def eval_smooth_show(config_obj):
test_preprocess_obj=data_preprocessing.test_preprocess.test_preprocess(config_obj.tfrecord_test_addr, config_obj.class_num)
net_name=config_obj.net_type
test_net_obj=None
if net_name=='vgg16':
test_net_obj=net.vgg16.vgg16(False, 'vgg16', config_obj.class_num)
elif net_name=='mobilenet_v2':
test_net_obj=net.mobilenet_v2.mobilenet_v2(True, 'mobilenet_v2', config_obj.class_num)
accu_name=config_obj.accuracy_type
accu_obj=None
if accu_name=='default':
accu_obj=default_accuracy.default_accuracy()
elif accu_name=='multi':
accu_obj=multi_accuracy.multi_accuracy()
images_test, labels_test = test_preprocess_obj.def_preposess()
net_test = test_net_obj.def_net(images_test)
inputs=tf.sigmoid(net_test)
predict=tf.cast(inputs> 0.5, tf.float32)
accuracy_perfect, accuracy, precision, recall, acc_list, pre_list, rec_list = accu_obj.def_accuracy(net_test, labels_test)
saver = tf.train.Saver()
img_mean = utils.global_var.means
with tf.Session() as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
saver.restore(sess, config_obj.result_addr)
accuracy_prefect_v, accuracy_v, precision_v, recall_v, acc_list_v, pre_list_v, rec_list_v, predict_v, labels_test_v, images_test_v = sess.run([accuracy_perfect, accuracy, precision, recall, acc_list, pre_list, rec_list, predict, labels_test, images_test])
print('accuracy_prefect_v: %s' % accuracy_prefect_v)
print('accuracy_v: ', accuracy_v)
print('precision:' , precision_v)
print('recall:' , recall_v)
print('acc_list_v:' , acc_list_v)
print('pre_list_v:' , pre_list_v)
print('rec_list_v:' , rec_list_v)
print('len(predict_V):', len(predict_v))
for k in range(len(predict_v)):
#print(labels_test_v[k])
mat_show=[]
for i in range(len(predict_v[k])):
if predict_v[k][i] == 1:
mat_show.append(data_scraping.materil_name.material_list[i])
print(str(data_scraping.materil_name.material_list[i]))
print(predict_v[k])
show_img=images_test_v[k]
show_img=show_img+img_mean
show_img = abs(show_img) / 256.0
plt.imshow(show_img)
zhfont = matplotlib.font_manager.FontProperties(fname='/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc')
for i in range(len(mat_show)):
plt.text(150, 25*(i+1), str(mat_show[i]), fontproperties = zhfont, fontsize=15, color='red')
plt.show()
coord.request_stop()
coord.join(threads)
y_temp = net_obj.def_net(x_data)
# y = tf.nn.sigmoid(net_obj.def_net(x_data))
y = tf.nn.sigmoid(y_temp)
# 通过预测的y值与真实的y_data值进行对比得出误差
loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=y, labels=y_data))
# 定义训练时采用的算法
optmizer = tf.train.GradientDescentOptimizer(learning_rate)
train = optmizer.minimize(loss)
acc = tf.reduce_mean(tf.cast(tf.abs(y - y_data) < 0.1, tf.float32))
obj_acc = multi_accuracy.multi_accuracy()
acc_total, accu, precision, recall, acc_list, precision_list, recall_list = obj_acc.def_accuracy(
y_temp, y_data)
obj_de_acc = default_accuracy.default_accuracy()
acc_default = obj_de_acc.def_accuracy(y_temp, y_data)
saver = tf.train.Saver()
with tf.Session() as sess:
file_writer = tf.summary.FileWriter('./logs_simple', sess.graph)
if not is_training:
saver.restore(sess, sess_path)
else:
# 初始化相关变量
init = tf.global_variables_initializer()
sess.run(init)
# 训练无数次，每训练1000次，输出一次训练的结果
def eval_smooth(config_name, repeat_time):
config_name = config_name
print('choose config: ' + config_name)
config_obj = None
if config_name == 'chamo':
config_obj = config.chamo.get_config()
elif config_name == 'chamo_full_run':
config_obj = config.chamo_full_run.get_config()
test_preprocess_obj = data_preprocessing.test_preprocess.test_preprocess(config_obj.tfrecord_test_addr,
config_obj.class_num)
net_name = config_obj.net_type
test_net_obj = None
if net_name == 'vgg16':
test_net_obj = net.vgg16.vgg16(False, 'vgg16', config_obj.class_num)
elif net_name == 'mobilenet_v2':
test_net_obj = net.mobilenet_v2.mobilenet_v2(True, 'mobilenet_v2', config_obj.class_num)
accu_name = config_obj.accuracy_type
accu_obj = None
if accu_name == 'default':
accu_obj = default_accuracy.default_accuracy()
elif accu_name == 'multi':
accu_obj = multi_accuracy.multi_accuracy()
images_test, labels_test = test_preprocess_obj.def_preposess()
net_test = test_net_obj.def_net(images_test)
inputs = tf.sigmoid(net_test)
predict = tf.cast(inputs > 0.5, tf.float32)
accuracy_perfect, accuracy, precision, recall, f1, acc_list, \
pre_list, pre_list_nume, pre_list_deno, rec_list, \
rec_list_nume, rec_list_deno = accu_obj.def_accuracy(net_test, labels_test)
saver = tf.train.Saver()
with tf.Session() as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
saver.restore(sess, config_obj.result_addr)
# len_all = len(labels_test)
each_size = labels_test.get_shape().as_list()[0]
len_all = labels_test.get_shape().as_list()[1]
acc_perfect_all = 0.0
acc_all = 0.0
precision_all = 0.0
recall_all = 0.0
acc_list_all = np.zeros(shape=[len_all], dtype=np.float32)
precision_all_nume = np.zeros(shape=[len_all], dtype=np.float32)
precision_all_deno = np.zeros(shape=[len_all], dtype=np.float32)
precision_list_all = np.zeros(shape=[len_all], dtype=np.float32)
recall_all_nume = np.zeros(shape=[len_all], dtype=np.float32)
recall_all_deno = np.zeros(shape=[len_all], dtype=np.float32)
recall_list_all = np.zeros(shape=[len_all], dtype=np.float32)
for repeat_i in range(1, repeat_time+1):
accuracy_perfect_v, accuracy_v, precision_v, recall_v, acc_list_v, pre_list_nume_v, pre_list_deno_v, \
rec_list_nume_v, rec_list_deno_v, predict_v, labels_test_v, images_test_v = sess.run(
[accuracy_perfect, accuracy, precision, recall, acc_list,
pre_list_nume, pre_list_deno, rec_list_nume, rec_list_deno, predict, labels_test,
images_test])
acc_perfect_all = acc_perfect_all + accuracy_perfect_v
acc_all = acc_all + accuracy_v
precision_all = precision_all + precision_v
recall_all = recall_all + recall_v
acc_list_all = np.nan_to_num(acc_list_all) + acc_list_v
precision_all_nume = precision_all_nume + pre_list_nume_v
precision_all_deno = precision_all_deno + pre_list_deno_v
recall_all_nume = recall_all_nume + rec_list_nume_v
recall_all_deno = recall_all_deno + rec_list_deno_v
repeat_i = float(repeat_i)
print('step: %d total pictures: %d' % (repeat_i, each_size*repeat_i))
print('accuracy_prefect_v:', acc_perfect_all/repeat_i)
print('accuracy_v: ', acc_all/repeat_i)
print('precision:', precision_all/repeat_i)
print('recall:', recall_all/repeat_i)
print('acc_list_v:', acc_list_all/repeat_i)
print('pre_list_v:', precision_all_nume/precision_all_deno)
print('rec_list_v:', recall_all_nume/recall_all_deno)
coord.request_stop()
coord.join(threads)
repeat_time = float(repeat_time)
acc_perfect_all = acc_perfect_all/repeat_time
acc_all = acc_all/repeat_time
precision_all = precision_all/repeat_time
recall_all = recall_all/repeat_time
acc_list_all = acc_list_all/repeat_time
precision_list_all = precision_all_nume/precision_all_deno
recall_list_all = recall_all_nume/recall_all_deno
return acc_perfect_all, acc_all, precision_all, recall_all, acc_list_all, precision_list_all, recall_list_all
def eval_smooth_show(config_obj, one_pic_path=None, isDeconv=False):
test_preprocess_obj = data_preprocessing.test_preprocess.test_preprocess(
config_obj.tfrecord_test_addr, config_obj.class_num)
net_name = config_obj.net_type
test_net_obj = None
if net_name == 'vgg16':
test_net_obj = net.vgg16.vgg16(False, 'vgg16', config_obj.class_num)
elif net_name == 'mobilenet_v2':
test_net_obj = net.mobilenet_v2.mobilenet_v2(False, 'mobilenet_v2',
config_obj.class_num)
accu_name = config_obj.accuracy_type
accu_obj = None
if accu_name == 'default':
accu_obj = default_accuracy.default_accuracy()
elif accu_name == 'multi':
accu_obj = multi_accuracy.multi_accuracy()
images_test, labels_test = test_preprocess_obj.def_preposess(batch_size=1)
net_test = test_net_obj.def_net(images_test)
inputs = tf.sigmoid(net_test)
predict = tf.cast(inputs > 0.1, tf.float32)
accuracy_TOTAL = accu_obj.def_accuracy(net_test, labels_test)
saver = tf.train.Saver()
img_mean = utils.global_var.means
sess = tf.Session()
writer = tf.summary.FileWriter("Log/", tf.get_default_graph())
with sess.as_default():
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
saver.restore(sess, config_obj.result_addr)
[accu_TOTAL, predict_v, labels_test_v, images_test_v] \
= sess.run([accuracy_TOTAL, predict, labels_test, images_test])
print('len(predict_V):', len(predict_v))
for k in range(len(predict_v)):
#print(labels_test_v[k])
mat_show = []
for i in range(len(predict_v[k])):
if predict_v[k][i] == 1:
mat_show.append(
data_scraping.materil_name_73.material_list[i])
print(str(data_scraping.materil_name_73.material_list[i]))
print(predict_v[k])
show_img = images_test_v[k]
show_img = show_img + img_mean
show_img = abs(show_img) / 256.0
plt.imshow(show_img)
# zhfont = matplotlib.font_manager.FontProperties(
# fname='/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc')
for i in range(len(mat_show)):
plt.text(150,
25 * (i + 1),
str(mat_show[i]),
fontproperties='Simhei',
fontsize=15,
color='red')
plt.show()
coord.request_stop()
coord.join(threads)
writer.close()
if isDeconv:
#show_cnnvis(sess, feed_dict={X: image_list, Y: tempY}, input_tensor=images_test)
show_cnnvis(sess, feed_dict={}, input_tensor=images_test)
def eval_reconstruct_slim(config_obj, one_pic_path):
'''
对slim的图片预处理进行了重现
:param config_obj:
:param one_pic_path:
:return:
'''
net_name = config_obj.net_type
test_net_obj = None
if net_name == 'vgg16':
test_net_obj = net.vgg16.vgg16(False, 'vgg16', config_obj.class_num)
elif net_name == 'mobilenet_v2':
test_net_obj = net.mobilenet_v2.mobilenet_v2(False, 'mobilenet_v2',
config_obj.class_num)
accu_name = config_obj.accuracy_type
accu_obj = None
if accu_name == 'default':
accu_obj = default_accuracy.default_accuracy()
elif accu_name == 'multi':
accu_obj = multi_accuracy.multi_accuracy()
images_test_p = tf.placeholder(shape=[config_obj.batchsize, 224, 224, 3],
dtype=tf.float32)
labels_test_p = tf.placeholder(
shape=[config_obj.batchsize, config_obj.class_num], dtype=tf.float32)
images_test, labels_test = read_a_pic_reconstruct_slim(
one_pic_path, config_obj.class_num)
net_test = test_net_obj.def_net(images_test_p)
inputs = tf.sigmoid(net_test)
predict = tf.cast(inputs > 0.1, tf.float32)
accuracy_TOTAL = accu_obj.def_accuracy(net_test, labels_test_p, 0.5)
saver = tf.train.Saver()
img_mean = utils.global_var.means
sess = tf.Session()
writer = tf.summary.FileWriter("Log/", tf.get_default_graph())
with sess.as_default():
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
saver.restore(sess, config_obj.result_addr)
feed_dict = {images_test_p: images_test, labels_test_p: labels_test}
[accu_TOTAL, predict_v] \
= sess.run([accuracy_TOTAL, predict], feed_dict=feed_dict)
print('len(predict_V):', len(predict_v))
for k in range(len(predict_v)):
# print(labels_test_v[k])
mat_show = []
for i in range(len(predict_v[k])):
if predict_v[k][i] == 1:
mat_show.append(
data_scraping.materil_name_73.material_list[i])
print(str(data_scraping.materil_name_73.material_list[i]))
print(predict_v[k])
show_img = images_test[k]
show_img = np.array(show_img)
# show_img = show_img + img_mean
# show_img = abs(show_img) / 256.0
# show_img = map(abs, show_img)
# show_img = [i/256.0 for i in show_img]
# plt.imshow(show_img)
plt.imshow(show_img)
# zhfont = matplotlib.font_manager.FontProperties(
# fname='/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc')
for i in range(len(mat_show)):
plt.text(150,
25 * (i + 1),
str(mat_show[i]),
fontproperties='Simhei',
fontsize=15,
color='red')
plt.show()
coord.request_stop()
coord.join(threads)
writer.close()
show_cnnvis(sess, feed_dict=feed_dict, input_tensor=images_test_p)
def eval_smooth_divide(config_obj, des_path, repeat_time):
print('read tfrecord from:', config_obj.tfrecord_test_addr)
RIGHT_PATH = des_path + 'right/'
PART_PATH = des_path + 'part/'
ERROR_PATH = des_path + 'error/'
if not os.path.exists(des_path):
os.makedirs(des_path)
if not os.path.exists(RIGHT_PATH):
os.makedirs(RIGHT_PATH)
if not os.path.exists(PART_PATH):
os.makedirs(PART_PATH)
if not os.path.exists(ERROR_PATH):
os.makedirs(ERROR_PATH)
test_preprocess_obj = data_preprocessing.test_preprocess.test_preprocess(
config_obj.tfrecord_test_addr, config_obj.class_num)
net_name = config_obj.net_type
test_net_obj = None
if net_name == 'vgg16':
test_net_obj = net.vgg16.vgg16(False, 'vgg16', config_obj.class_num)
elif net_name == 'mobilenet_v2':
test_net_obj = net.mobilenet_v2.mobilenet_v2(False, 'mobilenet_v2',
config_obj.class_num)
accu_name = config_obj.accuracy_type
accu_obj = None
if accu_name == 'default':
accu_obj = default_accuracy.default_accuracy()
elif accu_name == 'multi':
accu_obj = multi_accuracy.multi_accuracy()
images_test, labels_test = test_preprocess_obj.def_preposess()
labels_right = labels_test
net_test = test_net_obj.def_net(images_test)
inputs = tf.sigmoid(net_test)
predict = tf.cast(inputs > 0.5, tf.float32)
accuracy_TOTAL = accu_obj.def_accuracy(net_test, labels_test)
saver = tf.train.Saver()
img_mean = utils.global_var.means
sess = tf.Session()
with sess.as_default():
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
saver.restore(sess, config_obj.result_addr)
batch_size = 0
for repeat_time_i in range(repeat_time):
[accu_TOTAL, predict_v, labels_test_v, images_test_v
] = sess.run([accuracy_TOTAL, predict, labels_right, images_test])
print('len(predict_V):', len(predict_v))
batch_size = len(predict_v)
for k in range(len(predict_v)):
# print(labels_test_v[k])
mat_predict = []
mat_right = []
for i in range(len(predict_v[k])):
if predict_v[k][i] == 1:
mat_predict.append(
data_scraping.materil_name_73.material_list[i])
print(
str(data_scraping.materil_name_73.material_list[i])
)
print('===========')
for m in range(len(labels_test_v[k])):
if labels_test_v[k][m] == 1:
mat_right.append(
data_scraping.materil_name_73.material_list[m])
print(
str(data_scraping.materil_name_73.material_list[m])
)
print(predict_v[k])
show_img = images_test_v[k]
show_img = show_img + img_mean
show_img = abs(show_img) / 256.0
ret_num = is_right(predict_v[k], labels_test_v[k])
if ret_num == RIGHT:
tag_pic_and_save(show_img, mat_predict, RIGHT_PATH,
mat_right)
elif ret_num == PART:
tag_pic_and_save(show_img, mat_predict, PART_PATH,
mat_right)
else:
tag_pic_and_save(show_img, mat_predict, ERROR_PATH,
mat_right)
#plt.show()
coord.request_stop()
coord.join(threads)
print('eval done, batch_size: %d; repeat_time: %d; total eval: %d' %
(batch_size, repeat_time, batch_size * repeat_time))