def maxpool(data, factor, getPos=True):
"""
Return max pooled data and the pooled pixel positions.
Args:
-----
data: An N x k x m x n array.
factor: Pooling factor.
Returns:
--------
An N x k x (m/factor) x (n/factor), N x k x m x n arrays.
"""
_data = np.asarray(data, dtype='float32')
x = tn.ftensor4('x')
f = thn.function([], max_pool(x, factor, True), givens={x: shared(_data)})
g = thn.function([],
max_pool_same(x, factor) / x,
givens={x: shared(_data + 0.0000000001)})
pooled = f()
if not getPos:
return pooled
positions = g()
positions[np.where(np.isnan(positions))] = 0
return pooled, positions
def maxpool(data, factor, getPos=True):
"""
Return max pooled data and the pooled pixel positions.
Args:
-----
data: An N x k x m x n array.
factor: Pooling factor.
Returns:
--------
An N x k x (m/factor) x (n/factor), N x k x m x n arrays.
"""
_data = np.asarray(data, dtype='float32')
x = tn.ftensor4('x')
f = thn.function([], max_pool(x, factor, True), givens={x: shared(_data)})
g = thn.function([], max_pool_same(x, factor)/x, givens={x: shared(_data + 0.0000000001)})
pooled = f()
if not getPos:
return pooled
positions = g()
positions[np.where(np.isnan(positions))] = 0
return pooled, positions
def feedf(self, data):
"""
Pool features within a given receptive from the input data.
Args:
-----
data: An N x k x m1 x n1 array of input plains.
Returns:
-------
A N x k x m2 x n2 array of output plains.
"""
if self.type == 'max':
_data = np.asarray(data, dtype='float32')
x = tn.ftensor4('x')
f = thn.function([], max_pool(x, self.factor), givens={x: shared(_data)})
g = thn.function([], max_pool_same(x, self.factor)/x, givens={x: shared(_data + 0.0000000001)})
self.grad = g()
self.grad[np.where(np.isnan(self.grad))] = 0
return f()
else:
return downsample(data, (1, 1, self.factor[0], self.factor[1]))
