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ParakeetRebeccaRosario/parakeet/modules/weight_norm.py

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add models & modules back
2019-11-25 11:40:52 +08:00
# Copyright (c) 2019 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.
import math
import numpy as np
from six.moves import reduce
from copy import deepcopy
import paddle
from paddle import fluid
import paddle.fluid.dygraph as dg
from paddle.fluid import core
from paddle.fluid.layers import utils
from paddle.fluid.framework import Variable
from paddle.fluid.initializer import Normal, Constant, NumpyArrayInitializer
def _norm(p, dim):
"""Computes the norm over all dimensions except dim.
It differs from pytorch implementation that it does not keep dim.
This difference is related with the broadcast mechanism in paddle.
Read elementeise_mul for more.
"""
if dim is None:
return np.linalg.norm(p, ord=2, axis=None)
elif dim == 0:
p = np.reshape(p, newshape=(p.shape[0], -1))
return np.linalg.norm(p, ord=2, axis=1)
elif dim == p.ndim - 1:
p = np.reshape(p, newshape=(-1, p.shape[-1]))
return np.linalg.norm(p, ord=2, axis=0)
else:
perm = list(range(p.ndim))
perm[0] = dim
perm[dim] = 0
return _norm(np.transpose(p, axes=perm))
class FC(dg.Layer):
"""
**Fully Connected Layer**
This function creates a fully connected layer in the network. It can take
one or multiple tensors as its inputs(input can be a list of Variable, see
Args in detail). It creates a pair of variables called (magnitude(g),
direction(V)) for each input tensor. Elementwise_mul(V, g) 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 [M, `size`],
where M is batch size. If multiple input tensors are given, the results of
multiple output tensors with shape [M, `size`] will be summed up. If bias_attr
is not None, a bias variable will be created and added to the output.
Finally, if activation is not None, it will be applied to the output as well.
When the input is single tensor:
.. math::
Out = Act({X(normalize(V)g) + b})
When the input are multiple tensors:
.. math::
Out = Act({\sum_{i=0}^{N-1}X_i(V_ig_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:`V_i`: The i-th direction matrix corresponding i-th input tensor.
* :math:`g_i`: The i-th magnitude vector 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.
See below for an example.
.. code-block:: text
Given:
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:
name_scope(str): The name of this class.
size(int): The number of output units in this layer.
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-dimensional matrix. The parameter `num_flatten_dims` determines how the input
tensor is flattened: the first `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 `rank(X) - num_flatten_dims` dimensions are flattened to
form the second dimension of the final matrix (width of the matrix). For example, suppose
`X` is a 5-dimensional tensor with a shape [2, 3, 4, 5, 6], and `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|list of ParamAttr|None): The parameter attribute for learnable
parameters/weights of this layer.
bias_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for the bias
of this layer. If it is set to False, no bias will be added to the output units.
If it is set to None, the bias is initialized zero. Default: None.
act (str|None): Activation to be applied to the output of this layer.
is_test(bool): A flag indicating whether execution is in test phase. Default: False
dtype(str): Dtype used for weight
Raises:
ValueError: If rank of the input tensor is less than 2.
Examples:
.. code-block:: python
from paddle.fluid.dygraph.base import to_variable
import paddle.fluid as fluid
from paddle.fluid.dygraph import FC
import numpy as np
data = np.random.uniform( -1, 1, [30, 10, 32] ).astype('float32')
with fluid.dygraph.guard():
fc = FC( "fc", 64, num_flatten_dims=2)
data = to_variable( data )
conv = fc( data )
"""
def __init__(self,
name_scope,
size,
num_flatten_dims=1,
epsilon=1e-30,
param_attr=None,
bias_attr=None,
act=None,
is_test=False,
dtype="float32"):
super(FC, self).__init__(name_scope, dtype)
self._size = size
self._num_flatten_dims = num_flatten_dims
self._epsilon = epsilon
self._dtype = dtype
self._param_attr = param_attr
self._bias_attr = bias_attr
self._act = act
self.__g = list()
self.__v = list()
@property
def _v(self, i=0):
return self.__v[i]
@property
def _g(self, i=0):
return self.__g[i]
@_v.setter
def _v(self, value, i=0):
assert isinstance(value, Parameter)
self.__v[i] = value
@_g.setter
def _g(self, value, i=0):
assert isinstance(value, Parameter)
self.__g[i] = value
def _build_once(self, input):
i = 0
for inp, param in self._helper.iter_inputs_and_params(input,
self._param_attr):
input_shape = inp.shape
param_shape = [
reduce(lambda a, b: a * b, input_shape[self._num_flatten_dims:],
1)
] + [self._size]
self.__v.append(
self.add_parameter(
"_v%d" % i,
self.create_parameter(
attr=param,
shape=param_shape,
dtype=self._dtype,
is_bias=False)))
magnitude_shape = param_shape[1:]
magnitude_value = np.linalg.norm(self.__v[i].numpy(), ord=2, axis=0)
self.__g.append(
self.add_parameter(
"_g%d" % i,
self.create_parameter(
attr=fluid.ParamAttr(
initializer=fluid.initializer.NumpyArrayInitializer(
magnitude_value)),
shape=magnitude_shape,
dtype=self._dtype,
is_bias=False)))
i += 1
size = list([self._size])
self._b = self.create_parameter(
attr=self._bias_attr, shape=size, dtype=self._dtype, is_bias=True)
def forward(self, input):
mul_results = list()
i = 0
for inp, param in self._helper.iter_inputs_and_params(input,
self._param_attr):
v_norm = self._helper.create_variable_for_type_inference(
self._dtype)
v_normalized = self._helper.create_variable_for_type_inference(
self._dtype)
self._helper.append_op(
type="norm",
inputs={"X": self.__v[i]},
outputs={"Out": v_normalized,
"Norm": v_norm},
attrs={"axis": 0,
"epsilon": self._epsilon})
weight = self._helper.create_variable_for_type_inference(
self._dtype)
self._helper.append_op(
type="elementwise_mul",
inputs={"X": [v_normalized],
"Y": [self.__g[i]]},
outputs={"Out": [weight]},
attrs={"axis": 1})
tmp = self._helper.create_variable_for_type_inference(self._dtype)
self._helper.append_op(
type="mul",
inputs={"X": inp,
"Y": weight},
outputs={"Out": tmp},
attrs={
"x_num_col_dims": self._num_flatten_dims,
"y_num_col_dims": 1
})
i += 1
mul_results.append(tmp)
if len(mul_results) == 1:
pre_bias = mul_results[0]
else:
pre_bias = self._helper.create_variable_for_type_inference(
self._dtype)
self._helper.append_op(
type="sum",
inputs={"X": mul_results},
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 dygraph mode
return self._helper.append_activation(pre_activation, act=self._act)
class Conv2D(dg.Layer):
"""
The convolution2D layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input and
Output 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.
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
<http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`
for more detials.
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 ((Vg) \\ast X + b)
Where:
* :math:`X`: Input value, a tensor with NCHW format.
* :math:`V`: Filter direction value, a tensor with MCHW format.
* :math:`g`: Filter magnitude value, a tensor with M 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:
name_scope(str) : The name for this class.
num_filters(int): The number of filter. It is as same as the output
image channel.
filter_size (int|tuple|None): 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 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
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
Examples:
.. code-block:: python
from paddle.fluid.dygraph.base import to_variable
import paddle.fluid as fluid
from paddle.fluid.dygraph import Conv2D
import numpy as np
data = np.random.uniform( -1, 1, [10, 3, 32, 32] ).astype('float32')
with fluid.dygraph.guard():
conv2d = Conv2D( "conv2d", 2, 3)
data = to_variable( data )
conv = conv2d( data )
"""
def __init__(self,
name_scope,
num_filters,
filter_size,
stride=1,
padding=0,
dilation=1,
groups=None,
param_attr=None,
bias_attr=None,
use_cudnn=True,
act=None,
epsilon=1e-30,
dtype="float32"):
assert param_attr is not False, "param_attr should not be False here."
super(Conv2D, self).__init__(name_scope, dtype)
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._filter_size = filter_size
self._num_filters = num_filters
self._param_attr = param_attr
self._bias_attr = bias_attr
self._epsilon = epsilon
self._dtype = dtype
# if (self._num_channels == self._groups and
# num_filters % self._num_channels == 0 and not self._use_cudnn):
# self._l_type = 'depthwise_conv2d'
# else:
# TODO(jiabin): recover the usage of depthwise_conv2d when it's
# kernel fixed https://github.com/PaddlePaddle/Paddle/issues/17275
self._l_type = "conv2d"
def _build_once(self, input):
self._num_channels = input.shape[1]
if self._groups is None:
num_filter_channels = self._num_channels
else:
if self._num_channels % self._groups != 0:
raise ValueError("num_channels must be divisible by groups.")
num_filter_channels = self._num_channels // self._groups
filter_size = utils.convert_to_list(self._filter_size, 2, "filter_size")
filter_shape = [self._num_filters, int(num_filter_channels)
] + filter_size
def _get_default_param_initializer():
filter_elem_num = filter_size[0] * filter_size[
1] * self._num_channels
std = (2.0 / filter_elem_num)**0.5
return Normal(0.0, std, 0)
# weight_v
self._filter_param_v = self.create_parameter(
attr=self._param_attr,
shape=filter_shape,
dtype=self._dtype,
default_initializer=_get_default_param_initializer())
# weight_g
norm_value = _norm(
self._filter_param_v.numpy(), dim=0) # CAUTION: hard-code
self._filter_param_g = self.create_parameter(
attr=fluid.ParamAttr(
initializer=fluid.initializer.NumpyArrayInitializer(
norm_value)),
shape=norm_value.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=self._bias_attr,
shape=[self._num_filters],
dtype=self._dtype,
is_bias=True)
def forward(self, input):
matrix = self._helper.create_variable_for_type_inference(self._dtype)
tmp = self._helper.create_variable_for_type_inference(self._dtype)
new_shape = [
self._filter_param_v.shape[0],
reduce(lambda x, y: x * y, self._filter_param_v.shape[1:], 1),
]
self._helper.append_op(
type="reshape2",
inputs={"X": self._filter_param_v},
attrs={"shape": new_shape},
outputs={"Out": matrix,
"XShape": tmp})
m_norm = self._helper.create_variable_for_type_inference(self._dtype)
m_normalized = self._helper.create_variable_for_type_inference(
self._dtype)
self._helper.append_op(
type="norm",
inputs={"X": matrix},
outputs={"Out": m_normalized,
"Norm": m_norm},
attrs={"axis": 1,
"epsilon": self._epsilon})
v_normalized = self._helper.create_variable_for_type_inference(
self._dtype)
tmp2 = self._helper.create_variable_for_type_inference(self._dtype)
self._helper.append_op(
type="reshape2",
inputs={"X": m_normalized},
attrs={"shape": self._filter_param_v.shape},
outputs={"Out": v_normalized,
"XShape": tmp2})
filter_param = self._helper.create_variable_for_type_inference(
self._dtype)
self._helper.append_op(
type="elementwise_mul",
inputs={"X": [v_normalized],
"Y": [self._filter_param_g]},
outputs={"Out": [filter_param]},
attrs={"axis": 0}, # CAUTION: hard-code
)
pre_bias = self._helper.create_variable_for_type_inference(
dtype=self._dtype)
self._helper.append_op(
type=self._l_type,
inputs={"Input": input,
"Filter": 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,
})
if self._bias_param is not None:
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})
else:
pre_act = pre_bias
# Currently, we don't support inplace in dygraph mode
return self._helper.append_activation(pre_act, act=self._act)
class Conv2DTranspose(dg.Layer):
"""
**Convlution2D transpose layer**
The convolution2D transpose layer calculates the output based on the input,
filter, and dilations, strides, paddings. Input(Input) and output(Output)
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(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>`_.
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 ((Vg) \\ast X + b)
Where:
* :math:`X`: Input value, a tensor with NCHW format.
* :math:`V`: Filter value, a tensor with MCHW format.
* :math:`g`: Filter value, a tensor with M 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_{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] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\\\
W^\prime_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + 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] )
Args:
name_scope(str): The name of this class.
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 two integers, (image_H, image_W). 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.
filter_size(int|tuple|None): 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. None if use output size to
calculate filter_size. Default: None.
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.
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.
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 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|None): 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|None): 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): 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.
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
import paddle.fluid as fluid
import numpy
with fluid.dygraph.guard():
data = numpy.random.random((3, 32, 32)).astype('float32')
conv2DTranspose = fluid.dygraph.nn.Conv2DTranspose(
'Conv2DTranspose', num_filters=2, filter_size=3)
ret = conv2DTranspose(fluid.dygraph.base.to_variable(data))
"""
def __init__(self,
name_scope,
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,
epsilon=1e-30,
act=None,
dtype="float32"):
super(Conv2DTranspose, self).__init__(name_scope, dtype)
assert (param_attr is not False
), "param_attr should not be False in conv2d_transpose."
self._param_attr = param_attr
self._bias_attr = bias_attr
self._groups = groups
self._num_filters = num_filters
self._use_cudnn = use_cudnn
self._padding = padding
self._stride = stride
self._dilation = dilation
self._filter_size = filter_size
self._output_size = output_size
self._op_type = "conv2d_transpose"
self._epsilon = epsilon
def _build_once(self, input):
input_channel = input.shape[1]
if (input_channel == self._groups and
self._num_filters == input_channel and not self._use_cudnn):
self._op_type = "depthwise_conv2d_transpose"
if not isinstance(input, Variable):
raise TypeError("Input of conv2d_transpose must be Variable")
self._padding = utils.convert_to_list(self._padding, 2, "padding")
self._stride = utils.convert_to_list(self._stride, 2, "stride")
self._dilation = utils.convert_to_list(self._dilation, 2, "dilation")
if not isinstance(self._use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
if self._filter_size is None:
if self._output_size is None:
raise ValueError(
"output_size must be set when filter_size is None")
if isinstance(self._output_size, int):
self._output_size = [self._output_size, self._output_size]
h_in = input.shape[2]
w_in = input.shape[3]
filter_size_h = (self._output_size[0] -
(h_in - 1) * self._stride[0] + 2 * self._padding[0]
- 1) // self._dilation[0] + 1
filter_size_w = (self._output_size[1] -
(w_in - 1) * self._stride[1] + 2 * self._padding[1]
- 1) // self._dilation[1] + 1
self._filter_size = [filter_size_h, filter_size_w]
else:
self._filter_size = utils.convert_to_list(
self._filter_size, 2, "conv2d_transpose.filter_size")
if self._output_size is None:
self._output_size = []
elif isinstance(self._output_size, list) or isinstance(
self._output_size, int):
self._output_size = utils.convert_to_list(self._output_size, 2,
"output_size")
else:
raise ValueError("output_size should be list or int")
self._padding = utils.convert_to_list(self._padding, 2, "padding")
self._groups = 1 if self._groups is None else self._groups
filter_shape = [
input_channel,
self._num_filters // self._groups,
] + self._filter_size
# img filter v (direction)
self._img_filter_v = self.create_parameter(
dtype=input.dtype, shape=filter_shape, attr=self._param_attr)
# img filter g (magnitude)
img_filter_magnitude = _norm(
self._img_filter_v.numpy(), dim=0) # CAUTION: hard-code
self._img_filter_g = self.create_parameter(
dtype=input.dtype,
shape=img_filter_magnitude.shape,
attr=fluid.ParamAttr(
initializer=NumpyArrayInitializer(img_filter_magnitude)))
self._img_bias = self.create_parameter(
attr=self._bias_attr,
shape=[self._num_filters],
dtype=self._dtype,
is_bias=True)
def forward(self, input):
matrix = self._helper.create_variable_for_type_inference(self._dtype)
tmp = self._helper.create_variable_for_type_inference(self._dtype)
new_shape = [
self._img_filter_v.shape[0],
reduce(lambda x, y: x * y, self._img_filter_v.shape[1:], 1),
]
self._helper.append_op(
type="reshape2",
inputs={"X": self._img_filter_v},
attrs={"shape": new_shape},
outputs={"Out": matrix,
"XShape": tmp})
m_norm = self._helper.create_variable_for_type_inference(self._dtype)
m_normalized = self._helper.create_variable_for_type_inference(
self._dtype)
self._helper.append_op(
type="norm",
inputs={"X": matrix},
outputs={"Out": m_normalized,
"Norm": m_norm},
attrs={"axis": 1,
"epsilon": self._epsilon})
v_normalized = self._helper.create_variable_for_type_inference(
self._dtype)
tmp2 = self._helper.create_variable_for_type_inference(self._dtype)
self._helper.append_op(
type="reshape2",
inputs={"X": m_normalized},
attrs={"shape": self._img_filter_v.shape},
outputs={"Out": v_normalized,
"XShape": tmp2})
img_filter = self._helper.create_variable_for_type_inference(
self._dtype)
self._helper.append_op(
type="elementwise_mul",
inputs={"X": [v_normalized],
"Y": [self._img_filter_g]},
outputs={"Out": [img_filter]},
attrs={"axis": 0}, # CAUTION: hard-code
)
pre_bias = self._helper.create_variable_for_type_inference(
dtype=input.dtype)
self._helper.append_op(
type=self._op_type,
inputs={"Input": [input],
"Filter": [img_filter]},
outputs={"Output": pre_bias},
attrs={
"output_size": self._output_size,
"strides": self._stride,
"paddings": self._padding,
"dilations": self._dilation,
"groups": self._groups,
"use_cudnn": self._use_cudnn,
})
if self._img_bias is not None:
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._img_bias]},
outputs={"Out": [pre_act]},
attrs={"axis": 1})
else:
pre_act = pre_bias
out = self._helper.append_activation(pre_act)
return out