def check_forward(self, x, W, cp, pp):
x_in = convolution2D.Forward(array(x), array(W), None, cp)
y_act, self.indexes_act = pooling2D.Forward(x_in, pp)
x_in_npy = numpy.array(x_in, dtype=self.dtype)
y_act_npy = numpy.array(y_act, dtype=self.dtype)
indexes_act_npy = numpy.array(self.indexes_act, dtype=self.dtype)
col = im2col_cpu(x_in_npy,
3,
3,
2,
2,
0,
0,
pval=-float('inf'),
cover_all=True)
n, c, kh, kw, out_h, out_w = col.shape
col = col.reshape(n, c, kh * kw, out_h, out_w)
self.indexes_ref = col.argmax(axis=2)
y_ref = col.max(axis=2)
numpy.testing.assert_allclose(y_act_npy, y_ref,
**self.check_forward_options)
numpy.testing.assert_allclose(indexes_act_npy, self.indexes_ref,
**self.check_forward_options)
def send_array(self, array):
if isinstance(array, ideep.mdarray):
return array
if not isinstance(array, numpy.ndarray):
array = _cpu._to_cpu(array) # to numpy.ndarray
if (isinstance(array, numpy.ndarray) and array.ndim in (1, 2, 4)
and 0 not in array.shape):
# TODO(kmaehashi): Remove ndim validation once iDeep has fixed.
# Currently iDeep only supports (1, 2, 4)-dim arrays.
# Note that array returned from `ideep.array` may not be an
# iDeep mdarray, e.g., when the dtype is not float32.
array = ideep.array(array, itype=ideep.wgt_array)
return array
def check_backward(self, gy, W, pp):
gy_in = linear.BackwardData(array(W), array(gy))
gy_in = gy_in.reshape(self.bs, self.oc, 6, 6)
gx_act = pooling2D.Backward(gy_in, self.indexes_act, pp)
gy_in_npy = numpy.array(gy_in, dtype=self.dtype)
gx_act_npy = numpy.array(gx_act, dtype=self.dtype)
n, c, out_h, out_w = gy_in_npy.shape
h = 13
w = 13
kh = 3
kw = 3
gcol = numpy.zeros((n * c * out_h * out_w * 3 * 3), dtype=self.dtype)
indexes = self.indexes_ref.flatten()
indexes += numpy.arange(0, indexes.size * kh * kw, kh * kw)
gcol[indexes] = gy_in.ravel()
gcol = gcol.reshape(n, c, out_h, out_w, kh, kw)
gcol = numpy.swapaxes(gcol, 2, 4)
gcol = numpy.swapaxes(gcol, 3, 5)
gx_ref = col2im_cpu(gcol, 2, 2, 0, 0, h, w)
numpy.testing.assert_allclose(gx_act_npy, gx_ref,
**self.check_backward_options)
def send_array(self, array):
if isinstance(array, ideep.mdarray):
return array
if not isinstance(array, numpy.ndarray):
array = _cpu._to_cpu(array) # to numpy.ndarray
if (isinstance(array, numpy.ndarray) and
array.ndim in (1, 2, 4) and
0 not in array.shape):
# TODO(kmaehashi): Remove ndim validation once iDeep has fixed.
# Currently iDeep only supports (1, 2, 4)-dim arrays.
# Note that array returned from `ideep.array` may not be an
# iDeep mdarray, e.g., when the dtype is not float32.
array = ideep.array(array, itype=ideep.wgt_array)
return array
