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support dynamic time shape #191

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support dynamic time shape #191

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18 changes: 17 additions & 1 deletion onnxsim/onnx_simplifier.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,7 +106,9 @@ def get_np_type_from_elem_type(elem_type: int):


def get_inputs(model: onnx.ModelProto) -> List[onnx.ValueInfoProto]:
# weights
initializer_names = [x.name for x in model.graph.initializer]
# input not in weights
return [ipt for ipt in model.graph.input if ipt.name not in initializer_names]


Expand All @@ -127,9 +129,11 @@ def remove_unused_output(model: onnx.ModelProto, unused_output: Sequence[str]) -
if unused_output_name not in output_names:
raise RuntimeError(
f'The model doesn\'t have output named "{unused_output_name}"')

for graph_output in copy.deepcopy(model.graph.output):
if graph_output.name in unused_output_names:
model.graph.output.remove(graph_output)

model = onnxoptimizer.optimize(model, ['eliminate_deadend'],
fixed_point=True)
onnx.checker.check_model(model)
Expand All @@ -144,9 +148,20 @@ def generate_specific_rand_input(model, input_shapes: TensorShapes):
for key, shape in input_shapes.items():
shape_np = np.array(shape)
if not np.all(shape_np > 0):
# treat batch size as 1 automatically if dynamic_input_shape is True

if config.dynamic_input_shape and len(shape_np) >= 3 and np.all(shape_np[1:] > 0):
# treat batch size as 1 automatically if dynamic_input_shape is True
input_shapes[key] = [1] + shape[1:]
print(f"dynamic input shape: {key} {shape} -> {input_shapes[key]}")
continue

if config.dynamic_input_shape:
dyn_dim = np.where(shape_np==-1)[0]
shape_tmp = copy.deepcopy(shape)
for i, idx in enumerate(dyn_dim):
shape_tmp[idx.item()] = 10 + i
input_shapes[key] = shape_tmp
print(f"dynamic input shape: {key} {shape} -> {shape_tmp}")
continue

raise RuntimeError(
Expand Down Expand Up @@ -362,6 +377,7 @@ def optimize(model: onnx.ModelProto, skip_fuse_bn: bool, skipped_optimizers: Opt
onnx.checker.check_model(model)
onnx.helper.strip_doc_string(model)
optimizers_list = onnxoptimizer.get_fuse_and_elimination_passes()
print(f'opt passes: {optimizers_list}')
if skip_fuse_bn:
optimizers_list.remove('fuse_bn_into_conv')
if skipped_optimizers is not None:
Expand Down