img_root = '/media/ubuntu/65db2e03-ffde-4f3d-8f33-55d73836211a/dataset/VOCdevkit/VOC2007/Test/JPEGImages'
labelfiles = '/media/ubuntu/65db2e03-ffde-4f3d-8f33-55d73836211a/dataset/VOCdevkit/VOC2007/Test/ImageSets/Main'
checkpoint_dir = '../../model/yolol2sum_epoch_SGD'
classes = voc.list_image_sets()
val_list = voc.imgs_from_category_as_list('', 'test', labelfiles)
yolo_old = YOLO_tiny_tf.YOLO_TF()
with tf.device('/gpu:0'):
#Vanilla YOLO_tiny Weight
x = tf.placeholder(tf.float32, (None, 448, 448, 3))
label = tf.placeholder(tf.float32, (None, 1470), name='labels')
keep_prob = tf.placeholder(tf.float32)
modelTicket_G = {'root': 'yolo_tiny', 'branch': 'vanilla'}
init_layers = mu.model_zoo(modelTicket_G)
var_dict = recursive_create_var('recursive', 1, 0.2, init_layers)
yolo_ds = nf.glosso_train("recursive_0", 'test', x, var_dict, keep_prob,
False)
tlossTicket = {'loss': 'smoothL1'}
loss_pair = {'prob': yolo_ds}
loss = mu.loss_zoo(tlossTicket, loss_pair, label)
tp = 0
fp = 0
tp_old = 0
fp_old = 0
num = 0
import tensorflow as tf
import numpy as np
import yolo_netfactory as nf
import random_batch as rb
import YOLO_tiny_tf
import cv2
import model_utility as mut
import time
#Vanilla Yolo
scope = 'train'
yolo = YOLO_tiny_tf.YOLO_TF()
#Target Model
model_ticket = {'root': yolo_tiny, 'branch': 'double_cut89'}
ds_yolo = mut.create_var_tnorm(scope, mut.model_zoo(model_ticket))
batch_file = "/media/ubuntu/65db2e03-ffde-4f3d-8f33-55d73836211a/dataset/VOC_train"
test_file = "/media/ubuntu/65db2e03-ffde-4f3d-8f33-55d73836211a/dataset/VOC_val"
filename = "../../model/test/fcann_v1.ckpt"
logfile = '../../log/test'
graph_model = '../../model/test/fcann_v1.ckpt-4000.meta'
checkpoint_dir = '../../model/test'
continue_training = 1
loop_num = 5900
batch_size = 64
keep_prob = tf.placeholder(tf.float32)
x = tf.placeholder(tf.float32, (None, 448, 448, 3))
label = tf.placeholder(tf.float32, (None, 1470)) #
save_epoch = 200
test_epoch = 500
weight_decay = 0.0005
yolo = YOLO_tiny_tf.YOLO_TF()
with tf.device('/gpu:0'):
keep_prob = tf.placeholder(tf.float32, name='dropout_prob')
x = tf.placeholder(tf.float32, (None, 448, 448, 3), name='input_batch')
label = tf.placeholder(tf.float32, (None, 1470), name='labels')
tlabel = tf.placeholder(tf.float32, (None, 1470), name='test_labels')
learning_rate = tf.placeholder(tf.float32, shape=[], name='learning_rate')
model_ticket = {'root': 'yolo_tiny', 'branch': 'vanilla'}
init_layers = mu.model_zoo(model_ticket)
var_dict = recursive_create_var('recursive', 1, 0.2, init_layers)
var_list = var_dict['recursive_0']
with tf.name_scope('Weight_sum'):
with tf.variable_scope('recursive_0') as scope:
scope.reuse_variables()
weight_sum = tf.reduce_sum([
0.5 *
tf.reduce_sum(tf.square(tf.get_variable(x) * weight_decay))
for x in var_list
])
w1 = tf.get_variable(var_list[0])
glosso_train = nf.glosso_train("recursive_0", 'train', x, var_dict,
keep_prob, True)
save_epoch = 200
test_epoch = 500
modelTicket_G = {'root':'yolo_tiny', 'branch':'double_cut89'}
modelTicket_D = {'root':'discriminator', 'branch':'4layer'}
keep_prob = tf.placeholder(tf.float32)
x = tf.placeholder(tf.float32,(None,448,448,3))
test = tf.placeholder(tf.float32,(None,448,448,3))
label = tf.placeholder(tf.float32,(None,1470))
yolo = YOLO_tiny_tf.YOLO_TF()
gen_var = mut.create_var_xavier('train',mut.model_zoo(modelTicket_G))
dis_var = mut.create_var_xavier('discriminator', mut.model_zoo(modelTicket_D))
theta_D = []
theta_G = []
for i in mut.model_zoo(modelTicket_G):
theta_G.append(gen_var[i[0]])
for i in mut.model_zoo(modelTicket_D):
theta_D.append(dis_var[i[0]])
##Train Phase
yolo_ds_train = nf.yolo_dinception("yolo_train", x, gen_var, keep_prob, True)
lossTicket = {'loss':'L2norm'}
loss = mut.loss_zoo(lossTicket, yolo_ds_train, label)