mirror of
https://github.com/luxfi/onnx-source-snapshot.git
synced 2026-10-30 15:23:38 +00:00
74 lines
2.5 KiB
Python
74 lines
2.5 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 Div(Base):
|
|
@staticmethod
|
|
def export() -> None:
|
|
node = onnx.helper.make_node(
|
|
"Div",
|
|
inputs=["x", "y"],
|
|
outputs=["z"],
|
|
)
|
|
|
|
x = np.array([3, 4]).astype(np.float32)
|
|
y = np.array([1, 2]).astype(np.float32)
|
|
z = x / y # expected output [3., 2.]
|
|
expect(node, inputs=[x, y], outputs=[z], name="test_div_example")
|
|
|
|
x = np.random.randn(3, 4, 5).astype(np.float32)
|
|
y = np.random.rand(3, 4, 5).astype(np.float32) + 1.0
|
|
z = x / y
|
|
expect(node, inputs=[x, y], outputs=[z], name="test_div")
|
|
|
|
x = np.random.randint(24, size=(3, 4, 5), dtype=np.int8)
|
|
y = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) + 1
|
|
z = x // y
|
|
expect(node, inputs=[x, y], outputs=[z], name="test_div_int8")
|
|
|
|
x = np.random.randint(24, size=(3, 4, 5), dtype=np.int16)
|
|
y = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) + 1
|
|
z = x // y
|
|
expect(node, inputs=[x, y], outputs=[z], name="test_div_int16")
|
|
|
|
x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
|
|
y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) + 1
|
|
z = x // y
|
|
expect(node, inputs=[x, y], outputs=[z], name="test_div_uint8")
|
|
|
|
x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
|
|
y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) + 1
|
|
z = x // y
|
|
expect(node, inputs=[x, y], outputs=[z], name="test_div_uint16")
|
|
|
|
x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
|
|
y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) + 1
|
|
z = x // y
|
|
expect(node, inputs=[x, y], outputs=[z], name="test_div_uint32")
|
|
|
|
x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
|
|
y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) + 1
|
|
z = x // y
|
|
expect(node, inputs=[x, y], outputs=[z], name="test_div_uint64")
|
|
|
|
@staticmethod
|
|
def export_div_broadcast() -> None:
|
|
node = onnx.helper.make_node(
|
|
"Div",
|
|
inputs=["x", "y"],
|
|
outputs=["z"],
|
|
)
|
|
|
|
x = np.random.randn(3, 4, 5).astype(np.float32)
|
|
y = np.random.rand(5).astype(np.float32) + 1.0
|
|
z = x / y
|
|
expect(node, inputs=[x, y], outputs=[z], name="test_div_bcast")
|