def __init__(self,
w=0,
h=0,
kernel_size=3,
threshold=0,
scales=None,
border=3,
mrSize=1.0):
super(NMS3dAndComposeA, self).__init__()
self.eps = 1e-7
self.ks = 3
self.th = threshold
self.cube_idxs = []
self.border = border
self.mrSize = mrSize
self.beta = 1.0
self.grid_ones = Variable(torch.ones(3, 3, 3, 3), requires_grad=False)
self.NMS3d = NMS3d(kernel_size, threshold)
if (w > 0) and (h > 0):
self.spatial_grid = generate_2dgrid(h, w, False).view(
1, h, w, 2).permute(3, 1, 2, 0)
self.spatial_grid = Variable(self.spatial_grid)
else:
self.spatial_grid = None
return
def forward(self, low, cur, high, num_features = 0, octaveMap = None, scales = None):
assert low.size() == cur.size() == high.size()
#Filter responce map
self.is_cuda = low.is_cuda;
resp3d = torch.cat([low,cur,high], dim = 1)
mrSize_border = int(self.mrSize);
if octaveMap is not None:
nmsed_resp = zero_response_at_border(self.NMS3d(resp3d.unsqueeze(1)).squeeze(1)[:,1:2,:,:], mrSize_border) * (1. - octaveMap.float())
else:
nmsed_resp = zero_response_at_border(self.NMS3d(resp3d.unsqueeze(1)).squeeze(1)[:,1:2,:,:], mrSize_border)
num_of_nonzero_responces = (nmsed_resp > 0).sum().data[0]
if (num_of_nonzero_responces == 0):
return None,None,None
if octaveMap is not None:
octaveMap = (octaveMap.float() + nmsed_resp.float()).byte()
nmsed_resp = nmsed_resp.view(-1)
if (num_features > 0) and (num_features < num_of_nonzero_responces):
nmsed_resp, idxs = torch.topk(nmsed_resp, k = num_features);
else:
idxs = nmsed_resp.data.nonzero().squeeze()
nmsed_resp = nmsed_resp[idxs]
#Get point coordinates grid
if type(scales) is not list:
self.grid = generate_3dgrid(3,self.ks,self.ks)
else:
self.grid = generate_3dgrid(scales,self.ks,self.ks)
self.grid = Variable(self.grid.t().contiguous().view(3,3,3,3), requires_grad=False)
if self.spatial_grid is None:
self.spatial_grid = generate_2dgrid(low.size(2), low.size(3), False).view(1, low.size(2), low.size(3),2).permute(3,1, 2, 0)
self.spatial_grid = Variable(self.spatial_grid)
if self.is_cuda:
self.spatial_grid = self.spatial_grid.cuda()
self.grid_ones = self.grid_ones.cuda()
self.grid = self.grid.cuda()
#residual_to_patch_center
sc_y_x = F.conv2d(resp3d, self.grid,
padding = 1) // (F.conv2d(resp3d, self.grid_ones, padding = 1) + 1e-8)
##maxima coords
sc_y_x[0,1:,:,:] = sc_y_x[0,1:,:,:] + self.spatial_grid[:,:,:,0]
sc_y_x = sc_y_x.view(3,-1).t()
sc_y_x = sc_y_x[idxs,:]
min_size = float(min((cur.size(2)), cur.size(3)))
sc_y_x[:,0] = sc_y_x[:,0] // min_size
sc_y_x[:,1] = sc_y_x[:,1] // float(cur.size(2))
sc_y_x[:,2] = sc_y_x[:,2] // float(cur.size(3))
return nmsed_resp, sc_y_x2LAFs(sc_y_x), octaveMap
