Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

[backport] Support all float dtypes in F.spatial_transformer_sampler #6003

Merged
merged 1 commit into from Jan 17, 2019
Merged

[backport] Support all float dtypes in F.spatial_transformer_sampler #6003

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
14 changes: 8 additions & 6 deletions chainer/functions/array/spatial_transformer_sampler.py
View file
Open in desktop
Expand Up @@ -23,8 +23,8 @@ def check_type_forward(self, in_types):
x_type = in_types[0]
grid_type = in_types[1]
type_check.expect(
x_type.dtype.char == 'f',
grid_type.dtype.char == 'f',
x_type.dtype.kind == 'f',
grid_type.dtype == x_type.dtype,
x_type.ndim == 4,
grid_type.ndim == 4,
grid_type.shape[1] == 2,
Expand Down Expand Up @@ -53,8 +53,9 @@ def forward_gpu(self, inputs):
cuda.cupy.cudnn.create_spatial_transformer_descriptor(
_sampler_type, grid.dtype, len(shape), shape.ctypes.data)

one = numpy.array(1, dtype=x.dtype).ctypes
zero = numpy.array(0, dtype=x.dtype).ctypes
dtype = numpy.float64 if x.dtype == numpy.float64 else numpy.float32
one = numpy.array(1, dtype=dtype).ctypes
zero = numpy.array(0, dtype=dtype).ctypes
libcudnn.spatialTfSamplerForward(
handle, self.st_desc.value, one.data,
x_desc.value, x.data.ptr, grid_t.data.ptr, zero.data,
Expand Down Expand Up @@ -139,8 +140,9 @@ def backward_gpu(self, inputs, grad_outputs):
dx_desc = cudnn.create_tensor_descriptor(gx)
dy_desc = cudnn.create_tensor_descriptor(gy)

one = numpy.array(1, dtype=x.dtype).ctypes
zero = numpy.array(0, dtype=x.dtype).ctypes
dtype = numpy.float64 if x.dtype == numpy.float64 else numpy.float32
one = numpy.array(1, dtype=dtype).ctypes
zero = numpy.array(0, dtype=dtype).ctypes
libcudnn.spatialTfSamplerBackward(
handle, self.st_desc.value,
one.data,
Expand Down
40 changes: 27 additions & 13 deletions tests/chainer_tests/functions_tests/array_tests/test_spatial_transformer_sampler.py
View file
Open in desktop
Expand Up @@ -42,6 +42,7 @@ def _rotate_BCHW(x):


@testing.parameterize(*testing.product({
'dtype': [numpy.float16, numpy.float32, numpy.float64],
'use_cudnn': ['always', 'never'],
}))
class TestSpatialTransformerSampler(unittest.TestCase):
Expand All @@ -52,11 +53,11 @@ class TestSpatialTransformerSampler(unittest.TestCase):

def setUp(self):
self.x = numpy.random.uniform(
size=self.in_shape).astype(numpy.float32)
size=self.in_shape).astype(self.dtype)
self.grid = numpy.random.uniform(
low=-2., high=2., size=self.grid_shape).astype(numpy.float32)
low=-2., high=2., size=self.grid_shape).astype(self.dtype)
self.grads = numpy.random.uniform(
size=self.out_shape).astype(numpy.float32)
size=self.out_shape).astype(self.dtype)

def check_forward(self, x, grid):
y = functions.spatial_transformer_sampler(x, grid)
Expand Down Expand Up @@ -90,19 +91,26 @@ def test_backward_gpu(self):
cuda.to_gpu(self.grads))


@testing.parameterize(*testing.product({
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
class TestSpatialTransformerSamplerConsistencyWithCuDNN(unittest.TestCase):

in_shape = (2, 2, 4, 4)
out_shape = (2, 2, 3, 3)
grid_shape = (2, 2, 3, 3)

def setUp(self):
self.x = numpy.random.uniform(
size=self.in_shape).astype(numpy.float32)
self.x = numpy.random.uniform(size=self.in_shape).astype(self.dtype)
self.grid = numpy.random.uniform(
low=-2, high=2, size=self.grid_shape).astype(numpy.float32)
low=-2, high=2, size=self.grid_shape).astype(self.dtype)
self.grads = numpy.random.uniform(
size=self.out_shape).astype(numpy.float32)
size=self.out_shape).astype(self.dtype)

if self.dtype == numpy.float16:
self.assert_options = {'atol': 1e-2}
else:
self.assert_options = {}

def _apply_backward(self, x, grid, grads):
x = Variable(x)
Expand All @@ -125,9 +133,12 @@ def test_consistency_with_cudnn_cpu(self):
cuda.to_gpu(self.x), cuda.to_gpu(self.grid),
cuda.to_gpu(self.grads))

testing.assert_allclose(y_cpu.data, y_cudnn.data)
testing.assert_allclose(x_cpu.grad, x_cudnn.grad)
testing.assert_allclose(grid_cpu.grad, grid_cudnn.grad)
testing.assert_allclose(
y_cpu.data, y_cudnn.data, **self.assert_options)
testing.assert_allclose(
x_cpu.grad, x_cudnn.grad, **self.assert_options)
testing.assert_allclose(
grid_cpu.grad, grid_cudnn.grad, **self.assert_options)

@attr.gpu
@attr.cudnn
Expand All @@ -141,9 +152,12 @@ def test_consistency_with_cudnn_gpu(self):
cuda.to_gpu(self.x), cuda.to_gpu(self.grid),
cuda.to_gpu(self.grads))

testing.assert_allclose(y_gpu.data, y_cudnn.data)
testing.assert_allclose(x_gpu.grad, x_cudnn.grad)
testing.assert_allclose(grid_gpu.grad, grid_cudnn.grad)
testing.assert_allclose(
y_gpu.data, y_cudnn.data, **self.assert_options)
testing.assert_allclose(
x_gpu.grad, x_cudnn.grad, **self.assert_options)
testing.assert_allclose(
grid_gpu.grad, grid_cudnn.grad, **self.assert_options)


@testing.parameterize(
Expand Down