def ConfigPats(ss):
dt = ss.Pats
sch = etable.Schema(
[etable.Column("Name", etensor.STRING, go.nil, go.nil),
etable.Column("Input", etensor.FLOAT32, go.Slice_int([4, 5]), go.Slice_string(["Y", "X"])),
etable.Column("Output", etensor.FLOAT32, go.Slice_int([4, 5]), go.Slice_string(["Y", "X"]))]
)
dt.SetFromSchema(sch, 3)
ss.Pats.SetMetaData("name", "Pats")
ss.Pats.SetMetaData("desc", "Training patterns")
patgen.PermutedBinaryRows(dt.Cols[1], 6, 1, 0)
patgen.PermutedBinaryRows(dt.Cols[2], 6, 1, 0)
cn = etensor.String(dt.Cols[0])
cn.Values.copy(["any", "baker", "cheese"])
def Numpy(ss):
"""
test conversions to / from numpy
"""
dt = ss.Pats
print("\n\n##############################")
print("to / from numpy")
etf = etensor.Float32(dt.Cols[1])
npf = pyet.etensor_to_numpy(etf)
print(npf)
ctf = pyet.numpy_to_etensor(npf)
print(ctf)
etu32 = etensor.NewUint32(go.Slice_int([3,4,5]), go.nil, go.nil)
sz = etf.Len()
for i in range(sz):
etu32.Values[i] = int(etf.Values[i])
print(etu32)
npu32 = pyet.etensor_to_numpy(etu32)
print(npu32)
ctu32 = pyet.numpy_to_etensor(npu32)
print(ctu32)
pyet.copy_etensor_to_numpy(npu32, etu32)
pyet.copy_numpy_to_etensor(etu32, npu32)
ets = etensor.String(dt.Cols[0])
nps = pyet.etensor_to_numpy(ets)
print(nps)
cts = pyet.numpy_to_etensor(nps)
print(cts)
pyet.copy_etensor_to_numpy(nps, ets)
pyet.copy_numpy_to_etensor(ets, nps)
etb = etensor.NewBits(go.Slice_int([3,4,5]), go.nil, go.nil)
sz = etb.Len()
for i in range(sz):
etb.Set1D(i, erand.BoolProb(.2, -1))
print(etb)
npb = pyet.etensor_to_numpy(etb)
print(npb)
ctb = pyet.numpy_to_etensor(npb)
print(ctb)
pyet.copy_etensor_to_numpy(npb, etb)
pyet.copy_numpy_to_etensor(etb, npb)
def etensor_to_numpy(et):
"""
returns a numpy ndarray constructed from the given etensor.Tensor.
data is copied into the numpy ndarray -- it is not a view.
"""
nar = 0
if et.DataType() == etensor.UINT8:
nar = np.array(etensor.Uint8(et).Values, dtype=np.uint8)
elif et.DataType() == etensor.INT8:
nar = np.array(etensor.Int8(et).Values, dtype=np.int8)
elif et.DataType() == etensor.UINT16:
nar = np.array(etensor.Uint16(et).Values, dtype=np.uint16)
elif et.DataType() == etensor.INT16:
nar = np.array(etensor.Int16(et).Values, dtype=np.int16)
elif et.DataType() == etensor.UINT32:
nar = np.array(etensor.Uint32(et).Values, dtype=np.uint32)
elif et.DataType() == etensor.INT32:
nar = np.array(etensor.Int32(et).Values, dtype=np.int32)
elif et.DataType() == etensor.UINT64:
nar = np.array(etensor.Uint64(et).Values, dtype=np.uint64)
elif et.DataType() == etensor.INT64:
nar = np.array(etensor.Int64(et).Values, dtype=np.int64)
elif et.DataType() == etensor.FLOAT32:
nar = np.array(etensor.Float32(et).Values, dtype=np.float32)
elif et.DataType() == etensor.FLOAT64:
nar = np.array(etensor.Float64(et).Values, dtype=np.float64)
elif et.DataType() == etensor.STRING:
nar = np.array(etensor.String(et).Values)
elif et.DataType() == etensor.INT:
nar = np.array(etensor.Int(et).Values, dtype=np.intc)
elif et.DataType() == etensor.BOOL:
etb = etensor.Bits(et)
sz = etb.Len()
nar = np.zeros(sz, dtype=np.bool_)
for i in range(sz):
nar[i] = etb.Value1D(i)
else:
raise TypeError("tensor with type %s cannot be converted" %
(et.DataType().String()))
return 0
# there does not appear to be a way to set the shape at the same time as initializing
return nar.reshape(et.Shapes())
def copy_numpy_to_etensor(et, nar):
"""
copies data from numpy ndarray (nar, source) to existing etensor.Tensor (et, dest)
"""
narf = np.reshape(nar, -1)
etv = et
if et.DataType() == etensor.UINT8:
etv = etensor.Uint8(et).Values
elif et.DataType() == etensor.INT8:
etv = etensor.Int8(et).Values
elif et.DataType() == etensor.UINT16:
etv = etensor.Uint16(et).Values
elif et.DataType() == etensor.INT16:
etv = etensor.Int16(et).Values
elif et.DataType() == etensor.UINT32:
etv = etensor.Uint32(et).Values
elif et.DataType() == etensor.INT32:
etv = etensor.Int32(et).Values
elif et.DataType() == etensor.UINT64:
etv = etensor.Uint64(et).Values
elif et.DataType() == etensor.INT64:
etv = etensor.Int64(et).Values
elif et.DataType() == etensor.FLOAT32:
etv = etensor.Float32(et).Values
elif et.DataType() == etensor.FLOAT64:
etv = etensor.Float64(et).Values
elif et.DataType() == etensor.STRING:
etv = etensor.String(et).Values
elif et.DataType() == etensor.INT:
etv = etensor.Int(et).Values
elif et.DataType() == etensor.BOOL:
etb = etensor.Bits(et)
sz = min(etb.Len(), len(narf))
for i in range(sz):
narf[i] = etb.Value1D(i)
return
else:
raise TypeError("tensor with type %s cannot be copied" %
(et.DataType().String()))
return 0
etv.copy(narf) # go slice copy, not python copy = clone