03/24/2020 19:12:13 MainProcess _training_0 _base name DEBUG model name: 'dfl_sae'
03/24/2020 19:12:13 MainProcess _training_0 _base add_network DEBUG name: 'decoder_a', filename: 'dfl_sae_decoder_A.h5'
03/24/2020 19:12:13 MainProcess _training_0 _base init DEBUG Initializing NNMeta: (filename: 'T:\Goodnight-Alice2\dfl_sae_decoder_A.h5', network_type: 'decoder', side: 'a', network: <keras.engine.training.Model object at 0x0000025AE5B63308>, is_output: True
03/24/2020 19:12:13 MainProcess _training_0 _base init DEBUG Initialized NNMeta
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks upscale DEBUG inp: Tensor("input_3:0", shape=(?, 16, 16, 512), dtype=float32), filters: 504, kernel_size: 3, use_instance_norm: False, kwargs: {})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale_16_1
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000025AE5B631C8>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("input_3:0", shape=(?, 16, 16, 512), dtype=float32), filters: 2016, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale_16_1_conv2d', 'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000025AE5B631C8>})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000025AE5B631C8>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks res_block DEBUG inp: Tensor("upscale_16_1_pixelshuffler/Reshape_1:0", shape=(?, 32, 32, 504), dtype=float32), filters: 504, kernel_size: 3, kwargs: {})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: residual_32_2
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_32_2_leakyrelu_0/LeakyRelu:0", shape=(?, 32, 32, 504), dtype=float32), filters: 504, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'residual_32_2_conv2d_0'})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000025AE5B9A408>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks switch_kernel_initializer DEBUG Switched kernel_initializer from None to <keras.initializers.VarianceScaling object at 0x0000025AE5BB27C8>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_32_2_leakyrelu_1/LeakyRelu:0", shape=(?, 32, 32, 504), dtype=float32), filters: 504, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000025AE5BB27C8>})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv2d_32_2
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000025AE5BB27C8>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks switch_kernel_initializer DEBUG Switched kernel_initializer from <keras.initializers.VarianceScaling object at 0x0000025AE5BB27C8> to None
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks res_block DEBUG inp: Tensor("residual_32_2_leakyrelu_3/LeakyRelu:0", shape=(?, 32, 32, 504), dtype=float32), filters: 504, kernel_size: 3, kwargs: {})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: residual_32_3
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_32_3_leakyrelu_0/LeakyRelu:0", shape=(?, 32, 32, 504), dtype=float32), filters: 504, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'residual_32_3_conv2d_0'})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000025AE5BB1E48>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks switch_kernel_initializer DEBUG Switched kernel_initializer from None to <keras.initializers.VarianceScaling object at 0x0000025AE5BC1D48>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_32_3_leakyrelu_1/LeakyRelu:0", shape=(?, 32, 32, 504), dtype=float32), filters: 504, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000025AE5BC1D48>})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv2d_32_3
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000025AE5BC1D48>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks switch_kernel_initializer DEBUG Switched kernel_initializer from <keras.initializers.VarianceScaling object at 0x0000025AE5BC1D48> to None
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks upscale DEBUG inp: Tensor("residual_32_3_leakyrelu_3/LeakyRelu:0", shape=(?, 32, 32, 504), dtype=float32), filters: 252, kernel_size: 3, use_instance_norm: False, kwargs: {})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale_32_1
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000025AE5BD4108>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_32_3_leakyrelu_3/LeakyRelu:0", shape=(?, 32, 32, 504), dtype=float32), filters: 1008, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale_32_1_conv2d', 'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000025AE5BD4108>})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000025AE5BD4108>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks res_block DEBUG inp: Tensor("upscale_32_1_pixelshuffler/Reshape_1:0", shape=(?, 64, 64, 252), dtype=float32), filters: 252, kernel_size: 3, kwargs: {})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: residual_64_2
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_64_2_leakyrelu_0/LeakyRelu:0", shape=(?, 64, 64, 252), dtype=float32), filters: 252, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'residual_64_2_conv2d_0'})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000025AE5BE0DC8>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks switch_kernel_initializer DEBUG Switched kernel_initializer from None to <keras.initializers.VarianceScaling object at 0x0000025AE5BEC0C8>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_64_2_leakyrelu_1/LeakyRelu:0", shape=(?, 64, 64, 252), dtype=float32), filters: 252, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000025AE5BEC0C8>})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv2d_64_2
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000025AE5BEC0C8>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks switch_kernel_initializer DEBUG Switched kernel_initializer from <keras.initializers.VarianceScaling object at 0x0000025AE5BEC0C8> to None
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks res_block DEBUG inp: Tensor("residual_64_2_leakyrelu_3/LeakyRelu:0", shape=(?, 64, 64, 252), dtype=float32), filters: 252, kernel_size: 3, kwargs: {})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: residual_64_3
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_64_3_leakyrelu_0/LeakyRelu:0", shape=(?, 64, 64, 252), dtype=float32), filters: 252, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'residual_64_3_conv2d_0'})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000025AE5C01C48>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks switch_kernel_initializer DEBUG Switched kernel_initializer from None to <keras.initializers.VarianceScaling object at 0x0000025AE5C06108>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_64_3_leakyrelu_1/LeakyRelu:0", shape=(?, 64, 64, 252), dtype=float32), filters: 252, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000025AE5C06108>})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv2d_64_3
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000025AE5C06108>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks switch_kernel_initializer DEBUG Switched kernel_initializer from <keras.initializers.VarianceScaling object at 0x0000025AE5C06108> to None
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks upscale DEBUG inp: Tensor("residual_64_3_leakyrelu_3/LeakyRelu:0", shape=(?, 64, 64, 252), dtype=float32), filters: 126, kernel_size: 3, use_instance_norm: False, kwargs: {})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale_64_1
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000025AE5C0DF88>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_64_3_leakyrelu_3/LeakyRelu:0", shape=(?, 64, 64, 252), dtype=float32), filters: 504, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale_64_1_conv2d', 'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000025AE5C0DF88>})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000025AE5C0DF88>
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks res_block DEBUG inp: Tensor("upscale_64_1_pixelshuffler/Reshape_1:0", shape=(?, 128, 128, 126), dtype=float32), filters: 126, kernel_size: 3, kwargs: {})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: residual_128_2
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_128_2_leakyrelu_0/LeakyRelu:0", shape=(?, 128, 128, 126), dtype=float32), filters: 126, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'residual_128_2_conv2d_0'})
03/24/2020 19:12:13 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000025AE5C3CAC8>
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks switch_kernel_initializer DEBUG Switched kernel_initializer from None to <keras.initializers.VarianceScaling object at 0x0000025AE5C4C448>
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_128_2_leakyrelu_1/LeakyRelu:0", shape=(?, 128, 128, 126), dtype=float32), filters: 126, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000025AE5C4C448>})
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv2d_128_2
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000025AE5C4C448>
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks switch_kernel_initializer DEBUG Switched kernel_initializer from <keras.initializers.VarianceScaling object at 0x0000025AE5C4C448> to None
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks res_block DEBUG inp: Tensor("residual_128_2_leakyrelu_3/LeakyRelu:0", shape=(?, 128, 128, 126), dtype=float32), filters: 126, kernel_size: 3, kwargs: {})
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: residual_128_3
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_128_3_leakyrelu_0/LeakyRelu:0", shape=(?, 128, 128, 126), dtype=float32), filters: 126, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'residual_128_3_conv2d_0'})
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000025AE5C43888>
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks switch_kernel_initializer DEBUG Switched kernel_initializer from None to <keras.initializers.VarianceScaling object at 0x0000025AE5C6DD88>
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_128_3_leakyrelu_1/LeakyRelu:0", shape=(?, 128, 128, 126), dtype=float32), filters: 126, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'kernel_initializer': <keras.initializers.VarianceScaling object at 0x0000025AE5C6DD88>})
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv2d_128_3
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <keras.initializers.VarianceScaling object at 0x0000025AE5C6DD88>
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks switch_kernel_initializer DEBUG Switched kernel_initializer from <keras.initializers.VarianceScaling object at 0x0000025AE5C6DD88> to None
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("residual_128_3_leakyrelu_3/LeakyRelu:0", shape=(?, 128, 128, 126), dtype=float32), filters: 3, kernel_size: 5, strides: (1, 1), padding: same, kwargs: {'activation': 'sigmoid', 'name': 'face_out_128'})
03/24/2020 19:12:14 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <keras.initializers.VarianceScaling object at 0x0000025AE5C62DC8>
03/24/2020 19:12:14 MainProcess _training_0 _base add_network DEBUG network_type: 'decoder', side: 'b', network: '<keras.engine.training.Model object at 0x0000025AE5C810C8>', is_output: True
03/24/2020 19:12:14 MainProcess _training_0 _base name DEBUG model name: 'dfl_sae'
03/24/2020 19:12:14 MainProcess _training_0 _base add_network DEBUG name: 'decoder_b', filename: 'dfl_sae_decoder_B.h5'
03/24/2020 19:12:14 MainProcess _training_0 _base init DEBUG Initializing NNMeta: (filename: 'T:\Goodnight-Alice2\dfl_sae_decoder_B.h5', network_type: 'decoder', side: 'b', network: <keras.engine.training.Model object at 0x0000025AE5C810C8>, is_output: True
03/24/2020 19:12:14 MainProcess _training_0 _base init DEBUG Initialized NNMeta
03/24/2020 19:12:14 MainProcess _training_0 dfl_sae add_networks DEBUG Added networks
03/24/2020 19:12:14 MainProcess _training_0 _base load_models DEBUG Load model: (swapped: False)
03/24/2020 19:12:14 MainProcess _training_0 _base models_exist DEBUG Pre-existing models exist: False
03/24/2020 19:12:14 MainProcess _training_0 _base name DEBUG model name: 'dfl_sae'
03/24/2020 19:12:14 MainProcess _training_0 _base load_models INFO Creating new 'dfl_sae' model in folder: 'T:\Goodnight-Alice2'
03/24/2020 19:12:14 MainProcess _training_0 _base get_inputs DEBUG Getting inputs
03/24/2020 19:12:14 MainProcess _training_0 _base get_inputs DEBUG Got inputs: [<tf.Tensor 'face_in:0' shape=(?, 128, 128, 3) dtype=float32>]
03/24/2020 19:12:14 MainProcess _training_0 dfl_sae build_autoencoders DEBUG Initializing model
03/24/2020 19:12:14 MainProcess _training_0 dfl_sae build_df_autoencoder DEBUG Adding Autoencoder. Side: a
03/24/2020 19:12:14 MainProcess _training_0 _base add_predictor DEBUG Adding predictor: (side: 'a', model: <keras.engine.training.Model object at 0x0000025AE5C81B88>)
03/24/2020 19:12:14 MainProcess _training_0 _base add_predictor DEBUG Converting to multi-gpu: side a
03/24/2020 19:12:14 MainProcess training_0 multithreading run DEBUG Error in thread (training_0): To call multi_gpu_model
with gpus=2
, we expect the following devices to be available: ['/cpu:0', '/gpu:0', '/gpu:1']. However this machine only has: ['/cpu:0', '/gpu:0']. Try reducing gpus
.
03/24/2020 19:12:15 MainProcess MainThread train _monitor DEBUG Thread error detected
03/24/2020 19:12:15 MainProcess MainThread train _monitor DEBUG Closed Monitor
03/24/2020 19:12:15 MainProcess MainThread train _end_thread DEBUG Ending Training thread
03/24/2020 19:12:15 MainProcess MainThread train end_thread CRITICAL Error caught! Exiting...
03/24/2020 19:12:15 MainProcess MainThread multithreading join DEBUG Joining Threads: 'training'
03/24/2020 19:12:15 MainProcess MainThread multithreading join DEBUG Joining Thread: 'training_0'
03/24/2020 19:12:15 MainProcess MainThread multithreading join ERROR Caught exception in thread: 'training_0'
03/24/2020 19:12:15 MainProcess MainThread cli execute_script ERROR To call multi_gpu_model
with gpus=2
, we expect the following devices to be available: ['/cpu:0', '/gpu:0', '/gpu:1']. However this machine only has: ['/cpu:0', '/gpu:0']. Try reducing gpus
.
Traceback (most recent call last):
File "s:\Users\AbigFlea\faceswap\plugins\train\model\_base.py", line 248, in build
self.build_autoencoders(inputs)
File "s:\Users\AbigFlea\faceswap\plugins\train\model\dfl_sae.py", line 70, in build_autoencoders
getattr(self, "build{}autoencoder".format(self.architecture))(inputs)
File "s:\Users\AbigFlea\faceswap\plugins\train\model\dfl_sae.py", line 94, in build_df_autoencoder
self.add_predictor(side, autoencoder)
File "s:\Users\AbigFlea\faceswap\plugins\train\model\_base.py", line 326, in add_predictor
model = multi_gpu_model(model, self.gpus)
File "C:\Users\AbigFlea\MiniConda3\envs\faceswap\lib\site-packages\keras\utils\multi_gpu_utils.py", line 181, in multi_gpu_model
available_devices))
ValueError: To call multi_gpu_model
with gpus=2
, we expect the following devices to be available: ['/cpu:0', '/gpu:0', '/gpu:1']. However this machine only has: ['/cpu:0', '/gpu:0']. Try reducing gpus
.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "s:\Users\AbigFlea\faceswap\lib\cli.py", line 128, in execute_script
process.process()
File "s:\Users\AbigFlea\faceswap\scripts\train.py", line 159, in process
self._end_thread(thread, err)
File "s:\Users\AbigFlea\faceswap\scripts\train.py", line 199, in end_thread
thread.join()
File "s:\Users\AbigFlea\faceswap\lib\multithreading.py", line 121, in join
raise thread.err[1].with_traceback(thread.err[2])
File "s:\Users\AbigFlea\faceswap\lib\multithreading.py", line 37, in run
self.target(self.args, **self.kwargs)
File "s:\Users\AbigFlea\faceswap\scripts\train.py", line 224, in _training
raise err
File "s:\Users\AbigFlea\faceswap\scripts\train.py", line 212, in training
model = self.load_model()
File "s:\Users\AbigFlea\faceswap\scripts\train.py", line 253, in _load_model
predict=False)
File "s:\Users\AbigFlea\faceswap\plugins\train\model\dfl_sae.py", line 23, in init
super().init(args, **kwargs)
File "s:\Users\AbigFlea\faceswap\plugins\train\model\_base.py", line 126, in init
self.build()
File "s:\Users\AbigFlea\faceswap\plugins\train\model\_base.py", line 257, in build
raise FaceswapError(str(err)) from err
lib.utils.FaceswapError: To call multi_gpu_model
with gpus=2
, we expect the following devices to be available: ['/cpu:0', '/gpu:0', '/gpu:1']. However this machine only has: ['/cpu:0', '/gpu:0']. Try reducing gpus
.
============ System Information ============
encoding: cp1252
git_branch: master
git_commits: 4153a7e Tools Restructure (#990)
gpu_cuda: 10.2
gpu_cudnn: No global version found. Check Conda packages for Conda cuDNN
gpu_devices: GPU_0: P106-090, GPU_1: P106-090
gpu_devices_active: GPU_0, GPU_1
gpu_driver: 441.22
gpu_vram: GPU_0: 6077MB, GPU_1: 6077MB
os_machine: AMD64
os_platform: Windows-10-10.0.18362-SP0
os_release: 10
py_command: s:\Users\AbigFlea\faceswap\faceswap.py train -A S:/Extracted/Alice/Extract -ala S:/Extracted/Alice/origional/alignments.fsa -B S:/Extracted/GoodNight/Extracted -alb S:/Extracted/GoodNight/Origional/alignments.fsa -m T:/Goodnight-Alice2 -t dfl-sae -bs 10 -it 1000000 -g 2 -s 100 -ss 25000 -ps 50 -ag -L INFO -gui
py_conda_version: conda 4.8.3
py_implementation: CPython
py_version: 3.7.7
py_virtual_env: True
sys_cores: 8
sys_processor: AMD64 Family 21 Model 2 Stepping 0, AuthenticAMD
sys_ram: Total: 16341MB, Available: 9730MB, Used: 6611MB, Free: 9730MB
=============== Pip Packages ===============
absl-py==0.9.0
asn1crypto==1.3.0
astor==0.8.0
blinker==1.4
cachetools==3.1.1
certifi==2019.11.28
cffi==1.14.0
chardet==3.0.4
click==7.1.1
cloudpickle==1.3.0
cryptography==2.8
cycler==0.10.0
cytoolz==0.10.1
dask==2.12.0
decorator==4.4.2
fastcluster==1.1.26
ffmpy==0.2.2
gast==0.2.2
google-auth==1.11.2
google-auth-oauthlib==0.4.1
google-pasta==0.1.8
grpcio==1.27.2
h5py==2.9.0
idna==2.9
imageio==2.6.1
imageio-ffmpeg==0.4.1
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==3.1.3
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
oauthlib==3.1.0
olefile==0.46
opencv-python==4.1.2.30
opt-einsum==3.1.0
pathlib==1.0.1
Pillow==6.2.1
protobuf==3.11.4
psutil==5.7.0
pyasn1==0.4.8
pyasn1-modules==0.2.7
pycparser==2.20
PyJWT==1.7.1
pyOpenSSL==19.1.0
pyparsing==2.4.6
pyreadline==2.1
PySocks==1.7.1
python-dateutil==2.8.1
pytz==2019.3
PyWavelets==1.1.1
pywin32==227
PyYAML==5.3.1
requests==2.23.0
requests-oauthlib==1.3.0
rsa==4.0
scikit-image==0.16.2
scikit-learn==0.22.1
scipy==1.4.1
six==1.14.0
tensorboard==2.1.0
tensorflow==1.15.0
tensorflow-estimator==1.15.1
termcolor==1.1.0
toolz==0.10.0
toposort==1.5
tornado==6.0.4
tqdm==4.43.0
urllib3==1.25.8
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\AbigFlea\MiniConda3\envs\faceswap:
#
Name Version Build Channel
_tflow_select 2.1.0 gpu
absl-py 0.9.0 py37_0
asn1crypto 1.3.0 py37_0
astor 0.8.0 py37_0
blas 1.0 mkl
blinker 1.4 py37_0
ca-certificates 2020.1.1 0
cachetools 3.1.1 py_0
certifi 2019.11.28 py37_1
cffi 1.14.0 py37h7a1dbc1_0
chardet 3.0.4 py37_1003
click 7.1.1 py_0
cloudpickle 1.3.0 py_0
cryptography 2.8 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 2.12.0 py_0
decorator 4.4.2 py_0
fastcluster 1.1.26 py37he350917_0 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-auth 1.11.2 py_0
google-auth-oauthlib 0.4.1 py_2
google-pasta 0.1.8 py_0
grpcio 1.27.2 py37h351948d_0
h5py 2.9.0 py37h5e291fa_0
hdf5 1.10.4 h7ebc959_0
icc_rt 2019.0.0 h0cc432a_1
icu 58.2 ha66f8fd_1
idna 2.9 py_1
imageio 2.6.1 py37_0
imageio-ffmpeg 0.4.1 py_0 conda-forge
intel-openmp 2020.0 166
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
libpng 1.6.37 h2a8f88b_0
libprotobuf 3.11.4 h7bd577a_0
libtiff 4.1.0 h56a325e_0
markdown 3.1.1 py37_0
matplotlib 3.1.1 py37hc8f65d3_0
matplotlib-base 3.1.3 py37h64f37c6_0
mkl 2020.0 166
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
oauthlib 3.1.0 py_0
olefile 0.46 py37_0
opencv-python 4.1.2.30 pypi_0 pypi
openssl 1.1.1e he774522_0
opt_einsum 3.1.0 py_0
pathlib 1.0.1 py37_1
pillow 6.2.1 py37hdc69c19_0
pip 20.0.2 py37_1
protobuf 3.11.4 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_0
pyjwt 1.7.1 py37_0
pyopenssl 19.1.0 py37_0
pyparsing 2.4.6 py_0
pyqt 5.9.2 py37h6538335_2
pyreadline 2.1 py37_1
pysocks 1.7.1 py37_0
python 3.7.7 h60c2a47_0_cpython
python-dateutil 2.8.1 py_0
python_abi 3.7 1_cp37m conda-forge
pytz 2019.3 py_0
pywavelets 1.1.1 py37he774522_0
pywin32 227 py37he774522_1
pyyaml 5.3.1 py37he774522_0
qt 5.9.7 vc14h73c81de_0
requests 2.23.0 py37_0
requests-oauthlib 1.3.0 py_0
rsa 4.0 py_0
scikit-image 0.16.2 py37h47e9c7a_0
scikit-learn 0.22.1 py37h6288b17_0
scipy 1.4.1 py37h9439919_0
setuptools 46.1.1 py37_0
sip 4.19.8 py37h6538335_0
six 1.14.0 py37_0
sqlite 3.31.1 he774522_0
tensorboard 2.1.0 py3_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
tk 8.6.8 hfa6e2cd_0
toolz 0.10.0 py_0
toposort 1.5 py_3 conda-forge
tornado 6.0.4 py37he774522_1
tqdm 4.43.0 py_0
urllib3 1.25.8 py37_0
vc 14.1 h0510ff6_4
vs2015_runtime 14.16.27012 hf0eaf9b_1
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 5.2.4 h2fa13f4_4
yaml 0.1.7 hc54c509_2
zlib 1.2.11 h62dcd97_3
zstd 1.3.7 h508b16e_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
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