"""
Modified from https://github.com/pytorch/vision/blob/main/torchvision/models/detection/retinanet.py
"""
import math
from collections import OrderedDict
import warnings
import oneflow as flow
from oneflow import nn, Tensor
from typing import Dict, List, Tuple, Optional
from ..utils import load_state_dict_from_url
from . import det_utils
from .anchor_utils import AnchorGenerator
from .transform import GeneralizedRCNNTransform
from .backbone_utils import resnet_fpn_backbone, _validate_trainable_layers
from flowvision.layers.blocks.feature_pyramid_network import LastLevelP6P7
from flowvision.layers.blocks import boxes as box_ops
from flowvision.layers import sigmoid_focal_loss
from ..registry import ModelCreator
__all__ = ["RetinaNet", "retinanet_resnet50_fpn"]
model_urls = {
"retinanet_resnet50_fpn_coco": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/detection/retinanet/retinanet_resnet50_fpn_coco.tar.gz",
}
def _sum(x: List[Tensor]) -> Tensor:
res = x[0]
for i in x[1:]:
res = res + i
return res
class RetinaNetHead(nn.Module):
"""
A regression and classification head for use in RetinaNet.
Args:
in_channels (int): number of channels of the input feature
num_anchors (int): number of anchors to be predicted
num_classes (int): number of classes to be predicted
"""
def __init__(self, in_channels, num_anchors, num_classes):
super().__init__()
self.classification_head = RetinaNetClassificationHead(
in_channels, num_anchors, num_classes
)
self.regression_head = RetinaNetRegressionHead(in_channels, num_anchors)
def compute_loss(
self,
targets: List[Dict[str, Tensor]],
head_outputs: Dict[str, Tensor],
anchors: List[Tensor],
matched_idxs: List[Tensor],
) -> Dict[str, Tensor]:
return {
"classification": self.classification_head.compute_loss(
targets, head_outputs, matched_idxs
),
"bbox_regression": self.regression_head.compute_loss(
targets, head_outputs, anchors, matched_idxs
),
}
def forward(self, x: List[Tensor]) -> Dict[str, Tensor]:
return {
"cls_logits": self.classification_head(x),
"bbox_regression": self.regression_head(x),
}
class RetinaNetClassificationHead(nn.Module):
"""
A classification head for use in RetinaNet.
Args:
in_channels (int): number of channels of the input feature
num_anchors (int): number of anchors to be predicted
num_classes (int): number of classes to be predicted
"""
def __init__(self, in_channels, num_anchors, num_classes, prior_probability=0.01):
super().__init__()
conv = []
for _ in range(4):
conv.append(
nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
)
conv.append(nn.ReLU())
self.conv = nn.Sequential(*conv)
for layer in self.conv.children():
if isinstance(layer, nn.Conv2d):
flow.nn.init.normal_(layer.weight, std=0.01)
flow.nn.init.constant_(layer.bias, 0)
self.cls_logits = nn.Conv2d(
in_channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1
)
flow.nn.init.normal_(self.cls_logits.weight, std=0.01)
flow.nn.init.constant_(
self.cls_logits.bias, -math.log((1 - prior_probability) / prior_probability)
)
self.num_classes = num_classes
self.num_anchors = num_anchors
self.BETWEEN_THRESHOLDS = det_utils.Matcher.BETWEEN_THRESHOLDS
def compute_loss(
self,
targets: List[Dict[str, Tensor]],
head_outputs: Dict[str, Tensor],
matched_idxs: List[Tensor],
) -> Tensor:
losses = []
cls_logits = head_outputs["cls_logits"]
for targets_per_image, cls_logits_per_image, matched_idxs_per_image in zip(
targets, cls_logits, matched_idxs
):
# determin only the foreground
foreground_idxs_per_image = matched_idxs_per_image >= 0
num_foreground = foreground_idxs_per_image.sum()
# create the target classification
gt_classes_target = flow.zeros_like(cls_logits_per_image)
gt_classes_target[
foreground_idxs_per_image,
targets_per_image["labels"][
matched_idxs_per_image[foreground_idxs_per_image]
],
] = 1.0
# find indices for which anchors should be ignored
valid_idxs_per_image = matched_idxs_per_image != self.BETWEEN_THRESHOLDS
# compute the classification loss
losses.append(
sigmoid_focal_loss(
cls_logits_per_image[valid_idxs_per_image],
gt_classes_target[valid_idxs_per_image],
reduction="sum",
)
/ max(1, num_foreground)
)
return _sum(losses) / len(targets)
def forward(self, x: List[Tensor]) -> Tensor:
all_cls_logits = []
for features in x:
cls_logits = self.conv(features)
cls_logits = self.cls_logits(cls_logits)
# Permute classification output from (N, A * K, H, W) to (N, HWA, K).
N, _, H, W = cls_logits.shape
cls_logits = cls_logits.view(N, -1, self.num_classes, H, W)
cls_logits = cls_logits.permute(0, 3, 4, 1, 2)
cls_logits = cls_logits.reshape(N, -1, self.num_classes) # Size=(N, HWA, 4)
all_cls_logits.append(cls_logits)
return flow.cat(all_cls_logits, dim=1)
class RetinaNetRegressionHead(nn.Module):
"""
A regression head for use in RetinaNet.
Args:
in_channels (int): number of channels of the input feature
num_anchors (int): number of anchors to be predicted
"""
__annotations__ = {
"box_coder": det_utils.BoxCoder,
}
def __init__(self, in_channels, num_anchors):
super().__init__()
conv = []
for _ in range(4):
conv.append(
nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
)
conv.append(nn.ReLU())
self.conv = nn.Sequential(*conv)
self.bbox_reg = nn.Conv2d(
in_channels, num_anchors * 4, kernel_size=3, stride=1, padding=1
)
flow.nn.init.normal_(self.bbox_reg.weight, std=0.01)
flow.nn.init.zeros_(self.bbox_reg.bias)
for layer in self.conv.children():
if isinstance(layer, nn.Conv2d):
flow.nn.init.normal_(layer.weight, std=0.01)
flow.nn.init.zeros_(layer.bias)
self.box_coder = det_utils.BoxCoder(weights=(1.0, 1.0, 1.0, 1.0))
def compute_loss(
self,
targets: List[Dict[str, Tensor]],
head_outputs: Dict[str, Tensor],
anchors: List[Tensor],
matched_idxs: List[Tensor],
) -> Tensor:
losses = []
bbox_regression = head_outputs["bbox_regression"]
for (
targets_per_image,
bbox_regression_per_image,
anchors_per_image,
matched_idxs_per_image,
) in zip(targets, bbox_regression, anchors, matched_idxs):
# determine only the foreground indices, ignore the rest
foreground_idxs_per_image = flow.where(matched_idxs_per_image >= 0)[0]
num_foreground = foreground_idxs_per_image.numel()
# select only the foreground boxes
matched_gt_boxes_per_image = targets_per_image["boxes"][
matched_idxs_per_image[foreground_idxs_per_image]
]
bbox_regression_per_image = bbox_regression_per_image[
foreground_idxs_per_image, :
]
anchors_per_image = anchors_per_image[foreground_idxs_per_image, :]
# compute the regression targets
target_regression = self.box_coder.encode_single(
matched_gt_boxes_per_image, anchors_per_image
)
# compute the loss
losses.append(
flow._C.l1_loss(
bbox_regression_per_image, target_regression, reduction="sum"
)
/ max(1, num_foreground)
)
return _sum(losses) / max(1, len(targets))
def forward(self, x: List[Tensor]) -> Tensor:
all_bbox_regression = []
for features in x:
bbox_regression = self.conv(features)
bbox_regression = self.bbox_reg(bbox_regression)
# Permute bbox regression output from (N, 4 * A, H, W) to (N, HWA, 4).
N, _, H, W = bbox_regression.shape
bbox_regression = bbox_regression.view(N, -1, 4, H, W)
bbox_regression = bbox_regression.permute(0, 3, 4, 1, 2)
bbox_regression = bbox_regression.reshape(N, -1, 4) # Size=(N, HWA, 4)
all_bbox_regression.append(bbox_regression)
return flow.cat(all_bbox_regression, dim=1)
class RetinaNet(nn.Module):
"""
Implements RetinaNet.
The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each
image, and should be in 0-1 range. Different images can have different sizes.
The behavior of the model changes depending if it is in training or evaluation mode.
During training, the model expects both the input tensors, as well as a targets (list of dictionary),
containing:
- boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with
``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
- labels (Int64Tensor[N]): the class label for each ground-truth box
The model returns a Dict[Tensor] during training, containing the classification and regression
losses.
During inference, the model requires only the input tensors, and returns the post-processed
predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as
follows:
- boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with
``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
- labels (Int64Tensor[N]): the predicted labels for each image
- scores (Tensor[N]): the scores for each prediction
Args:
backbone (nn.Module): the network used to compute the features for the model.
It should contain an out_channels attribute, which indicates the number of output
channels that each feature map has (and it should be the same for all feature maps).
The backbone should return a single Tensor or an OrderedDict[Tensor].
num_classes (int): number of output classes of the model (including the background).
min_size (int): minimum size of the image to be rescaled before feeding it to the backbone
max_size (int): maximum size of the image to be rescaled before feeding it to the backbone
image_mean (Tuple[float, float, float]): mean values used for input normalization.
They are generally the mean values of the dataset on which the backbone has been trained
on
image_std (Tuple[float, float, float]): std values used for input normalization.
They are generally the std values of the dataset on which the backbone has been trained on
anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature
maps.
head (nn.Module): Module run on top of the feature pyramid.
Defaults to a module containing a classification and regression module.
score_thresh (float): Score threshold used for postprocessing the detections.
nms_thresh (float): NMS threshold used for postprocessing the detections.
detections_per_img (int): Number of best detections to keep after NMS.
fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be
considered as positive during training.
bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be
considered as negative during training.
topk_candidates (int): Number of best detections to keep before NMS.
"""
__annotations__ = {
"box_coder": det_utils.BoxCoder,
"proposal_matcher": det_utils.Matcher,
}
def __init__(
self,
backbone,
num_classes,
# transform parameters
min_size=800,
max_size=1333,
image_mean=None,
image_std=None,
# Anchor parameters
anchor_generator=None,
head=None,
proposal_matcher=None,
score_thresh=0.05,
nms_thresh=0.5,
detections_per_img=300,
fg_iou_thresh=0.5,
bg_iou_thresh=0.4,
topk_candidates=1000,
**kwargs,
):
super().__init__()
if not hasattr(backbone, "out_channels"):
raise ValueError(
"backbone should contain an attribute out_channels "
"specifying the number of output channels (assumed to be the "
"same for all the levels)"
)
self.backbone = backbone
if not isinstance(anchor_generator, (AnchorGenerator, type(None))):
raise TypeError(
f"anchor_generator should be of type AnchorGenerator or None instead of {type(anchor_generator)}"
)
if anchor_generator is None:
anchor_sizes = tuple(
(x, int(x * 2 ** (1.0 / 3)), int(x * 2 ** (2.0 / 3)))
for x in [32, 64, 128, 256, 512]
)
aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
anchor_generator = AnchorGenerator(anchor_sizes, aspect_ratios)
self.anchor_generator = anchor_generator
if head is None:
head = RetinaNetHead(
backbone.out_channels,
anchor_generator.num_anchors_per_location()[0],
num_classes,
)
self.head = head
if proposal_matcher is None:
proposal_matcher = det_utils.Matcher(
fg_iou_thresh, bg_iou_thresh, allow_low_quality_matches=True,
)
self.proposal_matcher = proposal_matcher
self.box_coder = det_utils.BoxCoder(weights=(1.0, 1.0, 1.0, 1.0))
if image_mean is None:
image_mean = [0.485, 0.456, 0.406]
if image_std is None:
image_std = [0.229, 0.224, 0.225]
self.transform = GeneralizedRCNNTransform(
min_size, max_size, image_mean, image_std, **kwargs
)
self.score_thresh = score_thresh
self.nms_thresh = nms_thresh
self.detections_per_img = detections_per_img
self.topk_candidates = topk_candidates
def compute_loss(
self,
targets: List[Dict[str, Tensor]],
head_outputs: Dict[str, Tensor],
anchors: List[Tensor],
) -> Dict[str, Tensor]:
matched_idxs = []
for anchors_per_image, targets_per_image in zip(anchors, targets):
if targets_per_image["boxes"].numel() == 0:
matched_idxs.append(
flow.full(
(anchors_per_image.size(0),),
-1,
dtype=flow.int64,
device=anchors_per_image.device,
)
)
continue
match_quality_matrix = box_ops.box_iou(
targets_per_image["boxes"], anchors_per_image
)
matched_idxs.append(self.proposal_matcher(match_quality_matrix))
return self.head.compute_loss(targets, head_outputs, anchors, matched_idxs)
def postprocess_detections(
self,
head_outputs: Dict[str, List[Tensor]],
anchors: List[List[Tensor]],
image_shapes: List[Tuple[int, int]],
) -> List[Dict[str, Tensor]]:
class_logits = head_outputs["cls_logits"]
box_regression = head_outputs["bbox_regression"]
num_images = len(image_shapes)
detections: List[Dict[str, Tensor]] = []
for index in range(num_images):
box_regression_per_image = [br[index] for br in box_regression]
logits_per_image = [cl[index] for cl in class_logits]
anchors_per_image, image_shape = anchors[index], image_shapes[index]
image_boxes = []
image_scores = []
image_labels = []
for box_regression_per_level, logits_per_level, anchors_per_level in zip(
box_regression_per_image, logits_per_image, anchors_per_image
):
num_classes = logits_per_level.shape[-1]
# remove low scoring boxes
scores_per_level = flow.sigmoid(logits_per_level).flatten()
keep_idxs = scores_per_level > self.score_thresh
scores_per_level = scores_per_level[keep_idxs]
topk_idxs = flow.where(keep_idxs)[0]
# keep only topk scoring predictions
num_topk = min(self.topk_candidates, topk_idxs.size(0))
scores_per_level, idxs = scores_per_level.topk(num_topk)
topk_idxs = topk_idxs[idxs]
anchor_idxs = flow.floor_divide(topk_idxs, num_classes)
labels_per_level = topk_idxs % num_classes
boxes_per_level = self.box_coder.decode_single(
box_regression_per_level[anchor_idxs],
anchors_per_level[anchor_idxs],
)
boxes_per_level = box_ops.clip_boxes_to_image(
boxes_per_level, image_shape
)
image_boxes.append(boxes_per_level)
image_scores.append(scores_per_level)
image_labels.append(labels_per_level)
image_boxes = flow.cat(image_boxes, dim=0)
image_scores = flow.cat(image_scores, dim=0)
image_labels = flow.cat(image_labels, dim=0)
# non-maximum suppression
keep = box_ops.batched_nms(
image_boxes, image_scores, image_labels, self.nms_thresh
)
keep = keep[: self.detections_per_img]
detections.append(
{
"boxes": image_boxes[keep],
"scores": image_scores[keep],
"labels": image_labels[keep],
}
)
return detections
def forward(
self, images: List[Tensor], targets: Optional[List[Dict[str, Tensor]]] = None
) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]:
"""
Args:
images (list[Tensor]): images to be processed
targets (list[Dict[Tensor]]): ground-truth boxes present in the image (optional)
Returns:
result (list[BoxList] or dict[Tensor]): the output from the model.
During training, it returns a dict[Tensor] which contains the losses.
During testing, it returns list[BoxList] contains additional fields
like `scores`, `labels` and `mask` (for Mask R-CNN models).
"""
if self.training and targets is None:
raise ValueError("In training mode, targets should be passed")
if self.training:
assert targets is not None
for target in targets:
boxes = target["boxes"]
if isinstance(boxes, flow.Tensor):
if len(boxes.shape) != 2 or boxes.shape[-1] != 4:
raise ValueError(
"Expected target boxes to be a tensor"
"of shape [N, 4], got {:}.".format(boxes.shape)
)
else:
raise ValueError(
"Expected target boxes to be of type "
"Tensor, got {:}.".format(type(boxes))
)
# get the original image sizes
original_image_sizes: List[Tuple[int, int]] = []
for img in images:
val = img.shape[-2:]
if len(val) != 2:
raise ValueError(
f"expecting the last two dimensions of the Tensor to be H and W instead got {img.shape[-2:]}",
)
original_image_sizes.append((val[0], val[1]))
# transform the input
images, targets = self.transform(images, targets)
# Check for degenerate boxes
if targets is not None:
for target_idx, target in enumerate(targets):
boxes = target["boxes"]
degenerate_boxes = boxes[:, 2:] <= boxes[:, :2]
if degenerate_boxes.any():
# print the first degenerate box
bb_idx = flow.where(degenerate_boxes.sum(dim=1))[0][0]
degen_bb: List[float] = boxes[bb_idx].tolist()
raise ValueError(
"All bounding boxes should have positive height and width."
" Found invalid box {} for target at index {}.".format(
degen_bb, target_idx
)
)
# get the features from the backbone
features = self.backbone(images.tensors)
if isinstance(features, flow.Tensor):
features = OrderedDict([("0", features)])
features = list(features.values())
# compute the retinanet heads outputs using the features
head_outputs = self.head(features)
# create the set of anchors
anchors = self.anchor_generator(images, features)
losses = {}
detections: List[Dict[str, Tensor]] = []
if self.training:
if targets is None:
raise ValueError("targets should not be none when in training mode")
else:
# compute the losses
losses = self.compute_loss(targets, head_outputs, anchors)
else:
# recover level sizes
num_anchors_per_level = [x.size(2) * x.size(3) for x in features]
HW = 0
for v in num_anchors_per_level:
HW += v
HWA = head_outputs["cls_logits"].size(1)
A = HWA // HW
num_anchors_per_level = [hw * A for hw in num_anchors_per_level]
# split outputs per level
split_head_outputs: Dict[str, List[Tensor]] = {}
for k in head_outputs:
split_head_outputs[k] = list(
head_outputs[k].split(num_anchors_per_level, dim=1)
)
split_anchors = [list(a.split(num_anchors_per_level)) for a in anchors]
# compute the detections
detections = self.postprocess_detections(
split_head_outputs, split_anchors, images.image_sizes
)
detections = self.transform.postprocess(
detections, images.image_sizes, original_image_sizes
)
if self.training:
return losses
return detections
def _retinanet_resnet_fpn(
weights_name: Optional[str] = None,
backbone_name: Optional[str] = None,
pretrained: bool = False,
progress: bool = True,
num_classes: Optional[int] = 91,
pretrained_backbone: bool = True,
trainable_backbone_layers: Optional[int] = None,
**kwargs,
):
trainable_backbone_layers = _validate_trainable_layers(
pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3
)
if pretrained:
# no need to download the backbone if pretrained is set
pretrained_backbone = False
# skip P2 because it generates too many anchors
backbone = resnet_fpn_backbone(
backbone_name,
pretrained_backbone,
returned_layers=[2, 3, 4],
extra_blocks=LastLevelP6P7(256, 256),
trainable_layers=trainable_backbone_layers,
)
model = RetinaNet(backbone, num_classes, **kwargs)
if pretrained:
state_dict = load_state_dict_from_url(
model_urls[weights_name], progress=progress
)
model.load_state_dict(state_dict)
det_utils.overwrite_eps(model, 0.0)
return model
[docs]@ModelCreator.register_model
def retinanet_resnet50_fpn(
pretrained: bool = False,
progress: bool = True,
num_classes: Optional[int] = 91,
pretrained_backbone: bool = True,
trainable_backbone_layers: Optional[int] = None,
**kwargs,
):
"""
Constructs a RetinaNet model with a ResNet-50-FPN backbone.
Reference: `"Focal Loss for Dense Object Detection" <https://arxiv.org/abs/1708.02002>`_.
The input to the model is expected to be a list of tensors, each of shape ``[C, H, W]``, one for each
image, and should be in ``0-1`` range. Different images can have different sizes.
The behavior of the model changes depending if it is in training or evaluation mode.
During training, the model expects both the input tensors, as well as a targets (list of dictionary),
containing:
- boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with
``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
- labels (``Int64Tensor[N]``): the class label for each ground-truth box
The model returns a ``Dict[Tensor]`` during training, containing the classification and regression
losses.
During inference, the model requires only the input tensors, and returns the post-processed
predictions as a ``List[Dict[Tensor]]``, one for each input image. The fields of the ``Dict`` are as
follows, where ``N`` is the number of detections:
- boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with
``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
- labels (``Int64Tensor[N]``): the predicted labels for each detection
- scores (``Tensor[N]``): the scores of each detection
For more details on the output, you may refer to :ref:`instance_seg_output`.
Args:
pretrained (bool): If True, returns a model pre-trained on COCO train2017
progress (bool): If True, displays a progress bar of the download to stderr
num_classes (int): number of output classes of the model (including the background)
pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.
For example:
.. code-block:: python
>>> import flowvision
>>> retinanet_resnet50_fpn = flowvision.models.detection.retinanet_resnet50_fpn(pretrained=False, progress=True)
"""
weights_name = "retinanet_resnet50_fpn_coco"
backbone_name = "resnet50"
return _retinanet_resnet_fpn(
weights_name,
backbone_name,
pretrained=pretrained,
progress=progress,
num_classes=num_classes,
pretrained_backbone=pretrained_backbone,
trainable_backbone_layers=trainable_backbone_layers,
**kwargs,
)
@ModelCreator.register_model
def retinanet_resnet101_fpn(
pretrained: bool = False,
progress: bool = True,
num_classes: Optional[int] = 91,
pretrained_backbone: bool = True,
trainable_backbone_layers: Optional[int] = None,
**kwargs,
):
"""
See details in `retinanet_resnet101_fpn`.
For example:
.. code-block:: python
>>> import flowvision
>>> retinanet_resnet101_fpn = flowvision.models.detection.retinanet_resnet101_fpn(pretrained=False, progress=True)
"""
weights_name = "retinanet_resnet101_fpn_coco"
backbone_name = "resnet101"
return _retinanet_resnet_fpn(
weights_name,
backbone_name,
pretrained=pretrained,
progress=progress,
num_classes=num_classes,
pretrained_backbone=pretrained_backbone,
trainable_backbone_layers=trainable_backbone_layers,
**kwargs,
)
@ModelCreator.register_model
def retinanet_resnext50_32x4d_fpn(
pretrained: bool = False,
progress: bool = True,
num_classes: Optional[int] = 91,
pretrained_backbone: bool = True,
trainable_backbone_layers: Optional[int] = None,
**kwargs,
):
"""
See details in `retinanet_resnext50_32x4d_fpn`.
For example:
.. code-block:: python
>>> import flowvision
>>> retinanet_resnext50_32x4d_fpn = flowvision.models.detection.retinanet_resnext50_32x4d_fpn(pretrained=False, progress=True)
"""
weights_name = "retinanet_resnext50_32x4d_fpn_coco"
backbone_name = "resnext50_32x4d"
return _retinanet_resnet_fpn(
weights_name,
backbone_name,
pretrained=pretrained,
progress=progress,
num_classes=num_classes,
pretrained_backbone=pretrained_backbone,
trainable_backbone_layers=trainable_backbone_layers,
**kwargs,
)