def test_encoder_decoder():
# test 1 decode head, w.o. aux head
cfg = dict(type='EncoderDecoder',
backbone=dict(type='ExampleBackbone'),
decode_head=dict(type='ExampleDecodeHead'))
test_cfg = mmcv.Config(dict(mode='whole'))
segmentor = build_segmentor(cfg, train_cfg=None, test_cfg=test_cfg)
_segmentor_forward_train_test(segmentor)
# test slide mode
test_cfg = mmcv.Config(dict(mode='slide', crop_size=(3, 3), stride=(2, 2)))
segmentor = build_segmentor(cfg, train_cfg=None, test_cfg=test_cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 1 aux head
cfg = dict(type='EncoderDecoder',
backbone=dict(type='ExampleBackbone'),
decode_head=dict(type='ExampleDecodeHead'),
auxiliary_head=dict(type='ExampleDecodeHead'))
test_cfg = mmcv.Config(dict(mode='whole'))
segmentor = build_segmentor(cfg, train_cfg=None, test_cfg=test_cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 2 aux head
cfg = dict(type='EncoderDecoder',
backbone=dict(type='ExampleBackbone'),
decode_head=dict(type='ExampleDecodeHead'),
auxiliary_head=[
dict(type='ExampleDecodeHead'),
dict(type='ExampleDecodeHead')
])
test_cfg = mmcv.Config(dict(mode='whole'))
segmentor = build_segmentor(cfg, train_cfg=None, test_cfg=test_cfg)
_segmentor_forward_train_test(segmentor)
def load_swin(cfg, ck_path):
# make temp_cfg
temp_cfg = copy.deepcopy(cfg)
temp_cfg.model.decode_head.num_classes = 150
temp_cfg.model.auxiliary_head.num_classes = 150
# make original swin
pre_trained = build_segmentor(temp_cfg.model,
train_cfg=None,
test_cfg=None)
load_checkpoint(pre_trained, ck_path, map_location='cpu')
model = build_segmentor(cfg.model, train_cfg=None, test_cfg=None)
# make pretrained state dict
pretrained_dict = pre_trained.backbone.state_dict()
model_dict = model.backbone.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
model_dict.update(pretrained_dict)
del pre_trained
torch.cuda.empty_cache()
model.backbone.load_state_dict(model_dict)
return model
def __init__(
self,
teacher_cfg,
student_cfg,
distill_cfg=None,
teacher_pretrained=None,
):
super(SegmentationDistiller, self).__init__()
self.teacher = build_segmentor(teacher_cfg.model,
train_cfg=teacher_cfg.get('train_cfg'),
test_cfg=teacher_cfg.get('test_cfg'))
self.init_weights_teacher(teacher_pretrained)
self.teacher.eval()
self.student = build_segmentor(student_cfg.model,
train_cfg=student_cfg.get('train_cfg'),
test_cfg=student_cfg.get('test_cfg'))
self.distill_losses = nn.ModuleDict()
self.distill_cfg = distill_cfg
student_modules = dict(self.student.named_modules())
teacher_modules = dict(self.teacher.named_modules())
def regitster_hooks(student_module, teacher_module):
def hook_teacher_forward(module, input, output):
self.register_buffer(teacher_module, output)
def hook_student_forward(module, input, output):
self.register_buffer(student_module, output)
return hook_teacher_forward, hook_student_forward
for item_loc in distill_cfg:
student_module = 'student_' + item_loc.student_module.replace(
'.', '_')
teacher_module = 'teacher_' + item_loc.teacher_module.replace(
'.', '_')
self.register_buffer(student_module, None)
self.register_buffer(teacher_module, None)
hook_teacher_forward, hook_student_forward = regitster_hooks(
student_module, teacher_module)
teacher_modules[item_loc.teacher_module].register_forward_hook(
hook_teacher_forward)
student_modules[item_loc.student_module].register_forward_hook(
hook_student_forward)
for item_loss in item_loc.methods:
loss_name = item_loss.name
self.distill_losses[loss_name] = build_distill_loss(item_loss)
def test_cascade_encoder_decoder():
# test 1 decode head, w.o. aux head
cfg = ConfigDict(
type='CascadeEncoderDecoder',
num_stages=2,
backbone=dict(type='ExampleBackbone'),
decode_head=[
dict(type='ExampleDecodeHead'),
dict(type='ExampleCascadeDecodeHead')
])
cfg.test_cfg = ConfigDict(mode='whole')
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test slide mode
cfg.test_cfg = ConfigDict(mode='slide', crop_size=(3, 3), stride=(2, 2))
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 1 aux head
cfg = ConfigDict(
type='CascadeEncoderDecoder',
num_stages=2,
backbone=dict(type='ExampleBackbone'),
decode_head=[
dict(type='ExampleDecodeHead'),
dict(type='ExampleCascadeDecodeHead')
],
auxiliary_head=dict(type='ExampleDecodeHead'))
cfg.test_cfg = ConfigDict(mode='whole')
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 2 aux head
cfg = ConfigDict(
type='CascadeEncoderDecoder',
num_stages=2,
backbone=dict(type='ExampleBackbone'),
decode_head=[
dict(type='ExampleDecodeHead'),
dict(type='ExampleCascadeDecodeHead')
],
auxiliary_head=[
dict(type='ExampleDecodeHead'),
dict(type='ExampleDecodeHead')
])
cfg.test_cfg = ConfigDict(mode='whole')
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
def main():
args = parse_args()
if len(args.shape) == 1:
input_shape = (3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
input_shape = (3, ) + tuple(args.shape)
else:
raise ValueError('invalid input shape')
cfg = Config.fromfile(args.config)
model = build_segmentor(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg).cuda()
model.eval()
if hasattr(model, 'forward_dummy'):
model.forward = model.forward_dummy
else:
raise NotImplementedError(
'FLOPs counter is currently not currently supported with {}'.
format(model.__class__.__name__))
flops, params = get_model_complexity_info(model, input_shape)
split_line = '=' * 30
print('{0}\nInput shape: {1}\nFlops: {2}\nParams: {3}\n{0}'.format(
split_line, input_shape, flops, params))
print('!!!Please be cautious if you use the results in papers. '
'You may need to check if all ops are supported and verify that the '
'flops computation is correct.')
def test_config_build_segmentor():
"""Test that all segmentation models defined in the configs can be
initialized."""
config_dpath = _get_config_directory()
print('Found config_dpath = {!r}'.format(config_dpath))
config_fpaths = []
# one config each sub folder
for sub_folder in os.listdir(config_dpath):
if isdir(sub_folder):
config_fpaths.append(
list(glob.glob(join(config_dpath, sub_folder, '*.py')))[0])
config_fpaths = [p for p in config_fpaths if p.find('_base_') == -1]
config_names = [relpath(p, config_dpath) for p in config_fpaths]
print('Using {} config files'.format(len(config_names)))
for config_fname in config_names:
config_fpath = join(config_dpath, config_fname)
config_mod = Config.fromfile(config_fpath)
config_mod.model
print('Building segmentor, config_fpath = {!r}'.format(config_fpath))
# Remove pretrained keys to allow for testing in an offline environment
if 'pretrained' in config_mod.model:
config_mod.model['pretrained'] = None
print('building {}'.format(config_fname))
segmentor = build_segmentor(config_mod.model)
assert segmentor is not None
head_config = config_mod.model['decode_head']
_check_decode_head(head_config, segmentor.decode_head)
def __init__(self,
# num_stages,
backbone,
decode_head,
neck=None,
auxiliary_head=None,
train_cfg=None,
test_cfg=None,
pretrained=None,
with_aux_kd=None,
teacher_config=None):
# self.num_stages = num_stages
super(KDEncoderDecoder, self).__init__(
backbone=backbone,
decode_head=decode_head,
neck=neck,
auxiliary_head=auxiliary_head,
train_cfg=train_cfg,
test_cfg=test_cfg,
pretrained=pretrained)
# self.with_aux_kd = False
self.with_aux_kd = with_aux_kd
if teacher_config:
T_cfg = Config.fromfile(teacher_config.config_file)
self.teacher = build_segmentor(T_cfg.model, train_cfg=None, test_cfg=None)
checkpoint = load_checkpoint(self.teacher, teacher_config.checkpoint)
self.teacher.eval()
def init_segmentor(config, checkpoint=None, device='cuda:0'):
"""Initialize a segmentor from config file.
Args:
config (str or :obj:`mmcv.Config`): Config file path or the config
object.
checkpoint (str, optional): Checkpoint path. If left as None, the model
will not load any weights.
device (str, optional) CPU/CUDA device option. Default 'cuda:0'.
Use 'cpu' for loading model on CPU.
Returns:
nn.Module: The constructed segmentor.
"""
if isinstance(config, str):
config = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_segmentor(config.model, test_cfg=config.test_cfg)
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint, map_location='cpu')
model.CLASSES = checkpoint['meta']['CLASSES']
model.PALETTE = checkpoint['meta']['PALETTE']
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
return model
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 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():
args = parse_args()
if len(args.shape) == 1:
input_shape = (3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
input_shape = (3, ) + tuple(args.shape)
else:
raise ValueError('invalid input shape')
cfg = Config.fromfile(args.config)
cfg.model.pretrained = None
if args.net_params:
tag, input_channels, block1, block2, block3, block4, last_channel = args.net_params.split(
'-')
input_channels = [int(item) for item in input_channels.split('_')]
block1 = [int(item) for item in block1.split('_')]
block2 = [int(item) for item in block2.split('_')]
block3 = [int(item) for item in block3.split('_')]
block4 = [int(item) for item in block4.split('_')]
last_channel = int(last_channel)
inverted_residual_setting = []
for item in [block1, block2, block3, block4]:
for _ in range(item[0]):
inverted_residual_setting.append([
item[1], item[2:-int(len(item) / 2 - 1)],
item[-int(len(item) / 2 - 1):]
])
cfg.model.backbone.input_channel = input_channels
cfg.model.backbone.inverted_residual_setting = inverted_residual_setting
cfg.model.backbone.last_channel = last_channel
model = build_segmentor(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg).cuda()
model.eval()
if hasattr(model, 'forward_dummy'):
model.forward = model.forward_dummy
else:
raise NotImplementedError(
'FLOPs counter is currently not currently supported with {}'.
format(model.__class__.__name__))
flops, params = get_model_complexity_info(model, input_shape)
split_line = '=' * 30
print('{0}\nInput shape: {1}\nFlops: {2}\nParams: {3}\n{0}'.format(
split_line, input_shape, flops, params))
print('!!!Please be cautious if you use the results in papers. '
'You may need to check if all ops are supported and verify that the '
'flops computation is correct.')
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 _test_encoder_decoder_forward(cfg_file):
model = _get_segmentor_cfg(cfg_file)
model['pretrained'] = None
model['test_cfg']['mode'] = 'whole'
from mmseg.models import build_segmentor
segmentor = build_segmentor(model)
segmentor.init_weights()
if isinstance(segmentor.decode_head, nn.ModuleList):
num_classes = segmentor.decode_head[-1].num_classes
else:
num_classes = segmentor.decode_head.num_classes
# batch_size=2 for BatchNorm
input_shape = (2, 3, 32, 32)
mm_inputs = _demo_mm_inputs(input_shape, num_classes=num_classes)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
gt_semantic_seg = mm_inputs['gt_semantic_seg']
# convert to cuda Tensor if applicable
if torch.cuda.is_available():
segmentor = segmentor.cuda()
imgs = imgs.cuda()
gt_semantic_seg = gt_semantic_seg.cuda()
else:
segmentor = _convert_batchnorm(segmentor)
# Test forward train
losses = segmentor.forward(imgs,
img_metas,
gt_semantic_seg=gt_semantic_seg,
return_loss=True)
assert isinstance(losses, dict)
# Test forward test
with torch.no_grad():
segmentor.eval()
# pack into lists
img_list = [img[None, :] for img in imgs]
img_meta_list = [[img_meta] for img_meta in img_metas]
segmentor.forward(img_list, img_meta_list, return_loss=False)
def __init__(self, opt, num_classes=10, final_conv=False):
super().__init__()
self.opt = opt
cfg = mmcv.Config.fromfile(
os.path.join(self.opt.jg_dir, self.opt.config_segformer))
cfg.model.pretrained = None
cfg.model.train_cfg = None
cfg.model.decode_head.num_classes = num_classes
self.net = build_segmentor(cfg.model,
train_cfg=None,
test_cfg=cfg.get('test_cfg'))
configure_encoder_decoder(self.net)
configure_new_forward_mit(self.net.backbone)
self.use_final_conv = final_conv
if self.use_final_conv:
self.final_conv = MobileResnetDecoder(num_classes, 3, ngf=64)
def __init__(
self,
opt=None,
final_conv=False
): #nb_attn : total number of attention masks, nb_mask_input :number of attention mask applied to input img directly
super(SegformerGenerator_attn, self).__init__()
self.opt = opt
self.nb_attn = self.opt.nb_mask_attn
self.nb_mask_input = self.opt.nb_mask_input
self.use_final_conv = final_conv
self.tanh = nn.Tanh()
cfg = mmcv.Config.fromfile(
os.path.join(self.opt.jg_dir, self.opt.config_segformer))
cfg.model.pretrained = None
cfg.model.train_cfg = None
cfg.model.auxiliary_head = cfg.model.decode_head.copy()
if self.use_final_conv:
num_cls = 256
else:
num_cls = 3 * (self.nb_attn - self.nb_mask_input)
cfg.model.decode_head.num_classes = num_cls
cfg.model.auxiliary_head.num_classes = self.nb_attn
self.segformer = build_segmentor(cfg.model,
train_cfg=None,
test_cfg=cfg.get('test_cfg'))
self.segformer.train()
self.softmax_ = nn.Softmax(dim=1)
configure_encoder_decoder(self.segformer)
configure_new_forward_mit(self.segformer.backbone)
self.use_final_conv = final_conv
if self.use_final_conv:
self.final_conv = MobileResnetDecoder(
num_cls, 3 * (self.nb_attn - self.nb_mask_input), ngf=64)
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)
if len(args.shape) == 1:
input_shape = (1, 3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
input_shape = (
1,
3,
) + tuple(args.shape)
else:
raise ValueError('invalid input shape')
cfg = mmcv.Config.fromfile(args.config)
cfg.model.pretrained = None
# build the model and load checkpoint
segmentor = build_segmentor(cfg.model,
train_cfg=None,
test_cfg=cfg.test_cfg)
# convert SyncBN to BN
segmentor = _convert_batchnorm(segmentor)
if args.checkpoint:
load_checkpoint(segmentor, args.checkpoint, map_location='cpu')
# conver model to onnx file
pytorch2onnx(segmentor,
input_shape,
opset_version=args.opset_version,
show=args.show,
output_file=args.output_file,
verify=args.verify)
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()
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)
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())
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)
input_shape = (1, 3, img_scale[1], img_scale[0])
elif len(args.shape) == 1:
input_shape = (1, 3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
input_shape = (
1,
3,
) + tuple(args.shape)
else:
raise ValueError('invalid input shape')
test_mode = cfg.model.test_cfg.mode
# build the model and load checkpoint
cfg.model.train_cfg = None
segmentor = build_segmentor(
cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))
# convert SyncBN to BN
segmentor = _convert_batchnorm(segmentor)
if args.checkpoint:
checkpoint = load_checkpoint(
segmentor, args.checkpoint, map_location='cpu')
segmentor.CLASSES = checkpoint['meta']['CLASSES']
segmentor.PALETTE = checkpoint['meta']['PALETTE']
# read input or create dummpy input
if args.input_img is not None:
preprocess_shape = (input_shape[2], input_shape[3])
rescale_shape = None
if args.rescale_shape is not None:
rescale_shape = [args.rescale_shape[0], args.rescale_shape[1]]
def __init__(self, args):
cudnn.enabled = True
self.args = args
self.mode_name = 'kd_mmseg'
device = args.device
######## skd
# self.S_device = 'cuda:0'
# self.T_device = 'cuda:1'
# student = Res_pspnet(BasicBlock, [2, 2, 2, 2], num_classes = args.classes_num, deep_base=False)
# load_S_model(args, student, False)
# # print(student)
#
# # print(student.device)
# print_model_parm_nums(student, 'student_model')
# self.student = self.DataParallelModelProcess(student, 2, 'train', device=self.S_device)
# self.student = student
# # self.student.cuda()
# # self.student.to('cuda:0')
# # self.student.train()
# teacher = Res_pspnet(Bottleneck, [3, 4, 23, 3], num_classes = args.classes_num)
# load_T_model(teacher, args.T_ckpt_path)
# print_model_parm_nums(teacher, 'teacher_model')
# self.teacher = self.DataParallelModelProcess(teacher, 2, 'eval', device=self.T_device)
# self.teacher = teacher
# # self.teacher.to('cuda:1')
# # self.teacher.eval()
########################## mmseg
self.S_device = 'cuda:0'
S_config = 'configs/pspnet/pspnet_r18-d8_512x512_40k_cityscapes_1gpu.py'
S_cfg = Config.fromfile(S_config)
# print(S_cfg)
# S_cfg.model.pretrained = args.student_pretrain_model_imgnet
self.student = build_segmentor(S_cfg.model, train_cfg=S_cfg.train_cfg, test_cfg=S_cfg.test_cfg)
# self.student = build_segmentor(S_cfg.model, train_cfg=None, test_cfg=None)
# checkpoint = args.student_pretrain_model_imgnet
# print(checkpoint)
# checkpoint = load_checkpoint(self.student, checkpoint)
# load_S_model(args, self.student, False)
# self.student = self.DataParallelModelProcess(self.student, 2, 'train', device=self.S_device)
self.student.train()
self.student.to(self.S_device)
# print(self.student)
# for name, parameters in self.student.named_parameters():
# print(name, ':', parameters.size(), parameters.requires_grad)
#
# # print(self.student.parameters())
#
# # for parameters in self.student.parameters():
# # print(parameters)
if self.args.pi or self.args.pa or self.args.ho:
self.T_device = 'cuda:1'
T_config = 'configs/pspnet/pspnet_r101-d8_512x512_80k_cityscapes_1gpu.py'
T_cfg = Config.fromfile(T_config)
# print(T_cfg)
# self.teacher = build_segmentor(T_cfg.model, train_cfg=T_cfg.train_cfg, test_cfg=T_cfg.test_cfg)
self.teacher = build_segmentor(T_cfg.model, train_cfg=None, test_cfg=None)
checkpoint = 'work_dirs/models_zoo/pspnet_r101-d8_512x512_80k_cityscapes.2.2_iter_80000.pth'
checkpoint = load_checkpoint(self.teacher, checkpoint)
self.teacher = self.DataParallelModelProcess(self.teacher, 2, 'eval', device=self.T_device)
####################################################
D_model = Discriminator(args.preprocess_GAN_mode, args.classes_num, args.batch_size, args.imsize_for_adv,
args.adv_conv_dim)
load_D_model(args, D_model, False)
print_model_parm_nums(D_model, 'D_model')
# self.parallel_D = self.DataParallelModelProcess(D_model, 2, 'train', device)
self.parallel_D = self.DataParallelModelProcess(D_model, 2, 'train', device='cuda:0')
self.D_model = D_model
self.G_solver = optim.SGD(
[{'params': filter(lambda p: p.requires_grad, self.student.parameters()), 'initial_lr': args.lr_g}],
lr=args.lr_g, momentum=args.momentum, weight_decay=args.weight_decay)
# self.G_solver = optim.SGD(self.student.parameters(),
# lr=args.lr_g, momentum=args.momentum, weight_decay=args.weight_decay)
self.D_solver = optim.SGD(
[{'params': filter(lambda p: p.requires_grad, D_model.parameters()), 'initial_lr': args.lr_d}],
lr=args.lr_d, momentum=args.momentum, weight_decay=args.weight_decay)
self.best_mean_IU = args.best_mean_IU
self.criterion = self.DataParallelCriterionProcess(CriterionDSN()) # CriterionCrossEntropy()
self.criterion_ce = self.DataParallelCriterionProcess(CriterionCE()) # CriterionCrossEntropy()
self.criterion_pixel_wise = self.DataParallelCriterionProcess(CriterionPixelWise())
# self.criterion_pair_wise_for_interfeat = [self.DataParallelCriterionProcess(CriterionPairWiseforWholeFeatAfterPool(scale=args.pool_scale[ind], feat_ind=-(ind+1))) for ind in range(len(args.lambda_pa))]
self.criterion_pair_wise_for_interfeat = self.DataParallelCriterionProcess(
CriterionPairWiseforWholeFeatAfterPool(scale=args.pool_scale, feat_ind=-5))
self.criterion_adv = self.DataParallelCriterionProcess(CriterionAdv(args.adv_loss_type))
if args.adv_loss_type == 'wgan-gp':
self.criterion_AdditionalGP = self.DataParallelCriterionProcess(
CriterionAdditionalGP(self.parallel_D, args.lambda_gp))
self.criterion_adv_for_G = self.DataParallelCriterionProcess(CriterionAdvForG(args.adv_loss_type))
self.mc_G_loss = 0.0
self.pi_G_loss = 0.0
self.pa_G_loss = 0.0
self.D_loss = 0.0
self.criterion_AT = self.DataParallelCriterionProcess(AT(p=2))
cudnn.benchmark = True
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
print('init finish')
def __init__(self, cfgfile):
super().__init__()
cfg = Config.fromfile(cfgfile)
temp = build_segmentor(cfg.model)
self.backbone = temp.backbone
self.decode_head = temp.decode_head
def test_calculate_metric(iter_nums):
if args.net == 'unet':
# timm-efficientnet performs slightly worse.
if not args.vis_mode:
backbone_type = re.sub("^eff", "efficientnet", args.backbone_type)
net = smp.Unet(backbone_type, classes=args.num_classes, encoder_weights='imagenet')
else:
net = VanillaUNet(n_channels=3, num_classes=args.num_classes)
elif args.net == 'unet-scratch':
# net = UNet(num_classes=args.num_classes)
net = VanillaUNet(n_channels=3, num_classes=args.num_classes,
use_polyformer=args.polyformer_mode,
num_modes=args.num_modes)
elif args.net == 'nestedunet':
net = NestedUNet(num_classes=args.num_classes)
elif args.net == 'unet3plus':
net = UNet_3Plus(num_classes=args.num_classes)
elif args.net == 'pranet':
net = PraNet(num_classes=args.num_classes - 1)
elif args.net == 'attunet':
net = AttU_Net(output_ch=args.num_classes)
elif args.net == 'attr2unet':
net = R2AttU_Net(output_ch=args.num_classes)
elif args.net == 'dunet':
net = DeformableUNet(n_channels=3, n_classes=args.num_classes)
elif args.net == 'setr':
# Install mmcv first:
# pip install mmcv-full==1.2.2 -f https://download.openmmlab.com/mmcv/dist/cu{Your CUDA Version}/torch{Your Pytorch Version}/index.html
# E.g.: pip install mmcv-full==1.2.2 -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.7.1/index.html
from mmcv.utils import Config
sys.path.append("networks/setr")
from mmseg.models import build_segmentor
task2config = { 'refuge': 'SETR_PUP_288x288_10k_refuge_context_bs_4.py',
'polyp': 'SETR_PUP_320x320_10k_polyp_context_bs_4.py' }
setr_cfg = Config.fromfile("networks/setr/configs/SETR/{}".format(task2config[args.task_name]))
net = build_segmentor(setr_cfg.model, train_cfg=setr_cfg.train_cfg, test_cfg=setr_cfg.test_cfg)
# By default, net() calls forward_train(), which receives extra parameters, and returns losses.
# net.forward_dummy() receives/returns the traditional input/output.
# Relevant file: mmseg/models/segmentors/encoder_decoder.py
net.forward = net.forward_dummy
elif args.net == 'transunet':
transunet_config = TransUNet_CONFIGS[args.backbone_type]
transunet_config.n_classes = args.num_classes
if args.backbone_type.find('R50') != -1:
# The "patch" in TransUNet means grid-like patches of the input image.
# The "patch" in our code means the whole input image after cropping/resizing (part of the augmentation)
transunet_config.patches.grid = (int(args.patch_size[0] / transunet_config.patches.size[0]),
int(args.patch_size[1] / transunet_config.patches.size[1]))
net = TransUNet(transunet_config, img_size=args.patch_size, num_classes=args.num_classes)
elif args.net.startswith('deeplab'):
use_smp_deeplab = args.net.endswith('smp')
if use_smp_deeplab:
backbone_type = re.sub("^eff", "efficientnet", args.backbone_type)
net = smp.DeepLabV3Plus(backbone_type, classes=args.num_classes, encoder_weights='imagenet')
else:
model_name = args.net + "_" + args.backbone_type
model_map = {
'deeplabv3_resnet50': deeplab.deeplabv3_resnet50,
'deeplabv3plus_resnet50': deeplab.deeplabv3plus_resnet50,
'deeplabv3_resnet101': deeplab.deeplabv3_resnet101,
'deeplabv3plus_resnet101': deeplab.deeplabv3plus_resnet101,
'deeplabv3_mobilenet': deeplab.deeplabv3_mobilenet,
'deeplabv3plus_mobilenet': deeplab.deeplabv3plus_mobilenet
}
net = model_map[model_name](num_classes=args.num_classes, output_stride=8)
elif args.net == 'nnunet':
from nnunet.network_architecture.initialization import InitWeights_He
from nnunet.network_architecture.generic_UNet import Generic_UNet
net = Generic_UNet(
input_channels=3,
base_num_features=32,
num_classes=args.num_classes,
num_pool=7,
num_conv_per_stage=2,
feat_map_mul_on_downscale=2,
norm_op=nn.InstanceNorm2d,
norm_op_kwargs={'eps': 1e-05, 'affine': True},
dropout_op_kwargs={'p': 0, 'inplace': True},
nonlin_kwargs={'negative_slope': 0.01, 'inplace': True},
final_nonlin=(lambda x: x),
weightInitializer=InitWeights_He(1e-2),
pool_op_kernel_sizes=[[2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]],
conv_kernel_sizes=([[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]]),
upscale_logits=False,
convolutional_pooling=True,
convolutional_upsampling=True,
)
net.inference_apply_nonlin = (lambda x: F.softmax(x, 1))
elif args.net == 'segtran':
get_default(args, 'num_modes', default_settings, -1, [args.net, 'num_modes', args.in_fpn_layers])
set_segtran2d_config(args)
print(args)
net = Segtran2d(config)
else:
breakpoint()
net.cuda()
net.eval()
if args.robust_ref_cp_path:
refnet = copy.deepcopy(net)
print("Reference network created")
load_model(refnet, args, args.robust_ref_cp_path)
else:
refnet = None
# Currently colormap is used only for OCT task.
colormap = get_seg_colormap(args.num_classes, return_torch=True).cuda()
# prepred: pre-prediction. postpred: post-prediction.
task2mask_prepred = { 'refuge': refuge_map_mask, 'polyp': polyp_map_mask,
'oct': partial(index_to_onehot, num_classes=args.num_classes) }
task2mask_postpred = { 'refuge': refuge_inv_map_mask, 'polyp': polyp_inv_map_mask,
'oct': partial(onehot_inv_map, colormap=colormap) }
mask_prepred_mapping_func = task2mask_prepred[args.task_name]
mask_postpred_mapping_funcs = [ task2mask_postpred[args.task_name] ]
if args.do_remove_frag:
remove_frag = lambda segmap: remove_fragmentary_segs(segmap, 255)
mask_postpred_mapping_funcs.append(remove_frag)
if not args.checkpoint_dir:
if args.vis_mode is not None:
visualize_model(net, args.vis_mode, args.vis_layers, args.patch_size, db_test)
return
if args.eval_robustness:
eval_robustness(args, net, refnet, testloader, mask_prepred_mapping_func)
return
allcls_avg_metric = None
all_results = np.zeros((args.num_classes, len(iter_nums)))
for iter_idx, iter_num in enumerate(iter_nums):
if args.checkpoint_dir:
checkpoint_path = os.path.join(args.checkpoint_dir, 'iter_' + str(iter_num) + '.pth')
load_model(net, args, checkpoint_path)
if args.vis_mode is not None:
visualize_model(net, args.vis_mode, args.vis_layers, args.patch_size, db_test)
continue
if args.eval_robustness:
eval_robustness(args, net, refnet, testloader, mask_prepred_mapping_func)
continue
save_results = args.save_results and (not args.test_interp)
if save_results:
test_save_paths = []
test_save_dirs = []
test_save_dir_tmpl = "%s-%s-%s-%d" %(args.net, args.job_name, timestamp, iter_num)
for suffix in ("-soft", "-%.1f" %args.mask_thres):
test_save_dir = test_save_dir_tmpl + suffix
test_save_path = "../prediction/%s" %(test_save_dir)
if not os.path.exists(test_save_path):
os.makedirs(test_save_path)
test_save_dirs.append(test_save_dir)
test_save_paths.append(test_save_path)
else:
test_save_paths = None
test_save_dirs = None
if args.save_features_img_count > 0:
args.save_features_file_path = "%s-%s-feat-%s.pth" %(args.net, args.job_name, timestamp)
else:
args.save_features_file_path = None
allcls_avg_metric, allcls_metric_count = \
test_all_cases(net, testloader, task_name=args.task_name,
num_classes=args.num_classes,
mask_thres=args.mask_thres,
model_type=args.net,
orig_input_size=args.orig_input_size,
patch_size=args.patch_size,
stride=(args.orig_input_size[0] // 2, args.orig_input_size[1] // 2),
test_save_paths=test_save_paths,
out_origsize=args.out_origsize,
mask_prepred_mapping_func=mask_prepred_mapping_func,
mask_postpred_mapping_funcs=mask_postpred_mapping_funcs,
reload_mask=args.reload_mask,
test_interp=args.test_interp,
save_features_img_count=args.save_features_img_count,
save_features_file_path=args.save_features_file_path,
verbose=args.verbose_output)
print("Iter-%d scores on %d images:" %(iter_num, allcls_metric_count[0]))
dice_sum = 0
for cls in range(1, args.num_classes):
dice = allcls_avg_metric[cls-1]
print('class %d: dice = %.3f' %(cls, dice))
dice_sum += dice
all_results[cls, iter_idx] = dice
avg_dice = dice_sum / (args.num_classes - 1)
print("Average dice: %.3f" %avg_dice)
if args.net == 'segtran':
max_attn, avg_attn, clamp_count, call_count = \
[ segtran_shared.__dict__[v] for v in ('max_attn', 'avg_attn', 'clamp_count', 'call_count') ]
print("max_attn={:.2f}, avg_attn={:.2f}, clamp_count={}, call_count={}".format(
max_attn, avg_attn, clamp_count, call_count))
if save_results:
FNULL = open(os.devnull, 'w')
for pred_type, test_save_dir, test_save_path in zip(('soft', 'hard'), test_save_dirs, test_save_paths):
do_tar = subprocess.run(["tar", "cvf", "%s.tar" %test_save_dir, test_save_dir], cwd="../prediction",
stdout=FNULL, stderr=subprocess.STDOUT)
# print(do_tar)
print("{} tarball:\n{}.tar".format(pred_type, os.path.abspath(test_save_path)))
np.set_printoptions(precision=3, suppress=True)
print(all_results[1:])
return allcls_avg_metric
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)
# Let's have a look at the final config used for training
print(f'Config:\n{cfg.pretty_text}')
# Build the dataset
datasets = [build_dataset(cfg.data.train)]
# checkpoint_file = osp.join(data_root, 'model', 'iter_1600.pth')
# model = init_segmentor(cfg, checkpoint_file, device='cuda:0')
# Build the detector
model = build_segmentor(cfg.model,
train_cfg=cfg.get('train_cfg'),
test_cfg=cfg.get('test_cfg'))
# print('datasets[0]: ', datasets[0].PALETTE)
# Add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
model.PALETTE = datasets[0].PALETTE
# Create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
train_segmentor(model,
datasets,
cfg,
distributed=False,
validate=True,
meta=dict())
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)
# 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
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,
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)
# 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
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,
