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.pad_value)
self.assertEqual(y.data.dtype, self.dtype)
y_data = cuda.to_cpu(y.data)
def denom(idx):
if self.pad_value is None:
s = 1
for slic in idx:
s *= slic.stop - slic.start
return s
else:
return functools.reduce(operator.mul, ksize)
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]
expect = numpy.array(
[x[idx].sum() / denom(idx) for idx in patches])
expect = expect.reshape(y_data.shape[2:])
testing.assert_allclose(expect, y_data[k, c],
**self.check_forward_options)
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.pad_value)
self.assertEqual(y.data.dtype, self.dtype)
y_data = cuda.to_cpu(y.data)
def denom(idx):
if self.pad_value is None:
s = 1
for slic in idx:
s *= slic.stop - slic.start
return s
else:
return functools.reduce(operator.mul, ksize)
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]
expect = numpy.array(
[x[idx].sum() / denom(idx) for idx in patches])
expect = expect.reshape(y_data.shape[2:])
testing.assert_allclose(
expect, y_data[k, c], **self.check_forward_options)
def forward_expected(self, inputs):
x, = inputs
patches = pooling_nd_helper.pooling_patches(
self.dims, self.ksize, self.stride, self.pad, False)
def denom(idx):
if self.pad_value is None:
s = 1
for slic in idx:
s *= slic.stop - slic.start
return s
else:
return functools.reduce(operator.mul, self.ksize)
y = []
for k in six.moves.range(2):
tmp = []
for c in six.moves.range(3):
x_ = x[k, c]
expect = numpy.array(
[x_[idx].sum() / denom(idx) for idx in patches])
expect = expect.reshape(self.output_shape[2:])
tmp.append(expect)
y.append(tmp)
return numpy.asarray(y, dtype=self.dtype),
def forward_expected(self, inputs):
x, = inputs
patches = pooling_nd_helper.pooling_patches(self.dims, self.ksize,
self.stride, self.pad,
False)
def denom(idx):
if self.pad_value is None:
s = 1
for slic in idx:
s *= slic.stop - slic.start
return s
else:
return functools.reduce(operator.mul, self.ksize)
y = []
for k in six.moves.range(2):
tmp = []
for c in six.moves.range(3):
x_ = x[k, c]
expect = numpy.array(
[x_[idx].sum() / denom(idx) for idx in patches])
expect = expect.reshape(self.output_shape[2:])
tmp.append(expect)
y.append(tmp)
return numpy.asarray(y, dtype=self.dtype),
def forward_expected(self, inputs):
in_dims = self.in_dims
ksize = self.ksize
stride = self.stride
pad = self.pad
cover_all = self.cover_all
patches = pooling_nd_helper.pooling_patches(in_dims, ksize, stride,
pad, cover_all)
x, = inputs
out_dims = self._get_out_dims(x.shape[2:])
y_shape = x.shape[:2] + out_dims
x = x.astype(numpy.float64)
y = numpy.empty(y_shape, numpy.float64)
for i in six.moves.range(2):
for c in six.moves.range(3):
d = numpy.array([x[i, c][idx].max() for idx in patches])
y[i, c, ...] = d.reshape(out_dims)
return y.astype(self.dtype),
def forward_expected(self, inputs):
in_dims = self.in_dims
ksize = self.ksize
stride = self.stride
pad = self.pad
cover_all = self.cover_all
patches = pooling_nd_helper.pooling_patches(
in_dims, ksize, stride, pad, cover_all)
x, = inputs
out_dims = self._get_out_dims(x.shape[2:])
y_shape = x.shape[:2] + out_dims
x = x.astype(numpy.float64)
y = numpy.empty(y_shape, numpy.float64)
for i in six.moves.range(2):
for c in six.moves.range(3):
d = numpy.array([x[i, c][idx].max() for idx in patches])
y[i, c, ...] = d.reshape(out_dims)
return y.astype(self.dtype),
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.max_pooling_nd(x, ksize, stride=stride, pad=pad,
cover_all=self.cover_all)
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])
