完整的错误消息如下所示：
(FYP_v2) sg97-ubuntu@SG97-ubuntu:~/SGSN$ python2 ./train.py
Traceback (most recent call last):
File "./train.py", line 165, in <module>
main()
File "./train.py", line 65, in main
tf.set_random_seed(args.random_seed)
AttributeError: 'module' object has no attribute 'set_random_seed'
(FYP_v2) sg97-ubuntu@SG97-ubuntu:~/SGSN$
我检查了关于stackoverflow的（AttributeError: 'module' object has no attribute 'set_random_seed'）问题，但是由于我没有使用Caffe，因此它实际上不适用于我的情况。
我还提供了以下python代码供参考
from __future__ import print_function
import argparse
from datetime import datetime
from random import shuffle
import os
import sys
import time
import math
import tensorflow as tf
import numpy as np
from utils import *
from train_image_reader import *
from net import *
parser = argparse.ArgumentParser(description='')
parser.add_argument("--snapshot_dir", default='./snapshots', help="path of snapshots")
parser.add_argument("--image_size", type=int, default=256, help="load image size")
parser.add_argument("--x_data_txt_path", default='./datasets/x_traindata.txt', help="txt of x images")
parser.add_argument("--y_data_txt_path", default='./datasets/y_traindata.txt', help="txt of y images")
parser.add_argument("--random_seed", type=int, default=1234, help="random seed")
parser.add_argument('--base_lr', type=float, default=0.0002, help='initial learning rate for adam')
parser.add_argument('--epoch', dest='epoch', type=int, default=50, help='# of epoch')
parser.add_argument('--epoch_step', dest='epoch_step', type=int, default=20, help='# of epoch to decay lr')
parser.add_argument("--lamda", type=float, default=10.0, help="L1 lamda")
parser.add_argument('--beta1', dest='beta1', type=float, default=0.5, help='momentum term of adam')
parser.add_argument("--summary_pred_every", type=int, default=200, help="times to summary.")
parser.add_argument("--save_pred_every", type=int, default=8000, help="times to save.")
parser.add_argument("--x_image_forpath", default='./datasets/train/X/images/', help="forpath of x training datas.")
parser.add_argument("--x_label_forpath", default='./datasets/train/X/labels/', help="forpath of x training labels.")
parser.add_argument("--y_image_forpath", default='./datasets/train/Y/images/', help="forpath of y training datas.")
parser.add_argument("--y_label_forpath", default='./datasets/train/Y/labels/', help="forpath of y training labels.")
args = parser.parse_args()
def save(saver, sess, logdir, step):
model_name = 'model'
checkpoint_path = os.path.join(logdir, model_name)
if not os.path.exists(logdir):
os.makedirs(logdir)
saver.save(sess, checkpoint_path, global_step=step)
print('The checkpoint has been created.')
def get_data_lists(data_path):
f = open(data_path, 'r')
datas=[]
for line in f:
data = line.strip("\n")
datas.append(data)
return datas
def l1_loss(src, dst):
return tf.reduce_mean(tf.abs(src - dst))
def gan_loss(src, dst):
return tf.reduce_mean((src-dst)**2)
def main():
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
x_datalists = get_data_lists(args.x_data_txt_path) # a list of x images
y_datalists = get_data_lists(args.y_data_txt_path) # a list of y images
tf.set_random_seed(args.random_seed)
x_img = tf.placeholder(tf.float32,shape=[1, args.image_size, args.image_size,3],name='x_img')
x_label = tf.placeholder(tf.float32,shape=[1, args.image_size, args.image_size,3],name='x_label')
y_img = tf.placeholder(tf.float32,shape=[1, args.image_size, args.image_size,3],name='y_img')
y_label = tf.placeholder(tf.float32,shape=[1, args.image_size, args.image_size,3],name='y_label')
fake_y = generator(image=x_img, reuse=False, name='generator_x2y') # G
fake_x_ = generator(image=fake_y, reuse=False, name='generator_y2x') # S
fake_x = generator(image=y_img, reuse=True, name='generator_y2x') # G'
fake_y_ = generator(image=fake_x, reuse=True, name='generator_x2y') # S'
dy_fake = discriminator(image=fake_y, gen_label = x_label, reuse=False, name='discriminator_y') # D
dx_fake = discriminator(image=fake_x, gen_label = y_label, reuse=False, name='discriminator_x') # D'
dy_real = discriminator(image=y_img, gen_label = y_label, reuse=True, name='discriminator_y') # D
dx_real = discriminator(image=x_img, gen_label = x_label, reuse=True, name='discriminator_x') #D'
final_loss = gan_loss(dy_fake, tf.ones_like(dy_fake)) + gan_loss(dx_fake, tf.ones_like(dx_fake)) + args.lamda*l1_loss(x_label, fake_x_) + args.lamda*l1_loss(y_label, fake_y_) # final objective function
dy_loss_real = gan_loss(dy_real, tf.ones_like(dy_real))
dy_loss_fake = gan_loss(dy_fake, tf.zeros_like(dy_fake))
dy_loss = (dy_loss_real + dy_loss_fake) / 2
dx_loss_real = gan_loss(dx_real, tf.ones_like(dx_real))
dx_loss_fake = gan_loss(dx_fake, tf.zeros_like(dx_fake))
dx_loss = (dx_loss_real + dx_loss_fake) / 2
dis_loss = dy_loss + dx_loss # discriminator loss
final_loss_sum = tf.summary.scalar("final_objective", final_loss)
dx_loss_sum = tf.summary.scalar("dx_loss", dx_loss)
dy_loss_sum = tf.summary.scalar("dy_loss", dy_loss)
dis_loss_sum = tf.summary.scalar("dis_loss", dis_loss)
discriminator_sum = tf.summary.merge([dx_loss_sum, dy_loss_sum, dis_loss_sum])
x_images_summary = tf.py_func(cv_inv_proc, [x_img], tf.float32) #(1, 256, 256, 3) float32
y_fake_cv2inv_images_summary = tf.py_func(cv_inv_proc, [fake_y], tf.float32) #(1, 256, 256, 3) float32
x_label_summary = tf.py_func(label_proc, [x_label], tf.float32) #(1, 256, 256, 3) float32
x_gen_label_summary = tf.py_func(label_inv_proc, [fake_x_], tf.float32) #(1, 256, 256, 3) float32
image_summary = tf.summary.image('images', tf.concat(axis=2, values=[x_images_summary, y_fake_cv2inv_images_summary, x_label_summary, x_gen_label_summary]), max_outputs=3)
summary_writer = tf.summary.FileWriter(args.snapshot_dir, graph=tf.get_default_graph())
g_vars = [v for v in tf.trainable_variables() if 'generator' in v.name]
d_vars = [v for v in tf.trainable_variables() if 'discriminator' in v.name]
lr = tf.placeholder(tf.float32, None, name='learning_rate')
d_optim = tf.train.AdamOptimizer(lr, beta1=args.beta1)
g_optim = tf.train.AdamOptimizer(lr, beta1=args.beta1)
d_grads_and_vars = d_optim.compute_gradients(dis_loss, var_list=d_vars)
d_train = d_optim.apply_gradients(d_grads_and_vars) # update weights of D and D'
g_grads_and_vars = g_optim.compute_gradients(final_loss, var_list=g_vars)
g_train = g_optim.apply_gradients(g_grads_and_vars) # update weights of G, G', S and S'
train_op = tf.group(d_train, g_train)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=50)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
counter = 0 # training step
for epoch in range(args.epoch):
shuffle(x_datalists) # change the order of x images
shuffle(y_datalists) # change the order of y images
lrate = args.base_lr if epoch < args.epoch_step else args.base_lr*(args.epoch-epoch)/(args.epoch-args.epoch_step)
for step in range(len(x_datalists)):
counter += 1
x_image_resize, x_label_resize, y_image_resize, y_label_resize = TrainImageReader(args.x_image_forpath, args.x_label_forpath, args.y_image_forpath, args.y_label_forpath, x_datalists, y_datalists, step, args.image_size)
batch_x_image = np.expand_dims(np.array(x_image_resize).astype(np.float32), axis = 0)
batch_x_label = np.expand_dims(np.array(x_label_resize).astype(np.float32), axis = 0)
batch_y_image = np.expand_dims(np.array(y_image_resize).astype(np.float32), axis = 0)
batch_y_label = np.expand_dims(np.array(y_label_resize).astype(np.float32), axis = 0)
start_time = time.time()
feed_dict = { lr : lrate, x_img : batch_x_image, x_label : batch_x_label, y_img : batch_y_image, y_label : batch_y_label}
if counter % args.save_pred_every == 0:
final_loss_value, dis_loss_value, _ = sess.run([final_loss, dis_loss, train_op], feed_dict=feed_dict)
save(saver, sess, args.snapshot_dir, counter)
elif counter % args.summary_pred_every == 0:
final_loss_value, dis_loss_value, final_loss_sum_value, discriminator_sum_value, image_summary_value, _ = \
sess.run([final_loss, dis_loss, final_loss_sum, discriminator_sum, image_summary, train_op], feed_dict=feed_dict)
summary_writer.add_summary(final_loss_sum_value, counter)
summary_writer.add_summary(discriminator_sum_value, counter)
summary_writer.add_summary(image_summary_value, counter)
else:
final_loss_value, dis_loss_value, _ = \
sess.run([final_loss, dis_loss, train_op], feed_dict=feed_dict)
print('epoch {:d} step {:d} \t final_loss = {:.3f}, dis_loss = {:.3f}'.format(epoch, step, final_loss_value, dis_loss_value))
coord.request_stop()
coord.join(threads)
if __name__ == '__main__':
main()
答案 0 :(得分：16)
使用tf.random.set_seed()代替tf.set_random_seed。在此处链接到tensorflow文档：https://www.tensorflow.org/api_docs/python/tf/random/set_seed?version=stable