def check_forward(self, x_data, use_cudnn=True):
dims = self.dims
ksize = self.ksize
stride = self.stride
pad = self.pad
x = chainer.Variable(x_data)
y = functions.average_pooling_nd(x,
ksize,
stride,
pad,
use_cudnn=use_cudnn)
self.assertEqual(y.data.dtype, self.dtype)
y_data = cuda.to_cpu(y.data)
self.assertEqual(self.gy.shape, y_data.shape)
patches = pooling_nd_helper.pooling_patches(dims, ksize, stride, pad,
False)
for k in six.moves.range(2):
for c in six.moves.range(3):
x = self.x[k, c]
size = functools.reduce(operator.mul, ksize)
expect = numpy.array([x[idx].sum() for idx in patches])
expect = expect.reshape(y_data.shape[2:]) / size
testing.assert_allclose(expect, y_data[k, c],
**self.check_forward_options)
def check_forward(self, x_data, use_cudnn=True):
dims = self.dims
ksize = self.ksize
stride = self.stride
pad = self.pad
x = chainer.Variable(x_data)
y = functions.max_pooling_nd(x, ksize, stride=stride, pad=pad,
cover_all=self.cover_all,
use_cudnn=use_cudnn)
self.assertEqual(y.data.dtype, self.dtype)
y_data = cuda.to_cpu(y.data)
self.assertEqual(self.gy.shape, y_data.shape)
patches = pooling_nd_helper.pooling_patches(
dims, ksize, stride, pad, self.cover_all)
for k in six.moves.range(2):
for c in six.moves.range(3):
x = self.x[k, c]
expect = numpy.array([x[idx].max() for idx in patches])
expect = expect.reshape(y_data.shape[2:])
testing.assert_allclose(expect, y_data[k, c])
def check_forward(self, x_data, use_cudnn=True):
dims = self.dims
ksize = self.ksize
stride = self.stride
pad = self.pad
x = chainer.Variable(x_data)
y = functions.max_pooling_nd(x, ksize, stride=stride, pad=pad,
cover_all=self.cover_all,
use_cudnn=use_cudnn)
self.assertEqual(y.data.dtype, self.dtype)
y_data = cuda.to_cpu(y.data)
self.assertEqual(self.gy.shape, y_data.shape)
patches = pooling_nd_helper.pooling_patches(
dims, ksize, stride, pad, self.cover_all)
for k in six.moves.range(2):
for c in six.moves.range(3):
x = self.x[k, c]
expect = numpy.array([x[idx].max() for idx in patches])
expect = expect.reshape(y_data.shape[2:])
testing.assert_allclose(expect, y_data[k, c])
def check_forward(self, x_data, use_cudnn='always'):
dims = self.dims
ksize = self.ksize
stride = self.stride
pad = self.pad
x = chainer.Variable(x_data)
with chainer.using_config('use_cudnn', use_cudnn):
y = functions.average_pooling_nd(x, ksize, stride, pad)
self.assertEqual(y.data.dtype, self.dtype)
y_data = cuda.to_cpu(y.data)
self.assertEqual(self.gy.shape, y_data.shape)
patches = pooling_nd_helper.pooling_patches(
dims, ksize, stride, pad, False)
for k in six.moves.range(2):
for c in six.moves.range(3):
x = self.x[k, c]
size = functools.reduce(operator.mul, ksize)
expect = numpy.array([x[idx].sum() for idx in patches])
expect = expect.reshape(y_data.shape[2:]) / size
testing.assert_allclose(
expect, y_data[k, c], **self.check_forward_options)