def test_args_invalid_model_path(self):
device_id = 0
model_path = "xxx_invalid.om"
options = aclruntime.session_options()
with pytest.raises(RuntimeError) as e:
aclruntime.InferenceSession(model_path, device_id, options)
print("when om_path invalid error msg is %s", e)
def infer_dynamicshape():
device_id = 0
options = aclruntime.session_options()
session = aclruntime.InferenceSession(model_path, device_id, options)
# only need call this functon compare infer_simple
session.set_dynamic_shape("actual_input_1:1,3,224,224")
session.set_custom_outsize([10000])
# create new numpy data according inputs info
barray = bytearray(session.get_inputs()[0].realsize)
ndata = np.frombuffer(barray)
# convert numpy to pytensors in device
tensor = aclruntime.Tensor(ndata)
tensor.to_device(device_id)
outnames = [session.get_outputs()[0].name]
feeds = {session.get_inputs()[0].name: tensor}
outputs = session.run(outnames, feeds)
print("outputs:", outputs)
for out in outputs:
out.to_host()
print(session.sumary())
def infer_step():
device_id = 0
options = aclruntime.session_options()
options.log_level = 1
session = aclruntime.InferenceSession(model_path, device_id, options)
# create new numpy data according inputs info
barray = bytearray(session.get_inputs()[0].realsize)
ndata = np.frombuffer(barray)
# convert numpy to pytensors in device
tensor = aclruntime.Tensor(ndata)
tensor.to_device(device_id)
outnames = [session.get_outputs()[0].name]
feeds = {session.get_inputs()[0].name: tensor}
# infer three step function
session.run_setinputs(feeds)
session.run_execute()
outputs = session.run_getoutputs(outnames)
# outputs = session.run(outnames, feeds)
print("step outputs:", outputs)
for out in outputs:
out.to_host()
# sumary inference throughput
print(session.sumary())
def test_args_invalid_device_id(self):
device_id = 100
model_path = self.get_resnet_static_om_path(1)
options = aclruntime.session_options()
with pytest.raises(RuntimeError) as e:
aclruntime.InferenceSession(model_path, device_id, options)
print("when device_id invalid error msg is %s", e)
def infer_run_simultaneous():
device_id = 0
# one call
options = aclruntime.session_options()
session = aclruntime.InferenceSession(model_path, device_id, options)
# create new numpy data according inputs info
barray = bytearray(session.get_inputs()[0].realsize)
ndata = np.frombuffer(barray)
# convert numpy to pytensors in device
tensor = aclruntime.Tensor(ndata)
tensor.to_device(device_id)
outnames = [session.get_outputs()[0].name]
feeds = {session.get_inputs()[0].name: tensor}
# another call
options1 = aclruntime.session_options()
model_path1 = sys.argv[2]
session1 = aclruntime.InferenceSession(model_path1, device_id, options1)
# create new numpy data according inputs info
barray1 = bytearray(session1.get_inputs()[0].realsize)
ndata1 = np.frombuffer(barray1)
# convert numpy to pytensors in device
tensor1 = aclruntime.Tensor(ndata1)
tensor1.to_device(device_id)
outnames1 = [session1.get_outputs()[0].name]
feeds1 = {session1.get_inputs()[0].name: tensor1}
# one run
outputs = session.run(outnames, feeds)
print("outputs:", outputs)
for out in outputs:
out.to_host()
# sumary inference throughput
print(session.sumary())
# another run
outputs1 = session1.run(outnames1, feeds1)
print("outputs1:", outputs1)
for out in outputs1:
out.to_host()
# sumary inference throughput
print(session1.sumary())
def init_inference_session(args):
options = aclruntime.session_options()
if args.acl_json_path != None:
options.acl_json_path = args.acl_json_path
if args.debug == True:
logger.setLevel(logging.DEBUG)
options.log_level = 1
session = aclruntime.InferenceSession(args.model, args.device_id, options)
set_session_options(session, args)
logger.debug("session info:{}".format(session))
return session
def test_infer_invalid_device_id(self):
device_id = 0
options = aclruntime.session_options()
model_path = self.get_resnet_static_om_path(1)
session = aclruntime.InferenceSession(model_path, device_id, options)
# create new numpy data according inputs info
barray = bytearray(session.get_inputs()[0].realsize)
ndata = np.frombuffer(barray)
# convert numpy to pytensors in device
tensor = aclruntime.Tensor(ndata)
with pytest.raises(RuntimeError) as e:
tensor.to_device(device_id + 100)
def test_infer_invalid_input_type(self):
device_id = 0
options = aclruntime.session_options()
model_path = self.get_resnet_static_om_path(1)
session = aclruntime.InferenceSession(model_path, device_id, options)
# create new numpy data according inputs info
barray = bytearray(session.get_inputs()[0].realsize)
ndata = np.frombuffer(barray)
outnames = [session.get_outputs()[0].name]
feeds = {session.get_inputs()[0].name: ndata}
with pytest.raises(TypeError) as e:
outputs = session.run(outnames, feeds)
print("outputs:", outputs)
def load(self, model_path, inputs=None, outputs=None, device_id=0):
self.device_id = device_id
self.model_path = model_path
options = aclruntime.session_options()
#options.log_level = 1
self.session = aclruntime.InferenceSession(model_path, device_id,
options)
if not inputs:
self.inputs = [meta.name for meta in self.session.get_inputs()]
else:
self.inputs = inputs
if not outputs:
self.outputs = [meta.name for meta in self.session.get_outputs()]
else:
self.outputs = outputs
warmup(self.session, self.session.get_inputs(),
self.session.get_outputs(), device_id)
def test_infer_invalid_outname(self):
device_id = 0
options = aclruntime.session_options()
model_path = self.get_resnet_static_om_path(1)
session = aclruntime.InferenceSession(model_path, device_id, options)
# create new numpy data according inputs info
barray = bytearray(session.get_inputs()[0].realsize)
ndata = np.frombuffer(barray)
# convert numpy to pytensors in device
tensor = aclruntime.Tensor(ndata)
tensor.to_device(device_id)
outnames = [session.get_outputs()[0].name + "xxx"]
feeds = {session.get_inputs()[0].name: tensor}
with pytest.raises(RuntimeError) as e:
outputs = session.run(outnames, feeds)
print("outputs:", outputs)
def get_model_info():
device_id = 0
options = aclruntime.session_options()
# 方法1设置级别为debug模式后可以打印模型信息
options.log_level = 1
session = aclruntime.InferenceSession(model_path, device_id, options)
# 方法2 直接打印session 也可以获取模型信息
print(session)
# 方法3 也可以直接通过get接口去获取
intensors_desc = session.get_outputs()
for i, info in enumerate(intensors_desc):
print("input info i:{} shape:{} type:{} val:{} realsize:{} size:{}".
format(i, info.shape, info.datatype, int(info.datatype),
info.realsize, info.size))
intensors_desc = session.get_outputs()
for i, info in enumerate(intensors_desc):
print("outputs info i:{} shape:{} type:{} val:{} realsize:{} size:{}".
format(i, info.shape, info.datatype, int(info.datatype),
info.realsize, info.size))
def test_infer_runcase_empty_outputname(self):
device_id = 0
options = aclruntime.session_options()
model_path = self.get_resnet_static_om_path(1)
session = aclruntime.InferenceSession(model_path, device_id, options)
# create new numpy data according inputs info
barray = bytearray(session.get_inputs()[0].realsize)
ndata = np.frombuffer(barray)
# convert numpy to pytensors in device
tensor = aclruntime.Tensor(ndata)
tensor.to_device(device_id)
outnames = []
feeds = [tensor]
outputs = session.run(outnames, feeds)
print("outputs:", outputs)
for out in outputs:
out.to_host()
print(session.sumary())
def test_infer_no_set_dynamicshape(self):
device_id = 0
options = aclruntime.session_options()
input_tensor_name = self.get_input_tensor_name()
model_path = self.get_resnet_dymshape_om_path()
session = aclruntime.InferenceSession(model_path, device_id, options)
session.set_custom_outsize([10000])
# create new numpy data according inputs info
barray = bytearray(session.get_inputs()[0].realsize)
ndata = np.frombuffer(barray)
# convert numpy to pytensors in device
tensor = aclruntime.Tensor(ndata)
tensor.to_device(device_id)
outnames = [ session.get_outputs()[0].name ]
feeds = { session.get_inputs()[0].name : tensor}
with pytest.raises(RuntimeError) as e:
outputs = session.run(outnames, feeds)
print("outputs:", outputs)
def test_get_input_info(self):
device_id = 0
options = aclruntime.session_options()
input_tensor_name = self.get_input_tensor_name()
model_path = self.get_resnet_dymshape_om_path()
session = aclruntime.InferenceSession(model_path, device_id, options)
session.set_dynamic_shape(input_tensor_name+":1,3,112,112")
session.set_custom_outsize([80000])
basesize = session.get_inputs()[0].realsize
session.set_dynamic_shape(input_tensor_name+":1,3,224,224")
basesize1 = session.get_inputs()[0].realsize
assert basesize1 == basesize*4
session.set_dynamic_shape(input_tensor_name+":2,3,224,224")
basesize2 = session.get_inputs()[0].realsize
assert basesize2 == basesize1*2
session.set_dynamic_shape(input_tensor_name+":8,3,224,224")
basesize3 = session.get_inputs()[0].realsize
assert basesize3 == basesize2*4
def acljson_run():
device_id = 0
options = aclruntime.session_options()
options.acl_json_path = "./acl.json"
session = aclruntime.InferenceSession(model_path, device_id, options)
