def set_configuration(self, cfg_in):
cfg = self.get_configuration()
# Imports used across this func
import os
import torch
from bioharn import detect_predict
# HACK: merge config doesn't support dictionary input
_vital_config_update(cfg, cfg_in)
for key in self._kwiver_config.keys():
self._kwiver_config[key] = str(cfg.get_value(key))
self._kwiver_config['thresh'] = float(self._kwiver_config['thresh'])
self._thresh = float(self._kwiver_config['thresh'])
if self._kwiver_config['batch_size'] == "auto":
self._kwiver_config['batch_size'] = 2
if torch.cuda.is_available():
gpu_mem = 0
if len(self._kwiver_config['xpu']) == 1 and \
self._kwiver_config['xpu'] != 0:
gpu_id = int(self._kwiver_config['xpu'])
gpu_mem = torch.cuda.get_device_properties(
gpu_id).total_memory
else:
self._gpu_count = torch.cuda.device_count()
for i in range(self._gpu_count):
single_gpu_mem = torch.cuda.get_device_properties(
i).total_memory
if gpu_mem == 0:
gpu_mem = single_gpu_mem
else:
gpu_mem = min(gpu_mem, single_gpu_mem)
if gpu_mem > 9e9:
self._kwiver_config['batch_size'] = 4
elif gpu_mem >= 7e9:
self._kwiver_config['batch_size'] = 3
if os.name == 'nt':
os.environ["KWIMAGE_DISABLE_TORCHVISION_NMS"] = "1"
pred_config = detect_predict.DetectPredictConfig()
pred_config['batch_size'] = self._kwiver_config['batch_size']
pred_config['deployed'] = self._kwiver_config['deployed']
if torch.cuda.is_available():
pred_config['xpu'] = self._kwiver_config['xpu']
else:
pred_config['xpu'] = "cpu"
self.predictor = detect_predict.DetectPredictor(pred_config)
self.predictor._ensure_model()
return True
def set_configuration(self, cfg_in):
cfg = self.get_configuration()
# HACK: merge config doesn't support dictionary input
_vital_config_update(cfg, cfg_in)
for key in self._kwiver_config.keys():
self._kwiver_config[key] = str(cfg.get_value(key))
self._kwiver_config['thresh'] = float(self._kwiver_config['thresh'])
self._thresh = float(self._kwiver_config['thresh'])
if self._kwiver_config['batch_size'] == "auto":
self._kwiver_config['batch_size'] = 2
import torch
if torch.cuda.is_available():
gpu_mem = 0
if len(self._kwiver_config['xpu']) == 1 and \
self._kwiver_config['xpu'] != 0:
gpu_id = int(self._kwiver_config['xpu'])
gpu_mem = torch.cuda.get_device_properties(
gpu_id).total_memory
else:
self._gpu_count = torch.cuda.device_count()
for i in range(self._gpu_count):
single_gpu_mem = torch.cuda.get_device_properties(
i).total_memory
if gpu_mem == 0:
gpu_mem = single_gpu_mem
else:
gpu_mem = min(gpu_mem, single_gpu_mem)
if gpu_mem > 9e9:
self._kwiver_config['batch_size'] = 4
elif gpu_mem >= 7e9:
self._kwiver_config['batch_size'] = 3
from bioharn import detect_predict
pred_config = detect_predict.DetectPredictConfig()
pred_config['batch_size'] = self._kwiver_config['batch_size']
pred_config['deployed'] = self._kwiver_config['deployed']
pred_config['xpu'] = self._kwiver_config['xpu']
self.predictor = detect_predict.DetectPredictor(pred_config)
self.predictor._ensure_model()
return True