def setupEOgmaNeo():
global _system, _hierarchy, _inputImage, _inputValue
print("Initializing EOgmaNeo::ComputeSystem")
_system = eogmaneo.ComputeSystem(4)
print("Initializing EOgmaNeo::Hierarchy")
lds = eogmaneo.StdVecLayerDesc()
numLayers = 6
layerSize = 36
for l in range(0, numLayers):
ld = eogmaneo.LayerDesc()
ld._width = layerSize
ld._height = layerSize
ld._forwardRadius = 9
ld._backwardRadius = 9
ld._ticksPerUpdate = 2
ld._temporalHorizon = 2
ld._alpha = 0.1
ld._beta = 0.1
ld._gamma = 0.01
lds.push_back(ld)
#layerSize = 16
_hierarchy = eogmaneo.Hierarchy()
_hierarchy.create([(hiddenWidth, hiddenHeight),
(steerChunkSize, steerChunkSize)],
[lineChunkSize, steerChunkSize], [False, True], lds, 41)
_inputValue = eogmaneo.StdVeci(1 * 1)
camera.resolution = (camWidth, camHeight)
camera.framerate = 24
#camera.start_preview()
time.sleep(2)
######################
ser = serial.Serial(
'/dev/serial/by-id/usb-Arduino_Srl_Arduino_Uno_556393038343514082D0-if00',
115200)
esystem = eogmaneo.ComputeSystem(4)
ld = 6 * [eogmaneo.LayerDesc()]
for l in range(0, len(ld)):
ld[l]._width = 36
ld[l]._height = 36
ld[l]._chunkSize = 6
if l == 0:
ld[l]._forwardRadius = 9
ld[l]._backwardRadius = 9
else:
ld[l]._forwardRadius = 9
ld[l]._backwardRadius = 9
ld[l]._ticksPerUpdate = 2
ld[l]._temporalHorizon = 2
# After training an ImageEncoder will output unique sparse chunked representations
# that can be input into an EOgmaNeo hierarchy. Consequently it can take predicted
# sparse chunked representations output from the hierarchy and reconstruct an
# image it has learned.
preEncoder = eogmaneo.ImageEncoder()
preEncoder.create(IMAGE_WIDTH, IMAGE_HEIGHT, HIDDEN_WIDTH, HIDDEN_HEIGHT,
CHUNK_SIZE, int(16), 123)
# Make sure EOgmaNeo uses all CPU cores available
cs = eogmaneo.ComputeSystem(multiprocessing.cpu_count())
# Construct parameter descriptions for all hierarchy encoder-decoder layers
lds = []
for i in range(NUM_LAYERS):
ld = eogmaneo.LayerDesc()
ld._width = HIDDEN_WIDTH
ld._height = HIDDEN_HEIGHT
ld._chunkSize = CHUNK_SIZE
ld._radius = RADIUS
ld._ticksPerUpdate = 2
ld._temporalHorizon = 2
ld._alpha = 0.1
ld._beta = 0.1
# Disable reinforcement learning
ld._gamma = 0.0
lds.append(ld)
time.sleep(2)
######################
ser = serial.Serial(
'/dev/serial/by-id/usb-Arduino_Srl_Arduino_Uno_556393038343514082D0-if00',
115200)
s = eogmaneo.ComputeSystem(4)
#lr = eogmaneo.LocalRegressor()
#lr.load("localregSave.txt")
ld = 2 * [eogmaneo.LayerDesc()]
for l in range(0, len(ld)):
ld[l]._width = 18
ld[l]._height = 18
ld[l]._chunkWidth = ld[l]._chunkHeight = 6
if l == 0:
ld[l]._forwardRadius = 6
ld[l]._backwardRadius = 6
else:
ld[l]._forwardRadius = 6
ld[l]._backwardRadius = 6
ld[l]._ticksPerUpdate = 2
ld[l]._temporalHorizon = 2
time.sleep(2)
######################
ser = serial.Serial(
'/dev/serial/by-id/usb-Arduino_Srl_Arduino_Uno_556393038343514082D0-if00',
115200)
s = eogmaneo.ComputeSystem(4)
#lr = eogmaneo.LocalRegressor()
#lr.load("localregSave.txt")
ld = 4 * [eogmaneo.LayerDesc()]
for l in range(0, len(ld)):
ld[l]._width = 36
ld[l]._height = 36
ld[l]._chunkSize = 6
if l == 0:
ld[l]._forwardRadius = 9
ld[l]._backwardRadius = 9
else:
ld[l]._forwardRadius = 9
ld[l]._backwardRadius = 9
ld[l]._ticksPerUpdate = 2
ld[l]._temporalHorizon = 2
