I run this project on Colab,Then I encounter a problem about colorization-pytorch HOT 13 OPEN

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?

Oh im almost there! i did as per instructed.. but when running python test.py --name siggraph_caffemodel --mask_cent 0 I get: Traceback (most recent call last): File "test.py", line 54, in <module> data_raw[0] = util.crop_mult(data_raw[0], mult=8) File "/home/malcomtan/colorization-pytorch/util/util.py", line 277, in crop_mult return data[:,:,h:h+Hnew,w:w+Wnew] TypeError: slice indices must be integers or None or have an __index__ method Hmmm, may i know if u did any changes to test.py? Sorry its 11 pm here.. still trying to get it working hahahhaa xDD im so close!!

oh yes, i realized its a numpy thing, installed many different numpy packages! But still to no avail! hmmm may i know what numpy version you were using? Because now, current numpy version (1.19) doesnt support such indexing. Just wondering if you're still able to run this repo! So sorry for the trouble!! but yes, hope you have a good good day!! ^.^

yes! definitely!! im trying to solve this problem as well. But if i do, i will DEFINITELY update you! rest assured! you have my word! ^.^

You're very welcome! Thank you too for helping!! ^.^