forked from mindspore-Ecosystem/mindspore
124 lines
4.9 KiB
Python
124 lines
4.9 KiB
Python
# Copyright 2020 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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"""evaluate_imagenet"""
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import time
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import os
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from src.model_utils.config import config
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from src.model_utils.moxing_adapter import moxing_wrapper
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from src.model_utils.device_adapter import get_device_id, get_device_num
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from src.dataset import create_dataset_imagenet, create_dataset_cifar10
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from src.inceptionv4 import Inceptionv4
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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def modelarts_process():
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""" modelarts process """
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def unzip(zip_file, save_dir):
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import zipfile
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s_time = time.time()
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if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)):
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zip_isexist = zipfile.is_zipfile(zip_file)
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if zip_isexist:
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fz = zipfile.ZipFile(zip_file, 'r')
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data_num = len(fz.namelist())
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print('Extract Start...')
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print('unzip file num: {}'.format(data_num))
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data_print = int(data_num / 100) if data_num > 100 else 1
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i = 0
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for file in fz.namelist():
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if i % data_print == 0:
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print('unzip percent: {}%'.format(int(i * 100 / data_num)), flush=True)
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i += 1
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fz.extract(file, save_dir)
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print('cost time: {}min:{}s.'.format(int((time.time() - s_time) / 60),\
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int(int(time.time() - s_time) % 60)))
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print('Extract Done')
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else:
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print('This is not zip.')
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else:
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print('Zip has been extracted.')
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if config.need_modelarts_dataset_unzip:
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zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + '.zip')
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save_dir_1 = os.path.join(config.data_path)
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sync_lock = '/tmp/unzip_sync.lock'
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# Each server contains 8 devices as most
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if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
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print('Zip file path: ', zip_file_1)
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print('Unzip file save dir: ', save_dir_1)
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unzip(zip_file_1, save_dir_1)
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print('===Finish extract data synchronization===')
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try:
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os.mknod(sync_lock)
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except IOError:
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pass
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while True:
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if os.path.exists(sync_lock):
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break
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time.sleep(1)
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print('Device: {}, Finish sync unzip data from {} to {}.'.format(get_device_id(), zip_file_1, save_dir_1))
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print('#' * 200, os.listdir(save_dir_1))
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print('#' * 200, os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name)))
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config.dataset_path = os.path.join(config.data_path, config.modelarts_dataset_unzip_name)
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config.checkpoint_path = os.path.join(config.dataset_path, config.checkpoint_path)
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DS_DICT = {
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"imagenet": create_dataset_imagenet,
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"cifar10": create_dataset_cifar10,
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}
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@moxing_wrapper(pre_process=modelarts_process)
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def inception_v4_eval():
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if config.platform == 'Ascend':
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device_id = int(os.getenv('DEVICE_ID', '0'))
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context.set_context(device_id=device_id)
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create_dataset = DS_DICT[config.ds_type]
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context.set_context(mode=context.GRAPH_MODE, device_target=config.platform)
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net = Inceptionv4(classes=config.num_classes)
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ckpt = load_checkpoint(config.checkpoint_path)
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load_param_into_net(net, ckpt)
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net.set_train(False)
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config.rank = 0
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config.group_size = 1
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dataset = create_dataset(dataset_path=config.dataset_path, do_train=False, cfg=config)
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loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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eval_metrics = {'Loss': nn.Loss(),
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'Top1-Acc': nn.Top1CategoricalAccuracy(),
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'Top5-Acc': nn.Top5CategoricalAccuracy()}
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model = Model(net, loss, optimizer=None, metrics=eval_metrics)
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print('=' * 20, 'Evalute start', '=' * 20)
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metrics = model.eval(dataset, dataset_sink_mode=config.ds_sink_mode)
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print("metric: ", metrics)
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if __name__ == '__main__':
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config.dataset_path = os.path.join(config.dataset_path, 'validation_preprocess')
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inception_v4_eval()
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