Spaces:
Build error
Build error
File size: 3,484 Bytes
6250360 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
"""
This file contains specific functions for computing losses on the RetinaNet
file
"""
import torch
from torch.nn import functional as F
from ..utils import concat_box_prediction_layers
from maskrcnn_benchmark.layers import smooth_l1_loss
from maskrcnn_benchmark.layers import SigmoidFocalLoss
from maskrcnn_benchmark.modeling.matcher import Matcher
from maskrcnn_benchmark.modeling.utils import cat
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
from maskrcnn_benchmark.modeling.rpn.loss import RPNLossComputation
class RetinaNetLossComputation(RPNLossComputation):
"""
This class computes the RetinaNet loss.
"""
def __init__(self, proposal_matcher, box_coder,
generate_labels_func,
sigmoid_focal_loss,
bbox_reg_beta=0.11,
regress_norm=1.0):
"""
Arguments:
proposal_matcher (Matcher)
box_coder (BoxCoder)
"""
self.proposal_matcher = proposal_matcher
self.box_coder = box_coder
self.box_cls_loss_func = sigmoid_focal_loss
self.bbox_reg_beta = bbox_reg_beta
self.copied_fields = ['labels']
self.generate_labels_func = generate_labels_func
self.discard_cases = ['between_thresholds']
self.regress_norm = regress_norm
def __call__(self, anchors, box_cls, box_regression, targets):
"""
Arguments:
anchors (list[BoxList])
box_cls (list[Tensor])
box_regression (list[Tensor])
targets (list[BoxList])
Returns:
retinanet_cls_loss (Tensor)
retinanet_regression_loss (Tensor
"""
anchors = [cat_boxlist(anchors_per_image) for anchors_per_image in anchors]
labels, regression_targets = self.prepare_targets(anchors, targets)
N = len(labels)
box_cls, box_regression = \
concat_box_prediction_layers(box_cls, box_regression)
labels = torch.cat(labels, dim=0)
regression_targets = torch.cat(regression_targets, dim=0)
pos_inds = torch.nonzero(labels > 0).squeeze(1)
retinanet_regression_loss = smooth_l1_loss(
box_regression[pos_inds],
regression_targets[pos_inds],
beta=self.bbox_reg_beta,
size_average=False,
) / (max(1, pos_inds.numel() * self.regress_norm))
labels = labels.int()
retinanet_cls_loss = self.box_cls_loss_func(
box_cls,
labels
) / (pos_inds.numel() + N)
return retinanet_cls_loss, retinanet_regression_loss
def generate_retinanet_labels(matched_targets):
labels_per_image = matched_targets.get_field("labels")
return labels_per_image
def make_retinanet_loss_evaluator(cfg, box_coder):
matcher = Matcher(
cfg.MODEL.RETINANET.FG_IOU_THRESHOLD,
cfg.MODEL.RETINANET.BG_IOU_THRESHOLD,
allow_low_quality_matches=True,
)
sigmoid_focal_loss = SigmoidFocalLoss(
cfg.MODEL.RETINANET.LOSS_GAMMA,
cfg.MODEL.RETINANET.LOSS_ALPHA
)
loss_evaluator = RetinaNetLossComputation(
matcher,
box_coder,
generate_retinanet_labels,
sigmoid_focal_loss,
bbox_reg_beta = cfg.MODEL.RETINANET.BBOX_REG_BETA,
regress_norm = cfg.MODEL.RETINANET.BBOX_REG_WEIGHT,
)
return loss_evaluator
|