heyy!! hmm the train_options.py is as shown: from .base_options import BaseOptions class TrainOptions(BaseOptions): def initialize(self, parser): BaseOptions.initialize(self, parser) parser.add_argument('--display_freq', type=int, default=10000, help='frequency of showing training results on screen') parser.add_argument('--display_ncols', type=int, default=5, help='if positive, display all images in a single visdom web panel with certain number of images per row.') parser.add_argument('--update_html_freq', type=int, default=10000, help='frequency of saving training results to html') parser.add_argument('--print_freq', type=int, default=200, help='frequency of showing training results on console') parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results') parser.add_argument('--save_epoch_freq', type=int, default=1, help='frequency of saving checkpoints at the end of epochs') parser.add_argument('--epoch_count', type=int, default=0, help='the starting epoch count, we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>, ...') parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate') parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero') parser.add_argument('--beta1', type=float, default=0.9, help='momentum term of adam') parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate for adam') parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN') parser.add_argument('--lambda_GAN', type=float, default=0., help='weight for GAN loss') parser.add_argument('--lambda_A', type=float, default=1., help='weight for cycle loss (A -> B -> A)') parser.add_argument('--lambda_B', type=float, default=1., help='weight for cycle loss (B -> A -> B)') parser.add_argument('--lambda_identity', type=float, default=0.5, help='use identity mapping. Setting lambda_identity other than 0 has an effect of scaling the weight of the identity mapping loss.' 'For example, if the weight of the identity loss should be 10 times smaller than the weight of the reconstruction loss, please set lambda_identity = 0.1') parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images') parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name ]/web/') parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau') parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations') parser.add_argument('--avg_loss_alpha', type=float, default=.986, help='exponential averaging weight for displaying loss') self.isTrain = True return parser which part of it did u change?

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On Wed, Nov 25, 2020 at 6:49 PM meiguoofa ***@***.***> wrote: What's your error information? you should change the dataset path that you specify in the train_options.py — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#15 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/APZCGIVXTW43E4KQLY74ELTSRTOLVANCNFSM4SMXUBGA> .

I run this project on Colab,Then I encounter a problem #15

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