problem starting dlight model
Hi, just want to double verify the problem as i think i might have read somewhere that dlight can't run with optimizing savings and multi gpu. Is this the case? cause i can't seem to get my dlight model to start.
The place to discuss Faceswap and Deepfakes
https://forum.faceswap.dev/
Hi, just want to double verify the problem as i think i might have read somewhere that dlight can't run with optimizing savings and multi gpu. Is this the case? cause i can't seem to get my dlight model to start.
I don't believe that to be the case. Afaik there is nothing intrinsic to dlight to stop it behaving like any other model. What is the specific issue you are having?
Code: Select all
Loading...
Setting Faceswap backend to NVIDIA
02/05/2020 21:36:55 INFO Log level set to: INFO
Using TensorFlow backend.
02/05/2020 21:36:57 INFO Model A Directory: D:\Faceswap Images\A_src\A_aligned384x384
02/05/2020 21:36:57 INFO Model B Directory: D:\Faceswap Images\fan_jilamika_src\combined_faceset
02/05/2020 21:36:57 INFO Training data directory: D:\Faceswap Images\jilamikariona_dlightmod
02/05/2020 21:36:57 INFO ===================================================
02/05/2020 21:36:57 INFO Starting
02/05/2020 21:36:57 INFO Press 'Stop' to save and quit
02/05/2020 21:36:57 INFO ===================================================
02/05/2020 21:36:58 INFO Loading data, this may take a while...
02/05/2020 21:36:58 INFO Loading Model from Dlight plugin...
02/05/2020 21:36:58 INFO Using Optimizer Savings
02/05/2020 21:36:58 INFO No existing state file found. Generating.
02/05/2020 21:36:58 INFO Using Convolutional Aware Initialization. Model generation will take a few minutes...
02/05/2020 21:36:58 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 1024, 512)
02/05/2020 21:36:59 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 1024, 128)
02/05/2020 21:37:00 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 256, 256)
02/05/2020 21:37:00 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 256, 64)
02/05/2020 21:37:00 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 128, 128)
02/05/2020 21:37:00 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 128, 32)
02/05/2020 21:37:00 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 64, 64)
02/05/2020 21:37:00 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 64, 16)
02/05/2020 21:37:00 INFO Calculating Convolution Aware Initializer for shape: (5, 5, 32, 3)
02/05/2020 21:37:00 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 1024, 256)
02/05/2020 21:37:01 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 1024, 64)
02/05/2020 21:37:01 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 128, 128)
02/05/2020 21:37:01 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 128, 32)
02/05/2020 21:37:01 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 64, 64)
02/05/2020 21:37:01 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 64, 16)
02/05/2020 21:37:01 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 32, 32)
02/05/2020 21:37:01 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 32, 8)
02/05/2020 21:37:01 INFO Calculating Convolution Aware Initializer for shape: (5, 5, 16, 1)
02/05/2020 21:37:04 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 1024, 9216)
02/05/2020 21:37:30 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 1024, 256)
02/05/2020 21:37:31 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 512)
02/05/2020 21:37:32 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 512)
02/05/2020 21:37:32 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 512)
02/05/2020 21:37:33 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 512)
02/05/2020 21:37:34 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 512)
02/05/2020 21:37:34 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 512)
02/05/2020 21:37:35 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 1024)
02/05/2020 21:37:37 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 256)
02/05/2020 21:37:37 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 512)
02/05/2020 21:37:38 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 512)
02/05/2020 21:37:38 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 512)
02/05/2020 21:37:39 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 512)
02/05/2020 21:37:52 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 512)
02/05/2020 21:37:53 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 128)
02/05/2020 21:37:53 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 256, 256)
02/05/2020 21:37:53 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 256, 256)
02/05/2020 21:37:53 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 256, 256)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 256, 64)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 128, 128)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 128, 128)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 128, 128)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 128, 32)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (5, 5, 64, 3)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 256)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 64)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 128, 128)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 128, 32)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 64, 64)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 64, 16)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 32, 32)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (3, 3, 32, 8)
02/05/2020 21:37:54 INFO Calculating Convolution Aware Initializer for shape: (5, 5, 16, 1)
02/05/2020 21:37:58 INFO Calculating Convolution Aware Initializer for shape: (5, 5, 3, 32)
02/05/2020 21:37:58 INFO Calculating Convolution Aware Initializer for shape: (5, 5, 35, 64)
02/05/2020 21:37:58 INFO Calculating Convolution Aware Initializer for shape: (5, 5, 99, 128)
02/05/2020 21:37:58 INFO Calculating Convolution Aware Initializer for shape: (5, 5, 227, 256)
02/05/2020 21:37:58 INFO Calculating Convolution Aware Initializer for shape: (5, 5, 483, 512)
02/05/2020 21:38:04 INFO Creating new 'dlight' model in folder: 'D:\Faceswap Images\jilamikariona_dlightmod'
02/05/2020 21:38:07 CRITICAL Error caught! Exiting...
02/05/2020 21:38:07 ERROR Caught exception in thread: '_training_0'
02/05/2020 21:38:10 ERROR Got Exception on main handler:
Traceback (most recent call last):
File "C:\Users\Howard\faceswap\lib\cli.py", line 128, in execute_script
process.process()
File "C:\Users\Howard\faceswap\scripts\train.py", line 159, in process
self._end_thread(thread, err)
File "C:\Users\Howard\faceswap\scripts\train.py", line 199, in _end_thread
thread.join()
File "C:\Users\Howard\faceswap\lib\multithreading.py", line 121, in join
raise thread.err[1].with_traceback(thread.err[2])
File "C:\Users\Howard\faceswap\lib\multithreading.py", line 37, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\Howard\faceswap\scripts\train.py", line 224, in _training
raise err
File "C:\Users\Howard\faceswap\scripts\train.py", line 212, in _training
model = self._load_model()
File "C:\Users\Howard\faceswap\scripts\train.py", line 253, in _load_model
predict=False)
File "C:\Users\Howard\faceswap\plugins\train\model\dlight.py", line 85, in __init__
super().__init__(*args, **kwargs)
File "C:\Users\Howard\faceswap\plugins\train\model\original.py", line 25, in __init__
super().__init__(*args, **kwargs)
File "C:\Users\Howard\faceswap\plugins\train\model\_base.py", line 126, in __init__
self.build()
File "C:\Users\Howard\faceswap\plugins\train\model\dlight.py", line 132, in build
super().build()
File "C:\Users\Howard\faceswap\plugins\train\model\_base.py", line 248, in build
self.build_autoencoders(inputs)
File "C:\Users\Howard\faceswap\plugins\train\model\original.py", line 44, in build_autoencoders
self.add_predictor(side, autoencoder)
File "C:\Users\Howard\faceswap\plugins\train\model\_base.py", line 326, in add_predictor
model = multi_gpu_model(model, self.gpus)
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\utils\multi_gpu_utils.py", line 227, in multi_gpu_model
outputs = model(inputs)
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\base_layer.py", line 457, in __call__
output = self.call(inputs, **kwargs)
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\network.py", line 564, in call
output_tensors, _, _ = self.run_internal_graph(inputs, masks)
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\network.py", line 721, in run_internal_graph
layer.call(computed_tensor, **kwargs))
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\network.py", line 564, in call
output_tensors, _, _ = self.run_internal_graph(inputs, masks)
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\network.py", line 721, in run_internal_graph
layer.call(computed_tensor, **kwargs))
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\layers\normalization.py", line 185, in call
epsilon=self.epsilon)
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\backend\tensorflow_backend.py", line 1858, in normalize_batch_in_training
if not _has_nchw_support() and list(reduction_axes) == [0, 2, 3]:
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\backend\tensorflow_backend.py", line 291, in _has_nchw_support
explicitly_on_cpu = _is_current_explicit_device('CPU')
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\backend\tensorflow_backend.py", line 266, in _is_current_explicit_device
device = _get_current_tf_device()
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\backend\tensorflow_backend.py", line 247, in _get_current_tf_device
g._apply_device_functions(op)
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\tensorflow_core\python\framework\ops.py", line 4398, in _apply_device_functions
op._set_device_from_string(device_string)
AttributeError: '_TfDeviceCaptureOp' object has no attribute '_set_device_from_string'
02/05/2020 21:38:10 CRITICAL An unexpected crash has occurred. Crash report written to 'C:\Users\Howard\faceswap\crash_report.2020.02.05.213809959053.log'. You MUST provide this file if seeking assistance. Please verify you are running the latest version of faceswap before reporting
Process exited.
Hi torzdf, above is what i'm seeing. Below is the crash log
Code: Select all
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <lib.model.initializers.ConvolutionAware object at 0x0000024607A0CD08>
02/05/2020 21:37:54 MainProcess _training_0 initializers __call__ INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 256)
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("upscale2x_hyb_8_concatenate/concat:0", shape=(?, 24, 24, 512), dtype=float32), filters: 64, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale2x_hyb_13_conv2d'})
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <lib.model.initializers.ConvolutionAware object at 0x0000024607A0C788>
02/05/2020 21:37:54 MainProcess _training_0 initializers __call__ INFO Calculating Convolution Aware Initializer for shape: (3, 3, 512, 64)
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale2x_hyb_14
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks upscale DEBUG inp: Tensor("upscale2x_hyb_13_concatenate/concat:0", shape=(?, 48, 48, 128), dtype=float32), filters: 32, kernel_size: 3, use_instance_norm: False, kwargs: {})
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale_48_3
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <lib.model.initializers.ConvolutionAware object at 0x0000024606288788>
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("upscale2x_hyb_13_concatenate/concat:0", shape=(?, 48, 48, 128), dtype=float32), filters: 128, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale_48_3_conv2d', 'kernel_initializer': <lib.model.initializers.ConvolutionAware object at 0x0000024606288788>})
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <lib.model.initializers.ConvolutionAware object at 0x0000024606288788>
02/05/2020 21:37:54 MainProcess _training_0 initializers __call__ INFO Calculating Convolution Aware Initializer for shape: (3, 3, 128, 128)
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("upscale2x_hyb_13_concatenate/concat:0", shape=(?, 48, 48, 128), dtype=float32), filters: 32, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale2x_hyb_14_conv2d'})
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <lib.model.initializers.ConvolutionAware object at 0x0000024606282C48>
02/05/2020 21:37:54 MainProcess _training_0 initializers __call__ INFO Calculating Convolution Aware Initializer for shape: (3, 3, 128, 32)
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale2x_hyb_15
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks upscale DEBUG inp: Tensor("upscale2x_hyb_14_concatenate/concat:0", shape=(?, 96, 96, 64), dtype=float32), filters: 16, kernel_size: 3, use_instance_norm: False, kwargs: {})
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale_96_3
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <lib.model.initializers.ConvolutionAware object at 0x00000246062B4D08>
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("upscale2x_hyb_14_concatenate/concat:0", shape=(?, 96, 96, 64), dtype=float32), filters: 64, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale_96_3_conv2d', 'kernel_initializer': <lib.model.initializers.ConvolutionAware object at 0x00000246062B4D08>})
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <lib.model.initializers.ConvolutionAware object at 0x00000246062B4D08>
02/05/2020 21:37:54 MainProcess _training_0 initializers __call__ INFO Calculating Convolution Aware Initializer for shape: (3, 3, 64, 64)
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("upscale2x_hyb_14_concatenate/concat:0", shape=(?, 96, 96, 64), dtype=float32), filters: 16, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale2x_hyb_15_conv2d'})
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <lib.model.initializers.ConvolutionAware object at 0x00000246062B4F08>
02/05/2020 21:37:54 MainProcess _training_0 initializers __call__ INFO Calculating Convolution Aware Initializer for shape: (3, 3, 64, 16)
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale2x_hyb_16
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks upscale DEBUG inp: Tensor("upscale2x_hyb_15_concatenate/concat:0", shape=(?, 192, 192, 32), dtype=float32), filters: 8, kernel_size: 3, use_instance_norm: False, kwargs: {})
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: upscale_192_3
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <lib.model.initializers.ConvolutionAware object at 0x00000246047FCA08>
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("upscale2x_hyb_15_concatenate/concat:0", shape=(?, 192, 192, 32), dtype=float32), filters: 32, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale_192_3_conv2d', 'kernel_initializer': <lib.model.initializers.ConvolutionAware object at 0x00000246047FCA08>})
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Using model specified initializer: <lib.model.initializers.ConvolutionAware object at 0x00000246047FCA08>
02/05/2020 21:37:54 MainProcess _training_0 initializers __call__ INFO Calculating Convolution Aware Initializer for shape: (3, 3, 32, 32)
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("upscale2x_hyb_15_concatenate/concat:0", shape=(?, 192, 192, 32), dtype=float32), filters: 8, kernel_size: 3, strides: (1, 1), padding: same, kwargs: {'name': 'upscale2x_hyb_16_conv2d'})
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <lib.model.initializers.ConvolutionAware object at 0x00000246047EC588>
02/05/2020 21:37:54 MainProcess _training_0 initializers __call__ INFO Calculating Convolution Aware Initializer for shape: (3, 3, 32, 8)
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("upscale2x_hyb_16_concatenate/concat:0", shape=(?, 384, 384, 16), dtype=float32), filters: 1, kernel_size: 5, strides: (1, 1), padding: same, kwargs: {'activation': 'sigmoid', 'name': 'mask_out'})
02/05/2020 21:37:54 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <lib.model.initializers.ConvolutionAware object at 0x00000246044E5FC8>
02/05/2020 21:37:54 MainProcess _training_0 initializers __call__ INFO Calculating Convolution Aware Initializer for shape: (5, 5, 16, 1)
02/05/2020 21:37:54 MainProcess _training_0 _base add_network DEBUG network_type: 'decoder', side: 'b', network: '<keras.engine.training.Model object at 0x00000246044EE9C8>', is_output: True
02/05/2020 21:37:54 MainProcess _training_0 _base name DEBUG model name: 'dlight'
02/05/2020 21:37:54 MainProcess _training_0 _base add_network DEBUG name: 'decoder_b', filename: 'dlight_decoder_B.h5'
02/05/2020 21:37:54 MainProcess _training_0 _base __init__ DEBUG Initializing NNMeta: (filename: 'D:\Faceswap Images\jilamikariona_dlightmod\dlight_decoder_B.h5', network_type: 'decoder', side: 'b', network: <keras.engine.training.Model object at 0x00000246044EE9C8>, is_output: True
02/05/2020 21:37:58 MainProcess _training_0 _base __init__ DEBUG Initialized NNMeta
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks conv DEBUG inp: Tensor("input_3:0", shape=(?, 128, 128, 3), dtype=float32), filters: 32, kernel_size: 5, strides: 2, use_instance_norm: False, kwargs: {})
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv_128_0
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("input_3:0", shape=(?, 128, 128, 3), dtype=float32), filters: 32, kernel_size: 5, strides: 2, padding: same, kwargs: {'name': 'conv_128_0_conv2d'})
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <lib.model.initializers.ConvolutionAware object at 0x0000024604D10DC8>
02/05/2020 21:37:58 MainProcess _training_0 initializers __call__ INFO Calculating Convolution Aware Initializer for shape: (5, 5, 3, 32)
02/05/2020 21:37:58 MainProcess _training_0 module_wrapper _tfmw_add_deprecation_warning DEBUG From C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\backend\tensorflow_backend.py:3980: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks conv DEBUG inp: Tensor("concatenate_1/concat:0", shape=(?, 64, 64, 35), dtype=float32), filters: 64, kernel_size: 5, strides: 2, use_instance_norm: False, kwargs: {})
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv_64_0
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("concatenate_1/concat:0", shape=(?, 64, 64, 35), dtype=float32), filters: 64, kernel_size: 5, strides: 2, padding: same, kwargs: {'name': 'conv_64_0_conv2d'})
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <lib.model.initializers.ConvolutionAware object at 0x000002460481A348>
02/05/2020 21:37:58 MainProcess _training_0 initializers __call__ INFO Calculating Convolution Aware Initializer for shape: (5, 5, 35, 64)
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks conv DEBUG inp: Tensor("concatenate_2/concat:0", shape=(?, 32, 32, 99), dtype=float32), filters: 128, kernel_size: 5, strides: 2, use_instance_norm: False, kwargs: {})
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv_32_0
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("concatenate_2/concat:0", shape=(?, 32, 32, 99), dtype=float32), filters: 128, kernel_size: 5, strides: 2, padding: same, kwargs: {'name': 'conv_32_0_conv2d'})
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <lib.model.initializers.ConvolutionAware object at 0x0000024607A43C08>
02/05/2020 21:37:58 MainProcess _training_0 initializers __call__ INFO Calculating Convolution Aware Initializer for shape: (5, 5, 99, 128)
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks conv DEBUG inp: Tensor("concatenate_3/concat:0", shape=(?, 16, 16, 227), dtype=float32), filters: 256, kernel_size: 5, strides: 2, use_instance_norm: False, kwargs: {})
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv_16_0
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("concatenate_3/concat:0", shape=(?, 16, 16, 227), dtype=float32), filters: 256, kernel_size: 5, strides: 2, padding: same, kwargs: {'name': 'conv_16_0_conv2d'})
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <lib.model.initializers.ConvolutionAware object at 0x0000024607A4B708>
02/05/2020 21:37:58 MainProcess _training_0 initializers __call__ INFO Calculating Convolution Aware Initializer for shape: (5, 5, 227, 256)
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks conv DEBUG inp: Tensor("concatenate_4/concat:0", shape=(?, 8, 8, 483), dtype=float32), filters: 512, kernel_size: 5, strides: 2, use_instance_norm: False, kwargs: {})
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks get_name DEBUG Generating block name: conv_8_0
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks conv2d DEBUG inp: Tensor("concatenate_4/concat:0", shape=(?, 8, 8, 483), dtype=float32), filters: 512, kernel_size: 5, strides: 2, padding: same, kwargs: {'name': 'conv_8_0_conv2d'})
02/05/2020 21:37:58 MainProcess _training_0 nn_blocks set_default_initializer DEBUG Set default kernel_initializer to: <lib.model.initializers.ConvolutionAware object at 0x0000024607A69D08>
02/05/2020 21:37:58 MainProcess _training_0 initializers __call__ INFO Calculating Convolution Aware Initializer for shape: (5, 5, 483, 512)
02/05/2020 21:38:00 MainProcess _training_0 module_wrapper _tfmw_add_deprecation_warning DEBUG From C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\backend\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n
02/05/2020 21:38:00 MainProcess _training_0 deprecation new_func DEBUG From C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\backend\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
02/05/2020 21:38:00 MainProcess _training_0 _base add_network DEBUG network_type: 'encoder', side: 'None', network: '<keras.engine.training.Model object at 0x0000024829588E88>', is_output: False
02/05/2020 21:38:00 MainProcess _training_0 _base name DEBUG model name: 'dlight'
02/05/2020 21:38:00 MainProcess _training_0 _base add_network DEBUG name: 'encoder', filename: 'dlight_encoder.h5'
02/05/2020 21:38:00 MainProcess _training_0 _base __init__ DEBUG Initializing NNMeta: (filename: 'D:\Faceswap Images\jilamikariona_dlightmod\dlight_encoder.h5', network_type: 'encoder', side: 'None', network: <keras.engine.training.Model object at 0x0000024829588E88>, is_output: False
02/05/2020 21:38:04 MainProcess _training_0 _base __init__ DEBUG Initialized NNMeta
02/05/2020 21:38:04 MainProcess _training_0 dlight add_networks DEBUG Added networks
02/05/2020 21:38:04 MainProcess _training_0 _base load_models DEBUG Load model: (swapped: False)
02/05/2020 21:38:04 MainProcess _training_0 _base models_exist DEBUG Pre-existing models exist: False
02/05/2020 21:38:04 MainProcess _training_0 _base name DEBUG model name: 'dlight'
02/05/2020 21:38:04 MainProcess _training_0 _base load_models INFO Creating new 'dlight' model in folder: 'D:\Faceswap Images\jilamikariona_dlightmod'
02/05/2020 21:38:04 MainProcess _training_0 _base get_inputs DEBUG Getting inputs
02/05/2020 21:38:04 MainProcess _training_0 _base get_inputs DEBUG Got inputs: [<tf.Tensor 'face_in:0' shape=(?, 128, 128, 3) dtype=float32>, <tf.Tensor 'mask_in:0' shape=(?, 384, 384, 1) dtype=float32>]
02/05/2020 21:38:04 MainProcess _training_0 original build_autoencoders DEBUG Initializing model
02/05/2020 21:38:04 MainProcess _training_0 original build_autoencoders DEBUG Adding Autoencoder. Side: a
02/05/2020 21:38:04 MainProcess _training_0 _base add_predictor DEBUG Adding predictor: (side: 'a', model: <keras.engine.training.Model object at 0x0000024607467888>)
02/05/2020 21:38:04 MainProcess _training_0 _base add_predictor DEBUG Converting to multi-gpu: side a
02/05/2020 21:38:04 MainProcess _training_0 _base store_input_shapes DEBUG Adding input shapes to state for model
02/05/2020 21:38:04 MainProcess _training_0 _base store_input_shapes DEBUG Added input shapes: {'face_in:0': (128, 128, 3), 'mask_in:0': (384, 384, 1)}
02/05/2020 21:38:04 MainProcess _training_0 original build_autoencoders DEBUG Adding Autoencoder. Side: b
02/05/2020 21:38:04 MainProcess _training_0 _base add_predictor DEBUG Adding predictor: (side: 'b', model: <keras.engine.training.Model object at 0x00000246073F3E48>)
02/05/2020 21:38:04 MainProcess _training_0 _base add_predictor DEBUG Converting to multi-gpu: side b
02/05/2020 21:38:07 MainProcess _training_0 multithreading run DEBUG Error in thread (_training_0): '_TfDeviceCaptureOp' object has no attribute '_set_device_from_string'
02/05/2020 21:38:07 MainProcess MainThread train _monitor DEBUG Thread error detected
02/05/2020 21:38:07 MainProcess MainThread train _monitor DEBUG Closed Monitor
02/05/2020 21:38:07 MainProcess MainThread train _end_thread DEBUG Ending Training thread
02/05/2020 21:38:07 MainProcess MainThread train _end_thread CRITICAL Error caught! Exiting...
02/05/2020 21:38:07 MainProcess MainThread multithreading join DEBUG Joining Threads: '_training'
02/05/2020 21:38:07 MainProcess MainThread multithreading join DEBUG Joining Thread: '_training_0'
02/05/2020 21:38:07 MainProcess MainThread multithreading join ERROR Caught exception in thread: '_training_0'
Traceback (most recent call last):
File "C:\Users\Howard\faceswap\lib\cli.py", line 128, in execute_script
process.process()
File "C:\Users\Howard\faceswap\scripts\train.py", line 159, in process
self._end_thread(thread, err)
File "C:\Users\Howard\faceswap\scripts\train.py", line 199, in _end_thread
thread.join()
File "C:\Users\Howard\faceswap\lib\multithreading.py", line 121, in join
raise thread.err[1].with_traceback(thread.err[2])
File "C:\Users\Howard\faceswap\lib\multithreading.py", line 37, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\Howard\faceswap\scripts\train.py", line 224, in _training
raise err
File "C:\Users\Howard\faceswap\scripts\train.py", line 212, in _training
model = self._load_model()
File "C:\Users\Howard\faceswap\scripts\train.py", line 253, in _load_model
predict=False)
File "C:\Users\Howard\faceswap\plugins\train\model\dlight.py", line 85, in __init__
super().__init__(*args, **kwargs)
File "C:\Users\Howard\faceswap\plugins\train\model\original.py", line 25, in __init__
super().__init__(*args, **kwargs)
File "C:\Users\Howard\faceswap\plugins\train\model\_base.py", line 126, in __init__
self.build()
File "C:\Users\Howard\faceswap\plugins\train\model\dlight.py", line 132, in build
super().build()
File "C:\Users\Howard\faceswap\plugins\train\model\_base.py", line 248, in build
self.build_autoencoders(inputs)
File "C:\Users\Howard\faceswap\plugins\train\model\original.py", line 44, in build_autoencoders
self.add_predictor(side, autoencoder)
File "C:\Users\Howard\faceswap\plugins\train\model\_base.py", line 326, in add_predictor
model = multi_gpu_model(model, self.gpus)
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\utils\multi_gpu_utils.py", line 227, in multi_gpu_model
outputs = model(inputs)
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\base_layer.py", line 457, in __call__
output = self.call(inputs, **kwargs)
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\network.py", line 564, in call
output_tensors, _, _ = self.run_internal_graph(inputs, masks)
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\network.py", line 721, in run_internal_graph
layer.call(computed_tensor, **kwargs))
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\network.py", line 564, in call
output_tensors, _, _ = self.run_internal_graph(inputs, masks)
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\engine\network.py", line 721, in run_internal_graph
layer.call(computed_tensor, **kwargs))
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\layers\normalization.py", line 185, in call
epsilon=self.epsilon)
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\backend\tensorflow_backend.py", line 1858, in normalize_batch_in_training
if not _has_nchw_support() and list(reduction_axes) == [0, 2, 3]:
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\backend\tensorflow_backend.py", line 291, in _has_nchw_support
explicitly_on_cpu = _is_current_explicit_device('CPU')
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\backend\tensorflow_backend.py", line 266, in _is_current_explicit_device
device = _get_current_tf_device()
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\keras\backend\tensorflow_backend.py", line 247, in _get_current_tf_device
g._apply_device_functions(op)
File "C:\Users\Howard\MiniConda3\envs\faceswap\lib\site-packages\tensorflow_core\python\framework\ops.py", line 4398, in _apply_device_functions
op._set_device_from_string(device_string)
AttributeError: '_TfDeviceCaptureOp' object has no attribute '_set_device_from_string'
============ System Information ============
encoding: cp1252
git_branch: master
git_commits: 76bf610 plugins.extract.mask - Enable allow_growth option for mask tool
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: GeForce RTX 2070 SUPER, GPU_1: GeForce RTX 2070 SUPER
gpu_devices_active: GPU_0, GPU_1
gpu_driver: 442.19
gpu_vram: GPU_0: 8192MB, GPU_1: 8192MB
os_machine: AMD64
os_platform: Windows-10-10.0.18362-SP0
os_release: 10
py_command: C:\Users\Howard\faceswap\faceswap.py train -A D:/Faceswap Images/A_src/A_aligned384x384 -ala D:/Faceswap Images/A_src/alignments.fsa -B D:/Faceswap Images/fan_jilamika_src/combined_faceset -alb D:/Faceswap Images/fan_jilamika_src/alignments_merged_20200204_043810.fsa -m D:/Faceswap Images/jilamikariona_dlightmod -t dlight -bs 4 -it 5000000 -g 2 -o -s 1000 -ss 50000 -ps 50 -L INFO -gui
py_conda_version: conda 4.8.1
py_implementation: CPython
py_version: 3.7.6
py_virtual_env: True
sys_cores: 24
sys_processor: AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD
sys_ram: Total: 32697MB, Available: 21252MB, Used: 11445MB, Free: 21252MB
=============== Pip Packages ===============
absl-py==0.8.1
astor==0.8.0
certifi==2019.11.28
cloudpickle==1.2.2
cycler==0.10.0
cytoolz==0.10.1
dask==2.10.0
decorator==4.4.1
fastcluster==1.1.26
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
protobuf==3.11.2
psutil==5.6.7
pyparsing==2.4.6
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==0.22.1
scipy==1.3.2
six==1.14.0
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==4.42.0
Werkzeug==0.16.0
wincertstore==0.2
wrapt==1.11.2
============== Conda Packages ==============
# packages in environment at C:\Users\Howard\MiniConda3\envs\faceswap:
#
# Name Version Build Channel
_tflow_select 2.1.0 gpu
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
cloudpickle 1.2.2 py_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 2.10.0 py_0
decorator 4.4.1 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-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 20.0.2 py37_0
protobuf 3.11.2 py37h33f27b4_0
psutil 5.6.7 py37he774522_0
pyparsing 2.4.6 py_0
pyqt 5.9.2 py37h6538335_2
pyreadline 2.1 py37_1
python 3.7.6 h60c2a47_2
python-dateutil 2.8.1 py_0
pytz 2019.3 py_0
pywavelets 1.1.1 py37he774522_0
pywin32 227 py37he774522_1
pyyaml 5.2 py37he774522_0
qt 5.9.7 vc14h73c81de_0
scikit-image 0.15.0 py37ha925a31_0
scikit-learn 0.22.1 py37h6288b17_0
scipy 1.3.2 py37h29ff71c_0
setuptools 45.1.0 py37_0
sip 4.19.8 py37h6538335_0
six 1.14.0 py37_0
sqlite 3.30.1 he774522_0
tensorboard 2.0.0 pyhb38c66f_1
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 6.0.3 py37he774522_0
tqdm 4.42.0 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
================= 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: gaussian
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: jpg
draw_transparent: False
jpg_quality: 85
png_compress_level: 3
[writer.pillow]
format: jpg
draw_transparent: False
optimize: False
gif_interlace: True
jpg_quality: 95
png_compress_level: 3
tif_compression: tiff_deflate
--------- extract.ini ---------
[global]
allow_growth: True
[align.fan]
batch-size: 64
[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: 75
batch-size: 64
[mask.unet_dfl]
batch-size: 64
[mask.vgg_clear]
batch-size: 64
[mask.vgg_obstructed]
batch-size: 64
--------- 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: 72.0
mask_type: vgg-obstructed
mask_blur_kernel: 3
mask_threshold: 4
learn_mask: True
icnr_init: False
conv_aware_init: True
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: 256
clipnorm: True
architecture: df
autoencoder_dims: 0
encoder_dims: 42
decoder_dims: 21
multiscale_decoder: True
[model.dlight]
features: best
details: good
output_size: 384
[model.original]
lowmem: False
[model.realface]
input_size: 128
output_size: 256
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
Hi, I can start dlight on 256 output with single gpu at bs 16 but i can't get mgpu to work. i have 2xrtx 2070s. Once i change to 2 gpu the model will not even start.
I also tried training 384 output on dlight with single gpu since i succeeded with 256 single gpu but have failed to start as well. my extracted faceset is at 384x384 if this is any help.
Ok, the problem is with Keras and tf 1.15 with multi-gpu....
If you downgrade Tensorflow to 1.13.1 you should get it working fine.
should i also downgrade my CUDA? since faceswap auto downloads the needed configs and pip downloaded 1.15 is it because i'm running CUDA 10.2?
Assuming you have used the installer (and it looks like you have) I would just let conda handle it.
Off the top of my head (so I apologize if I miss a step)
Start > Anaconda Prompt
Inside your anaconda prompt:
Code: Select all
conda activate faceswap
conda remove tensorflow*
conda remove cudatoolkit
conda remove cudnn
conda install tensorflow-gpu==1.13
torzdf wrote: ↑Wed Feb 05, 2020 3:44 pmAssuming you have used the installer (and it looks like you have) I would just let conda handle it.
Off the top of my head (so I apologize if I miss a step)
Start > Anaconda Prompt
Inside your anaconda prompt:Code: Select all
conda activate faceswap conda remove tensorflow* conda remove cudatoolkit conda remove cudnn conda install tensorflow-gpu==1.13
thanks torzdf. i followed your instruction and installed 1.13
conda remove tensorflow* ---------- worked
conda remove cudatoolkit and conda remove cudnn didn't work though
will this be of any problem?
conda install tensorflow-gpu=1.13 ----------- worked
To be honest, Conda should be clever enough to work it out. Try it, and if you still have problems, let us know.