plaidml.exceptions.Unknown: No enough memory for the current schedule: required 2163167232, available 1825361152

If training is failing to start, and you are not receiving an error message telling you what to do, tell us about it here


Forum rules

Read the FAQs and search the forum before posting a new topic.

This forum is for reporting errors with the Training process. If you want to get tips, or better understand the Training process, then you should look in the Training Discussion forum.

Please mark any answers that fixed your problems so others can find the solutions.

Locked
User avatar
garwooi
Posts: 2
Joined: Tue Aug 18, 2020 12:19 am
Answers: 0

plaidml.exceptions.Unknown: No enough memory for the current schedule: required 2163167232, available 1825361152

Post by garwooi »

Hi Faceswap dev.
No enough memory for the current schedule: required 2163167232, available 1825361152.
My system has 12GB of memory.

Not sure what's went wrong, been update to the latest version.

Below was the failed log.


Code: Select all

08/20/2020 22:05:44 MainProcess     _training_0     multithreading  __init__                  DEBUG    Initializing BackgroundGenerator: (target: '_run', thread_count: 2)
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  __init__                  DEBUG    Initialized BackgroundGenerator: '_run'
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Starting thread(s): '_run'
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Starting thread 1 of 2: '_run_0'
08/20/2020 22:05:44 MainProcess     _run_0          training_data   _minibatch                DEBUG    Loading minibatch generator: (image_count: 7557, side: 'a', do_shuffle: True)
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Starting thread 2 of 2: '_run_1'
08/20/2020 22:05:44 MainProcess     _run_1          training_data   _minibatch                DEBUG    Loading minibatch generator: (image_count: 7557, side: 'a', do_shuffle: True)
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Started all threads '_run': 2
08/20/2020 22:05:44 MainProcess     _training_0     _base           _load_generator           DEBUG    Loading generator
08/20/2020 22:05:44 MainProcess     _training_0     _base           _load_generator           DEBUG    input_size: 64, output_shapes: [(64, 64, 3)]
08/20/2020 22:05:44 MainProcess     _training_0     _base           coverage_ratio            DEBUG    Requested coverage_ratio: 0.875
08/20/2020 22:05:44 MainProcess     _training_0     _base           coverage_ratio            DEBUG    Final coverage_ratio: 0.875
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Initializing TrainingDataGenerator: (model_input_size: 64, model_output_shapes: [(64, 64, 3)], coverage_ratio: 0.875, augment_color: True, no_flip: False, warp_to_landmarks: False, alignments: ['masks'], config: {'coverage': 87.5, 'mask_type': 'extended', 'mask_blur_kernel': 3, 'mask_threshold': 4, 'learn_mask': False, 'penalized_mask_loss': True, 'loss_function': 'mae', 'icnr_init': False, 'conv_aware_init': False, 'optimizer': 'adam', 'learning_rate': 5e-05, 'reflect_padding': False, 'allow_growth': False, 'mixed_precision': False, 'preview_images': 14, 'zoom_amount': 5, 'rotation_range': 10, 'shift_range': 5, 'flip_chance': 50, 'color_lightness': 30, 'color_ab': 8, 'color_clahe_chance': 50, 'color_clahe_max_size': 4})
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Initialized TrainingDataGenerator
08/20/2020 22:05:44 MainProcess     _training_0     training_data   minibatch_ab              DEBUG    Queue batches: (image_count: 1728, batchsize: 12, side: 'b', do_shuffle: True, is_preview, False, is_timelapse: False)
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Initializing ImageAugmentation: (batchsize: 12, is_display: False, input_size: 64, output_shapes: [(64, 64, 3)], coverage_ratio: 0.875, config: {'coverage': 87.5, 'mask_type': 'extended', 'mask_blur_kernel': 3, 'mask_threshold': 4, 'learn_mask': False, 'penalized_mask_loss': True, 'loss_function': 'mae', 'icnr_init': False, 'conv_aware_init': False, 'optimizer': 'adam', 'learning_rate': 5e-05, 'reflect_padding': False, 'allow_growth': False, 'mixed_precision': False, 'preview_images': 14, 'zoom_amount': 5, 'rotation_range': 10, 'shift_range': 5, 'flip_chance': 50, 'color_lightness': 30, 'color_ab': 8, 'color_clahe_chance': 50, 'color_clahe_max_size': 4})
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Output sizes: [64]
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Initialized ImageAugmentation
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  __init__                  DEBUG    Initializing BackgroundGenerator: (target: '_run', thread_count: 2)
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  __init__                  DEBUG    Initialized BackgroundGenerator: '_run'
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Starting thread(s): '_run'
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Starting thread 1 of 2: '_run_0'
08/20/2020 22:05:44 MainProcess     _run_0          training_data   _minibatch                DEBUG    Loading minibatch generator: (image_count: 1728, side: 'b', do_shuffle: True)
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Starting thread 2 of 2: '_run_1'
08/20/2020 22:05:44 MainProcess     _run_1          training_data   _minibatch                DEBUG    Loading minibatch generator: (image_count: 1728, side: 'b', do_shuffle: True)
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Started all threads '_run': 2
08/20/2020 22:05:44 MainProcess     _training_0     _base           _set_preview_feed         DEBUG    Setting preview feed: (side: 'a')
08/20/2020 22:05:44 MainProcess     _training_0     _base           _load_generator           DEBUG    Loading generator
08/20/2020 22:05:44 MainProcess     _training_0     _base           _load_generator           DEBUG    input_size: 64, output_shapes: [(64, 64, 3)]
08/20/2020 22:05:44 MainProcess     _training_0     _base           coverage_ratio            DEBUG    Requested coverage_ratio: 0.875
08/20/2020 22:05:44 MainProcess     _training_0     _base           coverage_ratio            DEBUG    Final coverage_ratio: 0.875
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Initializing TrainingDataGenerator: (model_input_size: 64, model_output_shapes: [(64, 64, 3)], coverage_ratio: 0.875, augment_color: True, no_flip: False, warp_to_landmarks: False, alignments: ['masks'], config: {'coverage': 87.5, 'mask_type': 'extended', 'mask_blur_kernel': 3, 'mask_threshold': 4, 'learn_mask': False, 'penalized_mask_loss': True, 'loss_function': 'mae', 'icnr_init': False, 'conv_aware_init': False, 'optimizer': 'adam', 'learning_rate': 5e-05, 'reflect_padding': False, 'allow_growth': False, 'mixed_precision': False, 'preview_images': 14, 'zoom_amount': 5, 'rotation_range': 10, 'shift_range': 5, 'flip_chance': 50, 'color_lightness': 30, 'color_ab': 8, 'color_clahe_chance': 50, 'color_clahe_max_size': 4})
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Initialized TrainingDataGenerator
08/20/2020 22:05:44 MainProcess     _training_0     training_data   minibatch_ab              DEBUG    Queue batches: (image_count: 7557, batchsize: 14, side: 'a', do_shuffle: True, is_preview, True, is_timelapse: False)
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Initializing ImageAugmentation: (batchsize: 14, is_display: True, input_size: 64, output_shapes: [(64, 64, 3)], coverage_ratio: 0.875, config: {'coverage': 87.5, 'mask_type': 'extended', 'mask_blur_kernel': 3, 'mask_threshold': 4, 'learn_mask': False, 'penalized_mask_loss': True, 'loss_function': 'mae', 'icnr_init': False, 'conv_aware_init': False, 'optimizer': 'adam', 'learning_rate': 5e-05, 'reflect_padding': False, 'allow_growth': False, 'mixed_precision': False, 'preview_images': 14, 'zoom_amount': 5, 'rotation_range': 10, 'shift_range': 5, 'flip_chance': 50, 'color_lightness': 30, 'color_ab': 8, 'color_clahe_chance': 50, 'color_clahe_max_size': 4})
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Output sizes: [64]
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Initialized ImageAugmentation
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  __init__                  DEBUG    Initializing BackgroundGenerator: (target: '_run', thread_count: 2)
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  __init__                  DEBUG    Initialized BackgroundGenerator: '_run'
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Starting thread(s): '_run'
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Starting thread 1 of 2: '_run_0'
08/20/2020 22:05:44 MainProcess     _run_0          training_data   _minibatch                DEBUG    Loading minibatch generator: (image_count: 7557, side: 'a', do_shuffle: True)
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Starting thread 2 of 2: '_run_1'
08/20/2020 22:05:44 MainProcess     _run_1          training_data   _minibatch                DEBUG    Loading minibatch generator: (image_count: 7557, side: 'a', do_shuffle: True)
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Started all threads '_run': 2
08/20/2020 22:05:44 MainProcess     _training_0     _base           _set_preview_feed         DEBUG    Setting preview feed: (side: 'b')
08/20/2020 22:05:44 MainProcess     _training_0     _base           _load_generator           DEBUG    Loading generator
08/20/2020 22:05:44 MainProcess     _training_0     _base           _load_generator           DEBUG    input_size: 64, output_shapes: [(64, 64, 3)]
08/20/2020 22:05:44 MainProcess     _training_0     _base           coverage_ratio            DEBUG    Requested coverage_ratio: 0.875
08/20/2020 22:05:44 MainProcess     _training_0     _base           coverage_ratio            DEBUG    Final coverage_ratio: 0.875
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Initializing TrainingDataGenerator: (model_input_size: 64, model_output_shapes: [(64, 64, 3)], coverage_ratio: 0.875, augment_color: True, no_flip: False, warp_to_landmarks: False, alignments: ['masks'], config: {'coverage': 87.5, 'mask_type': 'extended', 'mask_blur_kernel': 3, 'mask_threshold': 4, 'learn_mask': False, 'penalized_mask_loss': True, 'loss_function': 'mae', 'icnr_init': False, 'conv_aware_init': False, 'optimizer': 'adam', 'learning_rate': 5e-05, 'reflect_padding': False, 'allow_growth': False, 'mixed_precision': False, 'preview_images': 14, 'zoom_amount': 5, 'rotation_range': 10, 'shift_range': 5, 'flip_chance': 50, 'color_lightness': 30, 'color_ab': 8, 'color_clahe_chance': 50, 'color_clahe_max_size': 4})
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Initialized TrainingDataGenerator
08/20/2020 22:05:44 MainProcess     _training_0     training_data   minibatch_ab              DEBUG    Queue batches: (image_count: 1728, batchsize: 14, side: 'b', do_shuffle: True, is_preview, True, is_timelapse: False)
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Initializing ImageAugmentation: (batchsize: 14, is_display: True, input_size: 64, output_shapes: [(64, 64, 3)], coverage_ratio: 0.875, config: {'coverage': 87.5, 'mask_type': 'extended', 'mask_blur_kernel': 3, 'mask_threshold': 4, 'learn_mask': False, 'penalized_mask_loss': True, 'loss_function': 'mae', 'icnr_init': False, 'conv_aware_init': False, 'optimizer': 'adam', 'learning_rate': 5e-05, 'reflect_padding': False, 'allow_growth': False, 'mixed_precision': False, 'preview_images': 14, 'zoom_amount': 5, 'rotation_range': 10, 'shift_range': 5, 'flip_chance': 50, 'color_lightness': 30, 'color_ab': 8, 'color_clahe_chance': 50, 'color_clahe_max_size': 4})
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Output sizes: [64]
08/20/2020 22:05:44 MainProcess     _training_0     training_data   __init__                  DEBUG    Initialized ImageAugmentation
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  __init__                  DEBUG    Initializing BackgroundGenerator: (target: '_run', thread_count: 2)
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  __init__                  DEBUG    Initialized BackgroundGenerator: '_run'
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Starting thread(s): '_run'
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Starting thread 1 of 2: '_run_0'
08/20/2020 22:05:44 MainProcess     _run_0          training_data   _minibatch                DEBUG    Loading minibatch generator: (image_count: 1728, side: 'b', do_shuffle: True)
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Starting thread 2 of 2: '_run_1'
08/20/2020 22:05:44 MainProcess     _run_1          training_data   _minibatch                DEBUG    Loading minibatch generator: (image_count: 1728, side: 'b', do_shuffle: True)
08/20/2020 22:05:44 MainProcess     _training_0     multithreading  start                     DEBUG    Started all threads '_run': 2
08/20/2020 22:05:44 MainProcess     _training_0     _base           _set_preview_feed         DEBUG    Set preview feed. Batchsize: 14
08/20/2020 22:05:44 MainProcess     _training_0     _base           __init__                  DEBUG    Initialized _Feeder:
08/20/2020 22:05:44 MainProcess     _training_0     _base           _set_tensorboard          DEBUG    Enabling TensorBoard Logging
08/20/2020 22:05:44 MainProcess     _training_0     _base           _set_tensorboard          DEBUG    Setting up TensorBoard Logging
08/20/2020 22:05:45 MainProcess     _run_1          training_data   initialize                DEBUG    Initializing constants. training_size: 256
08/20/2020 22:05:45 MainProcess     _run_0          training_data   initialize                DEBUG    Initializing constants. training_size: 256
08/20/2020 22:05:45 MainProcess     _run_0          training_data   initialize                DEBUG    Initializing constants. training_size: 256
08/20/2020 22:05:45 MainProcess     _run_0          training_data   initialize                DEBUG    Initialized constants: {'clahe_base_contrast': 2, 'tgt_slices': slice(16, 240, None), 'warp_mapx': '[[[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]]', 'warp_mapy': '[[[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]]', 'warp_pad': 80, 'warp_slices': slice(8, -8, None), 'warp_lm_edge_anchors': '[[[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]]', 'warp_lm_grids': '[[[  0.   0.   0. ...   0.   0.   0.]\n  [  1.   1.   1. ...   1.   1.   1.]\n  [  2.   2.   2. ...   2.   2.   2.]\n  ...\n  [253. 253. 253. ... 253. 253. 253.]\n  [254. 254. 254. ... 254. 254. 254.]\n  [255. 255. 255. ... 255. 255. 255.]]\n\n [[  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]\n  ...\n  [  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]]]'}
08/20/2020 22:05:45 MainProcess     _run_1          training_data   initialize                DEBUG    Initialized constants: {'clahe_base_contrast': 2, 'tgt_slices': slice(16, 240, None), 'warp_mapx': '[[[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]]', 'warp_mapy': '[[[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]]', 'warp_pad': 80, 'warp_slices': slice(8, -8, None), 'warp_lm_edge_anchors': '[[[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]]', 'warp_lm_grids': '[[[  0.   0.   0. ...   0.   0.   0.]\n  [  1.   1.   1. ...   1.   1.   1.]\n  [  2.   2.   2. ...   2.   2.   2.]\n  ...\n  [253. 253. 253. ... 253. 253. 253.]\n  [254. 254. 254. ... 254. 254. 254.]\n  [255. 255. 255. ... 255. 255. 255.]]\n\n [[  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]\n  ...\n  [  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]]]'}
08/20/2020 22:05:45 MainProcess     _run_0          training_data   initialize                DEBUG    Initializing constants. training_size: 256
08/20/2020 22:05:45 MainProcess     _run_0          training_data   initialize                DEBUG    Initialized constants: {'clahe_base_contrast': 2, 'tgt_slices': slice(16, 240, None), 'warp_mapx': '[[[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]]', 'warp_mapy': '[[[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]]', 'warp_pad': 80, 'warp_slices': slice(8, -8, None), 'warp_lm_edge_anchors': '[[[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]]', 'warp_lm_grids': '[[[  0.   0.   0. ...   0.   0.   0.]\n  [  1.   1.   1. ...   1.   1.   1.]\n  [  2.   2.   2. ...   2.   2.   2.]\n  ...\n  [253. 253. 253. ... 253. 253. 253.]\n  [254. 254. 254. ... 254. 254. 254.]\n  [255. 255. 255. ... 255. 255. 255.]]\n\n [[  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]\n  ...\n  [  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]]]'}
08/20/2020 22:05:45 MainProcess     _run_0          training_data   initialize                DEBUG    Initialized constants: {'clahe_base_contrast': 2, 'tgt_slices': slice(16, 240, None), 'warp_mapx': '[[[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]\n\n [[ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]\n  [ 16.  72. 128. 184. 240.]]]', 'warp_mapy': '[[[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]\n\n [[ 16.  16.  16.  16.  16.]\n  [ 72.  72.  72.  72.  72.]\n  [128. 128. 128. 128. 128.]\n  [184. 184. 184. 184. 184.]\n  [240. 240. 240. 240. 240.]]]', 'warp_pad': 80, 'warp_slices': slice(8, -8, None), 'warp_lm_edge_anchors': '[[[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]\n\n [[  0   0]\n  [  0 255]\n  [255 255]\n  [255   0]\n  [127   0]\n  [127 255]\n  [255 127]\n  [  0 127]]]', 'warp_lm_grids': '[[[  0.   0.   0. ...   0.   0.   0.]\n  [  1.   1.   1. ...   1.   1.   1.]\n  [  2.   2.   2. ...   2.   2.   2.]\n  ...\n  [253. 253. 253. ... 253. 253. 253.]\n  [254. 254. 254. ... 254. 254. 254.]\n  [255. 255. 255. ... 255. 255. 255.]]\n\n [[  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]\n  ...\n  [  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]\n  [  0.   1.   2. ... 253. 254. 255.]]]'}
08/20/2020 22:05:45 MainProcess     _training_0     _base           _set_tensorboard          VERBOSE  Enabled TensorBoard Logging
08/20/2020 22:05:45 MainProcess     _training_0     _base           coverage_ratio            DEBUG    Requested coverage_ratio: 0.875
08/20/2020 22:05:45 MainProcess     _training_0     _base           coverage_ratio            DEBUG    Final coverage_ratio: 0.875
08/20/2020 22:05:45 MainProcess     _training_0     _base           __init__                  DEBUG    Initializing _Samples: model: '<plugins.train.model.original.Model object at 0x0000027C593453D0>', coverage_ratio: 0.875)
08/20/2020 22:05:45 MainProcess     _training_0     _base           __init__                  DEBUG    Initialized _Samples
08/20/2020 22:05:45 MainProcess     _training_0     _base           coverage_ratio            DEBUG    Requested coverage_ratio: 0.875
08/20/2020 22:05:45 MainProcess     _training_0     _base           coverage_ratio            DEBUG    Final coverage_ratio: 0.875
08/20/2020 22:05:45 MainProcess     _training_0     _base           __init__                  DEBUG    Initializing _Timelapse: model: <plugins.train.model.original.Model object at 0x0000027C593453D0>, coverage_ratio: 0.875, image_count: 14, feeder: '<plugins.train.trainer._base._Feeder object at 0x0000027C579A6FD0>')
08/20/2020 22:05:45 MainProcess     _training_0     _base           __init__                  DEBUG    Initializing _Samples: model: '<plugins.train.model.original.Model object at 0x0000027C593453D0>', coverage_ratio: 0.875)
08/20/2020 22:05:45 MainProcess     _training_0     _base           __init__                  DEBUG    Initialized _Samples
08/20/2020 22:05:45 MainProcess     _training_0     _base           __init__                  DEBUG    Initialized _Timelapse
08/20/2020 22:05:45 MainProcess     _training_0     _base           __init__                  DEBUG    Initialized Trainer
08/20/2020 22:05:45 MainProcess     _training_0     train           _load_trainer             DEBUG    Loaded Trainer
08/20/2020 22:05:45 MainProcess     _training_0     train           _run_training_cycle       DEBUG    Running Training Cycle
08/20/2020 22:05:48 MainProcess     _training_0     library         _logger_callback          INFO     Analyzing Ops: 450 of 826 operations complete
08/20/2020 22:05:50 MainProcess     _training_0     library         _logger_callback          INFO     Analyzing Ops: 488 of 826 operations complete
08/20/2020 22:06:11 MainProcess     _training_0     multithreading  run                       DEBUG    Error in thread (_training_0): No enough memory for the current schedule: required 2163167232, available 1825361152
08/20/2020 22:06:12 MainProcess     MainThread      train           _monitor                  DEBUG    Thread error detected
08/20/2020 22:06:12 MainProcess     MainThread      train           _monitor                  DEBUG    Closed Monitor
08/20/2020 22:06:12 MainProcess     MainThread      train           _end_thread               DEBUG    Ending Training thread
08/20/2020 22:06:12 MainProcess     MainThread      train           _end_thread               CRITICAL Error caught! Exiting...
08/20/2020 22:06:12 MainProcess     MainThread      multithreading  join                      DEBUG    Joining Threads: '_training'
08/20/2020 22:06:12 MainProcess     MainThread      multithreading  join                      DEBUG    Joining Thread: '_training_0'
08/20/2020 22:06:12 MainProcess     MainThread      multithreading  join                      ERROR    Caught exception in thread: '_training_0'
Traceback (most recent call last):
  File "C:\Users\karho\faceswap\lib\cli\launcher.py", line 156, in execute_script
    process.process()
  File "C:\Users\karho\faceswap\scripts\train.py", line 165, in process
    self._end_thread(thread, err)
  File "C:\Users\karho\faceswap\scripts\train.py", line 205, in _end_thread
    thread.join()
  File "C:\Users\karho\faceswap\lib\multithreading.py", line 121, in join
    raise thread.err[1].with_traceback(thread.err[2])
  File "C:\Users\karho\faceswap\lib\multithreading.py", line 37, in run
    self._target(*self._args, **self._kwargs)
  File "C:\Users\karho\faceswap\scripts\train.py", line 227, in _training
    raise err
  File "C:\Users\karho\faceswap\scripts\train.py", line 217, in _training
    self._run_training_cycle(model, trainer)
  File "C:\Users\karho\faceswap\scripts\train.py", line 298, in _run_training_cycle
    trainer.train_one_step(viewer, timelapse)
  File "C:\Users\karho\faceswap\plugins\train\trainer\_base.py", line 198, in train_one_step
    loss = self._model.model.train_on_batch(model_inputs, y=model_targets)
  File "C:\Users\karho\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\training.py", line 1217, in train_on_batch
    outputs = self.train_function(ins)
  File "C:\Users\karho\MiniConda3\envs\faceswap\lib\site-packages\plaidml\keras\backend.py", line 189, in __call__
    self._invoker.invoke()
  File "C:\Users\karho\MiniConda3\envs\faceswap\lib\site-packages\plaidml\__init__.py", line 1475, in invoke
    return Invocation(self._ctx, self)
  File "C:\Users\karho\MiniConda3\envs\faceswap\lib\site-packages\plaidml\__init__.py", line 1484, in __init__
    self._as_parameter_ = _lib().plaidml_schedule_invocation(ctx, invoker)
  File "C:\Users\karho\MiniConda3\envs\faceswap\lib\site-packages\plaidml\__init__.py", line 777, in _check_err
    self.raise_last_status()
  File "C:\Users\karho\MiniConda3\envs\faceswap\lib\site-packages\plaidml\library.py", line 131, in raise_last_status
    raise self.last_status()
plaidml.exceptions.Unknown: No enough memory for the current schedule: required 2163167232, available 1825361152

============ System Information ============
encoding:            cp1252
git_branch:          Not Found
git_commits:         Not Found
gpu_cuda:            No global version found. Check Conda packages for Conda Cuda
gpu_cudnn:           No global version found. Check Conda packages for Conda cuDNN
gpu_devices:         GPU_0: Advanced Micro Devices, Inc. - Bonaire (experimental)
gpu_devices_active:  GPU_0
gpu_driver:          ['3110.7']
gpu_vram:            GPU_0: 2048MB
os_machine:          AMD64
os_platform:         Windows-10-10.0.19041-SP0
os_release:          10
py_command:          C:\Users\karho\faceswap\faceswap.py train -A C:/TD/A/A -ala C:/TD/A/a_alignments.fsa -B C:/TD/B/B -alb C:/TD/B/b_alignments.fsa -m C:/TD/3 -t original -bs 12 -it 760000 -s 250 -ss 25000 -tia C:/TD/A/A -tib C:/TD/B/B -to C:/TD/4 -ps 50 -L INFO -gui
py_conda_version:    conda 4.8.4
py_implementation:   CPython
py_version:          3.8.5
py_virtual_env:      True
sys_cores:           4
sys_processor:       Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
sys_ram:             Total: 12248MB, Available: 8103MB, Used: 4144MB, Free: 8103MB

=============== Pip Packages ===============
absl-py==0.9.0
astunparse==1.6.3
cachetools==4.1.1
certifi==2020.6.20
cffi==1.14.2
chardet==3.0.4
cycler==0.10.0
enum34==1.1.10
fastcluster==1.1.26
ffmpy==0.2.3
gast==0.3.3
google-auth==1.20.1
google-auth-oauthlib==0.4.1
google-pasta==0.2.0
grpcio==1.31.0
h5py==2.10.0
idna==2.10
imageio @ file:///tmp/build/80754af9/imageio_1594161405741/work
imageio-ffmpeg @ file:///home/conda/feedstock_root/build_artifacts/imageio-ffmpeg_1589202782679/work
joblib @ file:///tmp/build/80754af9/joblib_1594236160679/work
Keras==2.2.4
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.2
kiwisolver==1.2.0
Markdown==3.2.2
matplotlib @ file:///C:/ci/matplotlib-base_1592837548929/work
mkl-fft==1.1.0
mkl-random==1.1.1
mkl-service==2.3.0
numpy @ file:///C:/ci/numpy_and_numpy_base_1596215850360/work
nvidia-ml-py3 @ git+https://github.com/deepfakes/nvidia-ml-py3.git@6fc29ac84b32bad877f078cb4a777c1548a00bf6
oauthlib==3.1.0
olefile==0.46
opencv-python==4.4.0.42
opt-einsum==3.3.0
pathlib==1.0.1
Pillow @ file:///C:/ci/pillow_1594298230227/work
plaidml==0.7.0
plaidml-keras==0.7.0
protobuf==3.13.0
psutil==5.7.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
pycparser==2.20
pyparsing==2.4.7
python-dateutil==2.8.1
pywin32==227
PyYAML==5.3.1
requests==2.24.0
requests-oauthlib==1.3.0
rsa==4.6
scikit-learn @ file:///C:/ci/scikit-learn_1592853510272/work
scipy==1.4.1
sip==4.19.13
six==1.15.0
tensorboard==2.2.2
tensorboard-plugin-wit==1.7.0
tensorflow==2.2.0
tensorflow-estimator==2.2.0
termcolor==1.1.0
threadpoolctl @ file:///tmp/tmp9twdgx9k/threadpoolctl-2.1.0-py3-none-any.whl
tornado==6.0.4
tqdm @ file:///tmp/build/80754af9/tqdm_1596810128862/work
urllib3==1.25.10
Werkzeug==1.0.1
wincertstore==0.2
wrapt==1.12.1

============== Conda Packages ==============
# packages in environment at C:\Users\karho\MiniConda3\envs\faceswap:
#
# Name                    Version                   Build  Channel
absl-py                   0.9.0                    pypi_0    pypi
astunparse                1.6.3                    pypi_0    pypi
blas                      1.0                         mkl  
ca-certificates 2020.6.24 0
cachetools 4.1.1 pypi_0 pypi certifi 2020.6.20 py38_0
cffi 1.14.2 pypi_0 pypi chardet 3.0.4 pypi_0 pypi cycler 0.10.0 py38_0
enum34 1.1.10 pypi_0 pypi fastcluster 1.1.26 py38hbe40bda_1 conda-forge ffmpeg 4.3.1 ha925a31_0 conda-forge ffmpy 0.2.3 pypi_0 pypi freetype 2.10.2 hd328e21_0
gast 0.3.3 pypi_0 pypi git 2.23.0 h6bb4b03_0
google-auth 1.20.1 pypi_0 pypi google-auth-oauthlib 0.4.1 pypi_0 pypi google-pasta 0.2.0 pypi_0 pypi grpcio 1.31.0 pypi_0 pypi h5py 2.10.0 pypi_0 pypi icc_rt 2019.0.0 h0cc432a_1
icu 58.2 ha925a31_3
idna 2.10 pypi_0 pypi imageio 2.9.0 py_0
imageio-ffmpeg 0.4.2 py_0 conda-forge intel-openmp 2020.1 216
joblib 0.16.0 py_0
jpeg 9b hb83a4c4_2
keras 2.2.4 pypi_0 pypi keras-applications 1.0.8 pypi_0 pypi keras-preprocessing 1.1.2 pypi_0 pypi kiwisolver 1.2.0 py38h74a9793_0
libpng 1.6.37 h2a8f88b_0
libtiff 4.1.0 h56a325e_1
lz4-c 1.9.2 h62dcd97_1
markdown 3.2.2 pypi_0 pypi matplotlib 3.2.2 0
matplotlib-base 3.2.2 py38h64f37c6_0
mkl 2020.1 216
mkl-service 2.3.0 py38hb782905_0
mkl_fft 1.1.0 py38h45dec08_0
mkl_random 1.1.1 py38h47e9c7a_0
numpy 1.19.1 py38h5510c5b_0
numpy-base 1.19.1 py38ha3acd2a_0
nvidia-ml-py3 7.352.1 pypi_0 pypi oauthlib 3.1.0 pypi_0 pypi olefile 0.46 py_0
opencv-python 4.4.0.42 pypi_0 pypi openssl 1.1.1g he774522_1
opt-einsum 3.3.0 pypi_0 pypi pathlib 1.0.1 py_1
pillow 7.2.0 py38hcc1f983_0
pip 20.2.2 py38_0
plaidml 0.7.0 pypi_0 pypi plaidml-keras 0.7.0 pypi_0 pypi protobuf 3.13.0 pypi_0 pypi psutil 5.7.0 py38he774522_0
pyasn1 0.4.8 pypi_0 pypi pyasn1-modules 0.2.8 pypi_0 pypi pycparser 2.20 pypi_0 pypi pyparsing 2.4.7 py_0
pyqt 5.9.2 py38ha925a31_4
python 3.8.5 he1778fa_0
python-dateutil 2.8.1 py_0
python_abi 3.8 1_cp38 conda-forge pywin32 227 py38he774522_1
pyyaml 5.3.1 pypi_0 pypi qt 5.9.7 vc14h73c81de_0
requests 2.24.0 pypi_0 pypi requests-oauthlib 1.3.0 pypi_0 pypi rsa 4.6 pypi_0 pypi scikit-learn 0.23.1 py38h25d0782_0
scipy 1.4.1 pypi_0 pypi setuptools 49.6.0 py38_0
sip 4.19.13 py38ha925a31_0
six 1.15.0 py_0
sqlite 3.32.3 h2a8f88b_0
tensorboard 2.2.2 pypi_0 pypi tensorboard-plugin-wit 1.7.0 pypi_0 pypi tensorflow 2.2.0 pypi_0 pypi tensorflow-estimator 2.2.0 pypi_0 pypi termcolor 1.1.0 pypi_0 pypi threadpoolctl 2.1.0 pyh5ca1d4c_0
tk 8.6.10 he774522_0
tornado 6.0.4 py38he774522_1
tqdm 4.48.2 py_0
urllib3 1.25.10 pypi_0 pypi vc 14.1 h0510ff6_4
vs2015_runtime 14.16.27012 hf0eaf9b_3
werkzeug 1.0.1 pypi_0 pypi wheel 0.34.2 py38_0
wincertstore 0.2 py38_0
wrapt 1.12.1 pypi_0 pypi xz 5.2.5 h62dcd97_0
zlib 1.2.11 h62dcd97_4
zstd 1.4.5 h04227a9_0 ================= Configs ================== --------- .faceswap --------- backend: amd --------- convert.ini --------- [color.color_transfer] clip: True preserve_paper: True [color.manual_balance] colorspace: HSV balance_1: 0.0 balance_2: 0.0 balance_3: 0.0 contrast: 0.0 brightness: 0.0 [color.match_hist] threshold: 99.0 [mask.box_blend] type: gaussian distance: 11.0 radius: 5.0 passes: 1 [mask.mask_blend] type: normalized kernel_size: 3 passes: 4 threshold: 4 erosion: 0.0 [scaling.sharpen] method: unsharp_mask amount: 150 radius: 0.3 threshold: 5.0 [writer.ffmpeg] container: mp4 codec: libx264 crf: 23 preset: medium tune: none profile: auto level: auto skip_mux: False [writer.gif] fps: 25 loop: 0 palettesize: 256 subrectangles: False [writer.opencv] format: png draw_transparent: False jpg_quality: 75 png_compress_level: 3 [writer.pillow] format: png draw_transparent: False optimize: False gif_interlace: True jpg_quality: 75 png_compress_level: 3 tif_compression: tiff_deflate --------- extract.ini --------- [global] allow_growth: False [align.fan] batch-size: 12 [detect.cv2_dnn] confidence: 50 [detect.mtcnn] minsize: 20 threshold_1: 0.6 threshold_2: 0.7 threshold_3: 0.7 scalefactor: 0.709 batch-size: 8 [detect.s3fd] confidence: 70 batch-size: 4 [mask.unet_dfl] batch-size: 8 [mask.vgg_clear] batch-size: 6 [mask.vgg_obstructed] batch-size: 2 --------- gui.ini --------- [global] fullscreen: False tab: extract options_panel_width: 30 console_panel_height: 20 icon_size: 14 font: default font_size: 9 autosave_last_session: prompt timeout: 120 auto_load_model_stats: True --------- train.ini --------- [global] coverage: 87.5 mask_type: extended mask_blur_kernel: 3 mask_threshold: 4 learn_mask: False penalized_mask_loss: True loss_function: mae icnr_init: False conv_aware_init: False optimizer: adam learning_rate: 5e-05 reflect_padding: False allow_growth: False mixed_precision: False [model.dfl_h128] lowmem: False [model.dfl_sae] input_size: 128 clipnorm: True architecture: df autoencoder_dims: 0 encoder_dims: 42 decoder_dims: 21 multiscale_decoder: False [model.dlight] features: best details: good output_size: 256 [model.original] lowmem: False [model.realface] input_size: 64 output_size: 128 dense_nodes: 1536 complexity_encoder: 128 complexity_decoder: 512 [model.unbalanced] input_size: 128 lowmem: False clipnorm: True nodes: 1024 complexity_encoder: 128 complexity_decoder_a: 384 complexity_decoder_b: 512 [model.villain] lowmem: False [trainer.original] preview_images: 14 zoom_amount: 5 rotation_range: 10 shift_range: 5 flip_chance: 50 color_lightness: 30 color_ab: 8 color_clahe_chance: 50 color_clahe_max_size: 4
by torzdf » Thu Aug 20, 2020 3:48 pm

This is referring to GPU Memory, not System Memory.

You have a 2GB GPU which may not be enough to run Faceswap (I haven't done extensive tests on the limits for AMD).

Try using the "Lightweight" model and lowering the batchsize to 4 or 2.

Go to full post
User avatar
torzdf
Posts: 2796
Joined: Fri Jul 12, 2019 12:53 am
Answers: 160
Has thanked: 142 times
Been thanked: 650 times

Re: plaidml.exceptions.Unknown: No enough memory for the current schedule: required 2163167232, available 1825361152

Post by torzdf »

This is referring to GPU Memory, not System Memory.

You have a 2GB GPU which may not be enough to run Faceswap (I haven't done extensive tests on the limits for AMD).

Try using the "Lightweight" model and lowering the batchsize to 4 or 2.

My word is final

User avatar
garwooi
Posts: 2
Joined: Tue Aug 18, 2020 12:19 am
Answers: 0

Re: plaidml.exceptions.Unknown: No enough memory for the current schedule: required 2163167232, available 1825361152

Post by garwooi »

Thanks alot torzdf..!
It works..yay..!!! :D :D :D :D

Locked