def __init__(self, cfg):
super(ReferExpRCNN_Old, self).__init__(cfg)
# Weight losses
self.weight_dict = {
'loss_objectness': cfg.LOSS_WEIGHTS.loss_objectness,
'loss_rpn_box_reg': cfg.LOSS_WEIGHTS.loss_rpn_box_reg,
'loss_classifier': cfg.LOSS_WEIGHTS.loss_classifier,
'loss_box_reg': cfg.LOSS_WEIGHTS.loss_box_reg,
'loss_mask': cfg.LOSS_WEIGHTS.loss_mask
}
# Text Embedding Network
self.wordnet = LanguageModel(cfg)
# Ref Localization Network
if self.hha_backbone:
self.ref_rpn = build_rpn(
cfg, self.image_backbone.out_channels * 2 + 1024)
self.ref_roi_heads = build_roi_heads(
cfg, self.image_backbone.out_channels * 2 + 1024)
else:
self.ref_rpn = build_rpn(cfg,
self.image_backbone.out_channels + 1024)
self.ref_roi_heads = build_roi_heads(
cfg, self.image_backbone.out_channels + 1024)
def __init__(self, cfg, weight_dict=None, extra_features=0):
super(DepthRCNN, self).__init__()
# Weight losses
if weight_dict is None:
self.weight_dict = {
'loss_objectness': cfg.LOSS_WEIGHTS.loss_objectness,
'loss_rpn_box_reg': cfg.LOSS_WEIGHTS.loss_rpn_box_reg,
'loss_classifier': cfg.LOSS_WEIGHTS.loss_classifier,
'loss_box_reg': cfg.LOSS_WEIGHTS.loss_box_reg,
'loss_mask': cfg.LOSS_WEIGHTS.loss_mask
}
else:
self.weight_dict = weight_dict
self.image_backbone = build_backbone(cfg)
self.freeze_backbone(self.image_backbone,
cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT)
self.roi_heads = None
if cfg.MODEL.BACKBONE.DEPTH:
self.hha_backbone = build_backbone(cfg)
self.freeze_backbone(self.hha_backbone,
cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT)
self.rpn = build_rpn(
cfg, self.image_backbone.out_channels * 2 + extra_features)
if not cfg.MODEL.RPN_ONLY:
self.roi_heads = build_roi_heads(
cfg, self.image_backbone.out_channels * 2 + extra_features)
else:
self.hha_backbone = None
self.rpn = build_rpn(
cfg, self.image_backbone.out_channels + extra_features)
if not cfg.MODEL.RPN_ONLY:
self.roi_heads = build_roi_heads(
cfg, self.image_backbone.out_channels + extra_features)
def __init__(self, exp_dict):
super().__init__()
cfg_base_path = "./models/configs/"
self.n_classes = 21
cfg_path = cfg_base_path + "e2e_mask_rcnn_R_50_FPN_1x.yaml"
self.cfg = cfg
self.cfg.merge_from_file(cfg_path)
self.cfg.MODEL.ROI_BOX_HEAD.NUM_CLASSES = self.n_classes
# ---------------
# build model
self.backbone_fpn = backbone.build_backbone(self.cfg)
self.rpn = rpn.build_rpn(self.cfg, self.backbone_fpn.out_channels)
self.roi_heads = roi_heads.build_roi_heads(
self.cfg, self.backbone_fpn.out_channels)
# ---------------
# load checkpoint
checkpoint = _load_file(self.cfg)
load_state_dict(self, checkpoint.pop("model"))
#--------
# Opt stage
self.cfg.SOLVER.BASE_LR = ((0.0025 * 8) /
(16 / float(exp_dict["batch_size"])))
optimizer = make_optimizer(self.cfg, self)
scheduler = make_lr_scheduler(self.cfg, optimizer)
self.opt = optimizer
self.scheduler = scheduler
def init_maskrcnn_benchmark(self, args):
if 'datasets' in sys.modules:
del sys.modules['datasets']
this_dir = os.path.dirname(__file__)
lib_path = os.path.join(this_dir, '../../external/maskrcnn-benchmark')
sys.path.insert(0, lib_path)
# config
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip
from maskrcnn_benchmark.config import cfg
self.cfg = cfg
#cfg.MODEL.BACKBONE.OUT_CHANNELS = 832
#cfg.MODEL.RESNETS.RES2_OUT_CHANNELS = 208
cfg.MODEL.RPN.PRE_NMS_TOP_N_TEST = 6000
cfg.MODEL.RPN.POST_NMS_TOP_N_TEST = 1000
cfg.MODEL.ROI_HEADS.SCORE_THRESH = 0
cfg.MODEL.ROI_HEADS.DETECTIONS_PER_IMG = 0
# bind classes/methods from MaskRCNN-Benchmark
from maskrcnn_benchmark.modeling.rpn.rpn import build_rpn
from maskrcnn_benchmark.modeling.roi_heads.roi_heads import build_roi_heads
from maskrcnn_benchmark.structures.image_list import to_image_list
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.modeling.backbone import build_backbone
self.basenet = build_backbone(cfg)
self.rpn = build_rpn(cfg)
self.roi_heads = build_roi_heads(cfg)
self.to_image_list = to_image_list
self.BoxList = BoxList
def __init__(self, cfg):
super(PanopticModel, self).__init__()
self.backbone = build_backbone(cfg)
self.loss = SegLoss()
self.training = True
self.rpn = build_rpn(cfg, self.backbone.out_channels)
self.roi_heads = build_roi_heads(cfg, self.backbone.out_channels)
self.panoptic = PanopticRCNN(
cfg, num_classes=cfg.MODEL.PANOPTIC.NUM_CLASSES)