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- 08/06/2020 16:25:19 MainProcess _training_0 multithreading start DEBUG Started all threads '_run': 2
- 08/06/2020 16:25:19 MainProcess _training_0 _base _set_preview_feed DEBUG Setting preview feed: (side: 'b')
- 08/06/2020 16:25:19 MainProcess _training_0 _base _load_generator DEBUG Loading generator: b
- 08/06/2020 16:25:19 MainProcess _training_0 _base _load_generator DEBUG input_size: 64, output_shapes: [(64, 64, 3)]
- 08/06/2020 16:25:19 MainProcess _training_0 training_data __init__ DEBUG Initializing TrainingDataGenerator: (model_input_size: 64, model_output_shapes: [(64, 64, 3)], training_opts: {'alignments': {'a': 'E:\\deepfake\\faceexport\\alignments.fsa', 'b': 'E:\\deepfake\\face2\\alignments.fsa'}, 'preview_scaling': 0.5, 'warp_to_landmarks': False, 'augment_color': True, 'no_flip': False, 'pingpong': False, 'snapshot_interval': 25000, 'training_size': 256, 'no_logs': False, 'coverage_ratio': 0.6875, 'mask_type': None, 'mask_blur_kernel': 3, 'mask_threshold': 4, 'learn_mask': False, 'penalized_mask_loss': False}, landmarks: {}, masks: {}, config: {'coverage': 68.75, 'mask_type': None, 'mask_blur_kernel': 3, 'mask_threshold': 4, 'learn_mask': False, 'icnr_init': False, 'conv_aware_init': False, 'reflect_padding': False, 'penalized_mask_loss': True, 'loss_function': 'mae', 'learning_rate': 5e-05, '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})
- 08/06/2020 16:25:19 MainProcess _training_0 training_data __init__ DEBUG Initialized TrainingDataGenerator
- 08/06/2020 16:25:19 MainProcess _training_0 training_data minibatch_ab DEBUG Queue batches: (image_count: 64, batchsize: 14, side: 'b', do_shuffle: True, is_preview, True, is_timelapse: False)
- 08/06/2020 16:25:19 MainProcess _training_0 training_data __init__ DEBUG Initializing ImageAugmentation: (batchsize: 14, is_display: True, input_size: 64, output_shapes: [(64, 64, 3)], coverage_ratio: 0.6875, config: {'coverage': 68.75, 'mask_type': None, 'mask_blur_kernel': 3, 'mask_threshold': 4, 'learn_mask': False, 'icnr_init': False, 'conv_aware_init': False, 'reflect_padding': False, 'penalized_mask_loss': True, 'loss_function': 'mae', 'learning_rate': 5e-05, '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})
- 08/06/2020 16:25:19 MainProcess _training_0 training_data __init__ DEBUG Output sizes: [64]
- 08/06/2020 16:25:19 MainProcess _training_0 training_data __init__ DEBUG Initialized ImageAugmentation
- 08/06/2020 16:25:19 MainProcess _training_0 multithreading __init__ DEBUG Initializing BackgroundGenerator: (target: '_run', thread_count: 2)
- 08/06/2020 16:25:19 MainProcess _training_0 multithreading __init__ DEBUG Initialized BackgroundGenerator: '_run'
- 08/06/2020 16:25:19 MainProcess _training_0 multithreading start DEBUG Starting thread(s): '_run'
- 08/06/2020 16:25:19 MainProcess _training_0 multithreading start DEBUG Starting thread 1 of 2: '_run_0'
- 08/06/2020 16:25:19 MainProcess _run_0 training_data _minibatch DEBUG Loading minibatch generator: (image_count: 64, side: 'b', do_shuffle: True)
- 08/06/2020 16:25:19 MainProcess _training_0 multithreading start DEBUG Starting thread 2 of 2: '_run_1'
- 08/06/2020 16:25:19 MainProcess _run_1 training_data _minibatch DEBUG Loading minibatch generator: (image_count: 64, side: 'b', do_shuffle: True)
- 08/06/2020 16:25:19 MainProcess _training_0 multithreading start DEBUG Started all threads '_run': 2
- 08/06/2020 16:25:19 MainProcess _training_0 _base _set_preview_feed DEBUG Set preview feed. Batchsize: 14
- 08/06/2020 16:25:19 MainProcess _training_0 _base _set_tensorboard DEBUG Enabling TensorBoard Logging
- 08/06/2020 16:25:19 MainProcess _training_0 _base _set_tensorboard DEBUG Setting up TensorBoard Logging. Side: a
- 08/06/2020 16:25:19 MainProcess _training_0 _base name DEBUG model name: 'original'
- 08/06/2020 16:25:19 MainProcess _training_0 _base _tensorboard_kwargs DEBUG Tensorflow version: [1, 15, 0]
- 08/06/2020 16:25:19 MainProcess _training_0 _base _tensorboard_kwargs DEBUG {'histogram_freq': 0, 'batch_size': 64, 'write_graph': True, 'write_grads': True, 'update_freq': 'batch', 'profile_batch': 0}
- 08/06/2020 16:25:21 MainProcess _training_0 _base _set_tensorboard DEBUG Setting up TensorBoard Logging. Side: b
- 08/06/2020 16:25:21 MainProcess _training_0 _base name DEBUG model name: 'original'
- 08/06/2020 16:25:21 MainProcess _training_0 _base _tensorboard_kwargs DEBUG Tensorflow version: [1, 15, 0]
- 08/06/2020 16:25:21 MainProcess _training_0 _base _tensorboard_kwargs DEBUG {'histogram_freq': 0, 'batch_size': 64, 'write_graph': True, 'write_grads': True, 'update_freq': 'batch', 'profile_batch': 0}
- 08/06/2020 16:25:22 MainProcess _training_0 _base _set_tensorboard INFO Enabled TensorBoard Logging
- 08/06/2020 16:25:22 MainProcess _training_0 _base _use_mask DEBUG False
- 08/06/2020 16:25:22 MainProcess _training_0 _base __init__ DEBUG Initializing Samples: model: '<plugins.train.model.original.Model object at 0x000001C337F73BC8>', use_mask: False, coverage_ratio: 0.6875)
- 08/06/2020 16:25:22 MainProcess _training_0 _base __init__ DEBUG Initialized Samples
- 08/06/2020 16:25:22 MainProcess _training_0 _base _use_mask DEBUG False
- 08/06/2020 16:25:22 MainProcess _training_0 _base __init__ DEBUG Initializing Timelapse: model: <plugins.train.model.original.Model object at 0x000001C337F73BC8>, use_mask: False, coverage_ratio: 0.6875, image_count: 14, batchers: '{'a': <plugins.train.trainer._base.Batcher object at 0x000001C3F1652748>, 'b': <plugins.train.trainer._base.Batcher object at 0x000001C3F1652448>}')
- 08/06/2020 16:25:22 MainProcess _training_0 _base __init__ DEBUG Initializing Samples: model: '<plugins.train.model.original.Model object at 0x000001C337F73BC8>', use_mask: False, coverage_ratio: 0.6875)
- 08/06/2020 16:25:22 MainProcess _training_0 _base __init__ DEBUG Initialized Samples
- 08/06/2020 16:25:22 MainProcess _training_0 _base __init__ DEBUG Initialized Timelapse
- 08/06/2020 16:25:22 MainProcess _training_0 _base __init__ DEBUG Initialized Trainer
- 08/06/2020 16:25:22 MainProcess _training_0 train _load_trainer DEBUG Loaded Trainer
- 08/06/2020 16:25:22 MainProcess _training_0 train _run_training_cycle DEBUG Running Training Cycle
- 08/06/2020 16:25:22 MainProcess _run_0 training_data initialize DEBUG Initializing constants. training_size: 256
- 08/06/2020 16:25:22 MainProcess _run_0 training_data initialize DEBUG Initialized constants: {'clahe_base_contrast': 2, 'tgt_slices': slice(40, 216, None), 'warp_mapx': '[[[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]]', 'warp_mapy': '[[[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]]', '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.]]]'}
- 08/06/2020 16:25:22 MainProcess _run_0 training_data initialize DEBUG Initializing constants. training_size: 265
- 08/06/2020 16:25:22 MainProcess _run_1 training_data initialize DEBUG Initializing constants. training_size: 265
- 08/06/2020 16:25:22 MainProcess _run_1 training_data initialize DEBUG Initialized constants: {'clahe_base_contrast': 2, 'tgt_slices': slice(41, 223, None), 'warp_mapx': '[[[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]]', 'warp_mapy': '[[[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]]', 'warp_pad': 80, 'warp_slices': slice(8, -8, None), 'warp_lm_edge_anchors': '[[[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]]', '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 [262. 262. 262. ... 262. 262. 262.]\n [263. 263. 263. ... 263. 263. 263.]\n [264. 264. 264. ... 264. 264. 264.]]\n\n [[ 0. 1. 2. ... 262. 263. 264.]\n [ 0. 1. 2. ... 262. 263. 264.]\n [ 0. 1. 2. ... 262. 263. 264.]\n ...\n [ 0. 1. 2. ... 262. 263. 264.]\n [ 0. 1. 2. ... 262. 263. 264.]\n [ 0. 1. 2. ... 262. 263. 264.]]]'}
- 08/06/2020 16:25:22 MainProcess _run_0 training_data initialize DEBUG Initialized constants: {'clahe_base_contrast': 2, 'tgt_slices': slice(41, 223, None), 'warp_mapx': '[[[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]]', 'warp_mapy': '[[[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]]', 'warp_pad': 80, 'warp_slices': slice(8, -8, None), 'warp_lm_edge_anchors': '[[[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]]', '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 [262. 262. 262. ... 262. 262. 262.]\n [263. 263. 263. ... 263. 263. 263.]\n [264. 264. 264. ... 264. 264. 264.]]\n\n [[ 0. 1. 2. ... 262. 263. 264.]\n [ 0. 1. 2. ... 262. 263. 264.]\n [ 0. 1. 2. ... 262. 263. 264.]\n ...\n [ 0. 1. 2. ... 262. 263. 264.]\n [ 0. 1. 2. ... 262. 263. 264.]\n [ 0. 1. 2. ... 262. 263. 264.]]]'}
- 08/06/2020 16:25:22 MainProcess _run_1 training_data initialize DEBUG Initializing constants. training_size: 265
- 08/06/2020 16:25:22 MainProcess _run_1 training_data initialize DEBUG Initialized constants: {'clahe_base_contrast': 2, 'tgt_slices': slice(41, 223, None), 'warp_mapx': '[[[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]\n\n [[ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]\n [ 41. 86.5 132. 177.5 223. ]]]', 'warp_mapy': '[[[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]\n\n [[ 41. 41. 41. 41. 41. ]\n [ 86.5 86.5 86.5 86.5 86.5]\n [132. 132. 132. 132. 132. ]\n [177.5 177.5 177.5 177.5 177.5]\n [223. 223. 223. 223. 223. ]]]', 'warp_pad': 80, 'warp_slices': slice(8, -8, None), 'warp_lm_edge_anchors': '[[[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]\n\n [[ 0 0]\n [ 0 264]\n [264 264]\n [264 0]\n [131 0]\n [131 264]\n [264 131]\n [ 0 131]]]', '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 [262. 262. 262. ... 262. 262. 262.]\n [263. 263. 263. ... 263. 263. 263.]\n [264. 264. 264. ... 264. 264. 264.]]\n\n [[ 0. 1. 2. ... 262. 263. 264.]\n [ 0. 1. 2. ... 262. 263. 264.]\n [ 0. 1. 2. ... 262. 263. 264.]\n ...\n [ 0. 1. 2. ... 262. 263. 264.]\n [ 0. 1. 2. ... 262. 263. 264.]\n [ 0. 1. 2. ... 262. 263. 264.]]]'}
- 08/06/2020 16:25:22 MainProcess _run_1 training_data initialize DEBUG Initializing constants. training_size: 256
- 08/06/2020 16:25:22 MainProcess _run_1 training_data initialize DEBUG Initialized constants: {'clahe_base_contrast': 2, 'tgt_slices': slice(40, 216, None), 'warp_mapx': '[[[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]\n\n [[ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]\n [ 40. 84. 128. 172. 216.]]]', 'warp_mapy': '[[[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]\n\n [[ 40. 40. 40. 40. 40.]\n [ 84. 84. 84. 84. 84.]\n [128. 128. 128. 128. 128.]\n [172. 172. 172. 172. 172.]\n [216. 216. 216. 216. 216.]]]', '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]]\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]]\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.]]]'}
- 08/06/2020 16:25:23 MainProcess _training_0 module_wrapper _tfmw_add_deprecation_warning DEBUG From C:\Users\michalek\MiniConda3\envs\faceswap\lib\site-packages\keras\backend\tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n
- 08/06/2020 16:25:23 MainProcess _training_0 module_wrapper _tfmw_add_deprecation_warning DEBUG From C:\Users\michalek\MiniConda3\envs\faceswap\lib\site-packages\keras\backend\tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n
- 08/06/2020 16:25:31 MainProcess _training_0 _base generate_preview DEBUG Generating preview
- 08/06/2020 16:25:31 MainProcess _training_0 _base largest_face_index DEBUG 0
- 08/06/2020 16:25:31 MainProcess _training_0 _base compile_sample DEBUG Compiling samples: (side: 'a', samples: 14)
- 08/06/2020 16:25:36 MainProcess _training_0 _base generate_preview DEBUG Generating preview
- 08/06/2020 16:25:36 MainProcess _training_0 _base largest_face_index DEBUG 0
- 08/06/2020 16:25:36 MainProcess _training_0 _base compile_sample DEBUG Compiling samples: (side: 'b', samples: 14)
- 08/06/2020 16:25:36 MainProcess _training_0 _base show_sample DEBUG Showing sample
- 08/06/2020 16:25:36 MainProcess _training_0 _base _get_predictions DEBUG Getting Predictions
- 08/06/2020 16:25:37 MainProcess _training_0 _base _get_predictions DEBUG Returning predictions: {'a_a': (14, 64, 64, 3), 'b_a': (14, 64, 64, 3), 'a_b': (14, 64, 64, 3), 'b_b': (14, 64, 64, 3)}
- 08/06/2020 16:25:37 MainProcess _training_0 _base _to_full_frame DEBUG side: 'a', number of sample arrays: 3, prediction.shapes: [(14, 64, 64, 3), (14, 64, 64, 3)])
- 08/06/2020 16:25:37 MainProcess _training_0 _base _frame_overlay DEBUG full_size: 256, target_size: 176, color: (0, 0, 255)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _frame_overlay DEBUG Overlayed background. Shape: (14, 256, 256, 3)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'a', sample.shape: (14, 64, 64, 3), target_size: 176, scale: 2.75)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'a' shape: (14, 176, 176, 3))
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'a', sample.shape: (14, 64, 64, 3), target_size: 176, scale: 2.75)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'a' shape: (14, 176, 176, 3))
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'a', sample.shape: (14, 64, 64, 3), target_size: 176, scale: 2.75)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'a' shape: (14, 176, 176, 3))
- 08/06/2020 16:25:37 MainProcess _training_0 _base _overlay_foreground DEBUG Overlayed foreground. Shape: (14, 256, 256, 3)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _overlay_foreground DEBUG Overlayed foreground. Shape: (14, 256, 256, 3)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _overlay_foreground DEBUG Overlayed foreground. Shape: (14, 256, 256, 3)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'a', sample.shape: (14, 256, 256, 3), target_size: 128, scale: 0.5)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'a' shape: (14, 128, 128, 3))
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'a', sample.shape: (14, 256, 256, 3), target_size: 128, scale: 0.5)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'a' shape: (14, 128, 128, 3))
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'a', sample.shape: (14, 256, 256, 3), target_size: 128, scale: 0.5)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'a' shape: (14, 128, 128, 3))
- 08/06/2020 16:25:37 MainProcess _training_0 _base _get_headers DEBUG side: 'a', width: 128
- 08/06/2020 16:25:37 MainProcess _training_0 _base _get_headers DEBUG height: 32, total_width: 384
- 08/06/2020 16:25:37 MainProcess _training_0 _base _get_headers DEBUG texts: ['Original (A)', 'Original > Original', 'Original > Swap'], text_sizes: [(72, 9), (116, 9), (102, 9)], text_x: [28, 134, 269], text_y: 20
- 08/06/2020 16:25:37 MainProcess _training_0 _base _get_headers DEBUG header_box.shape: (32, 384, 3)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _to_full_frame DEBUG side: 'b', number of sample arrays: 3, prediction.shapes: [(14, 64, 64, 3), (14, 64, 64, 3)])
- 08/06/2020 16:25:37 MainProcess _training_0 _base _frame_overlay DEBUG full_size: 265, target_size: 182, color: (0, 0, 255)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _frame_overlay DEBUG Overlayed background. Shape: (14, 265, 190, 3)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'b', sample.shape: (14, 64, 64, 3), target_size: 182, scale: 2.84375)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'b' shape: (14, 182, 182, 3))
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'b', sample.shape: (14, 64, 64, 3), target_size: 182, scale: 2.84375)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'b' shape: (14, 182, 182, 3))
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'b', sample.shape: (14, 64, 64, 3), target_size: 182, scale: 2.84375)
- 08/06/2020 16:25:37 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'b' shape: (14, 182, 182, 3))
- 08/06/2020 16:25:37 MainProcess _training_0 multithreading run DEBUG Error in thread (_training_0): could not broadcast input array from shape (182,182,3) into shape (182,149,3)
- 08/06/2020 16:25:38 MainProcess MainThread train _monitor DEBUG Thread error detected
- 08/06/2020 16:25:38 MainProcess MainThread train _monitor DEBUG Closed Monitor
- 08/06/2020 16:25:38 MainProcess MainThread train _end_thread DEBUG Ending Training thread
- 08/06/2020 16:25:38 MainProcess MainThread train _end_thread CRITICAL Error caught! Exiting...
- 08/06/2020 16:25:38 MainProcess MainThread multithreading join DEBUG Joining Threads: '_training'
- 08/06/2020 16:25:38 MainProcess MainThread multithreading join DEBUG Joining Thread: '_training_0'
- 08/06/2020 16:25:38 MainProcess MainThread multithreading join ERROR Caught exception in thread: '_training_0'
- Traceback (most recent call last):
- File "C:\Users\michalek\faceswap\lib\cli\launcher.py", line 155, in execute_script
- process.process()
- File "C:\Users\michalek\faceswap\scripts\train.py", line 161, in process
- self._end_thread(thread, err)
- File "C:\Users\michalek\faceswap\scripts\train.py", line 201, in _end_thread
- thread.join()
- File "C:\Users\michalek\faceswap\lib\multithreading.py", line 121, in join
- raise thread.err[1].with_traceback(thread.err[2])
- File "C:\Users\michalek\faceswap\lib\multithreading.py", line 37, in run
- self._target(*self._args, **self._kwargs)
- File "C:\Users\michalek\faceswap\scripts\train.py", line 226, in _training
- raise err
- File "C:\Users\michalek\faceswap\scripts\train.py", line 216, in _training
- self._run_training_cycle(model, trainer)
- File "C:\Users\michalek\faceswap\scripts\train.py", line 305, in _run_training_cycle
- trainer.train_one_step(viewer, timelapse)
- File "C:\Users\michalek\faceswap\plugins\train\trainer\_base.py", line 316, in train_one_step
- raise err
- File "C:\Users\michalek\faceswap\plugins\train\trainer\_base.py", line 306, in train_one_step
- samples = self._samples.show_sample()
- File "C:\Users\michalek\faceswap\plugins\train\trainer\_base.py", line 635, in show_sample
- display = self._to_full_frame(side, samples, predictions)
- File "C:\Users\michalek\faceswap\plugins\train\trainer\_base.py", line 736, in _to_full_frame
- images = [self._overlay_foreground(frame, image) for image in images]
- File "C:\Users\michalek\faceswap\plugins\train\trainer\_base.py", line 736, in <listcomp>
- images = [self._overlay_foreground(frame, image) for image in images]
- File "C:\Users\michalek\faceswap\plugins\train\trainer\_base.py", line 828, in _overlay_foreground
- offset:offset + foregrounds[idx].shape[1], :3] = foregrounds[idx]
- ValueError: could not broadcast input array from shape (182,182,3) into shape (182,149,3)
- ============ System Information ============
- encoding: cp1252
- git_branch: master
- git_commits: 3fd26b5 Manual Tool (#1038)
- gpu_cuda: 11.0
- gpu_cudnn: No global version found. Check Conda packages for Conda cuDNN
- gpu_devices: GPU_0: GeForce GTX 1050 Ti
- gpu_devices_active: GPU_0
- gpu_driver: 451.48
- gpu_vram: GPU_0: 4096MB
- os_machine: AMD64
- os_platform: Windows-10-10.0.19041-SP0
- os_release: 10
- py_command: C:\Users\michalek\faceswap\faceswap.py train -A E:/deepfake/faceexport -B E:/deepfake/face2 -m E:/deepfake/model burger -t original -bs 32 -it 1000000 -g 1 -s 100 -ss 25000 -ps 50 -L INFO -gui
- py_conda_version: conda 4.8.3
- py_implementation: CPython
- py_version: 3.7.7
- py_virtual_env: True
- sys_cores: 4
- sys_processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
- sys_ram: Total: 8082MB, Available: 3350MB, Used: 4731MB, Free: 3350MB
- =============== Pip Packages ===============
- absl-py==0.9.0
- astor==0.8.0
- blinker==1.4
- brotlipy==0.7.0
- cachetools==4.1.0
- certifi==2020.6.20
- cffi==1.14.0
- chardet==3.0.4
- click==7.1.2
- cloudpickle @ file:///tmp/build/80754af9/cloudpickle_1594141588948/work
- cryptography==2.9.2
- cycler==0.10.0
- cytoolz==0.10.1
- dask @ file:///tmp/build/80754af9/dask-core_1594156306305/work
- decorator==4.4.2
- fastcluster==1.1.26
- ffmpy==0.2.3
- gast==0.2.2
- google-auth @ file:///tmp/build/80754af9/google-auth_1594357566944/work
- google-auth-oauthlib==0.4.1
- google-pasta==0.2.0
- grpcio==1.27.2
- h5py==2.10.0
- idna @ file:///tmp/build/80754af9/idna_1593446292537/work
- 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 @ file:///tmp/build/80754af9/keras-applications_1594366238411/work
- Keras-Preprocessing==1.1.0
- kiwisolver==1.2.0
- Markdown==3.1.1
- matplotlib @ file:///C:/ci/matplotlib-base_1592846084747/work
- mkl-fft==1.1.0
- mkl-random==1.1.1
- mkl-service==2.3.0
- networkx @ file:///tmp/build/80754af9/networkx_1594377231366/work
- numpy @ file:///C:/ci/numpy_and_numpy_base_1596233945180/work
- nvidia-ml-py3 @ git+https://github.com/deepfakes/nvidia-ml-py3.git@6fc29ac84b32bad877f078cb4a777c1548a00bf6
- oauthlib==3.1.0
- olefile==0.46
- opencv-python==4.3.0.36
- opt-einsum==3.1.0
- Pillow @ file:///C:/ci/pillow_1594298234712/work
- protobuf==3.12.3
- psutil==5.7.0
- pyasn1==0.4.8
- pyasn1-modules==0.2.7
- pycparser @ file:///tmp/build/80754af9/pycparser_1594388511720/work
- PyJWT==1.7.1
- pyOpenSSL @ file:///tmp/build/80754af9/pyopenssl_1594392929924/work
- pyparsing==2.4.7
- pyreadline==2.1
- PySocks @ file:///C:/ci/pysocks_1594394709107/work
- python-dateutil==2.8.1
- PyWavelets==1.1.1
- pywin32==227
- PyYAML==5.3.1
- requests @ file:///tmp/build/80754af9/requests_1592841827918/work
- requests-oauthlib==1.3.0
- rsa==4.0
- scikit-image==0.16.2
- scikit-learn @ file:///C:/ci/scikit-learn_1592847564598/work
- scipy @ file:///C:/ci/scipy_1592916958183/work
- six==1.15.0
- tensorboard==2.2.1
- tensorboard-plugin-wit==1.6.0
- tensorflow==1.15.0
- tensorflow-estimator==1.15.1
- termcolor==1.1.0
- threadpoolctl @ file:///tmp/tmp9twdgx9k/threadpoolctl-2.1.0-py3-none-any.whl
- toolz==0.10.0
- toposort==1.5
- tornado==6.0.4
- tqdm @ file:///tmp/build/80754af9/tqdm_1593446365756/work
- urllib3==1.25.9
- Werkzeug==0.16.1
- win-inet-pton==1.1.0
- wincertstore==0.2
- wrapt==1.12.1
- ============== Conda Packages ==============
- # packages in environment at C:\Users\michalek\MiniConda3\envs\faceswap:
- #
- # Name Version Build Channel
- _tflow_select 2.1.0 gpu
- absl-py 0.9.0 py37_0
- astor 0.8.0 py37_0
- blas 1.0 mkl
- blinker 1.4 py37_0
- brotlipy 0.7.0 py37he774522_1000
- ca-certificates 2020.6.24 0
- cachetools 4.1.0 py_1
- certifi 2020.6.20 py37_0
- cffi 1.14.0 py37h7a1dbc1_0
- chardet 3.0.4 py37_1003
- click 7.1.2 py_0
- cloudpickle 1.5.0 py_0
- cryptography 2.9.2 py37h7a1dbc1_0
- cudatoolkit 10.0.130 0
- cudnn 7.6.5 cuda10.0_0
- cycler 0.10.0 py37_0
- cytoolz 0.10.1 py37he774522_0
- dask-core 2.20.0 py_0
- decorator 4.4.2 py_0
- fastcluster 1.1.26 py37h9b59f54_1 conda-forge
- ffmpeg 4.3 ha925a31_1 conda-forge
- ffmpy 0.2.3 pypi_0 pypi
- freetype 2.10.2 hd328e21_0
- gast 0.2.2 py37_0
- git 2.23.0 h6bb4b03_0
- google-auth 1.17.2 py_0
- google-auth-oauthlib 0.4.1 py_2
- google-pasta 0.2.0 py_0
- grpcio 1.27.2 py37h351948d_0
- h5py 2.10.0 py37h5e291fa_0
- hdf5 1.10.4 h7ebc959_0
- icc_rt 2019.0.0 h0cc432a_1
- icu 58.2 ha925a31_3
- idna 2.10 py_0
- 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 0
- keras-applications 1.0.8 py_1
- keras-base 2.2.4 py37_0
- keras-preprocessing 1.1.0 py_1
- kiwisolver 1.2.0 py37h74a9793_0
- libpng 1.6.37 h2a8f88b_0
- libprotobuf 3.12.3 h7bd577a_0
- libtiff 4.1.0 h56a325e_1
- lz4-c 1.9.2 h62dcd97_1
- markdown 3.1.1 py37_0
- matplotlib 3.2.2 0
- matplotlib-base 3.2.2 py37h64f37c6_0
- mkl 2020.1 216
- mkl-service 2.3.0 py37hb782905_0
- mkl_fft 1.1.0 py37h45dec08_0
- mkl_random 1.1.1 py37h47e9c7a_0
- networkx 2.4 py_1
- numpy 1.19.1 py37h5510c5b_0
- numpy-base 1.19.1 py37ha3acd2a_0
- nvidia-ml-py3 7.352.1 pypi_0 pypi
- oauthlib 3.1.0 py_0
- olefile 0.46 py37_0
- opencv-python 4.3.0.36 pypi_0 pypi
- openssl 1.1.1g he774522_0
- opt_einsum 3.1.0 py_0
- pathlib 1.0.1 py37_2
- pillow 7.2.0 py37hcc1f983_0
- pip 20.1.1 py37_1
- protobuf 3.12.3 py37h33f27b4_0
- psutil 5.7.0 py37he774522_0
- pyasn1 0.4.8 py_0
- pyasn1-modules 0.2.7 py_0
- pycparser 2.20 py_2
- pyjwt 1.7.1 py37_0
- pyopenssl 19.1.0 py_1
- pyparsing 2.4.7 py_0
- pyqt 5.9.2 py37h6538335_2
- pyreadline 2.1 py37_1
- pysocks 1.7.1 py37_1
- python 3.7.7 h81c818b_4
- python-dateutil 2.8.1 py_0
- python_abi 3.7 1_cp37m conda-forge
- pywavelets 1.1.1 py37he774522_0
- pywin32 227 py37he774522_1
- pyyaml 5.3.1 py37he774522_1
- qt 5.9.7 vc14h73c81de_0
- requests 2.24.0 py_0
- requests-oauthlib 1.3.0 py_0
- rsa 4.0 py_0
- scikit-image 0.16.2 py37h47e9c7a_0
- scikit-learn 0.23.1 py37h25d0782_0
- scipy 1.5.0 py37h9439919_0
- setuptools 49.2.0 py37_0
- sip 4.19.8 py37h6538335_0
- six 1.15.0 py_0
- sqlite 3.32.3 h2a8f88b_0
- tensorboard 2.2.1 pyh532a8cf_0
- tensorboard-plugin-wit 1.6.0 py_0
- tensorflow 1.15.0 gpu_py37hc3743a6_0
- tensorflow-base 1.15.0 gpu_py37h1afeea4_0
- tensorflow-estimator 1.15.1 pyh2649769_0
- tensorflow-gpu 1.15.0 h0d30ee6_0
- termcolor 1.1.0 py37_1
- threadpoolctl 2.1.0 pyh5ca1d4c_0
- tk 8.6.10 he774522_0
- toolz 0.10.0 py_0
- toposort 1.5 py_3 conda-forge
- tornado 6.0.4 py37he774522_1
- tqdm 4.47.0 py_0
- urllib3 1.25.9 py_0
- vc 14.1 h0510ff6_4
- vs2015_runtime 14.16.27012 hf0eaf9b_3
- werkzeug 0.16.1 py_0
- wheel 0.34.2 py37_0
- win_inet_pton 1.1.0 py37_0
- wincertstore 0.2 py37_0
- wrapt 1.12.1 py37he774522_1
- xz 5.2.5 h62dcd97_0
- yaml 0.2.5 he774522_0
- zlib 1.2.11 h62dcd97_4
- zstd 1.4.5 h04227a9_0
- ================= Configs ==================
- --------- .faceswap ---------
- backend: nvidia
- --------- 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
- [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: 68.75
- mask_type: none
- mask_blur_kernel: 3
- mask_threshold: 4
- learn_mask: False
- icnr_init: False
- conv_aware_init: False
- reflect_padding: False
- penalized_mask_loss: True
- loss_function: mae
- learning_rate: 5e-05
- [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
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