def getAffineShape(self, final_resp, LAFs, final_pyr_idxs, final_level_idxs, num_features = 0):
pe_time = 0
affnet_time = 0
pyr_inv_idxs = get_inverted_pyr_index(self.scale_pyr, final_pyr_idxs, final_level_idxs)
t = time.time()
patches_small = extract_patches_from_pyramid_with_inv_index(self.scale_pyr, pyr_inv_idxs, LAFs, PS = self.AffNet.PS)
pe_time+=time.time() - t
t = time.time()
base_A = torch.eye(2).unsqueeze(0).expand(final_pyr_idxs.size(0),2,2)
if final_resp.is_cuda:
base_A = base_A.cuda()
base_A = Variable(base_A)
is_good = None
n_patches = patches_small.size(0)
for i in range(self.num_Baum_iters):
t = time.time()
A = batched_forward(self.AffNet, patches_small, 256)
is_good_current = 1
affnet_time += time.time() - t
if is_good is None:
is_good = is_good_current
else:
is_good = is_good * is_good_current
base_A = torch.bmm(A, base_A);
new_LAFs = torch.cat([torch.bmm(base_A,LAFs[:,:,0:2]), LAFs[:,:,2:] ], dim =2)
#print torch.sqrt(new_LAFs[0,0,0]*new_LAFs[0,1,1] - new_LAFs[0,1,0] *new_LAFs[0,0,1]) * scale_pyr[0][0].size(2)
if i != self.num_Baum_iters - 1:
pe_time+=time.time() - t
t = time.time()
patches_small = extract_patches_from_pyramid_with_inv_index(self.scale_pyr, pyr_inv_idxs, new_LAFs, PS = self.AffNet.PS)
pe_time+= time.time() - t
l1,l2 = batch_eig2x2(A)
ratio1 = torch.abs(l1 / (l2 + 1e-8))
converged_mask = (ratio1 <= 1.2) * (ratio1 >= (0.8))
l1,l2 = batch_eig2x2(base_A)
ratio = torch.abs(l1 / (l2 + 1e-8))
idxs_mask = ((ratio < 6.0) * (ratio > (1./6.))) * checkTouchBoundary(new_LAFs)
num_survived = idxs_mask.float().sum()
if (num_features > 0) and (num_survived.data[0] > num_features):
final_resp = final_resp * idxs_mask.float() #zero bad points
final_resp, idxs = torch.topk(final_resp, k = num_features);
else:
idxs = Variable(torch.nonzero(idxs_mask.data).view(-1).long())
final_resp = final_resp[idxs]
final_pyr_idxs = final_pyr_idxs[idxs]
final_level_idxs = final_level_idxs[idxs]
base_A = torch.index_select(base_A, 0, idxs)
LAFs = torch.index_select(LAFs, 0, idxs)
new_LAFs = torch.cat([torch.bmm(base_A, LAFs[:,:,0:2]),
LAFs[:,:,2:]], dim =2)
print 'affnet_time',affnet_time
print 'pe_time', pe_time
return final_resp, new_LAFs, final_pyr_idxs, final_level_idxs
def multiScaleDetectorAff(self,x, num_features = 0):
t = time.time()
self.scale_pyr, self.sigmas, self.pix_dists = self.ScalePyrGen(x)
### Detect keypoints in scale space
aff_matrices = []
top_responces = []
pyr_idxs = []
level_idxs = []
det_t = 0
nmst = 0
for oct_idx in range(len(self.sigmas)):
#print oct_idx
octave = self.scale_pyr[oct_idx]
sigmas_oct = self.sigmas[oct_idx]
pix_dists_oct = self.pix_dists[oct_idx]
low = None
cur = None
high = None
octaveMap = (self.scale_pyr[oct_idx][0] * 0).byte()
nms_f = NMS3dAndComposeAAff(w = octave[0].size(3),
h = octave[0].size(2),
border = self.b, mrSize = self.mrSize)
#oct_aff_map = F.upsample(self.AffNet(octave[0]), (octave[0].size(2), octave[0].size(3)),mode='bilinear')
oct_aff_map = self.AffNet(octave[0])
for level_idx in range(1, len(octave)-1):
if cur is None:
low = torch.clamp(self.RespNet(octave[level_idx - 1], (sigmas_oct[level_idx - 1 ])) - self.th, min = 0)
else:
low = cur
if high is None:
cur = torch.clamp(self.RespNet(octave[level_idx ], (sigmas_oct[level_idx ])) - self.th, min = 0)
else:
cur = high
high = torch.clamp(self.RespNet(octave[level_idx + 1], (sigmas_oct[level_idx + 1 ])) - self.th, min = 0)
top_resp, aff_matrix, octaveMap_current = nms_f(low, cur, high,
num_features = num_features,
octaveMap = octaveMap,
scales = sigmas_oct[level_idx - 1:level_idx + 2],
aff_resp = oct_aff_map)
if top_resp is None:
continue
octaveMap = octaveMap_current
not_touch_boundary_idx = checkTouchBoundary(torch.cat([aff_matrix[:,:2,:2] *3.0, aff_matrix[:,:,2:]], dim =2))
aff_matrices.append(aff_matrix[not_touch_boundary_idx.byte()]), top_responces.append(top_resp[not_touch_boundary_idx.byte()])
pyr_id = Variable(oct_idx * torch.ones(aff_matrices[-1].size(0)))
lev_id = Variable((level_idx - 1) * torch.ones(aff_matrices[-1].size(0))) #prevBlur
if x.is_cuda:
pyr_id = pyr_id.cuda()
lev_id = lev_id.cuda()
pyr_idxs.append(pyr_id)
level_idxs.append(lev_id)
all_responses = torch.cat(top_responces, dim = 0)
aff_m_scales = torch.cat(aff_matrices,dim = 0)
pyr_idxs_scales = torch.cat(pyr_idxs,dim = 0)
level_idxs_scale = torch.cat(level_idxs, dim = 0)
if (num_features > 0) and (num_features < all_responses.size(0)):
all_responses, idxs = torch.topk(all_responses, k = num_features);
LAFs = torch.index_select(aff_m_scales, 0, idxs)
final_pyr_idxs = pyr_idxs_scales[idxs]
final_level_idxs = level_idxs_scale[idxs]
else:
return all_responses, aff_m_scales, pyr_idxs_scales , level_idxs_scale
return all_responses, LAFs, final_pyr_idxs, final_level_idxs,