def ref_sum_pooling_2d(x, kernel, stride, ignore_border, pad):
y = []
for xx in x.reshape((-1,) + x.shape[-3:]):
if xx.ndim == 2:
xx = xx[np.newaxis]
y += [refs.pooling_2d(xx, 'sum', kernel, stride,
pad, ignore_border)[np.newaxis]]
y = np.vstack(y)
if x.ndim == 2:
y = np.squeeze(y, 1)
return y.reshape(x.shape[:-3] + y.shape[1:])
def ref_max_pooling(x, kernel, stride, ignore_border, pad):
# Only 2d
y = []
for xx in x.reshape((-1,) + x.shape[-3:]):
if xx.ndim == 2:
xx = xx[np.newaxis]
y += [refs.pooling_2d(xx, 'max', kernel, stride,
pad, ignore_border)[np.newaxis]]
y = np.vstack(y)
if x.ndim == 2:
y = np.squeeze(y, 1)
return y.reshape(x.shape[:-3] + y.shape[1:])
def ref_average_pooling(x, kernel, stride, ignore_border, pad, including_pad):
# Only 2d
y = []
for xx in x.reshape((-1, ) + x.shape[-3:]):
if xx.ndim == 2:
xx = xx[np.newaxis]
y += [
refs.pooling_2d(xx, 'average', kernel, stride, pad, ignore_border,
including_pad)[np.newaxis]
]
y = np.vstack(y)
if x.ndim == 2:
y = np.squeeze(y, 1)
return y.reshape(x.shape[:-3] + y.shape[1:])