Spaces:
Running
Running
# 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 Clip(Base): | |
def export() -> None: | |
node = onnx.helper.make_node( | |
"Clip", | |
inputs=["x", "min", "max"], | |
outputs=["y"], | |
) | |
x = np.array([-2, 0, 2]).astype(np.float32) | |
min_val = np.float32(-1) | |
max_val = np.float32(1) | |
y = np.clip(x, min_val, max_val) # expected output [-1., 0., 1.] | |
expect( | |
node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_example" | |
) | |
x = np.random.randn(3, 4, 5).astype(np.float32) | |
y = np.clip(x, min_val, max_val) | |
expect(node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip") | |
node = onnx.helper.make_node( | |
"Clip", | |
inputs=["x", "min", "max"], | |
outputs=["y"], | |
) | |
min_val = np.float32(-5) | |
max_val = np.float32(5) | |
x = np.array([-1, 0, 1]).astype(np.float32) | |
y = np.array([-1, 0, 1]).astype(np.float32) | |
expect( | |
node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_inbounds" | |
) | |
x = np.array([-6, 0, 6]).astype(np.float32) | |
y = np.array([-5, 0, 5]).astype(np.float32) | |
expect( | |
node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_outbounds" | |
) | |
x = np.array([-1, 0, 6]).astype(np.float32) | |
y = np.array([-1, 0, 5]).astype(np.float32) | |
expect( | |
node, | |
inputs=[x, min_val, max_val], | |
outputs=[y], | |
name="test_clip_splitbounds", | |
) | |
def export_clip_default() -> None: | |
node = onnx.helper.make_node( | |
"Clip", | |
inputs=["x", "min"], | |
outputs=["y"], | |
) | |
min_val = np.float32(0) | |
x = np.random.randn(3, 4, 5).astype(np.float32) | |
y = np.clip(x, min_val, np.inf) | |
expect(node, inputs=[x, min_val], outputs=[y], name="test_clip_default_min") | |
no_min = "" # optional input, not supplied | |
node = onnx.helper.make_node( | |
"Clip", | |
inputs=["x", no_min, "max"], | |
outputs=["y"], | |
) | |
max_val = np.float32(0) | |
x = np.random.randn(3, 4, 5).astype(np.float32) | |
y = np.clip(x, -np.inf, max_val) | |
expect(node, inputs=[x, max_val], outputs=[y], name="test_clip_default_max") | |
no_max = "" # optional input, not supplied | |
node = onnx.helper.make_node( | |
"Clip", | |
inputs=["x", no_min, no_max], | |
outputs=["y"], | |
) | |
x = np.array([-1, 0, 1]).astype(np.float32) | |
y = np.array([-1, 0, 1]).astype(np.float32) | |
expect(node, inputs=[x], outputs=[y], name="test_clip_default_inbounds") | |
def export_clip_default_int8() -> None: | |
node = onnx.helper.make_node( | |
"Clip", | |
inputs=["x", "min"], | |
outputs=["y"], | |
) | |
min_val = np.int8(0) | |
x = np.random.randn(3, 4, 5).astype(np.int8) | |
y = np.clip(x, min_val, np.iinfo(np.int8).max) | |
expect( | |
node, inputs=[x, min_val], outputs=[y], name="test_clip_default_int8_min" | |
) | |
no_min = "" # optional input, not supplied | |
node = onnx.helper.make_node( | |
"Clip", | |
inputs=["x", no_min, "max"], | |
outputs=["y"], | |
) | |
max_val = np.int8(0) | |
x = np.random.randn(3, 4, 5).astype(np.int8) | |
y = np.clip(x, np.iinfo(np.int8).min, max_val) | |
expect( | |
node, inputs=[x, max_val], outputs=[y], name="test_clip_default_int8_max" | |
) | |
no_max = "" # optional input, not supplied | |
node = onnx.helper.make_node( | |
"Clip", | |
inputs=["x", no_min, no_max], | |
outputs=["y"], | |
) | |
x = np.array([-1, 0, 1]).astype(np.int8) | |
y = np.array([-1, 0, 1]).astype(np.int8) | |
expect(node, inputs=[x], outputs=[y], name="test_clip_default_int8_inbounds") | |