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# Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import Dict, List, Tuple
import torch
import torch.nn as nn
from mmcv.cnn import Linear
from mmengine.model import bias_init_with_prob, constant_init
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.utils import InstanceList, OptInstanceList
from ..layers import inverse_sigmoid
from .detr_head import DETRHead
@MODELS.register_module()
class DeformableDETRHead(DETRHead):
r"""Head of DeformDETR: Deformable DETR: Deformable Transformers for
End-to-End Object Detection.
Code is modified from the `official github repo
<https://github.com/fundamentalvision/Deformable-DETR>`_.
More details can be found in the `paper
<https://arxiv.org/abs/2010.04159>`_ .
Args:
share_pred_layer (bool): Whether to share parameters for all the
prediction layers. Defaults to `False`.
num_pred_layer (int): The number of the prediction layers.
Defaults to 6.
as_two_stage (bool, optional): Whether to generate the proposal
from the outputs of encoder. Defaults to `False`.
"""
def __init__(self,
*args,
share_pred_layer: bool = False,
num_pred_layer: int = 6,
as_two_stage: bool = False,
**kwargs) -> None:
self.share_pred_layer = share_pred_layer
self.num_pred_layer = num_pred_layer
self.as_two_stage = as_two_stage
super().__init__(*args, **kwargs)
def _init_layers(self) -> None:
"""Initialize classification branch and regression branch of head."""
fc_cls = Linear(self.embed_dims, self.cls_out_channels)
reg_branch = []
for _ in range(self.num_reg_fcs):
reg_branch.append(Linear(self.embed_dims, self.embed_dims))
reg_branch.append(nn.ReLU())
reg_branch.append(Linear(self.embed_dims, 4))
reg_branch = nn.Sequential(*reg_branch)
if self.share_pred_layer:
self.cls_branches = nn.ModuleList(
[fc_cls for _ in range(self.num_pred_layer)])
self.reg_branches = nn.ModuleList(
[reg_branch for _ in range(self.num_pred_layer)])
else:
self.cls_branches = nn.ModuleList(
[copy.deepcopy(fc_cls) for _ in range(self.num_pred_layer)])
self.reg_branches = nn.ModuleList([
copy.deepcopy(reg_branch) for _ in range(self.num_pred_layer)
])
def init_weights(self) -> None:
"""Initialize weights of the Deformable DETR head."""
if self.loss_cls.use_sigmoid:
bias_init = bias_init_with_prob(0.01)
for m in self.cls_branches:
nn.init.constant_(m.bias, bias_init)
for m in self.reg_branches:
constant_init(m[-1], 0, bias=0)
nn.init.constant_(self.reg_branches[0][-1].bias.data[2:], -2.0)
if self.as_two_stage:
for m in self.reg_branches:
nn.init.constant_(m[-1].bias.data[2:], 0.0)
def forward(self, hidden_states: Tensor,
references: List[Tensor]) -> Tuple[Tensor]:
"""Forward function.
Args:
hidden_states (Tensor): Hidden states output from each decoder
layer, has shape (num_decoder_layers, bs, num_queries, dim).
references (list[Tensor]): List of the reference from the decoder.
The first reference is the `init_reference` (initial) and the
other num_decoder_layers(6) references are `inter_references`
(intermediate). The `init_reference` has shape (bs,
num_queries, 4) when `as_two_stage` of the detector is `True`,
otherwise (bs, num_queries, 2). Each `inter_reference` has
shape (bs, num_queries, 4) when `with_box_refine` of the
detector is `True`, otherwise (bs, num_queries, 2). The
coordinates are arranged as (cx, cy) when the last dimension is
2, and (cx, cy, w, h) when it is 4.
Returns:
tuple[Tensor]: results of head containing the following tensor.
- all_layers_outputs_classes (Tensor): Outputs from the
classification head, has shape (num_decoder_layers, bs,
num_queries, cls_out_channels).
- all_layers_outputs_coords (Tensor): Sigmoid outputs from the
regression head with normalized coordinate format (cx, cy, w,
h), has shape (num_decoder_layers, bs, num_queries, 4) with the
last dimension arranged as (cx, cy, w, h).
"""
all_layers_outputs_classes = []
all_layers_outputs_coords = []
for layer_id in range(hidden_states.shape[0]):
reference = inverse_sigmoid(references[layer_id])
# NOTE The last reference will not be used.
hidden_state = hidden_states[layer_id]
outputs_class = self.cls_branches[layer_id](hidden_state)
tmp_reg_preds = self.reg_branches[layer_id](hidden_state)
if reference.shape[-1] == 4:
# When `layer` is 0 and `as_two_stage` of the detector
# is `True`, or when `layer` is greater than 0 and
# `with_box_refine` of the detector is `True`.
tmp_reg_preds += reference
else:
# When `layer` is 0 and `as_two_stage` of the detector
# is `False`, or when `layer` is greater than 0 and
# `with_box_refine` of the detector is `False`.
assert reference.shape[-1] == 2
tmp_reg_preds[..., :2] += reference
outputs_coord = tmp_reg_preds.sigmoid()
all_layers_outputs_classes.append(outputs_class)
all_layers_outputs_coords.append(outputs_coord)
all_layers_outputs_classes = torch.stack(all_layers_outputs_classes)
all_layers_outputs_coords = torch.stack(all_layers_outputs_coords)
return all_layers_outputs_classes, all_layers_outputs_coords
def loss(self, hidden_states: Tensor, references: List[Tensor],
enc_outputs_class: Tensor, enc_outputs_coord: Tensor,
batch_data_samples: SampleList) -> dict:
"""Perform forward propagation and loss calculation of the detection
head on the queries of the upstream network.
Args:
hidden_states (Tensor): Hidden states output from each decoder
layer, has shape (num_decoder_layers, num_queries, bs, dim).
references (list[Tensor]): List of the reference from the decoder.
The first reference is the `init_reference` (initial) and the
other num_decoder_layers(6) references are `inter_references`
(intermediate). The `init_reference` has shape (bs,
num_queries, 4) when `as_two_stage` of the detector is `True`,
otherwise (bs, num_queries, 2). Each `inter_reference` has
shape (bs, num_queries, 4) when `with_box_refine` of the
detector is `True`, otherwise (bs, num_queries, 2). The
coordinates are arranged as (cx, cy) when the last dimension is
2, and (cx, cy, w, h) when it is 4.
enc_outputs_class (Tensor): The score of each point on encode
feature map, has shape (bs, num_feat_points, cls_out_channels).
Only when `as_two_stage` is `True` it would be passed in,
otherwise it would be `None`.
enc_outputs_coord (Tensor): The proposal generate from the encode
feature map, has shape (bs, num_feat_points, 4) with the last
dimension arranged as (cx, cy, w, h). Only when `as_two_stage`
is `True` it would be passed in, otherwise it would be `None`.
batch_data_samples (list[:obj:`DetDataSample`]): The Data
Samples. It usually includes information such as
`gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
Returns:
dict: A dictionary of loss components.
"""
batch_gt_instances = []
batch_img_metas = []
for data_sample in batch_data_samples:
batch_img_metas.append(data_sample.metainfo)
batch_gt_instances.append(data_sample.gt_instances)
outs = self(hidden_states, references)
loss_inputs = outs + (enc_outputs_class, enc_outputs_coord,
batch_gt_instances, batch_img_metas)
losses = self.loss_by_feat(*loss_inputs)
return losses
def loss_by_feat(
self,
all_layers_cls_scores: Tensor,
all_layers_bbox_preds: Tensor,
enc_cls_scores: Tensor,
enc_bbox_preds: Tensor,
batch_gt_instances: InstanceList,
batch_img_metas: List[dict],
batch_gt_instances_ignore: OptInstanceList = None
) -> Dict[str, Tensor]:
"""Loss function.
Args:
all_layers_cls_scores (Tensor): Classification scores of all
decoder layers, has shape (num_decoder_layers, bs, num_queries,
cls_out_channels).
all_layers_bbox_preds (Tensor): Regression outputs of all decoder
layers. Each is a 4D-tensor with normalized coordinate format
(cx, cy, w, h) and has shape (num_decoder_layers, bs,
num_queries, 4) with the last dimension arranged as
(cx, cy, w, h).
enc_cls_scores (Tensor): The score of each point on encode
feature map, has shape (bs, num_feat_points, cls_out_channels).
Only when `as_two_stage` is `True` it would be passes in,
otherwise, it would be `None`.
enc_bbox_preds (Tensor): The proposal generate from the encode
feature map, has shape (bs, num_feat_points, 4) with the last
dimension arranged as (cx, cy, w, h). Only when `as_two_stage`
is `True` it would be passed in, otherwise it would be `None`.
batch_gt_instances (list[:obj:`InstanceData`]): Batch of
gt_instance. It usually includes ``bboxes`` and ``labels``
attributes.
batch_img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
Batch of gt_instances_ignore. It includes ``bboxes`` attribute
data that is ignored during training and testing.
Defaults to None.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
loss_dict = super().loss_by_feat(all_layers_cls_scores,
all_layers_bbox_preds,
batch_gt_instances, batch_img_metas,
batch_gt_instances_ignore)
# loss of proposal generated from encode feature map.
if enc_cls_scores is not None:
proposal_gt_instances = copy.deepcopy(batch_gt_instances)
for i in range(len(proposal_gt_instances)):
proposal_gt_instances[i].labels = torch.zeros_like(
proposal_gt_instances[i].labels)
enc_loss_cls, enc_losses_bbox, enc_losses_iou = \
self.loss_by_feat_single(
enc_cls_scores, enc_bbox_preds,
batch_gt_instances=proposal_gt_instances,
batch_img_metas=batch_img_metas)
loss_dict['enc_loss_cls'] = enc_loss_cls
loss_dict['enc_loss_bbox'] = enc_losses_bbox
loss_dict['enc_loss_iou'] = enc_losses_iou
return loss_dict
def predict(self,
hidden_states: Tensor,
references: List[Tensor],
batch_data_samples: SampleList,
rescale: bool = True) -> InstanceList:
"""Perform forward propagation and loss calculation of the detection
head on the queries of the upstream network.
Args:
hidden_states (Tensor): Hidden states output from each decoder
layer, has shape (num_decoder_layers, num_queries, bs, dim).
references (list[Tensor]): List of the reference from the decoder.
The first reference is the `init_reference` (initial) and the
other num_decoder_layers(6) references are `inter_references`
(intermediate). The `init_reference` has shape (bs,
num_queries, 4) when `as_two_stage` of the detector is `True`,
otherwise (bs, num_queries, 2). Each `inter_reference` has
shape (bs, num_queries, 4) when `with_box_refine` of the
detector is `True`, otherwise (bs, num_queries, 2). The
coordinates are arranged as (cx, cy) when the last dimension is
2, and (cx, cy, w, h) when it is 4.
batch_data_samples (list[:obj:`DetDataSample`]): The Data
Samples. It usually includes information such as
`gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
rescale (bool, optional): If `True`, return boxes in original
image space. Defaults to `True`.
Returns:
list[obj:`InstanceData`]: Detection results of each image
after the post process.
"""
batch_img_metas = [
data_samples.metainfo for data_samples in batch_data_samples
]
outs = self(hidden_states, references)
predictions = self.predict_by_feat(
*outs, batch_img_metas=batch_img_metas, rescale=rescale)
return predictions
def predict_by_feat(self,
all_layers_cls_scores: Tensor,
all_layers_bbox_preds: Tensor,
batch_img_metas: List[Dict],
rescale: bool = False) -> InstanceList:
"""Transform a batch of output features extracted from the head into
bbox results.
Args:
all_layers_cls_scores (Tensor): Classification scores of all
decoder layers, has shape (num_decoder_layers, bs, num_queries,
cls_out_channels).
all_layers_bbox_preds (Tensor): Regression outputs of all decoder
layers. Each is a 4D-tensor with normalized coordinate format
(cx, cy, w, h) and shape (num_decoder_layers, bs, num_queries,
4) with the last dimension arranged as (cx, cy, w, h).
batch_img_metas (list[dict]): Meta information of each image.
rescale (bool, optional): If `True`, return boxes in original
image space. Default `False`.
Returns:
list[obj:`InstanceData`]: Detection results of each image
after the post process.
"""
cls_scores = all_layers_cls_scores[-1]
bbox_preds = all_layers_bbox_preds[-1]
result_list = []
for img_id in range(len(batch_img_metas)):
cls_score = cls_scores[img_id]
bbox_pred = bbox_preds[img_id]
img_meta = batch_img_metas[img_id]
results = self._predict_by_feat_single(cls_score, bbox_pred,
img_meta, rescale)
result_list.append(results)
return result_list
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