def set_model(mdl_file, model_type):
""" This function takes a given model file (with full pathname) as input and returns a
variable that contains the loaded model. The model is loaded into the CPU or the GPU depending
on the type of the model. GMM-HMM model is loaded on the CPU, whereas Flow-based HMM models like
NVP-HMM and Glow-HMM have to be loaded on the GPU
----
Args:
- mdl_file : model file (.mdl extension) with full pathname
- model_type : string that indicates the type of model: "gaus" - GMM, "gen" - NVP, "glow" - Glow
Returns:
- mdl: Variable containing loaded model into the appropriate device
"""
if model_type == 'gaus':
# Loading the GMM-HMM model on the CPU
with open(mdl_file, "rb") as handle:
mdl = pkl.load(handle)
mdl.device = 'cpu'
elif model_type == 'gen' or model_type == 'glow':
# In case the model is NVP-HMM ("gen") or Glow-HMM ("glow")
mdl = load_model(mdl_file)
if torch.cuda.is_available():
if not options["Mul_gpu"]:
# default case, only one gpu
device = torch.device('cuda')
mdl.device = device
mdl.pushto(mdl.device)
else:
# In case Mul_gpu option is set, the model is pushed to mutiple available GPU cores
for i in range(4):
try:
time.sleep(np.random.randint(10))
device = torch.device('cuda:{}'.format(
int(get_freer_gpu())))
# print("Try to push to device: {}".format(device))
mdl.device = device
mdl.pushto(mdl.device)
break
except:
# if push error (maybe memory overflow, try again)
# print("Push to device cuda:{} fail, try again ...")
continue
else:
# In case no GPU device is available, the model is pushed to CPU
mdl.device = 'cpu'
mdl.pushto(mdl.device)
# Set model into eval mode so that no parameters are learned during "Testing" phase
mdl.eval()
# Returns the loaded model file
return mdl
def set_model(mdl_file, model_type):
if model_type == 'gaus':
with open(mdl_file, "rb") as handle:
mdl = pkl.load(handle)
mdl.device = 'cpu'
#f = lambda x: accuracy_fun(x, mdl=mdl)
elif model_type == 'gen' or model_type == 'glow':
mdl = load_model(mdl_file)
if torch.cuda.is_available():
if not options["Mul_gpu"]:
# default case, only one gpu
device = torch.device('cuda')
mdl.device = device
mdl.pushto(mdl.device)
else:
for i in range(4):
try:
time.sleep(np.random.randint(10))
device = torch.device('cuda:{}'.format(
int(get_freer_gpu())))
# print("Try to push to device: {}".format(device))
mdl.device = device
mdl.pushto(mdl.device)
break
except:
# if push error (maybe memory overflow, try again)
# print("Push to device cuda:{} fail, try again ...")
continue
else:
mdl.device = 'cpu'
mdl.pushto(mdl.device)
# set model into eval mode
mdl.eval()
return mdl
mdl.device = 'cpu'
f = lambda x: accuracy_fun(x, mdl=mdl)
elif model_type == 'gen':
mdl = load_model(mdl_file)
if torch.cuda.is_available():
if not options["Mul_gpu"]:
# default case, only one gpu
device = torch.device('cuda')
mdl.device = device
mdl.pushto(mdl.device)
else:
for i in range(4):
try:
time.sleep(np.random.randint(10))
device = torch.device('cuda:{}'.format(
int(get_freer_gpu())))
# print("Try to push to device: {}".format(device))
mdl.device = device
mdl.pushto(mdl.device)
break
except:
# if push error (maybe memory overflow, try again)
# print("Push to device cuda:{} fail, try again ...")
continue
# set model into eval mode
mdl.eval()
f = lambda x: accuracy_fun_torch(
x, mdl=mdl, batch_size_=options["Train"]["eval_batch_size"])
# print("[Acc:] epoch:{}\tclass:{}\tPush model to {}. Done.".format(epoch,iclass, mdl.device), file=sys.stdout)
mdl.iclass = iclass_str
mdl.device = 'cpu'
if torch.cuda.is_available():
if not options["Mul_gpu"]:
# default case, only one gpu
device = torch.device('cuda')
mdl.device = device
mdl.pushto(mdl.device)
else:
for i in range(4):
try:
time.sleep(np.random.randint(20))
device = torch.device('cuda:{}'.format(int(
get_freer_gpu())))
print("Try to push to device: {}".format(device))
mdl.device = device
mdl.pushto(mdl.device)
break
except:
# if push error (maybe memory overflow, try again)
print("Push to device cuda:{} fail, try again ...")
continue
print("epoch:{}\tclass:{}\tPush model to {}. Done.".format(
epoch_str, iclass_str, mdl.device),
file=sys.stdout)
# zero pad data for batch training
max_len_ = max([x.shape[0] for x in xtrain])
xtrain_padded = pad_data(xtrain, max_len_)