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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Tuple
import torch
from mmengine.structures import InstanceData
from torch import Tensor
import torch.nn.functional as F
from mmdet.registry import MODELS
from mmdet.structures import SampleList, OptSampleList, TrackDataSample
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from mmdet.models.detectors.single_stage import SingleStageDetector
from seg.models.utils import mask_pool
@MODELS.register_module()
class Mask2formerVideo(SingleStageDetector):
r"""Implementation of `Per-Pixel Classification is
NOT All You Need for Semantic Segmentation
<https://arxiv.org/pdf/2107.06278>`_."""
OVERLAPPING = None
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,
inference_sam: bool = False,
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
self.alpha = 0.4
self.beta = 0.8
self.inference_sam = inference_sam
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
"""
if isinstance(batch_data_samples[0], TrackDataSample):
bs, num_frames, three, h, w = batch_inputs.shape
assert three == 3, "Only supporting images with 3 channels."
x = batch_inputs.reshape((bs * num_frames, three, h, w))
x = self.extract_feat(x)
else:
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).
"""
if isinstance(batch_data_samples[0], TrackDataSample):
bs, num_frames, three, h, w = batch_inputs.shape
assert three == 3, "Only supporting images with 3 channels."
x = batch_inputs.reshape((bs * num_frames, three, h, w))
feats = self.extract_feat(x)
else:
num_frames = 0
bs = batch_inputs.shape[0]
feats = self.extract_feat(batch_inputs)
# in case no queries are provided for prompt.
if self.inference_sam and len(batch_data_samples[0].gt_instances) == 0:
for idx, data_sample in enumerate(batch_data_samples):
results = InstanceData()
data_sample.pred_instances = results
return batch_data_samples
mask_cls_results, mask_pred_results, iou_results = self.panoptic_head.predict(feats, batch_data_samples)
if self.OVERLAPPING is not None:
assert len(self.OVERLAPPING) == self.num_classes
mask_cls_results = self.open_voc_inference(feats, mask_cls_results, mask_pred_results)
if batch_data_samples[0].data_tag == 'sam':
return mask_pred_results.cpu().numpy()
# # if self.inference_sam:
# for idx, data_sample in enumerate(batch_data_samples):
# results = InstanceData()
# mask = mask_pred_results[idx]
# img_height, img_width = data_sample.metainfo['img_shape'][:2]
# mask = mask[:, :img_height, :img_width]
# ori_height, ori_width = data_sample.metainfo['ori_shape'][:2]
# mask = F.interpolate(
# mask[:, None],
# size=(ori_height, ori_width),
# mode='bilinear',
# align_corners=False)[:, 0]
# results.masks = mask.sigmoid() > 0.5
# data_sample.pred_instances = results
# return batch_data_samples
if num_frames > 0:
for frame_id in range(num_frames):
results_list_img = self.panoptic_fusion_head.predict(
mask_cls_results,
mask_pred_results[:, :, frame_id],
[batch_data_samples[idx][frame_id] for idx in range(bs)],
rescale=rescale
)
_ = self.add_track_pred_to_datasample(
[batch_data_samples[idx][frame_id] for idx in range(bs)], results_list_img
)
results = batch_data_samples
else:
results_list = self.panoptic_fusion_head.predict(
mask_cls_results,
mask_pred_results,
batch_data_samples,
iou_results=iou_results,
rescale=rescale
)
results = self.add_pred_to_datasample(batch_data_samples, results_list)
return results_list
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
return data_samples
def add_track_pred_to_datasample(self, data_samples: SampleList, results_list: List[dict]) -> SampleList:
for data_sample, pred_results in zip(data_samples, results_list):
if 'pan_results' in pred_results:
assert self.num_stuff_classes > 0
pred_results['pan_results'].sem_seg = pred_results['pan_results'].sem_seg.cpu()
data_sample.pred_track_panoptic_seg = pred_results['pan_results']
if 'ins_results' in pred_results:
bboxes = pred_results['ins_results']['bboxes']
labels = pred_results['ins_results']['labels']
track_ids = torch.arange(len(bboxes), dtype=labels.dtype, device=bboxes.device) + 1
pred_results['ins_results']['instances_id'] = track_ids
data_sample.pred_track_instances = pred_results['ins_results']
if 'pro_results' in pred_results:
data_sample.pred_track_proposal = pred_results['pro_results']
assert 'sem_results' not in pred_results
return 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 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.
"""
if isinstance(batch_data_samples[0], TrackDataSample):
bs, num_frames, three, h, w = batch_inputs.shape
assert three == 3, "Only supporting images with 3 channels."
x = batch_inputs.reshape((bs * num_frames, three, h, w))
feats = self.extract_feat(x)
else:
feats = self.extract_feat(batch_inputs)
results = self.panoptic_head.forward(feats, batch_data_samples)
return results
def open_voc_inference(self, feats, mask_cls_results, mask_pred_results):
if len(mask_pred_results.shape) == 5:
batch_size = mask_cls_results.shape[0]
num_frames = mask_pred_results.shape[2]
mask_pred_results = mask_pred_results.permute(0, 2, 1, 3, 4).flatten(0, 1)
else:
batch_size = mask_cls_results.shape[0]
num_frames = 0
clip_feat = self.backbone.get_clip_feature(feats[-1])
clip_feat_mask = F.interpolate(
mask_pred_results,
size=clip_feat.shape[-2:],
mode='bilinear',
align_corners=False
)
if num_frames > 0:
clip_feat_mask = clip_feat_mask.unflatten(0, (batch_size, num_frames)).permute(0, 2, 1, 3, 4).flatten(2, 3)
clip_feat = clip_feat.unflatten(0, (batch_size, num_frames)).permute(0, 2, 1, 3, 4).flatten(2, 3)
instance_feat = mask_pool(clip_feat, clip_feat_mask)
instance_feat = self.backbone.forward_feat(instance_feat)
clip_logit = self.panoptic_head.forward_logit(instance_feat)
clip_logit = clip_logit[..., :-1]
query_logit = mask_cls_results[..., :-1]
clip_logit = clip_logit.softmax(-1)
query_logit = query_logit.softmax(-1)
overlapping_mask = torch.tensor(self.OVERLAPPING, dtype=torch.float32, device=clip_logit.device)
valid_masking = ((clip_feat_mask > 0).to(dtype=torch.float32).flatten(-2).sum(-1) > 0).to(
torch.float32)[..., None]
alpha = torch.ones_like(clip_logit) * self.alpha * valid_masking
beta = torch.ones_like(clip_logit) * self.beta * valid_masking
cls_logits_seen = (
(query_logit ** (1 - alpha) * clip_logit ** alpha).log()
* overlapping_mask
)
cls_logits_unseen = (
(query_logit ** (1 - beta) * clip_logit ** beta).log()
* (1 - overlapping_mask)
)
cls_results = cls_logits_seen + cls_logits_unseen
is_void_prob = F.softmax(mask_cls_results, dim=-1)[..., -1:]
mask_cls_results = torch.cat([
cls_results.softmax(-1) * (1.0 - is_void_prob), is_void_prob], dim=-1)
mask_cls_results = torch.log(mask_cls_results + 1e-8)
return mask_cls_results
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