def GetOutput( self, input = input ):
"""
params : input : input image usually with one batch ( batch_size, channel, weight,height)
type : input : numpy.ndarray
"""
if input.dtype == theano.config.floatX:
conv_out = self.conv( input )
else:
conv_out = self.conv( input.astype(theano.config.floatX) )
if( conv_out.shape[1] == 1 ):
conv_out = conv_out.reshape( conv_out.shape[2:4] )
else:
conv_out = conv_out.reshape( conv_out.shape[1:4] )
pooled_out, switch_map = max_pool_2d( conv_out , poolsize = self.poolsize)
if( pooled_out.ndim == 2 ):
pooled_out = pooled_out.reshape( [1,1] + list(pooled_out.shape))
else:
pooled_out = pooled_out.reshape( [1] + list(pooled_out.shape))
output = self.activation( pooled_out )
return np.asarray(output, dtype = theano.config.floatX), switch_map
def test_max_pool(self):
input = np.asarray([[[0, 0, 1, 1], [0, 0, 1, 1], [2, 2, 3, 3],
[2, 2, 3, 3]],
[[1, 2, -1, -2], [3, 4, -3, -4], [0, 2, 1, 1],
[3, 5, 0, 1]]])
desired_output = np.asarray([[[0, 1], [2, 3]], [[4, -1], [5, 1]]])
desired_switch_map = np.asarray([[[0, 0], [0, 0]], [[3, 0], [3, 0]]])
poolsize = 2
actual_output, actual_switch_map = max_pool_2d(input, poolsize)
assert (desired_output == actual_output).all()
assert (desired_switch_map == actual_switch_map).all()
def test_max_pool( self ):
input = np.asarray( [ [ [0,0,1,1],
[0,0,1,1],
[2,2,3,3],
[2,2,3,3]],
[ [1,2,-1,-2],
[3,4,-3,-4],
[0,2,1,1],
[3,5,0,1] ] ] )
desired_output = np.asarray( [ [ [0,1],
[2,3]],
[ [4,-1],
[5,1] ] ] )
desired_switch_map = np.asarray( [ [ [0,0],
[0,0]],
[ [3,0],
[3,0] ] ] )
poolsize = 2
actual_output, actual_switch_map = max_pool_2d( input, poolsize )
assert ( desired_output == actual_output ).all()
assert ( desired_switch_map == actual_switch_map ).all()
