def test_dump_tensor(self):
model, dataloader = self.cv_session
augment = ONNXRTAugment(ONNXModel(model),
dataloader, [],
self.augment_path,
iterations=[0, 1],
white_nodes=["conv"])
map_dumped_tensors = augment.dump_tensor()
assert "conv" in map_dumped_tensors["activation"][0]
assert "C" in map_dumped_tensors["activation"][0]["conv"]
assert "conv" in map_dumped_tensors["activation"][1]
assert "C" in map_dumped_tensors["activation"][1]["conv"]
model, dataloader = self.cv_session
augment = ONNXRTAugment(ONNXModel(model),
dataloader, [],
self.augment_path,
iterations=[0],
white_nodes=["conv", "relu"])
map_dumped_tensors = augment.dump_tensor(weight=True)
assert "conv" in map_dumped_tensors["activation"][0]
assert "relu" in map_dumped_tensors["activation"][0]
assert "conv" in map_dumped_tensors["weight"]
model, dataloader = self.nlp_session
augment = ONNXRTAugment(ONNXModel(model),
dataloader, [],
self.augment_path,
iterations=[0],
white_nodes=["gather"])
map_dumped_tensors = augment.dump_tensor()
assert "gather" in map_dumped_tensors["activation"][0]
def test_dump_tensor(self):
model, dataloader = self.cv_session
augment = ONNXRTAugment(model,
dataloader, ["Conv", "Relu"],
self.augment_path,
iterations=[0])
dumped_tensors = augment.dump_tensor()
assert len(dumped_tensors) == 1
assert "A" in dumped_tensors[0] and "B" in dumped_tensors[0]
assert "C" in dumped_tensors[0] and "D" in dumped_tensors[0]
model, dataloader = self.nlp_session
augment = ONNXRTAugment(model,
dataloader, ["Gather"],
self.augment_path,
iterations=[0])
dumped_tensors = augment.dump_tensor()
assert len(dumped_tensors) == 1
assert "A" in dumped_tensors[0] and "C" in dumped_tensors[0]
def inspect_tensor(self,
model,
data_loader,
op_list=[],
iteration_list=[],
inspect_type='activation',
save_to_disk=False):
'''The function is used by tune strategy class for dumping tensor info.
'''
from lpot.adaptor.ox_utils.onnxrt_mid import ONNXRTAugment
from lpot.model.onnx_model import ONNXModel
if not isinstance(model, ONNXModel):
model = ONNXModel(model)
augment = ONNXRTAugment(model, data_loader, [], \
os.path.join(self.work_space, 'augment_for_inspect.onnx'), \
iterations=iteration_list,
white_nodes=op_list)
tensors = augment.dump_tensor(activation=(inspect_type != 'weight'),
weight=(inspect_type != 'activation'))
if save_to_disk:
np.savez(tensors,
os.path.join(self.work_space, 'dumped_tensors.npz'))
return tensors