def test_getMultiplicityMap(self):
"""Test generating a multiplicity map from a coreIdMap."""
inputShape = (10, 10, 2)
inputLayer = NxInputLayer(inputShape)
layer = NxConv2D(2, 3)
layer(inputLayer.input)
coreShape = np.array([4, 3, 2])
layerShape = layer.output_shape[1:]
numCoresPerAxis = np.ceil(layerShape / coreShape).astype(int)
coreIdMap = getCoreIdMapFromCoreShape(coreShape, layerShape,
numCoresPerAxis)
multiplicityMap = layer.getMultiplicityMap(coreIdMap)
if self.verbose:
plotMat(multiplicityMap, title='MultiplicityMap')
target = [[1, 1, 1, 2, 2, 1, 2, 2, 1,
1], [1, 1, 1, 2, 2, 1, 2, 2, 1, 1],
[1, 1, 1, 2, 2, 1, 2, 2, 1,
1], [1, 1, 1, 2, 2, 1, 2, 2, 1, 1],
[2, 2, 2, 4, 4, 2, 4, 4, 2,
2], [2, 2, 2, 4, 4, 2, 4, 4, 2, 2],
[1, 1, 1, 2, 2, 1, 2, 2, 1,
1], [1, 1, 1, 2, 2, 1, 2, 2, 1, 1],
[1, 1, 1, 2, 2, 1, 2, 2, 1, 1],
[1, 1, 1, 2, 2, 1, 2, 2, 1, 1]]
self.assertTrue(np.array_equal(multiplicityMap, target))
def tryCreatePartition(numCoresPerAxis, coreShape, postLayerPartition, layer,
logdir):
"""Try creating a partition of the layer.
Fails if proposed partition exceeds one of the Loihi limits.
:param np.ndarray | list | tuple numCoresPerAxis: Number of cores along
each layer dimension.
:param np.ndarray | list | tuple coreShape: The shape of the largest
core.
:param Layer postLayerPartition: The subsequent partitioned layer.
:param KerasLayer | NxConv2D layer: The layer to partition.
:param str logdir: Where to save plots.
:return: Valid partition candidate.
:rtype: Layer
"""
outputShape = layer.output_shape[1:]
# When using signed spikes the number of channels in the output
# is doubled.
if hasattr(layer, 'signed'):
if layer.signed:
outputShape = outputShape[:-1] + (2 * outputShape[-1],)
coreIdMap = getCoreIdMapFromCoreShape(coreShape, outputShape,
numCoresPerAxis)
coreOccupancy = getCoreOccupancy(coreIdMap, numCoresPerAxis)
if np.any(coreOccupancy > layer.maxNumCompartments):
return
multiplicityMap = layer.getMultiplicityMap(coreIdMap)
partitionCandidate = Layer(layer.name, layer.__class__.__name__,
layer.compartmentKwargs, layer.connectionKwargs,
coreIdMap, multiplicityMap, postLayerPartition)
# Pass coreOccupancy to partitionCandidate here only to be able to plot it
# later when the partition has been stored to disk.
partitionCandidate.coreOccupancy = coreOccupancy
partitionCandidate = layer.compile(partitionCandidate)
if partitionCandidate is None:
print('.', end='', flush=True)
return
layer.validatePartition(partitionCandidate)
layer.visualizePartition(logdir, partitionCandidate, coreIdMap,
coreOccupancy, multiplicityMap=multiplicityMap)
print('x', end='', flush=True)
return partitionCandidate
def test_getCoreIdMapFromCoreShape2(self):
"""Test generating the coreIdMap given a coreShape."""
inputShape = (4, 4, 2)
inputLayer = NxInputLayer(inputShape)
layer = NxConv2D(2, 3)
layer(inputLayer.input)
coreShape = np.array([2, 1, 2])
layerShape = layer.output_shape[1:]
numCoresPerAxis = np.ceil(layerShape / coreShape).astype(int)
coreIds = getCoreIdMapFromCoreShape(coreShape, layerShape,
numCoresPerAxis)
target = np.array([[[0, 0], [1, 1]], [[0, 0], [1, 1]]])
self.assertTrue(np.array_equal(coreIds, target))
if self.verbose:
plotMat(coreIds[:, :, 0], title='coreIds[:, :, 0]')
plotMat(coreIds[:, :, 1], title='coreIds[:, :, 1]')
def test_compressConnections(self):
"""Check axon compression."""
inputShapes = np.array([[7, 7, 2], [32, 32, 3],
[32, 32, 3], [27, 26, 2], [32, 30, 2],
[30, 35, 2], [7, 7, 2], [20, 25, 2], [7, 7, 2],
[30, 35, 2], [7, 7, 2], [30, 35, 2]])
layerArgs = np.array([[2, 3], [2, 3], [2, 3], [2, 1], [2, 2], [2, 3],
[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]])
layerKwargs = np.array([
{
'synapseEncoding': 'sparse'
},
{
'synapseEncoding': 'sparse'
},
{
'synapseEncoding': 'sparse',
'strides': 2
},
{
'synapseEncoding': 'sparse',
'strides': 2
},
{
'synapseEncoding': 'sparse',
'strides': 2
},
{
'synapseEncoding': 'sparse',
'strides': 2,
'padding': 'same'
},
{
'synapseEncoding': 'runlength'
},
{
'synapseEncoding': 'runlength',
'strides': 2,
'padding': 'same'
},
{
'synapseEncoding': 'dense1'
},
{
'synapseEncoding': 'dense1',
'strides': 2,
'padding': 'same'
},
{
'synapseEncoding': 'dense2'
},
{
'synapseEncoding': 'dense2',
'strides': 2,
'padding': 'same'
},
])
coreShapes = np.array([[5, 5, 2], [30, 10, 2], [10, 5, 2], [7, 13, 2],
[16, 5, 2], [8, 9, 2], [5, 5, 2], [6, 9, 2],
[5, 5, 2], [15, 10, 2], [5, 5, 2], [6, 9, 2]])
# numOutputAxons (second column) is expected to be zero because we are
# looking at the output layer.
expectedResults = np.array([[14, 0, 900, 281, 378],
[108, 0, 29160, 6873, 10080],
[198, 0, 4050, 2430, 2592],
[52, 0, 56, 42, 28],
[60, 0, 768, 528, 384],
[144, 0, 1408, 808, 868],
[14, 0, 900, 288, 371],
[104, 0, 928, 524, 580],
[14, 0, 900, 660, 427],
[72, 0, 1408, 1226, 840],
[14, 0, 3587, 290, 700],
[216, 0, 2920, 874, 1052]])
doTest = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], bool)
np.random.seed(0)
for inputShape, args, kwargs, coreShape, expectedResult in \
zip(inputShapes[doTest],
layerArgs[doTest],
layerKwargs[doTest],
coreShapes[doTest],
expectedResults[doTest]):
with self.subTest(inputShape=inputShape,
args=args,
kwargs=kwargs,
coreShape=coreShape,
expectedResult=expectedResult):
# The Keras layer initializer does not know how to handle
# np.int types. Convert to builtin int manually.
args = list(args)
args[1] = int(args[1])
inputLayer = NxInputLayer(tuple(inputShape))
layer = NxConv2D(*args, **kwargs)
layer(inputLayer.input)
# Assumes that this layer will never go beyond one chip.
layer.coreCounter = 0
layerShape = layer.output_shape[1:]
numCoresPerAxis = np.ceil(layerShape / coreShape).astype(int)
coreIdMap = getCoreIdMapFromCoreShape(coreShape, layerShape,
numCoresPerAxis)
multiplicityMapPre = np.ones(layer.input_shape[1:-1], int)
postLayerPartition = getDummyLayer(layer.output_shape[1:])
partitionCandidate = Layer(layer.name, '',
layer.compartmentKwargs,
layer.connectionKwargs, coreIdMap,
multiplicityMapPre,
postLayerPartition)
partitionCandidate = layer.compile(partitionCandidate)
try:
layer.validatePartition(partitionCandidate)
finally:
layer.deleteKernelIdMap()
layer.deleteKernelIdMap()
result = [
partitionCandidate.numInputAxons,
partitionCandidate.numOutputAxons,
partitionCandidate.numSyn,
partitionCandidate.numSynEntries,
partitionCandidate.numSynMemWords
]
self.verbose = True
if self.verbose:
print(result)
for er, r in zip(expectedResult, result):
self.assertEqual(er, r)
