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DSOD.py
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DSOD.py
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import os
import gc
import xml.etree.ElementTree as etxml
import math
import random
import skimage.io
import skimage.transform
import numpy as np
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import *
from tensorflow.python.ops import variables
import time
from imutils.object_detection import non_max_suppression
import imutils
import cv2
import matplotlib.pyplot as plt
batch_size = 16
running_count = 5000
file_name_list = os.listdir('./train_datasets/voc2012/JPEGImages/')
lable_arr = ['background','aeroplane','bicycle','bird','boat','bottle','bus','car','cat','chair','cow','diningtable','dog','horse','motorbike','person','pottedplant','sheep','sofa','train','tvmonitor']
img_size = [300, 300]
# 分类总数量
classes_size = 21
# 背景分类的值
background_classes_val = 0
# 每个特征图单元的default box数量
default_box_size = [6, 6, 6, 6, 6, 6]
# default box 尺寸长宽比例
box_aspect_ratio = [
[0.5, 1.0, 2.0, 3.0,1/3.0],
[0.5, 1.0, 2.0, 3.0, 1 / 3.0],
[0.5, 1.0, 2.0, 3.0, 1 / 3.0],
[0.5, 1.0, 2.0, 3.0, 1 / 3.0],
[0.5, 1.0, 2.0, 3.0, 1 / 3.0],
[0.5, 1.0, 2.0, 3.0, 1 / 3.0]
]
# 最小default box面积比例
min_box_scale = 0.1
# 最大default box面积比例
max_box_scale = 0.9
# 每个特征层的面积比例
# numpy生成等差数组，效果等同于论文中的s_k=s_min+(s_max-s_min)*(k-1)/(m-1)
default_box_scale = np.linspace(min_box_scale, max_box_scale, num=np.amax(default_box_size))
print('## default_box_scale:' + str(default_box_scale))
# 卷积步长
conv_strides_1 = [1, 1, 1, 1]
conv_strides_2 = [1, 2, 2, 1]
conv_strides_3 = [1, 3, 3, 1]
tl_strides_1 = (1, 1)
tl_strides_2 = (2, 2)
tl_strides_3 = (3, 3)
# 池化窗口
pool_size = [1, 2, 2, 1]
tl_pool_size = (2, 2)
# 池化步长
pool_strides = [1, 2, 2, 1]
tl_pool_strides = (2, 2)
# Batch Normalization 算法的 decay 参数
conv_bn_decay = 0.9999
# Batch Normalization 算法的 variance_epsilon 参数
conv_bn_epsilon = 0.001
# Jaccard相似度判断阀值
jaccard_value = 0.55
feature_maps_shape=[]
all_default_boxs_len=0
all_default_boxs=[]
jitter = 0.2
def get_traindata_voc(batch_size):
def get_actual_data_from_xml(xml_path):
actual_item = []
try:
annotation_node = etxml.parse(xml_path).getroot()
img_width = float(annotation_node.find('size').find('width').text.strip())
img_height = float(annotation_node.find('size').find('height').text.strip())
object_node_list = annotation_node.findall('object')
for obj_node in object_node_list:
lable = lable_arr.index(obj_node.find('name').text.strip())
bndbox = obj_node.find('bndbox')
x_min = float(bndbox.find('xmin').text.strip())
y_min = float(bndbox.find('ymin').text.strip())
x_max = float(bndbox.find('xmax').text.strip())
y_max = float(bndbox.find('ymax').text.strip())
# 位置数据用比例来表示，格式[center_x,center_y,width,height,lable]
actual_item.append([((x_min + x_max) / 2 / img_width), ((y_min + y_max) / 2 / img_height),
((x_max - x_min) / img_width), ((y_max - y_min) / img_height), lable])
return actual_item
except:
return None
train_data = []
actual_data = []
file_list = random.sample(file_name_list, batch_size)
for f_name in file_list:
img_path = './train_datasets/voc2012/JPEGImages/' + f_name
xml_path = './train_datasets/voc2012/Annotations/' + f_name.replace('.jpg', '.xml')
if os.path.splitext(img_path)[1].lower() == '.jpg':
actual_item = get_actual_data_from_xml(xml_path)
img = skimage.io.imread(img_path)
if actual_item != None:
countwhile=0
while True:
clas=[]
coords=[]
for x in actual_item:
clas.append(x[4])
coords.append([x[0],x[1],x[2],x[3]])
tmp0 = random.randint(-30, 50)
tmp1 = random.randint(-30, 50)
imgr=img.copy()
scale = np.max((400 / float(img.shape[1]),
400 / float(img.shape[0])))
im, coords = tl.prepro.obj_box_imresize(imgr, coords,
[int(img.shape[0] * scale) + tmp0, int(img.shape[1] * scale) + tmp1],
is_rescale=True, interp='bicubic')
# print(im.shape)
# print(coords)
for wi in range(7):
imt, clast, coordst = tl.prepro.obj_box_zoom(im, clas, coords, zoom_range=(1.0, 2.2),
fill_mode='nearest',
order=1, is_rescale=True, is_center=True,
is_random=True,
thresh_wh=0.04, thresh_wh2=8.0)
# print(im.shape)
if clast!=[]:
im=imt
clas= clast
coords =coordst
break
if wi>=6:
im, clas, coords = tl.prepro.obj_box_zoom(im, clas, coords, zoom_range=(0.7, 1.2),
fill_mode='nearest',
order=1, is_rescale=True, is_center=True,
is_random=True,
thresh_wh=0.05, thresh_wh2=8.0)
im, coords = tl.prepro.obj_box_left_right_flip(im,
coords, is_rescale=True, is_center=True, is_random=True)
# print(coords)
for wi in range(8):
imt, clast, coordst = tl.prepro.obj_box_crop(im, clas, coords,
wrg=300, hrg=300,
is_rescale=True, is_center=True, is_random=True,
thresh_wh=0.07, thresh_wh2=7.0)
if clast!=[]:
im=imt
clas= clast
coords =coordst
break
if wi==7:
im, clas, coords = tl.prepro.obj_box_crop(im, clas, coords,
wrg=300, hrg=300,
is_rescale=True, is_center=True,
is_random=True,
thresh_wh=0.07, thresh_wh2=8.0)
im = tl.prepro.illumination(im, gamma=(0.2, 1.2),
contrast=(0.2, 1.2), saturation=(0.2, 1.2), is_random=True)
im = tl.prepro.adjust_hue(im, hout=0.1, is_offset=True,
is_clip=True, is_random=True)
im = im / 127.5 - 1.
aitems = []
if clas!=[]:
for x in range(len(clas)):
aitem=[coords[x][0],coords[x][1],coords[x][2],coords[x][3],clas[x]]
aitems.append(aitem)
actual_data.append(aitems)
train_data.append(im)
break
countwhile+=1
if countwhile>=4:
clas = []
coords = []
for x in actual_item:
clas.append(x[4])
coords.append([x[0], x[1], x[2], x[3]])
tmp0 = random.randint(1, 30)
tmp1 = random.randint(1, 30)
imgr = img.copy()
im, coords = tl.prepro.obj_box_imresize(imgr, coords,
[300 + tmp0,
300 + tmp1],
is_rescale=True, interp='bicubic')
im, coords = tl.prepro.obj_box_left_right_flip(im,
coords, is_rescale=True, is_center=True,
is_random=True)
im, clas, coords = tl.prepro.obj_box_crop(im, clas, coords,
wrg=300, hrg=300,
is_rescale=True, is_center=True,
is_random=True,
thresh_wh=0.02, thresh_wh2=10.0)
im = tl.prepro.illumination(im, gamma=(0.8, 1.2),
contrast=(0.8, 1.2), saturation=(0.8, 1.2), is_random=True)
im = tl.prepro.pixel_value_scale(im, 0.1, [0, 255], is_random=True)
im = im / 127.5 - 1.
aitems = []
if len(clas) != 0:
for x in range(len(clas)):
aitem = [coords[x][0], coords[x][1], coords[x][2], coords[x][3], clas[x]]
aitems.append(aitem)
actual_data.append(aitems)
train_data.append(im)
break
else:
print('Error : ' + xml_path)
continue
return train_data, actual_data, file_list
def generate_groundtruth_data(input_actual_data):
# 生成空数组，用于保存groundtruth
input_actual_data_len = len(input_actual_data)
gt_class = np.zeros((input_actual_data_len, all_default_boxs_len))
gt_location = np.zeros((input_actual_data_len, all_default_boxs_len, 4))
gt_positives_jacc = np.zeros((input_actual_data_len, all_default_boxs_len))
gt_positives = np.zeros((input_actual_data_len, all_default_boxs_len))
gt_negatives = np.zeros((input_actual_data_len, all_default_boxs_len))
background_jacc = max(0, (jaccard_value - 0.2))
# 初始化正例训练数据
for img_index in range(input_actual_data_len):
for pre_actual in input_actual_data[img_index]:
gt_class_val = pre_actual[-1:][0]
if gt_class_val>20 or gt_class_val<0:
gt_class_val=0
gt_box_val = pre_actual[:-1]
for boxe_index in range(all_default_boxs_len):
jacc,gt_box_val_loc = jaccard(gt_box_val, all_default_boxs[boxe_index])
if jacc > jaccard_value or jacc == jaccard_value:
gt_class[img_index][boxe_index] = gt_class_val
gt_location[img_index][boxe_index] = gt_box_val_loc
gt_positives_jacc[img_index][boxe_index] = jacc
gt_positives[img_index][boxe_index] = 1
gt_negatives[img_index][boxe_index] = 0
# 如果没有正例，则随机创建一个正例，预防nan
if np.sum(gt_positives[img_index]) == 0:
# print('【没有匹配jacc】:'+str(input_actual_data[img_index]))
random_pos_index = np.random.randint(low=0, high=all_default_boxs_len, size=1)[0]
gt_class[img_index][random_pos_index] = background_classes_val
gt_location[img_index][random_pos_index] = [0.00001, 0.00001, 0.00001, 0.00001]
gt_positives_jacc[img_index][random_pos_index] = jaccard_value
gt_positives[img_index][random_pos_index] = 1
gt_negatives[img_index][random_pos_index] = 0
gt_neg_end_count = int(np.sum(gt_positives[img_index]) * 3)
if (gt_neg_end_count + np.sum(gt_positives[img_index])) > all_default_boxs_len:
gt_neg_end_count = all_default_boxs_len - np.sum(gt_positives[img_index])
gt_neg_index = np.random.randint(low=0, high=all_default_boxs_len, size=gt_neg_end_count)
for r_index in gt_neg_index:
if gt_positives_jacc[img_index][r_index] < background_jacc and gt_positives[img_index][r_index] != 1:
gt_class[img_index][r_index] = background_classes_val
gt_positives[img_index][r_index] = 0
gt_negatives[img_index][r_index] = 1
gt_class = check_numerics(gt_class, 'gt_class')
gt_location = check_numerics(gt_location, 'gt_class')
gt_positives = check_numerics(gt_positives, 'gt_positives')
gt_negatives = check_numerics(gt_negatives, 'gt_negatives')
return gt_class, gt_location, gt_positives, gt_negatives
def jaccard(rect1, rect2):
x_overlap = max(0, (min(rect1[0] + (rect1[2] / 2), rect2[0] + (rect2[2] / 2)) - max(rect1[0] - (rect1[2] / 2),
rect2[0] - (rect2[2] / 2))))
y_overlap = max(0, (min(rect1[1] + (rect1[3] / 2), rect2[1] + (rect2[3] / 2)) - max(rect1[1] - (rect1[3] / 2),
rect2[1] - (rect2[3] / 2))))
intersection = x_overlap * y_overlap
# 删除超出图像大小的部分
rect1_width_sub = 0
rect1_height_sub = 0
rect2_width_sub = 0
rect2_height_sub = 0
if (rect1[0] - rect1[2] / 2) < 0: rect1_width_sub += 0 - (rect1[0] - rect1[2] / 2)
if (rect1[0] + rect1[2] / 2) > 1: rect1_width_sub += (rect1[0] + rect1[2] / 2) - 1
if (rect1[1] - rect1[3] / 2) < 0: rect1_height_sub += 0 - (rect1[1] - rect1[3] / 2)
if (rect1[1] + rect1[3] / 2) > 1: rect1_height_sub += (rect1[1] + rect1[3] / 2) - 1
if (rect2[0] - rect2[2] / 2) < 0: rect2_width_sub += 0 - (rect2[0] - rect2[2] / 2)
if (rect2[0] + rect2[2] / 2) > 1: rect2_width_sub += (rect2[0] + rect2[2] / 2) - 1
if (rect2[1] - rect2[3] / 2) < 0: rect2_height_sub += 0 - (rect2[1] - rect2[3] / 2)
if (rect2[1] + rect2[3] / 2) > 1: rect2_height_sub += (rect2[1] + rect2[3] / 2) - 1
area_box_a = (rect1[2] - rect1_width_sub) * (rect1[3] - rect1_height_sub)
area_box_b = (rect2[2] - rect2_width_sub) * (rect2[3] - rect2_height_sub)
union = area_box_a + area_box_b - intersection
if intersection > 0 and union > 0:
return intersection / union,[(rect1[0]-(rect2[0]))/rect2[2],(rect1[1]-(rect2[1]))/rect2[3],math.log(rect1[2]/rect2[2]),math.log(rect1[3]/rect2[3])]
else:
return 0,[0.00001,0.00001,0.00001,0.00001]
def denseblock(input,blocknum=1,step=48,firstchannel=192,is_train=True,name='denseblock',reuse=None):
with tf.variable_scope(name, reuse=reuse):
tl.layers.set_name_reuse(reuse)
nettemp=LambdaLayer(input, lambda x: tf.identity(x), name="INPUTS")
for x in range(blocknum):
netbn = BatchNormLayer(nettemp, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name='bn/' + str(x))
net=Conv2d(netbn, firstchannel, (1, 1), (1, 1), padding='SAME',name='neta/'+str(x))
netbn = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name=name + 'bn2/' + str(x))
net=Conv2d(netbn, step, (3, 3), (1, 1), padding='SAME',name='netb/'+str(x))
nettemp= ConcatLayer([nettemp,net], -1,name='concattemp/'+str(x))
net = nettemp
return net
def denseblockpl(input,step=256,firstchannel=256,is_train=True,name='densepl',reuse=None):
with tf.variable_scope(name, reuse=reuse):
tl.layers.set_name_reuse(reuse)
input = LambdaLayer(input, lambda x: tf.identity(x), name="INPUTS")
netbn2=MaxPool2d(input,(2,2),(2,2),padding='SAME', name='bnpool2')
netbn2 = BatchNormLayer(netbn2, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name=name + 'bn2pl' )
netbn2 = Conv2d(netbn2, firstchannel, (1, 1), (1, 1), padding='SAME', name='bnconv2' )
netbn = BatchNormLayer(input, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name= 'bn' )
net=Conv2d(netbn, firstchannel, (1, 1), (1, 1), padding='SAME',name='neta')
netbn = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name='bn2')
net=Conv2d(netbn, step, (3, 3), (2, 2), padding='SAME',name='netb')
nettemp = ConcatLayer([net,netbn2], -1,name='concat')
return nettemp
def denseblockfin(input,step=256,firstchannel=256,is_train=True,name='densepl',reuse=None):
with tf.variable_scope(name, reuse=reuse):
tl.layers.set_name_reuse(reuse)
input = LambdaLayer(input, lambda x: tf.identity(x), name="INPUTS")
netbn2=MaxPool2d(input,(3,3),(1,1),padding='VALID', name='bnpool2')
netbn2 = BatchNormLayer(netbn2, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name=name + 'bn2pl' )
netbn2 = Conv2d(netbn2, firstchannel, (1, 1), (1, 1), padding='SAME', name='bnconv2' )
netbn = BatchNormLayer(input, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name= 'bn' )
net=Conv2d(netbn, firstchannel, (1, 1), (1, 1), padding='SAME',name='neta')
netbn = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name='bn2')
net=Conv2d(netbn, step, (3, 3), (1, 1), padding='VALID',name='netb')
nettemp = ConcatLayer([net,netbn2], -1,name='concat')
return nettemp
def inference(inputs, is_train, reuse):
W_init = tf.contrib.layers.xavier_initializer()
with tf.variable_scope("model", reuse=reuse):
tl.layers.set_name_reuse(reuse)
net = InputLayer(inputs, name='input')
net = Conv2d(net, 64, (3, 3), (2, 2), padding='SAME',
W_init=W_init, name='stem1')
net = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name='stem1_bn')
net = Conv2d(net, 64, (3, 3), (1, 1), padding='SAME',
W_init=W_init, name='stem2')
net = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name='stem2_bn')
net = Conv2d(net, 128, (3, 3), (1, 1), padding='SAME',
W_init=W_init, name='stem3')
net = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name='stem3_bn')
net = MaxPool2d(net, filter_size=(2, 2), strides=(2, 2), name='stem3_pool')
net = denseblock(net, blocknum=6, step=48, firstchannel=192, is_train=is_train, name='denseblock0', reuse=reuse)
net = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name='denseblock0_bn')
net = Conv2d(net, 416, (1, 1), (1, 1), padding='SAME',
W_init=W_init, name='denseblock0_cnn')
net = MaxPool2d(net, filter_size=(2, 2), strides=(2, 2), name='denseblock0_pool')
net = denseblock(net, blocknum=8, step=48, firstchannel=192, is_train=is_train, name='denseblock1', reuse=reuse)
net = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name='denseblock1_bn')
net = Conv2d(net, 800, (1, 1), (1, 1), padding='SAME',
W_init=W_init, name='denseblock1_cnn')
netfirst=BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name='feature_first_bn')
net = MaxPool2d(net, filter_size=(2, 2), strides=(2, 2), name='denseblock2_pool1')
net = denseblock(net, blocknum=8, step=48, firstchannel=192, is_train=is_train, name='denseblock2', reuse=reuse)
net = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name='denseblock2_bn')
net = Conv2d(net, 1184, (1, 1), (1, 1), padding='SAME',
W_init=W_init, name='denseblock2_cnn')
net = denseblock(net, blocknum=8, step=48, firstchannel=192, is_train=is_train, name='denseblock3', reuse=reuse)
net = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name='denseblock3_bn')
net = Conv2d(net, 256, (1, 1), (1, 1), padding='SAME',
W_init=W_init, name='denseblock2_cnna')
netpl=MaxPool2d(netfirst, filter_size=(2, 2), strides=(2, 2), name='First_pool')
netpl=BatchNormLayer(netpl, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name='First_bn')
netpl = Conv2d(netpl, 256, (1, 1), (1, 1), padding='SAME',
W_init=W_init, name='denseblock2_cnnb')
net=ConcatLayer([net,netpl],-1,"Second_Cat")
netsecond = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu, name='feature_second_bn')
net = denseblockpl(net, step=256, firstchannel=256, is_train=is_train, name='denseplz1', reuse=reuse)
netthird = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu,
name='feature_third_bn')
net = denseblockpl(net, step=128, firstchannel=128, is_train=is_train, name='denseplz2', reuse=reuse)
netfourth = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu,
name='feature_fourth_bn')
net = denseblockpl(net, step=128, firstchannel=128, is_train=is_train, name='denseplz3', reuse=reuse)
netfifth = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu,
name='feature_fifth_bn')
net = denseblockfin(net, step=128, firstchannel=128, is_train=is_train, name='denseplz4', reuse=reuse)
netsixth = BatchNormLayer(net, is_train=is_train, decay=conv_bn_decay, act=tf.nn.relu,
name='feature_sixth_bn')
outfirst=Conv2d(netfirst, default_box_size[0] * (classes_size + 4), (3, 3), (1, 1), padding='SAME',
W_init=W_init, name='firstout')
outsecond=Conv2d(netsecond, default_box_size[1] * (classes_size + 4), (3, 3), (1, 1), padding='SAME',
W_init=W_init, name='secondout')
outthird=Conv2d(netthird, default_box_size[2] * (classes_size + 4), (3, 3), (1, 1), padding='SAME',
W_init=W_init, name='thirdout')
outfourth=Conv2d(netfourth, default_box_size[3] * (classes_size + 4), (3, 3), (1, 1), padding='SAME',
W_init=W_init, name='fourthout')
outfifth=Conv2d(netfifth, default_box_size[4] * (classes_size + 4), (3, 3), (1, 1), padding='SAME',
W_init=W_init, name='fifthout')
outsixth=Conv2d(netsixth, default_box_size[5] * (classes_size + 4), (3, 3), (1, 1), padding='SAME',
W_init=W_init, name='sixthout')
features1=outfirst.outputs
features2=outsecond.outputs
features3=outthird.outputs
features4=outfourth.outputs
features5=outfifth.outputs
features6=outsixth.outputs
feature_maps = [features1, features2, features3, features4, features5,features6]
global feature_maps_shape
feature_maps_shape = [m.get_shape().as_list() for m in feature_maps]
tmp_all_feature = []
for i, fmap in zip(range(len(feature_maps)), feature_maps):
width = feature_maps_shape[i][1]
height = feature_maps_shape[i][2]
tmp_all_feature.append(
tf.reshape(fmap, [-1, (width * height * default_box_size[i]), (classes_size + 4)]))
tmp_all_feature = tf.concat(tmp_all_feature, axis=1)
feature_class = tmp_all_feature[:, :, :classes_size]
feature_location = tmp_all_feature[:, :, classes_size:]
print('## feature_class shape : ' + str(feature_class.get_shape().as_list()))
print('## feature_location shape : ' + str(feature_location.get_shape().as_list()))
# 生成所有default boxs
global all_default_boxs
all_default_boxs = generate_all_default_boxs()
# print(all_default_boxs)
global all_default_boxs_len
all_default_boxs_len = len(all_default_boxs)
print('## all default boxs : ' + str(all_default_boxs_len))
return feature_class,feature_location,all_default_boxs,all_default_boxs_len
def smooth_L1(x):
return tf.where(tf.less_equal(tf.abs(x), 1.0), tf.multiply(0.5, tf.pow(x, 2.0)), tf.subtract(tf.abs(x), 0.5))
def elloss(feature_class,feature_location,groundtruth_class,groundtruth_location,groundtruth_positives,groundtruth_count):
softmax_cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=feature_class,
labels=groundtruth_class)
loss_location = tf.div(tf.reduce_sum(tf.multiply(
tf.reduce_sum(smooth_L1(tf.subtract(groundtruth_location, feature_location)),
reduction_indices=2), groundtruth_positives), reduction_indices=1),
tf.reduce_sum(groundtruth_positives, reduction_indices=1))
loss_class = tf.div(
tf.reduce_sum(tf.multiply(softmax_cross_entropy, groundtruth_count), reduction_indices=1),
tf.reduce_sum(groundtruth_count, reduction_indices=1))
loss_all = tf.reduce_sum(tf.add(loss_class, loss_location*5))
return loss_all,loss_class,loss_location
def generate_all_default_boxs():
all_default_boxes = []
for index, map_shape in zip(range(len(feature_maps_shape)), feature_maps_shape):
width = int(map_shape[1])
height = int(map_shape[2])
cell_scale = default_box_scale[index]
for x in range(width):
for y in range(height):
for ratio in box_aspect_ratio[index]:
center_x = (x / float(width)) + (0.5 / float(width))
center_y = (y / float(height)) + (0.5 / float(height))
box_width = cell_scale*np.sqrt(ratio)/1.2
box_height = cell_scale/np.sqrt(ratio)/1.2
all_default_boxes.append([center_x, center_y, box_width, box_height])
all_default_boxes.append([(x / float(width)) + (0.5 / float(width)), (y / float(height)) + (0.5 / float(height)), cell_scale*1.5,cell_scale*1.4])
all_default_boxes = np.array(all_default_boxes)
all_default_boxes = check_numerics(all_default_boxes, 'all_default_boxes')
return all_default_boxes
def check_numerics(input_dataset, message):
if str(input_dataset).find('Tensor') == 0:
input_dataset = tf.check_numerics(input_dataset, message)
else:
dataset = np.array(input_dataset)
nan_count = np.count_nonzero(dataset != dataset)
inf_count = len(dataset[dataset == float("inf")])
n_inf_count = len(dataset[dataset == float("-inf")])
if nan_count > 0 or inf_count > 0 or n_inf_count > 0:
data_error = '【' + message + '】出现数据错误！【nan：' + str(nan_count) + '|inf：' + str(
inf_count) + '|-inf：' + str(n_inf_count) + '】'
raise Exception(data_error)
return input_dataset
if __name__ == '__main__':
imageinput=tf.placeholder(tf.float32,[None,300,300,3],"inputsimage")
imageinputtest = tf.placeholder(tf.float32, [None, 300, 300, 3], "inputsimage")
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
fc, fl, _, _ = inference(imageinput, True, None)
fc2, fl2, _, _ = inference(imageinputtest, False, True)
groundtruth_class = tf.placeholder(shape=[None, all_default_boxs_len], dtype=tf.int32,
name='groundtruth_class')
groundtruth_location = tf.placeholder(shape=[None, all_default_boxs_len, 4], dtype=tf.float32,
name='groundtruth_location')
groundtruth_positives = tf.placeholder(shape=[None, all_default_boxs_len], dtype=tf.float32,
name='groundtruth_positives')
groundtruth_negatives = tf.placeholder(shape=[None, all_default_boxs_len], dtype=tf.float32,
name='groundtruth_negatives')
groundtruth_count = tf.add(groundtruth_positives, groundtruth_negatives)
learning_rt=0.000001
learning_rate = tf.placeholder(tf.float32, None, 'learning_rate')
loss_allt, loss_classt, loss_locationt = elloss(fc, fl, groundtruth_class, groundtruth_location, groundtruth_positives, groundtruth_count)
train = tf.train.MomentumOptimizer(learning_rate,momentum=0.9).minimize(loss_allt)
tf.summary.scalar('loss_all_train', loss_allt)
tf.summary.scalar('loss_class_train', tf.reduce_sum(loss_classt) )
tf.summary.scalar('loss_location_train', tf.reduce_sum(loss_locationt))
merged = tf.summary.merge_all()
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
trainwrite = tf.summary.FileWriter("logs/", sess.graph)
sess.run(tf.global_variables_initializer())
saver2 = tf.train.Saver(var_list=tf.trainable_variables())
zzz = variables._all_saveable_objects().copy()
print(zzz)
saver = tf.train.Saver()
if os.path.exists('./session_paramsdddaleasy/session2.ckpt.index') :
print('\nStart Restore')
saver2.restore(sess, './session_paramsdddaleasy/session2.ckpt')
print('\nEnd Restore')
print('\nStart Training')
min_loss_location = 100000.
min_loss_class = 100000.
avg_loss=0
avg_lossloc=0
avg_losclass=0
ptlos=0
ptlosc=0
ptlosl=0
while((min_loss_location + min_loss_class) > 0.001 and running_count < 100000):
running_count += 1
train_data, actual_data, _ = get_traindata_voc(batch_size)
starttime = time.time()
gt_class, gt_location, gt_positives, gt_negatives=generate_groundtruth_data(actual_data)
if len(train_data) > 0:
loss_all,loss_class,loss_location,_,pred_class,pred_location = sess.run([loss_allt, loss_classt, loss_locationt,train,fc, fl],feed_dict={imageinput:train_data,groundtruth_class:gt_class,groundtruth_location:gt_location,groundtruth_positives:gt_positives,groundtruth_negatives:gt_negatives,learning_rate:learning_rt})
l = np.sum(loss_location)
c = np.sum(loss_class)
avg_loss +=loss_all
avg_lossloc += loss_class
avg_losclass += loss_location
if min_loss_location > l:
min_loss_location = l
if min_loss_class > c:
min_loss_class = c
print('Running:【' + str(running_count) + '】|Loss All:【' + str(
min_loss_location + min_loss_class) + '|' + str(loss_all) + '】|Location:【' + str(
np.sum(loss_location)) + '】|Class:【' + str(np.sum(loss_class)) + '】|pred_class:【' + str(
np.sum(pred_class)) + '|' + str(np.amax(pred_class)) + '|' + str(
np.min(pred_class)) + '】|pred_location:【' + str(np.sum(pred_location)) + '|' + str(
np.amax(pred_location)) + '|' + str(np.min(pred_location)) + '】TIME:'+str(time.time()-starttime))
if running_count % 100 == 0:
print('---------')
print('avgloss')
print(avg_loss/100.)
print(np.sum(avg_lossloc/100.) )
print(np.sum(avg_losclass/100.) )
print(ptlos-avg_loss/100.)
print(ptlosc-np.sum(avg_lossloc/100.) )
print(ptlosl-np.sum(avg_losclass/100.) )
ptlos = avg_loss/100.
ptlosc = np.sum(avg_lossloc/100. )
ptlosl = np.sum(avg_losclass/100. )
print('---------')
avg_loss=0
avg_lossloc = 0
avg_losclass = 0
if running_count % 100 == 0:
results = sess.run(merged,feed_dict={imageinput:train_data,groundtruth_class:gt_class,groundtruth_location:gt_location,groundtruth_positives:gt_positives,groundtruth_negatives:gt_negatives,learning_rate:learning_rt})
trainwrite.add_summary(results, running_count)
if running_count % 500 == 0:
saver.save(sess, './session_paramsdddaleasy/session.ckpt')
print('session.ckpt has been saved.')
gc.collect()
else:
print('No Data Exists!')
break