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chore: import upstream snapshot with attribution
2026-07-13 12:41:19 +08:00

179 lines
5.1 KiB
Python

# 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 Slice(Base):
@staticmethod
def export_slice() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
y = x[0:3, 0:10]
starts = np.array([0, 0], dtype=np.int64)
ends = np.array([3, 10], dtype=np.int64)
axes = np.array([0, 1], dtype=np.int64)
steps = np.array([1, 1], dtype=np.int64)
expect(
node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice"
)
@staticmethod
def export_slice_neg() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([0], dtype=np.int64)
ends = np.array([-1], dtype=np.int64)
axes = np.array([1], dtype=np.int64)
steps = np.array([1], dtype=np.int64)
y = x[:, 0:-1]
expect(
node,
inputs=[x, starts, ends, axes, steps],
outputs=[y],
name="test_slice_neg",
)
@staticmethod
def export_slice_start_out_of_bounds() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([1000], dtype=np.int64)
ends = np.array([1000], dtype=np.int64)
axes = np.array([1], dtype=np.int64)
steps = np.array([1], dtype=np.int64)
y = x[:, 1000:1000]
expect(
node,
inputs=[x, starts, ends, axes, steps],
outputs=[y],
name="test_slice_start_out_of_bounds",
)
@staticmethod
def export_slice_end_out_of_bounds() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([1], dtype=np.int64)
ends = np.array([1000], dtype=np.int64)
axes = np.array([1], dtype=np.int64)
steps = np.array([1], dtype=np.int64)
y = x[:, 1:1000]
expect(
node,
inputs=[x, starts, ends, axes, steps],
outputs=[y],
name="test_slice_end_out_of_bounds",
)
@staticmethod
def export_slice_default_axes() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([0, 0, 3], dtype=np.int64)
ends = np.array([20, 10, 4], dtype=np.int64)
y = x[:, :, 3:4]
expect(
node, inputs=[x, starts, ends], outputs=[y], name="test_slice_default_axes"
)
@staticmethod
def export_slice_default_steps() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([0, 0, 3], dtype=np.int64)
ends = np.array([20, 10, 4], dtype=np.int64)
axes = np.array([0, 1, 2], dtype=np.int64)
y = x[:, :, 3:4]
expect(
node,
inputs=[x, starts, ends, axes],
outputs=[y],
name="test_slice_default_steps",
)
@staticmethod
def export_slice_neg_steps() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([20, 10, 4], dtype=np.int64)
ends = np.array([0, 0, 1], dtype=np.int64)
axes = np.array([0, 1, 2], dtype=np.int64)
steps = np.array([-1, -3, -2]).astype(np.int64)
y = x[20:0:-1, 10:0:-3, 4:1:-2]
expect(
node,
inputs=[x, starts, ends, axes, steps],
outputs=[y],
name="test_slice_neg_steps",
)
@staticmethod
def export_slice_negative_axes() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([0, 0, 3], dtype=np.int64)
ends = np.array([20, 10, 4], dtype=np.int64)
axes = np.array([0, -2, -1], dtype=np.int64)
y = x[:, :, 3:4]
expect(
node,
inputs=[x, starts, ends, axes],
outputs=[y],
name="test_slice_negative_axes",
)