forked from mindspore-Ecosystem/mindspore
63 lines
2.9 KiB
Python
63 lines
2.9 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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"""postprocess for 310 inference"""
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import os
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import argparse
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import json
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import numpy as np
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from mindspore.nn import Top1CategoricalAccuracy, Top5CategoricalAccuracy
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from src.model_utils.config import config as cfg
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parser = argparse.ArgumentParser(description="postprocess")
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label_path = "./preprocess_Result/cifar10_label_ids.npy"
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parser.add_argument("--result_dir", type=str, default="./result_Files", help="result files path.")
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parser.add_argument('--dataset_name', type=str, choices=["cifar10", "imagenet2012"], default="cifar10")
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parser.add_argument("--label_dir", type=str, default=label_path, help="image file path.")
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parser.add_argument("--config_path", type=str, default="../default_config.yaml", help="config file path.")
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args = parser.parse_args()
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cfg.config_path = args.config_path
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def calcul_acc(lab, preds):
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return sum(1 for x, y in zip(lab, preds) if x == y) / len(lab)
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if __name__ == '__main__':
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if args.dataset_name == "cifar10":
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top1_acc = Top1CategoricalAccuracy()
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rst_path = args.result_dir
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labels = np.load(args.label_dir, allow_pickle=True)
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for idx, label in enumerate(labels):
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f_name = os.path.join(rst_path, "alexnet_data_bs" + str(cfg.batch_size) + "_" + str(idx) + "_0.bin")
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pred = np.fromfile(f_name, np.float32)
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pred = pred.reshape(cfg.batch_size, int(pred.shape[0] / cfg.batch_size))
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top1_acc.update(pred, labels[idx])
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print("acc: ", top1_acc.eval())
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else:
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batch_size = 1
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top1_acc = Top1CategoricalAccuracy()
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rst_path = args.result_dir
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label_list = []
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pred_list = []
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file_list = os.listdir(rst_path)
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top5_acc = Top5CategoricalAccuracy()
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with open('./preprocess_Result/imagenet_label.json', "r") as label:
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labels = json.load(label)
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for f in file_list:
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label = f.split("_0.bin")[0] + ".JPEG"
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label_list.append(labels[label])
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pred = np.fromfile(os.path.join(rst_path, f), np.float32)
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pred = pred.reshape(batch_size, int(pred.shape[0] / batch_size))
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top1_acc.update(pred, [labels[label],])
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top5_acc.update(pred, [labels[label],])
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print("Top1 acc: ", top1_acc.eval())
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print("Top5 acc: ", top5_acc.eval())
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