def build_model(
self,
batch_size=100,
input_size=784,
hidden1=100,
hidden2=100,
out_classes=10,
quant_param_path='../../mnist_mlp_quant_param.npz'): # 建立网络结构
self.batch_size = batch_size
self.out_classes = out_classes
# 在创建全连层时需要输入对应的量化参数，为了简化实验，本实验已经提供了网络的量化参数，
# 读取到了 input_quant_params 和 filter_quant_params 中，搭建网络时，只需要按顺序
# 为每个全连接层输入 input_quant_params 即可，加载参数时，同样也只需要按顺序把
# filter_quant_params 中的值输入。
# 加载量化参数
params = np.load(quant_param_path)
input_params = params['input']
filter_params = params['filter']
for i in range(0, len(input_params), 2):
self.input_quant_params.append(
pycnml.QuantParam(int(input_params[i]),
float(input_params[i + 1])))
for i in range(0, len(filter_params), 2):
self.filter_quant_params.append(
pycnml.QuantParam(int(filter_params[i]),
float(filter_params[i + 1])))
# TODO：使用 pycnml 建立三层神经网络结构
self.net.setInputShape(batch_size, input_size, 1, 1)
# fc1
self.net.createMlpLayer('fc1', hidden1, self.input_quant_params[0])
# relu
self.net.createReLuLayer('relu1')
# fc2
self.net.createMlpLayer('fc2', hidden2, self.input_quant_params[1])
# relu
self.net.createReLuLayer('relu2')
# fc3
self.net.createMlpLayer('fc3', self.out_classes,
self.input_quant_params[2])
# softmax
self.net.createSoftmaxLayer('sf', axis=1)
def build_model(self,
param_path='../../imagenet-vgg-verydeep-19.mat',
quant_param_path='../../vgg19_quant_param_new.npz'):
self.param_path = param_path
# loading quant params
# before creating layers, you should run through the net with cpu and get positions and scales used for quantizing input data. you can get quant params by using pycnml.QuantTool
# only conv and mlp layer need to be quantized
# in this experiment these quant params have already been created and saved into local files.
params = np.load(quant_param_path)
input_params = params['input']
filter_params = params['filter']
for i in range(0, len(input_params), 2):
self.input_quant_params.append(pycnml.QuantParam(int(input_params[i]), float(input_params[i + 1])))
for i in range(0, len(filter_params), 2):
self.filter_quant_params.append(pycnml.QuantParam(int(filter_params[i]), float(filter_params[i + 1])))
# TODO: 使用net的createXXXLayer接口搭建VGG19网络
# creating layers
self.net.setInputShape(1, 3, 224, 224)
# conv1_1
self.net.createConvLayer('conv1_1', 64, 3, 1, 1, 1, self.input_quant_params[0])
# relu1_1
self.net.createReLuLayer('relu1_1')
# conv1_2
self.net.createConvLayer('conv1_2', 64, 3, 1, 1, 1, self.input_quant_params[1])
# relu1_2
self.net.createReLuLayer('relu1_2')
# pool1
_______________________
# conv2_1
_______________________
# relu2_1
_______________________
# conv2_2
_______________________
# relu2_2
_______________________
# pool2
_______________________
# conv3_1
_______________________
# relu3_1
_______________________
# conv3_2
_______________________
# relu3_2
_______________________
# conv3_3
_______________________
# relu3_3
_______________________
# conv3_4
_______________________
# relu3_4
_______________________
# pool3
_______________________
# conv4_1
_______________________
# relu4_1
_______________________
# conv4_2
_______________________
# relu4_2
_______________________
# conv4_3
_______________________
# relu4_3
_______________________
# conv4_4
_______________________
# relu4_4
_______________________
# pool4
_______________________
# conv5_1
_______________________
# relu5_1
_______________________
# conv5_2
_______________________
# relu5_2
_______________________
# conv5_3
_______________________
# relu5_3
_______________________
# conv5_4
_______________________
# relu5_4
_______________________
# pool5
_______________________
# flatten
self.net.createFlattenLayer('flatten', [1, 512 * 7 * 7, 1, 1])
# fc6
_______________________
# relu6
_______________________
# fc7
_______________________
# relu7
_______________________
# fc8
self.net.createMlpLayer('fc8', 1000, self.input_quant_params[18])
# softmax
self.net.createSoftmaxLayer('softmax', 1)
def build_model(self,
param_path='../../imagenet-vgg-verydeep-19.mat',
quant_param_path='../../vgg19_quant_param_new.npz'):
self.param_path = param_path
# loading quant params
# before creating layers, you should run through the net with cpu and get positions and scales used for quantizing input data. you can get quant params by using pycnml.QuantTool
# only conv and mlp layer need to be quantized
# in this experiment these quant params have already been created and saved into local files.
params = np.load(quant_param_path)
input_params = params['input']
filter_params = params['filter']
for i in range(0, len(input_params), 2):
self.input_quant_params.append(
pycnml.QuantParam(int(input_params[i]),
float(input_params[i + 1])))
for i in range(0, len(filter_params), 2):
self.filter_quant_params.append(
pycnml.QuantParam(int(filter_params[i]),
float(filter_params[i + 1])))
# TODO: 使用net的createXXXLayer接口搭建VGG19网络
# createConvLayer: layer_name, out_channel, kernel_size, stride, dilation, pad, quant_param
# createPoolingLayer: layer_name, kernel_size, stride
# createFlattenLayer: layer_name, output_shape
# creating layers
self.net.setInputShape(1, 3, 224, 224)
# conv1_1
self.net.createConvLayer('conv1_1', 64, 3, 1, 1, 1,
self.input_quant_params[0])
# relu1_1
self.net.createReLuLayer('relu1_1')
# conv1_2
self.net.createConvLayer('conv1_2', 64, 3, 1, 1, 1,
self.input_quant_params[1])
# relu1_2
self.net.createReLuLayer('relu1_2')
# pool1
self.net.createPoolingLayer('pool1', 2, 2)
# conv2_1
self.net.createConvLayer('conv2_1', 128, 3, 1, 1, 1,
self.input_quant_params[2])
# relu2_1
self.net.createReLuLayer('relu2_1')
# conv2_2
self.net.createConvLayer('conv2_2', 128, 3, 1, 1, 1,
self.input_quant_params[3])
# relu2_2
self.net.createReLuLayer('relu2_2')
# pool2
self.net.createPoolingLayer('pool2', 2, 2)
# conv3_1
self.net.createConvLayer('conv3_1', 256, 3, 1, 1, 1,
self.input_quant_params[4])
# relu3_1
self.net.createReLuLayer('relu3_1')
# conv3_2
self.net.createConvLayer('conv3_2', 256, 3, 1, 1, 1,
self.input_quant_params[5])
# relu3_2
self.net.createReLuLayer('relu3_2')
# conv3_3
self.net.createConvLayer('conv3_3', 256, 3, 1, 1, 1,
self.input_quant_params[6])
# relu3_3
self.net.createReLuLayer('relu3_3')
# conv3_4
self.net.createConvLayer('conv3_4', 256, 3, 1, 1, 1,
self.input_quant_params[7])
# relu3_4
self.net.createReLuLayer('relu3_4')
# pool3
self.net.createPoolingLayer('pool3', 2, 2)
# conv4_1
self.net.createConvLayer('conv4_1', 512, 3, 1, 1, 1,
self.input_quant_params[8])
# relu4_1
self.net.createReLuLayer('relu4_1')
# conv4_2
self.net.createConvLayer('conv4_2', 512, 3, 1, 1, 1,
self.input_quant_params[9])
# relu4_2
self.net.createReLuLayer('relu4_2')
# conv4_3
self.net.createConvLayer('conv4_3', 512, 3, 1, 1, 1,
self.input_quant_params[10])
# relu4_3
self.net.createReLuLayer('relu4_3')
# conv4_4
self.net.createConvLayer('conv4_4', 512, 3, 1, 1, 1,
self.input_quant_params[11])
# relu4_4
self.net.createReLuLayer('relu4_4')
# pool4
self.net.createPoolingLayer('pool4', 2, 2)
# conv5_1
self.net.createConvLayer('conv5_1', 512, 3, 1, 1, 1,
self.input_quant_params[12])
# relu5_1
self.net.createReLuLayer('relu5_1')
# conv5_2
self.net.createConvLayer('conv5_2', 512, 3, 1, 1, 1,
self.input_quant_params[13])
# relu5_2
self.net.createReLuLayer('relu5_2')
# conv5_3
self.net.createConvLayer('conv5_3', 512, 3, 1, 1, 1,
self.input_quant_params[14])
# relu5_3
self.net.createReLuLayer('relu5_3')
# conv5_4
self.net.createConvLayer('conv5_4', 512, 3, 1, 1, 1,
self.input_quant_params[15])
# relu5_4
self.net.createReLuLayer('relu5_4')
# pool5
self.net.createPoolingLayer('pool5', 2, 2)
# flatten
self.net.createFlattenLayer('flatten', [1, 512 * 7 * 7, 1, 1])
# fc6
self.net.createMlpLayer('fc6', 4096, self.input_quant_params[16])
# relu6
self.net.createReLuLayer('relu6')
# fc7
self.net.createMlpLayer('fc7', 4096, self.input_quant_params[17])
# relu7
self.net.createReLuLayer('relu7')
# fc8
self.net.createMlpLayer('fc8', 1000, self.input_quant_params[18])
# softmax
self.net.createSoftmaxLayer('softmax', 1)