I got an error I cannot get passed after just one training session.

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LuCas23332
Posts: 2
Joined: Fri May 19, 2023 3:04 pm

I got an error I cannot get passed after just one training session.

Post by LuCas23332 »

I reinstalled python, recreated a new anaconda environment, let it update packages, then I started up my first training session from a previous model and I began to train a brand new model (I did not use any previously created model) for 15 hours straight. When I clicked "stop", then clicked "Train" (I didn't even close faceswap or restart my computer) it now gives this error that I keep getting no matter what I do. I retried restarting my faceswap but it didn't work

Code: Select all

File "C:\Users\pfftdammitchris\faceswap\plugins\train\model\_base.py", line 244, in build
self.load_models(swapped=False)
File "C:\Users\pfftdammitchris\faceswap\plugins\train\model\_base.py", line 456, in load_models
is_loaded = network.load(fullpath=model_mapping[network.side][network.type])
File "C:\Users\pfftdammitchris\faceswap\plugins\train\model\_base.py", line 834, in load
network = load_model(self.filename, custom_objects=get_custom_objects())
File "C:\Users\pfftdammitchris\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\saving.py", line 419, in load_model
model = _deserialize_model(f, custom_objects, compile)
File "C:\Users\pfftdammitchris\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\saving.py", line 224, in _deserialize_model
model_config = json.loads(model_config.decode('utf-8'))
AttributeError: 'str' object has no attribute 'decode'
05/22/2023 13:14:53 CRITICAL An unexpected crash has occurred. Crash report written to 'C:\Users\pfftdammitchris\faceswap\crash_report.2023.05.22.131449461273.log'. You MUST provide this file if seeking assistance. Please verify you are running the latest version of faceswap before reporting
Process exited.

Here is my crash report:

Code: Select all

05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       __init__                  DEBUG    Initializing NNBlocks: (use_icnr_init: False, use_convaware_init: False, use_reflect_padding: False, first_run: False)
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       __init__                  DEBUG    Initialized NNBlocks
05/22/2023 13:03:21 MainProcess     _training_0     _base           name                      DEBUG    model name: 'original'
05/22/2023 13:03:21 MainProcess     _training_0     _base           rename_legacy             DEBUG    Renaming legacy files
05/22/2023 13:03:21 MainProcess     _training_0     _base           name                      DEBUG    model name: 'original'
05/22/2023 13:03:21 MainProcess     _training_0     _base           rename_legacy             DEBUG    No legacy files to rename
05/22/2023 13:03:21 MainProcess     _training_0     _base           load_state_info           DEBUG    Loading Input Shape from State file
05/22/2023 13:03:21 MainProcess     _training_0     _base           load_state_info           DEBUG    Setting input shape from state file: (64, 64, 3)
05/22/2023 13:03:21 MainProcess     _training_0     _base           calculate_coverage_ratio  DEBUG    Requested coverage_ratio: 0.6875
05/22/2023 13:03:21 MainProcess     _training_0     _base           calculate_coverage_ratio  DEBUG    Final coverage_ratio: 0.6875
05/22/2023 13:03:21 MainProcess     _training_0     _base           __init__                  DEBUG    training_opts: {'alignments': {'a': 'C:\\Users\\pfftdammitchris\\Desktop\\faceswap\\input-A\\7_alignments.fsa', 'b': 'C:\\Users\\pfftdammitchris\\Desktop\\faceswap\\output-B\\alignments.fsa'}, 'preview_scaling': 0.5, 'warp_to_landmarks': False, 'augment_color': True, 'no_flip': False, 'pingpong': False, 'snapshot_interval': 25000, 'training_size': 512, 'no_logs': False, 'coverage_ratio': 0.6875, 'mask_type': None, 'mask_blur_kernel': 3, 'mask_threshold': 4, 'learn_mask': False, 'penalized_mask_loss': False}
05/22/2023 13:03:21 MainProcess     _training_0     _base           multiple_models_in_folder DEBUG    model_files: ['original_decoder_A.h5', 'original_decoder_B.h5', 'original_encoder.h5'], retval: False
05/22/2023 13:03:21 MainProcess     _training_0     original        add_networks              DEBUG    Adding networks
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       upscale                   DEBUG    input_tensor: input_1 Placeholder FLOAT32(<tile.Value SymbolicDim UINT64()>, 8, 8, 512), filters: 256, kernel_size: 3, use_instance_norm: False, kwargs: {})
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _get_name                 DEBUG    Generating block name: upscale_(<tile.Value SymbolicDim UINT64()>, 8, 8, 512)_0
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000012D0CDAB908>
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       conv2d                    DEBUG    input_tensor: input_1 Placeholder FLOAT32(<tile.Value SymbolicDim UINT64()>, 8, 8, 512), filters: 1024, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale_(<tile.Value SymbolicDim UINT64()>, 8, 8, 512)_0_conv2d', 'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000012D0CDAB908>})
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000012D0CDAB908>
05/22/2023 13:03:21 MainProcess     _training_0     library         _logger_callback          INFO     Opening device "opencl_nvidia_nvidia_geforce_rtx_3050_laptop_gpu.0"
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       upscale                   DEBUG    input_tensor: Reshape FLOAT32(<tile.Value FloorDiv FLOAT64()>, 16, 16, 256), filters: 128, kernel_size: 3, use_instance_norm: False, kwargs: {})
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _get_name                 DEBUG    Generating block name: upscale_(<tile.Value FloorDiv FLOAT64()>, 16, 16, 256)_0
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000012D0CDA0D88>
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       conv2d                    DEBUG    input_tensor: Reshape FLOAT32(<tile.Value FloorDiv FLOAT64()>, 16, 16, 256), filters: 512, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale_(<tile.Value FloorDiv FLOAT64()>, 16, 16, 256)_0_conv2d', 'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000012D0CDA0D88>})
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000012D0CDA0D88>
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       upscale                   DEBUG    input_tensor: Reshape FLOAT32(<tile.Value FloorDiv FLOAT64()>, 32, 32, 128), filters: 64, kernel_size: 3, use_instance_norm: False, kwargs: {})
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _get_name                 DEBUG    Generating block name: upscale_(<tile.Value FloorDiv FLOAT64()>, 32, 32, 128)_0
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000012D0CE72248>
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       conv2d                    DEBUG    input_tensor: Reshape FLOAT32(<tile.Value FloorDiv FLOAT64()>, 32, 32, 128), filters: 256, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale_(<tile.Value FloorDiv FLOAT64()>, 32, 32, 128)_0_conv2d', 'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000012D0CE72248>})
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000012D0CE72248>
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       conv2d                    DEBUG    input_tensor: Reshape FLOAT32(<tile.Value FloorDiv FLOAT64()>, 64, 64, 64), filters: 3, kernel_size: 5, strides: (1, 1), padding: same, kwargs: {'activation': 'sigmoid', 'name': 'face_out'})
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000012D0CE7A9C8>
05/22/2023 13:03:21 MainProcess     _training_0     _base           add_network               DEBUG    network_type: 'decoder', side: 'a', network: '<keras.engine.training.Model object at 0x0000012D0CE2B4C8>', is_output: True
05/22/2023 13:03:21 MainProcess     _training_0     _base           name                      DEBUG    model name: 'original'
05/22/2023 13:03:21 MainProcess     _training_0     _base           add_network               DEBUG    name: 'decoder_a', filename: 'original_decoder_A.h5'
05/22/2023 13:03:21 MainProcess     _training_0     _base           __init__                  DEBUG    Initializing NNMeta: (filename: 'C:\Users\pfftdammitchris\Desktop\faceswap\models\bigie-2\original_decoder_A.h5', network_type: 'decoder', side: 'a', network: <keras.engine.training.Model object at 0x0000012D0CE2B4C8>, is_output: True
05/22/2023 13:03:21 MainProcess     _training_0     _base           __init__                  DEBUG    Initialized NNMeta
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       upscale                   DEBUG    input_tensor: input_2 Placeholder FLOAT32(<tile.Value SymbolicDim UINT64()>, 8, 8, 512), filters: 256, kernel_size: 3, use_instance_norm: False, kwargs: {})
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _get_name                 DEBUG    Generating block name: upscale_(<tile.Value SymbolicDim UINT64()>, 8, 8, 512)_1
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000012D0CE9DC48>
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       conv2d                    DEBUG    input_tensor: input_2 Placeholder FLOAT32(<tile.Value SymbolicDim UINT64()>, 8, 8, 512), filters: 1024, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale_(<tile.Value SymbolicDim UINT64()>, 8, 8, 512)_1_conv2d', 'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000012D0CE9DC48>})
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000012D0CE9DC48>
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       upscale                   DEBUG    input_tensor: Reshape FLOAT32(<tile.Value FloorDiv FLOAT64()>, 16, 16, 256), filters: 128, kernel_size: 3, use_instance_norm: False, kwargs: {})
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _get_name                 DEBUG    Generating block name: upscale_(<tile.Value FloorDiv FLOAT64()>, 16, 16, 256)_1
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000012D0CEAA048>
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       conv2d                    DEBUG    input_tensor: Reshape FLOAT32(<tile.Value FloorDiv FLOAT64()>, 16, 16, 256), filters: 512, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale_(<tile.Value FloorDiv FLOAT64()>, 16, 16, 256)_1_conv2d', 'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000012D0CEAA048>})
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000012D0CEAA048>
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       upscale                   DEBUG    input_tensor: Reshape FLOAT32(<tile.Value FloorDiv FLOAT64()>, 32, 32, 128), filters: 64, kernel_size: 3, use_instance_norm: False, kwargs: {})
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _get_name                 DEBUG    Generating block name: upscale_(<tile.Value FloorDiv FLOAT64()>, 32, 32, 128)_1
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000012D0CEF5AC8>
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       conv2d                    DEBUG    input_tensor: Reshape FLOAT32(<tile.Value FloorDiv FLOAT64()>, 32, 32, 128), filters: 256, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale_(<tile.Value FloorDiv FLOAT64()>, 32, 32, 128)_1_conv2d', 'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000012D0CEF5AC8>})
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000012D0CEF5AC8>
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       conv2d                    DEBUG    input_tensor: Reshape FLOAT32(<tile.Value FloorDiv FLOAT64()>, 64, 64, 64), filters: 3, kernel_size: 5, strides: (1, 1), padding: same, kwargs: {'activation': 'sigmoid', 'name': 'face_out'})
05/22/2023 13:03:21 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000012D0CF02F88>
05/22/2023 13:03:21 MainProcess     _training_0     _base           add_network               DEBUG    network_type: 'decoder', side: 'b', network: '<keras.engine.training.Model object at 0x0000012D0CF0E0C8>', is_output: True
05/22/2023 13:03:21 MainProcess     _training_0     _base           name                      DEBUG    model name: 'original'
05/22/2023 13:03:21 MainProcess     _training_0     _base           add_network               DEBUG    name: 'decoder_b', filename: 'original_decoder_B.h5'
05/22/2023 13:03:21 MainProcess     _training_0     _base           __init__                  DEBUG    Initializing NNMeta: (filename: 'C:\Users\pfftdammitchris\Desktop\faceswap\models\bigie-2\original_decoder_B.h5', network_type: 'decoder', side: 'b', network: <keras.engine.training.Model object at 0x0000012D0CF0E0C8>, is_output: True
05/22/2023 13:03:22 MainProcess     _training_0     _base           __init__                  DEBUG    Initialized NNMeta
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       conv                      DEBUG    input_tensor: input_3 Placeholder FLOAT32(<tile.Value SymbolicDim UINT64()>, 64, 64, 3), filters: 128, kernel_size: 5, strides: 2, use_instance_norm: False, kwargs: {})
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       _get_name                 DEBUG    Generating block name: conv_(<tile.Value SymbolicDim UINT64()>, 64, 64, 3)_0
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       conv2d                    DEBUG    input_tensor: input_3 Placeholder FLOAT32(<tile.Value SymbolicDim UINT64()>, 64, 64, 3), filters: 128, kernel_size: 5, strides: 2, padding: same, kwargs: {'name': 'conv_(<tile.Value SymbolicDim UINT64()>, 64, 64, 3)_0_conv2d'})
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000012D0CF21888>
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       conv                      DEBUG    input_tensor: Relu FLOAT32(<tile.Value SymbolicDim UINT64()>, 32, 32, 128), filters: 256, kernel_size: 5, strides: 2, use_instance_norm: False, kwargs: {})
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       _get_name                 DEBUG    Generating block name: conv_(<tile.Value SymbolicDim UINT64()>, 32, 32, 128)_0
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       conv2d                    DEBUG    input_tensor: Relu FLOAT32(<tile.Value SymbolicDim UINT64()>, 32, 32, 128), filters: 256, kernel_size: 5, strides: 2, padding: same, kwargs: {'name': 'conv_(<tile.Value SymbolicDim UINT64()>, 32, 32, 128)_0_conv2d'})
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000012D0CF2E608>
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       conv                      DEBUG    input_tensor: Relu FLOAT32(<tile.Value SymbolicDim UINT64()>, 16, 16, 256), filters: 512, kernel_size: 5, strides: 2, use_instance_norm: False, kwargs: {})
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       _get_name                 DEBUG    Generating block name: conv_(<tile.Value SymbolicDim UINT64()>, 16, 16, 256)_0
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       conv2d                    DEBUG    input_tensor: Relu FLOAT32(<tile.Value SymbolicDim UINT64()>, 16, 16, 256), filters: 512, kernel_size: 5, strides: 2, padding: same, kwargs: {'name': 'conv_(<tile.Value SymbolicDim UINT64()>, 16, 16, 256)_0_conv2d'})
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000012D0CFB4E88>
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       conv                      DEBUG    input_tensor: Relu FLOAT32(<tile.Value SymbolicDim UINT64()>, 8, 8, 512), filters: 1024, kernel_size: 5, strides: 2, use_instance_norm: False, kwargs: {})
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       _get_name                 DEBUG    Generating block name: conv_(<tile.Value SymbolicDim UINT64()>, 8, 8, 512)_0
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       conv2d                    DEBUG    input_tensor: Relu FLOAT32(<tile.Value SymbolicDim UINT64()>, 8, 8, 512), filters: 1024, kernel_size: 5, strides: 2, padding: same, kwargs: {'name': 'conv_(<tile.Value SymbolicDim UINT64()>, 8, 8, 512)_0_conv2d'})
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000012D0CFC0A88>
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       upscale                   DEBUG    input_tensor: Reshape FLOAT32(<tile.Value SymbolicDim UINT64()>, 4, 4, 1024), filters: 512, kernel_size: 3, use_instance_norm: False, kwargs: {})
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       _get_name                 DEBUG    Generating block name: upscale_(<tile.Value SymbolicDim UINT64()>, 4, 4, 1024)_0
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000012D0CFDF048>
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       conv2d                    DEBUG    input_tensor: Reshape FLOAT32(<tile.Value SymbolicDim UINT64()>, 4, 4, 1024), filters: 2048, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale_(<tile.Value SymbolicDim UINT64()>, 4, 4, 1024)_0_conv2d', 'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000012D0CFDF048>})
05/22/2023 13:03:22 MainProcess     _training_0     nn_blocks       _set_default_initializer  DEBUG    Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000012D0CFDF048>
05/22/2023 13:03:22 MainProcess     _training_0     _base           add_network               DEBUG    network_type: 'encoder', side: 'None', network: '<keras.engine.training.Model object at 0x0000012D0CFCFC48>', is_output: False
05/22/2023 13:03:22 MainProcess     _training_0     _base           name                      DEBUG    model name: 'original'
05/22/2023 13:03:22 MainProcess     _training_0     _base           add_network               DEBUG    name: 'encoder', filename: 'original_encoder.h5'
05/22/2023 13:03:22 MainProcess     _training_0     _base           __init__                  DEBUG    Initializing NNMeta: (filename: 'C:\Users\pfftdammitchris\Desktop\faceswap\models\bigie-2\original_encoder.h5', network_type: 'encoder', side: 'None', network: <keras.engine.training.Model object at 0x0000012D0CFCFC48>, is_output: False
05/22/2023 13:03:22 MainProcess     _training_0     _base           __init__                  DEBUG    Initialized NNMeta
05/22/2023 13:03:22 MainProcess     _training_0     original        add_networks              DEBUG    Added networks
05/22/2023 13:03:22 MainProcess     _training_0     _base           load_models               DEBUG    Load model: (swapped: False)
05/22/2023 13:03:22 MainProcess     _training_0     _base           models_exist              DEBUG    Pre-existing models exist: True
05/22/2023 13:03:22 MainProcess     _training_0     _base           models_exist              DEBUG    Pre-existing models exist: True
05/22/2023 13:03:22 MainProcess     _training_0     _base           map_models                DEBUG    Map models: (swapped: False)
05/22/2023 13:03:22 MainProcess     _training_0     _base           map_models                DEBUG    Mapped models: (models_map: {'a': {'decoder': 'C:\\Users\\pfftdammitchris\\Desktop\\faceswap\\models\\bigie-2\\original_decoder_A.h5'}, 'b': {'decoder': 'C:\\Users\\pfftdammitchris\\Desktop\\faceswap\\models\\bigie-2\\original_decoder_B.h5'}})
05/22/2023 13:03:22 MainProcess     _training_0     _base           load                      DEBUG    Loading model: 'C:\Users\pfftdammitchris\Desktop\faceswap\models\bigie-2\original_decoder_A.h5'
05/22/2023 13:03:22 MainProcess     _training_0     attrs           __getitem__               DEBUG    Creating converter from 3 to 5
05/22/2023 13:03:22 MainProcess     _training_0     multithreading  run                       DEBUG    Error in thread (_training_0): 'str' object has no attribute 'decode'
05/22/2023 13:03:22 MainProcess     MainThread      train           _monitor                  DEBUG    Thread error detected
05/22/2023 13:03:22 MainProcess     MainThread      train           _monitor                  DEBUG    Closed Monitor
05/22/2023 13:03:22 MainProcess     MainThread      train           _end_thread               DEBUG    Ending Training thread
05/22/2023 13:03:22 MainProcess     MainThread      train           _end_thread               CRITICAL Error caught! Exiting...
05/22/2023 13:03:22 MainProcess     MainThread      multithreading  join                      DEBUG    Joining Threads: '_training'
05/22/2023 13:03:22 MainProcess     MainThread      multithreading  join                      DEBUG    Joining Thread: '_training_0'
05/22/2023 13:03:22 MainProcess     MainThread      multithreading  join                      ERROR    Caught exception in thread: '_training_0'
Traceback (most recent call last):
  File "C:\Users\pfftdammitchris\faceswap\lib\cli\launcher.py", line 155, in execute_script
    process.process()
  File "C:\Users\pfftdammitchris\faceswap\scripts\train.py", line 161, in process
    self._end_thread(thread, err)
  File "C:\Users\pfftdammitchris\faceswap\scripts\train.py", line 201, in _end_thread
    thread.join()
  File "C:\Users\pfftdammitchris\faceswap\lib\multithreading.py", line 121, in join
    raise thread.err[1].with_traceback(thread.err[2])
  File "C:\Users\pfftdammitchris\faceswap\lib\multithreading.py", line 37, in run
    self._target(*self._args, **self._kwargs)
  File "C:\Users\pfftdammitchris\faceswap\scripts\train.py", line 226, in _training
    raise err
  File "C:\Users\pfftdammitchris\faceswap\scripts\train.py", line 214, in _training
    model = self._load_model()
  File "C:\Users\pfftdammitchris\faceswap\scripts\train.py", line 255, in _load_model
    predict=False)
  File "C:\Users\pfftdammitchris\faceswap\plugins\train\model\original.py", line 25, in __init__
    super().__init__(*args, **kwargs)
  File "C:\Users\pfftdammitchris\faceswap\plugins\train\model\_base.py", line 125, in __init__
    self.build()
  File "C:\Users\pfftdammitchris\faceswap\plugins\train\model\_base.py", line 244, in build
    self.load_models(swapped=False)
  File "C:\Users\pfftdammitchris\faceswap\plugins\train\model\_base.py", line 456, in load_models
    is_loaded = network.load(fullpath=model_mapping[network.side][network.type])
  File "C:\Users\pfftdammitchris\faceswap\plugins\train\model\_base.py", line 834, in load
    network = load_model(self.filename, custom_objects=get_custom_objects())
  File "C:\Users\pfftdammitchris\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\saving.py", line 419, in load_model
    model = _deserialize_model(f, custom_objects, compile)
  File "C:\Users\pfftdammitchris\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\saving.py", line 224, in _deserialize_model
    model_config = json.loads(model_config.decode('utf-8'))
AttributeError: 'str' object has no attribute 'decode'

============ System Information ============
encoding:            cp1252
git_branch:          r1.0
git_commits:         a4ccfc6 GUI - Remove Switch Branch option Installers - Pin to r1.0
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: NVIDIA Corporation - NVIDIA GeForce RTX 3050 Laptop GPU (experimental), GPU_1: NVIDIA Corporation - NVIDIA GeForce RTX 3050 Laptop GPU (supported)
gpu_devices_active:  GPU_0, GPU_1
gpu_driver:          ['516.40', '516.40']
gpu_vram:            GPU_0: 4095MB, GPU_1: 4095MB
os_machine:          AMD64
os_platform:         Windows-10-10.0.22621-SP0
os_release:          10
py_command:          C:\Users\pfftdammitchris\faceswap\faceswap.py train -A C:/Users/pfftdammitchris/Desktop/faceswap/input-A/7-faces -ala C:/Users/pfftdammitchris/Desktop/faceswap/input-A/7_alignments.fsa -B C:/Users/pfftdammitchris/Desktop/faceswap/output-B -alb C:/Users/pfftdammitchris/Desktop/faceswap/output-B/alignments.fsa -m C:/Users/pfftdammitchris/Desktop/faceswap/models/bigie-2 -t original -bs 64 -it 1000000 -s 100 -ss 25000 -ps 50 -L DEBUG -gui
py_conda_version:    conda 23.3.1
py_implementation:   CPython
py_version:          3.7.16
py_virtual_env:      True
sys_cores:           16
sys_processor:       AMD64 Family 25 Model 68 Stepping 1, AuthenticAMD
sys_ram:             Total: 15629MB, Available: 701MB, Used: 14927MB, Free: 701MB

=============== Pip Packages ===============
absl-py @ file:///opt/conda/conda-bld/absl-py_1639803114343/work
aiohttp @ file:///C:/b/abs_c4zmy2l696/croot/aiohttp_1670009573673/work
aiosignal @ file:///tmp/build/80754af9/aiosignal_1637843061372/work
astor==0.8.1
async-timeout @ file:///C:/b/abs_43ozhz2a8g/croots/recipe/async-timeout_1664876362767/work
asynctest==0.13.0
attrs @ file:///C:/b/abs_09s3y775ra/croot/attrs_1668696195628/work
blinker==1.4
brotlipy==0.7.0
cachetools @ file:///tmp/build/80754af9/cachetools_1619597386817/work
certifi @ file:///C:/b/abs_85o_6fm0se/croot/certifi_1671487778835/work/certifi
cffi @ file:///C:/b/abs_49n3v2hyhr/croot/cffi_1670423218144/work
charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work
click @ file:///C:/ci/click_1646038601470/work
cloudpickle @ file:///tmp/build/80754af9/cloudpickle_1632508026186/work
colorama @ file:///C:/b/abs_a9ozq0l032/croot/colorama_1672387194846/work
cryptography @ file:///C:/b/abs_8ecplyc3n2/croot/cryptography_1677533105000/work
cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work
cytoolz @ file:///C:/b/abs_61m9vzb4qh/croot/cytoolz_1667465938275/work
dask==2021.10.0
enum34==1.1.10
fastcluster==1.1.26
ffmpy==0.2.3
flit_core @ file:///opt/conda/conda-bld/flit-core_1644941570762/work/source/flit_core
fonttools==4.25.0
frozenlist @ file:///C:/b/abs_2bb5uzghsi/croot/frozenlist_1670004511812/work
fsspec @ file:///C:/b/abs_5bjz6v0w_f/croot/fsspec_1670336608940/work
gast==0.2.2
google-auth @ file:///opt/conda/conda-bld/google-auth_1646735974934/work
google-auth-oauthlib @ file:///tmp/build/80754af9/google-auth-oauthlib_1617120569401/work
google-pasta @ file:///Users/ktietz/demo/mc3/conda-bld/google-pasta_1630577991354/work
grpcio @ file:///C:/ci/grpcio_1637590993074/work
h5py @ file:///C:/ci/h5py_1659089886851/work
idna @ file:///C:/b/abs_bdhbebrioa/croot/idna_1666125572046/work
imagecodecs @ file:///C:/b/abs_f0cr12h73p/croot/imagecodecs_1677576746499/work
imageio @ file:///C:/Windows/TEMP/abs_24c1b783-7540-4ca9-a1b1-0e8aa8e6ae64hb79ssux/croots/recipe/imageio_1658785038775/work
imageio-ffmpeg @ file:///home/conda/feedstock_root/build_artifacts/imageio-ffmpeg_1673483481485/work
importlib-metadata @ file:///C:/ci/importlib-metadata_1648562631189/work
joblib @ file:///C:/b/abs_e60_bwl1v6/croot/joblib_1666298845728/work
Keras==2.2.4
Keras-Applications @ file:///tmp/build/80754af9/keras-applications_1594366238411/work
Keras-Preprocessing @ file:///tmp/build/80754af9/keras-preprocessing_1612283640596/work
kiwisolver @ file:///C:/ci/kiwisolver_1644944417636/work
locket @ file:///C:/ci/locket_1652885902808/work
Markdown @ file:///C:/b/abs_98lv_ucina/croot/markdown_1671541919225/work
matplotlib @ file:///C:/b/abs_ae02atcfur/croot/matplotlib-suite_1667356722968/work
mkl-fft==1.3.1
mkl-random @ file:///C:/ci/mkl_random_1626186163140/work
mkl-service==2.4.0
multidict @ file:///C:/b/abs_6cx_8w3cv2/croot/multidict_1665674238352/work
munkres==1.1.4
networkx @ file:///tmp/build/80754af9/networkx_1633639043937/work
numpy @ file:///C:/ci/numpy_and_numpy_base_1653574840943/work
nvidia-ml-py3 @ git+https://github.com/deepfakes/nvidia-ml-py3.git@6fc29ac84b32bad877f078cb4a777c1548a00bf6
oauthlib @ file:///C:/b/abs_2eoymqc2ow/croot/oauthlib_1665490906043/work
opencv-python==4.7.0.72
opt-einsum @ file:///tmp/build/80754af9/opt_einsum_1621500238896/work
packaging @ file:///C:/b/abs_cfsup8ur87/croot/packaging_1671697442297/work
partd @ file:///opt/conda/conda-bld/partd_1647245470509/work
Pillow==9.4.0
plaidml==0.6.4
plaidml-keras==0.6.4
ply==3.11
protobuf==3.20.3
psutil @ file:///C:/Windows/Temp/abs_b2c2fd7f-9fd5-4756-95ea-8aed74d0039flsd9qufz/croots/recipe/psutil_1656431277748/work
pyasn1 @ file:///Users/ktietz/demo/mc3/conda-bld/pyasn1_1629708007385/work
pyasn1-modules==0.2.8
pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work
PyJWT @ file:///C:/ci/pyjwt_1657529476747/work
pyOpenSSL @ file:///C:/b/abs_552w85x1jz/croot/pyopenssl_1677607703691/work
pyparsing @ file:///C:/Users/BUILDE~1/AppData/Local/Temp/abs_7f_7lba6rl/croots/recipe/pyparsing_1661452540662/work
PyQt5==5.15.7
PyQt5-sip @ file:///C:/Windows/Temp/abs_d7gmd2jg8i/croots/recipe/pyqt-split_1659273064801/work/pyqt_sip
PySocks @ file:///C:/ci/pysocks_1594394709107/work
python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work
PyWavelets @ file:///C:/ci/pywavelets_1648728036674/work
pywin32==305.1
PyYAML==6.0
requests @ file:///C:/ci/requests_1657735288441/work
requests-oauthlib==1.3.0
rsa @ file:///tmp/build/80754af9/rsa_1614366226499/work
scikit-image @ file:///C:/b/abs_63r0vmx78u/croot/scikit-image_1669241746873/work
scikit-learn @ file:///C:/ci/scikit-learn_1642599122269/work
scipy @ file:///C:/ci/scipy_1661333074914/work
sip @ file:///C:/Windows/Temp/abs_b8fxd17m2u/croots/recipe/sip_1659012372737/work
six @ file:///tmp/build/80754af9/six_1644875935023/work
tensorboard @ file:///C:/tf/b/tensorboard_1660161540402/work/tensorboard-2.8.0-py3-none-any.whl
tensorboard-data-server @ file:///C:/b/abs_2fhvpo862s/croot/tensorboard-data-server_1670853600144/work/tensorboard_data_server-0.6.1-py3-none-any.whl
tensorboard-plugin-wit @ file:///C:/tf/b/tensorboard-plugin-wit_1660162132996/work/tensorboard_plugin_wit-1.8.1-py3-none-any.whl
tensorflow==1.15.0
tensorflow-estimator @ file:///home/builder/adipietro/tf/tensorflow-estimator_1630508970172/work/tensorflow_estimator-2.6.0-py2.py3-none-any.whl
termcolor==1.1.0
threadpoolctl @ file:///Users/ktietz/demo/mc3/conda-bld/threadpoolctl_1629802263681/work
tifffile @ file:///tmp/build/80754af9/tifffile_1627275862826/work
toml @ file:///tmp/build/80754af9/toml_1616166611790/work
toolz @ file:///C:/b/abs_cfvk6rc40d/croot/toolz_1667464080130/work
toposort==1.5
tornado @ file:///C:/ci/tornado_1662476933490/work
tqdm @ file:///C:/b/abs_0axbz66qik/croots/recipe/tqdm_1664392691071/work
typing_extensions @ file:///C:/b/abs_89eui86zuq/croot/typing_extensions_1669923792806/work
urllib3 @ file:///C:/b/abs_9bcwxczrvm/croot/urllib3_1673575521331/work
Werkzeug==0.16.1
win-inet-pton @ file:///C:/ci/win_inet_pton_1605306165655/work
wincertstore==0.2
wrapt @ file:///C:/Windows/Temp/abs_7c3dd407-1390-477a-b542-fd15df6a24085_diwiza/croots/recipe/wrapt_1657814452175/work
yarl @ file:///C:/Users/BUILDE~1/AppData/Local/Temp/abs_e5nlunspj6/croots/recipe/yarl_1661437086516/work
zipp @ file:///C:/b/abs_b9jfdr908q/croot/zipp_1672387552360/work

============== Conda Packages ==============
# packages in environment at C:\Users\pfftdammitchris\MiniConda3\envs\faceswap:
#
# Name                    Version                   Build  Channel
_tflow_select             2.2.0                     eigen  
absl-py                   0.15.0             pyhd3eb1b0_0  
aiohttp                   3.8.3            py37h2bbff1b_0  
aiosignal                 1.2.0              pyhd3eb1b0_0  
astor                     0.8.1            py37haa95532_0  
async-timeout             4.0.2            py37haa95532_0  
asynctest                 0.13.0                     py_0  
attrs                     22.1.0           py37haa95532_0  
blas                      1.0                         mkl  
blinker                   1.4              py37haa95532_0  
blosc                     1.21.3               h6c2663c_0  
brotli                    1.0.9                h2bbff1b_7  
brotli-bin                1.0.9                h2bbff1b_7  
brotlipy                  0.7.0           py37h2bbff1b_1003  
bzip2                     1.0.8                he774522_0  
ca-certificates           2023.01.10           haa95532_0  
cachetools                4.2.2              pyhd3eb1b0_0  
certifi                   2022.12.7        py37haa95532_0  
cffi                      1.15.1           py37h2bbff1b_3  
cfitsio                   3.470                h2bbff1b_7  
charls                    2.2.0                h6c2663c_0  
charset-normalizer        2.0.4              pyhd3eb1b0_0  
click                     8.0.4            py37haa95532_0  
cloudpickle               2.0.0              pyhd3eb1b0_0  
colorama                  0.4.6            py37haa95532_0  
cryptography              39.0.1           py37h21b164f_0  
cycler                    0.11.0             pyhd3eb1b0_0  
cytoolz                   0.12.0           py37h2bbff1b_0  
dask-core                 2021.10.0          pyhd3eb1b0_0  
enum34                    1.1.10                   pypi_0    pypi
fastcluster               1.1.26           py37h9386db6_3    conda-forge
ffmpeg                    4.3.1                ha925a31_0    conda-forge
ffmpy                     0.2.3                    pypi_0    pypi
fftw                      3.3.9                h2bbff1b_1  
flit-core                 3.6.0              pyhd3eb1b0_0  
fonttools                 4.25.0             pyhd3eb1b0_0  
freetype                  2.12.1               ha860e81_0  
frozenlist                1.3.3            py37h2bbff1b_0  
fsspec                    2022.11.0        py37haa95532_0  
gast                      0.2.2                    py37_0  
giflib                    5.2.1                h8cc25b3_3  
git                       2.34.1               haa95532_0  
glib                      2.69.1               h5dc1a3c_2  
google-auth               2.6.0              pyhd3eb1b0_0  
google-auth-oauthlib      0.4.4              pyhd3eb1b0_0  
google-pasta              0.2.0              pyhd3eb1b0_0  
grpcio                    1.42.0           py37hc60d5dd_0  
gst-plugins-base          1.18.5               h9e645db_0  
gstreamer                 1.18.5               hd78058f_0  
h5py                      3.7.0            py37h3de5c98_0  
hdf5                      1.10.6               h1756f20_1  
icc_rt                    2022.1.0             h6049295_2  
icu                       58.2                 ha925a31_3  
idna                      3.4              py37haa95532_0  
imagecodecs               2021.8.26        py37h319e4f4_2  
imageio                   2.19.3           py37haa95532_0  
imageio-ffmpeg            0.4.8              pyhd8ed1ab_0    conda-forge
importlib-metadata        4.11.3           py37haa95532_0  
intel-openmp              2021.4.0          haa95532_3556  
joblib                    1.1.1            py37haa95532_0  
jpeg                      9e                   h2bbff1b_1  
jxrlib                    1.1                  he774522_2  
keras                     2.2.4                         0  
keras-applications        1.0.8                      py_1  
keras-base                2.2.4                    py37_0  
keras-preprocessing       1.1.2              pyhd3eb1b0_0  
kiwisolver                1.3.2            py37hd77b12b_0  
krb5                      1.19.4               h5b6d351_0  
lcms2                     2.12                 h83e58a3_0  
lerc                      3.0                  hd77b12b_0  
libaec                    1.0.4                h33f27b4_1  
libbrotlicommon           1.0.9                h2bbff1b_7  
libbrotlidec              1.0.9                h2bbff1b_7  
libbrotlienc              1.0.9                h2bbff1b_7  
libclang                  14.0.6          default_hb5a9fac_1  
libclang13                14.0.6          default_h8e68704_1  
libdeflate                1.17                 h2bbff1b_0  
libffi                    3.4.4                hd77b12b_0  
libiconv                  1.16                 h2bbff1b_2  
libogg                    1.3.5                h2bbff1b_1  
libpng                    1.6.39               h8cc25b3_0  
libprotobuf               3.20.3               h23ce68f_0  
libtiff                   4.5.0                h6c2663c_2  
libvorbis                 1.3.7                he774522_0  
libwebp                   1.2.4                hbc33d0d_1  
libwebp-base              1.2.4                h2bbff1b_1  
libxml2                   2.10.3               h0ad7f3c_0  
libxslt                   1.1.37               h2bbff1b_0  
libzopfli                 1.0.3                ha925a31_0  
locket                    1.0.0            py37haa95532_0  
lz4-c                     1.9.4                h2bbff1b_0  
markdown                  3.4.1            py37haa95532_0  
matplotlib                3.5.3            py37haa95532_0  
matplotlib-base           3.5.3            py37hd77b12b_0  
mkl                       2021.4.0           haa95532_640  
mkl-service               2.4.0            py37h2bbff1b_0  
mkl_fft                   1.3.1            py37h277e83a_0  
mkl_random                1.2.2            py37hf11a4ad_0  
multidict                 6.0.2            py37h2bbff1b_0  
munkres                   1.1.4                      py_0  
networkx                  2.6.3              pyhd3eb1b0_0  
numpy                     1.21.5           py37h7a0a035_3  
numpy-base                1.21.5           py37hca35cd5_3  
nvidia-ml-py3             7.352.1                  pypi_0    pypi
oauthlib                  3.2.1            py37haa95532_0  
opencv-python             4.7.0.72                 pypi_0    pypi
openjpeg                  2.4.0                h4fc8c34_0  
openssl                   1.1.1t               h2bbff1b_0  
opt_einsum                3.3.0              pyhd3eb1b0_1  
packaging                 22.0             py37haa95532_0  
partd                     1.2.0              pyhd3eb1b0_1  
pathlib                   1.0.1                    py37_2  
pcre                      8.45                 hd77b12b_0  
pillow                    9.4.0            py37hd77b12b_0  
pip                       22.3.1           py37haa95532_0  
plaidml                   0.6.4                    pypi_0    pypi
plaidml-keras             0.6.4                    pypi_0    pypi
ply                       3.11                     py37_0  
protobuf                  3.20.3           py37hd77b12b_0  
psutil                    5.9.0            py37h2bbff1b_0  
pyasn1                    0.4.8              pyhd3eb1b0_0  
pyasn1-modules            0.2.8                      py_0  
pycparser                 2.21               pyhd3eb1b0_0  
pyjwt                     2.4.0            py37haa95532_0  
pyopenssl                 23.0.0           py37haa95532_0  
pyparsing                 3.0.9            py37haa95532_0  
pyqt                      5.15.7           py37hd77b12b_0  
pyqt5-sip                 12.11.0          py37hd77b12b_0  
pysocks                   1.7.1                    py37_1  
python                    3.7.16               h6244533_0  
python-dateutil           2.8.2              pyhd3eb1b0_0  
python_abi                3.7                     2_cp37m    conda-forge
pywavelets                1.3.0            py37h2bbff1b_0  
pywin32                   305              py37h2bbff1b_0  
pyyaml                    6.0              py37h2bbff1b_1  
qt-main                   5.15.2               he8e5bd7_8  
qt-webengine              5.15.9               hb9a9bb5_5  
qtwebkit                  5.212                h2bbfb41_5  
requests                  2.28.1           py37haa95532_0  
requests-oauthlib         1.3.0                      py_0  
rsa                       4.7.2              pyhd3eb1b0_1  
scikit-image              0.19.3           py37hd77b12b_1  
scikit-learn              1.0.2            py37hf11a4ad_1  
scipy                     1.7.3            py37h7a0a035_2  
setuptools                65.6.3           py37haa95532_0  
sip                       6.6.2            py37hd77b12b_0  
six                       1.16.0             pyhd3eb1b0_1  
snappy                    1.1.9                h6c2663c_0  
sqlite                    3.41.2               h2bbff1b_0  
tensorboard               2.8.0            py37haa95532_0  
tensorboard-data-server   0.6.1            py37haa95532_0  
tensorboard-plugin-wit    1.8.1            py37haa95532_0  
tensorflow                1.15.0          eigen_py37h9f89a44_0  
tensorflow-base           1.15.0          eigen_py37h07d2309_0  
tensorflow-estimator      2.6.0              pyh7b7c402_0  
termcolor                 1.1.0            py37haa95532_1  
threadpoolctl             2.2.0              pyh0d69192_0  
tifffile                  2021.7.2           pyhd3eb1b0_2  
tk                        8.6.12               h2bbff1b_0  
toml                      0.10.2             pyhd3eb1b0_0  
toolz                     0.12.0           py37haa95532_0  
toposort                  1.5                        py_3    conda-forge
tornado                   6.2              py37h2bbff1b_0  
tqdm                      4.64.1           py37haa95532_0  
typing-extensions         4.4.0            py37haa95532_0  
typing_extensions         4.4.0            py37haa95532_0  
urllib3                   1.26.14          py37haa95532_0  
vc                        14.2                 h21ff451_1  
vs2015_runtime            14.27.29016          h5e58377_2  
werkzeug                  0.16.1                     py_0  
wheel                     0.38.4           py37haa95532_0  
win_inet_pton             1.1.0            py37haa95532_0  
wincertstore              0.2              py37haa95532_2  
wrapt                     1.14.1           py37h2bbff1b_0  
xz                        5.4.2                h8cc25b3_0  
yaml                      0.2.5                he774522_0  
yarl                      1.8.1            py37h2bbff1b_0  
zfp                       0.5.5                hd77b12b_6  
zipp                      3.11.0           py37haa95532_0  
zlib                      1.2.13               h8cc25b3_0  
zstd                      1.5.5                hd43e919_0  

=============== State File =================
{
  "name": "original",
  "sessions": {
    "1": {
      "timestamp": 1684731234.8941696,
      "no_logs": false,
      "pingpong": false,
      "loss_names": {
        "a": [
          "face_loss"
        ],
        "b": [
          "face_loss"
        ]
      },
      "batchsize": 64,
      "iterations": 23677,
      "config": {
        "learning_rate": 5e-05
      }
    }
  },
  "lowest_avg_loss": {
    "a": 0.03764933113008737,
    "b": 0.03583359196782112
  },
  "iterations": 23677,
  "inputs": {
    "face_in": [
      64,
      64,
      3
    ]
  },
  "training_size": 512,
  "config": {
    "coverage": 68.75,
    "mask_type": null,
    "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,
    "lowmem": false
  }
}

================= 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:                 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
Last edited by torzdf on Thu May 25, 2023 11:39 am, edited 6 times in total.
User avatar
LuCas23332
Posts: 2
Joined: Fri May 19, 2023 3:04 pm

Re: I got an error I cannot get passed after just one training session.

Post by LuCas23332 »

It seems like I might have installed with the wrong options. This is all confusing because there are usually two options to choose in some windows: NVIDIA and AMD
But I looked in my specs and it says my graphics is NVIDIA GeForce RTX 3050 4GB GDDR6 while my CPU is AMD Ryzen 7 6800H 4800MHz DDR5 RAM

So I have both things in my hardware that have both keywords "NVIDIA" and "AMD" but we are given two options to choose when installing faceswap and tells us to choose one of these. Does anyone know which one I should choose then?

User avatar
torzdf
Posts: 2649
Joined: Fri Jul 12, 2019 12:53 am
Answers: 159
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Re: I got an error I cannot get passed after just one training session.

Post by torzdf »

Yes, you have installed for the incorrect back end. You should select Nvidia as you have an Nvidia GPU

My word is final

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