import argparse
import os
import os.path as osp
import shutil
import tempfile
import mmcv
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, load_checkpoint
from mmdet.apis import init_dist
from mmdet.core import coco_eval, results2json, wrap_fp16_model
from mmdet.datasets import build_dataloader, build_dataset
from mmdet.models import build_detector
def single_gpu_test(model, data_loader, show=True):
model.eval()
results = []
dataset = data_loader.dataset
prog_bar = mmcv.ProgressBar(len(dataset))
for i, data in enumerate(data_loader):
with torch.no_grad():
result = model(return_loss=False, rescale=not show, **data)
results.append(result)
if show:
model.module.show_result(data, result, score_thr=0.9)
# print(data, result)
batch_size = data['img'][0].size(0)
for _ in range(batch_size):
prog_bar.update()
# print(i, result)
return results
def multi_gpu_test(model, data_loader, tmpdir=None):
model.eval()
results = []
dataset = data_loader.dataset
rank, world_size = get_dist_info()
if rank == 0:
prog_bar = mmcv.ProgressBar(len(dataset))
for i, data in enumerate(data_loader):
with torch.no_grad():
result = model(return_loss=False, rescale=True, **data)
results.append(result)
if rank == 0:
batch_size = data['img'][0].size(0)
for _ in range(batch_size * world_size):
prog_bar.update()
# collect results from all ranks
results = collect_results(results, len(dataset), tmpdir)
return results
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
def parse_args():
parser = argparse.ArgumentParser(description='MMDet test detector')
parser.add_argument('--config', help='test config file path', default='/home/hdkj/mmdetection/configs/faster_rcnn_r50_fpn_1x.py', type=str)#用哪个训练
parser.add_argument('--checkpoint', help='checkpoint file', default='/home/hdkj/mmdetection/work_dirs/faster_rcnn_r50_fpn_1x/drinks0511/epoch_12.pth', type=str)#刚得到的模型
parser.add_argument('--out', help='output result file')
parser.add_argument(
'--json_out',
help='output result file name without extension',
type=str)
parser.add_argument(
'--eval',
type=str,
nargs='+',
choices=['proposal', 'proposal_fast', 'bbox', 'segm', 'keypoints'],
help='eval types')
parser.add_argument('--show', action='store_true', help='show results',default='True')
parser.add_argument('--tmpdir', help='tmp dir for writing some results')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = str(args.local_rank)
return args
def main():
args = parse_args()
from mmdet.apis import init_detector, inference_detector, show_result
# assert args.out or args.show or args.json_out, \
# ('Please specify at least one operation (save or show the results) '
# 'with the argument "--out" or "--show" or "--json_out"')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
if args.json_out is not None and args.json_out.endswith('.json'):
args.json_out = args.json_out[:-5]
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
# cfg = mmcv.Config.fromfile('/home/zs/mmdetection/configs/fast_rcnn_r50_fpn_1x.py')
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
# old versions did not save class info in checkpoints, this walkaround is
# for backward compatibility
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
model.CLASSES = dataset.CLASSES
if not distributed:
model = MMDataParallel(model, device_ids=[0])
print(checkpoint['meta']['CLASSES'])
outputs = single_gpu_test(model, data_loader, args.show)
# outputs = inference_detector(model, data_loader)
else:
model = MMDistributedDataParallel(model.cuda())
outputs = multi_gpu_test(model, data_loader, args.tmpdir)
rank, _ = get_dist_info()
if args.out and rank == 0:
print('\nwriting results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
eval_types = args.eval
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = args.out
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_files = results2json(dataset, outputs, args.out)
coco_eval(result_files, eval_types, dataset.coco)
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = args.out + '.{}'.format(name)
result_files = results2json(dataset, outputs_,
result_file)
coco_eval(result_files, eval_types, dataset.coco)
# Save predictions in the COCO json format
if args.json_out and rank == 0:
if not isinstance(outputs[0], dict):
pass
# results2json(dataset, outputs, args.json_out)
else:
for name in outputs[0]:
outputs_ = [out[name] for out in outputs]
result_file = args.json_out + '.{}'.format(name)
results2json(dataset, outputs_, result_file)
if __name__ == '__main__':
main()
