# coding=utf-8
# Copyright 2021 The OneFlow Authors. All rights reserved.
#
# 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.
import copy
from collections import OrderedDict
from libai.utils import distributed as dist
from .evaluator import DatasetEvaluator
def accuracy(output, target, topk=(1,)):
maxk = min(max(topk), output.size()[1])
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.reshape(1, -1).expand_as(pred))
return [
(correct[: min(k, maxk)].reshape(-1).float().sum(0) * 100.0 / batch_size).item()
for k in topk
]
[docs]class ClsEvaluator(DatasetEvaluator):
"""
Evaluate accuracy for classification.
The metrics range from 0 to 100 (instead of 0 to 1).
We support evaluate different topk accuracy.
You can reset `cfg.train.topk=(1, 5, N)` according to your needs.
"""
def __init__(self, topk=(1, 5)):
self.topk = topk
self._predictions = []
[docs] def reset(self):
self._predictions = []
[docs] def process(self, inputs, outputs):
pred_logits = outputs["prediction_scores"]
labels = inputs["labels"]
# measure accuracy
topk_acc = accuracy(pred_logits, labels, topk=self.topk)
num_correct_acc_topk = [acc * labels.size(0) / 100 for acc in topk_acc]
self._predictions.append(
{"num_correct_topk": num_correct_acc_topk, "num_samples": labels.size(0)}
)
[docs] def evaluate(self):
if not dist.is_main_process():
return {}
else:
predictions = self._predictions
total_correct_num = OrderedDict()
for top_k in self.topk:
total_correct_num["Acc@" + str(top_k)] = 0
total_samples = 0
for prediction in predictions:
for top_k, num_correct_n in zip(self.topk, prediction["num_correct_topk"]):
total_correct_num["Acc@" + str(top_k)] += int(num_correct_n)
total_samples += int(prediction["num_samples"])
self._results = OrderedDict()
for top_k, topk_correct_num in total_correct_num.items():
self._results[top_k] = topk_correct_num / total_samples * 100
return copy.deepcopy(self._results)