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# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Dict, List, Tuple, Union
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures import OptSampleList, SampleList
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .base import BaseDetector
@MODELS.register_module()
class DetectionTransformer(BaseDetector, metaclass=ABCMeta):
r"""Base class for Detection Transformer.
In Detection Transformer, an encoder is used to process output features of
neck, then several queries interact with the encoder features using a
decoder and do the regression and classification with the bounding box
head.
Args:
backbone (:obj:`ConfigDict` or dict): Config of the backbone.
neck (:obj:`ConfigDict` or dict, optional): Config of the neck.
Defaults to None.
encoder (:obj:`ConfigDict` or dict, optional): Config of the
Transformer encoder. Defaults to None.
decoder (:obj:`ConfigDict` or dict, optional): Config of the
Transformer decoder. Defaults to None.
bbox_head (:obj:`ConfigDict` or dict, optional): Config for the
bounding box head module. Defaults to None.
positional_encoding (:obj:`ConfigDict` or dict, optional): Config
of the positional encoding module. Defaults to None.
num_queries (int, optional): Number of decoder query in Transformer.
Defaults to 100.
train_cfg (:obj:`ConfigDict` or dict, optional): Training config of
the bounding box head module. Defaults to None.
test_cfg (:obj:`ConfigDict` or dict, optional): Testing config of
the bounding box head module. Defaults to None.
data_preprocessor (dict or ConfigDict, optional): The pre-process
config of :class:`BaseDataPreprocessor`. it usually includes,
``pad_size_divisor``, ``pad_value``, ``mean`` and ``std``.
Defaults to None.
init_cfg (:obj:`ConfigDict` or dict, optional): the config to control
the initialization. Defaults to None.
"""
def __init__(self,
backbone: ConfigType,
neck: OptConfigType = None,
encoder: OptConfigType = None,
decoder: OptConfigType = None,
bbox_head: OptConfigType = None,
positional_encoding: OptConfigType = None,
num_queries: int = 100,
train_cfg: OptConfigType = None,
test_cfg: OptConfigType = None,
data_preprocessor: OptConfigType = None,
init_cfg: OptMultiConfig = None) -> None:
super().__init__(
data_preprocessor=data_preprocessor, init_cfg=init_cfg)
# process args
bbox_head.update(train_cfg=train_cfg)
bbox_head.update(test_cfg=test_cfg)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.encoder = encoder
self.decoder = decoder
self.positional_encoding = positional_encoding
self.num_queries = num_queries
# init model layers
self.backbone = MODELS.build(backbone)
if neck is not None:
self.neck = MODELS.build(neck)
self.bbox_head = MODELS.build(bbox_head)
self._init_layers()
@abstractmethod
def _init_layers(self) -> None:
"""Initialize layers except for backbone, neck and bbox_head."""
pass
def loss(self, batch_inputs: Tensor,
batch_data_samples: SampleList) -> Union[dict, list]:
"""Calculate losses from a batch of inputs and data samples.
Args:
batch_inputs (Tensor): Input images of shape (bs, dim, H, W).
These should usually be mean centered and std scaled.
batch_data_samples (List[:obj:`DetDataSample`]): The batch
data samples. It usually includes information such
as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
Returns:
dict: A dictionary of loss components
"""
img_feats = self.extract_feat(batch_inputs)
head_inputs_dict = self.forward_transformer(img_feats,
batch_data_samples)
losses = self.bbox_head.loss(
**head_inputs_dict, batch_data_samples=batch_data_samples)
return losses
def predict(self,
batch_inputs: Tensor,
batch_data_samples: SampleList,
rescale: bool = True) -> SampleList:
"""Predict results from a batch of inputs and data samples with post-
processing.
Args:
batch_inputs (Tensor): Inputs, has shape (bs, dim, H, W).
batch_data_samples (List[:obj:`DetDataSample`]): The batch
data samples. It usually includes information such
as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
rescale (bool): Whether to rescale the results.
Defaults to True.
Returns:
list[:obj:`DetDataSample`]: Detection results of the input images.
Each DetDataSample usually contain 'pred_instances'. And the
`pred_instances` usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
"""
img_feats = self.extract_feat(batch_inputs)
head_inputs_dict = self.forward_transformer(img_feats,
batch_data_samples)
results_list = self.bbox_head.predict(
**head_inputs_dict,
rescale=rescale,
batch_data_samples=batch_data_samples)
batch_data_samples = self.add_pred_to_datasample(
batch_data_samples, results_list)
return batch_data_samples
def _forward(
self,
batch_inputs: Tensor,
batch_data_samples: OptSampleList = None) -> Tuple[List[Tensor]]:
"""Network forward process. Usually includes backbone, neck and head
forward without any post-processing.
Args:
batch_inputs (Tensor): Inputs, has shape (bs, dim, H, W).
batch_data_samples (List[:obj:`DetDataSample`], optional): The
batch data samples. It usually includes information such
as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
Defaults to None.
Returns:
tuple[Tensor]: A tuple of features from ``bbox_head`` forward.
"""
img_feats = self.extract_feat(batch_inputs)
head_inputs_dict = self.forward_transformer(img_feats,
batch_data_samples)
results = self.bbox_head.forward(**head_inputs_dict)
return results
def forward_transformer(self,
img_feats: Tuple[Tensor],
batch_data_samples: OptSampleList = None) -> Dict:
"""Forward process of Transformer, which includes four steps:
'pre_transformer' -> 'encoder' -> 'pre_decoder' -> 'decoder'. We
summarized the parameters flow of the existing DETR-like detector,
which can be illustrated as follow:
.. code:: text
img_feats & batch_data_samples
|
V
+-----------------+
| pre_transformer |
+-----------------+
| |
| V
| +-----------------+
| | forward_encoder |
| +-----------------+
| |
| V
| +---------------+
| | pre_decoder |
| +---------------+
| | |
V V |
+-----------------+ |
| forward_decoder | |
+-----------------+ |
| |
V V
head_inputs_dict
Args:
img_feats (tuple[Tensor]): Tuple of feature maps from neck. Each
feature map has shape (bs, dim, H, W).
batch_data_samples (list[:obj:`DetDataSample`], optional): The
batch data samples. It usually includes information such
as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
Defaults to None.
Returns:
dict: The dictionary of bbox_head function inputs, which always
includes the `hidden_states` of the decoder output and may contain
`references` including the initial and intermediate references.
"""
encoder_inputs_dict, decoder_inputs_dict = self.pre_transformer(
img_feats, batch_data_samples)
encoder_outputs_dict = self.forward_encoder(**encoder_inputs_dict)
tmp_dec_in, head_inputs_dict = self.pre_decoder(**encoder_outputs_dict)
decoder_inputs_dict.update(tmp_dec_in)
decoder_outputs_dict = self.forward_decoder(**decoder_inputs_dict)
head_inputs_dict.update(decoder_outputs_dict)
return head_inputs_dict
def extract_feat(self, batch_inputs: Tensor) -> Tuple[Tensor]:
"""Extract features.
Args:
batch_inputs (Tensor): Image tensor, has shape (bs, dim, H, W).
Returns:
tuple[Tensor]: Tuple of feature maps from neck. Each feature map
has shape (bs, dim, H, W).
"""
x = self.backbone(batch_inputs)
if self.with_neck:
x = self.neck(x)
return x
@abstractmethod
def pre_transformer(
self,
img_feats: Tuple[Tensor],
batch_data_samples: OptSampleList = None) -> Tuple[Dict, Dict]:
"""Process image features before feeding them to the transformer.
Args:
img_feats (tuple[Tensor]): Tuple of feature maps from neck. Each
feature map has shape (bs, dim, H, W).
batch_data_samples (list[:obj:`DetDataSample`], optional): The
batch data samples. It usually includes information such
as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
Defaults to None.
Returns:
tuple[dict, dict]: The first dict contains the inputs of encoder
and the second dict contains the inputs of decoder.
- encoder_inputs_dict (dict): The keyword args dictionary of
`self.forward_encoder()`, which includes 'feat', 'feat_mask',
'feat_pos', and other algorithm-specific arguments.
- decoder_inputs_dict (dict): The keyword args dictionary of
`self.forward_decoder()`, which includes 'memory_mask', and
other algorithm-specific arguments.
"""
pass
@abstractmethod
def forward_encoder(self, feat: Tensor, feat_mask: Tensor,
feat_pos: Tensor, **kwargs) -> Dict:
"""Forward with Transformer encoder.
Args:
feat (Tensor): Sequential features, has shape (bs, num_feat_points,
dim).
feat_mask (Tensor): ByteTensor, the padding mask of the features,
has shape (bs, num_feat_points).
feat_pos (Tensor): The positional embeddings of the features, has
shape (bs, num_feat_points, dim).
Returns:
dict: The dictionary of encoder outputs, which includes the
`memory` of the encoder output and other algorithm-specific
arguments.
"""
pass
@abstractmethod
def pre_decoder(self, memory: Tensor, **kwargs) -> Tuple[Dict, Dict]:
"""Prepare intermediate variables before entering Transformer decoder,
such as `query`, `query_pos`, and `reference_points`.
Args:
memory (Tensor): The output embeddings of the Transformer encoder,
has shape (bs, num_feat_points, dim).
Returns:
tuple[dict, dict]: The first dict contains the inputs of decoder
and the second dict contains the inputs of the bbox_head function.
- decoder_inputs_dict (dict): The keyword dictionary args of
`self.forward_decoder()`, which includes 'query', 'query_pos',
'memory', and other algorithm-specific arguments.
- head_inputs_dict (dict): The keyword dictionary args of the
bbox_head functions, which is usually empty, or includes
`enc_outputs_class` and `enc_outputs_class` when the detector
support 'two stage' or 'query selection' strategies.
"""
pass
@abstractmethod
def forward_decoder(self, query: Tensor, query_pos: Tensor, memory: Tensor,
**kwargs) -> Dict:
"""Forward with Transformer decoder.
Args:
query (Tensor): The queries of decoder inputs, has shape
(bs, num_queries, dim).
query_pos (Tensor): The positional queries of decoder inputs,
has shape (bs, num_queries, dim).
memory (Tensor): The output embeddings of the Transformer encoder,
has shape (bs, num_feat_points, dim).
Returns:
dict: The dictionary of decoder outputs, which includes the
`hidden_states` of the decoder output, `references` including
the initial and intermediate reference_points, and other
algorithm-specific arguments.
"""
pass
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