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inception_module.py
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inception_module.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: inception_module.py
# Author: Qian Ge <[email protected]>
import numpy as np
import tensorflow as tf
from tensorflow.contrib.framework import add_arg_scope
import src.models.layers as L
def sub_rgb2bgr_mean(inputs):
with tf.name_scope('sub_mean'):
red, green, blue = tf.split(axis=3,
num_or_size_splits=3,
value=inputs)
imagenet_mean = [103.939, 116.779, 123.68]
input_bgr = tf.concat(axis=3, values=[
blue - imagenet_mean[0],
green - imagenet_mean[1],
red - imagenet_mean[2],
])
return input_bgr
@add_arg_scope
def inception_layer(conv_11_size, conv_33_reduce_size, conv_33_size,
conv_55_reduce_size, conv_55_size, pool_size,
layer_dict, inputs=None,
bn=False, wd=0, init_w=None,
pretrained_dict=None, trainable=True, is_training=True,
name='inception'):
if inputs is None:
inputs = layer_dict['cur_input']
layer_dict['cur_input'] = inputs
arg_scope = tf.contrib.framework.arg_scope
with arg_scope([L.conv], layer_dict=layer_dict, pretrained_dict=pretrained_dict,
bn=bn, nl=tf.nn.relu, init_w=init_w, trainable=trainable,
is_training=is_training, wd=wd, add_summary=False):
conv_11 = L.conv(filter_size=1, out_dim=conv_11_size,
inputs=inputs, name='{}_1x1'.format(name))
L.conv(filter_size=1, out_dim=conv_33_reduce_size,
inputs=inputs, name='{}_3x3_reduce'.format(name))
conv_33 = L.conv(filter_size=3, out_dim=conv_33_size,
name='{}_3x3'.format(name))
L.conv(filter_size=1, out_dim=conv_55_reduce_size,
inputs=inputs, name='{}_5x5_reduce'.format(name))
conv_55 = L.conv(filter_size=5, out_dim=conv_55_size,
name='{}_5x5'.format(name))
L.max_pool(layer_dict=layer_dict, inputs=inputs, stride=1,
filter_size=3, padding='SAME', name='{}_pool'.format(name))
convpool = L.conv(filter_size=1, out_dim=pool_size,
name='{}_pool_proj'.format(name))
output = tf.concat([conv_11, conv_33, conv_55, convpool], 3,
name='{}_concat'.format(name))
layer_dict['cur_input'] = output
layer_dict[name] = output
return output
def inception_conv_layers(layer_dict, inputs=None, pretrained_dict=None,
bn=False, wd=0, init_w=None,
is_training=True, trainable=True,
conv_stride=2):
if inputs is None:
inputs = layer_dict['cur_input']
layer_dict['cur_input'] = inputs
arg_scope = tf.contrib.framework.arg_scope
with arg_scope([L.conv], layer_dict=layer_dict, pretrained_dict=pretrained_dict,
bn=bn, nl=tf.nn.relu, init_w=init_w, trainable=trainable,
is_training=is_training, wd=wd, add_summary=False):
conv1 = L.conv(7, 64, inputs=inputs, name='conv1_7x7_s2', stride=conv_stride)
padding1 = tf.constant([[0, 0], [0, 1], [0, 1], [0, 0]])
conv1_pad = tf.pad(conv1, padding1, 'CONSTANT')
pool1, _ = L.max_pool(
layer_dict=layer_dict, inputs=conv1_pad, stride=2,
filter_size=3, padding='VALID', name='pool1')
pool1_lrn = tf.nn.local_response_normalization(
pool1, depth_radius=2, alpha=2e-05, beta=0.75,
name='pool1_lrn')
conv2_reduce = L.conv(1, 64, inputs=pool1_lrn, name='conv2_3x3_reduce')
conv2 = L.conv(3, 192, inputs=conv2_reduce, name='conv2_3x3')
padding2 = tf.constant([[0, 0], [0, 1], [0, 1], [0, 0]])
conv2_pad = tf.pad(conv2, padding2, 'CONSTANT')
pool2, _ = L.max_pool(
layer_dict=layer_dict, inputs=conv2_pad, stride=2,
filter_size=3, padding='VALID', name='pool2')
pool2_lrn = tf.nn.local_response_normalization(
pool2, depth_radius=2, alpha=2e-05, beta=0.75,
name='pool2_lrn')
layer_dict['cur_input'] = pool2_lrn
return pool2_lrn
def inception_layers(layer_dict, inputs=None, pretrained_dict=None,
bn=False, init_w=None, wd=0,
trainable=True, is_training=True):
if inputs is not None:
layer_dict['cur_input'] = inputs
arg_scope = tf.contrib.framework.arg_scope
with arg_scope([inception_layer], layer_dict=layer_dict,
pretrained_dict=pretrained_dict,
bn=bn, init_w=init_w, trainable=trainable,
is_training=is_training, wd=wd):
inception_layer(64, 96, 128, 16, 32, 32, name='inception_3a')
inception_layer(128, 128, 192, 32, 96, 64, name='inception_3b')
L.max_pool(layer_dict, stride=2, filter_size=3, name='pool3')
inception_layer(192, 96, 208, 16, 48, 64, name='inception_4a')
inception_layer(160, 112, 224, 24, 64, 64, name='inception_4b')
inception_layer(128, 128, 256, 24, 64, 64, name='inception_4c')
inception_layer(112, 144, 288, 32, 64, 64, name='inception_4d')
inception_layer(256, 160, 320, 32, 128, 128, name='inception_4e')
L.max_pool(layer_dict, stride=2, filter_size=3, name='pool4')
inception_layer(256, 160, 320, 32, 128, 128, name='inception_5a')
inception_layer(384, 192, 384, 48, 128, 128, name='inception_5b')
return layer_dict['cur_input']
def inception_fc(layer_dict, n_class, keep_prob=1., inputs=None,
pretrained_dict=None, is_training=True,
bn=False, init_w=None, trainable=True, wd=0):
if inputs is not None:
layer_dict['cur_input'] = inputs
layer_dict['cur_input'] = L.global_avg_pool(layer_dict['cur_input'], keepdims=True)
# layer_dict['cur_input'] = tf.expand_dims(layer_dict['cur_input'], [1, 2])
L.drop_out(layer_dict, is_training, keep_prob=keep_prob)
L.conv(filter_size=1, out_dim=n_class, layer_dict=layer_dict,
pretrained_dict=pretrained_dict, trainable=trainable,
bn=False, init_w=init_w, wd=wd, is_training=is_training,
name='loss3_classifier')
layer_dict['cur_input'] = tf.squeeze(layer_dict['cur_input'], [1, 2])
return layer_dict['cur_input']
def inception_conv_layers_cifar(layer_dict, inputs=None, pretrained_dict=None,
bn=False, wd=0, init_w=None,
is_training=True, trainable=True,
conv_stride=2):
if inputs is None:
inputs = layer_dict['cur_input']
layer_dict['cur_input'] = inputs
arg_scope = tf.contrib.framework.arg_scope
with arg_scope([L.conv], layer_dict=layer_dict, pretrained_dict=pretrained_dict,
bn=bn, nl=tf.nn.relu, init_w=init_w, trainable=trainable,
is_training=is_training, wd=wd, add_summary=False):
L.conv(7, 64, name='conv1_7x7_s2', stride=conv_stride)
# L.max_pool(layer_dict=layer_dict, stride=2,
# filter_size=3, padding='VALID', name='pool1')
L.conv(1, 64, name='conv2_3x3_reduce')
L.conv(3, 192, name='conv2_3x3')
# L.max_pool(layer_dict=layer_dict, stride=2,
# filter_size=3, padding='VALID', name='pool2')
return layer_dict['cur_input']
def auxiliary_classifier(layer_dict, n_class, keep_prob=1., inputs=None,
pretrained_dict=None, is_training=True,
bn=False, init_w=None, trainable=True, wd=0):
if inputs is not None:
layer_dict['cur_input'] = inputs
# layer_dict['cur_input'] = tf.layers.average_pooling2d(
# inputs=layer_dict['cur_input'],
# pool_size=5, strides=3,
# padding='valid', name='averagepool')
layer_dict['cur_input'] = L.global_avg_pool(layer_dict['cur_input'], keepdims=True)
arg_scope = tf.contrib.framework.arg_scope
with arg_scope([L.conv, L.linear], layer_dict=layer_dict,
bn=bn, init_w=init_w, trainable=trainable,
is_training=is_training, wd=wd, add_summary=False):
L.conv(1, 128, name='conv', stride=1, nl=tf.nn.relu)
L.linear(out_dim=512, name='fc_1', nl=tf.nn.relu)
L.drop_out(layer_dict, is_training, keep_prob=keep_prob)
L.linear(out_dim=512, name='fc_2', nl=tf.nn.relu)
L.drop_out(layer_dict, is_training, keep_prob=keep_prob)
L.linear(out_dim=n_class, name='classifier', bn=False)
return layer_dict['cur_input']