Source code for mmdet.models.dense_heads.detr_head
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import Conv2d, Linear, build_activation_layer
from mmcv.cnn.bricks.transformer import FFN, build_positional_encoding
from mmcv.runner import force_fp32
from mmdet.core import (bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh,
build_assigner, build_sampler, multi_apply,
reduce_mean)
from mmdet.models.utils import build_transformer
from ..builder import HEADS, build_loss
from .anchor_free_head import AnchorFreeHead
[docs]@HEADS.register_module()
class DETRHead(AnchorFreeHead):
"""Implements the DETR transformer head.
See `paper: End-to-End Object Detection with Transformers
<https://arxiv.org/pdf/2005.12872>`_ for details.
Args:
num_classes (int): Number of categories excluding the background.
in_channels (int): Number of channels in the input feature map.
num_query (int): Number of query in Transformer.
num_reg_fcs (int, optional): Number of fully-connected layers used in
`FFN`, which is then used for the regression head. Default 2.
transformer (obj:`mmcv.ConfigDict`|dict): Config for transformer.
Default: None.
sync_cls_avg_factor (bool): Whether to sync the avg_factor of
all ranks. Default to False.
positional_encoding (obj:`mmcv.ConfigDict`|dict):
Config for position encoding.
loss_cls (obj:`mmcv.ConfigDict`|dict): Config of the
classification loss. Default `CrossEntropyLoss`.
loss_bbox (obj:`mmcv.ConfigDict`|dict): Config of the
regression loss. Default `L1Loss`.
loss_iou (obj:`mmcv.ConfigDict`|dict): Config of the
regression iou loss. Default `GIoULoss`.
tran_cfg (obj:`mmcv.ConfigDict`|dict): Training config of
transformer head.
test_cfg (obj:`mmcv.ConfigDict`|dict): Testing config of
transformer head.
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None
"""
_version = 2
def __init__(self,
num_classes,
in_channels,
num_query=100,
num_reg_fcs=2,
transformer=None,
sync_cls_avg_factor=False,
positional_encoding=dict(
type='SinePositionalEncoding',
num_feats=128,
normalize=True),
loss_cls=dict(
type='CrossEntropyLoss',
bg_cls_weight=0.1,
use_sigmoid=False,
loss_weight=1.0,
class_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=5.0),
loss_iou=dict(type='GIoULoss', loss_weight=2.0),
train_cfg=dict(
assigner=dict(
type='HungarianAssigner',
cls_cost=dict(type='ClassificationCost', weight=1.),
reg_cost=dict(type='BBoxL1Cost', weight=5.0),
iou_cost=dict(
type='IoUCost', iou_mode='giou', weight=2.0))),
test_cfg=dict(max_per_img=100),
init_cfg=None,
**kwargs):
# NOTE here use `AnchorFreeHead` instead of `TransformerHead`,
# since it brings inconvenience when the initialization of
# `AnchorFreeHead` is called.
super(AnchorFreeHead, self).__init__(init_cfg)
self.bg_cls_weight = 0
self.sync_cls_avg_factor = sync_cls_avg_factor
class_weight = loss_cls.get('class_weight', None)
if class_weight is not None and (self.__class__ is DETRHead):
assert isinstance(class_weight, float), 'Expected ' \
'class_weight to have type float. Found ' \
f'{type(class_weight)}.'
# NOTE following the official DETR rep0, bg_cls_weight means
# relative classification weight of the no-object class.
bg_cls_weight = loss_cls.get('bg_cls_weight', class_weight)
assert isinstance(bg_cls_weight, float), 'Expected ' \
'bg_cls_weight to have type float. Found ' \
f'{type(bg_cls_weight)}.'
class_weight = torch.ones(num_classes + 1) * class_weight
# set background class as the last indice
class_weight[num_classes] = bg_cls_weight
loss_cls.update({'class_weight': class_weight})
if 'bg_cls_weight' in loss_cls:
loss_cls.pop('bg_cls_weight')
self.bg_cls_weight = bg_cls_weight
if train_cfg:
assert 'assigner' in train_cfg, 'assigner should be provided '\
'when train_cfg is set.'
assigner = train_cfg['assigner']
assert loss_cls['loss_weight'] == assigner['cls_cost']['weight'], \
'The classification weight for loss and matcher should be' \
'exactly the same.'
assert loss_bbox['loss_weight'] == assigner['reg_cost'][
'weight'], 'The regression L1 weight for loss and matcher ' \
'should be exactly the same.'
assert loss_iou['loss_weight'] == assigner['iou_cost']['weight'], \
'The regression iou weight for loss and matcher should be' \
'exactly the same.'
self.assigner = build_assigner(assigner)
# DETR sampling=False, so use PseudoSampler
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.num_query = num_query
self.num_classes = num_classes
self.in_channels = in_channels
self.num_reg_fcs = num_reg_fcs
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.fp16_enabled = False
self.loss_cls = build_loss(loss_cls)
self.loss_bbox = build_loss(loss_bbox)
self.loss_iou = build_loss(loss_iou)
if self.loss_cls.use_sigmoid:
self.cls_out_channels = num_classes
else:
self.cls_out_channels = num_classes + 1
self.act_cfg = transformer.get('act_cfg',
dict(type='ReLU', inplace=True))
self.activate = build_activation_layer(self.act_cfg)
self.positional_encoding = build_positional_encoding(
positional_encoding)
self.transformer = build_transformer(transformer)
self.embed_dims = self.transformer.embed_dims
assert 'num_feats' in positional_encoding
num_feats = positional_encoding['num_feats']
assert num_feats * 2 == self.embed_dims, 'embed_dims should' \
f' be exactly 2 times of num_feats. Found {self.embed_dims}' \
f' and {num_feats}.'
self._init_layers()
def _init_layers(self):
"""Initialize layers of the transformer head."""
self.input_proj = Conv2d(
self.in_channels, self.embed_dims, kernel_size=1)
self.fc_cls = Linear(self.embed_dims, self.cls_out_channels)
self.reg_ffn = FFN(
self.embed_dims,
self.embed_dims,
self.num_reg_fcs,
self.act_cfg,
dropout=0.0,
add_residual=False)
self.fc_reg = Linear(self.embed_dims, 4)
self.query_embedding = nn.Embedding(self.num_query, self.embed_dims)
[docs] def init_weights(self):
"""Initialize weights of the transformer head."""
# The initialization for transformer is important
self.transformer.init_weights()
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
"""load checkpoints."""
# NOTE here use `AnchorFreeHead` instead of `TransformerHead`,
# since `AnchorFreeHead._load_from_state_dict` should not be
# called here. Invoking the default `Module._load_from_state_dict`
# is enough.
# Names of some parameters in has been changed.
version = local_metadata.get('version', None)
if (version is None or version < 2) and self.__class__ is DETRHead:
convert_dict = {
'.self_attn.': '.attentions.0.',
'.ffn.': '.ffns.0.',
'.multihead_attn.': '.attentions.1.',
'.decoder.norm.': '.decoder.post_norm.'
}
state_dict_keys = list(state_dict.keys())
for k in state_dict_keys:
for ori_key, convert_key in convert_dict.items():
if ori_key in k:
convert_key = k.replace(ori_key, convert_key)
state_dict[convert_key] = state_dict[k]
del state_dict[k]
super(AnchorFreeHead,
self)._load_from_state_dict(state_dict, prefix, local_metadata,
strict, missing_keys,
unexpected_keys, error_msgs)
[docs] def forward(self, feats, img_metas):
"""Forward function.
Args:
feats (tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
img_metas (list[dict]): List of image information.
Returns:
tuple[list[Tensor], list[Tensor]]: Outputs for all scale levels.
- all_cls_scores_list (list[Tensor]): Classification scores \
for each scale level. Each is a 4D-tensor with shape \
[nb_dec, bs, num_query, cls_out_channels]. Note \
`cls_out_channels` should includes background.
- all_bbox_preds_list (list[Tensor]): Sigmoid regression \
outputs for each scale level. Each is a 4D-tensor with \
normalized coordinate format (cx, cy, w, h) and shape \
[nb_dec, bs, num_query, 4].
"""
num_levels = len(feats)
img_metas_list = [img_metas for _ in range(num_levels)]
return multi_apply(self.forward_single, feats, img_metas_list)
[docs] def forward_single(self, x, img_metas):
""""Forward function for a single feature level.
Args:
x (Tensor): Input feature from backbone's single stage, shape
[bs, c, h, w].
img_metas (list[dict]): List of image information.
Returns:
all_cls_scores (Tensor): Outputs from the classification head,
shape [nb_dec, bs, num_query, cls_out_channels]. Note
cls_out_channels should includes background.
all_bbox_preds (Tensor): Sigmoid outputs from the regression
head with normalized coordinate format (cx, cy, w, h).
Shape [nb_dec, bs, num_query, 4].
"""
# construct binary masks which used for the transformer.
# NOTE following the official DETR repo, non-zero values representing
# ignored positions, while zero values means valid positions.
batch_size = x.size(0)
input_img_h, input_img_w = img_metas[0]['batch_input_shape']
masks = x.new_ones((batch_size, input_img_h, input_img_w))
for img_id in range(batch_size):
img_h, img_w, _ = img_metas[img_id]['img_shape']
masks[img_id, :img_h, :img_w] = 0
x = self.input_proj(x)
# interpolate masks to have the same spatial shape with x
masks = F.interpolate(
masks.unsqueeze(1), size=x.shape[-2:]).to(torch.bool).squeeze(1)
# position encoding
pos_embed = self.positional_encoding(masks) # [bs, embed_dim, h, w]
# outs_dec: [nb_dec, bs, num_query, embed_dim]
outs_dec, _ = self.transformer(x, masks, self.query_embedding.weight,
pos_embed)
all_cls_scores = self.fc_cls(outs_dec)
all_bbox_preds = self.fc_reg(self.activate(
self.reg_ffn(outs_dec))).sigmoid()
return all_cls_scores, all_bbox_preds
[docs] @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list'))
def loss(self,
all_cls_scores_list,
all_bbox_preds_list,
gt_bboxes_list,
gt_labels_list,
img_metas,
gt_bboxes_ignore=None):
""""Loss function.
Only outputs from the last feature level are used for computing
losses by default.
Args:
all_cls_scores_list (list[Tensor]): Classification outputs
for each feature level. Each is a 4D-tensor with shape
[nb_dec, bs, num_query, cls_out_channels].
all_bbox_preds_list (list[Tensor]): Sigmoid regression
outputs for each feature level. Each is a 4D-tensor with
normalized coordinate format (cx, cy, w, h) and shape
[nb_dec, bs, num_query, 4].
gt_bboxes_list (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 (list[Tensor]): Ground truth class indices for each
image with shape (num_gts, ).
img_metas (list[dict]): List of image meta information.
gt_bboxes_ignore (list[Tensor], optional): Bounding boxes
which can be ignored for each image. Default None.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
# NOTE defaultly only the outputs from the last feature scale is used.
all_cls_scores = all_cls_scores_list[-1]
all_bbox_preds = all_bbox_preds_list[-1]
assert gt_bboxes_ignore is None, \
'Only supports for gt_bboxes_ignore setting to None.'
num_dec_layers = len(all_cls_scores)
all_gt_bboxes_list = [gt_bboxes_list for _ in range(num_dec_layers)]
all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)]
all_gt_bboxes_ignore_list = [
gt_bboxes_ignore for _ in range(num_dec_layers)
]
img_metas_list = [img_metas for _ in range(num_dec_layers)]
losses_cls, losses_bbox, losses_iou = multi_apply(
self.loss_single, all_cls_scores, all_bbox_preds,
all_gt_bboxes_list, all_gt_labels_list, img_metas_list,
all_gt_bboxes_ignore_list)
loss_dict = dict()
# loss from the last decoder layer
loss_dict['loss_cls'] = losses_cls[-1]
loss_dict['loss_bbox'] = losses_bbox[-1]
loss_dict['loss_iou'] = losses_iou[-1]
# loss from other decoder layers
num_dec_layer = 0
for loss_cls_i, loss_bbox_i, loss_iou_i in zip(losses_cls[:-1],
losses_bbox[:-1],
losses_iou[:-1]):
loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i
loss_dict[f'd{num_dec_layer}.loss_bbox'] = loss_bbox_i
loss_dict[f'd{num_dec_layer}.loss_iou'] = loss_iou_i
num_dec_layer += 1
return loss_dict
[docs] def loss_single(self,
cls_scores,
bbox_preds,
gt_bboxes_list,
gt_labels_list,
img_metas,
gt_bboxes_ignore_list=None):
""""Loss function for outputs from a single decoder layer of a single
feature level.
Args:
cls_scores (Tensor): Box score logits from a single decoder layer
for all images. Shape [bs, num_query, cls_out_channels].
bbox_preds (Tensor): Sigmoid outputs from a single decoder layer
for all images, with normalized coordinate (cx, cy, w, h) and
shape [bs, num_query, 4].
gt_bboxes_list (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 (list[Tensor]): Ground truth class indices for each
image with shape (num_gts, ).
img_metas (list[dict]): List of image meta information.
gt_bboxes_ignore_list (list[Tensor], optional): Bounding
boxes which can be ignored for each image. Default None.
Returns:
dict[str, Tensor]: A dictionary of loss components for outputs from
a single decoder layer.
"""
num_imgs = cls_scores.size(0)
cls_scores_list = [cls_scores[i] for i in range(num_imgs)]
bbox_preds_list = [bbox_preds[i] for i in range(num_imgs)]
cls_reg_targets = self.get_targets(cls_scores_list, bbox_preds_list,
gt_bboxes_list, gt_labels_list,
img_metas, gt_bboxes_ignore_list)
(labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
num_total_pos, num_total_neg) = cls_reg_targets
labels = torch.cat(labels_list, 0)
label_weights = torch.cat(label_weights_list, 0)
bbox_targets = torch.cat(bbox_targets_list, 0)
bbox_weights = torch.cat(bbox_weights_list, 0)
# classification loss
cls_scores = cls_scores.reshape(-1, self.cls_out_channels)
# construct weighted avg_factor to match with the official DETR repo
cls_avg_factor = num_total_pos * 1.0 + \
num_total_neg * self.bg_cls_weight
if self.sync_cls_avg_factor:
cls_avg_factor = reduce_mean(
cls_scores.new_tensor([cls_avg_factor]))
cls_avg_factor = max(cls_avg_factor, 1)
loss_cls = self.loss_cls(
cls_scores, labels, label_weights, avg_factor=cls_avg_factor)
# Compute the average number of gt boxes across all gpus, for
# normalization purposes
num_total_pos = loss_cls.new_tensor([num_total_pos])
num_total_pos = torch.clamp(reduce_mean(num_total_pos), min=1).item()
# construct factors used for rescale bboxes
factors = []
for img_meta, bbox_pred in zip(img_metas, bbox_preds):
img_h, img_w, _ = img_meta['img_shape']
factor = bbox_pred.new_tensor([img_w, img_h, img_w,
img_h]).unsqueeze(0).repeat(
bbox_pred.size(0), 1)
factors.append(factor)
factors = torch.cat(factors, 0)
# DETR regress the relative position of boxes (cxcywh) in the image,
# thus the learning target is normalized by the image size. So here
# we need to re-scale them for calculating IoU loss
bbox_preds = bbox_preds.reshape(-1, 4)
bboxes = bbox_cxcywh_to_xyxy(bbox_preds) * factors
bboxes_gt = bbox_cxcywh_to_xyxy(bbox_targets) * factors
# regression IoU loss, defaultly GIoU loss
loss_iou = self.loss_iou(
bboxes, bboxes_gt, bbox_weights, avg_factor=num_total_pos)
# regression L1 loss
loss_bbox = self.loss_bbox(
bbox_preds, bbox_targets, bbox_weights, avg_factor=num_total_pos)
return loss_cls, loss_bbox, loss_iou
[docs] def get_targets(self,
cls_scores_list,
bbox_preds_list,
gt_bboxes_list,
gt_labels_list,
img_metas,
gt_bboxes_ignore_list=None):
""""Compute regression and classification targets for a batch image.
Outputs from a single decoder layer of a single feature level are used.
Args:
cls_scores_list (list[Tensor]): Box score logits from a single
decoder layer for each image with shape [num_query,
cls_out_channels].
bbox_preds_list (list[Tensor]): Sigmoid outputs from a single
decoder layer for each image, with normalized coordinate
(cx, cy, w, h) and shape [num_query, 4].
gt_bboxes_list (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 (list[Tensor]): Ground truth class indices for each
image with shape (num_gts, ).
img_metas (list[dict]): List of image meta information.
gt_bboxes_ignore_list (list[Tensor], optional): Bounding
boxes which can be ignored for each image. Default None.
Returns:
tuple: a tuple containing the following targets.
- labels_list (list[Tensor]): Labels for all images.
- label_weights_list (list[Tensor]): Label weights for all \
images.
- bbox_targets_list (list[Tensor]): BBox targets for all \
images.
- bbox_weights_list (list[Tensor]): BBox weights for all \
images.
- num_total_pos (int): Number of positive samples in all \
images.
- num_total_neg (int): Number of negative samples in all \
images.
"""
assert gt_bboxes_ignore_list is None, \
'Only supports for gt_bboxes_ignore setting to None.'
num_imgs = len(cls_scores_list)
gt_bboxes_ignore_list = [
gt_bboxes_ignore_list for _ in range(num_imgs)
]
(labels_list, label_weights_list, bbox_targets_list,
bbox_weights_list, pos_inds_list, neg_inds_list) = multi_apply(
self._get_target_single, cls_scores_list, bbox_preds_list,
gt_bboxes_list, gt_labels_list, img_metas, gt_bboxes_ignore_list)
num_total_pos = sum((inds.numel() for inds in pos_inds_list))
num_total_neg = sum((inds.numel() for inds in neg_inds_list))
return (labels_list, label_weights_list, bbox_targets_list,
bbox_weights_list, num_total_pos, num_total_neg)
def _get_target_single(self,
cls_score,
bbox_pred,
gt_bboxes,
gt_labels,
img_meta,
gt_bboxes_ignore=None):
""""Compute regression and classification targets for one image.
Outputs from a single decoder layer of a single feature level are used.
Args:
cls_score (Tensor): Box score logits from a single decoder layer
for one image. Shape [num_query, cls_out_channels].
bbox_pred (Tensor): Sigmoid outputs from a single decoder layer
for one image, with normalized coordinate (cx, cy, w, h) and
shape [num_query, 4].
gt_bboxes (Tensor): Ground truth bboxes for one image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels (Tensor): Ground truth class indices for one image
with shape (num_gts, ).
img_meta (dict): Meta information for one image.
gt_bboxes_ignore (Tensor, optional): Bounding boxes
which can be ignored. Default None.
Returns:
tuple[Tensor]: a tuple containing the following for one image.
- labels (Tensor): Labels of each image.
- label_weights (Tensor]): Label weights of each image.
- bbox_targets (Tensor): BBox targets of each image.
- bbox_weights (Tensor): BBox weights of each image.
- pos_inds (Tensor): Sampled positive indices for each image.
- neg_inds (Tensor): Sampled negative indices for each image.
"""
num_bboxes = bbox_pred.size(0)
# assigner and sampler
assign_result = self.assigner.assign(bbox_pred, cls_score, gt_bboxes,
gt_labels, img_meta,
gt_bboxes_ignore)
sampling_result = self.sampler.sample(assign_result, bbox_pred,
gt_bboxes)
pos_inds = sampling_result.pos_inds
neg_inds = sampling_result.neg_inds
# label targets
labels = gt_bboxes.new_full((num_bboxes, ),
self.num_classes,
dtype=torch.long)
labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds]
label_weights = gt_bboxes.new_ones(num_bboxes)
# bbox targets
bbox_targets = torch.zeros_like(bbox_pred)
bbox_weights = torch.zeros_like(bbox_pred)
bbox_weights[pos_inds] = 1.0
img_h, img_w, _ = img_meta['img_shape']
# DETR regress the relative position of boxes (cxcywh) in the image.
# Thus the learning target should be normalized by the image size, also
# the box format should be converted from defaultly x1y1x2y2 to cxcywh.
factor = bbox_pred.new_tensor([img_w, img_h, img_w,
img_h]).unsqueeze(0)
pos_gt_bboxes_normalized = sampling_result.pos_gt_bboxes / factor
pos_gt_bboxes_targets = bbox_xyxy_to_cxcywh(pos_gt_bboxes_normalized)
bbox_targets[pos_inds] = pos_gt_bboxes_targets
return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
neg_inds)
# over-write because img_metas are needed as inputs for bbox_head.
[docs] def forward_train(self,
x,
img_metas,
gt_bboxes,
gt_labels=None,
gt_bboxes_ignore=None,
proposal_cfg=None,
**kwargs):
"""Forward function for training mode.
Args:
x (list[Tensor]): Features from backbone.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes (Tensor): Ground truth bboxes of the image,
shape (num_gts, 4).
gt_labels (Tensor): Ground truth labels of each box,
shape (num_gts,).
gt_bboxes_ignore (Tensor): Ground truth bboxes to be
ignored, shape (num_ignored_gts, 4).
proposal_cfg (mmcv.Config): Test / postprocessing configuration,
if None, test_cfg would be used.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
assert proposal_cfg is None, '"proposal_cfg" must be None'
outs = self(x, img_metas)
if gt_labels is None:
loss_inputs = outs + (gt_bboxes, img_metas)
else:
loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
return losses
[docs] @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list'))
def get_bboxes(self,
all_cls_scores_list,
all_bbox_preds_list,
img_metas,
rescale=False):
"""Transform network outputs for a batch into bbox predictions.
Args:
all_cls_scores_list (list[Tensor]): Classification outputs
for each feature level. Each is a 4D-tensor with shape
[nb_dec, bs, num_query, cls_out_channels].
all_bbox_preds_list (list[Tensor]): Sigmoid regression
outputs for each feature level. Each is a 4D-tensor with
normalized coordinate format (cx, cy, w, h) and shape
[nb_dec, bs, num_query, 4].
img_metas (list[dict]): Meta information of each image.
rescale (bool, optional): If True, return boxes in original
image space. Default False.
Returns:
list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple. \
The first item is an (n, 5) tensor, where the first 4 columns \
are bounding box positions (tl_x, tl_y, br_x, br_y) and the \
5-th column is a score between 0 and 1. The second item is a \
(n,) tensor where each item is the predicted class label of \
the corresponding box.
"""
# NOTE defaultly only using outputs from the last feature level,
# and only the outputs from the last decoder layer is used.
cls_scores = all_cls_scores_list[-1][-1]
bbox_preds = all_bbox_preds_list[-1][-1]
result_list = []
for img_id in range(len(img_metas)):
cls_score = cls_scores[img_id]
bbox_pred = bbox_preds[img_id]
img_shape = img_metas[img_id]['img_shape']
scale_factor = img_metas[img_id]['scale_factor']
proposals = self._get_bboxes_single(cls_score, bbox_pred,
img_shape, scale_factor,
rescale)
result_list.append(proposals)
return result_list
def _get_bboxes_single(self,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=False):
"""Transform outputs from the last decoder layer into bbox predictions
for each image.
Args:
cls_score (Tensor): Box score logits from the last decoder layer
for each image. Shape [num_query, cls_out_channels].
bbox_pred (Tensor): Sigmoid outputs from the last decoder layer
for each image, with coordinate format (cx, cy, w, h) and
shape [num_query, 4].
img_shape (tuple[int]): Shape of input image, (height, width, 3).
scale_factor (ndarray, optional): Scale factor of the image arange
as (w_scale, h_scale, w_scale, h_scale).
rescale (bool, optional): If True, return boxes in original image
space. Default False.
Returns:
tuple[Tensor]: Results of detected bboxes and labels.
- det_bboxes: Predicted bboxes with shape [num_query, 5], \
where the first 4 columns are bounding box positions \
(tl_x, tl_y, br_x, br_y) and the 5-th column are scores \
between 0 and 1.
- det_labels: Predicted labels of the corresponding box with \
shape [num_query].
"""
assert len(cls_score) == len(bbox_pred)
max_per_img = self.test_cfg.get('max_per_img', self.num_query)
# exclude background
if self.loss_cls.use_sigmoid:
cls_score = cls_score.sigmoid()
scores, indexes = cls_score.view(-1).topk(max_per_img)
det_labels = indexes % self.num_classes
bbox_index = indexes // self.num_classes
bbox_pred = bbox_pred[bbox_index]
else:
scores, det_labels = F.softmax(cls_score, dim=-1)[..., :-1].max(-1)
scores, bbox_index = scores.topk(max_per_img)
bbox_pred = bbox_pred[bbox_index]
det_labels = det_labels[bbox_index]
det_bboxes = bbox_cxcywh_to_xyxy(bbox_pred)
det_bboxes[:, 0::2] = det_bboxes[:, 0::2] * img_shape[1]
det_bboxes[:, 1::2] = det_bboxes[:, 1::2] * img_shape[0]
det_bboxes[:, 0::2].clamp_(min=0, max=img_shape[1])
det_bboxes[:, 1::2].clamp_(min=0, max=img_shape[0])
if rescale:
det_bboxes /= det_bboxes.new_tensor(scale_factor)
det_bboxes = torch.cat((det_bboxes, scores.unsqueeze(1)), -1)
return det_bboxes, det_labels
[docs] def simple_test_bboxes(self, feats, img_metas, rescale=False):
"""Test det bboxes without test-time augmentation.
Args:
feats (tuple[torch.Tensor]): Multi-level features from the
upstream network, each is a 4D-tensor.
img_metas (list[dict]): List of image information.
rescale (bool, optional): Whether to rescale the results.
Defaults to False.
Returns:
list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
The first item is ``bboxes`` with shape (n, 5),
where 5 represent (tl_x, tl_y, br_x, br_y, score).
The shape of the second tensor in the tuple is ``labels``
with shape (n,)
"""
# forward of this head requires img_metas
outs = self.forward(feats, img_metas)
results_list = self.get_bboxes(*outs, img_metas, rescale=rescale)
return results_list
[docs] def forward_onnx(self, feats, img_metas):
"""Forward function for exporting to ONNX.
Over-write `forward` because: `masks` is directly created with
zero (valid position tag) and has the same spatial size as `x`.
Thus the construction of `masks` is different from that in `forward`.
Args:
feats (tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
img_metas (list[dict]): List of image information.
Returns:
tuple[list[Tensor], list[Tensor]]: Outputs for all scale levels.
- all_cls_scores_list (list[Tensor]): Classification scores \
for each scale level. Each is a 4D-tensor with shape \
[nb_dec, bs, num_query, cls_out_channels]. Note \
`cls_out_channels` should includes background.
- all_bbox_preds_list (list[Tensor]): Sigmoid regression \
outputs for each scale level. Each is a 4D-tensor with \
normalized coordinate format (cx, cy, w, h) and shape \
[nb_dec, bs, num_query, 4].
"""
num_levels = len(feats)
img_metas_list = [img_metas for _ in range(num_levels)]
return multi_apply(self.forward_single_onnx, feats, img_metas_list)
[docs] def forward_single_onnx(self, x, img_metas):
""""Forward function for a single feature level with ONNX exportation.
Args:
x (Tensor): Input feature from backbone's single stage, shape
[bs, c, h, w].
img_metas (list[dict]): List of image information.
Returns:
all_cls_scores (Tensor): Outputs from the classification head,
shape [nb_dec, bs, num_query, cls_out_channels]. Note
cls_out_channels should includes background.
all_bbox_preds (Tensor): Sigmoid outputs from the regression
head with normalized coordinate format (cx, cy, w, h).
Shape [nb_dec, bs, num_query, 4].
"""
# Note `img_shape` is not dynamically traceable to ONNX,
# since the related augmentation was done with numpy under
# CPU. Thus `masks` is directly created with zeros (valid tag)
# and the same spatial shape as `x`.
# The difference between torch and exported ONNX model may be
# ignored, since the same performance is achieved (e.g.
# 40.1 vs 40.1 for DETR)
batch_size = x.size(0)
h, w = x.size()[-2:]
masks = x.new_zeros((batch_size, h, w)) # [B,h,w]
x = self.input_proj(x)
# interpolate masks to have the same spatial shape with x
masks = F.interpolate(
masks.unsqueeze(1), size=x.shape[-2:]).to(torch.bool).squeeze(1)
pos_embed = self.positional_encoding(masks)
outs_dec, _ = self.transformer(x, masks, self.query_embedding.weight,
pos_embed)
all_cls_scores = self.fc_cls(outs_dec)
all_bbox_preds = self.fc_reg(self.activate(
self.reg_ffn(outs_dec))).sigmoid()
return all_cls_scores, all_bbox_preds
[docs] def onnx_export(self, all_cls_scores_list, all_bbox_preds_list, img_metas):
"""Transform network outputs into bbox predictions, with ONNX
exportation.
Args:
all_cls_scores_list (list[Tensor]): Classification outputs
for each feature level. Each is a 4D-tensor with shape
[nb_dec, bs, num_query, cls_out_channels].
all_bbox_preds_list (list[Tensor]): Sigmoid regression
outputs for each feature level. Each is a 4D-tensor with
normalized coordinate format (cx, cy, w, h) and shape
[nb_dec, bs, num_query, 4].
img_metas (list[dict]): Meta information of each image.
Returns:
tuple[Tensor, Tensor]: dets of shape [N, num_det, 5]
and class labels of shape [N, num_det].
"""
assert len(img_metas) == 1, \
'Only support one input image while in exporting to ONNX'
cls_scores = all_cls_scores_list[-1][-1]
bbox_preds = all_bbox_preds_list[-1][-1]
# Note `img_shape` is not dynamically traceable to ONNX,
# here `img_shape_for_onnx` (padded shape of image tensor)
# is used.
img_shape = img_metas[0]['img_shape_for_onnx']
max_per_img = self.test_cfg.get('max_per_img', self.num_query)
batch_size = cls_scores.size(0)
# `batch_index_offset` is used for the gather of concatenated tensor
batch_index_offset = torch.arange(batch_size).to(
cls_scores.device) * max_per_img
batch_index_offset = batch_index_offset.unsqueeze(1).expand(
batch_size, max_per_img)
# supports dynamical batch inference
if self.loss_cls.use_sigmoid:
cls_scores = cls_scores.sigmoid()
scores, indexes = cls_scores.view(batch_size, -1).topk(
max_per_img, dim=1)
det_labels = indexes % self.num_classes
bbox_index = indexes // self.num_classes
bbox_index = (bbox_index + batch_index_offset).view(-1)
bbox_preds = bbox_preds.view(-1, 4)[bbox_index]
bbox_preds = bbox_preds.view(batch_size, -1, 4)
else:
scores, det_labels = F.softmax(
cls_scores, dim=-1)[..., :-1].max(-1)
scores, bbox_index = scores.topk(max_per_img, dim=1)
bbox_index = (bbox_index + batch_index_offset).view(-1)
bbox_preds = bbox_preds.view(-1, 4)[bbox_index]
det_labels = det_labels.view(-1)[bbox_index]
bbox_preds = bbox_preds.view(batch_size, -1, 4)
det_labels = det_labels.view(batch_size, -1)
det_bboxes = bbox_cxcywh_to_xyxy(bbox_preds)
# use `img_shape_tensor` for dynamically exporting to ONNX
img_shape_tensor = img_shape.flip(0).repeat(2) # [w,h,w,h]
img_shape_tensor = img_shape_tensor.unsqueeze(0).unsqueeze(0).expand(
batch_size, det_bboxes.size(1), 4)
det_bboxes = det_bboxes * img_shape_tensor
# dynamically clip bboxes
x1, y1, x2, y2 = det_bboxes.split((1, 1, 1, 1), dim=-1)
from mmdet.core.export import dynamic_clip_for_onnx
x1, y1, x2, y2 = dynamic_clip_for_onnx(x1, y1, x2, y2, img_shape)
det_bboxes = torch.cat([x1, y1, x2, y2], dim=-1)
det_bboxes = torch.cat((det_bboxes, scores.unsqueeze(-1)), -1)
return det_bboxes, det_labels