forked from mindspore-Ecosystem/mindspore
245 lines
9.5 KiB
Python
245 lines
9.5 KiB
Python
# 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.
|
|
# ============================================================================
|
|
|
|
"""
|
|
##############test densenet example#################
|
|
python eval.py --data_dir /PATH/TO/DATASET --pretrained /PATH/TO/CHECKPOINT
|
|
"""
|
|
|
|
import os
|
|
import argparse
|
|
import datetime
|
|
import glob
|
|
import numpy as np
|
|
from mindspore import context
|
|
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
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.datasets import classification_dataset
|
|
from src.network import DenseNet121
|
|
from src.config import config
|
|
|
|
devid = int(os.getenv('DEVICE_ID'))
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="Davinci",
|
|
save_graphs=True, device_id=devid)
|
|
|
|
|
|
class ParameterReduce(nn.Cell):
|
|
"""
|
|
reduce parameter
|
|
"""
|
|
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='', help='eval data dir')
|
|
parser.add_argument('--num_classes', type=int, default=1000, help='num of classes in dataset')
|
|
parser.add_argument('--image_size', type=str, default='224,224', help='image size of the dataset')
|
|
# network related
|
|
parser.add_argument('--backbone', default='resnet50', help='backbone')
|
|
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=1, help='if multi device')
|
|
parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
|
|
parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
|
|
|
|
# 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.per_batch_size = config.per_batch_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 and cloud_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):
|
|
"""
|
|
network eval function. Get top1 and top5 ACC from classification.
|
|
The result will be save at [./outputs] by default.
|
|
"""
|
|
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')))
|
|
|
|
f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split('_')[0])
|
|
|
|
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 = DenseNet121(args.num_classes)
|
|
|
|
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))
|
|
|
|
network.add_flags_recursive(fp16=True)
|
|
|
|
img_tot = 0
|
|
top1_correct = 0
|
|
top5_correct = 0
|
|
network.set_train(False)
|
|
for data, gt_classes in eval_dataloader:
|
|
output = network(Tensor(data, mstype.float32))
|
|
output = output.asnumpy()
|
|
gt_classes = gt_classes.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
|
|
|
|
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 = '{}/top1_rank_{}_{}.npy'.format(tmp_dir, args.rank, model_md5)
|
|
top5_correct_npy = '{}/top5_rank_{}_{}.npy'.format(tmp_dir, args.rank, model_md5)
|
|
img_tot_npy = '{}/img_tot_rank_{}_{}.npy'.format(tmp_dir, 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 = '{}/top1_rank_{}_{}.npy'.format(tmp_dir, other_rank, model_md5)
|
|
top5_correct_npy = '{}/top5_rank_{}_{}.npy'.format(tmp_dir, other_rank, model_md5)
|
|
img_tot_npy = '{}/img_tot_rank_{}_{}.npy'.format(tmp_dir, 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 = '{}/top1_rank_{}_{}.npy'.format(tmp_dir, other_rank, model_md5)
|
|
top5_correct_npy = '{}/top5_rank_{}_{}.npy'.format(tmp_dir, other_rank, model_md5)
|
|
img_tot_npy = '{}/img_tot_rank_{}_{}.npy'.format(tmp_dir, 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}%'.format(top1_correct,
|
|
img_tot,
|
|
acc1))
|
|
args.logger.info('after allreduce eval: top5_correct={}, tot={}, acc={:.2f}%'.format(top5_correct,
|
|
img_tot,
|
|
acc5))
|
|
if args.is_distributed:
|
|
release()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test()
|