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vgg16.py
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vgg16.py
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# --------------------------------------------------------
# Tensorflow Two Stream Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Hangyan Jiang
# --------------------------------------------------------
import tensorflow as tf
import tensorflow.contrib.slim as slim
from lib.utils.compact_bilinear_pooling import compact_bilinear_pooling_layer
import numpy as np
import lib.config.config as cfg
from lib.nets.network import Network
class vgg16(Network):
def __init__(self, batch_size=1):
Network.__init__(self, batch_size=batch_size)
def build_network(self, sess, is_training=True):
with tf.variable_scope('vgg_16', 'vgg_16'):
# select initializer
if cfg.FLAGS.initializer == "truncated":
initializer = tf.truncated_normal_initializer(mean=0.0, stddev=0.01)
initializer_bbox = tf.truncated_normal_initializer(mean=0.0, stddev=0.001)
else:
initializer = tf.random_normal_initializer(mean=0.0, stddev=0.01)
initializer_bbox = tf.random_normal_initializer(mean=0.0, stddev=0.001)
q = [4.0, 12.0, 2.0]
filter1 = [[0, 0, 0, 0, 0],
[0, -1, 2, -1, 0],
[0, 2, -4, 2, 0],
[0, -1, 2, -1, 0],
[0, 0, 0, 0, 0]]
filter2 = [[-1, 2, -2, 2, -1],
[2, -6, 8, -6, 2],
[-2, 8, -12, 8, -2],
[2, -6, 8, -6, 2],
[-1, 2, -2, 2, -1]]
filter3 = [[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 1, -2, 1, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
filter1 = np.asarray(filter1, dtype=float) / q[0]
filter2 = np.asarray(filter2, dtype=float) / q[1]
filter3 = np.asarray(filter3, dtype=float) / q[2]
filters = [[filter1, filter1, filter1], [filter2, filter2, filter2], [filter3, filter3, filter3]]
filters = np.einsum('klij->ijlk', filters)
filters = filters.flatten()
initializer_srm = tf.constant_initializer(filters)
# Build RGB stream head
net = self.build_head(is_training)
# Build Noise stream head
net2 = self.build_head_forNoise(is_training, initializer, initializer_srm)
# Build rpn
rpn_cls_prob, rpn_bbox_pred, rpn_cls_score, rpn_cls_score_reshape = self.build_rpn(net, is_training, initializer)
# Build proposals
rois = self.build_proposals(is_training, rpn_cls_prob, rpn_bbox_pred, rpn_cls_score)
# Build predictions
cls_score, cls_prob, bbox_pred = self.build_predictions(net, net2, rois, is_training, initializer, initializer_bbox)
self._predictions["rpn_cls_score"] = rpn_cls_score
self._predictions["rpn_cls_score_reshape"] = rpn_cls_score_reshape
self._predictions["rpn_cls_prob"] = rpn_cls_prob
self._predictions["rpn_bbox_pred"] = rpn_bbox_pred
self._predictions["cls_score"] = cls_score
self._predictions["cls_prob"] = cls_prob
self._predictions["bbox_pred"] = bbox_pred
self._predictions["rois"] = rois
self._score_summaries.update(self._predictions)
return rois, cls_prob, bbox_pred
def get_variables_to_restore(self, variables, var_keep_dic, sess, pretrained_model):
variables_to_restore = []
noise_variable = {}
for v in variables:
# exclude the conv weights that are fc weights in vgg16
if v.name == 'vgg_16/fc6/weights:0' or v.name == 'vgg_16/fc7/weights:0' \
or v.name == 'vgg_16/cbp_fc6/weights:0' or v.name == 'vgg_16/cbp_fc7/weights:0':
self._variables_to_fix[v.name] = v
continue
# exclude the first conv layer to swap RGB to BGR
if v.name == 'vgg_16/conv1/conv1_1/weights:0' or v.name == 'vgg_16/conv1n/conv1n_1/weights:0':
self._variables_to_fix[v.name] = v
continue
if v.name.split(':')[0] in var_keep_dic:
print('Variables restored: %s' % v.name)
variables_to_restore.append(v)
# # From VGG pretrained weights file(RGB weights), load weights for noise stream
# name = v.name.split('/')
# if len(name) < 4:
# continue
# name[1] += 'n'
# name[2] = name[1] + name[2][len(name[1])-1:]
# noise_counterpart = name[0] + '/' + name[1] + '/' + name[2] + '/' + name[3]
# for u in variables:
# if u.name == noise_counterpart:
# noise_variable[v.name.split(':')[0]] = u
# print('Variables restored: %s' % u.name)
#
# with tf.variable_scope('Restore_Noise_Variables'):
# with tf.device("/cpu:0"):
# # fix the vgg16 noise stream variables
# restorer = tf.train.Saver(noise_variable)
# restorer.restore(sess, pretrained_model)
# # From VGG pretrained weights file(RGB weights), load weights for noise stream except for conv layer 1, 2
# name = v.name.split('/')
# if len(name) < 4 or (name[1] != 'conv1' and name[1] != 'conv2'):
# continue
# name[1] += 'n'
# name[2] = name[1] + name[2][len(name[1]) - 1:]
# noise_counterpart = name[0] + '/' + name[1] + '/' + name[2] + '/' + name[3]
# for u in variables:
# if u.name == noise_counterpart:
# noise_variable[v.name.split(':')[0]] = u
# print('Variables restored: %s' % u.name)
#
# with tf.variable_scope('Restore_Noise_Variables'):
# with tf.device("/cpu:0"):
# # fix the vgg16 noise stream variables
# restorer = tf.train.Saver(noise_variable)
# restorer.restore(sess, pretrained_model)
return variables_to_restore
def fix_variables(self, sess, pretrained_model):
print('Fix VGG16 layers..')
with tf.variable_scope('Fix_VGG16'):
with tf.device("/cpu:0"):
# fix the vgg16 issue from conv weights to fc weights
# fix RGB to BGR
fc6_conv = tf.get_variable("fc6_conv", [7, 7, 512, 4096], trainable=False)
fc7_conv = tf.get_variable("fc7_conv", [1, 1, 4096, 4096], trainable=False)
conv1_rgb = tf.get_variable("conv1_rgb", [3, 3, 3, 64], trainable=False)
# cbp_fc6_conv = tf.get_variable("cbp_fc6_conv", [7, 7, 512, 4096], trainable=False)
# cbp_fc7_conv = tf.get_variable("cbp_fc7_conv", [1, 1, 4096, 4096], trainable=False)
# noise_conv1_rgb = tf.get_variable("noise_conv1_rgb", [3, 3, 3, 64], trainable=False)
restorer_fc = tf.train.Saver({"vgg_16/fc6/weights": fc6_conv,
"vgg_16/fc7/weights": fc7_conv,
"vgg_16/conv1/conv1_1/weights": conv1_rgb})
restorer_fc.restore(sess, pretrained_model)
sess.run(tf.assign(self._variables_to_fix['vgg_16/fc6/weights:0'], tf.reshape(fc6_conv,
self._variables_to_fix[
'vgg_16/fc6/weights:0'].get_shape())))
sess.run(tf.assign(self._variables_to_fix['vgg_16/fc7/weights:0'], tf.reshape(fc7_conv,
self._variables_to_fix[
'vgg_16/fc7/weights:0'].get_shape())))
sess.run(tf.assign(self._variables_to_fix['vgg_16/conv1/conv1_1/weights:0'],
tf.reverse(conv1_rgb, [2])))
# restorer_cbp_noise = tf.train.Saver({"vgg_16/fc6/weights": cbp_fc6_conv,
# "vgg_16/fc7/weights": cbp_fc7_conv,
# "vgg_16/conv1/conv1_1/weights": noise_conv1_rgb})
# restorer_cbp_noise.restore(sess, pretrained_model)
#
# sess.run(tf.assign(self._variables_to_fix['vgg_16/cbp_fc6/weights:0'], tf.reshape(cbp_fc6_conv,
# self._variables_to_fix[
# 'vgg_16/cbp_fc6/weights:0'].get_shape())))
# sess.run(tf.assign(self._variables_to_fix['vgg_16/cbp_fc7/weights:0'], tf.reshape(cbp_fc7_conv,
# self._variables_to_fix[
# 'vgg_16/cbp_fc7/weights:0'].get_shape())))
# sess.run(tf.assign(self._variables_to_fix['vgg_16/conv1n/conv1n_1/weights:0'],
# tf.reverse(noise_conv1_rgb, [2])))
def build_head(self, is_training):
# Main network
# Layer 1
net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3], trainable=False, scope='conv1')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1')
# Layer 2
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], trainable=False, scope='conv2')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2')
# Layer 3
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], trainable=is_training, scope='conv3')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3')
# Layer 4
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, scope='conv4')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4')
# Layer 5
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, scope='conv5')
# Append network to summaries
self._act_summaries.append(net)
# Append network as head layer
self._layers['head'] = net
return net
def build_head_forNoise(self, is_training, initializer, initializer_srm):
def truncate_2(x):
neg = ((x + 2) + abs(x + 2)) / 2 - 2
return -(2 - neg + abs(2 - neg)) / 2 + 2
# Main network
# Layer SRM
net = slim.conv2d(self._image, 3, [5, 5], trainable=False, weights_initializer=initializer_srm,
activation_fn=None, padding='SAME', stride=1, scope='srm')
net = truncate_2(net)
# Layer 1
net = slim.repeat(net, 2, slim.conv2d, 64, [3, 3], trainable=is_training, weights_initializer=initializer, scope='conv1n')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1n')
# Layer 2
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], trainable=is_training, weights_initializer=initializer, scope='conv2n')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2n')
# Layer 3
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], trainable=is_training, weights_initializer=initializer, scope='conv3n')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3n')
# Layer 4
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, weights_initializer=initializer, scope='conv4n')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4n')
# Layer 5
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, weights_initializer=initializer, scope='conv5n')
# Append network to summaries
self._act_summaries.append(net)
# Append network as head layer
self._layers['head2'] = net
return net
def build_rpn(self, net, is_training, initializer):
# Build anchor component
self._anchor_component()
# Create RPN Layer
rpn = slim.conv2d(net, 512, [3, 3], trainable=is_training, weights_initializer=initializer, scope="rpn_conv/3x3")
self._act_summaries.append(rpn)
rpn_cls_score = slim.conv2d(rpn, self._num_anchors * 2, [1, 1], trainable=is_training, weights_initializer=initializer, padding='VALID', activation_fn=None, scope='rpn_cls_score')
# Change it so that the score has 2 as its channel size
rpn_cls_score_reshape = self._reshape_layer(rpn_cls_score, 2, 'rpn_cls_score_reshape')
rpn_cls_prob_reshape = self._softmax_layer(rpn_cls_score_reshape, "rpn_cls_prob_reshape")
rpn_cls_prob = self._reshape_layer(rpn_cls_prob_reshape, self._num_anchors * 2, "rpn_cls_prob")
rpn_bbox_pred = slim.conv2d(rpn, self._num_anchors * 4, [1, 1], trainable=is_training, weights_initializer=initializer, padding='VALID', activation_fn=None, scope='rpn_bbox_pred')
return rpn_cls_prob, rpn_bbox_pred, rpn_cls_score, rpn_cls_score_reshape
def build_proposals(self, is_training, rpn_cls_prob, rpn_bbox_pred, rpn_cls_score):
if is_training:
rois, roi_scores = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
rpn_labels = self._anchor_target_layer(rpn_cls_score, "anchor")
# Try to have a deterministic order for the computing graph, for reproducibility
with tf.control_dependencies([rpn_labels]):
rois, _ = self._proposal_target_layer(rois, roi_scores, "rpn_rois")
else:
if cfg.FLAGS.test_mode == 'nms':
rois, _ = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
elif cfg.FLAGS.test_mode == 'top':
rois, _ = self._proposal_top_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
else:
raise NotImplementedError
return rois
def build_predictions(self, net, net2, rois, is_training, initializer, initializer_bbox):
# Crop image ROIs
pool5 = self._crop_pool_layer(net, rois, "pool5")
# pool5_flat = slim.flatten(pool5, scope='flatten')
pool5_forNoise = self._crop_pool_layer(net2, rois, "pool5_forNoise")
# Compact Bilinear Pooling
cbp = compact_bilinear_pooling_layer(pool5, pool5_forNoise, 512)
cbp_flat = slim.flatten(cbp, scope='cbp_flatten')
# Fully connected layers
# fc6 = slim.fully_connected(pool5_flat, 4096, scope='bbox_fc6')
fc6_cbp = slim.fully_connected(cbp_flat, 4096, scope='fc6')
if is_training:
# fc6 = slim.dropout(fc6, keep_prob=0.5, is_training=True, scope='dropout6')
fc6_cbp = slim.dropout(fc6_cbp, keep_prob=0.5, is_training=True, scope='cbp_dropout6')
# fc7 = slim.fully_connected(fc6, 4096, scope='bbox_fc7')
fc7_cbp = slim.fully_connected(fc6_cbp, 4096, scope='fc7')
if is_training:
# fc7 = slim.dropout(fc7, keep_prob=0.5, is_training=True, scope='dropout7')
fc7_cbp = slim.dropout(fc7_cbp, keep_prob=0.5, is_training=True, scope='cbp_dropout7')
# Scores and predictions
cls_score = slim.fully_connected(fc7_cbp, self._num_classes, weights_initializer=initializer, trainable=is_training, activation_fn=None, scope='cls_score')
cls_prob = self._softmax_layer(cls_score, "cls_prob")
bbox_prediction = slim.fully_connected(fc7_cbp, self._num_classes * 4, weights_initializer=initializer_bbox, trainable=is_training, activation_fn=None, scope='bbox_pred')
return cls_score, cls_prob, bbox_prediction