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Generate name for each member of list arg #571

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Generate name for each member of list arg #571

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6c73264
Generate name for each member of list arg
xuzijian629 Jul 12, 2022
0e9f943
Merge branch 'master' of https://github.com/pfnet/pytorch-pfn-extras …
xuzijian629 Jan 30, 2023
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25 changes: 19 additions & 6 deletions pytorch_pfn_extras/onnx/export_testcase.py
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Original file line number Diff line number Diff line change
Expand Up @@ -310,10 +310,23 @@ def export_testcase(
os.makedirs(out_dir, exist_ok=True)
if isinstance(args, torch.Tensor):
args = args,
input_names = kwargs.pop(
'input_names',
['input_{}'.format(i) for i in range(len(args))])
assert len(input_names) == len(args)

# We unroll list args and generate names for each tensor.
gen_input_names = []
unrolled_args = []

def append_input_name(prefix: str, arg: Any) -> None:
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How about supporting also dict and namedtuple types like _normalize_outputs function in _logic.py?

pytorch-pfn-extras/pytorch_pfn_extras/handler/_logic.py

Lines 27 to 38 in c40cd02

def _normalize_outputs(outputs: Any) -> Dict[str, Any]:
target: Dict[str, Any]
if isinstance(outputs, tuple) and hasattr(outputs, '_fields'):
# namedtuple
target = outputs._asdict() # type: ignore[attr-defined]
elif isinstance(outputs, dict):
target = outputs
elif isinstance(outputs, (list, tuple)):
target = {str(i): out for i, out in enumerate(outputs)}
else:
target = {"0": outputs}
return target

if isinstance(arg, list):
for i, a in enumerate(arg):
append_input_name(prefix + f"_{i}", a)
else:
gen_input_names.append(prefix)
unrolled_args.append(arg)
for i, arg in enumerate(args):
append_input_name(f"input_{i}", arg)

input_names = kwargs.pop('input_names', gen_input_names)
assert len(input_names) == len(unrolled_args)
assert not isinstance(args, torch.Tensor)

onnx_graph, outs = _export(
Expand All @@ -335,7 +348,7 @@ def export_testcase(
if used_input.name not in initializer_names:
used_input_index_list.append(input_names.index(used_input.name))
input_names = [input_names[i] for i in used_input_index_list]
args = [args[i] for i in used_input_index_list]
unrolled_args = [unrolled_args[i] for i in used_input_index_list]

output_path = os.path.join(out_dir, 'model.onnx')
is_on_memory = True
Expand Down Expand Up @@ -374,7 +387,7 @@ def write_to_pb(f: str, tensor: torch.Tensor, name: Optional[str] = None) -> Non
os.makedirs(data_set_path, exist_ok=True)
for pb_name in glob.glob(os.path.join(data_set_path, "*.pb")):
os.remove(pb_name)
for i, (arg, name) in enumerate(zip(args, input_names)):
for i, (arg, name) in enumerate(zip(unrolled_args, input_names)):
f = os.path.join(data_set_path, 'input_{}.pb'.format(i))
write_to_pb(f, arg, name)

Expand Down