def test_max_pool_invalid(
device, x_shape, ksize, stride, pad, cover_all, float_dtype):
with pytest.raises(chainerx.DimensionError):
chainerx.max_pool(
**_create_max_pool_args(
chainerx, device, x_shape, ksize, stride, pad, cover_all,
float_dtype))
def test_max_pool_invalid(
device, x_shape, ksize, stride, pad, cover_all, float_dtype):
x = numpy.random.uniform(-1, 1, x_shape).astype(float_dtype)
x = chainerx.array(x)
with pytest.raises(chainerx.DimensionError):
chainerx.max_pool(
x, ksize=ksize, stride=stride, pad=pad, cover_all=cover_all)
def test_max_pool_invalid(device, x_shape, ksize, stride, pad, cover_all,
float_dtype):
x = numpy.random.uniform(-1, 1, x_shape).astype(float_dtype)
x = chainerx.array(x)
with pytest.raises(chainerx.DimensionError):
chainerx.max_pool(x,
ksize=ksize,
stride=stride,
pad=pad,
cover_all=cover_all)
def forward_chainerx(self, x):
# TODO(sonots): Support return_indices in ChainerX
if self.return_indices:
return chainer.Fallback
if x[0].device.backend.name == 'cuda':
# TODO(sonots): Support more ndim in ChainerX
if self.ndim not in [2, 3]:
return chainer.Fallback
return chainerx.max_pool(x[0], self.ksize, self.stride, self.pad,
self.cover_all),
def forward_chainerx(self, x):
# TODO(sonots): Support return_indices in ChainerX
if self.return_indices:
return chainer.Fallback
if x[0].device.backend.name == 'cuda':
# TODO(sonots): Support more ndim in ChainerX
if self.ndim not in [2, 3]:
return chainer.Fallback
return chainerx.max_pool(x[0], self.ksize, self.stride, self.pad,
self.cover_all),
def chainerx_max_pool():
y = chainerx.max_pool(**create_args(chainerx))
# In the case of CUDA, we get huge negative numbers instead of -inf
# around boundaries.
# Align them to chainer (native) results.
if device.backend.name == 'cuda':
y = chainerx.to_numpy(y)
y[y < -3.e+34] = -float('inf')
y = chainerx.array(y)
return y
def chainerx_max_pool():
y = chainerx.max_pool(**create_args(chainerx))
# In the case of CUDA, we get huge negative numbers instead of -inf
# around boundaries.
# Align them to chainer (native) results.
if device.backend.name == 'cuda':
y = chainerx.to_numpy(y)
y[y < -3.e+34] = -float('inf')
y = chainerx.array(y)
return y
def __call__(self, x):
h = self.bn1(self.conv1(x))
h = chx.max_pool(chx.maximum(0, h), 3, stride=2)
h = self.res2(h)
h = self.res3(h)
h = self.res4(h)
h = self.res5(h)
h = chx.average_pool(h, 7, stride=1)
h = self.fc(h)
return h
def __call__(self, x):
h = self.bn1(self.conv1(x))
h = chx.max_pool(chx.maximum(0, h), 3, stride=2)
h = self.res2(h)
h = self.res3(h)
h = self.res4(h)
h = self.res5(h)
h = chx.average_pool(h, 7, stride=1)
h = self.fc(h)
return h
def forward_chainerx(self, inputs):
x, = inputs
y = chainerx.max_pool(
x, ksize=self.ksize, stride=self.stride, pad=self.pad,
cover_all=self.cover_all)
# This function can return -inf (or huge negative numbers in case of
# CUDA) around boundaries.
# Convert them to finite numbers in order to properly calculate numeric
# gradients.
y = chainerx.maximum(y, -1e4)
return y,
def forward_chainerx(self, inputs):
x, = inputs
y = chainerx.max_pool(x,
ksize=self.ksize,
stride=self.stride,
pad=self.pad,
cover_all=self.cover_all)
# This function can return -inf (or huge negative numbers in case of
# CUDA) around boundaries.
# Convert them to finite numbers in order to properly calculate numeric
# gradients.
y = chainerx.maximum(y, -1e4)
return y,
def forward_chainerx(self, x):
ndim = self.ndim
ksize = self.ksize
stride = self.stride
pad = self.pad
cover_all = self.cover_all
# TODO(sonots): Support return_indices in ChainerX
if self.return_indices:
return chainer.Fallback
if x[0].device.backend.name == 'cuda':
# TODO(sonots): Support more ndim in ChainerX
if ndim not in [2, 3]:
return chainer.Fallback
y = chainerx.max_pool(x[0], ksize, stride, pad, cover_all)
return y,
def forward_chainerx(self, x):
# TODO(sonots): Support return_indices in ChainerX
if self.return_indices:
return chainer.Fallback
return chainerx.max_pool(x[0], (self.kh, self.kw), (self.sy, self.sx),
(self.ph, self.pw), self.cover_all),
def test_max_pool_invalid(device, x_shape, ksize, stride, pad, cover_all,
float_dtype):
with pytest.raises(chainerx.DimensionError):
chainerx.max_pool(
**_create_max_pool_args(chainerx, device, x_shape, ksize, stride,
pad, cover_all, float_dtype))
def forward_chainerx(self, x):
# TODO(sonots): Support return_indices in ChainerX
if self.return_indices:
return chainer.Fallback
return chainerx.max_pool(x[0], (self.kh, self.kw), (self.sy, self.sx),
(self.ph, self.pw), self.cover_all),