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chore: import upstream snapshot with attribution
2026-07-13 12:49:27 +08:00

58 lines
1.6 KiB
Python

# LICENSE HEADER MANAGED BY add-license-header
#
# Copyright 2018 Kornia Team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from __future__ import annotations
import torch
from kornia.metrics import AverageMeter
class TestAverageMeter:
def test_initial_state(self):
m = AverageMeter()
assert m.val == 0
assert m.avg == 0
assert m.count == 0
def test_update_scalar(self):
m = AverageMeter()
m.update(0.8, n=1)
m.update(0.4, n=1)
assert abs(m.avg - 0.6) < 1e-6
def test_update_weighted(self):
m = AverageMeter()
m.update(1.0, n=3)
m.update(0.0, n=1)
assert abs(m.avg - 0.75) < 1e-6
def test_update_tensor(self):
m = AverageMeter()
m.update(torch.tensor(0.9), n=1)
# avg property converts tensor to float
assert isinstance(m.avg, float)
assert abs(m.avg - 0.9) < 1e-6
def test_reset(self):
m = AverageMeter()
m.update(1.0, n=5)
m.reset()
assert m.val == 0
assert m.avg == 0
assert m.count == 0