Closed LZL-CS closed 1 year ago
Hi, I had the same problem as you, did you solve it?
Hi, I had the same problem as you, did you solve it?
Hi, sorry for the late reply. I finally used the MobaXterm on Windows with an X-server to connect my remote server to successfully run READ train.py.
Hi, when I run the train.py I got the error: `/public/home/luanzl/anaconda3/envs/READ/bin/python3.9 /public/home/luanzl/.pycharm_helpers/pydev/pydevd.py --multiprocess --qt-support=auto --client localhost --port 39309 --file /public/home/luanzl/WorkSpace/READ/train.py Connected to pydev debugger (build 223.8836.43) experiment dir: data/logs/03-21_19-10-01___
ARGV:
config : /public/home/luanzl/WorkSpace/READ/configs/train_example.yaml
conv_block : gated
criterion_args : {'partialconv': False}
criterion_module : READ.criterions.vgg_loss.VGGLoss
crop_size : (256, 256)
dataloader_workers : 8
dataset_names : []
descriptor_size : 8
epochs : 100
eval : False
eval_in_test : True
eval_in_train : True
eval_in_train_epoch : -1
exclude_datasets : None
freeze_net : False
ignore_changed_args : ['ignore_changed_args', 'save_dir', 'dataloader_workers', 'epochs', 'max_ds', 'batch_size_val', 'config', 'pipeline']
inference : False
input_channels : None
input_format : uv_1d_p1, uv_1d_p1_ds1, uv_1d_p1_ds2, uv_1d_p1_ds3, uv_1d_p1_ds4
log_freq : 5
log_freq_images : 100
lr : 0.0001
max_ds : 3
merge_loss : True
multigpu : True
n_points : 0
net_ckpt : None
net_size : 4
num_mipmap : 5
paths_file : configs/paths_example.yaml
pipeline : READ.pipelines.ogl.TexturePipeline
reg_weight : 0.0
save_dir : data/logs
save_freq : 5
seed : 2019
simple_name : True
splitter_args : {'val_step': 10, 'train_drop': 0.0}
splitter_module : READ.datasets.splitter.split_by_step
supersampling : 1
texture_activation : none
texture_ckpt : None
texture_lr : 0.1
texture_size : None
train_dataset_args : {'keep_fov': False, 'random_zoom': [0.7, 2.0], 'random_shift': [-1.0, 1.0], 'drop_points': 0.0, 'num_samples': 3000}
use_mask : None
use_mesh : False
val_dataset_args : {'keep_fov': False, 'drop_points': 0.0}
Changed args
Using all datasets
loading pointcloud...
no normals in /public/home/luanzl/WorkSpace/READ/Data/pointcloud.ply
=== 3D model ===
VERTICES: 2547322
EXTENT: [ -84.91281891 -12.33472538 -234.02790833] [ 68.9091568 47.1550293 685.07629395]
/public/home/luanzl/WorkSpace/READ/train.py Unchanged args batch_size : 3 batch_size_val : 3 comment :
[1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 71, 72, 73, 74, 75, 76, 77, 78, 79, 81, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 132, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 151, 152, 153, 154, 155, 156, 157, 158, 159, 161, 162, 163, 164, 165, 166, 167, 168, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 201, 202, 203, 204, 205, 206, 207, 208, 209, 211, 212, 213, 214, 215, 216, 217, 218, 219, 221, 222, 223, 224, 225, 226, 227, 228, 229, 231, 232, 233, 234, 235, 236, 237, 238, 239, 241, 242, 243, 244, 245, 246, 247, 248, 249, 251, 252, 253, 254, 255, 256, 257, 258, 259, 261, 262, 263, 264, 265, 266, 267, 268, 269, 271, 272, 273, 274, 275, 276, 277, 278, 279, 281, 282, 283, 284, 285, 286, 287, 288, 289, 291, 292, 293, 294, 295, 296, 297, 298, 299, 301, 302, 303, 304, 305, 306, 307, 308, 309, 311, 312, 313, 314, 315, 316, 317, 318, 319, 321, 322, 323, 324, 325, 326, 327, 328, 329, 331, 332, 333, 334, 335, 336, 337, 338, 339, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 354, 355, 356, 357, 358, 359, 361, 362, 363, 364, 365, 366, 367, 368, 369, 371, 372, 373, 374, 375, 376, 377, 378, 379, 381, 382, 383, 384, 385, 386, 387, 388, 389, 391, 392, 393, 394, 395, 396, 397, 398, 399, 401, 402, 403, 404, 405, 406, 407, 408, 409, 411, 412, 413, 414, 415, 416, 417, 418, 419, 421, 422, 423, 424, 425, 426, 427, 428, 429, 431, 432, 433, 434, 435, 436, 437, 438, 439, 441, 442, 443, 444, 445, 446, 447, 448, 449, 451, 452, 453, 454, 455, 456, 457, 458, 459, 461, 462, 463, 464, 465, 466, 467, 468, 469, 471, 472, 473, 474, 475, 476, 477, 478, 479, 481, 482, 483, 484, 485, 486, 487, 488, 489, 491, 492, 493, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 511, 512, 513, 514, 515, 516, 517, 518, 519, 521, 522, 523, 524, 525, 526, 527, 528, 529, 531, 532, 533, 534, 535, 536, 537, 538, 539, 541, 542, 543, 544, 545, 546, 547, 548, 549, 551, 552, 553, 554, 555, 556, 557, 558, 559, 561, 562, 563, 564, 565, 566, 567, 568, 569, 571, 572, 573, 574, 575, 576, 577, 578, 579, 581, 582, 583, 584, 585, 586, 587, 588, 589, 591, 592, 593, 594, 595, 596, 597, 598, 599, 601, 602, 603, 604, 605, 606, 607, 608, 609, 611, 612, 613, 614, 615, 616, 617, 618, 619, 621, 622, 623, 624, 625, 626, 627, 628, 629, 631, 632, 633, 634, 635, 636, 637, 638, 639, 641, 642, 643, 644, 645, 646, 647, 648, 649, 651, 652, 653, 654, 655, 656, 657, 658, 659, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 675, 676, 677, 678, 679, 681, 682, 683, 684, 685, 686, 687, 688, 689, 691, 692, 693, 694, 695, 696, 697, 698, 699, 701, 702, 703, 704, 705, 706, 707, 708, 709, 711, 712, 713, 714, 715, 716, 717, 718, 719, 721, 722, 723, 724, 725, 726, 727, 728, 729, 731, 732, 733, 734, 735, 736, 737, 738, 739, 741, 742, 743, 744, 745, 746, 747, 748, 749, 751, 752, 753, 754, 755, 756, 757, 758, 759, 761, 762, 763, 764, 765, 766, 767, 768, 769, 771, 772, 773, 774, 775, 776, 777, 778, 779, 781, 782, 783, 784, 785, 786, 787, 788, 789, 791, 792, 793, 794, 795, 796, 797, 798, 799, 801, 802, 803, 804, 805, 806, 807, 808, 809, 811, 812, 813, 814, 815, 816, 817, 818] [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810] ds_train: 3000 ds_val: 82
EPOCH 0
May I know how to resolve this error, hope for your reply, thanks!