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Running
on
Zero
File size: 6,088 Bytes
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import torch
import numpy as np
from torch.utils.data.dataloader import default_collate
from halt import _C
class HAFMencoder(object):
def __init__(self, cfg):
self.dis_th = cfg.ENCODER.DIS_TH
self.ang_th = cfg.ENCODER.ANG_TH
self.num_static_pos_lines = cfg.ENCODER.NUM_STATIC_POS_LINES
self.num_static_neg_lines = cfg.ENCODER.NUM_STATIC_NEG_LINES
def __call__(self,annotations):
targets = []
metas = []
for ann in annotations:
t,m = self._process_per_image(ann)
targets.append(t)
metas.append(m)
return default_collate(targets),metas
def adjacent_matrix(self, n, edges, device):
mat = torch.zeros(n+1,n+1,dtype=torch.bool,device=device)
if edges.size(0)>0:
mat[edges[:,0], edges[:,1]] = 1
mat[edges[:,1], edges[:,0]] = 1
return mat
def _process_per_image(self,ann):
junctions = ann['junctions']
device = junctions.device
height, width = ann['height'], ann['width']
jmap = torch.zeros((height,width),device=device)
joff = torch.zeros((2,height,width),device=device,dtype=torch.float32)
# junctions[:,0] = junctions[:,0].clamp(min=0,max=width-1)
# junctions[:,1] = junctions[:,1].clamp(min=0,max=height-1)
xint,yint = junctions[:,0].long(), junctions[:,1].long()
off_x = junctions[:,0] - xint.float()-0.5
off_y = junctions[:,1] - yint.float()-0.5
jmap[yint,xint] = 1
joff[0,yint,xint] = off_x
joff[1,yint,xint] = off_y
edges_positive = ann['edges_positive']
edges_negative = ann['edges_negative']
pos_mat = self.adjacent_matrix(junctions.size(0),edges_positive,device)
neg_mat = self.adjacent_matrix(junctions.size(0),edges_negative,device)
lines = torch.cat((junctions[edges_positive[:,0]], junctions[edges_positive[:,1]]),dim=-1)
lines_neg = torch.cat((junctions[edges_negative[:2000,0]],junctions[edges_negative[:2000,1]]),dim=-1)
lmap, _, _ = _C.encodels(lines,height,width,height,width,lines.size(0))
center_points = (lines[:,:2] + lines[:,2:])/2.0
cmap = torch.zeros((height,width),device=device)
cxint, cyint = center_points[:,0].long(), center_points[:,1].long()
cmap[cyint,cxint] = 1
# yy,xx = torch.meshgrid(torch.arange(width,device=device),torch.arange(width,device=device))
# gaussian = torch.exp(-((yy[:,:,None]-center_points[None,None,:,1])**2 + (xx[:,:,None]-center_points[None,None,:,0])**2)/(2*(2*2)))
# cmap = gaussian.max(dim=-1)[0]
lpos = np.random.permutation(lines.cpu().numpy())[:self.num_static_pos_lines]
lneg = np.random.permutation(lines_neg.cpu().numpy())[:self.num_static_neg_lines]
# lpos = lines[torch.randperm(lines.size(0),device=device)][:self.num_static_pos_lines]
# lneg = lines_neg[torch.randperm(lines_neg.size(0),device=device)][:self.num_static_neg_lines]
lpos = torch.from_numpy(lpos).to(device)
lneg = torch.from_numpy(lneg).to(device)
lpre = torch.cat((lpos,lneg),dim=0)
_swap = (torch.rand(lpre.size(0))>0.5).to(device)
lpre[_swap] = lpre[_swap][:,[2,3,0,1]]
lpre_label = torch.cat(
[
torch.ones(lpos.size(0),device=device),
torch.zeros(lneg.size(0),device=device)
])
meta = {
'junc': junctions,
'Lpos': pos_mat,
'Lneg': neg_mat,
'lpre': lpre,
'lpre_label': lpre_label,
'lines': lines,
}
dismap = torch.sqrt(lmap[0]**2+lmap[1]**2)[None]
def _normalize(inp):
mag = torch.sqrt(inp[0]*inp[0]+inp[1]*inp[1])
return inp/(mag+1e-6)
md_map = _normalize(lmap[:2])
st_map = _normalize(lmap[2:4])
ed_map = _normalize(lmap[4:])
st_map = lmap[2:4]
ed_map = lmap[4:]
md_ = md_map.reshape(2,-1).t()
st_ = st_map.reshape(2,-1).t()
ed_ = ed_map.reshape(2,-1).t()
Rt = torch.cat(
(torch.cat((md_[:,None,None,0],md_[:,None,None,1]),dim=2),
torch.cat((-md_[:,None,None,1], md_[:,None,None,0]),dim=2)),dim=1)
R = torch.cat(
(torch.cat((md_[:,None,None,0], -md_[:,None,None,1]),dim=2),
torch.cat((md_[:,None,None,1], md_[:,None,None,0]),dim=2)),dim=1)
Rtst_ = torch.matmul(Rt, st_[:,:,None]).squeeze(-1).t()
Rted_ = torch.matmul(Rt, ed_[:,:,None]).squeeze(-1).t()
swap_mask = (Rtst_[1]<0)*(Rted_[1]>0)
pos_ = Rtst_.clone()
neg_ = Rted_.clone()
temp = pos_[:,swap_mask]
pos_[:,swap_mask] = neg_[:,swap_mask]
neg_[:,swap_mask] = temp
pos_[0] = pos_[0].clamp(min=1e-9)
pos_[1] = pos_[1].clamp(min=1e-9)
neg_[0] = neg_[0].clamp(min=1e-9)
neg_[1] = neg_[1].clamp(max=-1e-9)
mask = (dismap.view(-1)<=self.dis_th).float()
pos_map = pos_.reshape(-1,height,width)
neg_map = neg_.reshape(-1,height,width)
md_angle = torch.atan2(md_map[1], md_map[0])
pos_angle = torch.atan2(pos_map[1],pos_map[0])
neg_angle = torch.atan2(neg_map[1],neg_map[0])
mask *= (pos_angle.reshape(-1)>self.ang_th*np.pi/2.0)
mask *= (neg_angle.reshape(-1)<-self.ang_th*np.pi/2.0)
pos_angle_n = pos_angle/(np.pi/2)
neg_angle_n = -neg_angle/(np.pi/2)
md_angle_n = md_angle/(np.pi*2) + 0.5
mask = mask.reshape(height,width)
hafm_ang = torch.cat((md_angle_n[None],pos_angle_n[None],neg_angle_n[None],),dim=0)
hafm_dis = dismap.clamp(max=self.dis_th)/self.dis_th
mask = mask[None]
target = {'jloc':jmap[None],
'joff':joff,
'cloc': cmap[None],
'md': hafm_ang,
'dis': hafm_dis,
'mask': mask
}
return target, meta |