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now you can train models on multiple GPU's on same workspace without cloning any folders.

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Model files names will be prefixed with GPU index if GPU choosed explicitly on train/convert start.
if you leave GPU idx choice default, then best GPU idx will be choosed and model file names will not contain index prefix.
It gives you possibility to train same fake with various models or options on multiple GPUs.

H64 and H128: now you can choose 'Lighter autoencoder'. It is same as vram gb <= 4 before this update.

added archived_models.zip contains old experiments

RecycleGAN: archived

devicelib: if your system has no NVML installed (some old cards), then it will work with gpu_idx=0 as 'Generic GeForce GPU' with 2GB vram.

refactorings
This commit is contained in:
iperov 2019-01-14 10:48:23 +04:00
parent e2f4677987
commit 1f2b1481ef
9 changed files with 180 additions and 479 deletions
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4
mainscripts/Extractor.py
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@ -68,7 +68,7 @@ class ExtractSubprocessor(SubprocessorBase):
if not multi_gpu or len(devices) == 0:
devices = [nnlib.device.getBestDeviceIdx()]
if len(devices) == 0 or devices[0] == -1:
if len(devices) == 0:
devices = [0]
devices = [ (idx, nnlib.device.getDeviceName(idx), nnlib.device.getDeviceVRAMTotalGb(idx) ) for idx in devices]
@ -263,7 +263,7 @@ class ExtractSubprocessor(SubprocessorBase):
self.e = None
device_config = nnlib.DeviceConfig ( cpu_only=self.cpu_only, force_best_gpu_idx=self.device_idx, allow_growth=True)
device_config = nnlib.DeviceConfig ( cpu_only=self.cpu_only, force_gpu_idx=self.device_idx, allow_growth=True)
if self.type == 'rects':
if self.detector is not None:
if self.detector == 'mt':