def _get_outputs_using_onnxruntime(self, output_nodes_name):
"""Get outputs using onnxruntime."""
feed_dict = build_feed_dict(self.inferred_model, self.input_nodes)
outputs_infer = fetch_output_from_onnx_model(self.model,
self.model_path,
feed_dict,
output_nodes_name)
return outputs_infer
def _get_outputs_using_onnxruntime(self, output_nodes_name):
"""Get outputs using onnxruntime."""
onnx_inputs = self.inferred_model.graph.input
dtype_dict = dict()
for onnx_input in onnx_inputs:
dtype_dict[onnx_input.name] = DTYPE_MAP[
onnx_input.type.tensor_type.elem_type]
feed_dict = build_feed_dict(self.inferred_model, self.input_nodes)
outputs_infer = fetch_output_from_onnx_model(self.model, feed_dict,
output_nodes_name)
return outputs_infer
def _for_resize():
"""Do resize nodes."""
nonlocal self
output_tensors = []
if not self.dynamic_resize_node:
return
for node in self.dynamic_resize_node:
shape_ref = self._nodes_dict[node].input_name_list[3]
output_tensors.append(shape_ref)
feed_dict = build_feed_dict(self.model, self.input_nodes)
fetch_dict = fetch_output_from_onnx_model(
self.model, feed_dict=feed_dict, output_nodes=output_tensors)
for opt_tensor_name, value in fetch_dict.items():
self.tensors_dict[opt_tensor_name] = OnnxTensor(
value, opt_tensor_name)
def _onnx_infer(self, infer_inputs_shape):
"""
Run onnx inference to get outputs of constant nodes.
Args:
infer_inputs_shape (dict): Input shape for running inference.
"""
feed_dict = build_feed_dict(self._onnx_model, infer_inputs_shape)
output_nodes_name = list()
for node in self._constant_nodes:
output_nodes_name.extend(node.output)
original_outputs = [nd.name for nd in self._onnx_model.graph.output]
self._outputs_infer = fetch_output_from_onnx_model(
self._onnx_model, self.model_path, feed_dict, output_nodes_name)
idx = 0
while idx < len(self._onnx_model.graph.output):
cur_opt = self._onnx_model.graph.output[idx]
if cur_opt.name not in original_outputs:
self._onnx_model.graph.output.remove(cur_opt)
continue
idx += 1
