mirror of
https://github.com/luxfi/onnx-source-snapshot.git
synced 2026-10-30 15:23:38 +00:00
305 lines
11 KiB
Python
Executable File
305 lines
11 KiB
Python
Executable File
# Copyright (c) ONNX Project Contributors
|
|
#
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
from __future__ import annotations
|
|
|
|
import numpy as np
|
|
|
|
import onnx
|
|
from onnx.backend.test.case.base import Base
|
|
from onnx.backend.test.case.node import expect
|
|
|
|
|
|
class SequenceMap(Base):
|
|
@staticmethod
|
|
def export_sequence_map_identity_1_sequence(): # type: () -> None
|
|
body = onnx.helper.make_graph(
|
|
[onnx.helper.make_node("Identity", ["in0"], ["out0"])],
|
|
"seq_map_body",
|
|
[onnx.helper.make_tensor_value_info("in0", onnx.TensorProto.FLOAT, ["N"])],
|
|
[onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["M"])],
|
|
)
|
|
|
|
node = onnx.helper.make_node(
|
|
"SequenceMap", inputs=["x"], outputs=["y"], body=body
|
|
)
|
|
|
|
x = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for _ in range(3)]
|
|
y = x
|
|
input_type_protos = [
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
|
|
),
|
|
]
|
|
output_type_protos = [
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
|
|
),
|
|
]
|
|
expect(
|
|
node,
|
|
inputs=[x],
|
|
outputs=[y],
|
|
input_type_protos=input_type_protos,
|
|
output_type_protos=output_type_protos,
|
|
name="test_sequence_map_identity_1_sequence",
|
|
)
|
|
|
|
@staticmethod
|
|
def export_sequence_map_identity_2_sequences(): # type: () -> None
|
|
body = onnx.helper.make_graph(
|
|
[
|
|
onnx.helper.make_node("Identity", ["in0"], ["out0"]),
|
|
onnx.helper.make_node("Identity", ["in1"], ["out1"]),
|
|
],
|
|
"seq_map_body",
|
|
[
|
|
onnx.helper.make_tensor_value_info(
|
|
"in0", onnx.TensorProto.FLOAT, ["N"]
|
|
),
|
|
onnx.helper.make_tensor_value_info(
|
|
"in1", onnx.TensorProto.FLOAT, ["M"]
|
|
),
|
|
],
|
|
[
|
|
onnx.helper.make_tensor_value_info(
|
|
"out0", onnx.TensorProto.FLOAT, ["N"]
|
|
),
|
|
onnx.helper.make_tensor_value_info(
|
|
"out1", onnx.TensorProto.FLOAT, ["M"]
|
|
),
|
|
],
|
|
)
|
|
|
|
node = onnx.helper.make_node(
|
|
"SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body
|
|
)
|
|
|
|
x0 = [
|
|
np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
|
|
for _ in range(3)
|
|
]
|
|
x1 = [
|
|
np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
|
|
for _ in range(3)
|
|
]
|
|
y0 = x0
|
|
y1 = x1
|
|
input_type_protos = [
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
|
|
),
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
|
|
),
|
|
]
|
|
output_type_protos = [
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
|
|
),
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
|
|
),
|
|
]
|
|
expect(
|
|
node,
|
|
inputs=[x0, x1],
|
|
outputs=[y0, y1],
|
|
input_type_protos=input_type_protos,
|
|
output_type_protos=output_type_protos,
|
|
name="test_sequence_map_identity_2_sequences",
|
|
)
|
|
|
|
@staticmethod
|
|
def export_sequence_map_identity_1_sequence_1_tensor(): # type: () -> None
|
|
body = onnx.helper.make_graph(
|
|
[
|
|
onnx.helper.make_node("Identity", ["in0"], ["out0"]),
|
|
onnx.helper.make_node("Identity", ["in1"], ["out1"]),
|
|
],
|
|
"seq_map_body",
|
|
[
|
|
onnx.helper.make_tensor_value_info(
|
|
"in0", onnx.TensorProto.FLOAT, ["N"]
|
|
),
|
|
onnx.helper.make_tensor_value_info(
|
|
"in1", onnx.TensorProto.FLOAT, ["M"]
|
|
),
|
|
],
|
|
[
|
|
onnx.helper.make_tensor_value_info(
|
|
"out0", onnx.TensorProto.FLOAT, ["N"]
|
|
),
|
|
onnx.helper.make_tensor_value_info(
|
|
"out1", onnx.TensorProto.FLOAT, ["M"]
|
|
),
|
|
],
|
|
)
|
|
|
|
node = onnx.helper.make_node(
|
|
"SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body
|
|
)
|
|
|
|
x0 = [
|
|
np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
|
|
for _ in range(3)
|
|
]
|
|
x1 = np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
|
|
y0 = x0
|
|
y1 = [x1 for _ in range(3)]
|
|
input_type_protos = [
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
|
|
),
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]),
|
|
]
|
|
output_type_protos = [
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
|
|
),
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
|
|
),
|
|
]
|
|
expect(
|
|
node,
|
|
inputs=[x0, x1],
|
|
outputs=[y0, y1],
|
|
input_type_protos=input_type_protos,
|
|
output_type_protos=output_type_protos,
|
|
name="test_sequence_map_identity_1_sequence_1_tensor",
|
|
)
|
|
|
|
@staticmethod
|
|
def export_sequence_map_add_2_sequences(): # type: () -> None
|
|
body = onnx.helper.make_graph(
|
|
[onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])],
|
|
"seq_map_body",
|
|
[
|
|
onnx.helper.make_tensor_value_info(
|
|
"in0", onnx.TensorProto.FLOAT, ["N"]
|
|
),
|
|
onnx.helper.make_tensor_value_info(
|
|
"in1", onnx.TensorProto.FLOAT, ["N"]
|
|
),
|
|
],
|
|
[onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])],
|
|
)
|
|
|
|
node = onnx.helper.make_node(
|
|
"SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body
|
|
)
|
|
|
|
N = [np.random.randint(1, 10) for _ in range(3)]
|
|
x0 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)]
|
|
x1 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)]
|
|
y0 = [x0[k] + x1[k] for k in range(3)]
|
|
input_type_protos = [
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
|
|
),
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
|
|
),
|
|
]
|
|
output_type_protos = [
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
|
|
),
|
|
]
|
|
expect(
|
|
node,
|
|
inputs=[x0, x1],
|
|
outputs=[y0],
|
|
input_type_protos=input_type_protos,
|
|
output_type_protos=output_type_protos,
|
|
name="test_sequence_map_add_2_sequences",
|
|
)
|
|
|
|
@staticmethod
|
|
def export_sequence_map_add_1_sequence_1_tensor(): # type: () -> None
|
|
body = onnx.helper.make_graph(
|
|
[onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])],
|
|
"seq_map_body",
|
|
[
|
|
onnx.helper.make_tensor_value_info(
|
|
"in0", onnx.TensorProto.FLOAT, ["N"]
|
|
),
|
|
onnx.helper.make_tensor_value_info(
|
|
"in1", onnx.TensorProto.FLOAT, ["N"]
|
|
),
|
|
],
|
|
[onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])],
|
|
)
|
|
|
|
node = onnx.helper.make_node(
|
|
"SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body
|
|
)
|
|
|
|
x0 = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for k in range(3)]
|
|
x1 = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
|
|
y0 = [x0[i] + x1 for i in range(3)]
|
|
input_type_protos = [
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
|
|
),
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]),
|
|
]
|
|
output_type_protos = [
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
|
|
),
|
|
]
|
|
expect(
|
|
node,
|
|
inputs=[x0, x1],
|
|
outputs=[y0],
|
|
input_type_protos=input_type_protos,
|
|
output_type_protos=output_type_protos,
|
|
name="test_sequence_map_add_1_sequence_1_tensor",
|
|
)
|
|
|
|
@staticmethod
|
|
def export_sequence_map_extract_shapes(): # type: () -> None
|
|
body = onnx.helper.make_graph(
|
|
[onnx.helper.make_node("Shape", ["x"], ["shape"])],
|
|
"seq_map_body",
|
|
[
|
|
onnx.helper.make_tensor_value_info(
|
|
"x", onnx.TensorProto.FLOAT, ["H", "W", "C"]
|
|
)
|
|
],
|
|
[onnx.helper.make_tensor_value_info("shape", onnx.TensorProto.INT64, [3])],
|
|
)
|
|
|
|
node = onnx.helper.make_node(
|
|
"SequenceMap", inputs=["in_seq"], outputs=["shapes"], body=body
|
|
)
|
|
|
|
shapes = [
|
|
np.array([40, 30, 3], dtype=np.int64),
|
|
np.array([20, 10, 3], dtype=np.int64),
|
|
np.array([10, 5, 3], dtype=np.int64),
|
|
]
|
|
x0 = [np.zeros(shape, dtype=np.float32) for shape in shapes]
|
|
input_type_protos = [
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(
|
|
onnx.TensorProto.FLOAT, ["H", "W", "C"]
|
|
)
|
|
),
|
|
]
|
|
output_type_protos = [
|
|
onnx.helper.make_sequence_type_proto(
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, [3])
|
|
),
|
|
]
|
|
expect(
|
|
node,
|
|
inputs=[x0],
|
|
outputs=[shapes],
|
|
input_type_protos=input_type_protos,
|
|
output_type_protos=output_type_protos,
|
|
name="test_sequence_map_extract_shapes",
|
|
)
|