#!/usr/bin/env python # -*- coding: utf-8 -*- # File: googlenet_model.py import tensorflow as tf from tensorflow.contrib.layers import variance_scaling_initializer from tensorpack.models import * from tensorpack.tfutils.argscope import argscope, get_arg_scope from learned_quantization import Conv2DQuant, getBNReLUQuant, QuantizedActiv def inception_block(l, name, ch_1x1, ch_3x3, ch_5x5, is_last_block=False, is_last=False): data_format = get_arg_scope()['Conv2DQuant']['data_format'] with tf.variable_scope(name): conv1x1 = Conv2DQuant('1x1', l, ch_1x1, 1, nl=getBNReLUQuant if not is_last_block else tf.identity) conv3x3_reduce = Conv2DQuant('3x3_reduce', l, ch_3x3, 1, nl=getBNReLUQuant) conv3x3 = Conv2DQuant('3x3', conv3x3_reduce, ch_3x3, 3, nl=getBNReLUQuant if not is_last_block else tf.identity) conv5x5_reduce = Conv2DQuant('5x5_reduce', l, ch_5x5, 1, nl=getBNReLUQuant) conv5x5 = Conv2DQuant('5x5', conv5x5_reduce, ch_5x5, 5, nl=getBNReLUQuant if not is_last_block else tf.identity) if is_last_block and not is_last: conv1x1 = MaxPooling('pool_1x1', conv1x1, shape=3, stride=2, padding='SAME') conv1x1 = BNReLU('conv1x1_bn', conv1x1) conv1x1 = QuantizedActiv('conv1x1_quant', conv1x1) conv3x3 = MaxPooling('pool_3x3', conv3x3, shape=3, stride=2, padding='SAME') conv3x3 = BNReLU('conv3x3_bn', conv3x3) conv3x3 = QuantizedActiv('conv3x3_quant', conv3x3) conv5x5 = MaxPooling('pool_5x5', conv5x5, shape=3, stride=2, padding='SAME') conv5x5 = BNReLU('conv5x5_bn', conv5x5) conv5x5 = QuantizedActiv('conv5x5_quant', conv5x5) l = tf.concat([ conv1x1, conv3x3, conv5x5], 1 if data_format == 'NCHW' else 3, name='concat') if is_last: l = BNReLU('output_bn', l) return l def googlenet_backbone(image, qw=1): with argscope(Conv2DQuant, nl=tf.identity, use_bias=False, W_init=variance_scaling_initializer(mode='FAN_IN'), data_format=get_arg_scope()['Conv2D']['data_format'], nbit=qw, is_quant=True if qw > 0 else False): logits = (LinearWrap(image) .Conv2DQuant('conv1', 64, 7, stride=2, is_quant=False) .MaxPooling('pool1', shape=3, stride=2, padding='SAME') .BNReLUQuant('pool1/out') .Conv2DQuant('conv2/3x3_reduce', 192, 1, nl=getBNReLUQuant) .Conv2DQuant('conv2/3x3', 192, 3) .MaxPooling('pool2', shape=3, stride=2, padding='SAME') .BNReLUQuant('pool2/out') .apply(inception_block, 'incpetion_3a', 96, 128, 32) .apply(inception_block, 'incpetion_3b', 192, 192, 96, is_last_block=True) .apply(inception_block, 'incpetion_4a', 256, 208, 48) .apply(inception_block, 'incpetion_4b', 224, 224, 64) .apply(inception_block, 'incpetion_4c', 192, 256, 64) .apply(inception_block, 'incpetion_4d', 176, 288, 64) .apply(inception_block, 'incpetion_4e', 384, 320, 128, is_last_block=True) .apply(inception_block, 'incpetion_5a', 384, 320, 128) .apply(inception_block, 'incpetion_5b', 512, 384, 128, is_last_block=True, is_last=True) .GlobalAvgPooling('pool5') .FullyConnected('linear', out_dim=1000, nl=tf.identity)()) return logits