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deconvolution_2d.py
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deconvolution_2d.py
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import numpy
import chainer
from chainer.backends import cuda
from chainer.backends import intel64
from chainer import configuration
from chainer import function_node
import chainer.functions
from chainer.functions.connection import convolution_2d
from chainer.utils import argument
from chainer.utils import conv
from chainer.utils import type_check
import chainerx
if cuda.cudnn_enabled:
_cudnn_version = cuda.cuda.cudnn.getVersion() # type: ignore
def _pair(x):
if hasattr(x, '__getitem__'):
return x
return x, x
class Deconvolution2DFunction(function_node.FunctionNode):
cover_all = None
_use_ideep = False
def __init__(self, stride=1, pad=0, outsize=None, **kwargs):
dilate, groups = argument.parse_kwargs(
kwargs, ('dilate', 1), ('groups', 1),
deterministic='deterministic argument is not supported anymore. '
'Use chainer.using_config(\'cudnn_deterministic\', value) context '
'where value is either `True` or `False`.',
requires_x_grad='requires_x_grad argument is not supported '
'anymore. Just remove the argument. Note that whether to compute '
'the gradient w.r.t. x is automatically decided during '
'backpropagation.')
self.sy, self.sx = _pair(stride)
self.ph, self.pw = _pair(pad)
self.outh, self.outw = (None, None) if outsize is None else outsize
self.dy, self.dx = _pair(dilate)
self.groups = groups
if self.dx < 1 or self.dy < 1:
raise ValueError('Dilate should be positive, but {} is '
'supplied.'.format(dilate))
def check_type_forward(self, in_types):
n_in = in_types.size()
type_check.expect(2 <= n_in, n_in <= 3)
x_type, w_type = in_types[:2]
type_check.expect(
x_type.dtype.kind == 'f',
w_type.dtype.kind == 'f',
x_type.ndim == 4,
w_type.ndim == 4,
x_type.shape[1] == w_type.shape[0]
)
if self.outh is not None:
lower_bound = conv.get_conv_outsize(
self.outh, w_type.shape[2], self.sy, self.ph,
d=self.dy)
upper_bound = conv.get_conv_outsize(
self.outh, w_type.shape[2], self.sy, self.ph, cover_all=True,
d=self.dy)
type_check.expect(
lower_bound <= x_type.shape[2],
x_type.shape[2] <= upper_bound)
if self.outw is not None:
lower_bound = conv.get_conv_outsize(
self.outw, w_type.shape[3], self.sx, self.pw,
d=self.dx)
upper_bound = conv.get_conv_outsize(
self.outw, w_type.shape[3], self.sx, self.pw, cover_all=True,
d=self.dx)
type_check.expect(
lower_bound <= x_type.shape[3],
x_type.shape[3] <= upper_bound)
if type_check.eval(n_in) == 3:
b_type = in_types[2]
type_check.expect(
b_type.dtype == x_type.dtype,
b_type.ndim == 1,
# Need to consider the case that group count > 1.
# b_type.shape[0] == w_type.shape[1],
)
def _calc_out_size(self, x, W):
"""Calculates and stores `outh` and `outw`."""
kh, kw = W.shape[2:]
_, _, in_h, in_w = x.shape
# - k, m, n: shape of out_channel
# - b: number of inputs
# - h, w: height and width of kernels
# k, m, n, b, h, w -> b, k, m, n, h, w
if self.outh is None:
self.outh = conv.get_deconv_outsize(
in_h, kh, self.sy, self.ph, d=self.dy)
if self.outh <= 0:
raise RuntimeError('Height in the output must be positive.')
if self.outw is None:
self.outw = conv.get_deconv_outsize(
in_w, kw, self.sx, self.pw, d=self.dx)
if self.outw <= 0:
raise RuntimeError('Width in the output must be positive.')
def forward_cpu(self, inputs):
if ((self.dy == 1 and self.dx == 1)
and intel64.should_use_ideep('>=auto')
and intel64.inputs_all_ready(inputs)):
self._use_ideep = True
self.retain_inputs((0, 1)) # only retain x and W
if len(inputs) == 2:
(x, W), b = inputs, None
else:
x, W, b = inputs
self._calc_out_size(x, W)
if self.groups > 1:
# Grouped convolution implementation
return self._forward_grouped_convolution(x, W, b)
elif (intel64.should_use_ideep('>=auto')
and intel64.inputs_all_ready(inputs)):
# iDeep implementation
self._use_ideep = True
return self._forward_ideep(x, W, b)
else:
return self._forward_cpu_core(x, W, b)
def _forward_cpu_core(self, x, W, b):
if self._use_ideep:
return self._forward_ideep(x, W, b)
gcol = numpy.tensordot(W, x, (0, 1)).astype(x.dtype, copy=False)
gcol = numpy.rollaxis(gcol, 3)
y = conv.col2im_cpu(
gcol, self.sy, self.sx, self.ph, self.pw, self.outh, self.outw,
dy=self.dy, dx=self.dx)
# b, k, h, w
if b is not None:
y += b.reshape((1, b.size, 1, 1))
return y,
def _forward_ideep(self, x, W, b):
_, in_c, kh, kw = W.shape
n, _, in_h, in_w = x.shape
pd = (self.sy * (in_h - 1)
+ (kh + (kh - 1) * (self.dy - 1))
- self.outh - self.ph)
pr = (self.sx * (in_w - 1)
+ (kw + (kw - 1) * (self.dx - 1))
- self.outw - self.pw)
param = intel64.ideep.convolution2DParam(
(n, in_c, self.outh, self.outw),
self.dy, self.dx,
self.sy, self.sx,
self.ph, self.pw,
pd, pr)
y = intel64.ideep.convolution2D.BackwardData(
intel64.ideep.array(W),
intel64.ideep.array(x),
param)
if b is not None:
y += b.reshape((1, b.size, 1, 1))
return y,
def forward_gpu(self, inputs):
self.retain_inputs((0, 1)) # only retain x and W
if len(inputs) == 2:
(x, W), b = inputs, None
else:
x, W, b = inputs
self._calc_out_size(x, W)
self._set_cover_all(x, W)
use_cudnn = (
chainer.should_use_cudnn('>=auto')
and not self.cover_all
and x.dtype == W.dtype
and ((self.dy == 1 and self.dx == 1)
or (_cudnn_version >= 6000
and not configuration.config.cudnn_deterministic))
and (self.groups <= 1 or _cudnn_version >= 7000)
)
if use_cudnn:
# cuDNN implementation
return self._forward_cudnn(x, W, b)
elif self.groups > 1:
return self._forward_grouped_convolution(x, W, b)
else:
return self._forward_gpu_core(x, W, b)
def _forward_gpu_core(self, x, W, b):
# Implementation using col2im
gcol = cuda.cupy.tensordot(W, x, (0, 1)).astype(x.dtype,
copy=False)
# - k, m, n: shape of out_channel
# - b: number of inputs
# - h, w: height and width of kernels
# k, m, n, b, h, w -> b, k, m, n, h, w
gcol = cuda.cupy.rollaxis(gcol, 3)
y = conv.col2im_gpu(
gcol, self.sy, self.sx, self.ph, self.pw, self.outh, self.outw,
dy=self.dy, dx=self.dx)
if b is not None:
y += b.reshape(1, b.size, 1, 1)
return y,
def _forward_grouped_convolution(self, x, W, b):
# G: group count
# N: batch size
# kH, kW: kernel height, kernel width
# xC, xH, xW: x channels, x height, x width
# yC, yH, yW: y channels, y height, y width
G = self.groups
N, xC, xH, xW = x.shape
xCg = xC // G
_, yCg, kH, kW = W.shape # _ == xC
yC = yCg * G
x = x.transpose(1, 0, 2, 3) # (xC, N, xH, xW)
x = x.reshape(G, xCg, N * xH * xW)
W = W.reshape(G, xCg, yCg * kH * kW)
W = W.transpose(0, 2, 1) # (G, yCg*kH*kW, xCg)
# (G, yCg*kH*kW, N*xH*xW) = (G, yCg*kH*kW, xCg) @ (G, xCg, N*xH*xW)
col = convolution_2d._matmul(W, x).astype(x.dtype, copy=False)
col = col.reshape(yC, kH, kW, N, xH, xW)
col = col.transpose(3, 0, 1, 2, 4, 5) # (N, yC, kH, kW, xH, xW)
y = conv.col2im(col, self.sy, self.sx, self.ph, self.pw,
self.outh, self.outw, dy=self.dy, dx=self.dx)
if b is not None:
y += b.reshape(1, b.size, 1, 1)
return y,
def _forward_cudnn(self, x, W, b):
n = len(x)
yC = W.shape[1] * self.groups
y = cuda.cupy.empty((n, yC, self.outh, self.outw), dtype=x.dtype)
pad = (self.ph, self.pw)
stride = (self.sy, self.sx)
dilation = (self.dy, self.dx)
deterministic = configuration.config.cudnn_deterministic
auto_tune = configuration.config.autotune
tensor_core = configuration.config.use_cudnn_tensor_core
cuda.cudnn.convolution_backward_data(
W, x, b, y, pad, stride, dilation, self.groups,
deterministic=deterministic, auto_tune=auto_tune,
tensor_core=tensor_core)
return y,
def forward_chainerx(self, inputs):
# TODO(imanishi): Support it
if self.dy != 1 or self.dx != 1:
return chainer.Fallback
# TODO(imanishi): Support it
if self.groups != 1:
return chainer.Fallback
# TODO(imanishi): Support it
if any(a.dtype != inputs[0].dtype for a in inputs):
return chainer.Fallback
# TODO(imanishi): Support it
self._calc_out_size(inputs[0], inputs[1])
self._set_cover_all(inputs[0], inputs[1])
if self.cover_all:
return chainer.Fallback
stride = (self.sy, self.sx)
pad = (self.ph, self.pw)
outsize = None if self.outh is None else (self.outh, self.outw)
return chainerx.conv_transpose(
*inputs, stride=stride, pad=pad, outsize=outsize),
def backward(self, indexes, grad_outputs):
x, W = self.get_retained_inputs()
gy, = grad_outputs
ret = []
if 0 in indexes:
if self.cover_all is None:
self._set_cover_all(x, W)
gx = chainer.functions.convolution_2d(
gy, W, stride=(self.sy, self.sx), pad=(self.ph, self.pw),
cover_all=self.cover_all, dilate=(self.dy, self.dx),
groups=self.groups)
ret.append(gx)
if 1 in indexes:
if self.cover_all is None:
self._set_cover_all(x, W)
gW, = convolution_2d.Convolution2DGradW(self).apply((gy, x))
ret.append(gW)
if 2 in indexes:
gb = chainer.functions.sum(gy, axis=(0, 2, 3))
ret.append(gb)
return ret
def _set_cover_all(self, x, W):
in_h, in_w = x.shape[2:]
kh, kw = W.shape[2:]
self.cover_all = (
in_h != conv.get_conv_outsize(self.outh, kh, self.sy,
self.ph, d=self.dy) or
in_w != conv.get_conv_outsize(self.outw, kw, self.sx,
self.pw, d=self.dx))
def deconvolution_2d(x, W, b=None, stride=1, pad=0, outsize=None, **kwargs):
"""deconvolution_2d(x, W, b=None, stride=1, pad=0, outsize=None, *, \
dilate=1, groups=1)
Two dimensional deconvolution function.
This is an implementation of two-dimensional deconvolution. In most of deep
learning frameworks and papers, this function is called
**transposed convolution**. But because of historical reasons (e.g. paper
by Ziller `Deconvolutional Networks`_) and backward compatibility, this
function is called **deconvolution** in Chainer.
.. _Deconvolutional Networks: \
http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf
It takes three variables: input image ``x``,
the filter weight ``W``, and the bias vector ``b``.
Notation: here is a notation for dimensionalities.
- :math:`n` is the batch size.
- :math:`c_I` and :math:`c_O` are the number of the input and output
channels, respectively.
- :math:`h_I` and :math:`w_I` are the height and width of the input image,
respectively.
- :math:`h_K` and :math:`w_K` are the height and width of the filters,
respectively.
- :math:`h_P` and :math:`w_P` are the height and width of the spatial
padding size, respectively.
Let :math:`(s_Y, s_X)` be the stride of filter application. Then, the
output size :math:`(h_O, w_O)` is estimated by the following equations:
.. math::
h_O &= s_Y (h_I - 1) + h_K - 2h_P,\\\\
w_O &= s_X (w_I - 1) + w_K - 2w_P.
The output of this function can be non-deterministic when it uses cuDNN.
If ``chainer.configuration.config.deterministic`` is ``True`` and
cuDNN version is >= v3, it forces cuDNN to use a deterministic algorithm.
Deconvolution links can use a feature of cuDNN called autotuning, which
selects the most efficient CNN algorithm for images of fixed-size,
can provide a significant performance boost for fixed neural nets.
To enable, set `chainer.using_config('autotune', True)`
Args:
x (:class:`~chainer.Variable` or :ref:`ndarray`):
Input variable of shape :math:`(n, c_I, h_I, w_I)`.
W (:class:`~chainer.Variable` or :ref:`ndarray`):
Weight variable of shape :math:`(c_I, c_O, h_K, w_K)`.
b (None or :class:`~chainer.Variable` or :ref:`ndarray`):
Bias variable of length :math:`c_O` (optional).
stride (:class:`int` or pair of :class:`int` s):
Stride of filter applications. ``stride=s`` and ``stride=(s, s)``
are equivalent.
pad (:class:`int` or pair of :class:`int` s):
Spatial padding width for input arrays.
``pad=p`` and ``pad=(p, p)`` are equivalent.
outsize (None or :class:`tuple` of :class:`int` s):
Expected output size of deconvolutional operation.
It should be pair of height and width :math:`(h_O, w_O)`.
Default value is ``None`` and the outsize is estimated by
input size, stride and pad.
dilate (:class:`int` or pair of :class:`int` s):
Dilation factor of filter applications.
``dilate=d`` and ``dilate=(d, d)`` are equivalent.
groups (:class:`int`):
The number of groups to use grouped deconvolution.
The default is one, where grouped deconvolution is not used.
Returns:
~chainer.Variable:
Output variable of shape :math:`(n, c_O, h_O, w_O)`.
.. seealso::
:class:`~chainer.links.Deconvolution2D` to manage the model parameters
``W`` and ``b``.
.. admonition:: Example
>>> n = 10
>>> c_i, c_o = 1, 3
>>> h_i, w_i = 5, 10
>>> h_k, w_k = 10, 10
>>> h_p, w_p = 5, 5
>>> x = np.random.uniform(0, 1, (n, c_i, h_i, w_i)).astype(np.float32)
>>> x.shape
(10, 1, 5, 10)
>>> W = np.random.uniform(0, 1, (c_i, c_o, h_k, w_k)).\
astype(np.float32)
>>> W.shape
(1, 3, 10, 10)
>>> b = np.random.uniform(0, 1, c_o).astype(np.float32)
>>> b.shape
(3,)
>>> s_y, s_x = 5, 5
>>> y = F.deconvolution_2d(x, W, b, stride=(s_y, s_x), pad=(h_p, w_p))
>>> y.shape
(10, 3, 20, 45)
>>> h_o = s_y * (h_i - 1) + h_k - 2 * h_p
>>> w_o = s_x * (w_i - 1) + w_k - 2 * w_p
>>> y.shape == (n, c_o, h_o, w_o)
True
"""
argument.check_unexpected_kwargs(
kwargs, deterministic='deterministic argument is not '
'supported anymore. '
'Use chainer.using_config(\'cudnn_deterministic\', value) '
'context where value is either `True` or `False`.')
dilate, groups = argument.parse_kwargs(kwargs,
('dilate', 1), ('groups', 1))
func = Deconvolution2DFunction(stride, pad, outsize, dilate=dilate,
groups=groups)
if b is None:
args = x, W
else:
args = x, W, b
y, = func.apply(args)
return y