Q&A
前提・実現したいこと
下記を利用して、私が用意した画像(256*256)を元に学習を行いました。
https://github.com/robgon-art/stylegan2-ada
Google colab1にて、下記コードを実行。
!python generate.py --seeds=1-4 --trunc=1.5 --outdir results_stylegan2_ada --network=drive/MyDrive/results_stylegan2_ada/00000-resize2-mirror-auto1-ada-target0/network-snapshot-000000.pkl
すると、下記エラーが表示されてしまい、原因がわからない状況です。
発生している問題・エラーメッセージ
・・・ Training options: { "G_args": { "func_name": "training.networks.G_main", "fmap_base": 8192, "fmap_max": 512, "mapping_layers": 2, "num_fp16_res": 4, "conv_clamp": 256 }, "D_args": { "func_name": "training.networks.D_main", "mbstd_group_size": 4, "fmap_base": 8192, "fmap_max": 512, "num_fp16_res": 4, "conv_clamp": 256 }, "G_opt_args": { "beta1": 0.0, "beta2": 0.99, "learning_rate": 0.0025 }, "D_opt_args": { "beta1": 0.0, "beta2": 0.99, "learning_rate": 0.0025 }, "loss_args": { "func_name": "training.loss.stylegan2", "r1_gamma": 0.8192 }, "augment_args": { "class_name": "training.augment.AdaptiveAugment", "tune_heuristic": "rt", "tune_target": 0.7, "apply_func": "training.augment.augment_pipeline", "apply_args": { "xflip": 1, "rotate90": 1, "xint": 1, "scale": 1, "rotate": 1, "aniso": 1, "xfrac": 1, "brightness": 1, "contrast": 1, "lumaflip": 1, "hue": 1, "saturation": 1 } }, "num_gpus": 1, "image_snapshot_ticks": 1, "network_snapshot_ticks": 1, "train_dataset_args": { "path": "./drive/MyDrive/resize2", "max_label_size": 0, "resolution": 256, "mirror_augment": true }, "metric_arg_list": [ { "name": "fid50k_full", "class_name": "metrics.frechet_inception_distance.FID", "max_reals": null, "num_fakes": 50000, "minibatch_per_gpu": 8, "force_dataset_args": { "shuffle": false, "max_images": null, "repeat": false, "mirror_augment": false } } ], "metric_dataset_args": { "path": "./drive/MyDrive/resize2", "max_label_size": 0, "resolution": 256, "mirror_augment": true }, "total_kimg": 25000, "minibatch_size": 16, "minibatch_gpu": 16, "G_smoothing_kimg": 5.0, "G_smoothing_rampup": 0.05, "run_dir": "./drive/MyDrive/results_stylegan2_ada/00002-resize2-mirror-auto1-ada-target0.7" } Output directory: ./drive/MyDrive/results_stylegan2_ada/00002-resize2-mirror-auto1-ada-target0.7 Training data: ./drive/MyDrive/resize2 Training length: 25000 kimg Resolution: 256 Number of GPUs: 1 Creating output directory... Loading training set... Image shape: [3, 256, 256] Label shape: [0] Constructing networks... Setting up TensorFlow plugin "fused_bias_act.cu": Loading... Done. Setting up TensorFlow plugin "upfirdn_2d.cu": Loading... Done. G Params OutputShape WeightShape --- --- --- --- latents_in - (?, 512) - labels_in - (?, 0) - G_mapping/Normalize - (?, 512) - G_mapping/Dense0 262656 (?, 512) (512, 512) G_mapping/Dense1 262656 (?, 512) (512, 512) G_mapping/Broadcast - (?, 14, 512) - dlatent_avg - (512,) - Truncation/Lerp - (?, 14, 512) - G_synthesis/4x4/Const 8192 (?, 512, 4, 4) (1, 512, 4, 4) G_synthesis/4x4/Conv 2622465 (?, 512, 4, 4) (3, 3, 512, 512) G_synthesis/4x4/ToRGB 264195 (?, 3, 4, 4) (1, 1, 512, 3) G_synthesis/8x8/Conv0_up 2622465 (?, 512, 8, 8) (3, 3, 512, 512) G_synthesis/8x8/Conv1 2622465 (?, 512, 8, 8) (3, 3, 512, 512) G_synthesis/8x8/Upsample - (?, 3, 8, 8) - G_synthesis/8x8/ToRGB 264195 (?, 3, 8, 8) (1, 1, 512, 3) G_synthesis/16x16/Conv0_up 2622465 (?, 512, 16, 16) (3, 3, 512, 512) G_synthesis/16x16/Conv1 2622465 (?, 512, 16, 16) (3, 3, 512, 512) G_synthesis/16x16/Upsample - (?, 3, 16, 16) - G_synthesis/16x16/ToRGB 264195 (?, 3, 16, 16) (1, 1, 512, 3) G_synthesis/32x32/Conv0_up 2622465 (?, 512, 32, 32) (3, 3, 512, 512) G_synthesis/32x32/Conv1 2622465 (?, 512, 32, 32) (3, 3, 512, 512) G_synthesis/32x32/Upsample - (?, 3, 32, 32) - G_synthesis/32x32/ToRGB 264195 (?, 3, 32, 32) (1, 1, 512, 3) G_synthesis/64x64/Conv0_up 1442561 (?, 256, 64, 64) (3, 3, 512, 256) G_synthesis/64x64/Conv1 721409 (?, 256, 64, 64) (3, 3, 256, 256) G_synthesis/64x64/Upsample - (?, 3, 64, 64) - G_synthesis/64x64/ToRGB 132099 (?, 3, 64, 64) (1, 1, 256, 3) G_synthesis/128x128/Conv0_up 426369 (?, 128, 128, 128) (3, 3, 256, 128) G_synthesis/128x128/Conv1 213249 (?, 128, 128, 128) (3, 3, 128, 128) G_synthesis/128x128/Upsample - (?, 3, 128, 128) - G_synthesis/128x128/ToRGB 66051 (?, 3, 128, 128) (1, 1, 128, 3) G_synthesis/256x256/Conv0_up 139457 (?, 64, 256, 256) (3, 3, 128, 64) G_synthesis/256x256/Conv1 69761 (?, 64, 256, 256) (3, 3, 64, 64) G_synthesis/256x256/Upsample - (?, 3, 256, 256) - G_synthesis/256x256/ToRGB 33027 (?, 3, 256, 256) (1, 1, 64, 3) --- --- --- --- Total 23191522 D Params OutputShape WeightShape --- --- --- --- images_in - (?, 3, 256, 256) - labels_in - (?, 0) - 256x256/FromRGB 256 (?, 64, 256, 256) (1, 1, 3, 64) 256x256/Conv0 36928 (?, 64, 256, 256) (3, 3, 64, 64) 256x256/Conv1_down 73856 (?, 128, 128, 128) (3, 3, 64, 128) 256x256/Skip 8192 (?, 128, 128, 128) (1, 1, 64, 128) 128x128/Conv0 147584 (?, 128, 128, 128) (3, 3, 128, 128) 128x128/Conv1_down 295168 (?, 256, 64, 64) (3, 3, 128, 256) 128x128/Skip 32768 (?, 256, 64, 64) (1, 1, 128, 256) 64x64/Conv0 590080 (?, 256, 64, 64) (3, 3, 256, 256) 64x64/Conv1_down 1180160 (?, 512, 32, 32) (3, 3, 256, 512) 64x64/Skip 131072 (?, 512, 32, 32) (1, 1, 256, 512) 32x32/Conv0 2359808 (?, 512, 32, 32) (3, 3, 512, 512) 32x32/Conv1_down 2359808 (?, 512, 16, 16) (3, 3, 512, 512) 32x32/Skip 262144 (?, 512, 16, 16) (1, 1, 512, 512) 16x16/Conv0 2359808 (?, 512, 16, 16) (3, 3, 512, 512) 16x16/Conv1_down 2359808 (?, 512, 8, 8) (3, 3, 512, 512) 16x16/Skip 262144 (?, 512, 8, 8) (1, 1, 512, 512) 8x8/Conv0 2359808 (?, 512, 8, 8) (3, 3, 512, 512) 8x8/Conv1_down 2359808 (?, 512, 4, 4) (3, 3, 512, 512) 8x8/Skip 262144 (?, 512, 4, 4) (1, 1, 512, 512) 4x4/MinibatchStddev - (?, 513, 4, 4) - 4x4/Conv 2364416 (?, 512, 4, 4) (3, 3, 513, 512) 4x4/Dense0 4194816 (?, 512) (8192, 512) Output 513 (?, 1) (512, 1) --- --- --- --- Total 24001089 Exporting sample images... Replicating networks across 1 GPUs... Initializing augmentations... Setting up optimizers... Constructing training graph... Finalizing training ops... Initializing metrics... Training for 25000 kimg... tick 0 kimg 0.1 time 1m 31s sec/tick 17.8 sec/kimg 278.72 maintenance 72.7 gpumem 7.2 augment 0.000 Evaluating metrics... Calculating real image statistics for fid50k_full... tcmalloc: large alloc 4294967296 bytes == 0x5571ec7fc000 @ 0x7fcdd101f001 0x7fcdce26254f 0x7fcdce2b2b58 0x7fcdce2b6b17 0x7fcdce355203 0x556d9b41e0a4 0x556d9b41dda0 0x556d9b492868 0x556d9b48d235 0x556d9b41ffec 0x556d9b460bc9 0x556d9b45dac4 0x556d9b41e8a9 0x556d9b492b0a 0x556d9b48cc35 0x556d9b35ee2c 0x556d9b48f318 0x556d9b41f65a 0x556d9b48dd67 0x556d9b510ae8 0x556d9b48dfb3 0x556d9b48cc35 0x556d9b41f73a 0x556d9b48e93b 0x556d9b48cc35 0x556d9b41f73a 0x556d9b48e93b 0x556d9b48cc35 0x556d9b35ee2c 0x556d9b48f318 0x556d9b48d235 ^C
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