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# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class ConvInteger(Base):
@staticmethod
def export_without_padding() -> None:
x = (
np.array([2, 3, 4, 5, 6, 7, 8, 9, 10])
.astype(np.uint8)
.reshape((1, 1, 3, 3))
)
x_zero_point = np.uint8(1)
w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
y = np.array([12, 16, 24, 28]).astype(np.int32).reshape(1, 1, 2, 2)
# ConvInteger without padding
convinteger_node = onnx.helper.make_node(
"ConvInteger", inputs=["x", "w", "x_zero_point"], outputs=["y"]
)
expect(
convinteger_node,
inputs=[x, w, x_zero_point],
outputs=[y],
name="test_convinteger_without_padding",
)
@staticmethod
def export_with_padding() -> None:
x = (
np.array([2, 3, 4, 5, 6, 7, 8, 9, 10])
.astype(np.uint8)
.reshape((1, 1, 3, 3))
)
x_zero_point = np.uint8(1)
w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
y = (
np.array([1, 3, 5, 3, 5, 12, 16, 9, 11, 24, 28, 15, 7, 15, 17, 9])
.astype(np.int32)
.reshape((1, 1, 4, 4))
)
# ConvInteger with padding
convinteger_node_with_padding = onnx.helper.make_node(
"ConvInteger",
inputs=["x", "w", "x_zero_point"],
outputs=["y"],
pads=[1, 1, 1, 1],
)
expect(
convinteger_node_with_padding,
inputs=[x, w, x_zero_point],
outputs=[y],
name="test_convinteger_with_padding",
)
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