wehub-resource-sync
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454 lines
12 KiB
Python
454 lines
12 KiB
Python
# Copyright (c) ONNX Project Contributors
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#
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# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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import numpy as np
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import onnx
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from onnx.backend.test.case.base import Base
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from onnx.backend.test.case.node import expect
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def triu_reference_implementation(x, k=0):
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return np.triu(x, k)
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def tril_reference_implementation(x, k=0):
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return np.tril(x, k)
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class Trilu(Base):
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@staticmethod
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def export_triu() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x"],
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outputs=["y"],
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)
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x = np.random.randint(10, size=(4, 5)).astype(np.int64)
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# X:
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# [[4, 7, 3, 7, 9],
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# [1, 2, 8, 6, 9],
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# [9, 4, 0, 8, 7],
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# [4, 3, 4, 2, 4]]
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# expect result:
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# [[4, 7, 3, 7, 9],
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# [0, 2, 8, 6, 9],
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# [0, 0, 0, 8, 7],
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# [0, 0, 0, 2, 4]]
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y = triu_reference_implementation(x)
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expect(node, inputs=[x], outputs=[y], name="test_triu")
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@staticmethod
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def export_triu_neg() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x", "k"],
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outputs=["y"],
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)
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x = np.random.randint(10, size=(4, 5)).astype(np.int64)
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k = np.array(-1).astype(np.int64)
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# X:
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# [[4, 7, 3, 7, 9],
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# [1, 2, 8, 6, 9],
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# [9, 4, 0, 8, 7],
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# [4, 3, 4, 2, 4]]
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# expect result:
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# [[4, 7, 3, 7, 9],
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# [1, 2, 8, 6, 9],
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# [0, 4, 0, 8, 7],
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# [0, 0, 4, 2, 4]]
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y = triu_reference_implementation(x, int(k))
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expect(node, inputs=[x, k], outputs=[y], name="test_triu_neg")
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@staticmethod
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def export_triu_out_neg_out() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x", "k"],
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outputs=["y"],
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)
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x = np.random.randint(10, size=(4, 5)).astype(np.int64)
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k = np.array(-7).astype(np.int64)
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# X:
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# [[4, 7, 3, 7, 9],
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# [1, 2, 8, 6, 9],
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# [9, 4, 0, 8, 7],
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# [4, 3, 4, 2, 4]]
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# expect result:
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# [[4, 7, 3, 7, 9],
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# [1, 2, 8, 6, 9],
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# [9, 4, 0, 8, 7],
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# [4, 3, 4, 2, 4]]
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y = triu_reference_implementation(x, int(k))
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expect(node, inputs=[x, k], outputs=[y], name="test_triu_out_neg_out")
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@staticmethod
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def export_triu_pos() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x", "k"],
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outputs=["y"],
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)
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x = np.random.randint(10, size=(4, 5)).astype(np.int64)
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k = np.array(2).astype(np.int64)
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# X:
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# [[4, 7, 3, 7, 9],
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# [1, 2, 8, 6, 9],
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# [9, 4, 0, 8, 7],
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# [4, 3, 4, 2, 4]]
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# expect result:
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# [[0, 0, 3, 7, 9],
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# [0, 0, 0, 6, 9],
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# [0, 0, 0, 0, 7],
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# [0, 0, 0, 0, 0]]
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y = triu_reference_implementation(x, int(k))
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expect(node, inputs=[x, k], outputs=[y], name="test_triu_pos")
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@staticmethod
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def export_triu_out_pos() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x", "k"],
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outputs=["y"],
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)
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x = np.random.randint(10, size=(4, 5)).astype(np.int64)
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k = np.array(6).astype(np.int64)
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# X:
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# [[4, 7, 3, 7, 9],
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# [1, 2, 8, 6, 9],
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# [9, 4, 0, 8, 7],
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# [4, 3, 4, 2, 4]]
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# expect result:
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# [[0, 0, 0, 0, 0],
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# [0, 0, 0, 0, 0],
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# [0, 0, 0, 0, 0],
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# [0, 0, 0, 0, 0]]
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y = triu_reference_implementation(x, int(k))
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expect(node, inputs=[x, k], outputs=[y], name="test_triu_out_pos")
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@staticmethod
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def export_triu_square() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x"],
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outputs=["y"],
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)
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x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
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y = triu_reference_implementation(x)
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# X:
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# [[[4, 6, 9],
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# [7, 5, 4],
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# [8, 1, 2]],
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#
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# [[1, 4, 9],
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# [9, 6, 3],
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# [8, 9, 8]]]
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# expect result:
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# [[[4, 6, 9],
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# [0, 5, 4],
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# [0, 0, 2]],
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#
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# [[1, 4, 9],
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# [0, 6, 3],
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# [0, 0, 8]]]
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expect(node, inputs=[x], outputs=[y], name="test_triu_square")
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@staticmethod
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def export_triu_square_neg() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x", "k"],
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outputs=["y"],
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)
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x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
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k = np.array(-1).astype(np.int64)
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# X:
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# [[[4, 6, 9],
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# [7, 5, 4],
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# [8, 1, 2]],
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#
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# [[1, 4, 9],
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# [9, 6, 3],
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# [8, 9, 8]]]
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# expect result:
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# [[[4, 6, 9],
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# [7, 5, 4],
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# [0, 1, 2]],
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#
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# [[1, 4, 9],
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# [9, 6, 3],
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# [0, 9, 8]]]
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y = triu_reference_implementation(x, int(k))
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expect(node, inputs=[x, k], outputs=[y], name="test_triu_square_neg")
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@staticmethod
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def export_triu_one_row() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x", "k"],
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outputs=["y"],
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)
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x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
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k = np.array(1).astype(np.int64)
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# X:
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# [[[1, 4, 9, 7, 1]],
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#
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# [[9, 2, 8, 8, 4]],
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#
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# [[3, 9, 7, 4, 2]]]
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# expect result:
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# [[[0, 4, 9, 7, 1]],
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#
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# [[0, 2, 8, 8, 4]],
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#
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# [[0, 9, 7, 4, 2]]]
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y = triu_reference_implementation(x, int(k))
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expect(node, inputs=[x, k], outputs=[y], name="test_triu_one_row")
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@staticmethod
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def export_triu_zero() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x", "k"],
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outputs=["y"],
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)
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x = np.random.randint(10, size=(0, 5)).astype(np.int64)
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k = np.array(6).astype(np.int64)
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# X:
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# []
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# expect result:
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# []
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y = triu_reference_implementation(x, int(k))
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expect(node, inputs=[x, k], outputs=[y], name="test_triu_zero")
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@staticmethod
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def export_tril() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x"],
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outputs=["y"],
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upper=0,
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)
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x = np.random.randint(10, size=(4, 5)).astype(np.int64)
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# X:
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# [[4, 7, 3, 7, 9],
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# [1, 2, 8, 6, 9],
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# [9, 4, 1, 8, 7],
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# [4, 3, 4, 2, 4]]
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# expect result:
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# [[4, 0, 0, 0, 0],
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# [1, 2, 0, 0, 0],
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# [9, 4, 1, 0, 0],
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# [4, 3, 4, 2, 0]]
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y = tril_reference_implementation(x)
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expect(node, inputs=[x], outputs=[y], name="test_tril")
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@staticmethod
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def export_tril_neg() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x", "k"],
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outputs=["y"],
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upper=0,
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)
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x = np.random.randint(10, size=(4, 5)).astype(np.int64)
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k = np.array(-1).astype(np.int64)
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# X:
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# [[4, 7, 3, 7, 9],
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# [1, 2, 8, 6, 9],
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# [9, 4, 1, 8, 7],
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# [4, 3, 4, 2, 4]]
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# expect result:
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# [[0, 0, 0, 0, 0],
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# [1, 0, 0, 0, 0],
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# [9, 4, 0, 0, 0],
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# [4, 3, 4, 0, 0]]
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y = tril_reference_implementation(x, int(k))
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expect(node, inputs=[x, k], outputs=[y], name="test_tril_neg")
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@staticmethod
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def export_tril_out_neg() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x", "k"],
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outputs=["y"],
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upper=0,
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)
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x = np.random.randint(10, size=(4, 5)).astype(np.int64)
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k = np.array(-7).astype(np.int64)
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# X:
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# [[4, 7, 3, 7, 9],
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# [1, 2, 8, 6, 9],
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# [9, 4, 1, 8, 7],
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# [4, 3, 4, 2, 4]]
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# expect result:
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# [[0, 0, 0, 0, 0],
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# [0, 0, 0, 0, 0],
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# [0, 0, 0, 0, 0],
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# [0, 0, 0, 0, 0]]
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y = tril_reference_implementation(x, int(k))
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expect(node, inputs=[x, k], outputs=[y], name="test_tril_out_neg")
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@staticmethod
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def export_tril_pos() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x", "k"],
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outputs=["y"],
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upper=0,
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)
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x = np.random.randint(10, size=(4, 5)).astype(np.int64)
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k = np.array(2).astype(np.int64)
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# X:
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# [[4, 7, 3, 7, 9],
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# [1, 2, 8, 6, 9],
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# [9, 4, 1, 8, 7],
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# [4, 3, 4, 2, 4]]
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# expect result:
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# [[4, 7, 3, 0, 0],
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# [1, 2, 8, 6, 0],
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# [9, 4, 1, 8, 7],
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# [4, 3, 4, 2, 4]]
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y = tril_reference_implementation(x, int(k))
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expect(node, inputs=[x, k], outputs=[y], name="test_tril_pos")
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@staticmethod
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def export_tril_out_pos() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x", "k"],
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outputs=["y"],
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upper=0,
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)
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x = np.random.randint(10, size=(4, 5)).astype(np.int64)
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k = np.array(6).astype(np.int64)
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# X:
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# [[4, 7, 3, 7, 9],
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# [1, 2, 8, 6, 9],
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# [9, 4, 1, 8, 7],
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# [4, 3, 4, 2, 4]]
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# expect result:
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# [[4, 7, 3, 7, 9],
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# [1, 2, 8, 6, 9],
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# [9, 4, 1, 8, 7],
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# [4, 3, 4, 2, 4]]
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y = tril_reference_implementation(x, int(k))
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expect(node, inputs=[x, k], outputs=[y], name="test_tril_out_pos")
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@staticmethod
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def export_tril_square() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x"],
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outputs=["y"],
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upper=0,
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)
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x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
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# X:
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# [[[0, 4, 3],
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# [2, 0, 9],
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# [8, 2, 5]],
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#
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# [[2, 7, 2],
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# [2, 6, 0],
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# [2, 6, 5]]]
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# expect result:
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# [[[0, 0, 0],
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# [2, 0, 0],
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# [8, 2, 5]],
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#
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# [[2, 0, 0],
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# [2, 6, 0],
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# [2, 6, 5]]]
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y = tril_reference_implementation(x)
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expect(node, inputs=[x], outputs=[y], name="test_tril_square")
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@staticmethod
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def export_tril_square_neg() -> None:
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node = onnx.helper.make_node(
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"Trilu",
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inputs=["x", "k"],
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outputs=["y"],
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upper=0,
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)
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x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
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k = np.array(-1).astype(np.int64)
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# X:
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# [[[0, 4, 3],
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# [2, 0, 9],
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# [8, 2, 5]],
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#
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# [[2, 7, 2],
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# [2, 6, 0],
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# [2, 6, 5]]]
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# expect result:
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# [[[0, 0, 0],
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# [2, 0, 0],
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# [8, 2, 0]],
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#
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# [[0, 0, 0],
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# [2, 0, 0],
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# [2, 6, 0]]]
|
|
y = tril_reference_implementation(x, int(k))
|
|
expect(node, inputs=[x, k], outputs=[y], name="test_tril_square_neg")
|
|
|
|
@staticmethod
|
|
def export_tril_one_row() -> None:
|
|
node = onnx.helper.make_node(
|
|
"Trilu",
|
|
inputs=["x"],
|
|
outputs=["y"],
|
|
upper=0,
|
|
)
|
|
|
|
x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
|
|
# X:
|
|
# [[[6, 2, 4, 1, 6]],
|
|
#
|
|
# [[8, 3, 8, 7, 0]],
|
|
#
|
|
# [[2, 2, 9, 5, 9]]]
|
|
# expect result:
|
|
# [[[6, 0, 0, 0, 0]],
|
|
#
|
|
# [[8, 0, 0, 0, 0]],
|
|
#
|
|
# [[2, 0, 0, 0, 0]]]
|
|
y = tril_reference_implementation(x)
|
|
expect(node, inputs=[x], outputs=[y], name="test_tril_one_row_neg")
|
|
|
|
@staticmethod
|
|
def export_tril_zero() -> None:
|
|
node = onnx.helper.make_node(
|
|
"Trilu",
|
|
inputs=["x", "k"],
|
|
outputs=["y"],
|
|
upper=0,
|
|
)
|
|
|
|
x = np.random.randint(10, size=(3, 0, 5)).astype(np.int64)
|
|
k = np.array(6).astype(np.int64)
|
|
# X:
|
|
# []
|
|
# expect result:
|
|
# []
|
|
y = tril_reference_implementation(x, int(k))
|
|
expect(node, inputs=[x, k], outputs=[y], name="test_tril_zero")
|