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import torch
import numpy as np
# Modified by Zhang Ruicheng on 2024.04.21
def snake_collator(batch):
ret = {'inp': default_collate([b['inp'] for b in batch])}
ret.update({'orig_img': default_collate([b['orig_img'] for b in batch])})
ret.update({'img_path': default_collate([b['img_path'] for b in batch])})
meta = default_collate([b['meta'] for b in batch])
ret.update({'meta': meta})
if 'vis_GT' in meta:
return ret
# detection
ct_hm = default_collate([b['ct_hm'] for b in batch])
batch_size = len(batch)
ct_num = torch.max(meta['ct_num'])
wh = torch.zeros([batch_size, ct_num, 2], dtype=torch.float)
# reg = torch.zeros([batch_size, ct_num, 2], dtype=torch.float)
ct_cls = torch.zeros([batch_size, ct_num], dtype=torch.int64)
ct_ind = torch.zeros([batch_size, ct_num], dtype=torch.int64)
ct_01 = torch.zeros([batch_size, ct_num], dtype=torch.uint8)
for i in range(batch_size):
ct_01[i, :meta['ct_num'][i]] = 1
if ct_num != 0:
wh[ct_01] = torch.Tensor(sum([b['wh'] for b in batch], []))
# reg[ct_01] = torch.Tensor(sum([b['reg'] for b in batch], []))
ct_cls[ct_01] = torch.LongTensor(sum([b['ct_cls'] for b in batch], []))
ct_ind[ct_01] = torch.LongTensor(sum([b['ct_ind'] for b in batch], []))
detection = {'ct_hm': ct_hm, 'wh': wh, 'ct_cls': ct_cls, 'ct_ind': ct_ind, 'ct_01': ct_01.float()}
# detection = {'ct_hm': ct_hm, 'wh': wh, 'reg': reg, 'ct_cls': ct_cls, 'ct_ind': ct_ind, 'ct_01': ct_01.float()}
ret.update(detection)
from lib.utils.snake import snake_config
# init
i_it_4pys = torch.zeros([batch_size, ct_num, snake_config.init_poly_num, 2], dtype=torch.float)
c_it_4pys = torch.zeros([batch_size, ct_num, snake_config.init_poly_num, 2], dtype=torch.float)
i_gt_4pys = torch.zeros([batch_size, ct_num, 4, 2], dtype=torch.float)
c_gt_4pys = torch.zeros([batch_size, ct_num, 4, 2], dtype=torch.float)
if ct_num != 0:
i_it_4pys[ct_01] = torch.Tensor(sum([b['i_it_4py'] for b in batch], []))
c_it_4pys[ct_01] = torch.Tensor(sum([b['c_it_4py'] for b in batch], []))
i_gt_4pys[ct_01] = torch.Tensor(sum([b['i_gt_4py'] for b in batch], []))
c_gt_4pys[ct_01] = torch.Tensor(sum([b['c_gt_4py'] for b in batch], []))
init = {'i_it_4py': i_it_4pys, 'c_it_4py': c_it_4pys, 'i_gt_4py': i_gt_4pys, 'c_gt_4py': c_gt_4pys}
ret.update(init)
# evolution
i_it_pys = torch.zeros([batch_size, ct_num, snake_config.poly_num, 2], dtype=torch.float)
c_it_pys = torch.zeros([batch_size, ct_num, snake_config.poly_num, 2], dtype=torch.float)
i_gt_pys = torch.zeros([batch_size, ct_num, snake_config.gt_poly_num, 2], dtype=torch.float)
c_gt_pys = torch.zeros([batch_size, ct_num, snake_config.gt_poly_num, 2], dtype=torch.float)
if ct_num != 0:
i_it_pys[ct_01] = torch.Tensor(sum([b['i_it_py'] for b in batch], []))
c_it_pys[ct_01] = torch.Tensor(sum([b['c_it_py'] for b in batch], []))
i_gt_pys[ct_01] = torch.Tensor(sum([b['i_gt_py'] for b in batch], []))
c_gt_pys[ct_01] = torch.Tensor(sum([b['c_gt_py'] for b in batch], []))
evolution = {'i_it_py': i_it_pys, 'c_it_py': c_it_pys, 'i_gt_py': i_gt_pys, 'c_gt_py': c_gt_pys}
ret.update(evolution)
# Aggregate YOLO targets if present in samples
has_yolo = any(('bboxes' in b and 'cls' in b and 'batch_idx' in b) for b in batch)
if has_yolo:
all_bboxes = []
all_cls = []
all_batch = []
for i, b in enumerate(batch):
if 'bboxes' in b and b['bboxes'] is not None and b['bboxes'].numel() > 0:
n = b['bboxes'].shape[0]
all_bboxes.append(b['bboxes'])
all_cls.append(b['cls'])
# override batch_idx with actual image index i
all_batch.append(torch.full((n, 1), float(i)))
if len(all_bboxes) > 0:
ret['bboxes'] = torch.cat(all_bboxes, dim=0)
ret['cls'] = torch.cat(all_cls, dim=0)
ret['batch_idx'] = torch.cat(all_batch, dim=0)
else:
ret['bboxes'] = torch.zeros((0, 4), dtype=torch.float32)
ret['cls'] = torch.zeros((0, 1), dtype=torch.float32)
ret['batch_idx'] = torch.zeros((0, 1), dtype=torch.float32)
return ret
def dsnake_collator(batch):
ret = snake_collator(batch)
meta = ret['meta']
# detection
act_hm = default_collate([b['act_hm'] for b in batch])
batch_size = len(batch)
act_num = torch.max(meta['act_num'])
awh = torch.zeros([batch_size, act_num, 2], dtype=torch.float)
act_ind = torch.zeros([batch_size, act_num], dtype=torch.int64)
act_01 = torch.zeros([batch_size, act_num], dtype=torch.uint8)
for i in range(batch_size):
act_01[i, :meta['act_num'][i]] = 1
awh[act_01] = torch.Tensor(sum([b['awh'] for b in batch], []))
act_ind[act_01] = torch.LongTensor(sum([b['act_ind'] for b in batch], []))
adet = {'act_hm': act_hm, 'awh': awh, 'act_ind': act_ind, 'act_01': act_01.float()}
ret.update(adet)
return ret
# Modified by Zhang Ruicheng on 2024.04.21
'''
def rcnn_snake_collator(batch):
ret = {'inp': default_collate([b['inp'] for b in batch])}
meta = default_collate([b['meta'] for b in batch])
ret.update({'meta': meta})
if 'vis_GT' in meta:
return ret
# detection
act_hm = default_collate([b['act_hm'] for b in batch])
batch_size = len(batch)
act_num = torch.max(meta['act_num'])
awh = torch.zeros([batch_size, act_num, 2], dtype=torch.float)
act_ind = torch.zeros([batch_size, act_num], dtype=torch.int64)
act_01 = torch.zeros([batch_size, act_num], dtype=torch.uint8)
for i in range(batch_size):
act_01[i, :meta['act_num'][i]] = 1
if act_num != 0:
awh[act_01] = torch.Tensor(sum([b['awh'] for b in batch], []))
act_ind[act_01] = torch.LongTensor(sum([b['act_ind'] for b in batch], []))
detection = {'act_hm': act_hm, 'awh': awh, 'act_ind': act_ind, 'act_01': act_01.float()}
ret.update(detection)
from lib.utils.rcnn_snake import rcnn_snake_config as snake_config
ct_num = torch.max(meta['ct_num'])
ct_01 = torch.zeros([batch_size, ct_num], dtype=torch.uint8)
for i in range(batch_size):
ct_01[i, :meta['ct_num'][i]] = 1
ret.update({'ct_01': ct_01.float()})
# component detection
cp_hm = default_collate(sum([[hm for hm in b['cp_hm']] for b in batch], []))
cp_num_list = sum([[len(wh) for wh in b['cp_wh']] for b in batch], [])
cp_num = max(cp_num_list)
cp_wh = torch.zeros([len(cp_hm), cp_num, 2], dtype=torch.float)
cp_ind = torch.zeros([len(cp_hm), cp_num], dtype=torch.int64)
cp_01 = torch.zeros([len(cp_hm), cp_num], dtype=torch.uint8)
for i in range(len(cp_hm)):
cp_01[i, :cp_num_list[i]] = 1
if cp_num != 0:
cp_wh[cp_01] = torch.Tensor(sum(sum([b['cp_wh'] for b in batch], []), []))
cp_ind[cp_01] = torch.LongTensor(sum(sum([b['cp_ind'] for b in batch], []), []))
cp_hm_ = torch.zeros([batch_size, act_num, 1, snake_config.cp_h, snake_config.cp_w], dtype=torch.float)
cp_wh_ = torch.zeros([batch_size, act_num, cp_num, 2], dtype=torch.float)
cp_ind_ = torch.zeros([batch_size, act_num, cp_num], dtype=torch.int64)
cp_01_ = torch.zeros([batch_size, act_num, cp_num], dtype=torch.uint8)
cp_hm_[act_01] = cp_hm
cp_wh_[act_01] = cp_wh
cp_ind_[act_01] = cp_ind
cp_01_[act_01] = cp_01
cp_detection = {'cp_hm': cp_hm_, 'cp_wh': cp_wh_, 'cp_ind': cp_ind_, 'cp_01': cp_01_.float()}
ret.update(cp_detection)
# init
i_it_4pys = torch.zeros([batch_size, ct_num, snake_config.init_poly_num, 2], dtype=torch.float)
c_it_4pys = torch.zeros([batch_size, ct_num, snake_config.init_poly_num, 2], dtype=torch.float)
i_gt_4pys = torch.zeros([batch_size, ct_num, 4, 2], dtype=torch.float)
c_gt_4pys = torch.zeros([batch_size, ct_num, 4, 2], dtype=torch.float)
if ct_num != 0:
i_it_4pys[ct_01] = torch.Tensor(sum([b['i_it_4py'] for b in batch], []))
c_it_4pys[ct_01] = torch.Tensor(sum([b['c_it_4py'] for b in batch], []))
i_gt_4pys[ct_01] = torch.Tensor(sum([b['i_gt_4py'] for b in batch], []))
c_gt_4pys[ct_01] = torch.Tensor(sum([b['c_gt_4py'] for b in batch], []))
init = {'i_it_4py': i_it_4pys, 'c_it_4py': c_it_4pys, 'i_gt_4py': i_gt_4pys, 'c_gt_4py': c_gt_4pys}
ret.update(init)
# evolution
i_it_pys = torch.zeros([batch_size, ct_num, snake_config.poly_num, 2], dtype=torch.float)
c_it_pys = torch.zeros([batch_size, ct_num, snake_config.poly_num, 2], dtype=torch.float)
i_gt_pys = torch.zeros([batch_size, ct_num, snake_config.gt_poly_num, 2], dtype=torch.float)
c_gt_pys = torch.zeros([batch_size, ct_num, snake_config.gt_poly_num, 2], dtype=torch.float)
if ct_num != 0:
i_it_pys[ct_01] = torch.Tensor(sum([b['i_it_py'] for b in batch], []))
c_it_pys[ct_01] = torch.Tensor(sum([b['c_it_py'] for b in batch], []))
i_gt_pys[ct_01] = torch.Tensor(sum([b['i_gt_py'] for b in batch], []))
c_gt_pys[ct_01] = torch.Tensor(sum([b['c_gt_py'] for b in batch], []))
evolution = {'i_it_py': i_it_pys, 'c_it_py': c_it_pys, 'i_gt_py': i_gt_pys, 'c_gt_py': c_gt_pys}
ret.update(evolution)
return ret
def ext_snake_collator(batch):
ret = {'inp': default_collate([b['inp'] for b in batch])}
meta = default_collate([b['meta'] for b in batch])
ret.update({'meta': meta})
if 'vis_GT' in meta:
return ret
# detection
ct_hm = default_collate([b['ct_hm'] for b in batch])
batch_size = len(batch)
ct_num = torch.max(meta['ct_num'])
ext = torch.zeros([batch_size, ct_num, 8], dtype=torch.float)
ct_cls = torch.zeros([batch_size, ct_num], dtype=torch.int64)
ct_ind = torch.zeros([batch_size, ct_num], dtype=torch.int64)
ct_01 = torch.zeros([batch_size, ct_num], dtype=torch.uint8)
for i in range(batch_size):
ct_01[i, :meta['ct_num'][i]] = 1
ext[ct_01] = torch.Tensor(sum([b['ext'] for b in batch], []))
ct_cls[ct_01] = torch.LongTensor(sum([b['ct_cls'] for b in batch], []))
ct_ind[ct_01] = torch.LongTensor(sum([b['ct_ind'] for b in batch], []))
detection = {'ct_hm': ct_hm, 'ext': ext, 'ct_cls': ct_cls, 'ct_ind': ct_ind, 'ct_01': ct_01.float()}
ret.update(detection)
from lib.utils.snake import snake_config
# init
i_it_4pys = torch.zeros([batch_size, ct_num, snake_config.init_poly_num, 2], dtype=torch.float)
c_it_4pys = torch.zeros([batch_size, ct_num, snake_config.init_poly_num, 2], dtype=torch.float)
i_gt_4pys = torch.zeros([batch_size, ct_num, 4, 2], dtype=torch.float)
c_gt_4pys = torch.zeros([batch_size, ct_num, 4, 2], dtype=torch.float)
i_it_4pys[ct_01] = torch.Tensor(sum([b['i_it_4py'] for b in batch], []))
c_it_4pys[ct_01] = torch.Tensor(sum([b['c_it_4py'] for b in batch], []))
i_gt_4pys[ct_01] = torch.Tensor(sum([b['i_gt_4py'] for b in batch], []))
c_gt_4pys[ct_01] = torch.Tensor(sum([b['c_gt_4py'] for b in batch], []))
init = {'i_it_4py': i_it_4pys, 'c_it_4py': c_it_4pys, 'i_gt_4py': i_gt_4pys, 'c_gt_4py': c_gt_4pys}
ret.update(init)
# evolution
i_it_pys = torch.zeros([batch_size, ct_num, snake_config.poly_num, 2], dtype=torch.float)
c_it_pys = torch.zeros([batch_size, ct_num, snake_config.poly_num, 2], dtype=torch.float)
i_gt_pys = torch.zeros([batch_size, ct_num, snake_config.gt_poly_num, 2], dtype=torch.float)
c_gt_pys = torch.zeros([batch_size, ct_num, snake_config.gt_poly_num, 2], dtype=torch.float)
i_it_pys[ct_01] = torch.Tensor(sum([b['i_it_py'] for b in batch], []))
c_it_pys[ct_01] = torch.Tensor(sum([b['c_it_py'] for b in batch], []))
i_gt_pys[ct_01] = torch.Tensor(sum([b['i_gt_py'] for b in batch], []))
c_gt_pys[ct_01] = torch.Tensor(sum([b['c_gt_py'] for b in batch], []))
evolution = {'i_it_py': i_it_pys, 'c_it_py': c_it_pys, 'i_gt_py': i_gt_pys, 'c_gt_py': c_gt_pys}
ret.update(evolution)
return ret
'''
# Modified by Zhang Ruicheng on 2024.04.21
_collators = {
'snake': snake_collator,
'ct': snake_collator,
#'rcnn_snake': rcnn_snake_collator,
#'ct_rcnn': rcnn_snake_collator
}
def make_collator(cfg):
if cfg.task in _collators:
return _collators[cfg.task]
else:
return default_collate
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