from logging import warning
from math import ceil, log
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, bias_init_with_prob
from mmcv.ops import CornerPool, batched_nms
from mmcv.runner import BaseModule
from mmdet.core import multi_apply
from ..builder import HEADS, build_loss
from ..utils import gaussian_radius, gen_gaussian_target
from ..utils.gaussian_target import (gather_feat, get_local_maximum,
get_topk_from_heatmap,
transpose_and_gather_feat)
from .base_dense_head import BaseDenseHead
from .dense_test_mixins import BBoxTestMixin
class BiCornerPool(BaseModule):
"""Bidirectional Corner Pooling Module (TopLeft, BottomRight, etc.)
Args:
in_channels (int): Input channels of module.
out_channels (int): Output channels of module.
feat_channels (int): Feature channels of module.
directions (list[str]): Directions of two CornerPools.
norm_cfg (dict): Dictionary to construct and config norm layer.
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None
"""
def __init__(self,
in_channels,
directions,
feat_channels=128,
out_channels=128,
norm_cfg=dict(type='BN', requires_grad=True),
init_cfg=None):
super(BiCornerPool, self).__init__(init_cfg)
self.direction1_conv = ConvModule(
in_channels, feat_channels, 3, padding=1, norm_cfg=norm_cfg)
self.direction2_conv = ConvModule(
in_channels, feat_channels, 3, padding=1, norm_cfg=norm_cfg)
self.aftpool_conv = ConvModule(
feat_channels,
out_channels,
3,
padding=1,
norm_cfg=norm_cfg,
act_cfg=None)
self.conv1 = ConvModule(
in_channels, out_channels, 1, norm_cfg=norm_cfg, act_cfg=None)
self.conv2 = ConvModule(
in_channels, out_channels, 3, padding=1, norm_cfg=norm_cfg)
self.direction1_pool = CornerPool(directions[0])
self.direction2_pool = CornerPool(directions[1])
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
"""Forward features from the upstream network.
Args:
x (tensor): Input feature of BiCornerPool.
Returns:
conv2 (tensor): Output feature of BiCornerPool.
"""
direction1_conv = self.direction1_conv(x)
direction2_conv = self.direction2_conv(x)
direction1_feat = self.direction1_pool(direction1_conv)
direction2_feat = self.direction2_pool(direction2_conv)
aftpool_conv = self.aftpool_conv(direction1_feat + direction2_feat)
conv1 = self.conv1(x)
relu = self.relu(aftpool_conv + conv1)
conv2 = self.conv2(relu)
return conv2
[docs]@HEADS.register_module()
class CornerHead(BaseDenseHead, BBoxTestMixin):
"""Head of CornerNet: Detecting Objects as Paired Keypoints.
Code is modified from the `official github repo
<https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/
kp.py#L73>`_ .
More details can be found in the `paper
<https://arxiv.org/abs/1808.01244>`_ .
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
num_feat_levels (int): Levels of feature from the previous module. 2
for HourglassNet-104 and 1 for HourglassNet-52. Because
HourglassNet-104 outputs the final feature and intermediate
supervision feature and HourglassNet-52 only outputs the final
feature. Default: 2.
corner_emb_channels (int): Channel of embedding vector. Default: 1.
train_cfg (dict | None): Training config. Useless in CornerHead,
but we keep this variable for SingleStageDetector. Default: None.
test_cfg (dict | None): Testing config of CornerHead. Default: None.
loss_heatmap (dict | None): Config of corner heatmap loss. Default:
GaussianFocalLoss.
loss_embedding (dict | None): Config of corner embedding loss. Default:
AssociativeEmbeddingLoss.
loss_offset (dict | None): Config of corner offset loss. Default:
SmoothL1Loss.
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None
"""
def __init__(self,
num_classes,
in_channels,
num_feat_levels=2,
corner_emb_channels=1,
train_cfg=None,
test_cfg=None,
loss_heatmap=dict(
type='GaussianFocalLoss',
alpha=2.0,
gamma=4.0,
loss_weight=1),
loss_embedding=dict(
type='AssociativeEmbeddingLoss',
pull_weight=0.25,
push_weight=0.25),
loss_offset=dict(
type='SmoothL1Loss', beta=1.0, loss_weight=1),
init_cfg=None):
assert init_cfg is None, 'To prevent abnormal initialization ' \
'behavior, init_cfg is not allowed to be set'
super(CornerHead, self).__init__(init_cfg)
self.num_classes = num_classes
self.in_channels = in_channels
self.corner_emb_channels = corner_emb_channels
self.with_corner_emb = self.corner_emb_channels > 0
self.corner_offset_channels = 2
self.num_feat_levels = num_feat_levels
self.loss_heatmap = build_loss(
loss_heatmap) if loss_heatmap is not None else None
self.loss_embedding = build_loss(
loss_embedding) if loss_embedding is not None else None
self.loss_offset = build_loss(
loss_offset) if loss_offset is not None else None
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self._init_layers()
def _make_layers(self, out_channels, in_channels=256, feat_channels=256):
"""Initialize conv sequential for CornerHead."""
return nn.Sequential(
ConvModule(in_channels, feat_channels, 3, padding=1),
ConvModule(
feat_channels, out_channels, 1, norm_cfg=None, act_cfg=None))
def _init_corner_kpt_layers(self):
"""Initialize corner keypoint layers.
Including corner heatmap branch and corner offset branch. Each branch
has two parts: prefix `tl_` for top-left and `br_` for bottom-right.
"""
self.tl_pool, self.br_pool = nn.ModuleList(), nn.ModuleList()
self.tl_heat, self.br_heat = nn.ModuleList(), nn.ModuleList()
self.tl_off, self.br_off = nn.ModuleList(), nn.ModuleList()
for _ in range(self.num_feat_levels):
self.tl_pool.append(
BiCornerPool(
self.in_channels, ['top', 'left'],
out_channels=self.in_channels))
self.br_pool.append(
BiCornerPool(
self.in_channels, ['bottom', 'right'],
out_channels=self.in_channels))
self.tl_heat.append(
self._make_layers(
out_channels=self.num_classes,
in_channels=self.in_channels))
self.br_heat.append(
self._make_layers(
out_channels=self.num_classes,
in_channels=self.in_channels))
self.tl_off.append(
self._make_layers(
out_channels=self.corner_offset_channels,
in_channels=self.in_channels))
self.br_off.append(
self._make_layers(
out_channels=self.corner_offset_channels,
in_channels=self.in_channels))
def _init_corner_emb_layers(self):
"""Initialize corner embedding layers.
Only include corner embedding branch with two parts: prefix `tl_` for
top-left and `br_` for bottom-right.
"""
self.tl_emb, self.br_emb = nn.ModuleList(), nn.ModuleList()
for _ in range(self.num_feat_levels):
self.tl_emb.append(
self._make_layers(
out_channels=self.corner_emb_channels,
in_channels=self.in_channels))
self.br_emb.append(
self._make_layers(
out_channels=self.corner_emb_channels,
in_channels=self.in_channels))
def _init_layers(self):
"""Initialize layers for CornerHead.
Including two parts: corner keypoint layers and corner embedding layers
"""
self._init_corner_kpt_layers()
if self.with_corner_emb:
self._init_corner_emb_layers()
[docs] def init_weights(self):
super(CornerHead, self).init_weights()
bias_init = bias_init_with_prob(0.1)
for i in range(self.num_feat_levels):
# The initialization of parameters are different between
# nn.Conv2d and ConvModule. Our experiments show that
# using the original initialization of nn.Conv2d increases
# the final mAP by about 0.2%
self.tl_heat[i][-1].conv.reset_parameters()
self.tl_heat[i][-1].conv.bias.data.fill_(bias_init)
self.br_heat[i][-1].conv.reset_parameters()
self.br_heat[i][-1].conv.bias.data.fill_(bias_init)
self.tl_off[i][-1].conv.reset_parameters()
self.br_off[i][-1].conv.reset_parameters()
if self.with_corner_emb:
self.tl_emb[i][-1].conv.reset_parameters()
self.br_emb[i][-1].conv.reset_parameters()
[docs] def forward(self, feats):
"""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 corner heatmaps, offset heatmaps and
embedding heatmaps.
- tl_heats (list[Tensor]): Top-left corner heatmaps for all
levels, each is a 4D-tensor, the channels number is
num_classes.
- br_heats (list[Tensor]): Bottom-right corner heatmaps for all
levels, each is a 4D-tensor, the channels number is
num_classes.
- tl_embs (list[Tensor] | list[None]): Top-left embedding
heatmaps for all levels, each is a 4D-tensor or None.
If not None, the channels number is corner_emb_channels.
- br_embs (list[Tensor] | list[None]): Bottom-right embedding
heatmaps for all levels, each is a 4D-tensor or None.
If not None, the channels number is corner_emb_channels.
- tl_offs (list[Tensor]): Top-left offset heatmaps for all
levels, each is a 4D-tensor. The channels number is
corner_offset_channels.
- br_offs (list[Tensor]): Bottom-right offset heatmaps for all
levels, each is a 4D-tensor. The channels number is
corner_offset_channels.
"""
lvl_ind = list(range(self.num_feat_levels))
return multi_apply(self.forward_single, feats, lvl_ind)
[docs] def forward_single(self, x, lvl_ind, return_pool=False):
"""Forward feature of a single level.
Args:
x (Tensor): Feature of a single level.
lvl_ind (int): Level index of current feature.
return_pool (bool): Return corner pool feature or not.
Returns:
tuple[Tensor]: A tuple of CornerHead's output for current feature
level. Containing the following Tensors:
- tl_heat (Tensor): Predicted top-left corner heatmap.
- br_heat (Tensor): Predicted bottom-right corner heatmap.
- tl_emb (Tensor | None): Predicted top-left embedding heatmap.
None for `self.with_corner_emb == False`.
- br_emb (Tensor | None): Predicted bottom-right embedding
heatmap. None for `self.with_corner_emb == False`.
- tl_off (Tensor): Predicted top-left offset heatmap.
- br_off (Tensor): Predicted bottom-right offset heatmap.
- tl_pool (Tensor): Top-left corner pool feature. Not must
have.
- br_pool (Tensor): Bottom-right corner pool feature. Not must
have.
"""
tl_pool = self.tl_pool[lvl_ind](x)
tl_heat = self.tl_heat[lvl_ind](tl_pool)
br_pool = self.br_pool[lvl_ind](x)
br_heat = self.br_heat[lvl_ind](br_pool)
tl_emb, br_emb = None, None
if self.with_corner_emb:
tl_emb = self.tl_emb[lvl_ind](tl_pool)
br_emb = self.br_emb[lvl_ind](br_pool)
tl_off = self.tl_off[lvl_ind](tl_pool)
br_off = self.br_off[lvl_ind](br_pool)
result_list = [tl_heat, br_heat, tl_emb, br_emb, tl_off, br_off]
if return_pool:
result_list.append(tl_pool)
result_list.append(br_pool)
return result_list
[docs] def get_targets(self,
gt_bboxes,
gt_labels,
feat_shape,
img_shape,
with_corner_emb=False,
with_guiding_shift=False,
with_centripetal_shift=False):
"""Generate corner targets.
Including corner heatmap, corner offset.
Optional: corner embedding, corner guiding shift, centripetal shift.
For CornerNet, we generate corner heatmap, corner offset and corner
embedding from this function.
For CentripetalNet, we generate corner heatmap, corner offset, guiding
shift and centripetal shift from this function.
Args:
gt_bboxes (list[Tensor]): Ground truth bboxes of each image, each
has shape (num_gt, 4).
gt_labels (list[Tensor]): Ground truth labels of each box, each has
shape (num_gt,).
feat_shape (list[int]): Shape of output feature,
[batch, channel, height, width].
img_shape (list[int]): Shape of input image,
[height, width, channel].
with_corner_emb (bool): Generate corner embedding target or not.
Default: False.
with_guiding_shift (bool): Generate guiding shift target or not.
Default: False.
with_centripetal_shift (bool): Generate centripetal shift target or
not. Default: False.
Returns:
dict: Ground truth of corner heatmap, corner offset, corner
embedding, guiding shift and centripetal shift. Containing the
following keys:
- topleft_heatmap (Tensor): Ground truth top-left corner
heatmap.
- bottomright_heatmap (Tensor): Ground truth bottom-right
corner heatmap.
- topleft_offset (Tensor): Ground truth top-left corner offset.
- bottomright_offset (Tensor): Ground truth bottom-right corner
offset.
- corner_embedding (list[list[list[int]]]): Ground truth corner
embedding. Not must have.
- topleft_guiding_shift (Tensor): Ground truth top-left corner
guiding shift. Not must have.
- bottomright_guiding_shift (Tensor): Ground truth bottom-right
corner guiding shift. Not must have.
- topleft_centripetal_shift (Tensor): Ground truth top-left
corner centripetal shift. Not must have.
- bottomright_centripetal_shift (Tensor): Ground truth
bottom-right corner centripetal shift. Not must have.
"""
batch_size, _, height, width = feat_shape
img_h, img_w = img_shape[:2]
width_ratio = float(width / img_w)
height_ratio = float(height / img_h)
gt_tl_heatmap = gt_bboxes[-1].new_zeros(
[batch_size, self.num_classes, height, width])
gt_br_heatmap = gt_bboxes[-1].new_zeros(
[batch_size, self.num_classes, height, width])
gt_tl_offset = gt_bboxes[-1].new_zeros([batch_size, 2, height, width])
gt_br_offset = gt_bboxes[-1].new_zeros([batch_size, 2, height, width])
if with_corner_emb:
match = []
# Guiding shift is a kind of offset, from center to corner
if with_guiding_shift:
gt_tl_guiding_shift = gt_bboxes[-1].new_zeros(
[batch_size, 2, height, width])
gt_br_guiding_shift = gt_bboxes[-1].new_zeros(
[batch_size, 2, height, width])
# Centripetal shift is also a kind of offset, from center to corner
# and normalized by log.
if with_centripetal_shift:
gt_tl_centripetal_shift = gt_bboxes[-1].new_zeros(
[batch_size, 2, height, width])
gt_br_centripetal_shift = gt_bboxes[-1].new_zeros(
[batch_size, 2, height, width])
for batch_id in range(batch_size):
# Ground truth of corner embedding per image is a list of coord set
corner_match = []
for box_id in range(len(gt_labels[batch_id])):
left, top, right, bottom = gt_bboxes[batch_id][box_id]
center_x = (left + right) / 2.0
center_y = (top + bottom) / 2.0
label = gt_labels[batch_id][box_id]
# Use coords in the feature level to generate ground truth
scale_left = left * width_ratio
scale_right = right * width_ratio
scale_top = top * height_ratio
scale_bottom = bottom * height_ratio
scale_center_x = center_x * width_ratio
scale_center_y = center_y * height_ratio
# Int coords on feature map/ground truth tensor
left_idx = int(min(scale_left, width - 1))
right_idx = int(min(scale_right, width - 1))
top_idx = int(min(scale_top, height - 1))
bottom_idx = int(min(scale_bottom, height - 1))
# Generate gaussian heatmap
scale_box_width = ceil(scale_right - scale_left)
scale_box_height = ceil(scale_bottom - scale_top)
radius = gaussian_radius((scale_box_height, scale_box_width),
min_overlap=0.3)
radius = max(0, int(radius))
gt_tl_heatmap[batch_id, label] = gen_gaussian_target(
gt_tl_heatmap[batch_id, label], [left_idx, top_idx],
radius)
gt_br_heatmap[batch_id, label] = gen_gaussian_target(
gt_br_heatmap[batch_id, label], [right_idx, bottom_idx],
radius)
# Generate corner offset
left_offset = scale_left - left_idx
top_offset = scale_top - top_idx
right_offset = scale_right - right_idx
bottom_offset = scale_bottom - bottom_idx
gt_tl_offset[batch_id, 0, top_idx, left_idx] = left_offset
gt_tl_offset[batch_id, 1, top_idx, left_idx] = top_offset
gt_br_offset[batch_id, 0, bottom_idx, right_idx] = right_offset
gt_br_offset[batch_id, 1, bottom_idx,
right_idx] = bottom_offset
# Generate corner embedding
if with_corner_emb:
corner_match.append([[top_idx, left_idx],
[bottom_idx, right_idx]])
# Generate guiding shift
if with_guiding_shift:
gt_tl_guiding_shift[batch_id, 0, top_idx,
left_idx] = scale_center_x - left_idx
gt_tl_guiding_shift[batch_id, 1, top_idx,
left_idx] = scale_center_y - top_idx
gt_br_guiding_shift[batch_id, 0, bottom_idx,
right_idx] = right_idx - scale_center_x
gt_br_guiding_shift[
batch_id, 1, bottom_idx,
right_idx] = bottom_idx - scale_center_y
# Generate centripetal shift
if with_centripetal_shift:
gt_tl_centripetal_shift[batch_id, 0, top_idx,
left_idx] = log(scale_center_x -
scale_left)
gt_tl_centripetal_shift[batch_id, 1, top_idx,
left_idx] = log(scale_center_y -
scale_top)
gt_br_centripetal_shift[batch_id, 0, bottom_idx,
right_idx] = log(scale_right -
scale_center_x)
gt_br_centripetal_shift[batch_id, 1, bottom_idx,
right_idx] = log(scale_bottom -
scale_center_y)
if with_corner_emb:
match.append(corner_match)
target_result = dict(
topleft_heatmap=gt_tl_heatmap,
topleft_offset=gt_tl_offset,
bottomright_heatmap=gt_br_heatmap,
bottomright_offset=gt_br_offset)
if with_corner_emb:
target_result.update(corner_embedding=match)
if with_guiding_shift:
target_result.update(
topleft_guiding_shift=gt_tl_guiding_shift,
bottomright_guiding_shift=gt_br_guiding_shift)
if with_centripetal_shift:
target_result.update(
topleft_centripetal_shift=gt_tl_centripetal_shift,
bottomright_centripetal_shift=gt_br_centripetal_shift)
return target_result
[docs] def loss(self,
tl_heats,
br_heats,
tl_embs,
br_embs,
tl_offs,
br_offs,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""Compute losses of the head.
Args:
tl_heats (list[Tensor]): Top-left corner heatmaps for each level
with shape (N, num_classes, H, W).
br_heats (list[Tensor]): Bottom-right corner heatmaps for each
level with shape (N, num_classes, H, W).
tl_embs (list[Tensor]): Top-left corner embeddings for each level
with shape (N, corner_emb_channels, H, W).
br_embs (list[Tensor]): Bottom-right corner embeddings for each
level with shape (N, corner_emb_channels, H, W).
tl_offs (list[Tensor]): Top-left corner offsets for each level
with shape (N, corner_offset_channels, H, W).
br_offs (list[Tensor]): Bottom-right corner offsets for each level
with shape (N, corner_offset_channels, H, W).
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [left, top, right, bottom] format.
gt_labels (list[Tensor]): Class indices corresponding to each box.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
boxes can be ignored when computing the loss.
Returns:
dict[str, Tensor]: A dictionary of loss components. Containing the
following losses:
- det_loss (list[Tensor]): Corner keypoint losses of all
feature levels.
- pull_loss (list[Tensor]): Part one of AssociativeEmbedding
losses of all feature levels.
- push_loss (list[Tensor]): Part two of AssociativeEmbedding
losses of all feature levels.
- off_loss (list[Tensor]): Corner offset losses of all feature
levels.
"""
targets = self.get_targets(
gt_bboxes,
gt_labels,
tl_heats[-1].shape,
img_metas[0]['pad_shape'],
with_corner_emb=self.with_corner_emb)
mlvl_targets = [targets for _ in range(self.num_feat_levels)]
det_losses, pull_losses, push_losses, off_losses = multi_apply(
self.loss_single, tl_heats, br_heats, tl_embs, br_embs, tl_offs,
br_offs, mlvl_targets)
loss_dict = dict(det_loss=det_losses, off_loss=off_losses)
if self.with_corner_emb:
loss_dict.update(pull_loss=pull_losses, push_loss=push_losses)
return loss_dict
[docs] def loss_single(self, tl_hmp, br_hmp, tl_emb, br_emb, tl_off, br_off,
targets):
"""Compute losses for single level.
Args:
tl_hmp (Tensor): Top-left corner heatmap for current level with
shape (N, num_classes, H, W).
br_hmp (Tensor): Bottom-right corner heatmap for current level with
shape (N, num_classes, H, W).
tl_emb (Tensor): Top-left corner embedding for current level with
shape (N, corner_emb_channels, H, W).
br_emb (Tensor): Bottom-right corner embedding for current level
with shape (N, corner_emb_channels, H, W).
tl_off (Tensor): Top-left corner offset for current level with
shape (N, corner_offset_channels, H, W).
br_off (Tensor): Bottom-right corner offset for current level with
shape (N, corner_offset_channels, H, W).
targets (dict): Corner target generated by `get_targets`.
Returns:
tuple[torch.Tensor]: Losses of the head's differnet branches
containing the following losses:
- det_loss (Tensor): Corner keypoint loss.
- pull_loss (Tensor): Part one of AssociativeEmbedding loss.
- push_loss (Tensor): Part two of AssociativeEmbedding loss.
- off_loss (Tensor): Corner offset loss.
"""
gt_tl_hmp = targets['topleft_heatmap']
gt_br_hmp = targets['bottomright_heatmap']
gt_tl_off = targets['topleft_offset']
gt_br_off = targets['bottomright_offset']
gt_embedding = targets['corner_embedding']
# Detection loss
tl_det_loss = self.loss_heatmap(
tl_hmp.sigmoid(),
gt_tl_hmp,
avg_factor=max(1,
gt_tl_hmp.eq(1).sum()))
br_det_loss = self.loss_heatmap(
br_hmp.sigmoid(),
gt_br_hmp,
avg_factor=max(1,
gt_br_hmp.eq(1).sum()))
det_loss = (tl_det_loss + br_det_loss) / 2.0
# AssociativeEmbedding loss
if self.with_corner_emb and self.loss_embedding is not None:
pull_loss, push_loss = self.loss_embedding(tl_emb, br_emb,
gt_embedding)
else:
pull_loss, push_loss = None, None
# Offset loss
# We only compute the offset loss at the real corner position.
# The value of real corner would be 1 in heatmap ground truth.
# The mask is computed in class agnostic mode and its shape is
# batch * 1 * width * height.
tl_off_mask = gt_tl_hmp.eq(1).sum(1).gt(0).unsqueeze(1).type_as(
gt_tl_hmp)
br_off_mask = gt_br_hmp.eq(1).sum(1).gt(0).unsqueeze(1).type_as(
gt_br_hmp)
tl_off_loss = self.loss_offset(
tl_off,
gt_tl_off,
tl_off_mask,
avg_factor=max(1, tl_off_mask.sum()))
br_off_loss = self.loss_offset(
br_off,
gt_br_off,
br_off_mask,
avg_factor=max(1, br_off_mask.sum()))
off_loss = (tl_off_loss + br_off_loss) / 2.0
return det_loss, pull_loss, push_loss, off_loss
[docs] def get_bboxes(self,
tl_heats,
br_heats,
tl_embs,
br_embs,
tl_offs,
br_offs,
img_metas,
rescale=False,
with_nms=True):
"""Transform network output for a batch into bbox predictions.
Args:
tl_heats (list[Tensor]): Top-left corner heatmaps for each level
with shape (N, num_classes, H, W).
br_heats (list[Tensor]): Bottom-right corner heatmaps for each
level with shape (N, num_classes, H, W).
tl_embs (list[Tensor]): Top-left corner embeddings for each level
with shape (N, corner_emb_channels, H, W).
br_embs (list[Tensor]): Bottom-right corner embeddings for each
level with shape (N, corner_emb_channels, H, W).
tl_offs (list[Tensor]): Top-left corner offsets for each level
with shape (N, corner_offset_channels, H, W).
br_offs (list[Tensor]): Bottom-right corner offsets for each level
with shape (N, corner_offset_channels, H, W).
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
rescale (bool): If True, return boxes in original image space.
Default: False.
with_nms (bool): If True, do nms before return boxes.
Default: True.
"""
assert tl_heats[-1].shape[0] == br_heats[-1].shape[0] == len(img_metas)
result_list = []
for img_id in range(len(img_metas)):
result_list.append(
self._get_bboxes_single(
tl_heats[-1][img_id:img_id + 1, :],
br_heats[-1][img_id:img_id + 1, :],
tl_offs[-1][img_id:img_id + 1, :],
br_offs[-1][img_id:img_id + 1, :],
img_metas[img_id],
tl_emb=tl_embs[-1][img_id:img_id + 1, :],
br_emb=br_embs[-1][img_id:img_id + 1, :],
rescale=rescale,
with_nms=with_nms))
if torch.onnx.is_in_onnx_export():
assert len(
img_metas
) == 1, 'Only support one input image while in exporting to ONNX'
detections, labels = result_list[0]
# batch_size 1 here, [1, num_det, 5], [1, num_det]
return detections.unsqueeze(0), labels.unsqueeze(0)
return result_list
def _get_bboxes_single(self,
tl_heat,
br_heat,
tl_off,
br_off,
img_meta,
tl_emb=None,
br_emb=None,
tl_centripetal_shift=None,
br_centripetal_shift=None,
rescale=False,
with_nms=True):
"""Transform outputs for a single batch item into bbox predictions.
Args:
tl_heat (Tensor): Top-left corner heatmap for current level with
shape (N, num_classes, H, W).
br_heat (Tensor): Bottom-right corner heatmap for current level
with shape (N, num_classes, H, W).
tl_off (Tensor): Top-left corner offset for current level with
shape (N, corner_offset_channels, H, W).
br_off (Tensor): Bottom-right corner offset for current level with
shape (N, corner_offset_channels, H, W).
img_meta (dict): Meta information of current image, e.g.,
image size, scaling factor, etc.
tl_emb (Tensor): Top-left corner embedding for current level with
shape (N, corner_emb_channels, H, W).
br_emb (Tensor): Bottom-right corner embedding for current level
with shape (N, corner_emb_channels, H, W).
tl_centripetal_shift: Top-left corner's centripetal shift for
current level with shape (N, 2, H, W).
br_centripetal_shift: Bottom-right corner's centripetal shift for
current level with shape (N, 2, H, W).
rescale (bool): If True, return boxes in original image space.
Default: False.
with_nms (bool): If True, do nms before return boxes.
Default: True.
"""
if isinstance(img_meta, (list, tuple)):
img_meta = img_meta[0]
batch_bboxes, batch_scores, batch_clses = self.decode_heatmap(
tl_heat=tl_heat.sigmoid(),
br_heat=br_heat.sigmoid(),
tl_off=tl_off,
br_off=br_off,
tl_emb=tl_emb,
br_emb=br_emb,
tl_centripetal_shift=tl_centripetal_shift,
br_centripetal_shift=br_centripetal_shift,
img_meta=img_meta,
k=self.test_cfg.corner_topk,
kernel=self.test_cfg.local_maximum_kernel,
distance_threshold=self.test_cfg.distance_threshold)
if rescale:
batch_bboxes /= batch_bboxes.new_tensor(img_meta['scale_factor'])
bboxes = batch_bboxes.view([-1, 4])
scores = batch_scores.view([-1, 1])
clses = batch_clses.view([-1, 1])
# use `sort` instead of `argsort` here, since currently exporting
# `argsort` to ONNX opset version 11 is not supported
scores, idx = scores.sort(dim=0, descending=True)
bboxes = bboxes[idx].view([-1, 4])
scores = scores.view(-1)
clses = clses[idx].view(-1)
detections = torch.cat([bboxes, scores.unsqueeze(-1)], -1)
keepinds = (detections[:, -1] > -0.1)
detections = detections[keepinds]
labels = clses[keepinds]
if with_nms:
detections, labels = self._bboxes_nms(detections, labels,
self.test_cfg)
return detections, labels
def _bboxes_nms(self, bboxes, labels, cfg):
if labels.numel() == 0:
return bboxes, labels
if 'nms_cfg' in cfg:
warning.warn('nms_cfg in test_cfg will be deprecated. '
'Please rename it as nms')
if 'nms' not in cfg:
cfg.nms = cfg.nms_cfg
out_bboxes, keep = batched_nms(bboxes[:, :4], bboxes[:, -1], labels,
cfg.nms)
out_labels = labels[keep]
if len(out_bboxes) > 0:
# use `sort` to replace with `argsort` here
_, idx = torch.sort(out_bboxes[:, -1], descending=True)
max_per_img = out_bboxes.new_tensor(cfg.max_per_img).to(torch.long)
nms_after = max_per_img
if torch.onnx.is_in_onnx_export():
# Always keep topk op for dynamic input in onnx
from mmdet.core.export import get_k_for_topk
nms_after = get_k_for_topk(max_per_img, out_bboxes.shape[0])
idx = idx[:nms_after]
out_bboxes = out_bboxes[idx]
out_labels = out_labels[idx]
return out_bboxes, out_labels
[docs] def decode_heatmap(self,
tl_heat,
br_heat,
tl_off,
br_off,
tl_emb=None,
br_emb=None,
tl_centripetal_shift=None,
br_centripetal_shift=None,
img_meta=None,
k=100,
kernel=3,
distance_threshold=0.5,
num_dets=1000):
"""Transform outputs for a single batch item into raw bbox predictions.
Args:
tl_heat (Tensor): Top-left corner heatmap for current level with
shape (N, num_classes, H, W).
br_heat (Tensor): Bottom-right corner heatmap for current level
with shape (N, num_classes, H, W).
tl_off (Tensor): Top-left corner offset for current level with
shape (N, corner_offset_channels, H, W).
br_off (Tensor): Bottom-right corner offset for current level with
shape (N, corner_offset_channels, H, W).
tl_emb (Tensor | None): Top-left corner embedding for current
level with shape (N, corner_emb_channels, H, W).
br_emb (Tensor | None): Bottom-right corner embedding for current
level with shape (N, corner_emb_channels, H, W).
tl_centripetal_shift (Tensor | None): Top-left centripetal shift
for current level with shape (N, 2, H, W).
br_centripetal_shift (Tensor | None): Bottom-right centripetal
shift for current level with shape (N, 2, H, W).
img_meta (dict): Meta information of current image, e.g.,
image size, scaling factor, etc.
k (int): Get top k corner keypoints from heatmap.
kernel (int): Max pooling kernel for extract local maximum pixels.
distance_threshold (float): Distance threshold. Top-left and
bottom-right corner keypoints with feature distance less than
the threshold will be regarded as keypoints from same object.
num_dets (int): Num of raw boxes before doing nms.
Returns:
tuple[torch.Tensor]: Decoded output of CornerHead, containing the
following Tensors:
- bboxes (Tensor): Coords of each box.
- scores (Tensor): Scores of each box.
- clses (Tensor): Categories of each box.
"""
with_embedding = tl_emb is not None and br_emb is not None
with_centripetal_shift = (
tl_centripetal_shift is not None
and br_centripetal_shift is not None)
assert with_embedding + with_centripetal_shift == 1
batch, _, height, width = tl_heat.size()
if torch.onnx.is_in_onnx_export():
inp_h, inp_w = img_meta['pad_shape_for_onnx'][:2]
else:
inp_h, inp_w, _ = img_meta['pad_shape']
# perform nms on heatmaps
tl_heat = get_local_maximum(tl_heat, kernel=kernel)
br_heat = get_local_maximum(br_heat, kernel=kernel)
tl_scores, tl_inds, tl_clses, tl_ys, tl_xs = get_topk_from_heatmap(
tl_heat, k=k)
br_scores, br_inds, br_clses, br_ys, br_xs = get_topk_from_heatmap(
br_heat, k=k)
# We use repeat instead of expand here because expand is a
# shallow-copy function. Thus it could cause unexpected testing result
# sometimes. Using expand will decrease about 10% mAP during testing
# compared to repeat.
tl_ys = tl_ys.view(batch, k, 1).repeat(1, 1, k)
tl_xs = tl_xs.view(batch, k, 1).repeat(1, 1, k)
br_ys = br_ys.view(batch, 1, k).repeat(1, k, 1)
br_xs = br_xs.view(batch, 1, k).repeat(1, k, 1)
tl_off = transpose_and_gather_feat(tl_off, tl_inds)
tl_off = tl_off.view(batch, k, 1, 2)
br_off = transpose_and_gather_feat(br_off, br_inds)
br_off = br_off.view(batch, 1, k, 2)
tl_xs = tl_xs + tl_off[..., 0]
tl_ys = tl_ys + tl_off[..., 1]
br_xs = br_xs + br_off[..., 0]
br_ys = br_ys + br_off[..., 1]
if with_centripetal_shift:
tl_centripetal_shift = transpose_and_gather_feat(
tl_centripetal_shift, tl_inds).view(batch, k, 1, 2).exp()
br_centripetal_shift = transpose_and_gather_feat(
br_centripetal_shift, br_inds).view(batch, 1, k, 2).exp()
tl_ctxs = tl_xs + tl_centripetal_shift[..., 0]
tl_ctys = tl_ys + tl_centripetal_shift[..., 1]
br_ctxs = br_xs - br_centripetal_shift[..., 0]
br_ctys = br_ys - br_centripetal_shift[..., 1]
# all possible boxes based on top k corners (ignoring class)
tl_xs *= (inp_w / width)
tl_ys *= (inp_h / height)
br_xs *= (inp_w / width)
br_ys *= (inp_h / height)
if with_centripetal_shift:
tl_ctxs *= (inp_w / width)
tl_ctys *= (inp_h / height)
br_ctxs *= (inp_w / width)
br_ctys *= (inp_h / height)
x_off, y_off = 0, 0 # no crop
if not torch.onnx.is_in_onnx_export():
# since `RandomCenterCropPad` is done on CPU with numpy and it's
# not dynamic traceable when exporting to ONNX, thus 'border'
# does not appears as key in 'img_meta'. As a tmp solution,
# we move this 'border' handle part to the postprocess after
# finished exporting to ONNX, which is handle in
# `mmdet/core/export/model_wrappers.py`. Though difference between
# pytorch and exported onnx model, it might be ignored since
# comparable performance is achieved between them (e.g. 40.4 vs
# 40.6 on COCO val2017, for CornerNet without test-time flip)
if 'border' in img_meta:
x_off = img_meta['border'][2]
y_off = img_meta['border'][0]
tl_xs -= x_off
tl_ys -= y_off
br_xs -= x_off
br_ys -= y_off
zeros = tl_xs.new_zeros(*tl_xs.size())
tl_xs = torch.where(tl_xs > 0.0, tl_xs, zeros)
tl_ys = torch.where(tl_ys > 0.0, tl_ys, zeros)
br_xs = torch.where(br_xs > 0.0, br_xs, zeros)
br_ys = torch.where(br_ys > 0.0, br_ys, zeros)
bboxes = torch.stack((tl_xs, tl_ys, br_xs, br_ys), dim=3)
area_bboxes = ((br_xs - tl_xs) * (br_ys - tl_ys)).abs()
if with_centripetal_shift:
tl_ctxs -= x_off
tl_ctys -= y_off
br_ctxs -= x_off
br_ctys -= y_off
tl_ctxs *= tl_ctxs.gt(0.0).type_as(tl_ctxs)
tl_ctys *= tl_ctys.gt(0.0).type_as(tl_ctys)
br_ctxs *= br_ctxs.gt(0.0).type_as(br_ctxs)
br_ctys *= br_ctys.gt(0.0).type_as(br_ctys)
ct_bboxes = torch.stack((tl_ctxs, tl_ctys, br_ctxs, br_ctys),
dim=3)
area_ct_bboxes = ((br_ctxs - tl_ctxs) * (br_ctys - tl_ctys)).abs()
rcentral = torch.zeros_like(ct_bboxes)
# magic nums from paper section 4.1
mu = torch.ones_like(area_bboxes) / 2.4
mu[area_bboxes > 3500] = 1 / 2.1 # large bbox have smaller mu
bboxes_center_x = (bboxes[..., 0] + bboxes[..., 2]) / 2
bboxes_center_y = (bboxes[..., 1] + bboxes[..., 3]) / 2
rcentral[..., 0] = bboxes_center_x - mu * (bboxes[..., 2] -
bboxes[..., 0]) / 2
rcentral[..., 1] = bboxes_center_y - mu * (bboxes[..., 3] -
bboxes[..., 1]) / 2
rcentral[..., 2] = bboxes_center_x + mu * (bboxes[..., 2] -
bboxes[..., 0]) / 2
rcentral[..., 3] = bboxes_center_y + mu * (bboxes[..., 3] -
bboxes[..., 1]) / 2
area_rcentral = ((rcentral[..., 2] - rcentral[..., 0]) *
(rcentral[..., 3] - rcentral[..., 1])).abs()
dists = area_ct_bboxes / area_rcentral
tl_ctx_inds = (ct_bboxes[..., 0] <= rcentral[..., 0]) | (
ct_bboxes[..., 0] >= rcentral[..., 2])
tl_cty_inds = (ct_bboxes[..., 1] <= rcentral[..., 1]) | (
ct_bboxes[..., 1] >= rcentral[..., 3])
br_ctx_inds = (ct_bboxes[..., 2] <= rcentral[..., 0]) | (
ct_bboxes[..., 2] >= rcentral[..., 2])
br_cty_inds = (ct_bboxes[..., 3] <= rcentral[..., 1]) | (
ct_bboxes[..., 3] >= rcentral[..., 3])
if with_embedding:
tl_emb = transpose_and_gather_feat(tl_emb, tl_inds)
tl_emb = tl_emb.view(batch, k, 1)
br_emb = transpose_and_gather_feat(br_emb, br_inds)
br_emb = br_emb.view(batch, 1, k)
dists = torch.abs(tl_emb - br_emb)
tl_scores = tl_scores.view(batch, k, 1).repeat(1, 1, k)
br_scores = br_scores.view(batch, 1, k).repeat(1, k, 1)
scores = (tl_scores + br_scores) / 2 # scores for all possible boxes
# tl and br should have same class
tl_clses = tl_clses.view(batch, k, 1).repeat(1, 1, k)
br_clses = br_clses.view(batch, 1, k).repeat(1, k, 1)
cls_inds = (tl_clses != br_clses)
# reject boxes based on distances
dist_inds = dists > distance_threshold
# reject boxes based on widths and heights
width_inds = (br_xs <= tl_xs)
height_inds = (br_ys <= tl_ys)
# No use `scores[cls_inds]`, instead we use `torch.where` here.
# Since only 1-D indices with type 'tensor(bool)' are supported
# when exporting to ONNX, any other bool indices with more dimensions
# (e.g. 2-D bool tensor) as input parameter in node is invalid
negative_scores = -1 * torch.ones_like(scores)
scores = torch.where(cls_inds, negative_scores, scores)
scores = torch.where(width_inds, negative_scores, scores)
scores = torch.where(height_inds, negative_scores, scores)
scores = torch.where(dist_inds, negative_scores, scores)
if with_centripetal_shift:
scores[tl_ctx_inds] = -1
scores[tl_cty_inds] = -1
scores[br_ctx_inds] = -1
scores[br_cty_inds] = -1
scores = scores.view(batch, -1)
scores, inds = torch.topk(scores, num_dets)
scores = scores.unsqueeze(2)
bboxes = bboxes.view(batch, -1, 4)
bboxes = gather_feat(bboxes, inds)
clses = tl_clses.contiguous().view(batch, -1, 1)
clses = gather_feat(clses, inds).float()
return bboxes, scores, clses