def test(self, output_dir=None, model_to_test=None):
if output_dir is not None:
self.cfg.OUTPUT_DIR = output_dir
model = build_detection_model(self.cfg)
device = torch.device(self.cfg.MODEL.DEVICE)
model.to(device)
arguments = {}
arguments["iteration"] = 0
output_dir = self.cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
self.cfg, model, None, None, output_dir, save_to_disk
)
if model_to_test is not None:
self.cfg.MODEL.WEIGHT = model_to_test
if self.cfg.MODEL.WEIGHT.startswith('/') or 'catalog' in self.cfg.MODEL.WEIGHT:
model_path = self.cfg.MODEL.WEIGHT
else:
model_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir, os.path.pardir, os.path.pardir, 'Data', 'pretrained_feature_extractors', self.cfg.MODEL.WEIGHT))
extra_checkpoint_data = checkpointer.load(model_path, use_latest=False)
checkpointer.optimizer = make_optimizer(self.cfg, checkpointer.model)
checkpointer.scheduler = make_lr_scheduler(self.cfg, checkpointer.optimizer)
# Initialize mixed-precision training
use_mixed_precision = self.cfg.DTYPE == "float16"
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
model, optimizer = amp.initialize(checkpointer.model, checkpointer.optimizer, opt_level=amp_opt_level)
if self.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[self.local_rank], output_device=self.local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
synchronize()
_ = inference( # The result can be used for additional logging, e. g. for TensorBoard
model,
# The method changes the segmentation mask format in a data loader,
# so every time a new data loader is created:
make_data_loader(self.cfg, is_train=False, is_distributed=(get_world_size() > 1), is_target_task=self.is_target_task),
dataset_name="[Test]",
iou_types=("bbox",),
box_only=False if cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY,
device=cfg.MODEL.DEVICE,
expected_results=cfg.TEST.EXPECTED_RESULTS,
expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
output_folder=None,
is_target_task=self.is_target_task,
)
synchronize()
logger = logging.getLogger("maskrcnn_benchmark")
logger.handlers=[]
def train(self,
output_dir=None,
fine_tune_last_layers=False,
fine_tune_rpn=False):
if output_dir is not None:
self.cfg.OUTPUT_DIR = output_dir
model = build_detection_model(self.cfg)
device = torch.device(self.cfg.MODEL.DEVICE)
model.to(device)
arguments = {}
arguments["iteration"] = 0
output_dir = self.cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(self.cfg, model, None, None,
output_dir, save_to_disk)
if self.cfg.MODEL.WEIGHT.startswith(
'/') or 'catalog' in self.cfg.MODEL.WEIGHT:
model_path = self.cfg.MODEL.WEIGHT
else:
model_path = os.path.abspath(
os.path.join(os.path.dirname(__file__), os.path.pardir,
os.path.pardir, os.path.pardir, os.path.pardir,
'Data', 'pretrained_feature_extractors',
self.cfg.MODEL.WEIGHT))
extra_checkpoint_data = checkpointer.load(model_path)
if self.cfg.MINIBOOTSTRAP.DETECTOR.NUM_CLASSES + 1 != self.cfg.MODEL.ROI_BOX_HEAD.NUM_CLASSES:
checkpointer.model.roi_heads.box.predictor.cls_score = torch.nn.Linear(
in_features=checkpointer.model.roi_heads.box.predictor.
cls_score.in_features,
out_features=self.cfg.MINIBOOTSTRAP.DETECTOR.NUM_CLASSES + 1,
bias=True)
checkpointer.model.roi_heads.box.predictor.bbox_pred = torch.nn.Linear(
in_features=checkpointer.model.roi_heads.box.predictor.
cls_score.in_features,
out_features=(self.cfg.MINIBOOTSTRAP.DETECTOR.NUM_CLASSES + 1)
* 4,
bias=True)
if hasattr(checkpointer.model.roi_heads, 'mask'):
checkpointer.model.roi_heads.mask.predictor.mask_fcn_logits = torch.nn.Conv2d(
in_channels=checkpointer.model.roi_heads.mask.predictor.
mask_fcn_logits.in_channels,
out_channels=self.cfg.MINIBOOTSTRAP.DETECTOR.NUM_CLASSES +
1,
kernel_size=(1, 1),
stride=(1, 1))
checkpointer.model.to(device)
if fine_tune_last_layers:
checkpointer.model.roi_heads.box.predictor.cls_score = torch.nn.Linear(
in_features=checkpointer.model.roi_heads.box.predictor.
cls_score.in_features,
out_features=self.cfg.MINIBOOTSTRAP.DETECTOR.NUM_CLASSES + 1,
bias=True)
checkpointer.model.roi_heads.box.predictor.bbox_pred = torch.nn.Linear(
in_features=checkpointer.model.roi_heads.box.predictor.
cls_score.in_features,
out_features=(self.cfg.MINIBOOTSTRAP.DETECTOR.NUM_CLASSES + 1)
* 4,
bias=True)
if hasattr(checkpointer.model.roi_heads, 'mask'):
checkpointer.model.roi_heads.mask.predictor.mask_fcn_logits = torch.nn.Conv2d(
in_channels=checkpointer.model.roi_heads.mask.predictor.
mask_fcn_logits.in_channels,
out_channels=self.cfg.MINIBOOTSTRAP.DETECTOR.NUM_CLASSES +
1,
kernel_size=(1, 1),
stride=(1, 1))
# Freeze backbone layers
for elem in checkpointer.model.backbone.parameters():
elem.requires_grad = False
if not fine_tune_rpn:
# Freeze RPN layers
for elem in checkpointer.model.rpn.parameters():
elem.requires_grad = False
else:
for elem in checkpointer.model.rpn.head.conv.parameters():
elem.requires_grad = False
checkpointer.model.rpn.head.cls_logits = torch.nn.Conv2d(
in_channels=checkpointer.model.rpn.head.cls_logits.
in_channels,
out_channels=checkpointer.model.rpn.head.cls_logits.
out_channels,
kernel_size=(1, 1),
stride=(1, 1))
checkpointer.model.rpn.head.bbox_pred = torch.nn.Conv2d(
in_channels=checkpointer.model.rpn.head.bbox_pred.
in_channels,
out_channels=checkpointer.model.rpn.head.bbox_pred.
out_channels,
kernel_size=(1, 1),
stride=(1, 1))
# Freeze roi_heads layers with the exception of the predictor ones
for elem in checkpointer.model.roi_heads.box.feature_extractor.parameters(
):
elem.requires_grad = False
for elem in checkpointer.model.roi_heads.box.predictor.parameters(
):
elem.requires_grad = True
if hasattr(checkpointer.model.roi_heads, 'mask'):
for elem in checkpointer.model.roi_heads.mask.predictor.parameters(
):
elem.requires_grad = False
for elem in checkpointer.model.roi_heads.mask.predictor.mask_fcn_logits.parameters(
):
elem.requires_grad = True
checkpointer.model.to(device)
checkpointer.optimizer = make_optimizer(self.cfg, checkpointer.model)
checkpointer.scheduler = make_lr_scheduler(self.cfg,
checkpointer.optimizer)
# Initialize mixed-precision training
use_mixed_precision = self.cfg.DTYPE == "float16"
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
model, optimizer = amp.initialize(checkpointer.model,
checkpointer.optimizer,
opt_level=amp_opt_level)
if self.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[self.local_rank],
output_device=self.local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
data_loader = make_data_loader(self.cfg,
is_train=True,
is_distributed=self.distributed,
start_iter=arguments["iteration"],
is_target_task=self.is_target_task)
test_period = self.cfg.SOLVER.TEST_PERIOD
if test_period > 0:
data_loader_val = make_data_loader(
self.cfg,
is_train=False,
is_distributed=self.distributed,
is_target_task=self.is_target_task)
else:
data_loader_val = None
checkpoint_period = self.cfg.SOLVER.CHECKPOINT_PERIOD
do_train(self.cfg,
model,
data_loader,
data_loader_val,
checkpointer.optimizer,
checkpointer.scheduler,
checkpointer,
device,
checkpoint_period,
test_period,
arguments,
is_target_task=self.is_target_task)
logger = logging.getLogger("maskrcnn_benchmark")
logger.handlers = []
