wehub-resource-sync
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305 lines
11 KiB
Python
Executable File
305 lines
11 KiB
Python
Executable File
# Copyright (c) ONNX Project Contributors
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#
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# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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import numpy as np
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import onnx
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from onnx.backend.test.case.base import Base
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from onnx.backend.test.case.node import expect
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class SequenceMap(Base):
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@staticmethod
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def export_sequence_map_identity_1_sequence(): # type: () -> None
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body = onnx.helper.make_graph(
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[onnx.helper.make_node("Identity", ["in0"], ["out0"])],
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"seq_map_body",
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[onnx.helper.make_tensor_value_info("in0", onnx.TensorProto.FLOAT, ["N"])],
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[onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["M"])],
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)
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node = onnx.helper.make_node(
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"SequenceMap", inputs=["x"], outputs=["y"], body=body
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)
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x = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for _ in range(3)]
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y = x
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input_type_protos = [
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
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),
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]
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output_type_protos = [
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
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),
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]
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expect(
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node,
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inputs=[x],
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outputs=[y],
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input_type_protos=input_type_protos,
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output_type_protos=output_type_protos,
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name="test_sequence_map_identity_1_sequence",
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)
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@staticmethod
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def export_sequence_map_identity_2_sequences(): # type: () -> None
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body = onnx.helper.make_graph(
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[
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onnx.helper.make_node("Identity", ["in0"], ["out0"]),
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onnx.helper.make_node("Identity", ["in1"], ["out1"]),
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],
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"seq_map_body",
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[
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onnx.helper.make_tensor_value_info(
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"in0", onnx.TensorProto.FLOAT, ["N"]
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),
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onnx.helper.make_tensor_value_info(
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"in1", onnx.TensorProto.FLOAT, ["M"]
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),
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],
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[
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onnx.helper.make_tensor_value_info(
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"out0", onnx.TensorProto.FLOAT, ["N"]
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),
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onnx.helper.make_tensor_value_info(
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"out1", onnx.TensorProto.FLOAT, ["M"]
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),
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],
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)
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node = onnx.helper.make_node(
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"SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body
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)
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x0 = [
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np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
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for _ in range(3)
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]
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x1 = [
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np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
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for _ in range(3)
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]
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y0 = x0
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y1 = x1
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input_type_protos = [
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
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),
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
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),
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]
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output_type_protos = [
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
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),
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
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),
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]
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expect(
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node,
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inputs=[x0, x1],
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outputs=[y0, y1],
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input_type_protos=input_type_protos,
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output_type_protos=output_type_protos,
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name="test_sequence_map_identity_2_sequences",
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)
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@staticmethod
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def export_sequence_map_identity_1_sequence_1_tensor(): # type: () -> None
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body = onnx.helper.make_graph(
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[
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onnx.helper.make_node("Identity", ["in0"], ["out0"]),
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onnx.helper.make_node("Identity", ["in1"], ["out1"]),
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],
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"seq_map_body",
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[
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onnx.helper.make_tensor_value_info(
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"in0", onnx.TensorProto.FLOAT, ["N"]
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),
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onnx.helper.make_tensor_value_info(
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"in1", onnx.TensorProto.FLOAT, ["M"]
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),
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],
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[
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onnx.helper.make_tensor_value_info(
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"out0", onnx.TensorProto.FLOAT, ["N"]
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),
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onnx.helper.make_tensor_value_info(
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"out1", onnx.TensorProto.FLOAT, ["M"]
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),
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],
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)
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node = onnx.helper.make_node(
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"SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body
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)
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x0 = [
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np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
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for _ in range(3)
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]
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x1 = np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
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y0 = x0
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y1 = [x1 for _ in range(3)]
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input_type_protos = [
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
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),
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]),
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]
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output_type_protos = [
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
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),
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
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),
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]
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expect(
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node,
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inputs=[x0, x1],
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outputs=[y0, y1],
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input_type_protos=input_type_protos,
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output_type_protos=output_type_protos,
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name="test_sequence_map_identity_1_sequence_1_tensor",
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)
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@staticmethod
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def export_sequence_map_add_2_sequences(): # type: () -> None
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body = onnx.helper.make_graph(
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[onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])],
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"seq_map_body",
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[
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onnx.helper.make_tensor_value_info(
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"in0", onnx.TensorProto.FLOAT, ["N"]
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),
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onnx.helper.make_tensor_value_info(
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"in1", onnx.TensorProto.FLOAT, ["N"]
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),
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],
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[onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])],
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)
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node = onnx.helper.make_node(
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"SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body
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)
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N = [np.random.randint(1, 10) for _ in range(3)]
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x0 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)]
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x1 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)]
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y0 = [x0[k] + x1[k] for k in range(3)]
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input_type_protos = [
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
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),
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
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),
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]
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output_type_protos = [
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
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),
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]
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expect(
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node,
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inputs=[x0, x1],
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outputs=[y0],
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input_type_protos=input_type_protos,
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output_type_protos=output_type_protos,
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name="test_sequence_map_add_2_sequences",
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)
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@staticmethod
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def export_sequence_map_add_1_sequence_1_tensor(): # type: () -> None
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body = onnx.helper.make_graph(
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[onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])],
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"seq_map_body",
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[
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onnx.helper.make_tensor_value_info(
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"in0", onnx.TensorProto.FLOAT, ["N"]
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),
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onnx.helper.make_tensor_value_info(
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"in1", onnx.TensorProto.FLOAT, ["N"]
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),
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],
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[onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])],
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)
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node = onnx.helper.make_node(
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"SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body
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)
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x0 = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for k in range(3)]
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x1 = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
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y0 = [x0[i] + x1 for i in range(3)]
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input_type_protos = [
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
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),
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]),
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]
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output_type_protos = [
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
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),
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]
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expect(
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node,
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inputs=[x0, x1],
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outputs=[y0],
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input_type_protos=input_type_protos,
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output_type_protos=output_type_protos,
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name="test_sequence_map_add_1_sequence_1_tensor",
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)
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@staticmethod
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def export_sequence_map_extract_shapes(): # type: () -> None
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body = onnx.helper.make_graph(
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[onnx.helper.make_node("Shape", ["x"], ["shape"])],
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"seq_map_body",
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[
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onnx.helper.make_tensor_value_info(
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"x", onnx.TensorProto.FLOAT, ["H", "W", "C"]
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)
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],
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[onnx.helper.make_tensor_value_info("shape", onnx.TensorProto.INT64, [3])],
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)
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node = onnx.helper.make_node(
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"SequenceMap", inputs=["in_seq"], outputs=["shapes"], body=body
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)
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shapes = [
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np.array([40, 30, 3], dtype=np.int64),
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np.array([20, 10, 3], dtype=np.int64),
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np.array([10, 5, 3], dtype=np.int64),
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]
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x0 = [np.zeros(shape, dtype=np.float32) for shape in shapes]
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input_type_protos = [
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(
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onnx.TensorProto.FLOAT, ["H", "W", "C"]
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)
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),
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]
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output_type_protos = [
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, [3])
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),
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]
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expect(
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node,
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inputs=[x0],
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outputs=[shapes],
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input_type_protos=input_type_protos,
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output_type_protos=output_type_protos,
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name="test_sequence_map_extract_shapes",
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)
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