def execute_onnx(model,
input_dict,
return_full_exec_context=False,
start_node=None,
end_node=None):
"""Executes given ONNX ModelWrapper with given named inputs.
If return_full_exec_context is False, a dict of named outputs is returned
as indicated by the model.graph.output.
If return return_full_exec_context is True, the full set of tensors used by
the execution (including inputs, weights, activations and final outputs)
will be returned as a dict.
When start_node and end_node are set to None, the whole graph is executed.
If they are set to particular ONNX nodes, only the subgraph between (and
including) those nodes is executed.
"""
if not model.check_all_tensor_shapes_specified():
raise Exception("Found unspecified tensor shapes, try infer_shapes")
ret = model.analysis(ta.nodes_topologically_sorted)
assert (ret["nodes_topologically_sorted"] is True), """Nodes must be
topologically sorted."""
graph = model.graph
# first, we need to make sure that every variable required by the graph has
# some buffer associated with it. this includes graph inputs (which includes
# the input data as well as the trained parameters) and the graph ValueInfo
# (intermediate tensors between layers)
# this is provided by the execution_context, which is a dict of np.ndarray
execution_context = model.make_empty_exec_context()
# fill in any inputs provided to this function
for inp_name in input_dict.keys():
if inp_name in execution_context:
if execution_context[inp_name].shape == input_dict[inp_name].shape:
execution_context[inp_name] = input_dict[inp_name]
else:
raise Exception(
"Shape mismatch for provided input %s: found %s expected %s "
% (
inp_name,
str(execution_context[inp_name].shape),
str(input_dict[inp_name].shape),
))
# else:
# raise Exception("Provided input not found in graph context: %s" % inp_name)
# check if model has an execution mode set
# if None, execute model node by node using execute_node()
# if set to "remote_pynq" execute model on PYNQ board
# if set to "rtlsim" execute model using pyverilator
model_exec_mode = model.get_metadata_prop("exec_mode")
if (model_exec_mode is None) or (model_exec_mode == ""):
# execute the model node by node
# we can simply walk down the list since the ONNX spec guarantees that it is
# topologically sorted
subgraph = []
if start_node is None:
start_node = model.graph.node[0]
if end_node is None:
end_node = model.graph.node[-1]
# select the nodes between specified start/end nodes
start_ind = model.get_node_index(start_node)
end_ind = model.get_node_index(end_node) + 1
assert end_ind >= start_ind, "Start/end nodes must define valid subgraph"
subgraph = graph.node[start_ind:end_ind]
for node in subgraph:
if get_sanitize_quant_tensors() != 0:
# round input values to match quantization annotation
execution_context = sanitize_quant_values(
model, node.input, execution_context)
execute_node(node, execution_context, graph,
return_full_exec_context)
if get_sanitize_quant_tensors() != 0:
# round output values to quantization annotation
execution_context = sanitize_quant_values(
model, node.output, execution_context)
elif model_exec_mode == "remote_pynq":
# use remote exec metadata built into model to execute on a remote PYNQ
remote_exec(model, execution_context)
elif model_exec_mode == "rtlsim":
# use stitched IP for rtlsim
rtlsim_exec(model, execution_context)
else:
raise Exception(
"""Metadata property "exec_mode" is set to an unknown value.
Can be left unset or has to be set to "remote_pynq" for remote execution
on PYNQ board or "rtlsim" for execution using pyverilator!""")
if return_full_exec_context:
return execution_context
else:
# provide outputs as dict
output_dict = dict()
for out_tensor in graph.output:
out_name = out_tensor.name
output_dict[out_name] = execution_context[out_name]
return output_dict
def execute_onnx(model, input_dict, return_full_exec_context=False):
"""Executes given ONNX ModelWrapper with given named inputs.
If return_full_exec_context is False, a dict of named outputs is returned
as indicated by the model.graph.output.
If return return_full_exec_context is True, the full set of tensors used by
the execution (including inputs, weights, activations and final outputs)
will be returned as a dict."""
if not model.check_all_tensor_shapes_specified():
raise Exception("Found unspecified tensor shapes, try infer_shapes")
graph = model.graph
# first, we need to make sure that every variable required by the graph has
# some buffer associated with it. this includes graph inputs (which includes
# the input data as well as the trained parameters) and the graph ValueInfo
# (intermediate tensors between layers)
# this is provided by the execution_context, which is a dict of np.ndarray
execution_context = model.make_empty_exec_context()
# fill in any inputs provided to this function
for inp_name in input_dict.keys():
if inp_name in execution_context:
if execution_context[inp_name].shape == input_dict[inp_name].shape:
execution_context[inp_name] = input_dict[inp_name]
else:
raise Exception(
"Shape mismatch for provided input %s: found %s expected %s "
% (
inp_name,
str(execution_context[inp_name].shape),
str(input_dict[inp_name].shape),
))
# else:
# raise Exception("Provided input not found in graph context: %s" % inp_name)
# check if model has an execution mode set
# if None, execute model node by node using execute_node()
# if set to "remote_pynq" execute model on PYNQ board
# if set to "rtlsim" execute model using pyverilator
model_exec_mode = model.get_metadata_prop("exec_mode")
if (model_exec_mode is None) or (model_exec_mode == ""):
# execute the model node by node
# we can simply walk down the list since the ONNX spec guarantees that it is
# topologically sorted
for node in graph.node:
execute_node(node, execution_context, graph)
elif model_exec_mode == "remote_pynq":
# use remote exec metadata built into model to execute on a remote PYNQ
remote_exec(model, execution_context)
elif model_exec_mode == "rtlsim":
# use stitched IP for rtlsim
rtlsim_exec(model, execution_context)
else:
raise Exception(
"""Metadata property "exec_mode" is set to an unknown value.
Can be left unset or has to be set to "remote_pynq" for remote execution
on PYNQ board or "rtlsim" for execution using pyverilator!""")
if return_full_exec_context:
return execution_context
else:
# provide outputs as dict
output_dict = dict()
for out_tensor in graph.output:
out_name = out_tensor.name
output_dict[out_name] = execution_context[out_name]
return output_dict