Code: Select all
05/25/2020 21:54:01 MainProcess _training_0 _base calculate_coverage_ratio DEBUG Final coverage_ratio: 0.6875
05/25/2020 21:54:01 MainProcess _training_0 _base __init__ DEBUG training_opts: {'alignments': {'a': 'C:\\Users\\ARG\\Desktop\\nathan\\extracted\\alignments.fsa', 'b': 'C:\\Users\\ARG\\Desktop\\nathan\\extracted-nathan\\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}
05/25/2020 21:54:01 MainProcess _training_0 _base multiple_models_in_folder DEBUG model_files: ['original_decoder_A.h5', 'original_decoder_B.h5', 'original_encoder.h5'], retval: False
05/25/2020 21:54:01 MainProcess _training_0 original add_networks DEBUG Adding networks
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks upscale DEBUG inp: input_1 Placeholder FLOAT32(<tile.Value SymbolicDim UINT64()>, 8, 8, 512), filters: 256, kernel_size: 3, use_instance_norm: False, kwargs: {})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale_(<tile.Value SymbolicDim UINT64()>, 8, 8, 512)_0
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x00000205A48B4A08>
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv2d DEBUG inp: 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 0x00000205A48B4A08>})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x00000205A48B4A08>
05/25/2020 21:54:01 MainProcess _training_0 library _logger_callback INFO Opening device "opencl_amd_ellesmere.0"
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks upscale DEBUG inp: Reshape FLOAT32(<tile.Value FloorDiv FLOAT64()>, 16, 16, 256), filters: 128, kernel_size: 3, use_instance_norm: False, kwargs: {})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale_(<tile.Value FloorDiv FLOAT64()>, 16, 16, 256)_0
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x00000205A49ED888>
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv2d DEBUG inp: 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 0x00000205A49ED888>})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x00000205A49ED888>
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks upscale DEBUG inp: Reshape FLOAT32(<tile.Value FloorDiv FLOAT64()>, 32, 32, 128), filters: 64, kernel_size: 3, use_instance_norm: False, kwargs: {})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale_(<tile.Value FloorDiv FLOAT64()>, 32, 32, 128)_0
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x00000205A45D6248>
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv2d DEBUG inp: 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 0x00000205A45D6248>})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x00000205A45D6248>
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv2d DEBUG inp: 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'})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x00000205A45DCB08>
05/25/2020 21:54:01 MainProcess _training_0 _base add_network DEBUG network_type: 'decoder', side: 'a', network: '<keras.engine.training.Model object at 0x00000205A45E8108>', is_output: True
05/25/2020 21:54:01 MainProcess _training_0 _base name DEBUG model name: 'original'
05/25/2020 21:54:01 MainProcess _training_0 _base add_network DEBUG name: 'decoder_a', filename: 'original_decoder_A.h5'
05/25/2020 21:54:01 MainProcess _training_0 _base __init__ DEBUG Initializing NNMeta: (filename: 'C:\Users\ARG\Desktop\nathan\take3\original_decoder_A.h5', network_type: 'decoder', side: 'a', network: <keras.engine.training.Model object at 0x00000205A45E8108>, is_output: True
05/25/2020 21:54:01 MainProcess _training_0 _base __init__ DEBUG Initialized NNMeta
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks upscale DEBUG inp: input_2 Placeholder FLOAT32(<tile.Value SymbolicDim UINT64()>, 8, 8, 512), filters: 256, kernel_size: 3, use_instance_norm: False, kwargs: {})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale_(<tile.Value SymbolicDim UINT64()>, 8, 8, 512)_1
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x00000205A45E8488>
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv2d DEBUG inp: 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 0x00000205A45E8488>})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x00000205A45E8488>
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks upscale DEBUG inp: Reshape FLOAT32(<tile.Value FloorDiv FLOAT64()>, 16, 16, 256), filters: 128, kernel_size: 3, use_instance_norm: False, kwargs: {})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale_(<tile.Value FloorDiv FLOAT64()>, 16, 16, 256)_1
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x00000205A4651F48>
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv2d DEBUG inp: 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 0x00000205A4651F48>})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x00000205A4651F48>
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks upscale DEBUG inp: Reshape FLOAT32(<tile.Value FloorDiv FLOAT64()>, 32, 32, 128), filters: 64, kernel_size: 3, use_instance_norm: False, kwargs: {})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale_(<tile.Value FloorDiv FLOAT64()>, 32, 32, 128)_1
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x00000205A465FB08>
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv2d DEBUG inp: 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 0x00000205A465FB08>})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x00000205A465FB08>
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv2d DEBUG inp: 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'})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x00000205A466A588>
05/25/2020 21:54:01 MainProcess _training_0 _base add_network DEBUG network_type: 'decoder', side: 'b', network: '<keras.engine.training.Model object at 0x00000205A4672708>', is_output: True
05/25/2020 21:54:01 MainProcess _training_0 _base name DEBUG model name: 'original'
05/25/2020 21:54:01 MainProcess _training_0 _base add_network DEBUG name: 'decoder_b', filename: 'original_decoder_B.h5'
05/25/2020 21:54:01 MainProcess _training_0 _base __init__ DEBUG Initializing NNMeta: (filename: 'C:\Users\ARG\Desktop\nathan\take3\original_decoder_B.h5', network_type: 'decoder', side: 'b', network: <keras.engine.training.Model object at 0x00000205A4672708>, is_output: True
05/25/2020 21:54:01 MainProcess _training_0 _base __init__ DEBUG Initialized NNMeta
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv DEBUG inp: input_3 Placeholder FLOAT32(<tile.Value SymbolicDim UINT64()>, 64, 64, 3), filters: 128, kernel_size: 5, strides: 2, use_instance_norm: False, kwargs: {})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv_(<tile.Value SymbolicDim UINT64()>, 64, 64, 3)_0
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv2d DEBUG inp: 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'})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x00000205A4672D08>
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv DEBUG inp: Relu FLOAT32(<tile.Value SymbolicDim UINT64()>, 32, 32, 128), filters: 256, kernel_size: 5, strides: 2, use_instance_norm: False, kwargs: {})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv_(<tile.Value SymbolicDim UINT64()>, 32, 32, 128)_0
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv2d DEBUG inp: 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'})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x00000205A468D3C8>
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv DEBUG inp: Relu FLOAT32(<tile.Value SymbolicDim UINT64()>, 16, 16, 256), filters: 512, kernel_size: 5, strides: 2, use_instance_norm: False, kwargs: {})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv_(<tile.Value SymbolicDim UINT64()>, 16, 16, 256)_0
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv2d DEBUG inp: 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'})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x00000205A4693C48>
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv DEBUG inp: Relu FLOAT32(<tile.Value SymbolicDim UINT64()>, 8, 8, 512), filters: 1024, kernel_size: 5, strides: 2, use_instance_norm: False, kwargs: {})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv_(<tile.Value SymbolicDim UINT64()>, 8, 8, 512)_0
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks conv2d DEBUG inp: 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'})
05/25/2020 21:54:01 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x00000205A46A3808>
05/25/2020 21:54:02 MainProcess _training_0 nn_blocks upscale DEBUG inp: Reshape FLOAT32(<tile.Value SymbolicDim UINT64()>, 4, 4, 1024), filters: 512, kernel_size: 3, use_instance_norm: False, kwargs: {})
05/25/2020 21:54:02 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale_(<tile.Value SymbolicDim UINT64()>, 4, 4, 1024)_0
05/25/2020 21:54:02 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x00000205A46B49C8>
05/25/2020 21:54:02 MainProcess _training_0 nn_blocks conv2d DEBUG inp: 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 0x00000205A46B49C8>})
05/25/2020 21:54:02 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x00000205A46B49C8>
05/25/2020 21:54:02 MainProcess _training_0 _base add_network DEBUG network_type: 'encoder', side: 'None', network: '<keras.engine.training.Model object at 0x00000205A469AB08>', is_output: False
05/25/2020 21:54:02 MainProcess _training_0 _base name DEBUG model name: 'original'
05/25/2020 21:54:02 MainProcess _training_0 _base add_network DEBUG name: 'encoder', filename: 'original_encoder.h5'
05/25/2020 21:54:02 MainProcess _training_0 _base __init__ DEBUG Initializing NNMeta: (filename: 'C:\Users\ARG\Desktop\nathan\take3\original_encoder.h5', network_type: 'encoder', side: 'None', network: <keras.engine.training.Model object at 0x00000205A469AB08>, is_output: False
05/25/2020 21:54:02 MainProcess _training_0 _base __init__ DEBUG Initialized NNMeta
05/25/2020 21:54:02 MainProcess _training_0 original add_networks DEBUG Added networks
05/25/2020 21:54:02 MainProcess _training_0 _base load_models DEBUG Load model: (swapped: False)
05/25/2020 21:54:02 MainProcess _training_0 _base models_exist DEBUG Pre-existing models exist: True
05/25/2020 21:54:02 MainProcess _training_0 _base models_exist DEBUG Pre-existing models exist: True
05/25/2020 21:54:02 MainProcess _training_0 _base map_models DEBUG Map models: (swapped: False)
05/25/2020 21:54:02 MainProcess _training_0 _base map_models DEBUG Mapped models: (models_map: {'a': {'decoder': 'C:\\Users\\ARG\\Desktop\\nathan\\take3\\original_decoder_A.h5'}, 'b': {'decoder': 'C:\\Users\\ARG\\Desktop\\nathan\\take3\\original_decoder_B.h5'}})
05/25/2020 21:54:02 MainProcess _training_0 _base load DEBUG Loading model: 'C:\Users\ARG\Desktop\nathan\take3\original_decoder_A.h5'
05/25/2020 21:54:02 MainProcess _training_0 library _logger_callback INFO Opening device "opencl_amd_ellesmere.0"
05/25/2020 21:54:02 MainProcess _training_0 _base load DEBUG Loading model: 'C:\Users\ARG\Desktop\nathan\take3\original_decoder_B.h5'
05/25/2020 21:54:02 MainProcess _training_0 _base load DEBUG Loading model: 'C:\Users\ARG\Desktop\nathan\take3\original_encoder.h5'
05/25/2020 21:54:03 MainProcess _training_0 multithreading run DEBUG Error in thread (_training_0): Unable to get link info (free block size is zero?)
05/25/2020 21:54:04 MainProcess MainThread train _monitor DEBUG Thread error detected
05/25/2020 21:54:04 MainProcess MainThread train _monitor DEBUG Closed Monitor
05/25/2020 21:54:04 MainProcess MainThread train _end_thread DEBUG Ending Training thread
05/25/2020 21:54:04 MainProcess MainThread train _end_thread CRITICAL Error caught! Exiting...
05/25/2020 21:54:04 MainProcess MainThread multithreading join DEBUG Joining Threads: '_training'
05/25/2020 21:54:04 MainProcess MainThread multithreading join DEBUG Joining Thread: '_training_0'
05/25/2020 21:54:04 MainProcess MainThread multithreading join ERROR Caught exception in thread: '_training_0'
05/25/2020 21:54:04 MainProcess MainThread plaidml_tools initialize DEBUG PlaidML already initialized
05/25/2020 21:54:04 MainProcess MainThread plaidml_tools get_supported_devices DEBUG [<plaidml._DeviceConfig object at 0x00000205A48D3188>]
05/25/2020 21:54:04 MainProcess MainThread plaidml_tools get_all_devices DEBUG Experimental Devices: [<plaidml._DeviceConfig object at 0x00000205A491BF48>]
05/25/2020 21:54:04 MainProcess MainThread plaidml_tools get_all_devices DEBUG [<plaidml._DeviceConfig object at 0x00000205A491BF48>, <plaidml._DeviceConfig object at 0x00000205A48D3188>]
05/25/2020 21:54:04 MainProcess MainThread plaidml_tools __init__ DEBUG Initialized: PlaidMLStats
05/25/2020 21:54:04 MainProcess MainThread plaidml_tools supported_indices DEBUG [1]
05/25/2020 21:54:04 MainProcess MainThread plaidml_tools supported_indices DEBUG [1]
Traceback (most recent call last):
File "C:\Users\ARG\faceswap\lib\cli.py", line 129, in execute_script
process.process()
File "C:\Users\ARG\faceswap\scripts\train.py", line 159, in process
self._end_thread(thread, err)
File "C:\Users\ARG\faceswap\scripts\train.py", line 199, in _end_thread
thread.join()
File "C:\Users\ARG\faceswap\lib\multithreading.py", line 121, in join
raise thread.err[1].with_traceback(thread.err[2])
File "C:\Users\ARG\faceswap\lib\multithreading.py", line 37, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\ARG\faceswap\scripts\train.py", line 224, in _training
raise err
File "C:\Users\ARG\faceswap\scripts\train.py", line 212, in _training
model = self._load_model()
File "C:\Users\ARG\faceswap\scripts\train.py", line 253, in _load_model
predict=False)
File "C:\Users\ARG\faceswap\plugins\train\model\original.py", line 25, in __init__
super().__init__(*args, **kwargs)
File "C:\Users\ARG\faceswap\plugins\train\model\_base.py", line 126, in __init__
self.build()
File "C:\Users\ARG\faceswap\plugins\train\model\_base.py", line 245, in build
self.load_models(swapped=False)
File "C:\Users\ARG\faceswap\plugins\train\model\_base.py", line 455, in load_models
is_loaded = network.load()
File "C:\Users\ARG\faceswap\plugins\train\model\_base.py", line 836, in load
network = load_model(self.filename, custom_objects=get_custom_objects())
File "C:\Users\ARG\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\ARG\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\saving.py", line 228, in _deserialize_model
if 'keras_version' in model_weights_group:
File "C:\Users\ARG\MiniConda3\envs\faceswap\lib\site-packages\keras\utils\io_utils.py", line 343, in __contains__
return (key in self.data) or (key in self.data.attrs)
File "h5py\_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py\_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "C:\Users\ARG\MiniConda3\envs\faceswap\lib\site-packages\h5py\_hl\group.py", line 413, in __contains__
return self._e(name) in self.id
File "h5py\h5g.pyx", line 461, in h5py.h5g.GroupID.__contains__
File "h5py\h5g.pyx", line 462, in h5py.h5g.GroupID.__contains__
File "h5py\_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py\_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "h5py\h5g.pyx", line 531, in h5py.h5g._path_valid
File "h5py\_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py\_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "h5py\h5l.pyx", line 212, in h5py.h5l.LinkProxy.exists
RuntimeError: Unable to get link info (free block size is zero?)
============ System Information ============
encoding: cp1252
git_branch: master
git_commits: ba41a9c bugfix: dlight model change "mask_type" to "learn_mask" in decoders
gpu_cuda: No global version found. Check Conda packages for Conda Cuda
gpu_cudnn: No global version found. Check Conda packages for Conda cuDNN
gpu_devices: GPU_0: Advanced Micro Devices, Inc. - Ellesmere (experimental), GPU_1: Advanced Micro Devices, Inc. - Ellesmere (supported)
gpu_devices_active: GPU_0, GPU_1
gpu_driver: ['2841.19', '2841.19']
gpu_vram: GPU_0: 4096MB, GPU_1: 4096MB
os_machine: AMD64
os_platform: Windows-10-10.0.18362-SP0
os_release: 10
py_command: C:\Users\ARG\faceswap\faceswap.py train -A C:/Users/ARG/Desktop/nathan/extracted -B C:/Users/ARG/Desktop/nathan/extracted-nathan -m C:/Users/ARG/Desktop/nathan/take3 -t original -bs 64 -it 1000000 -s 100 -ss 25000 -ps 50 -L INFO -gui
py_conda_version: conda 4.8.0
py_implementation: CPython
py_version: 3.7.5
py_virtual_env: True
sys_cores: 8
sys_processor: Intel64 Family 6 Model 158 Stepping 12, GenuineIntel
sys_ram: Total: 49090MB, Available: 37607MB, Used: 11482MB, Free: 37607MB
=============== Pip Packages ===============
absl-py==0.8.1
astor==0.8.0
certifi==2019.11.28
cffi==1.13.2
cloudpickle==1.2.2
cycler==0.10.0
cytoolz==0.10.1
dask==2.9.0
decorator==4.4.1
enum34==1.1.6
fastcluster==1.1.25
ffmpy==0.2.2
gast==0.2.2
google-pasta==0.1.8
grpcio==1.16.1
h5py==2.9.0
imageio==2.6.1
imageio-ffmpeg==0.3.0
joblib==0.14.1
Keras==2.2.4
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
kiwisolver==1.1.0
Markdown==3.1.1
matplotlib==3.1.1
mkl-fft==1.0.15
mkl-random==1.1.0
mkl-service==2.3.0
networkx==2.4
numpy==1.17.4
nvidia-ml-py3==7.352.1
olefile==0.46
opencv-python==4.1.2.30
opt-einsum==3.1.0
pathlib==1.0.1
Pillow==6.2.1
plaidml==0.6.4
plaidml-keras==0.6.4
protobuf==3.11.2
psutil==5.6.7
pycparser==2.19
pyparsing==2.4.5
pyreadline==2.1
python-dateutil==2.8.1
pytz==2019.3
PyWavelets==1.1.1
pywin32==227
PyYAML==5.2
scikit-image==0.15.0
scikit-learn==0.22
scipy==1.3.2
six==1.13.0
tensorboard==2.0.0
tensorflow==1.15.0
tensorflow-estimator==1.15.1
termcolor==1.1.0
toolz==0.10.0
toposort==1.5
tornado==6.0.3
tqdm==4.40.2
Werkzeug==0.16.0
wincertstore==0.2
wrapt==1.11.2
============== Conda Packages ==============
# packages in environment at C:\Users\ARG\MiniConda3\envs\faceswap:
#
# Name Version Build Channel
_tflow_select 2.3.0 mkl
absl-py 0.8.1 py37_0
astor 0.8.0 py37_0
blas 1.0 mkl
ca-certificates 2019.11.27 0
certifi 2019.11.28 py37_0
cffi 1.13.2 pypi_0 pypi
cloudpickle 1.2.2 py_0
cycler 0.10.0 py37_0
cytoolz 0.10.1 py37he774522_0
dask-core 2.9.0 py_0
decorator 4.4.1 py_0
enum34 1.1.6 pypi_0 pypi
fastcluster 1.1.25 py37he350917_1000 conda-forge
ffmpeg 4.2 h6538335_0 conda-forge
ffmpy 0.2.2 pypi_0 pypi
freetype 2.9.1 ha9979f8_1
gast 0.2.2 py37_0
git 2.23.0 h6bb4b03_0
google-pasta 0.1.8 py_0
grpcio 1.16.1 py37h351948d_1
h5py 2.9.0 py37h5e291fa_0
hdf5 1.10.4 h7ebc959_0
icc_rt 2019.0.0 h0cc432a_1
icu 58.2 ha66f8fd_1
imageio 2.6.1 py37_0
imageio-ffmpeg 0.3.0 py_0 conda-forge
intel-openmp 2019.4 245
joblib 0.14.1 py_0
jpeg 9b hb83a4c4_2
keras 2.2.4 0
keras-applications 1.0.8 py_0
keras-base 2.2.4 py37_0
keras-preprocessing 1.1.0 py_1
kiwisolver 1.1.0 py37ha925a31_0
libmklml 2019.0.5 0
libpng 1.6.37 h2a8f88b_0
libprotobuf 3.11.2 h7bd577a_0
libtiff 4.1.0 h56a325e_0
markdown 3.1.1 py37_0
matplotlib 3.1.1 py37hc8f65d3_0
mkl 2019.4 245
mkl-service 2.3.0 py37hb782905_0
mkl_fft 1.0.15 py37h14836fe_0
mkl_random 1.1.0 py37h675688f_0
networkx 2.4 py_0
numpy 1.17.4 py37h4320e6b_0
numpy-base 1.17.4 py37hc3f5095_0
nvidia-ml-py3 7.352.1 pypi_0 pypi
olefile 0.46 py37_0
opencv-python 4.1.2.30 pypi_0 pypi
openssl 1.1.1d he774522_3
opt_einsum 3.1.0 py_0
pathlib 1.0.1 py37_1
pillow 6.2.1 py37hdc69c19_0
pip 19.3.1 py37_0
plaidml 0.6.4 pypi_0 pypi
plaidml-keras 0.6.4 pypi_0 pypi
protobuf 3.11.2 py37h33f27b4_0
psutil 5.6.7 py37he774522_0
pycparser 2.19 pypi_0 pypi
pyparsing 2.4.5 py_0
pyqt 5.9.2 py37h6538335_2
pyreadline 2.1 py37_1
python 3.7.5 h8c8aaf0_0
python-dateutil 2.8.1 py_0
pytz 2019.3 py_0
pywavelets 1.1.1 py37he774522_0
pywin32 227 py37he774522_0
pyyaml 5.2 py37he774522_0
qt 5.9.7 vc14h73c81de_0
scikit-image 0.15.0 py37ha925a31_0
scikit-learn 0.22 py37h6288b17_0
scipy 1.3.2 py37h29ff71c_0
setuptools 42.0.2 py37_0
sip 4.19.8 py37h6538335_0
six 1.13.0 py37_0
sqlite 3.30.1 he774522_0
tensorboard 2.0.0 pyhb38c66f_1
tensorflow 1.15.0 mkl_py37h3789bd0_0
tensorflow-base 1.15.0 mkl_py37h190a33d_0
tensorflow-estimator 1.15.1 pyh2649769_0
termcolor 1.1.0 py37_1
tk 8.6.8 hfa6e2cd_0
toolz 0.10.0 py_0
toposort 1.5 py_3 conda-forge
tornado 6.0.3 py37he774522_0
tqdm 4.40.2 py_0
vc 14.1 h0510ff6_4
vs2015_runtime 14.16.27012 hf0eaf9b_1
werkzeug 0.16.0 py_0
wheel 0.33.6 py37_0
wincertstore 0.2 py37_0
wrapt 1.11.2 py37he774522_0
xz 5.2.4 h2fa13f4_4
yaml 0.1.7 hc54c509_2
zlib 1.2.11 h62dcd97_3
zstd 1.3.7 h508b16e_0
=============== State File =================
{
"name": "original",
"sessions": {
"1": {
"timestamp": 1585947161.7842915,
"no_logs": false,
"pingpong": false,
"loss_names": {
"a": [
"face_loss"
],
"b": [
"face_loss"
]
},
"batchsize": 64,
"iterations": 32601,
"config": {
"learning_rate": 5e-05
}
},
"2": {
"timestamp": 1586021959.994682,
"no_logs": false,
"pingpong": false,
"loss_names": {
"a": [
"face_loss"
],
"b": [
"face_loss"
]
},
"batchsize": 64,
"iterations": 1001,
"config": {
"learning_rate": 5e-05
}
},
"3": {
"timestamp": 1586187967.0968952,
"no_logs": false,
"pingpong": false,
"loss_names": {
"a": [
"face_loss"
],
"b": [
"face_loss"
]
},
"batchsize": 64,
"iterations": 18001,
"config": {
"learning_rate": 5e-05
}
},
"4": {
"timestamp": 1586270178.786016,
"no_logs": false,
"pingpong": false,
"loss_names": {
"a": [
"face_loss"
],
"b": [
"face_loss"
]
},
"batchsize": 64,
"iterations": 58901,
"config": {
"learning_rate": 5e-05
}
},
"5": {
"timestamp": 1586405021.1247506,
"no_logs": false,
"pingpong": false,
"loss_names": {
"a": [
"face_loss"
],
"b": [
"face_loss"
]
},
"batchsize": 64,
"iterations": 36501,
"config": {
"learning_rate": 5e-05
}
},
"6": {
"timestamp": 1586490699.4909968,
"no_logs": false,
"pingpong": false,
"loss_names": {
"a": [
"face_loss"
],
"b": [
"face_loss"
]
},
"batchsize": 64,
"iterations": 19184,
"config": {
"learning_rate": 5e-05
}
},
"7": {
"timestamp": 1586632998.9703188,
"no_logs": false,
"pingpong": false,
"loss_names": {
"a": [
"face_loss"
],
"b": [
"face_loss"
]
},
"batchsize": 64,
"iterations": 51001,
"config": {
"learning_rate": 5e-05
}
},
"8": {
"timestamp": 1586815724.416049,
"no_logs": false,
"pingpong": false,
"loss_names": {
"a": [
"face_loss"
],
"b": [
"face_loss"
]
},
"batchsize": 64,
"iterations": 36001,
"config": {
"learning_rate": 5e-05
}
}
},
"lowest_avg_loss": {
"a": 0.02381170665845275,
"b": 0.01903638871386647
},
"iterations": 253191,
"inputs": {
"face_in": [
64,
64,
3
]
},
"training_size": 256,
"config": {
"coverage": 68.75,
"mask_type": null,
"mask_blur_kernel": 3,
"mask_threshold": 4,
"learn_mask": false,
"icnr_init": false,
"conv_aware_init": false,
"subpixel_upscaling": 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
radius: 3.0
passes: 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
subpixel_upscaling: 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