def data_loader_notredame(data_path, name, dtype, device):
edges_path = os.path.join(data_path, name + '_Edges.txt')
edges = np.genfromtxt(edges_path, delimiter=',')
edges = (np.array(edges) - 1).astype('int64')
Qabs_path = os.path.join(data_path, name + '_Qabs.txt')
Qabs = np.genfromtxt(Qabs_path, delimiter=',')
Qrel_path = os.path.join(data_path, name + '_Qrel.txt')
Qrel = np.genfromtxt(Qrel_path, delimiter=',')
Qrel = tr.tensor(Qrel, dtype=dtype, device=device).unsqueeze(
1) # do not assigne to gpu for now (this matrix can be huge)
Qabs = tr.tensor(Qabs, dtype=dtype, device=device).unsqueeze(1)
Qabs[:, :, 1:] *= -1.
G, C = make_graphs(edges)
#I = np.array(list(G.edges()))
degree = np.array((G.degree()))
max_index = np.argmax(degree[:, 1])
G, edges, Qabs = swap_nodes(G, edges, Qabs, 0, max_index)
# Additional formatting : N x P x d
N, P, _ = Qabs.shape
Nrel, Prel, _ = Qrel.shape
# rotate all absolute poses so that the first camera becomes the reference
Qabs = utils.quaternion_X_times_Y_inv(
Qabs, Qabs[0, :, :].unsqueeze(0).repeat(N, 1, 1))
wabs = (1. / P) * tr.ones([N, P], dtype=dtype, device=device)
wrel = (1. / Prel) * tr.ones([Nrel, Prel], dtype=dtype, device=device)
if nx.is_connected(C):
return edges, G, Qrel, wrel, Qabs, wabs, None
def data_loader_artsquad(data_path, name, dtype, device):
edges_path = os.path.join(data_path, name + '_Edges.txt')
edges = np.genfromtxt(edges_path, delimiter=',')
edges = (np.array(edges) - 2).astype('int64')
Qabs_path = os.path.join(data_path, name + '_Qabs.txt')
Qabs = np.genfromtxt(Qabs_path, delimiter=',')
Qrel_path = os.path.join(data_path, name + '_Qrel.txt')
Qrel = np.genfromtxt(Qrel_path, delimiter=',')
Qrel = tr.tensor(Qrel, dtype=dtype, device=device).unsqueeze(
1) # do not assigne to gpu for now (this matrix can be huge)
Qabs = tr.tensor(Qabs, dtype=dtype, device=device).unsqueeze(1)
Qabs = Qabs[1:, :, :]
Qabs[:, :, 1:] *= -1.
vals = np.ones(edges.shape[0], dtype=np.double)
rows = (np.asarray(edges[:, 0])).astype(int)
cols = (np.asarray(edges[:, 1])).astype(int)
N = Qabs.shape[0]
G = np.zeros((N, N))
G[rows, cols] = vals
C = nx.from_numpy_matrix(G)
G = nx.from_numpy_matrix(G, create_using=nx.DiGraph())
connected_comp = list(nx.connected_components(C))
#H = G.subgraph(connected_comp[0])
#Qabs = Qabs[connected_comp[0],:,:]
connected_comp = list(connected_comp[0])
indx_1 = tr.norm(Qabs, dim=-1) > 0.5
indx_1 = indx_1.int()
indx = tr.zeros([Qabs.shape[0], 1, 1]).int().to(device)
indx[connected_comp, 0] = 1
indx = indx_1 * indx
indx = indx.to(device)
indx = indx.bool()
#I = np.array(list(G.edges()))
# Additional formatting : N x P x d
N, P, _ = Qabs.shape
Nrel, Prel, _ = Qrel.shape
# rotate all absolute poses so that the first camera becomes the reference
Qabs = utils.quaternion_X_times_Y_inv(
Qabs, Qabs[0, :, :].unsqueeze(0).repeat(N, 1, 1))
wabs = (1. / P) * tr.ones([N, P], dtype=dtype, device=device)
wrel = (1. / Prel) * tr.ones([Nrel, Prel], dtype=dtype, device=device)
#if nx.is_connected(C):
return edges, G, Qrel, wrel, Qabs, wabs, indx
def compute_ratios(self, particles, weights, edges):
ratios = []
RM_weights = []
i = edges[:, 0]
j = edges[:, 1]
xi = particles[i, :, :]
xj = particles[j, :, :]
if self.grad_type == 'euclidean':
ratios = utils.forward_quaternion_X_times_Y_inv(xi, xj)
elif self.grad_type == 'quaternion':
ratios = utils.quaternion_X_times_Y_inv(xi, xj)
#normalize = tr.norm(ratios,dim=-1).clone().detach()
#ratios = ratios/normalize.unsqueeze(-1)
#RM_weights = weights[i,:]*weights[j,:]
N = xi.shape[0]
RM_weights = weights[0, :].unsqueeze(0).repeat(N, 1)
#ratios = ratios.clone().detach()
return ratios, RM_weights
def data_loader_shapenet(data_path, name, dtype, device, conjugate=False):
edges_path = os.path.join(data_path, name + '_Edges.txt')
edges = np.genfromtxt(edges_path, delimiter=',')
edges = (np.array(edges) - 1).astype('int64')
Qabs_path = os.path.join(data_path, name + '_Qabs.txt')
Qabs = np.genfromtxt(Qabs_path, delimiter=',')
Qrel_path = os.path.join(data_path, name + '_Qrel.txt')
Qrel = np.genfromtxt(Qrel_path, delimiter=',')
Qrel = tr.tensor(Qrel, dtype=dtype, device=device).unsqueeze(
1) # do not assigne to gpu for now (this matrix can be huge)
#Qrel = reshape_flat_tensor(Qrel)
Qabs = tr.tensor(Qabs, dtype=dtype, device=device).unsqueeze(1)
#Qabs = Qabs[1:,:,:]
if conjugate:
Qabs[:, :, 1:] *= -1.
# test error to gt
N, P, _ = Qabs.shape
Nrel, Prel, _ = Qrel.shape
wabs = (1. / P) * tr.ones([N, P], dtype=dtype, device=device)
wrel = (1. / Prel) * tr.ones([Nrel, Prel], dtype=dtype, device=device)
true_args = make_true_dict(args)
# true_prior = get_prior(true_args, dtype, device)
# true_RM_map = get_true_rm_map(true_args, edges,dtype,device)
# rm, rm_weights = true_RM_map(Qabs, wabs , edges)
# dist = utils.quaternion_geodesic_distance(rm,Qrel)
# min_dist,_ = torch.min(dist,dim=-1)
# avg_best_dist = torch.mean(min_dist,dim=-1)
G, C = make_graphs(edges)
#connected_comp = list(nx.connected_components(C))
#H = G.subgraph(connected_comp[0])
#Qabs = Qabs[connected_comp[0],:,:]
#connected_comp = list(connected_comp[0])
#Qabs = Qabs[connected_comp,:,:]
#I = np.array(list(G.edges()))
degree = np.array((G.degree()))
max_index = np.argmax(degree[:, 1])
G, edges, Qabs = swap_nodes(G, edges, Qabs, 0, max_index)
# Additional formatting : N x P x d
N, P, _ = Qabs.shape
Nrel, Prel, _ = Qrel.shape
# rotate all absolute poses so that the first camera becomes the reference
Qabs = utils.quaternion_X_times_Y_inv(
Qabs, Qabs[0, :, :].unsqueeze(0).repeat(N, 1, 1))
mask = Qabs[:, :, 0] < 0
Qabs[mask] *= -1.
mask = Qrel[:, :, 0] < 0
Qrel[mask] *= -1.
wabs = (1. / P) * tr.ones([N, P], dtype=dtype, device=device)
wrel = (1. / Prel) * tr.ones([Nrel, Prel], dtype=dtype, device=device)
# reshaping Qrel
if nx.is_connected(C):
return edges, G, Qrel, wrel, Qabs, wabs, None
def compute_ratios(self, particles, couplings, edges):
weights, coupling_strenght = couplings
ratios = []
RM_weights = []
i = edges[:, 0]
j = edges[:, 1]
xi = particles[i, :, :]
xj = particles[j, :, :]
N, K, _ = xi.shape
N, L, _ = xj.shape
#xj = tr.ones_like(xi)
#xj = xj/tr.norm(xj,dim=-1).unsqueeze(-1)
A = self.coupling(weights[i, :], weights[j, :],
coupling_strenght).reshape([N, -1])
A = A / tr.sum(A, dim=-1).unsqueeze(-1).detach()
if self.subsample:
#mask =tr.multinomial(A,particles.shape[1], replacement=True)
mask = tr.multinomial(tr.ones_like(A),
particles.shape[1],
replacement=True)
mask_i = mask / K
mask_j = mask - mask_i * K
mask_i = tr.nn.functional.one_hot(mask_i, particles.shape[1]).to(
particles.device)
mask_i = mask_i.type(particles.dtype)
xi = tr.einsum('nki,nlk->nli', xi, mask_i)
mask_j = tr.nn.functional.one_hot(mask_j, particles.shape[1]).to(
particles.device)
mask_j = mask_j.type(particles.dtype)
xj = tr.einsum('nki,nlk->nli', xj, mask_j)
mask = tr.nn.functional.one_hot(mask,
A.shape[1]).to(particles.device)
mask = mask.type(particles.dtype)
A = tr.einsum('nk,nlk->nl', A, mask)
#w = tr.einsum('nk,nlk->nl',weights,mask_i)
if self.grad_type == 'euclidean':
ratios = utils.forward_quaternion_X_times_Y_inv(xi, xj)
elif self.grad_type == 'quaternion':
ratios = utils.quaternion_X_times_Y_inv(xi, xj)
#ratios = ratios.permute([0,3,1,2]).reshape([N,4,-1]).permute([0,2,1])
#RM_weights = self.coupling(weights[i,:],weights[j,:],coupling_strenght).reshape([N,-1])
RM_weights = A / tr.sum(A, dim=-1).unsqueeze(-1).detach()
else:
if self.grad_type == 'euclidean':
ratios = utils.forward_quaternion_X_times_Y_inv_prod(xi, xj)
elif self.grad_type == 'quaternion':
ratios = utils.quaternion_X_times_Y_inv_prod(xi, xj)
ratios = ratios.permute([0, 3, 1,
2]).reshape([N, 4, -1]).permute([0, 2, 1])
RM_weights = A / tr.sum(A, dim=-1).unsqueeze(-1).detach()
#RM_weights = A
#ratios = particles
#RM_weights = weights
return ratios, RM_weights