def test_autocrop_array_shapes(self):
from lasagne.layers.merge import autocrop_array_shapes
crop0 = None
crop1 = [None, 'lower', 'center', 'upper']
# Too few crop modes; should get padded with None
crop2 = ['lower', 'upper']
# Invalid crop modes
crop_bad = ['lower', 'upper', 'bad', 'worse']
assert autocrop_array_shapes(
[(1, 2, 3, 4), (5, 6, 7, 8), (5, 4, 3, 2)], crop0) == \
[(1, 2, 3, 4), (5, 6, 7, 8), (5, 4, 3, 2)]
assert autocrop_array_shapes(
[(1, 2, 3, 4), (5, 6, 7, 8), (5, 4, 3, 2)], crop1) == \
[(1, 2, 3, 2), (5, 2, 3, 2), (5, 2, 3, 2)]
assert autocrop_array_shapes(
[(1, 2, 3, 4), (5, 6, 7, 8), (5, 4, 3, 2)], crop2) == \
[(1, 2, 3, 4), (1, 2, 7, 8), (1, 2, 3, 2)]
assert autocrop_array_shapes(
[(None, 2, 3, 4), (5, 6, 7, 8), (5, 4, 3, 2)], crop2) == \
[(None, 2, 3, 4), (None, 2, 7, 8), (None, 2, 3, 2)]
with pytest.raises(ValueError):
autocrop_array_shapes([(1, 2, 3, 4), (5, 6, 7, 8), (5, 4, 3, 2)],
crop_bad)
# Inconsistent dimensionality
with pytest.raises(ValueError):
autocrop_array_shapes([(1, 2, 3, 4), (5, 6, 7), (5, 4, 3, 2, 10)],
crop1)
def test_autocrop_array_shapes(self):
from lasagne.layers.merge import autocrop_array_shapes
crop0 = None
crop1 = [None, 'lower', 'center', 'upper']
# Too few crop modes; should get padded with None
crop2 = ['lower', 'upper']
# Invalid crop modes
crop_bad = ['lower', 'upper', 'bad', 'worse']
assert autocrop_array_shapes(
[(1, 2, 3, 4), (5, 6, 7, 8), (5, 4, 3, 2)], crop0) == \
[(1, 2, 3, 4), (5, 6, 7, 8), (5, 4, 3, 2)]
assert autocrop_array_shapes(
[(1, 2, 3, 4), (5, 6, 7, 8), (5, 4, 3, 2)], crop1) == \
[(1, 2, 3, 2), (5, 2, 3, 2), (5, 2, 3, 2)]
assert autocrop_array_shapes(
[(1, 2, 3, 4), (5, 6, 7, 8), (5, 4, 3, 2)], crop2) == \
[(1, 2, 3, 4), (1, 2, 7, 8), (1, 2, 3, 2)]
assert autocrop_array_shapes(
[(None, 2, 3, 4), (5, 6, 7, 8), (5, 4, 3, 2)], crop2) == \
[(None, 2, 3, 4), (None, 2, 7, 8), (None, 2, 3, 2)]
with pytest.raises(ValueError):
autocrop_array_shapes(
[(1, 2, 3, 4), (5, 6, 7, 8), (5, 4, 3, 2)], crop_bad)
# Inconsistent dimensionality
with pytest.raises(ValueError):
autocrop_array_shapes(
[(1, 2, 3, 4), (5, 6, 7), (5, 4, 3, 2, 10)], crop1)
def get_output_shape_for(self, input_shapes):
input_shapes = autocrop_array_shapes(input_shapes, self.cropping)
# Infer the output shape by grabbing, for each axis, the first
# input size that is not `None` (if there is any)
output_shape = input_shapes[1]
return output_shape
def __init__(self, layers, croppings=[], axis=1, **kwargs):
from lasagne.layers.merge import autocrop_array_shapes
super(Concatenate, self).__init__(maxInConnections=None, **kwargs)
self.layers = list(layers)
self.croppings = croppings
self.axis = axis
self.croppedShapes = []
self.originalShapes = []
for l in self.layers:
self.originalShapes.append(l.getShape_abs())
self.croppedShapes = autocrop_array_shapes(self.originalShapes,
self.croppings)
self.shape = []
for i in xrange(len(self.croppedShapes[0])):
if i == self.axis:
v = 0
for s in self.croppedShapes:
v += s[i]
else:
v = self.croppedShapes[0][i]
self.shape.append(v)
self.shape = tuple(self.shape)
self.streams = None
for l in self.layers:
l.connect(self)
if self.streams is None:
self.streams = set(l.outputs.streams)
else:
sl = set(l.outputs.streams)
notCommon = self.streams - sl
if len(notCommon) > 0:
print MCAN.warning(
"Layers %s do not share streams: %s with other layers .Will take the intersection"
% (l.name, notCommon))
self.streams = self.streams & sl
self.outputs = MTYPES.Variable(streams=self.streams)