forked from mindspore-Ecosystem/mindspore
244 lines
9.4 KiB
Python
244 lines
9.4 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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"""Eval"""
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import os
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import time
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import argparse
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import datetime
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import glob
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor, context
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from mindspore.communication.management import init, get_rank, get_group_size, release
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.common import dtype as mstype
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from src.utils.logging import get_logger
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from src.image_classification import get_network
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from src.dataset import classification_dataset
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from src.config import config
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devid = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
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device_target="Ascend", save_graphs=False, device_id=devid)
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class ParameterReduce(nn.Cell):
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"""ParameterReduce"""
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def __init__(self):
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super(ParameterReduce, self).__init__()
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self.cast = P.Cast()
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self.reduce = P.AllReduce()
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def construct(self, x):
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one = self.cast(F.scalar_to_array(1.0), mstype.float32)
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out = x * one
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ret = self.reduce(out)
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return ret
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def parse_args(cloud_args=None):
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"""parse_args"""
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parser = argparse.ArgumentParser('mindspore classification test')
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# dataset related
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parser.add_argument('--data_dir', type=str, default='/opt/npu/datasets/classification/val', help='eval data dir')
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parser.add_argument('--per_batch_size', default=32, type=int, help='batch size for per npu')
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# network related
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parser.add_argument('--graph_ckpt', type=int, default=1, help='graph ckpt or feed ckpt')
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parser.add_argument('--pretrained', default='', type=str, help='fully path of pretrained model to load. '
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'If it is a direction, it will test all ckpt')
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# logging related
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parser.add_argument('--log_path', type=str, default='outputs/', help='path to save log')
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parser.add_argument('--is_distributed', type=int, default=0, help='if multi device')
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# roma obs
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parser.add_argument('--train_url', type=str, default="", help='train url')
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args, _ = parser.parse_known_args()
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args = merge_args(args, cloud_args)
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args.image_size = config.image_size
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args.num_classes = config.num_classes
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args.backbone = config.backbone
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args.rank = config.rank
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args.group_size = config.group_size
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args.image_size = list(map(int, args.image_size.split(',')))
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return 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 merge_args(args, cloud_args):
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"""merge_args"""
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args_dict = vars(args)
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if isinstance(cloud_args, dict):
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for key in cloud_args.keys():
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val = cloud_args[key]
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if key in args_dict and val:
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arg_type = type(args_dict[key])
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if arg_type is not type(None):
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val = arg_type(val)
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args_dict[key] = val
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return args
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def test(cloud_args=None):
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"""test"""
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args = parse_args(cloud_args)
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# init distributed
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if args.is_distributed:
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init()
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args.rank = get_rank()
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args.group_size = get_group_size()
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args.outputs_dir = os.path.join(args.log_path,
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datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
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args.logger = get_logger(args.outputs_dir, args.rank)
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args.logger.save_args(args)
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# network
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args.logger.important_info('start create network')
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if os.path.isdir(args.pretrained):
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models = list(glob.glob(os.path.join(args.pretrained, '*.ckpt')))
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print(models)
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if args.graph_ckpt:
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f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split('_')[0])
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else:
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f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('_')[-1])
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args.models = sorted(models, key=f)
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else:
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args.models = [args.pretrained,]
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for model in args.models:
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de_dataset = classification_dataset(args.data_dir, image_size=args.image_size,
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per_batch_size=args.per_batch_size,
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max_epoch=1, rank=args.rank, group_size=args.group_size,
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mode='eval')
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eval_dataloader = de_dataset.create_tuple_iterator()
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network = get_network(args.backbone, args.num_classes)
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if network is None:
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raise NotImplementedError('not implement {}'.format(args.backbone))
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param_dict = load_checkpoint(model)
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param_dict_new = {}
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for key, values in param_dict.items():
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if key.startswith('moments.'):
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continue
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elif key.startswith('network.'):
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param_dict_new[key[8:]] = values
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else:
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param_dict_new[key] = values
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load_param_into_net(network, param_dict_new)
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args.logger.info('load model {} success'.format(model))
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# must add
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network.add_flags_recursive(fp16=True)
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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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network.set_train(False)
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t_end = time.time()
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it = 0
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for data, gt_classes in eval_dataloader:
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output = network(Tensor(data, mstype.float32))
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output = output.asnumpy()
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top1_output = np.argmax(output, (-1))
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top5_output = np.argsort(output)[:, -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 += args.per_batch_size
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if args.rank == 0 and it == 0:
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t_end = time.time()
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it = 1
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if args.rank == 0:
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time_used = time.time() - t_end
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fps = (img_tot - args.per_batch_size) * args.group_size / time_used
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args.logger.info('Inference Performance: {:.2f} img/sec'.format(fps))
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results = [[top1_correct], [top5_correct], [img_tot]]
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args.logger.info('before results={}'.format(results))
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if args.is_distributed:
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model_md5 = model.replace('/', '')
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tmp_dir = '/cache'
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if not os.path.exists(tmp_dir):
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os.mkdir(tmp_dir)
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top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(args.rank, model_md5)
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top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(args.rank, model_md5)
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img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(args.rank, model_md5)
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np.save(top1_correct_npy, top1_correct)
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np.save(top5_correct_npy, top5_correct)
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np.save(img_tot_npy, img_tot)
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while True:
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rank_ok = True
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for other_rank in range(args.group_size):
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top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(other_rank, model_md5)
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top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(other_rank, model_md5)
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img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(other_rank, model_md5)
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if not os.path.exists(top1_correct_npy) or not os.path.exists(top5_correct_npy) or \
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not os.path.exists(img_tot_npy):
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rank_ok = False
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if rank_ok:
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break
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top1_correct_all = 0
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top5_correct_all = 0
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img_tot_all = 0
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for other_rank in range(args.group_size):
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top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(other_rank, model_md5)
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top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(other_rank, model_md5)
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img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(other_rank, model_md5)
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top1_correct_all += np.load(top1_correct_npy)
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top5_correct_all += np.load(top5_correct_npy)
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img_tot_all += np.load(img_tot_npy)
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results = [[top1_correct_all], [top5_correct_all], [img_tot_all]]
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results = np.array(results)
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else:
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results = np.array(results)
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args.logger.info('after results={}'.format(results))
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top1_correct = results[0, 0]
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top5_correct = results[1, 0]
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img_tot = results[2, 0]
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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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args.logger.info('after allreduce eval: top1_correct={}, tot={},'
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'acc={:.2f}%(TOP1)'.format(top1_correct, img_tot, acc1))
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args.logger.info('after allreduce eval: top5_correct={}, tot={},'
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'acc={:.2f}%(TOP5)'.format(top5_correct, img_tot, acc5))
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if args.is_distributed:
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release()
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if __name__ == "__main__":
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test()
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