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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
- """post process for 310 inference"""
- import os
- import json
- import numpy as np
- from src.model_utils.config import config
-
-
- batch_size = 1
-
- def get_top5_acc(top5_arg, gt_class):
- sub_count = 0
- for top5, gt in zip(top5_arg, gt_class):
- if gt in top5:
- sub_count += 1
- return sub_count
-
- def cal_acc_cifar10(result_path, label_path):
- img_tot = 0
- top1_correct = 0
- top5_correct = 0
- img_tot = 0
-
- result_shape = (1, 10)
-
- files = os.listdir(result_path)
- for file in files:
- full_file_path = os.path.join(result_path, file)
- if os.path.isfile(full_file_path):
- result = np.fromfile(full_file_path, dtype=np.float32).reshape(result_shape)
- label_file = os.path.join(label_path, file.split(".bin")[0][:-2] + ".bin")
- gt_classes = np.fromfile(label_file, dtype=np.int32)
-
- top1_output = np.argmax(result, (-1))
- top5_output = np.argsort(result)[:, -5:]
-
- t1_correct = np.equal(top1_output, gt_classes).sum()
- top1_correct += t1_correct
- top5_correct += get_top5_acc(top5_output, [gt_classes])
- img_tot += 1
-
- print(f"Total data: {img_tot}, top1 accuracy: {top1_correct / img_tot}, top5 accuracy: {top5_correct / img_tot}.")
-
- def cal_acc_imagenet(result_path, label_path):
- files = os.listdir(result_path)
- with open(label_path, "r") as label:
- labels = json.load(label)
- result_shape = (1, 1001)
- top1 = 0
- top5 = 0
- total_data = len(files)
- for file in files:
- img_ids_name = file.split('_0.')[0]
- data_path = os.path.join(result_path, img_ids_name + "_0.bin")
- result = np.fromfile(data_path, dtype=np.float32).reshape(result_shape)
- for batch in range(batch_size):
- predict = np.argsort(-result[batch], axis=-1)
- if labels[img_ids_name+".JPEG"] == predict[0]:
- top1 += 1
- if labels[img_ids_name+".JPEG"] in predict[:5]:
- top5 += 1
- print(f"Total data: {total_data}, top1 accuracy: {top1/total_data}, top5 accuracy: {top5/total_data}.")
-
-
- if __name__ == '__main__':
- if config.dataset.lower() == "cifar10":
- cal_acc_cifar10(config.result_path, config.label_path)
- else:
- cal_acc_imagenet(config.result_path, config.label_path)
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