def export_quant_config(self, export_file=None, adjust_pos=True):
if NndctOption.nndct_param_corr.value > 0:
if self.quant_mode == 1:
# gather bias correction, how to get nn module objec?
for node in self.Nndctgraph.nodes:
if node.op.type in [
NNDCT_OP.CONV1D, NNDCT_OP.CONV2D,
NNDCT_OP.CONVTRANSPOSE2D,
NNDCT_OP.DEPTHWISE_CONV2D, NNDCT_OP.DENSE,
NNDCT_OP.DEPTHWISE_CONVTRANSPOSE2D
]:
if node.module.bias is not None:
self.bias_corr[node.name] = node.module.bias_corr()
# export bias correction
torch.save(self.bias_corr, self.bias_corr_file)
self.bias_corrected = True
# export quant steps
file_name = export_file or self.export_file
if isinstance(file_name, str):
NndctScreenLogger().info(f"=>Exporting quant config.({file_name})")
if adjust_pos:
self.organize_quant_pos()
with open(file_name, 'w') as f:
f.write(nndct_utils.to_jsonstr(self.quant_config))
def export_quant_config(self, export_file=None):
file_name = export_file or self.export_file
if isinstance(file_name, str):
if self.quant_mode in [1, 3]:
self.organize_quant_pos()
with open(file_name, 'w') as f:
f.write(nndct_utils.to_jsonstr(self.quant_config))
def export_quant_config(self, export_file=None):
if self.quant_mode in [1, 3]:
file_name = export_file or self.export_file
if isinstance(file_name, str):
NndctScreenLogger().info(
f"=>Exporting quant config.({file_name})")
self.organize_quant_pos()
with open(file_name, 'w') as f:
f.write(nndct_utils.to_jsonstr(self.quant_config))
def export(self,
file_prefix='NndctGen_graph',
graph_format=False,
with_params=False,
prefix=''):
graph_des = self.as_description(prefix)
with open(file_prefix + NNDCT_KEYS.XMODEL_SUFFIX, 'w') as f:
if graph_format:
f.write(nndct_utils.to_jsonstr(graph_des))
else:
f.write(json.dumps(graph_des))
if with_params:
if os.path.exists(file_prefix + NNDCT_KEYS.XPARAM_SUFFIX):
os.remove(file_prefix + NNDCT_KEYS.XPARAM_SUFFIX)
with nndct_utils.HDFShapedStore(file_prefix +
NNDCT_KEYS.XPARAM_SUFFIX) as store:
for k, v in self._params.items():
store.save(k, v.shape, v.data)