def test_conv_transpose_invalid(
device, x_shape, w_shape, b_shape, stride, pad, outsize, float_dtype):
with pytest.raises(chainerx.DimensionError):
chainerx.conv_transpose(
*_create_conv_transpose_args(
chainerx, device, x_shape, w_shape, b_shape, stride, pad,
outsize, float_dtype))
def test_conv_transpose_invalid(
device, x_shape, w_shape, b_shape, stride, pad, outsize, float_dtype):
with pytest.raises(chainerx.DimensionError):
chainerx.conv_transpose(
*_create_conv_transpose_args(
chainerx, device, x_shape, w_shape, b_shape, stride, pad,
outsize, float_dtype))
def test_conv_transpose_invalid(
device, x_shape, w_shape, b_shape, stride, pad, outsize, float_dtype):
dtype = float_dtype
x = array_utils.create_dummy_ndarray(chainerx, x_shape, dtype)
w = array_utils.create_dummy_ndarray(chainerx, w_shape, dtype)
if b_shape is None:
b = None
else:
b = array_utils.create_dummy_ndarray(chainerx, b_shape, float_dtype)
with pytest.raises(chainerx.DimensionError):
chainerx.conv_transpose(x, w, b, stride, pad, outsize)
def test_conv_transpose_invalid(
device, x_shape, w_shape, b_shape, stride, pad, outsize, float_dtype):
dtype = float_dtype
x = array_utils.create_dummy_ndarray(chainerx, x_shape, dtype)
w = array_utils.create_dummy_ndarray(chainerx, w_shape, dtype)
if b_shape is None:
b = None
else:
b = array_utils.create_dummy_ndarray(chainerx, b_shape, float_dtype)
with pytest.raises(chainerx.DimensionError):
chainerx.conv_transpose(x, w, b, stride, pad, outsize)
def test_conv_transpose(
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(sonots): Support 1 dimensional convolution with CUDA.
return chainerx.testing.ignore()
if device.backend.name == 'cuda' and cover_all is True:
# outsize (for cover_all=True) is not supported by CUDA.
return chainerx.testing.ignore()
def create_args(xp):
if cover_all is None:
outsize = None
else:
outsize = _get_conv_transpose_outsize(
x_shape, w_shape, stride, pad, cover_all)
return _create_conv_transpose_args(
xp, device, x_shape, w_shape, b_shape, stride, pad, outsize,
float_dtype)
chainerx.testing.assert_allclose_ex(
chainerx.conv_transpose(
*create_args(chainerx)),
chainer.functions.deconvolution_nd(*create_args(numpy)).data,
rtol=1e-3, float16_rtol=1e-2, float16_atol=1e-2, strides_check=False)
def test_conv_transpose(
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(sonots): Support 1 dimensional convolution with CUDA.
return chainerx.testing.ignore()
if device.backend.name == 'cuda' and cover_all is True:
# outsize (for cover_all=True) is not supported by CUDA.
return chainerx.testing.ignore()
def create_args(xp):
if cover_all is None:
outsize = None
else:
outsize = _get_conv_transpose_outsize(
x_shape, w_shape, stride, pad, cover_all)
return _create_conv_transpose_args(
xp, device, x_shape, w_shape, b_shape, stride, pad, outsize,
float_dtype)
chainerx.testing.assert_allclose(
chainerx.conv_transpose(
*create_args(chainerx)),
chainer.functions.deconvolution_nd(*create_args(numpy)).data,
rtol=1e-3)
def forward_chainerx(self, inputs):
if len(inputs) == 3:
x, w, b = inputs
else:
(x, w), b = inputs, None
y = chainerx.conv_transpose(
x, w, b, self.stride, self.pad, self.outsize)
return y,
def forward_chainerx(self, inputs):
if len(inputs) == 3:
x, w, b = inputs
else:
(x, w), b = inputs, None
y = chainerx.conv_transpose(
x, w, b, self.stride, self.pad, self.outsize)
return y,
def forward_chainerx(self, inputs):
# TODO(imanishi): Support it
if any(d != 1 for d in self.dilate):
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): Supporft it
if inputs[0].device.backend.name == 'cuda' and self.ndim < 2:
return chainer.Fallback
stride = self.stride
pad = self.pad
return chainerx.conv_transpose(*inputs, stride=stride, pad=pad),
def forward_chainerx(self, inputs):
# TODO(imanishi): Support it
if any(d != 1 for d in self.dilate):
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): Supporft it
if inputs[0].device.backend.name == 'cuda' and self.ndim < 2:
return chainer.Fallback
stride = self.stride
pad = self.pad
return chainerx.conv_transpose(*inputs, stride=stride, pad=pad),
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),
