layer**
The convolution3D transpose layer calculates the output based on the input,
filter, and dilations, strides, paddings. Input(Input) and output(Output)
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(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 <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
For each input :math:`X`, the equation is:
.. math::
Out = W \\ast X
In the above equation:
* :math:`X`: Input value, a tensor with NCDHW format.
* :math:`W`: Filter value, a tensor with MCDHW format.
* :math:`\\ast` : Convolution transpose operation.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
different.
Example:
- Input:
Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$
Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$
- Output:
Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
Where
.. math::
D_{out} &= (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\
H_{out} &= (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\
W_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1
Args:
input(Variable): The input image with [N, C, D, H, W] format.
num_filters(int): The number of the filter. It is as same as the output
image channel.
output_size(int|tuple|None): The output image size. If output size is a
tuple, it must contain three integers, (image_D, image_H, image_W). This
parameter only works when filter_size is None.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
Otherwise, the filter will be a square. None if use output size to
calculate filter_size.
padding(int|tuple): The padding size. If padding is a tuple, it must
contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
padding_D = padding_H = padding_W = padding. Default: padding = 0.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
stride_D = stride_H = stride_W = stride. Default: stride = 1.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must
contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
groups(int): The groups number of the Conv3d transpose layer. Inspired by
grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
when group=2, the first half of the filters is only connected to the
first half of the input channels, while the second half of the
filters is only connected to the second half of the input channels.
Default: groups=1
param_attr(ParamAttr): The parameters to the Conv3d_transpose Layer.
Default: None
bias_attr(ParamAttr): Bias parameter for the Conv3d layer. 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. Default: None
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The tensor variable storing the convolution transpose result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 12, 32, 32], dtype='float32')
conv2d_transpose = fluid.layers.conv3d_transpose(
input=data, num_filters=2, filter_size=3)
"""
l_type = "conv3d_transpose"
helper = LayerHelper(l_type, **locals())
if not isinstance(input, Variable):
raise TypeError("Input of conv3d_transpose must be Variable")
input_channel = input.shape[1]
padding = utils.convert_to_list(padding, 3, 'padding')
stride = utils.convert_to_list(stride, 3, 'stride')
dilation = utils.convert_to_list(dilation, 3, 'dilation')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
if filter_size is None:
if output_size is None:
raise ValueError("output_size must be set when filter_size is None")
if isinstance(output_size, int):
output_size = [output_size, output_size]
d_in = input.shape[2]
h_in = input.shape[3]
w_in = input.shape[4]
filter_size_d = (output_size[0] - (d_in - 1) * stride[0] + 2 *
padding[0] - 1) / dilation[0] + 1
filter_size_h = (output_size[1] - (h_in - 1) * stride[1] + 2 *
padding[1] - 1) / dilation[1] + 1
filter_size_w = (output_size[2] - (w_in - 1) * stride[2] + 2 *
padding[2] - 1) / dilation[2] + 1
filter_size = [filter_size_d, filter_size_h, filter_size_w]
else:
filter_size = utils.convert_to_list(filter_size, 3,
'conv3d_transpose.filter_size')
groups = 1 if groups is None else groups
filter_shape = [input_channel, num_filters / groups] + filter_size
img_filter = helper.create_parameter(
dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
pre_bias = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type=l_type,
inputs={'Input': [input],
'Filter': [img_filter]},
outputs={'Output': pre_bias},
attrs={
'strides': stride,
'paddings': padding,
'dilations': dilation,
'groups': groups,
'use_cudnn': use_cudnn
})
pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
out = helper.append_activation(pre_act)
return out
def sequence_expand(x, y, ref_level=-1, name=None): def sequence_expand(x, y, ref_level=-1, name=None):
"""Sequence Expand Layer. This layer will expand the input variable **x** """Sequence Expand Layer. This layer will expand the input variable **x**
according to specified level lod of **y**. Please note that lod level of according to specified level lod of **y**. Please note that lod level of
......
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册