# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) OpenMMLab. All rights reserved.
"""Implementation modified from mmdet.models.dense_heads.rtmdet_head.py.
Reference : https://github.com/open-mmlab/mmdetection/blob/v3.2.0/mmdet/models/dense_heads/rtmdet_head.py
"""
from __future__ import annotations
from functools import partial
from typing import Any, Callable, ClassVar
import torch
from torch import Tensor, nn
from otx.algo.common.utils.coders import BaseBBoxCoder
from otx.algo.common.utils.nms import multiclass_nms
from otx.algo.common.utils.prior_generators import BasePriorGenerator
from otx.algo.common.utils.utils import distance2bbox, inverse_sigmoid, multi_apply
from otx.algo.detection.heads.atss_head import ATSSHeadModule
from otx.algo.detection.utils.prior_generators.utils import anchor_inside_flags
from otx.algo.detection.utils.utils import (
images_to_levels,
sigmoid_geometric_mean,
unmap,
)
from otx.algo.modules import build_activation_layer
from otx.algo.modules.conv_module import Conv2dModule, DepthwiseSeparableConvModule
from otx.algo.modules.norm import build_norm_layer, is_norm
from otx.algo.modules.scale import Scale
from otx.algo.utils.mmengine_utils import InstanceData
from otx.algo.utils.weight_init import bias_init_with_prob, constant_init, normal_init
from otx.core.data.entity.detection import DetBatchDataEntity
class RTMDetHead(ATSSHeadModule):
"""Detection Head of RTMDet.
Args:
num_classes (int): Number of categories excluding the background category.
in_channels (int): Number of channels in the input feature map.
with_objectness (bool): Whether to add an objectness branch.
Defaults to True.
activation (Callable[..., nn.Module]): Activation layer module.
Defaults to ``nn.ReLU``.
"""
def __init__(
self,
num_classes: int,
in_channels: int,
with_objectness: bool = True,
activation: Callable[..., nn.Module] = nn.ReLU,
**kwargs,
) -> None:
self.activation = activation
self.with_objectness = with_objectness
super().__init__(num_classes, in_channels, **kwargs)
if self.train_cfg:
self.assigner = self.train_cfg["assigner"]
def _init_layers(self) -> None:
"""Initialize layers of the head."""
self.cls_convs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
self.cls_convs.append(
Conv2dModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
normalization=build_norm_layer(self.normalization, num_features=self.feat_channels),
activation=build_activation_layer(self.activation),
),
)
self.reg_convs.append(
Conv2dModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
normalization=build_norm_layer(self.normalization, num_features=self.feat_channels),
activation=build_activation_layer(self.activation),
),
)
pred_pad_size = self.pred_kernel_size // 2
self.rtm_cls = nn.Conv2d(
self.feat_channels,
self.num_base_priors * self.cls_out_channels,
self.pred_kernel_size,
padding=pred_pad_size,
)
self.rtm_reg = nn.Conv2d(
self.feat_channels,
self.num_base_priors * 4,
self.pred_kernel_size,
padding=pred_pad_size,
)
if self.with_objectness:
self.rtm_obj = nn.Conv2d(self.feat_channels, 1, self.pred_kernel_size, padding=pred_pad_size)
self.scales = nn.ModuleList([Scale(1.0) for _ in self.prior_generator.strides])
def init_weights(self) -> None:
"""Initialize weights of the head."""
for m in self.modules():
if isinstance(m, nn.Conv2d):
normal_init(m, mean=0, std=0.01)
if is_norm(m):
constant_init(m, 1)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.rtm_cls, std=0.01, bias=bias_cls)
normal_init(self.rtm_reg, std=0.01)
if self.with_objectness:
normal_init(self.rtm_obj, std=0.01, bias=bias_cls)
def forward(self, feats: tuple[Tensor, ...]) -> tuple:
"""Forward features from the upstream network.
Args:
feats (tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
Returns:
tuple: Usually a tuple of classification scores and bbox prediction
- cls_scores (list[Tensor]): Classification scores for all scale
levels, each is a 4D-tensor, the channels number is
num_base_priors * num_classes.
- bbox_preds (list[Tensor]): Box energies / deltas for all scale
levels, each is a 4D-tensor, the channels number is
num_base_priors * 4.
"""
cls_scores = []
bbox_preds = []
for x, scale, stride in zip(feats, self.scales, self.prior_generator.strides):
cls_feat = x
reg_feat = x
for cls_layer in self.cls_convs:
cls_feat = cls_layer(cls_feat)
cls_score = self.rtm_cls(cls_feat)
for reg_layer in self.reg_convs:
reg_feat = reg_layer(reg_feat)
if self.with_objectness:
objectness = self.rtm_obj(reg_feat)
cls_score = inverse_sigmoid(sigmoid_geometric_mean(cls_score, objectness))
reg_dist = scale(self.rtm_reg(reg_feat).exp()).float() * stride[0]
cls_scores.append(cls_score)
bbox_preds.append(reg_dist)
return tuple(cls_scores), tuple(bbox_preds)
def prepare_loss_inputs(
self,
x: tuple[Tensor],
entity: DetBatchDataEntity,
) -> dict | tuple:
"""Perform forward propagation of the detection head and prepare for loss calculation.
Args:
x (tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
entity (DetBatchDataEntity): Entity from OTX dataset.
Returns:
dict: A dictionary of components for loss calculation.
"""
(cls_scores, bbox_preds), batch_gt_instances, batch_img_metas = super(ATSSHeadModule, self).prepare_loss_inputs(
x,
entity,
)
num_imgs = len(batch_img_metas)
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
if len(featmap_sizes) != self.prior_generator.num_levels:
msg = (
f"The number of featmap_sizes (={len(featmap_sizes)}) and the number of levels of prior_generator "
f"(={self.prior_generator.num_levels}) should be same."
)
raise ValueError(msg)
device = cls_scores[0].device
anchor_list, valid_flag_list = self.get_anchors(featmap_sizes, batch_img_metas, device=device)
flatten_cls_scores = torch.cat(
[cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1, self.cls_out_channels) for cls_score in cls_scores],
1,
)
decoded_bboxes = []
for anchor, bbox_pred in zip(anchor_list[0], bbox_preds):
anchor = anchor.reshape(-1, 4) # noqa: PLW2901
bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4) # noqa: PLW2901
bbox_pred = distance2bbox(anchor, bbox_pred) # noqa: PLW2901
decoded_bboxes.append(bbox_pred)
flatten_bboxes = torch.cat(decoded_bboxes, 1)
(
anchor_list,
labels_list,
label_weights_list,
bbox_targets_list,
assign_metrics_list,
sampling_results_list,
) = self.get_targets( # type: ignore[misc]
flatten_cls_scores,
flatten_bboxes,
anchor_list,
valid_flag_list,
batch_gt_instances,
batch_img_metas,
)
return {
"cls_score": cls_scores,
"bbox_pred": decoded_bboxes,
"labels": labels_list,
"label_weights": label_weights_list,
"bbox_targets": bbox_targets_list,
"assign_metrics": assign_metrics_list,
"stride": self.prior_generator.strides,
"sampling_results_list": sampling_results_list,
}
def export_by_feat( # type: ignore[override]
self,
cls_scores: list[Tensor],
bbox_preds: list[Tensor],
batch_img_metas: list[dict] | None = None,
cfg: dict | None = None,
rescale: bool = False,
with_nms: bool = True,
) -> tuple[Tensor, Tensor] | tuple[Tensor, Tensor, Tensor]:
"""Transform network output for a batch into bbox predictions.
Reference : https://github.com/open-mmlab/mmdeploy/blob/v1.3.1/mmdeploy/codebase/mmdet/models/dense_heads/rtmdet_head.py#L18-L108
Args:
cls_scores (list[Tensor]): Classification scores for all
scale levels, each is a 4D-tensor, has shape
(batch_size, num_priors * num_classes, H, W).
bbox_preds (list[Tensor]): Box energies / deltas for all
scale levels, each is a 4D-tensor, has shape
(batch_size, num_priors * 4, H, W).
batch_img_metas (list[dict], Optional): Batch image meta info.
Defaults to None.
cfg (dict, optional): Test / postprocessing
configuration, if None, test_cfg would be used.
Defaults to None.
rescale (bool): If True, return boxes in original image space.
Defaults to False.
with_nms (bool): If True, do nms before return boxes.
Defaults to True.
Returns:
tuple[Tensor, Tensor]: The first item is an (N, num_box, 5) tensor,
where 5 represent (tl_x, tl_y, br_x, br_y, score), N is batch
size and the score between 0 and 1. The shape of the second
tensor in the tuple is (N, num_box), and each element
represents the class label of the corresponding box.
"""
assert len(cls_scores) == len(bbox_preds) # noqa: S101
device = cls_scores[0].device
cfg = self.test_cfg if cfg is None else cfg
batch_size = bbox_preds[0].shape[0]
featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores]
mlvl_priors = self.prior_generator.grid_priors(featmap_sizes, device=device)
flatten_cls_scores = [
cls_score.permute(0, 2, 3, 1).reshape(batch_size, -1, self.cls_out_channels) for cls_score in cls_scores
]
flatten_bbox_preds = [bbox_pred.permute(0, 2, 3, 1).reshape(batch_size, -1, 4) for bbox_pred in bbox_preds]
flatten_cls_scores = torch.cat(flatten_cls_scores, dim=1).sigmoid()
flatten_bbox_preds = torch.cat(flatten_bbox_preds, dim=1)
priors = torch.cat(mlvl_priors)
tl_x = priors[..., 0] - flatten_bbox_preds[..., 0] # type: ignore[call-overload]
tl_y = priors[..., 1] - flatten_bbox_preds[..., 1] # type: ignore[call-overload]
br_x = priors[..., 0] + flatten_bbox_preds[..., 2] # type: ignore[call-overload]
br_y = priors[..., 1] + flatten_bbox_preds[..., 3] # type: ignore[call-overload]
bboxes = torch.stack([tl_x, tl_y, br_x, br_y], -1)
scores = flatten_cls_scores
if not with_nms:
return bboxes, scores
return multiclass_nms(
bboxes,
scores,
max_output_boxes_per_class=200,
iou_threshold=cfg["nms"]["iou_threshold"], # type: ignore[index]
score_threshold=cfg["score_thr"], # type: ignore[index]
pre_top_k=5000,
keep_top_k=cfg["max_per_img"], # type: ignore[index]
)
def get_targets( # type: ignore[override]
self,
cls_scores: Tensor,
bbox_preds: Tensor,
anchor_list: list[list[Tensor]],
valid_flag_list: list[list[Tensor]],
batch_gt_instances: list[InstanceData],
batch_img_metas: list[dict],
batch_gt_instances_ignore: list[InstanceData] | list[None] | None = None,
unmap_outputs: bool = True,
) -> tuple | None:
"""Compute regression and classification targets for anchors in multiple images.
Args:
cls_scores (Tensor): Classification predictions of images,
a 3D-Tensor with shape [num_imgs, num_priors, num_classes].
bbox_preds (Tensor): Decoded bboxes predictions of one image,
a 3D-Tensor with shape [num_imgs, num_priors, 4] in [tl_x,
tl_y, br_x, br_y] format.
anchor_list (list[list[Tensor]]): Multi level anchors of each
image. The outer list indicates images, and the inner list
corresponds to feature levels of the image. Each element of
the inner list is a tensor of shape (num_anchors, 4).
valid_flag_list (list[list[Tensor]]): Multi level valid flags of
each image. The outer list indicates images, and the inner list
corresponds to feature levels of the image. Each element of
the inner list is a tensor of shape (num_anchors, )
batch_gt_instances (list[InstanceData]): Batch of
gt_instance. It usually includes ``bboxes`` and ``labels``
attributes.
batch_img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
batch_gt_instances_ignore (list[InstanceData], Optional):
Batch of gt_instances_ignore. It includes ``bboxes`` attribute
data that is ignored during training and testing.
Defaults to None.
unmap_outputs (bool): Whether to map outputs back to the original
set of anchors. Defaults to True.
Returns:
tuple: a tuple containing learning targets.
- anchors_list (list[list[Tensor]]): Anchors of each level.
- labels_list (list[Tensor]): Labels of each level.
- label_weights_list (list[Tensor]): Label weights of each
level.
- bbox_targets_list (list[Tensor]): BBox targets of each level.
- assign_metrics_list (list[Tensor]): alignment metrics of each
level.
"""
num_imgs = len(batch_img_metas)
if len(anchor_list) != len(valid_flag_list) != num_imgs:
msg = (
f"The number of anchor_list (={len(anchor_list)}), the number of valid_flag_list "
f"(={len(valid_flag_list)}), and num_imgs (={num_imgs}) should be same."
)
raise ValueError(msg)
# anchor number of multi levels
num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
# concat all level anchors and flags to a single tensor
for i in range(num_imgs):
if len(anchor_list[i]) != len(valid_flag_list[i]):
msg = (
f"The number of anchor_list[i] (={len(anchor_list[i])}) and the number of valid_flag_list[i] "
f"(={len(valid_flag_list[i])}) should be same."
)
raise ValueError(msg)
anchor_list[i] = torch.cat(anchor_list[i])
valid_flag_list[i] = torch.cat(valid_flag_list[i])
# compute targets for each image
if batch_gt_instances_ignore is None:
batch_gt_instances_ignore = [None] * num_imgs
# anchor_list: list(b * [-1, 4])
(
all_anchors,
all_labels,
all_label_weights,
all_bbox_targets,
all_assign_metrics,
sampling_results_list,
) = multi_apply(
self._get_targets_single,
cls_scores.detach(),
bbox_preds.detach(),
anchor_list,
valid_flag_list,
batch_gt_instances,
batch_img_metas,
batch_gt_instances_ignore,
unmap_outputs=unmap_outputs,
)
# no valid anchors
if any(labels is None for labels in all_labels):
return None
# split targets to a list w.r.t. multiple levels
anchors_list = images_to_levels(all_anchors, num_level_anchors)
labels_list = images_to_levels(all_labels, num_level_anchors)
label_weights_list = images_to_levels(all_label_weights, num_level_anchors)
bbox_targets_list = images_to_levels(all_bbox_targets, num_level_anchors)
assign_metrics_list = images_to_levels(all_assign_metrics, num_level_anchors)
return (
anchors_list,
labels_list,
label_weights_list,
bbox_targets_list,
assign_metrics_list,
sampling_results_list,
)
def _get_targets_single( # type: ignore[override]
self,
cls_scores: Tensor,
bbox_preds: Tensor,
flat_anchors: Tensor,
valid_flags: Tensor,
gt_instances: InstanceData,
img_meta: dict,
gt_instances_ignore: InstanceData | None = None,
unmap_outputs: bool = True,
) -> tuple:
"""Compute regression, classification targets for anchors in a single image.
Args:
cls_scores (list[Tensor]): Box scores for each image.
bbox_preds (list[Tensor]): Box energies / deltas for each image.
flat_anchors (Tensor): Multi-level anchors of the image, which are
concatenated into a single tensor of shape (num_anchors ,4)
valid_flags (Tensor): Multi level valid flags of the image,
which are concatenated into a single tensor of
shape (num_anchors,).
gt_instances (InstanceData): Ground truth of instance
annotations. It usually includes ``bboxes`` and ``labels``
attributes.
img_meta (dict): Meta information for current image.
gt_instances_ignore (InstanceData, optional): Instances
to be ignored during training. It includes ``bboxes`` attribute
data that is ignored during training and testing.
Defaults to None.
unmap_outputs (bool): Whether to map outputs back to the original
set of anchors. Defaults to True.
Returns:
tuple: N is the number of total anchors in the image.
- anchors (Tensor): All anchors in the image with shape (N, 4).
- labels (Tensor): Labels of all anchors in the image with shape
(N,).
- label_weights (Tensor): Label weights of all anchor in the
image with shape (N,).
- bbox_targets (Tensor): BBox targets of all anchors in the
image with shape (N, 4).
- norm_alignment_metrics (Tensor): Normalized alignment metrics
of all priors in the image with shape (N,).
- sampling_result (SamplingResult): Sampling result.
"""
inside_flags = anchor_inside_flags(
flat_anchors,
valid_flags,
img_meta["img_shape"][:2],
self.train_cfg["allowed_border"],
)
if not inside_flags.any():
return (None,) * 7
# assign gt and sample anchors
anchors = flat_anchors[inside_flags, :]
pred_instances = InstanceData(
scores=cls_scores[inside_flags, :],
bboxes=bbox_preds[inside_flags, :],
priors=anchors,
)
assign_result = self.assigner.assign(pred_instances, gt_instances, gt_instances_ignore)
sampling_result = self.sampler.sample(assign_result, pred_instances, gt_instances)
num_valid_anchors = anchors.shape[0]
bbox_targets = torch.zeros_like(anchors)
labels = anchors.new_full((num_valid_anchors,), self.num_classes, dtype=torch.long)
label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
assign_metrics = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
pos_inds = sampling_result.pos_inds
neg_inds = sampling_result.neg_inds
if len(pos_inds) > 0:
# point-based
pos_bbox_targets = sampling_result.pos_gt_bboxes
bbox_targets[pos_inds, :] = pos_bbox_targets.to(bbox_targets)
labels[pos_inds] = sampling_result.pos_gt_labels
if self.train_cfg["pos_weight"] <= 0:
label_weights[pos_inds] = 1.0
else:
label_weights[pos_inds] = self.train_cfg["pos_weight"]
if len(neg_inds) > 0:
label_weights[neg_inds] = 1.0
class_assigned_gt_inds = torch.unique(sampling_result.pos_assigned_gt_inds)
for gt_inds in class_assigned_gt_inds:
gt_class_inds = pos_inds[sampling_result.pos_assigned_gt_inds == gt_inds]
assign_metrics[gt_class_inds] = assign_result.max_overlaps[gt_class_inds]
# map up to original set of anchors
if unmap_outputs:
num_total_anchors = flat_anchors.size(0)
anchors = unmap(anchors, num_total_anchors, inside_flags)
labels = unmap(labels, num_total_anchors, inside_flags, fill=self.num_classes)
label_weights = unmap(label_weights, num_total_anchors, inside_flags)
bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
assign_metrics = unmap(assign_metrics, num_total_anchors, inside_flags)
return (anchors, labels, label_weights, bbox_targets, assign_metrics, sampling_result)
def get_anchors(
self,
featmap_sizes: list[tuple[int, int]],
batch_img_metas: list[dict],
device: torch.device | str = "cuda",
) -> tuple[list[list[Tensor]], list[list[Tensor]]]:
"""Get anchors according to feature map sizes.
Args:
featmap_sizes (list[tuple]): Multi-level feature map sizes.
batch_img_metas (list[dict]): Image meta info.
device (torch.device or str): Device for returned tensors.
Defaults to cuda.
Returns:
tuple:
- anchor_list (list[list[Tensor]]): Anchors of each image.
- valid_flag_list (list[list[Tensor]]): Valid flags of each
image.
"""
num_imgs = len(batch_img_metas)
# since feature map sizes of all images are the same, we only compute
# anchors for one time
multi_level_anchors = self.prior_generator.grid_priors(featmap_sizes, device=device, with_stride=True)
anchor_list = [multi_level_anchors for _ in range(num_imgs)]
# for each image, we compute valid flags of multi level anchors
valid_flag_list = []
for img_meta in batch_img_metas:
multi_level_flags = self.prior_generator.valid_flags(featmap_sizes, img_meta["img_shape"], device)
valid_flag_list.append(multi_level_flags)
return anchor_list, valid_flag_list
class RTMDetSepBNHeadModule(RTMDetHead):
"""RTMDetHead with separated BN layers and shared conv layers.
Args:
num_classes (int): Number of categories excluding the background category.
in_channels (int): Number of channels in the input feature map.
share_conv (bool): Whether to share conv layers between stages.
Defaults to True.
use_depthwise (bool): Whether to use depthwise separable convolution in head.
Defaults to False.
normalization (Callable[..., nn.Module]): Normalization layer module.
Defaults to ``partial(nn.BatchNorm2d, momentum=0.03, eps=0.001)``.
activation (Callable[..., nn.Module]): Activation layer module.
Defaults to ``nn.SiLU``.
pred_kernel_size (int): Kernel size of prediction layer. Defaults to 1.
exp_on_reg (bool): Whether using exponential of regression features or not. Defaults to False.
use_sigmoid_cls (bool): Whether to use a sigmoid activation function
for classification prediction. Defaults to True.
"""
def __init__(
self,
num_classes: int,
in_channels: int,
share_conv: bool = True,
use_depthwise: bool = False,
normalization: Callable[..., nn.Module] = partial(nn.BatchNorm2d, momentum=0.03, eps=0.001),
activation: Callable[..., nn.Module] = nn.SiLU,
pred_kernel_size: int = 1,
exp_on_reg: bool = False,
use_sigmoid_cls: bool = True,
**kwargs,
) -> None:
self.share_conv = share_conv
self.exp_on_reg = exp_on_reg
self.use_depthwise = use_depthwise
super().__init__(
num_classes,
in_channels,
normalization=normalization,
activation=activation,
pred_kernel_size=pred_kernel_size,
use_sigmoid_cls=use_sigmoid_cls,
**kwargs,
)
def _init_layers(self) -> None:
"""Initialize layers of the head."""
conv = DepthwiseSeparableConvModule if self.use_depthwise else Conv2dModule
self.cls_convs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
self.rtm_cls = nn.ModuleList()
self.rtm_reg = nn.ModuleList()
if self.with_objectness:
self.rtm_obj = nn.ModuleList()
for _ in range(len(self.prior_generator.strides)):
cls_convs = nn.ModuleList()
reg_convs = nn.ModuleList()
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
cls_convs.append(
conv(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
normalization=build_norm_layer(self.normalization, num_features=self.feat_channels),
activation=build_activation_layer(self.activation),
),
)
reg_convs.append(
conv(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
normalization=build_norm_layer(self.normalization, num_features=self.feat_channels),
activation=build_activation_layer(self.activation),
),
)
self.cls_convs.append(cls_convs)
self.reg_convs.append(reg_convs)
self.rtm_cls.append(
nn.Conv2d(
self.feat_channels,
self.num_base_priors * self.cls_out_channels,
self.pred_kernel_size,
padding=self.pred_kernel_size // 2,
),
)
self.rtm_reg.append(
nn.Conv2d(
self.feat_channels,
self.num_base_priors * 4,
self.pred_kernel_size,
padding=self.pred_kernel_size // 2,
),
)
if self.with_objectness:
self.rtm_obj.append(
nn.Conv2d(self.feat_channels, 1, self.pred_kernel_size, padding=self.pred_kernel_size // 2),
)
if self.share_conv:
for n in range(len(self.prior_generator.strides)):
for i in range(self.stacked_convs):
self.cls_convs[n][i].conv = self.cls_convs[0][i].conv
self.reg_convs[n][i].conv = self.reg_convs[0][i].conv
def init_weights(self) -> None:
"""Initialize weights of the head."""
for m in self.modules():
if isinstance(m, nn.Conv2d):
normal_init(m, mean=0, std=0.01)
if is_norm(m):
constant_init(m, 1)
bias_cls = bias_init_with_prob(0.01)
for rtm_cls, rtm_reg in zip(self.rtm_cls, self.rtm_reg):
normal_init(rtm_cls, std=0.01, bias=bias_cls)
normal_init(rtm_reg, std=0.01)
if self.with_objectness:
for rtm_obj in self.rtm_obj:
normal_init(rtm_obj, std=0.01, bias=bias_cls)
def forward(self, feats: tuple[Tensor, ...]) -> tuple:
"""Forward features from the upstream network.
Args:
feats (tuple[Tensor]): Features from the upstream network, each is a 4D-tensor.
Returns:
tuple: Usually a tuple of classification scores and bbox prediction
- cls_scores (tuple[Tensor]): Classification scores for all scale
levels, each is a 4D-tensor, the channels number is
num_anchors * num_classes.
- bbox_preds (tuple[Tensor]): Box energies / deltas for all scale
levels, each is a 4D-tensor, the channels number is
num_anchors * 4.
"""
cls_scores = []
bbox_preds = []
for idx, (x, stride) in enumerate(zip(feats, self.prior_generator.strides)):
cls_feat = x
reg_feat = x
for cls_layer in self.cls_convs[idx]:
cls_feat = cls_layer(cls_feat)
cls_score = self.rtm_cls[idx](cls_feat)
for reg_layer in self.reg_convs[idx]:
reg_feat = reg_layer(reg_feat)
if self.with_objectness:
objectness = self.rtm_obj[idx](reg_feat)
cls_score = inverse_sigmoid(sigmoid_geometric_mean(cls_score, objectness))
if self.exp_on_reg:
reg_dist = self.rtm_reg[idx](reg_feat).exp() * stride[0]
else:
reg_dist = self.rtm_reg[idx](reg_feat) * stride[0]
cls_scores.append(cls_score)
bbox_preds.append(reg_dist)
return tuple(cls_scores), tuple(bbox_preds)
[docs]
class RTMDetSepBNHead:
"""RTMDetSepBNHead factory for detection."""
RTMDETSEPBNHEAD_CFG: ClassVar[dict[str, Any]] = {
"rtmdet_tiny": {
"in_channels": 96,
"stacked_convs": 2,
"feat_channels": 96,
"with_objectness": False,
"normalization": nn.BatchNorm2d,
"activation": partial(nn.SiLU, inplace=True),
"use_sigmoid_cls": True,
},
}
def __new__(
cls,
model_name: str,
num_classes: int,
anchor_generator: BasePriorGenerator,
bbox_coder: BaseBBoxCoder,
train_cfg: dict,
test_cfg: dict | None = None,
) -> RTMDetSepBNHeadModule:
"""Constructor for RTMDetSepBNHead."""
if model_name not in cls.RTMDETSEPBNHEAD_CFG:
msg = f"model type '{model_name}' is not supported"
raise KeyError(msg)
return RTMDetSepBNHeadModule(
**cls.RTMDETSEPBNHEAD_CFG[model_name],
num_classes=num_classes,
anchor_generator=anchor_generator,
bbox_coder=bbox_coder,
train_cfg=train_cfg, # TODO (sungchul, kirill): remove
test_cfg=test_cfg, # TODO (sungchul, kirill): remove
)