def test_conv_invalid(
device, x_shape, w_shape, b_shape, stride, pad, cover_all,
float_dtype):
with pytest.raises(chainerx.DimensionError):
chainerx.conv(
*_create_conv_args(
chainerx, device, x_shape, w_shape, b_shape, stride, pad,
cover_all, float_dtype))
def test_conv_invalid(
device, x_shape, w_shape, b_shape, stride, pad, cover_all,
float_dtype):
with pytest.raises(chainerx.DimensionError):
chainerx.conv(
*_create_conv_args(
chainerx, device, x_shape, w_shape, b_shape, stride, pad,
cover_all, float_dtype))
def __call__(self, x):
if self.b is not None:
return chx.conv(x,
self.W,
self.b,
stride=self.stride,
pad=self.pad)
else:
return chx.conv(x, self.W, stride=self.stride, pad=self.pad)
def forward_chainerx(self, inputs):
if len(inputs) == 2:
(x, w), b = inputs, None
else:
x, w, b = inputs
y = chainerx.conv(x, w, b, self.stride, self.pad, self.cover_all)
return y,
def forward_chainerx(self, inputs):
if len(inputs) == 2:
(x, w), b = inputs, None
else:
x, w, b = inputs
y = chainerx.conv(x, w, b, self.stride, self.pad, self.cover_all)
return y,
def test_conv(device, x_shape, w_shape, b_shape, stride, pad, cover_all,
float_dtype):
if device.backend.name == 'cuda' and len(x_shape) <= 3:
# cuDNN does not support 1 dimensional convolution and throws
# DimensionError.
# TODO(hvy): Support 1 dimensional convolution with CUDA.
return chainerx.testing.ignore()
def create_args(xp):
return _create_conv_args(xp, device, x_shape, w_shape, b_shape, stride,
pad, cover_all, float_dtype)
chainerx.testing.assert_allclose(
chainerx.conv(*create_args(chainerx)),
chainer.functions.convolution_nd(*create_args(numpy)).data)
def forward_chainerx(self, inputs):
# TODO(hvy): Support mixed precision.
if any([arr.dtype != inputs[0].dtype for arr in inputs[1:]]):
return chainer.Fallback
# TODO(hvy): Support dilate > 1.
if self.dy > 1 or self.dx > 1:
return chainer.Fallback
# TODO(hvy): Support groups > 1.
if self.groups > 1:
return chainer.Fallback
if inputs[0].device.backend.name == 'cuda' and self.cover_all:
return chainer.Fallback
return chainerx.conv(
*inputs, stride=(self.sy, self.sx), pad=(self.ph, self.pw),
cover_all=self.cover_all),
def forward_chainerx(self, inputs):
# TODO(hvy): Support mixed precision.
if any([arr.dtype != inputs[0].dtype for arr in inputs[1:]]):
return chainer.Fallback
# TODO(hvy): Support dilate > 1.
if self.dy > 1 or self.dx > 1:
return chainer.Fallback
# TODO(hvy): Support groups > 1.
if self.groups > 1:
return chainer.Fallback
if inputs[0].device.backend.name == 'cuda' and self.cover_all:
return chainer.Fallback
return chainerx.conv(
*inputs, stride=(self.sy, self.sx), pad=(self.ph, self.pw),
cover_all=self.cover_all),
def test_conv(
device, x_shape, w_shape, b_shape, stride, pad, cover_all,
float_dtype):
if device.backend.name == 'cuda' and len(x_shape) <= 3:
# cuDNN does not support 1 dimensional convolution and throws
# DimensionError.
# TODO(hvy): Support 1 dimensional convolution with CUDA.
return chainerx.testing.ignore()
def create_args(xp):
return _create_conv_args(
xp, device, x_shape, w_shape, b_shape, stride, pad, cover_all,
float_dtype)
chainerx.testing.assert_allclose(
chainerx.conv(*create_args(chainerx)),
chainer.functions.convolution_nd(*create_args(numpy)).data)
def forward_chainerx(self, inputs):
# TODO(hvy): Support mixed precision.
if any([arr.dtype != inputs[0].dtype for arr in inputs[1:]]):
return chainer.Fallback
# TODO(hvy): Support dilate > 1.
if any(d != 1 for d in self.dilate):
return chainer.Fallback
# TODO(hvy): Support groups > 1.
if self.groups > 1:
return chainer.Fallback
if inputs[0].device.backend.name == 'cuda' and (
self.cover_all or self.ndim < 2):
return chainer.Fallback
return chainerx.conv(
*inputs, stride=self.stride, pad=self.pad,
cover_all=self.cover_all),
def forward_chainerx(self, inputs):
# TODO(hvy): Support mixed precision.
if any([arr.dtype != inputs[0].dtype for arr in inputs[1:]]):
return chainer.Fallback
# TODO(hvy): Support dilate > 1.
if any(d != 1 for d in self.dilate):
return chainer.Fallback
# TODO(hvy): Support groups > 1.
if self.groups > 1:
return chainer.Fallback
if inputs[0].device.backend.name == 'cuda' and (
self.cover_all or self.ndim < 2):
return chainer.Fallback
return chainerx.conv(
*inputs, stride=self.stride, pad=self.pad,
cover_all=self.cover_all),
def __call__(self, x):
if self.b is not None:
return chx.conv(
x, self.W, self.b, stride=self.stride, pad=self.pad)
else:
return chx.conv(x, self.W, stride=self.stride, pad=self.pad)
