def create_expected_graph(data: np.ndarray) -> Graph: graph = Graph() # input x = Input('placeholder', [1, 5, 5, 3], Float32()) # constant and internal nodes w = Constant('weight', Float32(), data) q = BinaryMeanScalingQuantizer('qtz1', [1, 2, 2, 3], Float32(), {'input': w}) # Conv conv = Conv('conv', [1, 4, 4, 3], Float32(), { 'X': x, 'W': q }, kernel_shape=[2, 2]) # One output rs = Reshape('reshape', [1, 48], Float32(), {'data': conv}) y = Output( 'output', [1, 48], Float32(), {'input': rs}, ) # add ops to the graph graph.add_op_and_inputs(y) return graph
def make_simple_model(self) -> Graph: graph = Graph() # two inputs x = Input( 'input', [1, 5, 5, 3], Float32(), ) w = Constant( 'weight', Float32(), np.zeros([1, 2, 2, 3]), dimension_format='NHWC', ) # Conv conv = Conv('conv', [1, 4, 4, 1], Float32(), { 'X': x, 'W': w }, kernel_shape=[2, 2]) # One output y = Output('output', [1, 4, 4, 1], Float32(), {'input': conv}) # add ops to the graph graph.add_op_and_inputs(y) return graph
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', Float32(), np.array(1)) s2 = Constant('aq_const2', Float32(), np.array(2)) aq = LinearMidTreadHalfQuantizer('aqtz1', [1, 4, 4, 3], Float32(), {'X': conv1, 'Y': s1, 'Z': s2}) # Conv2 w2 = Constant('weight2', Float32(), data2) kq = BinaryMeanScalingQuantizer('kqtz1', [1, 2, 2, 3], Float32(), {'input': w2}) conv2 = Conv('conv2', [1, 3, 3, 3], Float32(), {'X': aq, 'W': kq}, kernel_shape=[2, 2]) conv2.a_quantizer = [aq] conv2.quantizer = kq # One output y = Output('output', [1, 3, 3, 3], Float32(), {'input': conv2}) # add ops to the graph graph.add_op_and_inputs(y) return graph
def create_sample_graph() -> Graph: graph = Graph() x = Input('placeholder', [2], Float32()) s1 = Constant('potato_1', Float32(), np.array([1, 2])) s2 = Constant('potato_2', Float32(), np.array([1, 3])) add1 = Add('potatoes', [2], Float32(), {'A': s1, 'B': s2}) add2 = Add('more_potatoes', [2], Float32(), {'A': x, 'B': add1}) # One output y = Output('output', [2], Float32(), {'input': add2}) # add ops to the graph graph.add_op_and_inputs(y) return graph
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 = LinearMidTreadHalfQuantizer('aqtz1', [1, 4, 4, 3], Float32(), {'X': conv1, 'Y': s1, 'Z': s2}) # Conv2 w2 = Constant('weight2', Float32(), data2) kq = BinaryMeanScalingQuantizer('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 = LinearMidTreadHalfQuantizer('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
def create_sample_graph(data1: 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], QUANTIZED_PACKED(), {'X': x, 'W': w1}, kernel_shape=[2, 2]) pool1 = SpaceToDepth('s2d', [1, 2, 2, 12], Float32(), {'input': conv1}) # One output y = Output('output', [1, 2, 2, 12], Float32(), {'input': pool1}) # add ops to the graph graph.add_op_and_inputs(y) return graph
def create_sample_graph_2(data1: 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]) s1 = Constant('const1', Float32(), np.zeros([1, 4, 4, 3])) add1 = Add('add', [1, 4, 4, 3], Float32(), {'A': conv1, 'B': s1}) y = Output('output', [1, 4, 4, 3], Float32(), {'input': add1}) # add ops to the graph graph.add_op_and_inputs(y) return graph
def create_expected_graph(data: np.ndarray) -> Graph: graph = Graph() data = data.transpose([3, 2, 1, 0]) # input x = Input('placeholder', [1, 5, 5, 3], Float32(), dimension_format='NHWC') # constant and internal nodes w = Constant('weight', Float32(), data, dimension_format='NHWC') i1 = Identity('identity1', [1, 2, 2, 3], Float32(), {'input': w}, dimension_format='NHWC') q = BinaryMeanScalingQuantizer('qtz1', [1, 2, 2, 3], Float32(), {'input': i1}, dimension_format='NHWC') # Conv conv = Conv('conv', [1, 4, 4, 3], Float32(), { 'X': x, 'W': q }, kernel_shape=[2, 2], dimension_format='NHWC') # One output rs = Reshape('reshape', [1, 48], Float32(), {'data': conv}) y = Output( 'output', [1, 48], Float32(), {'input': rs}, ) # add ops to the graph graph.add_op_and_inputs(y) return graph