# Copyright (c) 2019 Western Digital Corporation or its affiliates.
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import (ConvModule, bias_init_with_prob, constant_init, is_norm,
normal_init)
from mmcv.runner import force_fp32
from mmdet.core import (build_anchor_generator, build_assigner,
build_bbox_coder, build_sampler, images_to_levels,
multi_apply, multiclass_nms)
from ..builder import HEADS, build_loss
from .base_dense_head import BaseDenseHead
from .dense_test_mixins import BBoxTestMixin
[docs]@HEADS.register_module()
class YOLOV3Head(BaseDenseHead, BBoxTestMixin):
"""YOLOV3Head Paper link: https://arxiv.org/abs/1804.02767.
Args:
num_classes (int): The number of object classes (w/o background)
in_channels (List[int]): Number of input channels per scale.
out_channels (List[int]): The number of output channels per scale
before the final 1x1 layer. Default: (1024, 512, 256).
anchor_generator (dict): Config dict for anchor generator
bbox_coder (dict): Config of bounding box coder.
featmap_strides (List[int]): The stride of each scale.
Should be in descending order. Default: (32, 16, 8).
one_hot_smoother (float): Set a non-zero value to enable label-smooth
Default: 0.
conv_cfg (dict): Config dict for convolution layer. Default: None.
norm_cfg (dict): Dictionary to construct and config norm layer.
Default: dict(type='BN', requires_grad=True)
act_cfg (dict): Config dict for activation layer.
Default: dict(type='LeakyReLU', negative_slope=0.1).
loss_cls (dict): Config of classification loss.
loss_conf (dict): Config of confidence loss.
loss_xy (dict): Config of xy coordinate loss.
loss_wh (dict): Config of wh coordinate loss.
train_cfg (dict): Training config of YOLOV3 head. Default: None.
test_cfg (dict): Testing config of YOLOV3 head. Default: None.
init_cfg (dict or list[dict], optional): Initialization config dict.
"""
def __init__(self,
num_classes,
in_channels,
out_channels=(1024, 512, 256),
anchor_generator=dict(
type='YOLOAnchorGenerator',
base_sizes=[[(116, 90), (156, 198), (373, 326)],
[(30, 61), (62, 45), (59, 119)],
[(10, 13), (16, 30), (33, 23)]],
strides=[32, 16, 8]),
bbox_coder=dict(type='YOLOBBoxCoder'),
featmap_strides=[32, 16, 8],
one_hot_smoother=0.,
conv_cfg=None,
norm_cfg=dict(type='BN', requires_grad=True),
act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0),
loss_conf=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0),
loss_xy=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0),
loss_wh=dict(type='MSELoss', loss_weight=1.0),
train_cfg=None,
test_cfg=None,
init_cfg=dict(
type='Normal', std=0.01,
override=dict(name='convs_pred'))):
super(YOLOV3Head, self).__init__(init_cfg)
# Check params
assert (len(in_channels) == len(out_channels) == len(featmap_strides))
self.num_classes = num_classes
self.in_channels = in_channels
self.out_channels = out_channels
self.featmap_strides = featmap_strides
self.train_cfg = train_cfg
self.test_cfg = test_cfg
if self.train_cfg:
self.assigner = build_assigner(self.train_cfg.assigner)
if hasattr(self.train_cfg, 'sampler'):
sampler_cfg = self.train_cfg.sampler
else:
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.fp16_enabled = False
self.one_hot_smoother = one_hot_smoother
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.bbox_coder = build_bbox_coder(bbox_coder)
self.anchor_generator = build_anchor_generator(anchor_generator)
self.loss_cls = build_loss(loss_cls)
self.loss_conf = build_loss(loss_conf)
self.loss_xy = build_loss(loss_xy)
self.loss_wh = build_loss(loss_wh)
# usually the numbers of anchors for each level are the same
# except SSD detectors
self.num_anchors = self.anchor_generator.num_base_anchors[0]
assert len(
self.anchor_generator.num_base_anchors) == len(featmap_strides)
self._init_layers()
@property
def num_levels(self):
return len(self.featmap_strides)
@property
def num_attrib(self):
"""int: number of attributes in pred_map, bboxes (4) +
objectness (1) + num_classes"""
return 5 + self.num_classes
def _init_layers(self):
self.convs_bridge = nn.ModuleList()
self.convs_pred = nn.ModuleList()
for i in range(self.num_levels):
conv_bridge = ConvModule(
self.in_channels[i],
self.out_channels[i],
3,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
conv_pred = nn.Conv2d(self.out_channels[i],
self.num_anchors * self.num_attrib, 1)
self.convs_bridge.append(conv_bridge)
self.convs_pred.append(conv_pred)
[docs] def init_weights(self):
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)
# Use prior in model initialization to improve stability
for conv_pred, stride in zip(self.convs_pred, self.featmap_strides):
bias = conv_pred.bias.reshape(self.num_anchors, -1)
# init objectness with prior of 8 objects per feature map
# refer to https://github.com/ultralytics/yolov3
nn.init.constant_(bias.data[:, 4],
bias_init_with_prob(8 / (608 / stride)**2))
nn.init.constant_(bias.data[:, 5:], bias_init_with_prob(0.01))
[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[Tensor]: A tuple of multi-level predication map, each is a
4D-tensor of shape (batch_size, 5+num_classes, height, width).
"""
assert len(feats) == self.num_levels
pred_maps = []
for i in range(self.num_levels):
x = feats[i]
x = self.convs_bridge[i](x)
pred_map = self.convs_pred[i](x)
pred_maps.append(pred_map)
return tuple(pred_maps),
[docs] @force_fp32(apply_to=('pred_maps', ))
def get_bboxes(self,
pred_maps,
img_metas,
cfg=None,
rescale=False,
with_nms=True):
"""Transform network output for a batch into bbox predictions.
Args:
pred_maps (list[Tensor]): Raw predictions for a batch of images.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
cfg (mmcv.Config | None): Test / postprocessing configuration,
if None, test_cfg would be used. Default: None.
rescale (bool): If True, return boxes in original image space.
Default: False.
with_nms (bool): If True, do nms before return boxes.
Default: True.
Returns:
list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
The first item is an (n, 5) tensor, where 5 represent
(tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
The shape of the second tensor in the tuple is (n,), and
each element represents the class label of the corresponding
box.
"""
num_levels = len(pred_maps)
pred_maps_list = [pred_maps[i].detach() for i in range(num_levels)]
scale_factors = [
img_metas[i]['scale_factor']
for i in range(pred_maps_list[0].shape[0])
]
result_list = self._get_bboxes(pred_maps_list, scale_factors, cfg,
rescale, with_nms)
return result_list
def _get_bboxes(self,
pred_maps_list,
scale_factors,
cfg,
rescale=False,
with_nms=True):
"""Transform outputs for a single batch item into bbox predictions.
Args:
pred_maps_list (list[Tensor]): Prediction maps for different scales
of each single image in the batch.
scale_factors (list(ndarray)): Scale factor of the image arrange as
(w_scale, h_scale, w_scale, h_scale).
cfg (mmcv.Config | None): Test / postprocessing configuration,
if None, test_cfg would be used.
rescale (bool): If True, return boxes in original image space.
Default: False.
with_nms (bool): If True, do nms before return boxes.
Default: True.
Returns:
list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
The first item is an (n, 5) tensor, where 5 represent
(tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
The shape of the second tensor in the tuple is (n,), and
each element represents the class label of the corresponding
box.
"""
cfg = self.test_cfg if cfg is None else cfg
assert len(pred_maps_list) == self.num_levels
device = pred_maps_list[0].device
batch_size = pred_maps_list[0].shape[0]
featmap_sizes = [
pred_maps_list[i].shape[-2:] for i in range(self.num_levels)
]
multi_lvl_anchors = self.anchor_generator.grid_anchors(
featmap_sizes, device)
# convert to tensor to keep tracing
nms_pre_tensor = torch.tensor(
cfg.get('nms_pre', -1), device=device, dtype=torch.long)
multi_lvl_bboxes = []
multi_lvl_cls_scores = []
multi_lvl_conf_scores = []
for i in range(self.num_levels):
# get some key info for current scale
pred_map = pred_maps_list[i]
stride = self.featmap_strides[i]
# (b,h, w, num_anchors*num_attrib) ->
# (b,h*w*num_anchors, num_attrib)
pred_map = pred_map.permute(0, 2, 3,
1).reshape(batch_size, -1,
self.num_attrib)
# Inplace operation like
# ```pred_map[..., :2] = \torch.sigmoid(pred_map[..., :2])```
# would create constant tensor when exporting to onnx
pred_map_conf = torch.sigmoid(pred_map[..., :2])
pred_map_rest = pred_map[..., 2:]
pred_map = torch.cat([pred_map_conf, pred_map_rest], dim=-1)
pred_map_boxes = pred_map[..., :4]
multi_lvl_anchor = multi_lvl_anchors[i]
multi_lvl_anchor = multi_lvl_anchor.expand_as(pred_map_boxes)
bbox_pred = self.bbox_coder.decode(multi_lvl_anchor,
pred_map_boxes, stride)
# conf and cls
conf_pred = torch.sigmoid(pred_map[..., 4])
cls_pred = torch.sigmoid(pred_map[..., 5:]).view(
batch_size, -1, self.num_classes) # Cls pred one-hot.
# Get top-k prediction
from mmdet.core.export import get_k_for_topk
nms_pre = get_k_for_topk(nms_pre_tensor, bbox_pred.shape[1])
if nms_pre > 0:
_, topk_inds = conf_pred.topk(nms_pre)
batch_inds = torch.arange(batch_size).view(
-1, 1).expand_as(topk_inds).long()
# Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501
if torch.onnx.is_in_onnx_export():
transformed_inds = (
bbox_pred.shape[1] * batch_inds + topk_inds)
bbox_pred = bbox_pred.reshape(
-1, 4)[transformed_inds, :].reshape(batch_size, -1, 4)
cls_pred = cls_pred.reshape(
-1, self.num_classes)[transformed_inds, :].reshape(
batch_size, -1, self.num_classes)
conf_pred = conf_pred.reshape(-1,
1)[transformed_inds].reshape(
batch_size, -1)
else:
bbox_pred = bbox_pred[batch_inds, topk_inds, :]
cls_pred = cls_pred[batch_inds, topk_inds, :]
conf_pred = conf_pred[batch_inds, topk_inds]
# Save the result of current scale
multi_lvl_bboxes.append(bbox_pred)
multi_lvl_cls_scores.append(cls_pred)
multi_lvl_conf_scores.append(conf_pred)
# Merge the results of different scales together
batch_mlvl_bboxes = torch.cat(multi_lvl_bboxes, dim=1)
batch_mlvl_scores = torch.cat(multi_lvl_cls_scores, dim=1)
batch_mlvl_conf_scores = torch.cat(multi_lvl_conf_scores, dim=1)
# Replace multiclass_nms with ONNX::NonMaxSuppression in deployment
if torch.onnx.is_in_onnx_export() and with_nms:
from mmdet.core.export import add_dummy_nms_for_onnx
conf_thr = cfg.get('conf_thr', -1)
score_thr = cfg.get('score_thr', -1)
# follow original pipeline of YOLOv3
if conf_thr > 0:
mask = (batch_mlvl_conf_scores >= conf_thr).float()
batch_mlvl_conf_scores *= mask
if score_thr > 0:
mask = (batch_mlvl_scores > score_thr).float()
batch_mlvl_scores *= mask
batch_mlvl_conf_scores = batch_mlvl_conf_scores.unsqueeze(
2).expand_as(batch_mlvl_scores)
batch_mlvl_scores = batch_mlvl_scores * batch_mlvl_conf_scores
max_output_boxes_per_class = cfg.nms.get(
'max_output_boxes_per_class', 200)
iou_threshold = cfg.nms.get('iou_threshold', 0.5)
# keep aligned with original pipeline, improve
# mAP by 1% for YOLOv3 in ONNX
score_threshold = 0
nms_pre = cfg.get('deploy_nms_pre', -1)
return add_dummy_nms_for_onnx(
batch_mlvl_bboxes,
batch_mlvl_scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
nms_pre,
cfg.max_per_img,
)
if with_nms and (batch_mlvl_conf_scores.size(0) == 0):
return torch.zeros((0, 5)), torch.zeros((0, ))
if rescale:
batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor(
scale_factors).unsqueeze(1)
# In mmdet 2.x, the class_id for background is num_classes.
# i.e., the last column.
padding = batch_mlvl_scores.new_zeros(batch_size,
batch_mlvl_scores.shape[1], 1)
batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1)
# Support exporting to onnx without nms
if with_nms and cfg.get('nms', None) is not None:
det_results = []
for (mlvl_bboxes, mlvl_scores,
mlvl_conf_scores) in zip(batch_mlvl_bboxes, batch_mlvl_scores,
batch_mlvl_conf_scores):
# Filtering out all predictions with conf < conf_thr
conf_thr = cfg.get('conf_thr', -1)
if conf_thr > 0 and (not torch.onnx.is_in_onnx_export()):
# TensorRT not support NonZero
# add as_tuple=False for compatibility in Pytorch 1.6
# flatten would create a Reshape op with constant values,
# and raise RuntimeError when doing inference in ONNX
# Runtime with a different input image (#4221).
conf_inds = mlvl_conf_scores.ge(conf_thr).nonzero(
as_tuple=False).squeeze(1)
mlvl_bboxes = mlvl_bboxes[conf_inds, :]
mlvl_scores = mlvl_scores[conf_inds, :]
mlvl_conf_scores = mlvl_conf_scores[conf_inds]
det_bboxes, det_labels = multiclass_nms(
mlvl_bboxes,
mlvl_scores,
cfg.score_thr,
cfg.nms,
cfg.max_per_img,
score_factors=mlvl_conf_scores)
det_results.append(tuple([det_bboxes, det_labels]))
else:
det_results = [
tuple(mlvl_bs)
for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores,
batch_mlvl_conf_scores)
]
return det_results
[docs] @force_fp32(apply_to=('pred_maps', ))
def loss(self,
pred_maps,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""Compute loss of the head.
Args:
pred_maps (list[Tensor]): Prediction map for each scale level,
shape (N, num_anchors * num_attrib, H, W)
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] 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 (None | list[Tensor]): specify which bounding
boxes can be ignored when computing the loss.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
num_imgs = len(img_metas)
device = pred_maps[0][0].device
featmap_sizes = [
pred_maps[i].shape[-2:] for i in range(self.num_levels)
]
multi_level_anchors = self.anchor_generator.grid_anchors(
featmap_sizes, device)
anchor_list = [multi_level_anchors for _ in range(num_imgs)]
responsible_flag_list = []
for img_id in range(len(img_metas)):
responsible_flag_list.append(
self.anchor_generator.responsible_flags(
featmap_sizes, gt_bboxes[img_id], device))
target_maps_list, neg_maps_list = self.get_targets(
anchor_list, responsible_flag_list, gt_bboxes, gt_labels)
losses_cls, losses_conf, losses_xy, losses_wh = multi_apply(
self.loss_single, pred_maps, target_maps_list, neg_maps_list)
return dict(
loss_cls=losses_cls,
loss_conf=losses_conf,
loss_xy=losses_xy,
loss_wh=losses_wh)
[docs] def loss_single(self, pred_map, target_map, neg_map):
"""Compute loss of a single image from a batch.
Args:
pred_map (Tensor): Raw predictions for a single level.
target_map (Tensor): The Ground-Truth target for a single level.
neg_map (Tensor): The negative masks for a single level.
Returns:
tuple:
loss_cls (Tensor): Classification loss.
loss_conf (Tensor): Confidence loss.
loss_xy (Tensor): Regression loss of x, y coordinate.
loss_wh (Tensor): Regression loss of w, h coordinate.
"""
num_imgs = len(pred_map)
pred_map = pred_map.permute(0, 2, 3,
1).reshape(num_imgs, -1, self.num_attrib)
neg_mask = neg_map.float()
pos_mask = target_map[..., 4]
pos_and_neg_mask = neg_mask + pos_mask
pos_mask = pos_mask.unsqueeze(dim=-1)
if torch.max(pos_and_neg_mask) > 1.:
warnings.warn('There is overlap between pos and neg sample.')
pos_and_neg_mask = pos_and_neg_mask.clamp(min=0., max=1.)
pred_xy = pred_map[..., :2]
pred_wh = pred_map[..., 2:4]
pred_conf = pred_map[..., 4]
pred_label = pred_map[..., 5:]
target_xy = target_map[..., :2]
target_wh = target_map[..., 2:4]
target_conf = target_map[..., 4]
target_label = target_map[..., 5:]
loss_cls = self.loss_cls(pred_label, target_label, weight=pos_mask)
loss_conf = self.loss_conf(
pred_conf, target_conf, weight=pos_and_neg_mask)
loss_xy = self.loss_xy(pred_xy, target_xy, weight=pos_mask)
loss_wh = self.loss_wh(pred_wh, target_wh, weight=pos_mask)
return loss_cls, loss_conf, loss_xy, loss_wh
[docs] def get_targets(self, anchor_list, responsible_flag_list, gt_bboxes_list,
gt_labels_list):
"""Compute target maps for anchors in multiple images.
Args:
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_total_anchors, 4).
responsible_flag_list (list[list[Tensor]]): Multi level responsible
flags of each image. Each element is a tensor of shape
(num_total_anchors, )
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
gt_labels_list (list[Tensor]): Ground truth labels of each box.
Returns:
tuple: Usually returns a tuple containing learning targets.
- target_map_list (list[Tensor]): Target map of each level.
- neg_map_list (list[Tensor]): Negative map of each level.
"""
num_imgs = len(anchor_list)
# anchor number of multi levels
num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
results = multi_apply(self._get_targets_single, anchor_list,
responsible_flag_list, gt_bboxes_list,
gt_labels_list)
all_target_maps, all_neg_maps = results
assert num_imgs == len(all_target_maps) == len(all_neg_maps)
target_maps_list = images_to_levels(all_target_maps, num_level_anchors)
neg_maps_list = images_to_levels(all_neg_maps, num_level_anchors)
return target_maps_list, neg_maps_list
def _get_targets_single(self, anchors, responsible_flags, gt_bboxes,
gt_labels):
"""Generate matching bounding box prior and converted GT.
Args:
anchors (list[Tensor]): Multi-level anchors of the image.
responsible_flags (list[Tensor]): Multi-level responsible flags of
anchors
gt_bboxes (Tensor): Ground truth bboxes of single image.
gt_labels (Tensor): Ground truth labels of single image.
Returns:
tuple:
target_map (Tensor): Predication target map of each
scale level, shape (num_total_anchors,
5+num_classes)
neg_map (Tensor): Negative map of each scale level,
shape (num_total_anchors,)
"""
anchor_strides = []
for i in range(len(anchors)):
anchor_strides.append(
torch.tensor(self.featmap_strides[i],
device=gt_bboxes.device).repeat(len(anchors[i])))
concat_anchors = torch.cat(anchors)
concat_responsible_flags = torch.cat(responsible_flags)
anchor_strides = torch.cat(anchor_strides)
assert len(anchor_strides) == len(concat_anchors) == \
len(concat_responsible_flags)
assign_result = self.assigner.assign(concat_anchors,
concat_responsible_flags,
gt_bboxes)
sampling_result = self.sampler.sample(assign_result, concat_anchors,
gt_bboxes)
target_map = concat_anchors.new_zeros(
concat_anchors.size(0), self.num_attrib)
target_map[sampling_result.pos_inds, :4] = self.bbox_coder.encode(
sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes,
anchor_strides[sampling_result.pos_inds])
target_map[sampling_result.pos_inds, 4] = 1
gt_labels_one_hot = F.one_hot(
gt_labels, num_classes=self.num_classes).float()
if self.one_hot_smoother != 0: # label smooth
gt_labels_one_hot = gt_labels_one_hot * (
1 - self.one_hot_smoother
) + self.one_hot_smoother / self.num_classes
target_map[sampling_result.pos_inds, 5:] = gt_labels_one_hot[
sampling_result.pos_assigned_gt_inds]
neg_map = concat_anchors.new_zeros(
concat_anchors.size(0), dtype=torch.uint8)
neg_map[sampling_result.neg_inds] = 1
return target_map, neg_map
[docs] def aug_test(self, feats, img_metas, rescale=False):
"""Test function with test time augmentation.
Args:
feats (list[Tensor]): the outer list indicates test-time
augmentations and inner Tensor should have a shape NxCxHxW,
which contains features for all images in the batch.
img_metas (list[list[dict]]): the outer list indicates test-time
augs (multiscale, flip, etc.) and the inner list indicates
images in a batch. each dict has image information.
rescale (bool, optional): Whether to rescale the results.
Defaults to False.
Returns:
list[ndarray]: bbox results of each class
"""
return self.aug_test_bboxes(feats, img_metas, rescale=rescale)