mmyolo.models.dense_heads.rtmdet_ins_head 源代码
# Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import List, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, is_norm
from mmcv.ops import batched_nms
from mmdet.models.utils import filter_scores_and_topk
from mmdet.structures.bbox import get_box_tensor, get_box_wh, scale_boxes
from mmdet.utils import (ConfigType, InstanceList, OptConfigType,
OptInstanceList, OptMultiConfig)
from mmengine import ConfigDict
from mmengine.model import (BaseModule, bias_init_with_prob, constant_init,
normal_init)
from mmengine.structures import InstanceData
from torch import Tensor
from mmyolo.registry import MODELS
from .rtmdet_head import RTMDetHead, RTMDetSepBNHeadModule
class MaskFeatModule(BaseModule):
"""Mask feature head used in RTMDet-Ins. Copy from mmdet.
Args:
in_channels (int): Number of channels in the input feature map.
feat_channels (int): Number of hidden channels of the mask feature
map branch.
stacked_convs (int): Number of convs in mask feature branch.
num_levels (int): The starting feature map level from RPN that
will be used to predict the mask feature map.
num_prototypes (int): Number of output channel of the mask feature
map branch. This is the channel count of the mask
feature map that to be dynamically convolved with the predicted
kernel.
act_cfg (:obj:`ConfigDict` or dict): Config dict for activation layer.
Default: dict(type='ReLU', inplace=True)
norm_cfg (dict): Config dict for normalization layer. Default: None.
"""
def __init__(
self,
in_channels: int,
feat_channels: int = 256,
stacked_convs: int = 4,
num_levels: int = 3,
num_prototypes: int = 8,
act_cfg: ConfigType = dict(type='ReLU', inplace=True),
norm_cfg: ConfigType = dict(type='BN')
) -> None:
super().__init__(init_cfg=None)
self.num_levels = num_levels
self.fusion_conv = nn.Conv2d(num_levels * in_channels, in_channels, 1)
convs = []
for i in range(stacked_convs):
in_c = in_channels if i == 0 else feat_channels
convs.append(
ConvModule(
in_c,
feat_channels,
3,
padding=1,
act_cfg=act_cfg,
norm_cfg=norm_cfg))
self.stacked_convs = nn.Sequential(*convs)
self.projection = nn.Conv2d(
feat_channels, num_prototypes, kernel_size=1)
def forward(self, features: Tuple[Tensor, ...]) -> Tensor:
# multi-level feature fusion
fusion_feats = [features[0]]
size = features[0].shape[-2:]
for i in range(1, self.num_levels):
f = F.interpolate(features[i], size=size, mode='bilinear')
fusion_feats.append(f)
fusion_feats = torch.cat(fusion_feats, dim=1)
fusion_feats = self.fusion_conv(fusion_feats)
# pred mask feats
mask_features = self.stacked_convs(fusion_feats)
mask_features = self.projection(mask_features)
return mask_features
[文档]@MODELS.register_module()
class RTMDetInsSepBNHeadModule(RTMDetSepBNHeadModule):
"""Detection and Instance Segmentation Head of RTMDet.
Args:
num_classes (int): Number of categories excluding the background
category.
num_prototypes (int): Number of mask prototype features extracted
from the mask head. Defaults to 8.
dyconv_channels (int): Channel of the dynamic conv layers.
Defaults to 8.
num_dyconvs (int): Number of the dynamic convolution layers.
Defaults to 3.
use_sigmoid_cls (bool): Use sigmoid for class prediction.
Defaults to True.
"""
def __init__(self,
num_classes: int,
*args,
num_prototypes: int = 8,
dyconv_channels: int = 8,
num_dyconvs: int = 3,
use_sigmoid_cls: bool = True,
**kwargs):
self.num_prototypes = num_prototypes
self.num_dyconvs = num_dyconvs
self.dyconv_channels = dyconv_channels
self.use_sigmoid_cls = use_sigmoid_cls
if self.use_sigmoid_cls:
self.cls_out_channels = num_classes
else:
self.cls_out_channels = num_classes + 1
super().__init__(num_classes=num_classes, *args, **kwargs)
def _init_layers(self):
"""Initialize layers of the head."""
self.cls_convs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
self.kernel_convs = nn.ModuleList()
self.rtm_cls = nn.ModuleList()
self.rtm_reg = nn.ModuleList()
self.rtm_kernel = nn.ModuleList()
self.rtm_obj = nn.ModuleList()
# calculate num dynamic parameters
weight_nums, bias_nums = [], []
for i in range(self.num_dyconvs):
if i == 0:
weight_nums.append(
(self.num_prototypes + 2) * self.dyconv_channels)
bias_nums.append(self.dyconv_channels)
elif i == self.num_dyconvs - 1:
weight_nums.append(self.dyconv_channels)
bias_nums.append(1)
else:
weight_nums.append(self.dyconv_channels * self.dyconv_channels)
bias_nums.append(self.dyconv_channels)
self.weight_nums = weight_nums
self.bias_nums = bias_nums
self.num_gen_params = sum(weight_nums) + sum(bias_nums)
pred_pad_size = self.pred_kernel_size // 2
for n in range(len(self.featmap_strides)):
cls_convs = nn.ModuleList()
reg_convs = nn.ModuleList()
kernel_convs = nn.ModuleList()
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
cls_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg))
reg_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg))
kernel_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg))
self.cls_convs.append(cls_convs)
self.reg_convs.append(cls_convs)
self.kernel_convs.append(kernel_convs)
self.rtm_cls.append(
nn.Conv2d(
self.feat_channels,
self.num_base_priors * self.cls_out_channels,
self.pred_kernel_size,
padding=pred_pad_size))
self.rtm_reg.append(
nn.Conv2d(
self.feat_channels,
self.num_base_priors * 4,
self.pred_kernel_size,
padding=pred_pad_size))
self.rtm_kernel.append(
nn.Conv2d(
self.feat_channels,
self.num_gen_params,
self.pred_kernel_size,
padding=pred_pad_size))
if self.share_conv:
for n in range(len(self.featmap_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
self.mask_head = MaskFeatModule(
in_channels=self.in_channels,
feat_channels=self.feat_channels,
stacked_convs=4,
num_levels=len(self.featmap_strides),
num_prototypes=self.num_prototypes,
act_cfg=self.act_cfg,
norm_cfg=self.norm_cfg)
[文档] 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, rtm_kernel in zip(self.rtm_cls, self.rtm_reg,
self.rtm_kernel):
normal_init(rtm_cls, std=0.01, bias=bias_cls)
normal_init(rtm_reg, std=0.01, bias=1)
[文档] 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.
- kernel_preds (list[Tensor]): Dynamic conv kernels for all scale
levels, each is a 4D-tensor, the channels number is
num_gen_params.
- mask_feat (Tensor): Mask prototype features.
Has shape (batch_size, num_prototypes, H, W).
"""
mask_feat = self.mask_head(feats)
cls_scores = []
bbox_preds = []
kernel_preds = []
for idx, (x, stride) in enumerate(zip(feats, self.featmap_strides)):
cls_feat = x
reg_feat = x
kernel_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 kernel_layer in self.kernel_convs[idx]:
kernel_feat = kernel_layer(kernel_feat)
kernel_pred = self.rtm_kernel[idx](kernel_feat)
for reg_layer in self.reg_convs[idx]:
reg_feat = reg_layer(reg_feat)
reg_dist = self.rtm_reg[idx](reg_feat)
cls_scores.append(cls_score)
bbox_preds.append(reg_dist)
kernel_preds.append(kernel_pred)
return tuple(cls_scores), tuple(bbox_preds), tuple(
kernel_preds), mask_feat
[文档]@MODELS.register_module()
class RTMDetInsSepBNHead(RTMDetHead):
"""RTMDet Instance Segmentation head.
Args:
head_module(ConfigType): Base module used for RTMDetInsSepBNHead
prior_generator: Points generator feature maps in
2D points-based detectors.
bbox_coder (:obj:`ConfigDict` or dict): Config of bbox coder.
loss_cls (:obj:`ConfigDict` or dict): Config of classification loss.
loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss.
loss_mask (:obj:`ConfigDict` or dict): Config of mask loss.
train_cfg (:obj:`ConfigDict` or dict, optional): Training config of
anchor head. Defaults to None.
test_cfg (:obj:`ConfigDict` or dict, optional): Testing config of
anchor head. Defaults to None.
init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or
list[dict], optional): Initialization config dict.
Defaults to None.
"""
def __init__(self,
head_module: ConfigType,
prior_generator: ConfigType = dict(
type='mmdet.MlvlPointGenerator',
offset=0,
strides=[8, 16, 32]),
bbox_coder: ConfigType = dict(type='DistancePointBBoxCoder'),
loss_cls: ConfigType = dict(
type='mmdet.QualityFocalLoss',
use_sigmoid=True,
beta=2.0,
loss_weight=1.0),
loss_bbox: ConfigType = dict(
type='mmdet.GIoULoss', loss_weight=2.0),
loss_mask=dict(
type='mmdet.DiceLoss',
loss_weight=2.0,
eps=5e-6,
reduction='mean'),
train_cfg: OptConfigType = None,
test_cfg: OptConfigType = None,
init_cfg: OptMultiConfig = None):
super().__init__(
head_module=head_module,
prior_generator=prior_generator,
bbox_coder=bbox_coder,
loss_cls=loss_cls,
loss_bbox=loss_bbox,
train_cfg=train_cfg,
test_cfg=test_cfg,
init_cfg=init_cfg)
self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
if isinstance(self.head_module, RTMDetInsSepBNHeadModule):
assert self.use_sigmoid_cls == self.head_module.use_sigmoid_cls
self.loss_mask = MODELS.build(loss_mask)
[文档] def predict_by_feat(self,
cls_scores: List[Tensor],
bbox_preds: List[Tensor],
kernel_preds: List[Tensor],
mask_feats: Tensor,
score_factors: Optional[List[Tensor]] = None,
batch_img_metas: Optional[List[dict]] = None,
cfg: Optional[ConfigDict] = None,
rescale: bool = True,
with_nms: bool = True) -> List[InstanceData]:
"""Transform a batch of output features extracted from the head into
bbox results.
Note: When score_factors is not None, the cls_scores are
usually multiplied by it then obtain the real score used in NMS.
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).
kernel_preds (list[Tensor]): Kernel predictions of dynamic
convs for all scale levels, each is a 4D-tensor, has shape
(batch_size, num_params, H, W).
mask_feats (Tensor): Mask prototype features extracted from the
mask head, has shape (batch_size, num_prototypes, H, W).
score_factors (list[Tensor], optional): Score factor for
all scale level, each is a 4D-tensor, has shape
(batch_size, num_priors * 1, H, W). Defaults to None.
batch_img_metas (list[dict], Optional): Batch image meta info.
Defaults to None.
cfg (ConfigDict, 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:
list[:obj:`InstanceData`]: Object detection and instance
segmentation results of each image after the post process.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
- masks (Tensor): Has a shape (num_instances, h, w).
"""
cfg = self.test_cfg if cfg is None else cfg
cfg = copy.deepcopy(cfg)
multi_label = cfg.multi_label
multi_label &= self.num_classes > 1
cfg.multi_label = multi_label
num_imgs = len(batch_img_metas)
featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores]
# If the shape does not change, use the previous mlvl_priors
if featmap_sizes != self.featmap_sizes:
self.mlvl_priors = self.prior_generator.grid_priors(
featmap_sizes,
dtype=cls_scores[0].dtype,
device=cls_scores[0].device,
with_stride=True)
self.featmap_sizes = featmap_sizes
flatten_priors = torch.cat(self.mlvl_priors)
mlvl_strides = [
flatten_priors.new_full(
(featmap_size.numel() * self.num_base_priors, ), stride) for
featmap_size, stride in zip(featmap_sizes, self.featmap_strides)
]
flatten_stride = torch.cat(mlvl_strides)
# flatten cls_scores, bbox_preds
flatten_cls_scores = [
cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1,
self.num_classes)
for cls_score in cls_scores
]
flatten_bbox_preds = [
bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4)
for bbox_pred in bbox_preds
]
flatten_kernel_preds = [
kernel_pred.permute(0, 2, 3,
1).reshape(num_imgs, -1,
self.head_module.num_gen_params)
for kernel_pred in kernel_preds
]
flatten_cls_scores = torch.cat(flatten_cls_scores, dim=1).sigmoid()
flatten_bbox_preds = torch.cat(flatten_bbox_preds, dim=1)
flatten_decoded_bboxes = self.bbox_coder.decode(
flatten_priors[..., :2].unsqueeze(0), flatten_bbox_preds,
flatten_stride)
flatten_kernel_preds = torch.cat(flatten_kernel_preds, dim=1)
results_list = []
for (bboxes, scores, kernel_pred, mask_feat,
img_meta) in zip(flatten_decoded_bboxes, flatten_cls_scores,
flatten_kernel_preds, mask_feats,
batch_img_metas):
ori_shape = img_meta['ori_shape']
scale_factor = img_meta['scale_factor']
if 'pad_param' in img_meta:
pad_param = img_meta['pad_param']
else:
pad_param = None
score_thr = cfg.get('score_thr', -1)
if scores.shape[0] == 0:
empty_results = InstanceData()
empty_results.bboxes = bboxes
empty_results.scores = scores[:, 0]
empty_results.labels = scores[:, 0].int()
h, w = ori_shape[:2] if rescale else img_meta['img_shape'][:2]
empty_results.masks = torch.zeros(
size=(0, h, w), dtype=torch.bool, device=bboxes.device)
results_list.append(empty_results)
continue
nms_pre = cfg.get('nms_pre', 100000)
if cfg.multi_label is False:
scores, labels = scores.max(1, keepdim=True)
scores, _, keep_idxs, results = filter_scores_and_topk(
scores,
score_thr,
nms_pre,
results=dict(
labels=labels[:, 0],
kernel_pred=kernel_pred,
priors=flatten_priors))
labels = results['labels']
kernel_pred = results['kernel_pred']
priors = results['priors']
else:
out = filter_scores_and_topk(
scores,
score_thr,
nms_pre,
results=dict(
kernel_pred=kernel_pred, priors=flatten_priors))
scores, labels, keep_idxs, filtered_results = out
kernel_pred = filtered_results['kernel_pred']
priors = filtered_results['priors']
results = InstanceData(
scores=scores,
labels=labels,
bboxes=bboxes[keep_idxs],
kernels=kernel_pred,
priors=priors)
if rescale:
if pad_param is not None:
results.bboxes -= results.bboxes.new_tensor([
pad_param[2], pad_param[0], pad_param[2], pad_param[0]
])
results.bboxes /= results.bboxes.new_tensor(
scale_factor).repeat((1, 2))
if cfg.get('yolox_style', False):
# do not need max_per_img
cfg.max_per_img = len(results)
results = self._bbox_mask_post_process(
results=results,
mask_feat=mask_feat,
cfg=cfg,
rescale_bbox=False,
rescale_mask=rescale,
with_nms=with_nms,
pad_param=pad_param,
img_meta=img_meta)
results.bboxes[:, 0::2].clamp_(0, ori_shape[1])
results.bboxes[:, 1::2].clamp_(0, ori_shape[0])
results_list.append(results)
return results_list
def _bbox_mask_post_process(
self,
results: InstanceData,
mask_feat: Tensor,
cfg: ConfigDict,
rescale_bbox: bool = False,
rescale_mask: bool = True,
with_nms: bool = True,
pad_param: Optional[np.ndarray] = None,
img_meta: Optional[dict] = None) -> InstanceData:
"""bbox and mask post-processing method.
The boxes would be rescaled to the original image scale and do
the nms operation. Usually `with_nms` is False is used for aug test.
Args:
results (:obj:`InstaceData`): Detection instance results,
each item has shape (num_bboxes, ).
mask_feat (Tensor): Mask prototype features extracted from the
mask head, has shape (batch_size, num_prototypes, H, W).
cfg (ConfigDict): Test / postprocessing configuration,
if None, test_cfg would be used.
rescale_bbox (bool): If True, return boxes in original image space.
Default to False.
rescale_mask (bool): If True, return masks in original image space.
Default to True.
with_nms (bool): If True, do nms before return boxes.
Default to True.
img_meta (dict, optional): Image meta info. Defaults to None.
Returns:
:obj:`InstanceData`: Detection results of each image
after the post process.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
- masks (Tensor): Has a shape (num_instances, h, w).
"""
if rescale_bbox:
assert img_meta.get('scale_factor') is not None
scale_factor = [1 / s for s in img_meta['scale_factor']]
results.bboxes = scale_boxes(results.bboxes, scale_factor)
if hasattr(results, 'score_factors'):
# TODO： Add sqrt operation in order to be consistent with
# the paper.
score_factors = results.pop('score_factors')
results.scores = results.scores * score_factors
# filter small size bboxes
if cfg.get('min_bbox_size', -1) >= 0:
w, h = get_box_wh(results.bboxes)
valid_mask = (w > cfg.min_bbox_size) & (h > cfg.min_bbox_size)
if not valid_mask.all():
results = results[valid_mask]
# TODO: deal with `with_nms` and `nms_cfg=None` in test_cfg
assert with_nms, 'with_nms must be True for RTMDet-Ins'
if results.bboxes.numel() > 0:
bboxes = get_box_tensor(results.bboxes)
det_bboxes, keep_idxs = batched_nms(bboxes, results.scores,
results.labels, cfg.nms)
results = results[keep_idxs]
# some nms would reweight the score, such as softnms
results.scores = det_bboxes[:, -1]
results = results[:cfg.max_per_img]
# process masks
mask_logits = self._mask_predict_by_feat(mask_feat,
results.kernels,
results.priors)
stride = self.prior_generator.strides[0][0]
mask_logits = F.interpolate(
mask_logits.unsqueeze(0), scale_factor=stride, mode='bilinear')
if rescale_mask:
# TODO: When use mmdet.Resize or mmdet.Pad, will meet bug
# Use img_meta to crop and resize
ori_h, ori_w = img_meta['ori_shape'][:2]
if isinstance(pad_param, np.ndarray):
pad_param = pad_param.astype(np.int32)
crop_y1, crop_y2 = pad_param[
0], mask_logits.shape[-2] - pad_param[1]
crop_x1, crop_x2 = pad_param[
2], mask_logits.shape[-1] - pad_param[3]
mask_logits = mask_logits[..., crop_y1:crop_y2,
crop_x1:crop_x2]
mask_logits = F.interpolate(
mask_logits,
size=[ori_h, ori_w],
mode='bilinear',
align_corners=False)
masks = mask_logits.sigmoid().squeeze(0)
masks = masks > cfg.mask_thr_binary
results.masks = masks
else:
h, w = img_meta['ori_shape'][:2] if rescale_mask else img_meta[
'img_shape'][:2]
results.masks = torch.zeros(
size=(results.bboxes.shape[0], h, w),
dtype=torch.bool,
device=results.bboxes.device)
return results
def _mask_predict_by_feat(self, mask_feat: Tensor, kernels: Tensor,
priors: Tensor) -> Tensor:
"""Generate mask logits from mask features with dynamic convs.
Args:
mask_feat (Tensor): Mask prototype features.
Has shape (num_prototypes, H, W).
kernels (Tensor): Kernel parameters for each instance.
Has shape (num_instance, num_params)
priors (Tensor): Center priors for each instance.
Has shape (num_instance, 4).
Returns:
Tensor: Instance segmentation masks for each instance.
Has shape (num_instance, H, W).
"""
num_inst = kernels.shape[0]
h, w = mask_feat.size()[-2:]
if num_inst < 1:
return torch.empty(
size=(num_inst, h, w),
dtype=mask_feat.dtype,
device=mask_feat.device)
if len(mask_feat.shape) < 4:
mask_feat.unsqueeze(0)
coord = self.prior_generator.single_level_grid_priors(
(h, w), level_idx=0, device=mask_feat.device).reshape(1, -1, 2)
num_inst = priors.shape[0]
points = priors[:, :2].reshape(-1, 1, 2)
strides = priors[:, 2:].reshape(-1, 1, 2)
relative_coord = (points - coord).permute(0, 2, 1) / (
strides[..., 0].reshape(-1, 1, 1) * 8)
relative_coord = relative_coord.reshape(num_inst, 2, h, w)
mask_feat = torch.cat(
[relative_coord,
mask_feat.repeat(num_inst, 1, 1, 1)], dim=1)
weights, biases = self.parse_dynamic_params(kernels)
n_layers = len(weights)
x = mask_feat.reshape(1, -1, h, w)
for i, (weight, bias) in enumerate(zip(weights, biases)):
x = F.conv2d(
x, weight, bias=bias, stride=1, padding=0, groups=num_inst)
if i < n_layers - 1:
x = F.relu(x)
x = x.reshape(num_inst, h, w)
return x
[文档] def parse_dynamic_params(self, flatten_kernels: Tensor) -> tuple:
"""split kernel head prediction to conv weight and bias."""
n_inst = flatten_kernels.size(0)
n_layers = len(self.head_module.weight_nums)
params_splits = list(
torch.split_with_sizes(
flatten_kernels,
self.head_module.weight_nums + self.head_module.bias_nums,
dim=1))
weight_splits = params_splits[:n_layers]
bias_splits = params_splits[n_layers:]
for i in range(n_layers):
if i < n_layers - 1:
weight_splits[i] = weight_splits[i].reshape(
n_inst * self.head_module.dyconv_channels, -1, 1, 1)
bias_splits[i] = bias_splits[i].reshape(
n_inst * self.head_module.dyconv_channels)
else:
weight_splits[i] = weight_splits[i].reshape(n_inst, -1, 1, 1)
bias_splits[i] = bias_splits[i].reshape(n_inst)
return weight_splits, bias_splits
[文档] def loss_by_feat(
self,
cls_scores: List[Tensor],
bbox_preds: List[Tensor],
batch_gt_instances: InstanceList,
batch_img_metas: List[dict],
batch_gt_instances_ignore: OptInstanceList = None) -> dict:
raise NotImplementedError