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from typing import Optional, Sequence, List
import torch
import random
import numpy as np
from mmcv.transforms import to_tensor
from mmcv.transforms.base import BaseTransform
from mmdet.datasets.transforms import PackDetInputs
from mmdet.structures.bbox import BaseBoxes
from mmengine.structures import InstanceData, PixelData
from mmdet.registry import TRANSFORMS
from mmdet.structures import DetDataSample, TrackDataSample
@TRANSFORMS.register_module()
class PackVidSegInputs(BaseTransform):
"""Pack the inputs data for the multi object tracking and video instance
segmentation. All the information of images are packed to ``inputs``. All
the information except images are packed to ``data_samples``. In order to
get the original annotaiton and meta info, we add `instances` key into meta
keys.
Args:
meta_keys (Sequence[str]): Meta keys to be collected in
``data_sample.metainfo``. Defaults to None.
default_meta_keys (tuple): Default meta keys. Defaults to ('img_id',
'img_path', 'ori_shape', 'img_shape', 'scale_factor',
'flip', 'flip_direction', 'frame_id', 'is_video_data',
'video_id', 'video_length', 'instances').
"""
mapping_table = {
'gt_bboxes': 'bboxes',
'gt_bboxes_labels': 'labels',
'gt_masks': 'masks',
'gt_instances_ids': 'instances_ids'
}
def __init__(self,
meta_keys: Optional[dict] = None,
default_meta_keys: tuple = ('img_id', 'img_path', 'ori_shape',
'img_shape', 'scale_factor',
'flip', 'flip_direction',
'frame_id', 'video_id',
'video_length',
'ori_video_length', 'instances')):
self.meta_keys = default_meta_keys
if meta_keys is not None:
if isinstance(meta_keys, str):
meta_keys = (meta_keys,)
else:
assert isinstance(meta_keys, tuple), \
'meta_keys must be str or tuple'
self.meta_keys += meta_keys
def transform(self, results: dict) -> dict:
"""Method to pack the input data.
Args:
results (dict): Result dict from the data pipeline.
Returns:
dict:
- 'inputs' (dict[Tensor]): The forward data of models.
- 'data_samples' (obj:`TrackDataSample`): The annotation info of
the samples.
"""
packed_results = dict()
packed_results['inputs'] = dict()
# 1. Pack images
if 'img' in results:
imgs = results['img']
imgs = np.stack(imgs, axis=0)
imgs = imgs.transpose(0, 3, 1, 2)
packed_results['inputs'] = to_tensor(imgs)
# 2. Pack InstanceData
if 'gt_ignore_flags' in results:
gt_ignore_flags_list = results['gt_ignore_flags']
valid_idx_list, ignore_idx_list = [], []
for gt_ignore_flags in gt_ignore_flags_list:
valid_idx = np.where(gt_ignore_flags == 0)[0]
ignore_idx = np.where(gt_ignore_flags == 1)[0]
valid_idx_list.append(valid_idx)
ignore_idx_list.append(ignore_idx)
assert 'img_id' in results, "'img_id' must contained in the results "
'for counting the number of images'
num_imgs = len(results['img_id'])
instance_data_list = [InstanceData() for _ in range(num_imgs)]
ignore_instance_data_list = [InstanceData() for _ in range(num_imgs)]
for key in self.mapping_table.keys():
if key not in results:
continue
if key == 'gt_masks' or (isinstance(results[key], List) and isinstance(results[key][0], BaseBoxes)):
mapped_key = self.mapping_table[key]
gt_masks_list = results[key]
if 'gt_ignore_flags' in results:
for i, gt_mask in enumerate(gt_masks_list):
valid_idx, ignore_idx = valid_idx_list[
i], ignore_idx_list[i]
instance_data_list[i][mapped_key] = gt_mask[valid_idx]
ignore_instance_data_list[i][mapped_key] = gt_mask[
ignore_idx]
else:
for i, gt_mask in enumerate(gt_masks_list):
instance_data_list[i][mapped_key] = gt_mask
else:
anns_list = results[key]
if 'gt_ignore_flags' in results:
for i, ann in enumerate(anns_list):
valid_idx, ignore_idx = valid_idx_list[
i], ignore_idx_list[i]
instance_data_list[i][
self.mapping_table[key]] = to_tensor(
ann[valid_idx])
ignore_instance_data_list[i][
self.mapping_table[key]] = to_tensor(
ann[ignore_idx])
else:
for i, ann in enumerate(anns_list):
instance_data_list[i][
self.mapping_table[key]] = to_tensor(ann)
det_data_samples_list = []
for i in range(num_imgs):
det_data_sample = DetDataSample()
det_data_sample.gt_instances = instance_data_list[i]
det_data_sample.ignored_instances = ignore_instance_data_list[i]
if 'proposals' in results:
proposals = InstanceData(
bboxes=to_tensor(results['proposals'][i]),
scores=to_tensor(results['proposals_scores'][i]))
det_data_sample.proposals = proposals
if 'gt_seg_map' in results:
gt_sem_seg_data = dict(
sem_seg=to_tensor(results['gt_seg_map'][i][None, ...].copy()))
gt_sem_seg_data = PixelData(**gt_sem_seg_data)
if 'ignore_index' in results:
metainfo = dict(ignore_index=results['ignore_index'][i])
gt_sem_seg_data.set_metainfo(metainfo)
det_data_sample.gt_sem_seg = gt_sem_seg_data
det_data_samples_list.append(det_data_sample)
# 3. Pack metainfo
for key in self.meta_keys:
if key not in results:
continue
img_metas_list = results[key]
for i, img_meta in enumerate(img_metas_list):
det_data_samples_list[i].set_metainfo({f'{key}': img_meta})
track_data_sample = TrackDataSample()
track_data_sample.video_data_samples = det_data_samples_list
if 'key_frame_flags' in results:
key_frame_flags = np.asarray(results['key_frame_flags'])
key_frames_inds = np.where(key_frame_flags)[0].tolist()
ref_frames_inds = np.where(~key_frame_flags)[0].tolist()
track_data_sample.set_metainfo(
dict(key_frames_inds=key_frames_inds))
track_data_sample.set_metainfo(
dict(ref_frames_inds=ref_frames_inds))
packed_results['data_samples'] = track_data_sample
return packed_results
def __repr__(self) -> str:
repr_str = self.__class__.__name__
repr_str += f'meta_keys={self.meta_keys}, '
repr_str += f'default_meta_keys={self.default_meta_keys})'
return repr_str
@TRANSFORMS.register_module()
class PackSAMInputs(PackDetInputs):
mapping_table = {
'gt_bboxes': 'bboxes',
'gt_bboxes_labels': 'labels',
'gt_masks': 'masks',
'gt_point_coords': 'point_coords',
}
def transform(self, results: dict) -> dict:
if 'feat' in results:
gt_feats = results['feat']
results = super().transform(results)
results['data_samples'].gt_feats = gt_feats
return results
else:
return super().transform(results)
@TRANSFORMS.register_module()
class GeneratePoint(BaseTransform):
def __init__(self, num_proposals=60, num_mask_tokens=4):
self.num_proposals = num_proposals
self.num_mask_tokens = num_mask_tokens
def transform(self, results):
data_samples = results['data_samples']
gt_instances = data_samples.gt_instances
ori_num_instances = len(gt_instances)
ori_indices = torch.randperm(ori_num_instances)
if ori_num_instances < self.num_proposals:
repeat_cnt = (self.num_proposals // ori_num_instances) + 1
ori_indices = ori_indices.repeat(repeat_cnt)
indices = ori_indices[:self.num_proposals]
masks = gt_instances.masks.to_tensor(torch.bool, 'cpu')
gt_collected = []
for instance_idx in indices:
mask = masks[instance_idx]
candidate_indices = mask.nonzero()
assert len(candidate_indices) > 0
selected_index = random.randint(0, len(candidate_indices) - 1)
selected_point = candidate_indices[selected_index].flip(0)
selected_instances_idx = []
for instance_to_match_idx in range(len(gt_instances)):
mask_to_match = masks[instance_to_match_idx]
if mask_to_match[tuple(selected_point.flip(0))]:
selected_instances_idx.append(instance_to_match_idx)
assert len(selected_instances_idx) > 0
if len(selected_instances_idx) > self.num_mask_tokens:
random.shuffle(selected_instances_idx)
selected_instances_idx = selected_instances_idx[:self.num_mask_tokens]
selected_point = torch.cat([selected_point - 3, selected_point + 3], 0)
gt_collected.append({
'point_coords': selected_point,
'instances': selected_instances_idx,
})
data_samples.gt_instances_collected = InstanceData(
point_coords=torch.stack([itm['point_coords'] for itm in gt_collected]),
sub_instances=[itm['instances'] for itm in gt_collected],
idx=indices
)
return results
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