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# Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from typing import Sequence
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.model import expect
class ExpandDynamicShape(Base):
@staticmethod
def export() -> None:
def make_graph(
node: onnx.helper.NodeProto,
input_shape: Sequence[int],
shape_shape: Sequence[int],
output_shape: Sequence[int],
) -> onnx.helper.GraphProto:
graph = onnx.helper.make_graph(
nodes=[node],
name="Expand",
inputs=[
onnx.helper.make_tensor_value_info(
"X", onnx.TensorProto.FLOAT, input_shape
),
onnx.helper.make_tensor_value_info(
"shape", onnx.TensorProto.INT64, shape_shape
),
],
outputs=[
onnx.helper.make_tensor_value_info(
"Y", onnx.TensorProto.FLOAT, output_shape
)
],
)
return graph
node = onnx.helper.make_node("Expand", ["X", "shape"], ["Y"], name="test")
input_shape = [1, 3, 1]
x = np.ones(input_shape, dtype=np.float32)
# 1st testcase
shape = np.array([3, 1], dtype=np.int64)
y = x * np.ones(shape, dtype=np.float32)
graph = make_graph(node, input_shape, shape.shape, y.shape)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 9)],
)
expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model1")
# 2nd testcase
shape = np.array([1, 3], dtype=np.int64)
y = x * np.ones(shape, dtype=np.float32)
graph = make_graph(node, input_shape, shape.shape, y.shape)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 9)],
)
expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model2")
# 3rd testcase
shape = np.array([3, 1, 3], dtype=np.int64)
y = x * np.ones(shape, dtype=np.float32)
graph = make_graph(node, input_shape, shape.shape, y.shape)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 9)],
)
expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model3")
# 4th testcase
shape = np.array([3, 3, 1, 3], dtype=np.int64)
y = x * np.ones(shape, dtype=np.float32)
graph = make_graph(node, input_shape, shape.shape, y.shape)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 9)],
)
expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model4")
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