def __init__(self, cfg):
"""
Args:
cfg (CfgNode):
Use the custom checkpointer, which loads other backbone models
with matching heuristics.
"""
# Assume these objects must be constructed in this order.
model = self.build_model(cfg)
optimizer = self.build_optimizer(cfg, model)
data_loader = self.build_train_loader(cfg)
# Load GAN model
generator = esrgan_model.GeneratorRRDB(channels=3,
filters=64,
num_res_blocks=23).to(device)
discriminator = esrgan_model.Discriminator(
input_shape=(3, *hr_shape)).to(device)
feature_extractor = esrgan_model.FeatureExtractor().to(device)
feature_extractor.eval()
# GAN losses
criterion_GAN = torch.nn.BCEWithLogitsLoss().to(device)
criterion_content = torch.nn.L1Loss().to(device)
criterion_pixel = torch.nn.L1Loss().to(device)
# GAN optimizers
optimizer_G = torch.optim.Adam(generator.parameters(),
lr=.0002,
betas=(.9, .999))
optimizer_D = torch.optim.Adam(discriminator.parameters(),
lr=.0002,
betas=(.9, .999))
# For training, wrap with DDP. But don't need this for inference.
if comm.get_world_size() > 1:
model = DistributedDataParallel(model,
device_ids=[comm.get_local_rank()],
broadcast_buffers=False)
super(DefaultTrainer,
self).__init__(model, data_loader, optimizer, discriminator,
generator, feature_extractor, optimizer_G,
optimizer_D, criterion_pixel, criterion_content,
criterion_GAN)
self.scheduler = self.build_lr_scheduler(cfg, optimizer)
# Assume no other objects need to be checkpointed.
# We can later make it checkpoint the stateful hooks
self.checkpointer = AdetCheckpointer(
# Assume you want to save checkpoints together with logs/statistics
model,
cfg.OUTPUT_DIR,
optimizer=optimizer,
scheduler=self.scheduler,
)
self.start_iter = 0
self.max_iter = cfg.SOLVER.MAX_ITER
self.cfg = cfg
self.register_hooks(self.build_hooks())
def main(args):
cfg = setup(args)
if args.eval_only:
model = Trainer.build_model(cfg)
AdetCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.resume
)
res = Trainer.test(cfg, model) # d2 defaults.py
if comm.is_main_process():
verify_results(cfg, res)
if cfg.TEST.AUG.ENABLED:
res.update(Trainer.test_with_TTA(cfg, model))
return res
"""
If you'd like to do anything fancier than the standard training logic,
consider writing your own training loop or subclassing the trainer.
"""
trainer = Trainer(cfg)
trainer.resume_or_load(resume=args.resume)
if cfg.TEST.AUG.ENABLED:
trainer.register_hooks(
[hooks.EvalHook(0, lambda: trainer.test_with_TTA(cfg, trainer.model))]
)
return trainer.train()
def __init__(self, cfg):
"""
Args:
cfg (CfgNode):
Use the custom checkpointer, which loads other backbone models
with matching heuristics.
"""
# Assume these objects must be constructed in this order.
model = self.build_model(cfg)
optimizer = self.build_optimizer(cfg, model)
data_loader = self.build_train_loader(cfg)
# For training, wrap with DDP. But don't need this for inference.
if comm.get_world_size() > 1:
model = DistributedDataParallel(model,
device_ids=[comm.get_local_rank()],
broadcast_buffers=False)
super(DefaultTrainer, self).__init__(model, data_loader, optimizer)
self.scheduler = self.build_lr_scheduler(cfg, optimizer)
# Assume no other objects need to be checkpointed.
# We can later make it checkpoint the stateful hooks
self.checkpointer = AdetCheckpointer(
# Assume you want to save checkpoints together with logs/statistics
model,
cfg.OUTPUT_DIR,
optimizer=optimizer,
scheduler=self.scheduler,
)
self.start_iter = 0
self.max_iter = cfg.SOLVER.MAX_ITER
self.cfg = cfg
self.register_hooks(self.build_hooks())
def resume_or_load(self, resume=True):
if not isinstance(self.checkpointer, AdetCheckpointer):
# support loading a few other backbones
self.checkpointer = AdetCheckpointer(
self.model,
self.cfg.OUTPUT_DIR,
optimizer=self.optimizer,
scheduler=self.scheduler,
)
super().resume_or_load(resume=resume)
def __init__(self, cfg):
"""
Args:
cfg (CfgNode):
Use the custom checkpointer, which loads other backbone models
with matching heuristics.
"""
# Assume these objects must be constructed in this order.
dprint("build model")
model = self.build_model(cfg)
dprint('build optimizer')
optimizer = self.build_optimizer(cfg, model)
dprint("build train loader")
data_loader = self.build_train_loader(cfg)
images_per_batch = cfg.SOLVER.IMS_PER_BATCH
if isinstance(data_loader, AspectRatioGroupedDataset):
dataset_len = len(data_loader.dataset.dataset)
iters_per_epoch = dataset_len // images_per_batch
else:
dataset_len = len(data_loader.dataset)
iters_per_epoch = dataset_len // images_per_batch
self.iters_per_epoch = iters_per_epoch
total_iters = cfg.SOLVER.TOTAL_EPOCHS * iters_per_epoch
dprint("images_per_batch: ", images_per_batch)
dprint("dataset length: ", dataset_len)
dprint("iters per epoch: ", iters_per_epoch)
dprint("total iters: ", total_iters)
# For training, wrap with DDP. But don't need this for inference.
if comm.get_world_size() > 1:
model = DistributedDataParallel(model,
device_ids=[comm.get_local_rank()],
broadcast_buffers=False)
super(DefaultTrainer, self).__init__(model, data_loader, optimizer)
self.scheduler = self.build_lr_scheduler(cfg,
optimizer,
total_iters=total_iters)
# Assume no other objects need to be checkpointed.
# We can later make it checkpoint the stateful hooks
self.checkpointer = AdetCheckpointer(
# Assume you want to save checkpoints together with logs/statistics
model,
cfg.OUTPUT_DIR,
optimizer=optimizer,
scheduler=self.scheduler,
)
self.start_iter = 0
self.max_iter = total_iters # NOTE: ignore cfg.SOLVER.MAX_ITER
self.cfg = cfg
self.register_hooks(self.build_hooks())
def main(args):
cfg = setup(args)
model = Trainer.build_model(cfg)
net = parsingNet(pretrained=False,
backbone=model,
cls_dim=(200 + 1, 18, 4),
use_aux=False).cuda()
AdetCheckpointer(net, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.resume)
eval_lane(net, 'culane', '/home/ghr/CULANEROOT',
'/home/ghr/CULANEROOT/own_test_result', 200, False, False)
def main(args):
cfg = setup(args)
from detectron2.data.datasets import register_coco_instances
register_coco_instances("surgery_train2", {},
"data/coco/annotations/instances_train2017.json",
"data/coco/train2017")
MetadataCatalog.get("surgery_train2").thing_classes = [
'Cerebellum', 'CN8', 'CN5', 'CN7', 'SCA', 'AICA',
'SuperiorPetrosalVein', 'Vein', 'Brainstem', 'Suction', 'Bipolar',
'Forcep', 'BluntProbe', 'Drill', 'Kerrison', 'Cottonoid', 'Scissors',
'Unknown'
]
DatasetCatalog.get("surgery_train2")
register_coco_instances("surgery_val2", {},
"data/coco/annotations/instances_train2017.json",
"data/coco/train2017")
MetadataCatalog.get("surgery_val2").thing_classes = [
'Cerebellum', 'CN8', 'CN5', 'CN7', 'SCA', 'AICA',
'SuperiorPetrosalVein', 'Vein', 'Brainstem', 'Suction', 'Bipolar',
'Forcep', 'BluntProbe', 'Drill', 'Kerrison', 'Cottonoid', 'Scissors',
'Unknown'
]
DatasetCatalog.get("surgery_val2")
if args.eval_only:
model = Trainer.build_model(cfg)
AdetCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.resume)
res = Trainer.test(cfg, model) # d2 defaults.py
if comm.is_main_process():
verify_results(cfg, res)
if cfg.TEST.AUG.ENABLED:
res.update(Trainer.test_with_TTA(cfg, model))
return res
"""
If you'd like to do anything fancier than the standard training logic,
consider writing your own training loop or subclassing the trainer.
"""
trainer = Trainer(cfg)
trainer.resume_or_load(resume=args.resume)
if cfg.TEST.AUG.ENABLED:
trainer.register_hooks([
hooks.EvalHook(0,
lambda: trainer.test_with_TTA(cfg, trainer.model))
])
return trainer.train()
def resume_or_load(self, resume=True):
if not isinstance(self.checkpointer, AdetCheckpointer):
# support loading a few other backbones
self.checkpointer = AdetCheckpointer(
self.model,
self.cfg.OUTPUT_DIR,
optimizer=self.optimizer,
scheduler=self.scheduler,
)
self.checkpointer.path_manager.register_handler(
adet.utils.file_io.Detectron2Handler())
super().resume_or_load(resume=resume)
def build_hooks(self):
"""
Replace `DetectionCheckpointer` with `AdetCheckpointer`.
Build a list of default hooks, including timing, evaluation,
checkpointing, lr scheduling, precise BN, writing events.
"""
ret = super().build_hooks()
for i in range(len(ret)):
if isinstance(ret[i], hooks.PeriodicCheckpointer):
self.checkpointer = AdetCheckpointer(
self.model,
self.cfg.OUTPUT_DIR,
optimizer=self.optimizer,
scheduler=self.scheduler,
)
ret[i] = hooks.PeriodicCheckpointer(self.checkpointer, self.cfg.SOLVER.CHECKPOINT_PERIOD)
return ret
def do_train(cfg, model, resume=False):
model.train()
optimizer = build_optimizer(cfg, model)
scheduler = build_lr_scheduler(cfg, optimizer)
# checkpointer = DetectionCheckpointer(
# model, cfg.OUTPUT_DIR, optimizer=optimizer, scheduler=scheduler
# )
checkpointer = AdetCheckpointer(model,
cfg.OUTPUT_DIR,
optimizer=optimizer,
scheduler=scheduler)
start_iter = (checkpointer.resume_or_load(
cfg.MODEL.WEIGHTS, resume=resume).get("iteration", -1) + 1)
max_iter = cfg.SOLVER.MAX_ITER
periodic_checkpointer = PeriodicCheckpointer(checkpointer,
cfg.SOLVER.CHECKPOINT_PERIOD,
max_iter=max_iter)
writers = ([
CommonMetricPrinter(max_iter),
JSONWriter(os.path.join(cfg.OUTPUT_DIR, "metrics.json")),
TensorboardXWriter(cfg.OUTPUT_DIR),
] if comm.is_main_process() else [])
# compared to "train_net.py", we do not support accurate timing and
# precise BN here, because they are not trivial to implement in a small training loop
data_loader = build_detection_train_loader(cfg)
logger.info("Starting training from iteration {}".format(start_iter))
with EventStorage(start_iter) as storage:
for data, iteration in zip(data_loader, range(start_iter, max_iter)):
iteration = iteration + 1
storage.step()
loss_dict = model(data)
losses = sum(loss_dict.values())
assert torch.isfinite(losses).all(), loss_dict
loss_dict_reduced = {
k: v.item()
for k, v in comm.reduce_dict(loss_dict).items()
}
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
if comm.is_main_process():
storage.put_scalars(total_loss=losses_reduced,
**loss_dict_reduced)
optimizer.zero_grad()
losses.backward()
optimizer.step()
storage.put_scalar("lr",
optimizer.param_groups[0]["lr"],
smoothing_hint=False)
scheduler.step()
if (cfg.TEST.EVAL_PERIOD > 0
and iteration % cfg.TEST.EVAL_PERIOD == 0
and iteration != max_iter):
do_test(cfg, model)
# Compared to "train_net.py", the test results are not dumped to EventStorage
comm.synchronize()
if iteration - start_iter > 5 and (iteration % 20 == 0
or iteration == max_iter):
for writer in writers:
writer.write()
periodic_checkpointer.step(iteration)
