python - 当我运行 ' FileNotFoundError: [Errno 2] No such file or directory:' 时，为什么我在终端中收到 `python ./train.py`

Question

我正在按照一篇关于训练户外图像分类器的研究论文的作者提供的步骤进行操作。 (Github:https://github.com/yuxiaoz/SGSN) 然而，这是我在 Ubuntu 终端中遇到的错误:

Traceback (most recent call last):
  File "./train.py", line 165, in <module>
    main()
  File "./train.py", line 63, in main
    x_datalists = get_data_lists(args.x_data_txt_path) # a list of x images
  File "./train.py", line 47, in get_data_lists
    f = open(data_path, 'r')
FileNotFoundError: [Errno 2] No such file or directory: './datasets/x_traindata.txt'

下面是作者提供的训练Python代码:

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()

注意: 我偶然发现了一个有点类似的问题和建议的解决方案(IOError: [Errno 2] No such file or directory (when it really exist) Python)。但我不太确定在作者的代码中如何或在何处实现它。

Answer 1

您的调用是正确的，因为我可以从日志中看到运行的文件是./train.py。

现在，在存储库中我看不到任何 x_traindata.txt 文件。该文件夹中唯一可用的内容是 3 python files ，除非您自己创建了该文件，否则该错误似乎是合理的，因为其中没有 txt 文件。