def create_quantized_graph2(self, data1: np.ndarray, data2: np.ndarray, data3: np.ndarray) -> Graph: graph = Graph() # input x = Input( 'placeholder', [1, 5, 5, 3], Float32(), ) # constant and internal nodes scaling1, qdata1 = self.binary_mean_scaling(data1) w = Constant('weight', Float32(), qdata1 * scaling1) q = QTZ_binary_mean_scaling('qtz1', [3, 2, 2, 3], Float32(), {'input': w}) # Conv conv1 = Conv('conv1', [1, 4, 4, 3], Float32(), { 'X': x, 'W': w }, kernel_shape=[2, 2]) s1 = Constant('aq_const1', Float32(), np.array(1)) s2 = Constant('aq_const2', Float32(), np.array(2)) aq = QTZ_linear_mid_tread_half('aqtz1', [1, 4, 4, 3], QUANTIZED_NOT_PACKED(), { 'X': conv1, 'Y': s1, 'Z': s2 }) from modules.packer import Packer packer = Packer(1, 32) scaling2, qdata2 = self.binary_mean_scaling(data2) w2 = Constant('weight2', Uint32(), packer.run(qdata2), packed=True, actual_shape=[3, 2, 2, 3]) q2 = QTZ_binary_mean_scaling('qtz2', [3, 2, 2, 3], Float32(), {'input': w2}) q2.scaling_factor = scaling2 conv2 = Conv( 'conv2', [1, 3, 3, 3], Float32(), { 'X': aq, 'W': w2 }, kernel_shape=[2, 2], quantized=True, ) conv2.quantizer = q2 scaling3, qdata3 = self.binary_mean_scaling(data3) w3 = Constant('weight2', Uint32(), packer.run(qdata3), packed=True, actual_shape=[3, 2, 2, 3]) q3 = QTZ_binary_mean_scaling('qtz3', [3, 2, 2, 3], Float32(), {'input': w3}) q3.scaling_factor = scaling3 conv3 = Conv('conv3', [1, 3, 3, 3], Float32(), { 'X': aq, 'W': w3 }, kernel_shape=[2, 2], quantized=True) conv3.quantizer = q3 y1 = Output('output1', [1, 3, 3, 3], Float32(), {'input': conv2}) y2 = Output('output2', [1, 3, 3, 3], Float32(), {'input': conv3}) # add ops to the graph graph.add_op_and_inputs(y1) graph.add_op_and_inputs(y2) return graph, scaling2, scaling3
def create_quantized_graph(self, data: np.ndarray, data2: np.ndarray, data3: np.ndarray) \ -> Tuple[Graph, np.float32, np.float32]: graph = Graph() # two inputs x = Input( 'placeholder', [1, 5, 5, 3], Float32(), ) from modules.packer import Packer packer = Packer(1, 32) data = data.transpose([3, 2, 1, 0]) scaling, qdata = self.binary_mean_scaling(data) shape = list(data.shape) w = Constant( 'weight', Float32(), qdata * scaling, ) q = QTZ_binary_mean_scaling('qtz1', shape, Float32(), {'input': w}) q.scaling_factor = scaling # Conv conv1 = Conv( 'conv1', [1, 4, 4, 3], Float32(), { 'X': x, 'W': w }, kernel_shape=[2, 2], ) s1 = Constant('aq_const1', Float32(), np.array(1)) s2 = Constant('aq_const2', Float32(), np.array(2)) aq = QTZ_linear_mid_tread_half('aqtz1', [1, 4, 4, 3], QUANTIZED_NOT_PACKED(), { 'X': conv1, 'Y': s1, 'Z': s2 }) dummy = Transpose('dummy', [1, 4, 4, 3], QUANTIZED_NOT_PACKED(), {'data': aq}, perm=[0, 1, 2, 3]) scaling2, qdata2 = self.binary_mean_scaling(data2) w2 = Constant('weight2', Uint32(), packer.run(qdata2), packed=True, actual_shape=[3, 2, 2, 3]) # quantizer connected to conv2 as 'conv2.quantizer' q2 = QTZ_binary_mean_scaling('qtz2', [3, 2, 2, 3], Uint32(), {'input': w2}) q2.scaling_factor = scaling2 conv2 = Conv('conv2', [1, 3, 3, 3], Float32(), { 'X': dummy, 'W': w2 }, kernel_shape=[2, 2], quantized=True) conv2.quantizer = q2 s3 = Constant('aq_const1', Float32(), np.array(1)) s4 = Constant('aq_const2', Float32(), np.array(2)) aq2 = QTZ_linear_mid_tread_half('aqtz2', [1, 3, 3, 3], Float32(), { 'X': conv2, 'Y': s3, 'Z': s4 }) w3 = Constant('weight3', Float32(), data3) conv3 = Conv('conv3', [1, 2, 2, 3], Float32(), { 'X': aq2, 'W': w3 }, kernel_shape=[2, 2]) # One output y = Output('output', [1, 2, 2, 3], Float32(), {'input': conv3}) # add ops to the graph graph.add_op_and_inputs(y) return graph, scaling, scaling2