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added support of AMD videocards

Browse source 
added Intel's plaidML backend to use OpenCL engine. Check new requirements.
smart choosing of backend in device.py
env var 'force_plaidML' can be choosed to forced using plaidML
all tf functions transferred to pure keras
MTCNN transferred to pure keras, but it works slow on plaidML (forced to CPU in this case)
default batch size for all models and VRAMs now 4, feel free to adjust it on your own
SAE: default style options now ZERO, because there are no best values for all scenes, set them on your own.
SAE: return back option pixel_loss, feel free to enable it on your own.
SAE: added option multiscale_decoder default is true, but you can disable it to get 100% same as H,DF,LIAEF model behaviour.
fix converter output to .png
added linux fork reference to doc/doc_build_and_repository_info.md
This commit is contained in:
iperov 2019-02-19 17:33:12 +04:00
parent 3a9d450281
commit 72ba6b103c
24 changed files with 2694 additions and 1489 deletions
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79
mainscripts/Extractor.py
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@ -62,23 +62,35 @@ class ExtractSubprocessor(SubprocessorBase):
cv2.setMouseCallback(self.wnd_name, onMouse, self.param)
def get_devices_for_type (self, type, multi_gpu):
if (type == 'rects' or type == 'landmarks'):
if multi_gpu:
devices = nnlib.device.getDevicesWithAtLeastTotalMemoryGB(2)
if not multi_gpu or len(devices) == 0:
devices = [nnlib.device.getBestDeviceIdx()]
if len(devices) == 0:
devices = [0]
def get_devices_for_type (self, type, multi_gpu, cpu_only):
if not cpu_only and (type == 'rects' or type == 'landmarks'):
if type == 'rects' and self.detector == 'mt' and nnlib.device.backend == "plaidML":
cpu_only = True
else:
if multi_gpu:
devices = nnlib.device.getValidDevicesWithAtLeastTotalMemoryGB(2)
if not multi_gpu or len(devices) == 0:
devices = [nnlib.device.getBestValidDeviceIdx()]
if len(devices) == 0:
devices = [0]
devices = [ (idx, nnlib.device.getDeviceName(idx), nnlib.device.getDeviceVRAMTotalGb(idx) ) for idx in devices]
for idx in devices:
dev_name = nnlib.device.getDeviceName(idx)
dev_vram = nnlib.device.getDeviceVRAMTotalGb(idx)
if not self.manual and self.type == 'rects' and self.detector == 'mt':
for i in range ( int (max (1, dev_vram / 2) ) ):
yield (idx, 'GPU', '%s #%d' % (dev_name,i) , dev_vram)
else:
yield (idx, 'GPU', dev_name, dev_vram)
elif type == 'final':
devices = [ (i, 'CPU%d' % (i), 0 ) for i in range(0, multiprocessing.cpu_count()) ]
return devices
if cpu_only and (type == 'rects' or type == 'landmarks'):
for i in range( min(8, multiprocessing.cpu_count() // 2) ):
yield (i, 'CPU', 'CPU%d' % (i), 0 )
if type == 'final':
for i in range( min(8, multiprocessing.cpu_count()) ):
yield (i, 'CPU', 'CPU%d' % (i), 0 )
#override
def process_info_generator(self):
@ -89,31 +101,13 @@ class ExtractSubprocessor(SubprocessorBase):
'output_dir': str(self.output_path),
'detector': self.detector}
if not self.cpu_only:
for (device_idx, device_name, device_total_vram_gb) in self.get_devices_for_type(self.type, self.multi_gpu):
num_processes = 1
if not self.manual and self.type == 'rects' and self.detector == 'mt':
num_processes = int ( max (1, device_total_vram_gb / 2) )
for i in range(0, num_processes ):
client_dict = base_dict.copy()
client_dict['device_idx'] = device_idx
client_dict['device_name'] = device_name if num_processes == 1 else '%s #%d' % (device_name,i)
client_dict['device_type'] = 'GPU'
yield client_dict['device_name'], {}, client_dict
else:
num_processes = 1
if not self.manual and self.type == 'rects' and self.detector == 'mt':
num_processes = int ( max (1, multiprocessing.cpu_count() / 2 ) )
for i in range(0, num_processes ):
client_dict = base_dict.copy()
client_dict['device_idx'] = 0
client_dict['device_name'] = 'CPU' if num_processes == 1 else 'CPU #%d' % (i),
client_dict['device_type'] = 'CPU'
yield client_dict['device_name'], {}, client_dict
for (device_idx, device_type, device_name, device_total_vram_gb) in self.get_devices_for_type(self.type, self.multi_gpu, self.cpu_only):
client_dict = base_dict.copy()
client_dict['device_idx'] = device_idx
client_dict['device_name'] = device_name
client_dict['device_type'] = device_type
yield client_dict['device_name'], {}, client_dict
#override
def get_no_process_started_message(self):
@ -265,18 +259,17 @@ class ExtractSubprocessor(SubprocessorBase):
self.detector = client_dict['detector']
self.e = None
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':
nnlib.import_all (device_config)
self.e = facelib.MTCExtractor(nnlib.keras, nnlib.tf, nnlib.tf_sess)
self.e = facelib.MTCExtractor()
elif self.detector == 'dlib':
nnlib.import_dlib (device_config)
self.e = facelib.DLIBExtractor(nnlib.dlib)
self.e.__enter__()
elif self.type == 'landmarks':
nnlib.import_all (device_config)
self.e = facelib.LandmarksExtractor(nnlib.keras)