def main():
try:
# Setup argument parser
parser = ArgumentParser(
description="GoogleNet for image classification")
parser.add_argument("-p", "--port", default=9999, help="listen port")
parser.add_argument("-C", "--use_cpu", action="store_true")
parser.add_argument("--parameter_file",
default="bvlc_googlenet.pickle",
help="relative path")
# Process arguments
args = parser.parse_args()
port = args.port
# start to train
agent = Agent(port)
serve(agent, args.use_cpu, args.parameter_file)
agent.stop()
except SystemExit:
return
except:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
def main():
try:
# Setup argument parser
parser = ArgumentParser(description=\
"InceptionV3 and V4 for image classification")
parser.add_argument("--model", choices=['v3', 'v4'], default='v4')
parser.add_argument("-p", "--port", default=9999, help="listen port")
parser.add_argument("-C", "--use_cpu", action="store_true")
parser.add_argument("--parameter_file",
default="inception_v4.pickle",
help="relative path")
# Process arguments
args = parser.parse_args()
port = args.port
# start to train
agent = Agent(port)
serve(agent, args.model, args.use_cpu, args.parameter_file)
agent.stop()
except SystemExit:
return
except Exception:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
def main():
try:
# Setup argument parser
parser = ArgumentParser(description=\
"InceptionV3 and V4 for image classification")
parser.add_argument("--model", choices=['v3', 'v4'], default='v4')
parser.add_argument("-p", "--port", default=9999, help="listen port")
parser.add_argument("-C", "--use_cpu", action="store_true")
parser.add_argument("--parameter_file", default="inception_v4.pickle",
help="relative path")
# Process arguments
args = parser.parse_args()
port = args.port
# start to train
agent = Agent(port)
serve(agent, args.model, args.use_cpu, args.parameter_file)
agent.stop()
except SystemExit:
return
except:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
def main():
try:
# Setup argument parser
parser = ArgumentParser(description="VGG inference")
parser.add_argument("--port", default=9999, help="listen port")
parser.add_argument("--use_cpu",
action="store_true",
help="If set, load models onto CPU devices")
parser.add_argument("--parameter_file", default="")
parser.add_argument("--depth",
type=int,
choices=[11, 13, 16, 19],
default='11')
parser.add_argument("--batchnorm",
action='store_true',
help='use batchnorm or not')
# Process arguments
args = parser.parse_args()
port = args.port
# start to train
agent = Agent(port)
net = model.create_net(args.depth, 1000, args.batchnorm, args.use_cpu)
if args.use_cpu:
print('Using CPU')
dev = device.get_default_device()
else:
print('Using GPU')
dev = device.create_cuda_gpu()
net.to_device(dev)
model.init_params(net, args.parameter_file)
print('Finish loading models')
labels = np.loadtxt('synset_words.txt', str, delimiter='\t ')
serve(net, labels, dev, agent)
# acc = evaluate(net, '../val_list.txt', 'image/val', dev)
# print acc
# wait the agent finish handling http request
agent.stop()
except SystemExit:
return
except:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
def main():
try:
# Setup argument parser
parser = ArgumentParser(description="Wide residual network")
parser.add_argument("--port", default=9999, help="listen port")
parser.add_argument("--use_cpu",
action="store_true",
help="If set, load models onto CPU devices")
parser.add_argument("--parameter_file", default="wrn-50-2.pickle")
parser.add_argument("--model",
choices=['resnet', 'wrn', 'preact', 'addbn'],
default='wrn')
parser.add_argument("--depth",
type=int,
choices=[18, 34, 50, 101, 152, 200],
default='50')
# Process arguments
args = parser.parse_args()
port = args.port
# start to train
agent = Agent(port)
net = model.create_net(args.model, args.depth, args.use_cpu)
if args.use_cpu:
print('Using CPU')
dev = device.get_default_device()
else:
print('Using GPU')
dev = device.create_cuda_gpu()
net.to_device(dev)
model.init_params(net, args.parameter_file)
print('Finish loading models')
labels = np.loadtxt('synset_words.txt', str, delimiter='\t ')
serve(net, labels, dev, agent)
# acc = evaluate(net, '../val_list.txt', 'image/val', dev)
# print acc
# wait the agent finish handling http request
agent.stop()
except SystemExit:
return
except:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
def main():
'''Command line options'''
try:
# Setup argument parser
parser = ArgumentParser(description="Train CNN Readmission Model")
parser.add_argument('-inputfolder', type=str, help='inputfolder')
parser.add_argument('-outputfolder', type=str, help='outputfolder')
parser.add_argument('-visfolder', type=str, help='visfolder')
parser.add_argument('-trainratio',
type=float,
help='ratio of train samples')
parser.add_argument('-validationratio',
type=float,
help='ratio of validation samples')
parser.add_argument('-testratio',
type=float,
help='ratio of test samples')
parser.add_argument('-p',
'--port',
default=9989,
help='listening port')
parser.add_argument('-C', '--use_cpu', action="store_true")
parser.add_argument('--max_epoch', default=100)
# Process arguments
args = parser.parse_args()
port = args.port
use_cpu = args.use_cpu
if use_cpu:
print("runing with cpu")
dev = device.get_default_device()
else:
print("runing with gpu")
dev = device.create_cuda_gpu()
# start to train
agent = Agent(port)
train(args.inputfolder, args.outputfolder, args.visfolder,
args.trainratio, args.validationratio, args.testratio, dev,
agent, args.max_epoch, use_cpu)
# wait the agent finish handling http request
agent.stop()
except SystemExit:
return
except:
# p.terminate()
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
def main():
try:
# Setup argument parser
parser = ArgumentParser(description='DenseNet inference')
parser.add_argument("--port", default=9999, help="listen port")
parser.add_argument("--use_cpu",
action="store_true",
help="If set, load models onto CPU devices")
parser.add_argument("--parameter_file", default="densenet-121.pickle")
parser.add_argument("--depth",
type=int,
choices=[121, 169, 201, 161],
default=121)
parser.add_argument('--nb_classes', default=1000, type=int)
# Process arguments
args = parser.parse_args()
port = args.port
# start to train
agent = Agent(port)
net = model.create_net(args.depth, args.nb_classes, 0, args.use_cpu)
if args.use_cpu:
print('Using CPU')
dev = device.get_default_device()
else:
print('Using GPU')
dev = device.create_cuda_gpu()
net.to_device(dev)
print('start to load parameter_file')
model.init_params(net, args.parameter_file)
print('Finish loading models')
labels = np.loadtxt('synset_words.txt', str, delimiter='\t ')
serve(net, labels, dev, agent)
# wait the agent finish handling http request
agent.stop()
except SystemExit:
return
except:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
def main():
try:
# Setup argument parser
parser = ArgumentParser(description="Wide residual network")
parser.add_argument("--port", default=9999, help="listen port")
parser.add_argument("--use_cpu", action="store_true",
help="If set, load models onto CPU devices")
parser.add_argument("--parameter_file", default="wrn-50-2.pickle")
parser.add_argument("--model", choices=['resnet', 'wrn', 'preact',
'addbn'], default='wrn')
parser.add_argument("--depth", type=int, choices=[18, 34, 50, 101,
152, 200],
default='50')
# Process arguments
args = parser.parse_args()
port = args.port
# start to train
agent = Agent(port)
net = model.create_net(args.model, args.depth, args.use_cpu)
if args.use_cpu:
print('Using CPU')
dev = device.get_default_device()
else:
print('Using GPU')
dev = device.create_cuda_gpu()
net.to_device(dev)
model.init_params(net, args.parameter_file)
print('Finish loading models')
labels = np.loadtxt('synset_words.txt', str, delimiter='\t ')
serve(net, labels, dev, agent)
# acc = evaluate(net, '../val_list.txt', 'image/val', dev)
# print acc
# wait the agent finish handling http request
agent.stop()
except SystemExit:
return
except:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
def main():
try:
# Setup argument parser
parser = ArgumentParser(description="VGG inference")
parser.add_argument("--port", default=9999, help="listen port")
parser.add_argument("--use_cpu", action="store_true",
help="If set, load models onto CPU devices")
parser.add_argument("--parameter_file", default="")
parser.add_argument("--depth", type=int, choices=[11, 13, 16, 19],
default='11')
parser.add_argument("--batchnorm", action='store_true',
help='use batchnorm or not')
# Process arguments
args = parser.parse_args()
port = args.port
# start to train
agent = Agent(port)
net = model.create_net(args.depth, 1000, args.batchnorm, args.use_cpu)
if args.use_cpu:
print('Using CPU')
dev = device.get_default_device()
else:
print('Using GPU')
dev = device.create_cuda_gpu()
net.to_device(dev)
model.init_params(net, args.parameter_file)
print('Finish loading models')
labels = np.loadtxt('synset_words.txt', str, delimiter='\t ')
serve(net, labels, dev, agent)
# acc = evaluate(net, '../val_list.txt', 'image/val', dev)
# print acc
# wait the agent finish handling http request
agent.stop()
except SystemExit:
return
except:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
