def create_sample_graph(data1: np.ndarray, data2: np.ndarray) -> Graph:
graph = Graph()
# input
x = Input('placeholder', [1, 5, 5, 3], Float32())
# Conv1
w1 = Constant('weight1', Float32(), data1)
conv1 = Conv('conv1', [1, 4, 4, 3], Float32(), {'X': x, 'W': w1}, kernel_shape=[2, 2])
# activation quantizer
s1 = Constant('aq_const1', Int32(), np.array([2], dtype=np.int32))
s2 = Constant('aq_const2', Float32(), np.array([2.0], dtype=np.float32))
aq1 = QTZ_linear_mid_tread_half('aqtz1', [1, 4, 4, 3], Float32(), {'X': conv1, 'Y': s1, 'Z': s2})
# Conv2
w2 = Constant('weight2', Float32(), data2)
kq = QTZ_binary_mean_scaling('kqtz1', [1, 2, 2, 3], Float32(), {'input': w2})
conv2 = Conv('conv2', [1, 3, 3, 3], Float32(), {'X': aq1, 'W': kq}, kernel_shape=[2, 2])
conv2.a_quantizer = [aq1]
conv2.quantizer = kq
conv2.is_quantized = True
sc = Constant('bn_scale', Float32(), np.random.rand(3))
be = Constant('bn_b', Float32(), np.random.rand(3))
mu = Constant('bn_mu', Float32(), np.random.rand(3))
va = Constant('bn_var', Float32(), np.random.rand(3))
bn = BatchNormalization('bn', [1, 3, 3, 3], Float32(), {'X': conv2,
'scale': sc,
'B': be,
'mean': mu,
'var': va})
# activation quantizer
s3 = Constant('aq_const3', Int32(), np.array([2], dtype=np.int32))
s4 = Constant('aq_const4', Float32(), np.array([2.0], dtype=np.float32))
aq2 = QTZ_linear_mid_tread_half('aqtz2', [1, 3, 3, 3], Float32(), {'X': bn, 'Y': s3, 'Z': s4})
# One output
y = Output('output', [1, 3, 3, 3], Float32(), {'input': aq2})
# add ops to the graph
graph.add_op_and_inputs(y)
return graph
from core.data_types import DataType, Float32, Float64, Int8, Int16, Int32, \
Int64, Uint8, Uint16, Uint32, Uint64, Bool, String
from core.exceptions import UnsupportedNode, UnsupportedDataType
from core.graph import Graph
from core.operators import Operator, Conv, Identity, QTZ_binary_mean_scaling, \
BatchNormalization, QTZ_linear_mid_tread_half, Add, \
MaxPool, AveragePool, Reshape, Softmax, Transpose, Relu, SpaceToDepth, \
Mul, QTZ_binary_channel_wise_mean_scaling, ConcatOnDepth, Maximum, DepthToSpace, ResizeNearestNeighbor, \
Split, Pad, MatMul, Gather, Unique, Cast, Minimum, StridedSlice, Prod, Shape, LeakyRelu
DLK_DTYPE_MAP: Dict[str, Optional[DataType]] = {
# any
'DT_INVALID': Float32,
# primitives
'DT_FLOAT': Float32(),
'DT_INT32': Int32(),
'DT_UINT8': Uint8(),
'DT_INT8': Int8(),
'DT_UINT16': Uint16(),
'DT_INT16': Int16(),
'DT_INT64': Int64(),
'f': Float32(),
'i': Int32(),
# primitive vector
'FLOATS': None,
'INTS': None,
# custom
'DT_BOOL': Bool(),
'DT_STRING': String(),
Int64, Uint8, Uint16, Uint32, Uint64, Bool, String
from core.exceptions import UnsupportedNode, UnsupportedDataType
from core.operators import Operator, Conv, Identity, QTZ_binary_mean_scaling, \
BatchNormalization, QTZ_linear_mid_tread_half, Add, \
Pool, MaxPool, AveragePool, Reshape, Softmax, Transpose
import core.operators as dlk_op
import numpy as np
import importlib
import functools
DLK_DTYPE_MAP: Dict[str, Optional[DataType]] = {
# any
'UNDEFINED': None,
# primitives
'FLOAT': Float32(),
'INT': Int32(),
'UINT8': Uint8(),
'INT8': Int8(),
'UINT16': Uint16(),
'INT16': Int16(),
'INT32': Int32(),
'INT64': Int64(),
'f': Float32(),
'i': Int32(),
# primitive vector
'FLOATS': None,
'INTS': None,
# custom
'BOOL': Bool(),