forked from mindspore-Ecosystem/mindspore
100 lines
3.6 KiB
Python
100 lines
3.6 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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'''post process for 310 inference'''
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import os
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import argparse
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import numpy as np
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parser = argparse.ArgumentParser(description='postprocess for googlenet')
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parser.add_argument("--dataset", type=str, default="imagenet", help="result file path")
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parser.add_argument("--result_path", type=str, required=True, help="result file path")
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parser.add_argument("--label_file", type=str, required=True, help="label file")
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args = parser.parse_args()
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def get_top5_acc(top5_arg, gt_class):
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sub_count = 0
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for top5, gt in zip(top5_arg, gt_class):
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if gt in top5:
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sub_count += 1
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return sub_count
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def read_label(label_file):
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f = open(label_file, "r")
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lines = f.readlines()
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img_label = {}
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for line in lines:
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img_id = line.split(":")[0]
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label = line.split(":")[1]
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img_label[img_id] = label
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return img_label
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def cal_acc_cifar10(result_path, label_path):
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img_tot = 0
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top1_correct = 0
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result_shape = (1, 10)
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files = os.listdir(result_path)
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for file in files:
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full_file_path = os.path.join(result_path, file)
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if os.path.isfile(full_file_path):
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result = np.fromfile(full_file_path, dtype=np.float32).reshape(result_shape)
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label_file = os.path.join(label_path, file.split(".bin")[0][:-2] + ".bin")
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gt_classes = np.fromfile(label_file, dtype=np.int32)
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top1_output = np.argmax(result, (-1))
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t1_correct = np.equal(top1_output, gt_classes).sum()
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top1_correct += t1_correct
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img_tot += 1
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acc1 = 100.0 * top1_correct / img_tot
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print('after allreduce eval: top1_correct={}, tot={}, acc={:.2f}%'.format(top1_correct, img_tot, acc1))
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def cal_acc_imagenet(result_path, label_file):
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img_label = read_label(label_file)
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img_tot = 0
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top1_correct = 0
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top5_correct = 0
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files = os.listdir(result_path)
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for file in files:
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full_file_path = os.path.join(result_path, file)
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if os.path.isfile(full_file_path):
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result = np.fromfile(full_file_path, dtype=np.float32).reshape(1, 1000)
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gt_classes = int(img_label[file[:-6]])
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top1_output = np.argmax(result, (-1))
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top5_output = np.argsort(result)[:, -5:]
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t1_correct = np.equal(top1_output, gt_classes).sum()
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top1_correct += t1_correct
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top5_correct += get_top5_acc(top5_output, [gt_classes])
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img_tot += 1
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acc1 = 100.0 * top1_correct / img_tot
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acc5 = 100.0 * top5_correct / img_tot
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print('after allreduce eval: top1_correct={}, tot={}, acc={:.2f}%'.format(top1_correct, img_tot, acc1))
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print('after allreduce eval: top5_correct={}, tot={}, acc={:.2f}%'.format(top5_correct, img_tot, acc5))
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if __name__ == "__main__":
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if args.dataset.lower() == "cifar10":
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cal_acc_cifar10(args.result_path, args.label_file)
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else:
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cal_acc_imagenet(args.result_path, args.label_file)
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