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DSOD.py
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# Google Translated version of https://github.com/Windaway/DSOD-Tensorflow/blob/master/DSOD.py
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]
# Total number of categories
classes_size = 21
# Background classification value
background_classes_val = 0
# The number of default boxes per feature map unit
default_box_size = [6, 6, 6, 6, 6, 6]
# Default box size
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]
]
# Minimum default box area ratio
min_box_scale = 0.1
# Maximum default box area ratio
max_box_scale = 0.9
# Area ratio of each feature layer
# Numpy generates an array of equals, the effect is equivalent to the paper 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))
# Convolution step
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)
# Pooled window
pool_size = [1, 2, 2, 1]
tl_pool_size = (2, 2)
# Pooling step
pool_strides = [1, 2, 2, 1]
tl_pool_strides = (2, 2)
# Batch Normalization Algorithm's decay parameter
conv_bn_decay = 0.9999
# Batch Normalization The variance_epsilon parameter of the algorithm
conv_bn_epsilon = 0.001
# Jaccard similarity judgment threshold
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())
# Location data is expressed in proportions, format [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):
# Generate an empty array to hold the 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))
# Initialize positive training data
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
# If there is no positive case, randomly create a positive example to prevent nan
if np.sum(gt_positives[img_index]) == 0:
# print('【No match 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
# Delete the portion that exceeds the image size
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()))
# Generate all 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 + '】A data error has occurred!【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