forked from mindspore-Ecosystem/mindspore
87 lines
3.2 KiB
Python
87 lines
3.2 KiB
Python
# 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 argparse
|
|
import numpy as np
|
|
|
|
batch_size = 1
|
|
parser = argparse.ArgumentParser(description="resnet inference")
|
|
parser.add_argument("--dataset", type=str, required=True, help="dataset type.")
|
|
parser.add_argument("--result_path", type=str, required=True, help="result files path.")
|
|
parser.add_argument("--label_path", type=str, required=True, help="image file path.")
|
|
args = parser.parse_args()
|
|
|
|
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 args.dataset.lower() == "cifar10":
|
|
cal_acc_cifar10(args.result_path, args.label_path)
|
|
else:
|
|
cal_acc_imagenet(args.result_path, args.label_path)
|