# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Tuple
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register_module()
class MaskFormer(SingleStageDetector):
r"""Implementation of `Per-Pixel Classification is
NOT All You Need for Semantic Segmentation
<https://arxiv.org/pdf/2107.06278>`_."""
def __init__(self,
backbone: ConfigType,
neck: OptConfigType = None,
panoptic_head: OptConfigType = None,
panoptic_fusion_head: OptConfigType = None,
train_cfg: OptConfigType = None,
test_cfg: OptConfigType = None,
data_preprocessor: OptConfigType = None,
init_cfg: OptMultiConfig = None):
super(SingleStageDetector, self).__init__(
data_preprocessor=data_preprocessor, init_cfg=init_cfg)
self.backbone = MODELS.build(backbone)
if neck is not None:
self.neck = MODELS.build(neck)
panoptic_head_ = panoptic_head.deepcopy()
panoptic_head_.update(train_cfg=train_cfg)
panoptic_head_.update(test_cfg=test_cfg)
self.panoptic_head = MODELS.build(panoptic_head_)
panoptic_fusion_head_ = panoptic_fusion_head.deepcopy()
panoptic_fusion_head_.update(test_cfg=test_cfg)
self.panoptic_fusion_head = MODELS.build(panoptic_fusion_head_)
self.num_things_classes = self.panoptic_head.num_things_classes
self.num_stuff_classes = self.panoptic_head.num_stuff_classes
self.num_classes = self.panoptic_head.num_classes
self.train_cfg = train_cfg
self.test_cfg = test_cfg
def loss(self, batch_inputs: Tensor,
batch_data_samples: SampleList) -> Dict[str, Tensor]:
"""
Args:
batch_inputs (Tensor): Input images of shape (N, C, 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[str, Tensor]: a dictionary of loss components
"""
x = self.extract_feat(batch_inputs)
losses = self.panoptic_head.loss(x, 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 with shape (N, C, H, W).
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): 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 `pred_panoptic_seg`. 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).
- masks (Tensor): Has a shape (num_instances, H, W).
And the ``pred_panoptic_seg`` contains the following key
- sem_seg (Tensor): panoptic segmentation mask, has a
shape (1, h, w).
"""
feats = self.extract_feat(batch_inputs)
mask_cls_results, mask_pred_results = self.panoptic_head.predict(
feats, batch_data_samples)
results_list = self.panoptic_fusion_head.predict(
mask_cls_results,
mask_pred_results,
batch_data_samples,
rescale=rescale)
results = self.add_pred_to_datasample(batch_data_samples, results_list)
return results
def add_pred_to_datasample(self, data_samples: SampleList,
results_list: List[dict]) -> SampleList:
"""Add predictions to `DetDataSample`.
Args:
data_samples (list[:obj:`DetDataSample`], optional): A batch of
data samples that contain annotations and predictions.
results_list (List[dict]): Instance segmentation, segmantic
segmentation and panoptic segmentation results.
Returns:
list[:obj:`DetDataSample`]: Detection results of the
input images. Each DetDataSample usually contain
'pred_instances' and `pred_panoptic_seg`. 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).
- masks (Tensor): Has a shape (num_instances, H, W).
And the ``pred_panoptic_seg`` contains the following key
- sem_seg (Tensor): panoptic segmentation mask, has a
shape (1, h, w).
"""
for data_sample, pred_results in zip(data_samples, results_list):
if 'pan_results' in pred_results:
data_sample.pred_panoptic_seg = pred_results['pan_results']
if 'ins_results' in pred_results:
data_sample.pred_instances = pred_results['ins_results']
assert 'sem_results' not in pred_results, 'segmantic ' \
'segmentation results are not supported yet.'
return data_samples
def _forward(self, batch_inputs: Tensor,
batch_data_samples: SampleList) -> Tuple[List[Tensor]]:
"""Network forward process. Usually includes backbone, neck and head
forward without any post-processing.
Args:
batch_inputs (Tensor): Inputs with shape (N, C, 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`.
Returns:
tuple[List[Tensor]]: A tuple of features from ``panoptic_head``
forward.
"""
feats = self.extract_feat(batch_inputs)
results = self.panoptic_head.forward(feats, batch_data_samples)
return results