Huawei_Technology
/
mindspore

# Copyright 2020 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.
# ============================================================================
"""Eval"""
import os
import time
import argparse
import datetime
import glob
import numpy as np
import mindspore.nn as nn

from mindspore import Tensor, context
from mindspore.communication.management import init, get_rank, get_group_size, release
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.common import dtype as mstype

from src.utils.logging import get_logger
from src.image_classification import get_network
from src.dataset import classification_dataset
from src.config import config

devid = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
                    device_target="Ascend", save_graphs=False, device_id=devid)


class ParameterReduce(nn.Cell):
    """ParameterReduce"""
    def __init__(self):
        super(ParameterReduce, self).__init__()
        self.cast = P.Cast()
        self.reduce = P.AllReduce()

    def construct(self, x):
        one = self.cast(F.scalar_to_array(1.0), mstype.float32)
        out = x * one
        ret = self.reduce(out)
        return ret


def parse_args(cloud_args=None):
    """parse_args"""
    parser = argparse.ArgumentParser('mindspore classification test')

    # dataset related
    parser.add_argument('--data_dir', type=str, default='/opt/npu/datasets/classification/val', help='eval data dir')
    parser.add_argument('--per_batch_size', default=32, type=int, help='batch size for per npu')
    # network related
    parser.add_argument('--graph_ckpt', type=int, default=1, help='graph ckpt or feed ckpt')
    parser.add_argument('--pretrained', default='', type=str, help='fully path of pretrained model to load. '
                        'If it is a direction, it will test all ckpt')

    # logging related
    parser.add_argument('--log_path', type=str, default='outputs/', help='path to save log')
    parser.add_argument('--is_distributed', type=int, default=0, help='if multi device')

    # roma obs
    parser.add_argument('--train_url', type=str, default="", help='train url')

    args, _ = parser.parse_known_args()
    args = merge_args(args, cloud_args)
    args.image_size = config.image_size
    args.num_classes = config.num_classes
    args.backbone = config.backbone
    args.rank = config.rank
    args.group_size = config.group_size

    args.image_size = list(map(int, args.image_size.split(',')))

    return 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 merge_args(args, cloud_args):
    """merge_args"""
    args_dict = vars(args)
    if isinstance(cloud_args, dict):
        for key in cloud_args.keys():
            val = cloud_args[key]
            if key in args_dict and val:
                arg_type = type(args_dict[key])
                if arg_type is not type(None):
                    val = arg_type(val)
                args_dict[key] = val
    return args

def test(cloud_args=None):
    """test"""
    args = parse_args(cloud_args)

    # init distributed
    if args.is_distributed:
        init()
        args.rank = get_rank()
        args.group_size = get_group_size()

    args.outputs_dir = os.path.join(args.log_path,
                                    datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))

    args.logger = get_logger(args.outputs_dir, args.rank)
    args.logger.save_args(args)

    # network
    args.logger.important_info('start create network')
    if os.path.isdir(args.pretrained):
        models = list(glob.glob(os.path.join(args.pretrained, '*.ckpt')))
        print(models)
        if args.graph_ckpt:
            f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split('_')[0])
        else:
            f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('_')[-1])
        args.models = sorted(models, key=f)
    else:
        args.models = [args.pretrained,]

    for model in args.models:
        de_dataset = classification_dataset(args.data_dir, image_size=args.image_size,
                                            per_batch_size=args.per_batch_size,
                                            max_epoch=1, rank=args.rank, group_size=args.group_size,
                                            mode='eval')
        eval_dataloader = de_dataset.create_tuple_iterator()
        network = get_network(args.backbone, args.num_classes)
        if network is None:
            raise NotImplementedError('not implement {}'.format(args.backbone))

        param_dict = load_checkpoint(model)
        param_dict_new = {}
        for key, values in param_dict.items():
            if key.startswith('moments.'):
                continue
            elif key.startswith('network.'):
                param_dict_new[key[8:]] = values
            else:
                param_dict_new[key] = values

        load_param_into_net(network, param_dict_new)
        args.logger.info('load model {} success'.format(model))

        # must add
        network.add_flags_recursive(fp16=True)

        img_tot = 0
        top1_correct = 0
        top5_correct = 0
        network.set_train(False)
        t_end = time.time()
        it = 0
        for data, gt_classes in eval_dataloader:
            output = network(Tensor(data, mstype.float32))
            output = output.asnumpy()

            top1_output = np.argmax(output, (-1))
            top5_output = np.argsort(output)[:, -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 += args.per_batch_size

            if args.rank == 0 and it == 0:
                t_end = time.time()
                it = 1
        if args.rank == 0:
            time_used = time.time() - t_end
            fps = (img_tot - args.per_batch_size) * args.group_size / time_used
            args.logger.info('Inference Performance: {:.2f} img/sec'.format(fps))
        results = [[top1_correct], [top5_correct], [img_tot]]
        args.logger.info('before results={}'.format(results))
        if args.is_distributed:
            model_md5 = model.replace('/', '')
            tmp_dir = '/cache'
            if not os.path.exists(tmp_dir):
                os.mkdir(tmp_dir)
            top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(args.rank, model_md5)
            top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(args.rank, model_md5)
            img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(args.rank, model_md5)
            np.save(top1_correct_npy, top1_correct)
            np.save(top5_correct_npy, top5_correct)
            np.save(img_tot_npy, img_tot)
            while True:
                rank_ok = True
                for other_rank in range(args.group_size):
                    top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(other_rank, model_md5)
                    top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(other_rank, model_md5)
                    img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(other_rank, model_md5)
                    if not os.path.exists(top1_correct_npy) or not os.path.exists(top5_correct_npy) or \
                       not os.path.exists(img_tot_npy):
                        rank_ok = False
                if rank_ok:
                    break

            top1_correct_all = 0
            top5_correct_all = 0
            img_tot_all = 0
            for other_rank in range(args.group_size):
                top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(other_rank, model_md5)
                top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(other_rank, model_md5)
                img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(other_rank, model_md5)
                top1_correct_all += np.load(top1_correct_npy)
                top5_correct_all += np.load(top5_correct_npy)
                img_tot_all += np.load(img_tot_npy)
            results = [[top1_correct_all], [top5_correct_all], [img_tot_all]]
            results = np.array(results)
        else:
            results = np.array(results)

        args.logger.info('after results={}'.format(results))
        top1_correct = results[0, 0]
        top5_correct = results[1, 0]
        img_tot = results[2, 0]
        acc1 = 100.0 * top1_correct / img_tot
        acc5 = 100.0 * top5_correct / img_tot
        args.logger.info('after allreduce eval: top1_correct={}, tot={},'
                         'acc={:.2f}%(TOP1)'.format(top1_correct, img_tot, acc1))
        args.logger.info('after allreduce eval: top5_correct={}, tot={},'
                         'acc={:.2f}%(TOP5)'.format(top5_correct, img_tot, acc5))
    if args.is_distributed:
        release()


if __name__ == "__main__":
    test()