def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
results = mmcv.load(args.prediction_path)
assert isinstance(results, list)
if isinstance(results[0], np.ndarray):
pass
else:
raise TypeError('invalid type of prediction results')
if isinstance(cfg.data.test, dict):
cfg.data.test.test_mode = True
elif isinstance(cfg.data.test, list):
for ds_cfg in cfg.data.test:
ds_cfg.test_mode = True
dataset = build_dataset(cfg.data.test)
confusion_matrix = calculate_confusion_matrix(dataset, results)
plot_confusion_matrix(confusion_matrix,
dataset.CLASSES,
save_dir=args.save_dir,
show=args.show,
title=args.title,
color_theme=args.color_theme)
def __init__(
self,
image_size,
crop_size,
split,
config_path,
normalization,
**kwargs,
):
super().__init__()
self.image_size = image_size
self.crop_size = crop_size
self.split = split
self.normalization = STATS[normalization].copy()
self.ignore_label = None
for k, v in self.normalization.items():
v = np.round(255 * np.array(v), 2)
self.normalization[k] = tuple(v)
print(f"Use normalization: {self.normalization}")
config = Config.fromfile(config_path)
self.ratio = config.max_ratio
self.dataset = None
self.config = self.update_default_config(config)
self.dataset = build_dataset(getattr(self.config.data,
f"{self.split}"))
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
torch.backends.cudnn.benchmark = False
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# 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,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
# build the model and load checkpoint
cfg.model.train_cfg = None
model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
load_checkpoint(model, args.checkpoint, map_location='cpu')
model = MMDataParallel(model, device_ids=[0])
model.eval()
# the first several iterations may be very slow so skip them
num_warmup = 5
pure_inf_time = 0
total_iters = 200
# benchmark with 200 image and take the average
for i, data in enumerate(data_loader):
torch.cuda.synchronize()
start_time = time.perf_counter()
with torch.no_grad():
model(return_loss=False, rescale=True, **data)
torch.cuda.synchronize()
elapsed = time.perf_counter() - start_time
if i >= num_warmup:
pure_inf_time += elapsed
if (i + 1) % args.log_interval == 0:
fps = (i + 1 - num_warmup) / pure_inf_time
print(f'Done image [{i + 1:<3}/ {total_iters}], '
f'fps: {fps:.2f} img / s')
if (i + 1) == total_iters:
fps = (i + 1 - num_warmup) / pure_inf_time
print(f'Overall fps: {fps:.2f} img / s')
break
def main():
args = parse_args()
assert args.out or args.eval or args.format_only or args.show \
or args.show_dir, \
('Please specify at least one operation (save/eval/format/show the '
'results / save the results) with the argument "--out", "--eval"'
', "--format-only", "--show" or "--show-dir"')
if args.eval and args.format_only:
raise ValueError('--eval and --format_only cannot be both specified')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
if args.options is not None:
cfg.merge_from_dict(args.options)
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# init distributed env first, since logger depends on the dist info.
distributed = False
# 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,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# load onnx config and meta
cfg.model.train_cfg = None
model = ONNXRuntimeSegmentor(args.model, cfg=cfg, device_id=0)
model.CLASSES = dataset.CLASSES
model.PALETTE = dataset.PALETTE
efficient_test = False
if args.eval_options is not None:
efficient_test = args.eval_options.get('efficient_test', False)
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
efficient_test, args.opacity)
rank, _ = get_dist_info()
if rank == 0:
if args.out:
print(f'\nwriting results to {args.out}')
mmcv.dump(outputs, args.out)
kwargs = {} if args.eval_options is None else args.eval_options
if args.format_only:
dataset.format_results(outputs, **kwargs)
if args.eval:
dataset.evaluate(outputs, args.eval, **kwargs)
def predict_rsImage_mmseg(config_file,
trained_model,
image_path,
img_save_dir,
batch_size=1,
gpuid=0,
tile_width=480,
tile_height=480,
overlay_x=160,
overlay_y=160):
cfg = mmcv.Config.fromfile(config_file)
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.data.test.test_mode = True
distributed = False
# test_mode=False,rsimage='',rsImg_id=0,tile_width=480,tile_height=480,
# overlay_x=160,overlay_y=160
data_args = {
'rsImg_predict': True,
'rsimage': image_path,
'tile_width': tile_width,
'tile_height': tile_height,
'overlay_x': overlay_x,
'overlay_y': overlay_y
}
dataset = build_dataset(cfg.data.test, default_args=data_args)
data_loader = build_dataloader(dataset,
samples_per_gpu=batch_size,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
cfg.model.train_cfg = None
model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
checkpoint = load_checkpoint(model, trained_model, map_location='cpu')
if 'CLASSES' in checkpoint.get('meta', {}):
model.CLASSES = checkpoint['meta']['CLASSES']
else:
print('"CLASSES" not found in meta, use dataset.CLASSES instead')
model.CLASSES = dataset.CLASSES
if 'PALETTE' in checkpoint.get('meta', {}):
model.PALETTE = checkpoint['meta']['PALETTE']
else:
print('"PALETTE" not found in meta, use dataset.PALETTE instead')
model.PALETTE = dataset.PALETTE
# clean gpu memory when starting a new evaluation.
torch.cuda.empty_cache()
# no distributed
model = MMDataParallel(model, device_ids=[gpuid])
single_gpu_prediction_rsImage(model, data_loader, img_save_dir)
def main():
cfg = mmcv.Config.fromfile(args.cfg)
outs = mmcv.load(args.pkl_file)
test_set = build_dataset(cfg.data.test)
with ZipFile(args.zip_file, 'w') as myzip:
for img_info, binary in zip(tqdm(test_set.img_infos, ncols=80), outs):
mask = mutils.binary2array(binary, np.int32)
mask = out2mask(mask)
image_name = get_file_name(img_info['filename'])
save_path = os.path.join('results', image_name + '.png')
out_binary = mutils.array2binary(mask)
myzip.writestr(save_path, out_binary)
def main(args):
cfg = mmcv.Config.fromfile(
f'./experiments/config_standfordbackground_{args.version}.py')
if args.config:
print(cfg.pretty_text)
set_random_seed(cfg.seed, deterministic=False)
# Build the dataset
datasets = [build_dataset(cfg.data.train)]
# Build the detector
model = build_segmentor(cfg.model)
if args.model:
print(model)
# Launch training
if args.train:
print("Start training...")
# Create work_dir
mmcv.mkdir_or_exist(os.path.abspath(cfg.work_dir))
# Training process
train_segmentor(model,
datasets,
cfg,
distributed=False,
validate=True,
meta={
"CLASSES": classes,
"PALETTE": palette,
})
# evaluation
if args.evaluation:
eval_ = evaluate_dataset(checkpoint="{}/iter_{}.pth".format(
cfg.work_dir, args.evaluation),
device='cuda:{}'.format(cfg.gpu_ids[0]),
config=cfg)
print(f"Overall accuracy: {eval_[0]}")
print(f"Accuracies: {eval_[1]}")
print(f"IoUs: {eval_[2]}")
print(f"mIoU: {eval_[3]}")
def set_multiscale_mode(self):
self.config.data.val.pipeline[1]["img_ratios"] = [
0.5,
0.75,
1.0,
1.25,
1.5,
1.75,
]
self.config.data.val.pipeline[1]["flip"] = True
self.config.data.test.pipeline[1]["img_ratios"] = [
0.5,
0.75,
1.0,
1.25,
1.5,
1.75,
]
self.config.data.test.pipeline[1]["flip"] = True
self.dataset = build_dataset(getattr(self.config.data,
f"{self.split}"))
def main():
args = parse_args()
assert args.out or args.eval or args.format_only or args.show \
or args.show_dir, \
('Please specify at least one operation (save/eval/format/show the '
'results / save the results) with the argument "--out", "--eval"'
', "--format-only", "--show" or "--show-dir"')
if args.eval and args.format_only:
raise ValueError('--eval and --format_only cannot be both specified')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
if args.options is not None:
cfg.merge_from_dict(args.options)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
if args.aug_test:
# hard code index
cfg.data.test.pipeline[1].img_ratios = [
0.5, 0.75, 1.0, 1.25, 1.5, 1.75
]
cfg.data.test.pipeline[1].flip = 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,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
cfg.model.train_cfg = None
model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
model.CLASSES = checkpoint['meta']['CLASSES']
model.PALETTE = checkpoint['meta']['PALETTE']
efficient_test = False
if args.eval_options is not None:
efficient_test = args.eval_options.get('efficient_test', False)
if not distributed:
#for concatenated (multi) image input.
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test_multi(model, data_loader, args.show,
args.show_dir, args.show_original_dir,
efficient_test)
else:
#currently did not support for multi image
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False)
outputs = multi_gpu_test(model, data_loader, args.tmpdir,
args.gpu_collect, efficient_test)
rank, _ = get_dist_info()
if rank == 0:
if args.out:
print(f'\nwriting results to {args.out}')
mmcv.dump(outputs, args.out)
kwargs = {} if args.eval_options is None else args.eval_options
if args.format_only:
dataset.format_results(outputs, **kwargs)
if args.eval:
dataset.evaluate(outputs, args.eval, **kwargs)
def main():
cfg, config_fn = get_cfg()
_, config_name, _ = get_file_name_extension(config_fn)
# dataset_name = "SV3_roads"
dataset_name = "SN7_buildings"
args_work_dir = path_join("C:/_koray/korhun/mmsegmentation/data/space", dataset_name + "_" + config_name)
args_resume_from = path_join(args_work_dir, "latest.pth")
if not os.path.isfile(args_resume_from):
args_resume_from = None
args_launcher = "none"
args_seed = None
args_deterministic = False
args_no_validate = False
if not os.path.isdir(args_work_dir):
create_dir(args_work_dir)
# cfg = Config.fromfile(args_config)
# if args.options is not None:
# cfg.merge_from_dict(args.options)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.work_dir = args_work_dir
# if args.load_from is not None:
# cfg.load_from = args.load_from
if args_resume_from is not None:
cfg.resume_from = args_resume_from
# if args.gpu_ids is not None:
# cfg.gpu_ids = args.gpu_ids
# else:
# cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
cfg.gpu_ids = range(0, 1)
# 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)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# dump config
cfg.dump(osp.join(cfg.work_dir, config_name))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# init the meta dict to record some important information such as
# environment info and seed, which will be logged
meta = dict()
# log env info
env_info_dict = collect_env()
env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
dash_line)
meta['env_info'] = env_info
# log some basic info
logger.info(f'Distributed training: {distributed}')
logger.info(f'Config:\n{cfg.pretty_text}')
# set random seeds
if args_seed is not None:
logger.info(f'Set random seed to {args_seed}, deterministic: '
f'{args_deterministic}')
set_random_seed(args_seed, deterministic=args_deterministic)
cfg.seed = args_seed
meta['seed'] = args_seed
meta['exp_name'] = config_name
model = build_segmentor(
cfg.model,
train_cfg=cfg.get('train_cfg'),
test_cfg=cfg.get('test_cfg'))
logger.info(model)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
val_dataset.pipeline = cfg.data.train.pipeline
datasets.append(build_dataset(val_dataset))
if cfg.checkpoint_config is not None:
# save mmseg version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmseg_version=f'{__version__}+{get_git_hash()[:7]}',
config=cfg.pretty_text,
CLASSES=datasets[0].CLASSES,
PALETTE=datasets[0].PALETTE)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
train_segmentor(
model,
datasets,
cfg,
distributed=distributed,
validate=(not args_no_validate),
timestamp=timestamp,
meta=meta)
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)
cfg.runner.max_iters = 80000
# Let's have a look at the final config used for training
print(f'Config:\n{cfg.pretty_text}')
sys.exit()
from mmseg.datasets import build_dataset
from mmseg.models import build_segmentor
from mmseg.apis import train_segmentor
# Build the dataset
datasets = [build_dataset(cfg.data.train)]
# Build the detector
model = build_segmentor(cfg.model)
# Add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
# Create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
train_segmentor(model,
datasets,
cfg,
distributed=False,
validate=True,
meta=dict())
# print(all_acc, macc, miou, acc, iou )
#
# all_acc, macc, miou, acc, iou = evaluate_zzg(model1.student, valloader, num_classes=19, mode_name=model1.name)
# print(all_acc, macc, miou, acc, iou )
#
# mean_IU, IU_array = model1.evalute_model(model1.student, valloader, gpu_id='0', input_size='512,512', num_classes=19, whole=True)
# print('mean_IU:', mean_IU)
# print('IU_array:', IU_array)
## mmseg
checkpoint = load_checkpoint(model1.student, pretrain_model_1)
# model = model1.student
S_config = 'configs/pspnet/pspnet_r18-d8_512x512_40k_cityscapes_1gpu.py'
S_cfg = Config.fromfile(S_config)
dataset = build_dataset(S_cfg.data.train)
# dataset = build_dataset(S_cfg.data.test)
data_loader = build_dataloader(
dataset,
samples_per_gpu=1,
workers_per_gpu=1,
# workers_per_gpu=S_cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
# model.CLASSES = checkpoint['meta']['CLASSES']
# model.PALETTE = checkpoint['meta']['PALETTE']
# distributed = False
# if not distributed:
# model = MMDataParallel(model, device_ids=[0])
# # outputs = single_gpu_test(model, data_loader, args.show, args.show_dir)
# log some basic info
distributed = False
logger.info(f'Distributed training: {distributed}')
logger.info(f'Config:\n{cfg.pretty_text}')
model = build_segmentor(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
# checkpoint = '/home/zhouzhigong/Documents/mmsegmentation/work_dirs/pspnet_r18-d8_512x512_40k_cityscapes_skd/iter_40000.pth'
# checkpoint = load_checkpoint(model, checkpoint)
model.cuda(0)
model_paral = MMDataParallel(model, device_ids=[0])
train_dataset = build_dataset(cfg.data.train)
train_data_loader = build_dataloader(
train_dataset,
cfg.data.samples_per_gpu, # samples_per_gpu
cfg.data.workers_per_gpu, # workers_per_gpu
len(cfg.gpu_ids),
dist=False,
seed=cfg.seed,
drop_last=True)
val_dataset = build_dataset(cfg.data.test)
val_data_loader = build_dataloader(
val_dataset,
samples_per_gpu=4,
workers_per_gpu=4,
# workers_per_gpu=S_cfg.data.workers_per_gpu,
dist=False,
if seed is not None:
logger.info(f'Set random seed to {seed}, deterministic: ' f'{True}')
set_random_seed(seed, deterministic=True)
cfg.seed = seed
meta['seed'] = seed
# get gflops for model
# os.system('python tools/get_flops.py configs/hrnet/parkinglot.py --shape 1024 512')
# train and eval
from mmseg.datasets import build_dataset
from mmseg.models import build_segmentor
from mmseg.apis import inference_segmentor, init_segmentor, train_segmentor
# Build the dataset
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
val_dataset.pipeline = cfg.data.train.pipeline
datasets.append(build_dataset(val_dataset))
if cfg.checkpoint_config is not None:
# save mmseg version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmseg_version=f'{__version__}+{get_git_hash()[:7]}',
# config=cfg.pretty_text,
CLASSES=datasets[0].CLASSES,
PALETTE=datasets[0].PALETTE)
# save config
cfg.dump(os.path.join(cfg.work_dir, 'config.py'))
def main():
args = parse_args()
assert args.out or args.eval or args.format_only or args.show \
or args.show_dir, \
('Please specify at least one operation (save/eval/format/show the '
'results / save the results) with the argument "--out", "--eval"'
', "--format-only", "--show" or "--show-dir"')
if args.eval and args.format_only:
raise ValueError('--eval and --format_only cannot be both specified')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# init distributed env first, since logger depends on the dist info.
distributed = False
# 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,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# load onnx config and meta
cfg.model.train_cfg = None
if args.backend == 'onnxruntime':
model = ONNXRuntimeSegmentor(args.model, cfg=cfg, device_id=0)
elif args.backend == 'tensorrt':
model = TensorRTSegmentor(args.model, cfg=cfg, device_id=0)
model.CLASSES = dataset.CLASSES
model.PALETTE = dataset.PALETTE
# clean gpu memory when starting a new evaluation.
torch.cuda.empty_cache()
eval_kwargs = {} if args.eval_options is None else args.eval_options
# Deprecated
efficient_test = eval_kwargs.get('efficient_test', False)
if efficient_test:
warnings.warn(
'``efficient_test=True`` does not have effect in tools/test.py, '
'the evaluation and format results are CPU memory efficient by '
'default')
eval_on_format_results = (args.eval is not None
and 'cityscapes' in args.eval)
if eval_on_format_results:
assert len(args.eval) == 1, 'eval on format results is not ' \
'applicable for metrics other than ' \
'cityscapes'
if args.format_only or eval_on_format_results:
if 'imgfile_prefix' in eval_kwargs:
tmpdir = eval_kwargs['imgfile_prefix']
else:
tmpdir = '.format_cityscapes'
eval_kwargs.setdefault('imgfile_prefix', tmpdir)
mmcv.mkdir_or_exist(tmpdir)
else:
tmpdir = None
model = MMDataParallel(model, device_ids=[0])
results = single_gpu_test(model,
data_loader,
args.show,
args.show_dir,
False,
args.opacity,
pre_eval=args.eval is not None
and not eval_on_format_results,
format_only=args.format_only
or eval_on_format_results,
format_args=eval_kwargs)
rank, _ = get_dist_info()
if rank == 0:
if args.out:
warnings.warn(
'The behavior of ``args.out`` has been changed since MMSeg '
'v0.16, the pickled outputs could be seg map as type of '
'np.array, pre-eval results or file paths for '
'``dataset.format_results()``.')
print(f'\nwriting results to {args.out}')
mmcv.dump(results, args.out)
if args.eval:
dataset.evaluate(results, args.eval, **eval_kwargs)
if tmpdir is not None and eval_on_format_results:
# remove tmp dir when cityscapes evaluation
shutil.rmtree(tmpdir)
def test_build_dataset():
cfg = dict(type='ToyDataset')
dataset = build_dataset(cfg)
assert isinstance(dataset, ToyDataset)
assert dataset.cnt == 0
dataset = build_dataset(cfg, default_args=dict(cnt=1))
assert isinstance(dataset, ToyDataset)
assert dataset.cnt == 1
data_root = osp.join(osp.dirname(__file__), '../data/pseudo_dataset')
img_dir = 'imgs/'
ann_dir = 'gts/'
# We use same dir twice for simplicity
# with ann_dir
cfg = dict(type='CustomDataset',
pipeline=[],
data_root=data_root,
img_dir=[img_dir, img_dir],
ann_dir=[ann_dir, ann_dir])
dataset = build_dataset(cfg)
assert isinstance(dataset, ConcatDataset)
assert len(dataset) == 10
# with ann_dir, split
cfg = dict(type='CustomDataset',
pipeline=[],
data_root=data_root,
img_dir=img_dir,
ann_dir=ann_dir,
split=['splits/train.txt', 'splits/val.txt'])
dataset = build_dataset(cfg)
assert isinstance(dataset, ConcatDataset)
assert len(dataset) == 5
# with ann_dir, split
cfg = dict(type='CustomDataset',
pipeline=[],
data_root=data_root,
img_dir=img_dir,
ann_dir=[ann_dir, ann_dir],
split=['splits/train.txt', 'splits/val.txt'])
dataset = build_dataset(cfg)
assert isinstance(dataset, ConcatDataset)
assert len(dataset) == 5
# test mode
cfg = dict(type='CustomDataset',
pipeline=[],
data_root=data_root,
img_dir=[img_dir, img_dir],
test_mode=True)
dataset = build_dataset(cfg)
assert isinstance(dataset, ConcatDataset)
assert len(dataset) == 10
# test mode with splits
cfg = dict(type='CustomDataset',
pipeline=[],
data_root=data_root,
img_dir=[img_dir, img_dir],
split=['splits/val.txt', 'splits/val.txt'],
test_mode=True)
dataset = build_dataset(cfg)
assert isinstance(dataset, ConcatDataset)
assert len(dataset) == 2
# len(ann_dir) should be zero or len(img_dir) when len(img_dir) > 1
with pytest.raises(AssertionError):
cfg = dict(type='CustomDataset',
pipeline=[],
data_root=data_root,
img_dir=[img_dir, img_dir],
ann_dir=[ann_dir, ann_dir, ann_dir])
build_dataset(cfg)
# len(splits) should be zero or len(img_dir) when len(img_dir) > 1
with pytest.raises(AssertionError):
cfg = dict(
type='CustomDataset',
pipeline=[],
data_root=data_root,
img_dir=[img_dir, img_dir],
split=['splits/val.txt', 'splits/val.txt', 'splits/val.txt'])
build_dataset(cfg)
# len(splits) == len(ann_dir) when only len(img_dir) == 1 and len(
# ann_dir) > 1
with pytest.raises(AssertionError):
cfg = dict(
type='CustomDataset',
pipeline=[],
data_root=data_root,
img_dir=img_dir,
ann_dir=[ann_dir, ann_dir],
split=['splits/val.txt', 'splits/val.txt', 'splits/val.txt'])
build_dataset(cfg)
def main():
args = parse_args()
assert args.out or args.eval or args.format_only or args.show \
or args.show_dir, \
('Please specify at least one operation (save/eval/format/show the '
'results / save the results) with the argument "--out", "--eval"'
', "--format-only", "--show" or "--show-dir"')
if args.eval and args.format_only:
raise ValueError('--eval and --format_only cannot be both specified')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
# set multi-process settings
setup_multi_processes(cfg)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
if args.aug_test:
# hard code index
cfg.data.test.pipeline[1].img_ratios = [
0.5, 0.75, 1.0, 1.25, 1.5, 1.75
]
cfg.data.test.pipeline[1].flip = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
if args.gpu_id is not None:
cfg.gpu_ids = [args.gpu_id]
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
cfg.gpu_ids = [args.gpu_id]
distributed = False
if len(cfg.gpu_ids) > 1:
warnings.warn(f'The gpu-ids is reset from {cfg.gpu_ids} to '
f'{cfg.gpu_ids[0:1]} to avoid potential error in '
'non-distribute testing time.')
cfg.gpu_ids = cfg.gpu_ids[0:1]
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
rank, _ = get_dist_info()
# allows not to create
if args.work_dir is not None and rank == 0:
mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
if args.aug_test:
json_file = osp.join(args.work_dir,
f'eval_multi_scale_{timestamp}.json')
else:
json_file = osp.join(args.work_dir,
f'eval_single_scale_{timestamp}.json')
elif rank == 0:
work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
mmcv.mkdir_or_exist(osp.abspath(work_dir))
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
if args.aug_test:
json_file = osp.join(work_dir,
f'eval_multi_scale_{timestamp}.json')
else:
json_file = osp.join(work_dir,
f'eval_single_scale_{timestamp}.json')
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
# The default loader config
loader_cfg = dict(
# cfg.gpus will be ignored if distributed
num_gpus=len(cfg.gpu_ids),
dist=distributed,
shuffle=False)
# The overall dataloader settings
loader_cfg.update({
k: v
for k, v in cfg.data.items() if k not in [
'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
'test_dataloader'
]
})
test_loader_cfg = {
**loader_cfg,
'samples_per_gpu': 1,
'shuffle': False, # Not shuffle by default
**cfg.data.get('test_dataloader', {})
}
# build the dataloader
data_loader = build_dataloader(dataset, **test_loader_cfg)
# build the model and load checkpoint
cfg.model.train_cfg = None
model = build_segmentor(cfg.model, test_cfg=cfg.get('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')
if 'CLASSES' in checkpoint.get('meta', {}):
model.CLASSES = checkpoint['meta']['CLASSES']
else:
print('"CLASSES" not found in meta, use dataset.CLASSES instead')
model.CLASSES = dataset.CLASSES
if 'PALETTE' in checkpoint.get('meta', {}):
model.PALETTE = checkpoint['meta']['PALETTE']
else:
print('"PALETTE" not found in meta, use dataset.PALETTE instead')
model.PALETTE = dataset.PALETTE
# clean gpu memory when starting a new evaluation.
torch.cuda.empty_cache()
eval_kwargs = {} if args.eval_options is None else args.eval_options
# Deprecated
efficient_test = eval_kwargs.get('efficient_test', False)
if efficient_test:
warnings.warn(
'``efficient_test=True`` does not have effect in tools/test.py, '
'the evaluation and format results are CPU memory efficient by '
'default')
eval_on_format_results = (args.eval is not None
and 'cityscapes' in args.eval)
if eval_on_format_results:
assert len(args.eval) == 1, 'eval on format results is not ' \
'applicable for metrics other than ' \
'cityscapes'
if args.format_only or eval_on_format_results:
if 'imgfile_prefix' in eval_kwargs:
tmpdir = eval_kwargs['imgfile_prefix']
else:
tmpdir = '.format_cityscapes'
eval_kwargs.setdefault('imgfile_prefix', tmpdir)
mmcv.mkdir_or_exist(tmpdir)
else:
tmpdir = None
if not distributed:
warnings.warn(
'SyncBN is only supported with DDP. To be compatible with DP, '
'we convert SyncBN to BN. Please use dist_train.sh which can '
'avoid this error.')
if not torch.cuda.is_available():
assert digit_version(mmcv.__version__) >= digit_version('1.4.4'), \
'Please use MMCV >= 1.4.4 for CPU training!'
model = revert_sync_batchnorm(model)
model = MMDataParallel(model, device_ids=cfg.gpu_ids)
results = single_gpu_test(model,
data_loader,
args.show,
args.show_dir,
False,
args.opacity,
pre_eval=args.eval is not None
and not eval_on_format_results,
format_only=args.format_only
or eval_on_format_results,
format_args=eval_kwargs)
else:
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False)
results = multi_gpu_test(model,
data_loader,
args.tmpdir,
args.gpu_collect,
False,
pre_eval=args.eval is not None
and not eval_on_format_results,
format_only=args.format_only
or eval_on_format_results,
format_args=eval_kwargs)
rank, _ = get_dist_info()
if rank == 0:
if args.out:
warnings.warn(
'The behavior of ``args.out`` has been changed since MMSeg '
'v0.16, the pickled outputs could be seg map as type of '
'np.array, pre-eval results or file paths for '
'``dataset.format_results()``.')
print(f'\nwriting results to {args.out}')
mmcv.dump(results, args.out)
if args.eval:
eval_kwargs.update(metric=args.eval)
metric = dataset.evaluate(results, **eval_kwargs)
metric_dict = dict(config=args.config, metric=metric)
mmcv.dump(metric_dict, json_file, indent=4)
if tmpdir is not None and eval_on_format_results:
# remove tmp dir when cityscapes evaluation
shutil.rmtree(tmpdir)
def train_segmentor(model,
dataset,
cfg,
distributed=False,
validate=False,
timestamp=None,
meta=None):
"""Launch segmentor training."""
logger = get_root_logger(cfg.log_level)
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
data_loaders = [
build_dataloader(
ds,
cfg.data.samples_per_gpu,
cfg.data.workers_per_gpu,
# cfg.gpus will be ignored if distributed
len(cfg.gpu_ids),
dist=distributed,
seed=cfg.seed,
drop_last=True) for ds in dataset
]
# put model on gpus
if distributed:
find_unused_parameters = cfg.get('find_unused_parameters', False)
# Sets the `find_unused_parameters` parameter in
# torch.nn.parallel.DistributedDataParallel
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
else:
model = MMDataParallel(model.cuda(cfg.gpu_ids[0]),
device_ids=cfg.gpu_ids)
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
if cfg.get('runner') is None:
cfg.runner = {'type': 'IterBasedRunner', 'max_iters': cfg.total_iters}
warnings.warn(
'config is now expected to have a `runner` section, '
'please set `runner` in your config.', UserWarning)
runner = build_runner(cfg.runner,
default_args=dict(model=model,
batch_processor=None,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
meta=meta))
# register hooks
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config,
cfg.get('momentum_config', None))
# an ugly walkaround to make the .log and .log.json filenames the same
runner.timestamp = timestamp
# register eval hooks
if validate:
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
val_dataloader = build_dataloader(
val_dataset,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
eval_cfg = cfg.get('evaluation', {})
eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
eval_hook = DistEvalHook if distributed else EvalHook
# In this PR (https://github.com/open-mmlab/mmcv/pull/1193), the
# priority of IterTimerHook has been modified from 'NORMAL' to 'LOW'.
runner.register_hook(eval_hook(val_dataloader, **eval_cfg),
priority='LOW')
# user-defined hooks
if cfg.get('custom_hooks', None):
custom_hooks = cfg.custom_hooks
assert isinstance(custom_hooks, list), \
f'custom_hooks expect list type, but got {type(custom_hooks)}'
for hook_cfg in cfg.custom_hooks:
assert isinstance(hook_cfg, dict), \
'Each item in custom_hooks expects dict type, but got ' \
f'{type(hook_cfg)}'
hook_cfg = hook_cfg.copy()
priority = hook_cfg.pop('priority', 'NORMAL')
hook = build_from_cfg(hook_cfg, HOOKS)
runner.register_hook(hook, priority=priority)
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.options is not None:
cfg.merge_from_dict(args.options)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# work_dir is determined in this priority: CLI > segment in file > filename
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
if args.load_from is not None:
cfg.load_from = args.load_from
if args.resume_from is not None:
cfg.resume_from = args.resume_from
if args.gpu_ids is not None:
cfg.gpu_ids = args.gpu_ids
else:
cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
# 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)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# dump config
cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# init the meta dict to record some important information such as
# environment info and seed, which will be logged
meta = dict()
# log env info
env_info_dict = collect_env()
env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
dash_line)
meta['env_info'] = env_info
# log some basic info
logger.info(f'Distributed training: {distributed}')
logger.info(f'Config:\n{cfg.pretty_text}')
# set random seeds
if args.seed is not None:
logger.info(f'Set random seed to {args.seed}, deterministic: '
f'{args.deterministic}')
set_random_seed(args.seed, deterministic=args.deterministic)
cfg.seed = args.seed
meta['seed'] = args.seed
meta['exp_name'] = osp.basename(args.config)
model = build_segmentor(cfg.model,
train_cfg=cfg.get('train_cfg'),
test_cfg=cfg.get('test_cfg'))
logger.info(model)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
val_dataset.pipeline = cfg.data.train.pipeline
datasets.append(build_dataset(val_dataset))
if cfg.checkpoint_config is not None:
# save mmseg version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmseg_version=f'{__version__}+{get_git_hash()[:7]}',
config=cfg.pretty_text,
CLASSES=datasets[0].CLASSES,
PALETTE=datasets[0].PALETTE)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
train_segmentor(model,
datasets,
cfg,
distributed=distributed,
validate=(not args.no_validate),
timestamp=timestamp,
meta=meta)
def train_segmentor(model,
dataset,
cfg,
distributed=False,
validate=False,
timestamp=None,
meta=None):
"""Launch segmentor training."""
logger = get_root_logger(cfg.log_level)
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
# The default loader config
loader_cfg = dict(
# cfg.gpus will be ignored if distributed
num_gpus=len(cfg.gpu_ids),
dist=distributed,
seed=cfg.seed,
drop_last=True)
# The overall dataloader settings
loader_cfg.update({
k: v
for k, v in cfg.data.items() if k not in [
'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
'test_dataloader'
]
})
# The specific dataloader settings
train_loader_cfg = {**loader_cfg, **cfg.data.get('train_dataloader', {})}
data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]
# put model on gpus
if distributed:
find_unused_parameters = cfg.get('find_unused_parameters', False)
# Sets the `find_unused_parameters` parameter in
# torch.nn.parallel.DistributedDataParallel
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
else:
if not torch.cuda.is_available():
assert digit_version(mmcv.__version__) >= digit_version('1.4.4'), \
'Please use MMCV >= 1.4.4 for CPU training!'
model = MMDataParallel(model, device_ids=cfg.gpu_ids)
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
if cfg.get('runner') is None:
cfg.runner = {'type': 'IterBasedRunner', 'max_iters': cfg.total_iters}
warnings.warn(
'config is now expected to have a `runner` section, '
'please set `runner` in your config.', UserWarning)
runner = build_runner(cfg.runner,
default_args=dict(model=model,
batch_processor=None,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
meta=meta))
# register hooks
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config,
cfg.get('momentum_config', None))
if distributed:
# when distributed training by epoch, using`DistSamplerSeedHook` to set
# the different seed to distributed sampler for each epoch, it will
# shuffle dataset at each epoch and avoid overfitting.
if isinstance(runner, EpochBasedRunner):
runner.register_hook(DistSamplerSeedHook())
# an ugly walkaround to make the .log and .log.json filenames the same
runner.timestamp = timestamp
# register eval hooks
if validate:
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
# The specific dataloader settings
val_loader_cfg = {
**loader_cfg,
'samples_per_gpu': 1,
'shuffle': False, # Not shuffle by default
**cfg.data.get('val_dataloader', {}),
}
val_dataloader = build_dataloader(val_dataset, **val_loader_cfg)
eval_cfg = cfg.get('evaluation', {})
eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
eval_hook = DistEvalHook if distributed else EvalHook
# In this PR (https://github.com/open-mmlab/mmcv/pull/1193), the
# priority of IterTimerHook has been modified from 'NORMAL' to 'LOW'.
runner.register_hook(eval_hook(val_dataloader, **eval_cfg),
priority='LOW')
# user-defined hooks
if cfg.get('custom_hooks', None):
custom_hooks = cfg.custom_hooks
assert isinstance(custom_hooks, list), \
f'custom_hooks expect list type, but got {type(custom_hooks)}'
for hook_cfg in cfg.custom_hooks:
assert isinstance(hook_cfg, dict), \
'Each item in custom_hooks expects dict type, but got ' \
f'{type(hook_cfg)}'
hook_cfg = hook_cfg.copy()
priority = hook_cfg.pop('priority', 'NORMAL')
hook = build_from_cfg(hook_cfg, HOOKS)
runner.register_hook(hook, priority=priority)
if cfg.resume_from is None and cfg.get('auto_resume'):
resume_from = find_latest_checkpoint(cfg.work_dir)
if resume_from is not None:
cfg.resume_from = resume_from
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow)
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=0,
train=dict(type=dataset_type,
data_root=data_root,
img_dir='train/image',
ann_dir='train/label_cvt',
split='train/split/val_mini.txt',
mosaic_ratio=1,
pipeline=train_pipeline),
)
dataset = [build_dataset(data['train'])]
bs = 8
data_loaders = [
build_dataloader(
ds,
bs,
0,
# cfg.gpus will be ignored if distributed
len(range(0, 1)),
dist=False,
seed=41) for ds in dataset
]
data_loader = data_loaders[0]
data_per_batch = iter(data_loader).__next__()
def train_segmentor(model,
dataset,
cfg,
distributed=False,
validate=False,
timestamp=None,
meta=None):
"""Launch segmentor training."""
logger = get_root_logger(cfg.log_level)
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
if not torch.cuda.is_available():
len_gpu_ids = 2 # need to be changed
else:
len_gpu_ids = len(cfg.gpu_ids)
data_loaders = [
build_dataloader(
ds, # A PyTorch dataset.
cfg.data.
samples_per_gpu, # Number of training samples on each GPU, i.e., batch size of each GPU.
cfg.data.
workers_per_gpu, # How many subprocesses to use for data loading for each GPU.
# cfg.gpus will be ignored if distributed
len_gpu_ids,
# len(cfg.gpu_ids), # Number of GPUs. Only used in non-distributed training.
dist=distributed, # Distributed training/test or not. Default: True.
seed=cfg.seed,
drop_last=True) for ds in dataset
]
''' About build_dataloader
shuffle (bool): Whether to shuffle the data at every epoch.
Default: True.
seed (int | None): Seed to be used. Default: None.
drop_last (bool): Whether to drop the last incomplete batch in epoch.
Default: False
pin_memory (bool): Whether to use pin_memory in DataLoader.
Default: True
dataloader_type (str): Type of dataloader. Default: 'PoolDataLoader'
kwargs: any keyword argument to be used to initialize DataLoader'''
# put model on gpus
if distributed:
find_unused_parameters = cfg.get('find_unused_parameters', False)
# Sets the `find_unused_parameters` parameter in
# torch.nn.parallel.DistributedDataParallel
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
else:
if torch.cuda.is_available():
model = MMDataParallel(model.cuda(cfg.gpu_ids[0]),
device_ids=cfg.gpu_ids)
else:
model = MMDataParallel(model.to('cpu'))
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
if cfg.get('runner') is None:
cfg.runner = {'type': 'IterBasedRunner', 'max_iters': cfg.total_iters}
warnings.warn(
'config is now expected to have a `runner` section, '
'please set `runner` in your config.', UserWarning)
runner = build_runner(cfg.runner,
default_args=dict(model=model,
batch_processor=None,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
meta=meta))
# register hooks
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config,
cfg.get('momentum_config', None))
# an ugly walkaround to make the .log and .log.json filenames the same
runner.timestamp = timestamp
# register eval hooks
if validate:
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
val_dataloader = build_dataloader(
val_dataset,
samples_per_gpu=len_gpu_ids,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
eval_cfg = cfg.get('evaluation', {})
eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
eval_hook = DistEvalHook if distributed else EvalHook
runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow)
def main():
args = parse_args()
assert args.out or args.eval or args.format_only or args.show \
or args.show_dir, \
('Please specify at least one operation (save/eval/format/show the '
'results / save the results) with the argument "--out", "--eval"'
', "--format-only", "--show" or "--show-dir"')
if args.eval and args.format_only:
raise ValueError('--eval and --format_only cannot be both specified')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
# work_dir is determined in this priority: CLI > segment in file > filename
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
if args.options is not None:
cfg.merge_from_dict(args.options)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
if args.aug_test:
# hard code index
cfg.data.test.pipeline[1].img_ratios = [
0.5, 0.75, 1.0, 1.25, 1.5, 1.75
]
cfg.data.test.pipeline[1].flip = 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)
# init the logger before other steps
logger = None
if args.eval:
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, f'test_{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# set random seeds
if args.seed is not None:
set_random_seed(args.seed, deterministic=args.deterministic)
if logger is not None:
logger.info(f'Set random seed to {args.seed}, deterministic: '
f'{args.deterministic}')
else:
print(f'Set random seed to {args.seed}, deterministic: '
f'{args.deterministic}')
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.val, dict(test_mode=True))
data_loader = build_dataloader(
dataset,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_segmentor(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
model.CLASSES = checkpoint['meta']['CLASSES']
model.PALETTE = checkpoint['meta']['PALETTE']
if not distributed:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader, args.show, args.show_dir)
else:
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False)
outputs = multi_gpu_test(model, data_loader, args.tmpdir,
args.gpu_collect)
rank, _ = get_dist_info()
if rank == 0:
if args.out:
print(f'\nwriting results to {args.out}')
mmcv.dump(outputs, args.out)
kwargs = {} if args.eval_options is None else args.eval_options
if args.format_only:
dataset.format_results(outputs, **kwargs)
if args.eval:
dataset.evaluate(outputs, args.eval, logger, **kwargs)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
if args.work_dir is not None:
mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
json_file = osp.join(args.work_dir, f'fps_{timestamp}.json')
else:
# use config filename as default work_dir if cfg.work_dir is None
work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
mmcv.mkdir_or_exist(osp.abspath(work_dir))
json_file = osp.join(work_dir, f'fps_{timestamp}.json')
repeat_times = args.repeat_times
# set cudnn_benchmark
torch.backends.cudnn.benchmark = False
cfg.model.pretrained = None
cfg.data.test.test_mode = True
benchmark_dict = dict(config=args.config, unit='img / s')
overall_fps_list = []
for time_index in range(repeat_times):
print(f'Run {time_index + 1}:')
# 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,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
# build the model and load checkpoint
cfg.model.train_cfg = None
model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
if 'checkpoint' in args and osp.exists(args.checkpoint):
load_checkpoint(model, args.checkpoint, map_location='cpu')
model = MMDataParallel(model, device_ids=[0])
model.eval()
# the first several iterations may be very slow so skip them
num_warmup = 5
pure_inf_time = 0
total_iters = 200
# benchmark with 200 image and take the average
for i, data in enumerate(data_loader):
torch.cuda.synchronize()
start_time = time.perf_counter()
with torch.no_grad():
model(return_loss=False, rescale=True, **data)
torch.cuda.synchronize()
elapsed = time.perf_counter() - start_time
if i >= num_warmup:
pure_inf_time += elapsed
if (i + 1) % args.log_interval == 0:
fps = (i + 1 - num_warmup) / pure_inf_time
print(f'Done image [{i + 1:<3}/ {total_iters}], '
f'fps: {fps:.2f} img / s')
if (i + 1) == total_iters:
fps = (i + 1 - num_warmup) / pure_inf_time
print(f'Overall fps: {fps:.2f} img / s\n')
benchmark_dict[f'overall_fps_{time_index + 1}'] = round(fps, 2)
overall_fps_list.append(fps)
break
benchmark_dict['average_fps'] = round(np.mean(overall_fps_list), 2)
benchmark_dict['fps_variance'] = round(np.var(overall_fps_list), 4)
print(f'Average fps of {repeat_times} evaluations: '
f'{benchmark_dict["average_fps"]}')
print(f'The variance of {repeat_times} evaluations: '
f'{benchmark_dict["fps_variance"]}')
mmcv.dump(benchmark_dict, json_file, indent=4)
def test_eval_concat_custom_dataset(separate_eval):
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(128, 256),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data_root = osp.join(osp.dirname(__file__), '../data/pseudo_dataset')
img_dir = 'imgs/'
ann_dir = 'gts/'
cfg1 = dict(type='CustomDataset',
pipeline=test_pipeline,
data_root=data_root,
img_dir=img_dir,
ann_dir=ann_dir,
img_suffix='img.jpg',
seg_map_suffix='gt.png',
classes=tuple(['a'] * 7))
dataset1 = build_dataset(cfg1)
assert len(dataset1) == 5
# get gt seg map
gt_seg_maps = dataset1.get_gt_seg_maps(efficient_test=True)
assert isinstance(gt_seg_maps, Generator)
gt_seg_maps = list(gt_seg_maps)
assert len(gt_seg_maps) == 5
# test past evaluation
pseudo_results = []
for gt_seg_map in gt_seg_maps:
h, w = gt_seg_map.shape
pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
eval_results1 = dataset1.evaluate(pseudo_results,
metric=['mIoU', 'mDice', 'mFscore'])
# We use same dir twice for simplicity
# with ann_dir
cfg2 = dict(type='CustomDataset',
pipeline=test_pipeline,
data_root=data_root,
img_dir=[img_dir, img_dir],
ann_dir=[ann_dir, ann_dir],
img_suffix='img.jpg',
seg_map_suffix='gt.png',
classes=tuple(['a'] * 7),
separate_eval=separate_eval)
dataset2 = build_dataset(cfg2)
assert isinstance(dataset2, ConcatDataset)
assert len(dataset2) == 10
eval_results2 = dataset2.evaluate(pseudo_results * 2,
metric=['mIoU', 'mDice', 'mFscore'])
if separate_eval:
assert eval_results1['mIoU'] == eval_results2[
'0_mIoU'] == eval_results2['1_mIoU']
assert eval_results1['mDice'] == eval_results2[
'0_mDice'] == eval_results2['1_mDice']
assert eval_results1['mAcc'] == eval_results2[
'0_mAcc'] == eval_results2['1_mAcc']
assert eval_results1['aAcc'] == eval_results2[
'0_aAcc'] == eval_results2['1_aAcc']
assert eval_results1['mFscore'] == eval_results2[
'0_mFscore'] == eval_results2['1_mFscore']
assert eval_results1['mPrecision'] == eval_results2[
'0_mPrecision'] == eval_results2['1_mPrecision']
assert eval_results1['mRecall'] == eval_results2[
'0_mRecall'] == eval_results2['1_mRecall']
else:
assert eval_results1['mIoU'] == eval_results2['mIoU']
assert eval_results1['mDice'] == eval_results2['mDice']
assert eval_results1['mAcc'] == eval_results2['mAcc']
assert eval_results1['aAcc'] == eval_results2['aAcc']
assert eval_results1['mFscore'] == eval_results2['mFscore']
assert eval_results1['mPrecision'] == eval_results2['mPrecision']
assert eval_results1['mRecall'] == eval_results2['mRecall']
# test get dataset_idx and sample_idx from ConcateDataset
dataset_idx, sample_idx = dataset2.get_dataset_idx_and_sample_idx(3)
assert dataset_idx == 0
assert sample_idx == 3
dataset_idx, sample_idx = dataset2.get_dataset_idx_and_sample_idx(7)
assert dataset_idx == 1
assert sample_idx == 2
dataset_idx, sample_idx = dataset2.get_dataset_idx_and_sample_idx(-7)
assert dataset_idx == 0
assert sample_idx == 3
# test negative indice exceed length of dataset
with pytest.raises(ValueError):
dataset_idx, sample_idx = dataset2.get_dataset_idx_and_sample_idx(-11)
# test negative indice value
indice = -6
dataset_idx1, sample_idx1 = dataset2.get_dataset_idx_and_sample_idx(indice)
dataset_idx2, sample_idx2 = dataset2.get_dataset_idx_and_sample_idx(
len(dataset2) + indice)
assert dataset_idx1 == dataset_idx2
assert sample_idx1 == sample_idx2
# test evaluation with pre-eval and the dataset.CLASSES is necessary
pseudo_results = []
eval_results1 = []
for idx in range(len(dataset1)):
h, w = gt_seg_maps[idx].shape
pseudo_result = np.random.randint(low=0, high=7, size=(h, w))
pseudo_results.append(pseudo_result)
eval_results1.extend(dataset1.pre_eval(pseudo_result, idx))
assert len(eval_results1) == len(dataset1)
assert isinstance(eval_results1[0], tuple)
assert len(eval_results1[0]) == 4
assert isinstance(eval_results1[0][0], torch.Tensor)
eval_results1 = dataset1.evaluate(eval_results1,
metric=['mIoU', 'mDice', 'mFscore'])
pseudo_results = pseudo_results * 2
eval_results2 = []
for idx in range(len(dataset2)):
eval_results2.extend(dataset2.pre_eval(pseudo_results[idx], idx))
assert len(eval_results2) == len(dataset2)
assert isinstance(eval_results2[0], tuple)
assert len(eval_results2[0]) == 4
assert isinstance(eval_results2[0][0], torch.Tensor)
eval_results2 = dataset2.evaluate(eval_results2,
metric=['mIoU', 'mDice', 'mFscore'])
if separate_eval:
assert eval_results1['mIoU'] == eval_results2[
'0_mIoU'] == eval_results2['1_mIoU']
assert eval_results1['mDice'] == eval_results2[
'0_mDice'] == eval_results2['1_mDice']
assert eval_results1['mAcc'] == eval_results2[
'0_mAcc'] == eval_results2['1_mAcc']
assert eval_results1['aAcc'] == eval_results2[
'0_aAcc'] == eval_results2['1_aAcc']
assert eval_results1['mFscore'] == eval_results2[
'0_mFscore'] == eval_results2['1_mFscore']
assert eval_results1['mPrecision'] == eval_results2[
'0_mPrecision'] == eval_results2['1_mPrecision']
assert eval_results1['mRecall'] == eval_results2[
'0_mRecall'] == eval_results2['1_mRecall']
else:
assert eval_results1['mIoU'] == eval_results2['mIoU']
assert eval_results1['mDice'] == eval_results2['mDice']
assert eval_results1['mAcc'] == eval_results2['mAcc']
assert eval_results1['aAcc'] == eval_results2['aAcc']
assert eval_results1['mFscore'] == eval_results2['mFscore']
assert eval_results1['mPrecision'] == eval_results2['mPrecision']
assert eval_results1['mRecall'] == eval_results2['mRecall']
# test batch_indices for pre eval
eval_results2 = dataset2.pre_eval(pseudo_results,
list(range(len(pseudo_results))))
assert len(eval_results2) == len(dataset2)
assert isinstance(eval_results2[0], tuple)
assert len(eval_results2[0]) == 4
assert isinstance(eval_results2[0][0], torch.Tensor)
eval_results2 = dataset2.evaluate(eval_results2,
metric=['mIoU', 'mDice', 'mFscore'])
if separate_eval:
assert eval_results1['mIoU'] == eval_results2[
'0_mIoU'] == eval_results2['1_mIoU']
assert eval_results1['mDice'] == eval_results2[
'0_mDice'] == eval_results2['1_mDice']
assert eval_results1['mAcc'] == eval_results2[
'0_mAcc'] == eval_results2['1_mAcc']
assert eval_results1['aAcc'] == eval_results2[
'0_aAcc'] == eval_results2['1_aAcc']
assert eval_results1['mFscore'] == eval_results2[
'0_mFscore'] == eval_results2['1_mFscore']
assert eval_results1['mPrecision'] == eval_results2[
'0_mPrecision'] == eval_results2['1_mPrecision']
assert eval_results1['mRecall'] == eval_results2[
'0_mRecall'] == eval_results2['1_mRecall']
else:
assert eval_results1['mIoU'] == eval_results2['mIoU']
assert eval_results1['mDice'] == eval_results2['mDice']
assert eval_results1['mAcc'] == eval_results2['mAcc']
assert eval_results1['aAcc'] == eval_results2['aAcc']
assert eval_results1['mFscore'] == eval_results2['mFscore']
assert eval_results1['mPrecision'] == eval_results2['mPrecision']
assert eval_results1['mRecall'] == eval_results2['mRecall']
def main():
args = parse_args()
assert args.out or args.eval or args.format_only or args.show \
or args.show_dir, \
('Please specify at least one operation (save/eval/format/show the '
'results / save the results) with the argument "--out", "--eval"'
', "--format-only", "--show" or "--show-dir"')
if args.eval and args.format_only:
raise ValueError('--eval and --format_only cannot be both specified')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
if args.options is not None:
cfg.merge_from_dict(args.options)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
if args.aug_test:
# hard code index
cfg.data.test.pipeline[1].img_ratios = [
0.5, 0.75, 1.0, 1.25, 1.5, 1.75
]
cfg.data.test.pipeline[1].flip = 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)
rank, _ = get_dist_info()
# allows not to create
if args.work_dir is not None and rank == 0:
mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
json_file = osp.join(args.work_dir, f'eval_{timestamp}.json')
# 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,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
cfg.model.train_cfg = None
model = build_segmentor(cfg.model, test_cfg=cfg.get('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')
if 'CLASSES' in checkpoint.get('meta', {}):
model.CLASSES = checkpoint['meta']['CLASSES']
else:
print('"CLASSES" not found in meta, use dataset.CLASSES instead')
model.CLASSES = dataset.CLASSES
if 'PALETTE' in checkpoint.get('meta', {}):
model.PALETTE = checkpoint['meta']['PALETTE']
else:
print('"PALETTE" not found in meta, use dataset.PALETTE instead')
model.PALETTE = dataset.PALETTE
# clean gpu memory when starting a new evaluation.
torch.cuda.empty_cache()
eval_kwargs = {} if args.eval_options is None else args.eval_options
# Deprecated
efficient_test = eval_kwargs.get('efficient_test', False)
if efficient_test:
warnings.warn(
'``efficient_test=True`` does not have effect in tools/test.py, '
'the evaluation and format results are CPU memory efficient by '
'default')
eval_on_format_results = (args.eval is not None
and 'cityscapes' in args.eval)
if eval_on_format_results:
assert len(args.eval) == 1, 'eval on format results is not ' \
'applicable for metrics other than ' \
'cityscapes'
if args.format_only or eval_on_format_results:
if 'imgfile_prefix' in eval_kwargs:
tmpdir = eval_kwargs['imgfile_prefix']
else:
tmpdir = '.format_cityscapes'
eval_kwargs.setdefault('imgfile_prefix', tmpdir)
mmcv.mkdir_or_exist(tmpdir)
else:
tmpdir = None
if not distributed:
model = MMDataParallel(model, device_ids=[0])
results = single_gpu_test(model,
data_loader,
args.show,
args.show_dir,
False,
args.opacity,
pre_eval=args.eval is not None
and not eval_on_format_results,
format_only=args.format_only
or eval_on_format_results,
format_args=eval_kwargs)
else:
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False)
results = multi_gpu_test(model,
data_loader,
args.tmpdir,
args.gpu_collect,
False,
pre_eval=args.eval is not None
and not eval_on_format_results,
format_only=args.format_only
or eval_on_format_results,
format_args=eval_kwargs)
rank, _ = get_dist_info()
if rank == 0:
if args.out:
warnings.warn(
'The behavior of ``args.out`` has been changed since MMSeg '
'v0.16, the pickled outputs could be seg map as type of '
'np.array, pre-eval results or file paths for '
'``dataset.format_results()``.')
print(f'\nwriting results to {args.out}')
mmcv.dump(results, args.out)
if args.eval:
eval_kwargs.update(metric=args.eval)
metric = dataset.evaluate(results, **eval_kwargs)
metric_dict = dict(config=args.config, metric=metric)
if args.work_dir is not None and rank == 0:
mmcv.dump(metric_dict, json_file, indent=4)
if tmpdir is not None and eval_on_format_results:
# remove tmp dir when cityscapes evaluation
shutil.rmtree(tmpdir)
def train_segmentor(model,
dataset,
cfg,
distributed=False,
validate=False,
timestamp=None,
meta=None):
"""Launch segmentor training."""
logger = get_root_logger(cfg.log_level)
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
data_loaders = [
build_dataloader(
ds,
cfg.data.samples_per_gpu,
cfg.data.workers_per_gpu,
# cfg.gpus will be ignored if distributed
len(cfg.gpu_ids),
dist=distributed,
seed=cfg.seed,
drop_last=True) for ds in dataset
]
# put model on gpus
if distributed:
find_unused_parameters = cfg.get('find_unused_parameters', True)
# Sets the `find_unused_parameters` parameter in
# torch.nn.parallel.DistributedDataParallel
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
# model.ddp = model
else:
model = MMDataParallel(model.cuda(cfg.gpu_ids[0]),
device_ids=cfg.gpu_ids)
# print(model)
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
if cfg.get('runner') is None:
cfg.runner = {'type': 'IterBasedRunner', 'max_iters': cfg.total_iters}
warnings.warn(
'config is now expected to have a `runner` section, '
'please set `runner` in your config.', UserWarning)
runner = build_runner(cfg.runner,
default_args=dict(model=model,
batch_processor=None,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
meta=meta))
# print(cfg.optimizer)
# print(cfg.optimizer_config)
optimizer_config = OptimizerHookLW(**cfg.optimizer_config)
# register hooks
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config,
cfg.get('momentum_config', None))
# an ugly walkaround to make the .log and .log.json filenames the same
runner.timestamp = timestamp
# register eval hooks
if validate:
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
val_dataloader = build_dataloader(
val_dataset,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
eval_cfg = cfg.get('evaluation', {})
eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
eval_hook = DistEvalHook if distributed else EvalHook
runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow)
# ]
#
# iter_loaders = [IterLoader(x) for x in data_loaders]
# # print(len(iter_loaders))
model = build_segmentor(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
checkpoint = '/home/zhouzhigong/Documents/mmsegmentation/work_dirs/pspnet_r18-d8_512x512_40k_cityscapes_skd/iter_40000.pth'
checkpoint = load_checkpoint(model, checkpoint)
model.eval()
# model.cuda(0)
model = MMDataParallel(model, device_ids=[1])
# dataset = build_dataset(cfg.data.train)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
samples_per_gpu=4,
workers_per_gpu=4,
# workers_per_gpu=S_cfg.data.workers_per_gpu,
dist=False,
shuffle=False,
drop_last=True,
)
outputs = single_gpu_test(model, data_loader, show=False, out_dir=None)
eval_results = dataset.evaluate(outputs, metric='mIoU', logger=None)
print(
eval_results
) # {'mIoU': 0.5864396163552457, 'mAcc': 0.6625518273733926, 'aAcc': 0.9302386656807492}
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# work_dir is determined in this priority: CLI > segment in file > filename
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
if args.load_from is not None:
cfg.load_from = args.load_from
if args.resume_from is not None:
cfg.resume_from = args.resume_from
if args.gpus is not None:
cfg.gpu_ids = range(1)
warnings.warn('`--gpus` is deprecated because we only support '
'single GPU mode in non-distributed training. '
'Use `gpus=1` now.')
if args.gpu_ids is not None:
cfg.gpu_ids = args.gpu_ids[0:1]
warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
'Because we only support single GPU mode in '
'non-distributed training. Use the first GPU '
'in `gpu_ids` now.')
if args.gpus is None and args.gpu_ids is None:
cfg.gpu_ids = [args.gpu_id]
cfg.auto_resume = args.auto_resume
# 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)
# gpu_ids is used to calculate iter when resuming checkpoint
_, world_size = get_dist_info()
cfg.gpu_ids = range(world_size)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# dump config
cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# set multi-process settings
setup_multi_processes(cfg)
# init the meta dict to record some important information such as
# environment info and seed, which will be logged
meta = dict()
# log env info
env_info_dict = collect_env()
env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
dash_line)
meta['env_info'] = env_info
# log some basic info
logger.info(f'Distributed training: {distributed}')
logger.info(f'Config:\n{cfg.pretty_text}')
# set random seeds
seed = init_random_seed(args.seed)
seed = seed + dist.get_rank() if args.diff_seed else seed
logger.info(f'Set random seed to {seed}, '
f'deterministic: {args.deterministic}')
set_random_seed(seed, deterministic=args.deterministic)
cfg.seed = seed
meta['seed'] = seed
meta['exp_name'] = osp.basename(args.config)
model = build_segmentor(
cfg.model,
train_cfg=cfg.get('train_cfg'),
test_cfg=cfg.get('test_cfg'))
model.init_weights()
# SyncBN is not support for DP
if not distributed:
warnings.warn(
'SyncBN is only supported with DDP. To be compatible with DP, '
'we convert SyncBN to BN. Please use dist_train.sh which can '
'avoid this error.')
model = revert_sync_batchnorm(model)
logger.info(model)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
val_dataset.pipeline = cfg.data.train.pipeline
datasets.append(build_dataset(val_dataset))
if cfg.checkpoint_config is not None:
# save mmseg version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmseg_version=f'{__version__}+{get_git_hash()[:7]}',
config=cfg.pretty_text,
CLASSES=datasets[0].CLASSES,
PALETTE=datasets[0].PALETTE)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
# passing checkpoint meta for saving best checkpoint
meta.update(cfg.checkpoint_config.meta)
train_segmentor(
model,
datasets,
cfg,
distributed=distributed,
validate=(not args.no_validate),
timestamp=timestamp,
meta=meta)
def train_segmentor(model,
dataset,
cfg,
distributed=False,
validate=False,
timestamp=None,
meta=None):
"""Launch segmentor training."""
logger = get_root_logger(cfg.log_level)
# prepare data loaders
# print('----------------------------')
# print(type(dataset),len(dataset)) # <class 'list'> 1
# print(type(dataset[0])) # <class 'mmseg.datasets.cityscapes.CityscapesDataset'>
# print(len(dataset[0])) # 2975
# print(dataset[0][0]['img'].size())
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
data_loaders = [
build_dataloader(
ds,
cfg.data.samples_per_gpu,
cfg.data.workers_per_gpu,
# cfg.gpus will be ignored if distributed
len(cfg.gpu_ids),
dist=distributed,
seed=cfg.seed,
drop_last=True) for ds in dataset
]
print('---------------------------')
# print(data_loaders[0].next)
print('before')
print(cfg.gpu_ids)
print(next(model.parameters()).device)
# print(next(model.teacher.parameters()).device)
# put model on gpus
if distributed:
find_unused_parameters = cfg.get('find_unused_parameters', False)
find_unused_parameters = True
# Sets the `find_unused_parameters` parameter in
# torch.nn.parallel.DistributedDataParallel
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
else:
model = MMDataParallel(model.cuda(cfg.gpu_ids[0]),
device_ids=cfg.gpu_ids)
print('after')
print(next(model.parameters()).device)
# print(next(model.teacher.parameters()).device)
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = IterBasedRunner(model=model,
batch_processor=None,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
meta=meta)
# register hooks
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config,
cfg.get('momentum_config', None))
# an ugly walkaround to make the .log and .log.json filenames the same
runner.timestamp = timestamp
# register eval hooks
if validate:
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
val_dataloader = build_dataloader(
val_dataset,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
eval_cfg = cfg.get('evaluation', {})
eval_hook = DistEvalHook if distributed else EvalHook
runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_iters)
