def setUp(self):
ndim = self.ndim
dtype = self.dtype
batches = 2
in_channels_a_group = 3
out_channels_a_group = 2
in_channels = in_channels_a_group * self.groups
out_channels = out_channels_a_group * self.groups
ksize = 3
stride = 2
pad = ksize // stride * self.dilate
self.strides = (stride,) * ndim
self.pads = (pad,) * ndim
self.dilations = (self.dilate,) * ndim
self.x = cupy.zeros(
(batches, in_channels) + (ksize,) * ndim, dtype)
self.gy = cupy.zeros((batches, out_channels) + (2,) * ndim, dtype)
self.gW = cupy.ones(
(out_channels, in_channels_a_group) + (ksize,) * ndim, dtype)
version = libcudnn.getVersion()
deterministic = self.deterministic
self.err = None
if ((self.dilate > 1 and version < 6000) or
(self.groups > 1 and version < 7000)):
self.err = ValueError
elif ((self.dilate > 1 and deterministic and version < 7000) or
(ndim > 2 and deterministic and version < 6000) or
(ndim > 2 and deterministic and self.dtype == numpy.float64)):
self.err = libcudnn.CuDNNError
self._workspace_size = cudnn.get_max_workspace_size()
cudnn.set_max_workspace_size(self.max_workspace_size)
def setUp(self):
self.layout = libcudnn.CUDNN_TENSOR_NHWC
n = 16
x_c, y_c = 64, 64
x_h, x_w = 32, 32
y_h, y_w = x_h // self.stride, x_w // self.stride
self.pad = (self.ksize - 1) // 2
if self.layout == libcudnn.CUDNN_TENSOR_NHWC:
x_shape = (n, x_h, x_w, x_c)
y_shape = (n, y_h, y_w, y_c)
W_shape = (y_c, self.ksize, self.ksize, x_c)
else:
x_shape = (n, x_c, x_h, x_w)
y_shape = (n, y_c, y_h, y_w)
W_shape = (y_c, x_c, self.ksize, self.ksize)
self.x = cupy.ones(x_shape, dtype=self.dtype)
self.W = cupy.ones(W_shape, dtype=self.dtype)
self.y = cupy.empty(y_shape, dtype=self.dtype)
self.gx = cupy.empty(x_shape, dtype=self.dtype)
self.gW = cupy.empty(W_shape, dtype=self.dtype)
self.gy = cupy.ones(y_shape, dtype=self.dtype)
_workspace_size = cudnn.get_max_workspace_size()
cudnn.set_max_workspace_size(0)
yield
cudnn.set_max_workspace_size(_workspace_size)
def get_max_workspace_size():
"""Gets the workspace size for cuDNN.
Check "cuDNN Library User Guide" for detail.
Returns:
int: The workspace size for cuDNN.
"""
# To avoid error on no cuDNN environment
if cudnn_enabled:
return cudnn.get_max_workspace_size()
return 0
def get_max_workspace_size():
"""Gets the workspace size for cuDNN.
Check "cuDNN Library User Guide" for detail.
Returns:
int: The workspace size for cuDNN.
"""
# To avoid error on no cuDNN environment
if cudnn_enabled:
return cudnn.get_max_workspace_size()
return 0
def setUp(self):
ndim = self.ndim
dtype = self.dtype
batches = 2
in_channels_a_group = 3
out_channels_a_group = 2
in_channels = in_channels_a_group * self.groups
out_channels = out_channels_a_group * self.groups
# TODO(anaruse): increase test cases.
ksize = 3
stride = 2
pad = ksize // stride * self.dilate
self.strides = (stride, ) * ndim
self.pads = (pad, ) * ndim
self.dilations = (self.dilate, ) * ndim
self.W = cupy.zeros(
(out_channels, in_channels_a_group) + (ksize, ) * ndim, dtype)
self.gy = cupy.zeros((batches, out_channels) + (2, ) * ndim, dtype)
self.b = None
if self.bias:
self.b = cupy.zeros((in_channels, ), dtype)
self.gx = cupy.ones((batches, in_channels) + (ksize, ) * ndim, dtype)
version = libcudnn.getVersion()
deterministic = self.deterministic
self.err = None
if ((self.dilate > 1 and version < 6000)
or (self.groups > 1 and version < 7000)):
self.err = ValueError
elif (sys.platform.startswith('win32') and version == 7605
and deterministic and self.dtype == numpy.float16
and self.ndim == 3 and self.dilate == 2 and self.groups == 2):
# see https://github.com/cupy/cupy/pull/4893
self.err = RuntimeError
elif deterministic and (
(self.dilate > 1 and
(ndim != 2 and version < 8100 or version < 7300)) or
(ndim > 2 and version < 6000) or
(ndim > 2 and self.dtype == numpy.float64 and version < 8100)):
self.err = libcudnn.CuDNNError
elif (8000 <= version < 8100
and int(cupy.cuda.device.get_compute_capability()) < 70
and self.dilate > 1 and self.groups > 1 and ndim > 2
and self.dtype == numpy.float16):
self.err = RuntimeError
_workspace_size = cudnn.get_max_workspace_size()
cudnn.set_max_workspace_size(self.max_workspace_size)
yield
cudnn.set_max_workspace_size(_workspace_size)
def setUp(self):
ndim = self.ndim
dtype = self.dtype
batches = 2
if self.layout == libcudnn.CUDNN_TENSOR_NHWC:
# channel size must be multiple of 4
in_channels_a_group = 4
out_channels_a_group = 4
else:
in_channels_a_group = 3
out_channels_a_group = 2
in_channels = in_channels_a_group * self.groups
out_channels = out_channels_a_group * self.groups
# TODO(anaruse): increase test cases.
ksize = 3
stride = 2
pad = ksize // stride * self.dilate
self.strides = (stride, ) * ndim
self.pads = (pad, ) * ndim
self.dilations = (self.dilate, ) * ndim
if self.layout == libcudnn.CUDNN_TENSOR_NHWC:
self.x = cupy.zeros(
(batches, ) + (ksize, ) * ndim + (in_channels, ), dtype)
self.W = cupy.zeros(
(out_channels, ) + (ksize, ) * ndim + (in_channels_a_group, ),
dtype)
self.y = cupy.ones((batches, ) + (2, ) * ndim + (out_channels, ),
dtype)
else:
self.x = cupy.zeros((batches, in_channels) + (ksize, ) * ndim,
dtype)
self.W = cupy.zeros(
(out_channels, in_channels_a_group) + (ksize, ) * ndim, dtype)
self.y = cupy.ones((batches, out_channels) + (2, ) * ndim, dtype)
self.b = None
if self.bias:
self.b = cupy.zeros((out_channels, ), dtype)
version = libcudnn.getVersion()
self.err = None
if ((self.dilate > 1 and version < 6000)
or (self.groups > 1 and version < 7000)):
self.err = ValueError
elif ndim > 2 and self.dilate > 1:
self.err = libcudnn.CuDNNError
_workspace_size = cudnn.get_max_workspace_size()
cudnn.set_max_workspace_size(self.max_workspace_size)
yield
cudnn.set_max_workspace_size(_workspace_size)
def setUp(self):
ndim = self.ndim
dtype = self.dtype
batches = 2
in_channels_a_group = 3
out_channels_a_group = 2
in_channels = in_channels_a_group * self.groups
out_channels = out_channels_a_group * self.groups
# TODO(anaruse): increase test cases.
ksize = 3
stride = 2
pad = ksize // stride * self.dilate
self.strides = (stride,) * ndim
self.pads = (pad,) * ndim
self.dilations = (self.dilate,) * ndim
self.W = cupy.zeros(
(out_channels, in_channels_a_group) + (ksize,) * ndim, dtype)
self.gy = cupy.zeros((batches, out_channels) + (2,) * ndim, dtype)
self.b = None
if self.bias:
self.b = cupy.zeros((in_channels,), dtype)
self.gx = cupy.ones(
(batches, in_channels) + (ksize,) * ndim, dtype)
version = libcudnn.getVersion()
deterministic = self.deterministic
self.err = None
if ((self.dilate > 1 and version < 6000) or
(self.groups > 1 and version < 7000)):
self.err = ValueError
elif deterministic and (
(self.dilate > 1 and (ndim != 2 or version < 7300)) or
(ndim > 2 and version < 6000) or
(ndim > 2 and self.dtype == numpy.float64)):
self.err = libcudnn.CuDNNError
elif (8000 <= version and
int(cupy.cuda.device.get_compute_capability()) < 70 and
self.dilate > 1 and self.groups > 1 and ndim > 2 and
self.dtype == numpy.float16):
self.err = RuntimeError
self._workspace_size = cudnn.get_max_workspace_size()
cudnn.set_max_workspace_size(self.max_workspace_size)
