def __init__(self,
x,
y,
filter_regs,
precond,
sample_weights,
net,
pixel_weighting,
compute_norm=False):
super().__init__()
self.training_samples = x
self.training_labels = y
self.y_size = y.shape[-2:]
self.x = x
self.y = y
self.w = pixel_weighting
self.filter_regs = TensorList(filter_regs)
if sample_weights.size()[0] == 1:
self.sample_weights = x.new_zeros(x.shape[0])
self.sample_weights.fill_(sample_weights[0])
else:
self.sample_weights = sample_weights
self.diag_M = TensorList(precond)
self.pixel_weighting = pixel_weighting
self.net = net
self.compute_norm = compute_norm
def empirical_estimate(points, num_neighbors):
ps, batch = points.permute(1, 0).cat_tensors()
N = ps.shape[0]
val = knn(ps.contiguous(),
ps.contiguous(),
batch_x=batch,
batch_y=batch,
k=num_neighbors)
A = ps[val[1, :]].reshape(N, num_neighbors, 3)
A = A - A.mean(dim=1, keepdim=True)
Asqr = A.permute(0, 2, 1).bmm(A)
sigma, _ = Asqr.cpu().symeig()
w = (sigma[:, 2] * sigma[:, 1]).sqrt()
val = val[1, :].reshape(N, num_neighbors)
w, _ = torch.median(w[val].to(ps.device), dim=1, keepdim=True)
weights = TensorListList()
bi = 0
for point_list in points:
ww = TensorList()
for p in point_list:
ww.append(w[batch == bi])
bi = bi + 1
weights.append(ww)
return weights
def GARPointList(t1_list, t2_list, R1_list, R2_list, V, c, alpha):
err = TensorListList()
for t1, t2, R1, R2, Vb in zip(t1_list, t2_list, R1_list, R2_list, V):
M = len(t1)
err1 = TensorList()
for ind1 in range(M - 1):
for ind2 in range(ind1 + 1, M):
Rtest = R1[ind1].permute(1, 0) @ R1[ind2]
# estimated relative translation from ind2 to ind1
test = R1[ind1].permute(1, 0) @ t1[ind2] - R1[ind1].permute(
1, 0) @ t1[ind1]
Rgt = R2[ind1].permute(1, 0) @ R2[ind2]
# ground truth translation from ind2 to ind1
tgt = R2[ind1].permute(1, 0) @ t2[ind2] - R2[ind1].permute(
1, 0) @ t2[ind1]
diff = Rtest @ Vb[ind2] + test - (Rgt @ Vb[ind2] + tgt)
err_i = (diff * diff).sum(dim=-2).sqrt()
err1.append(lossfun(err_i, alpha=alpha, scale=c).mean())
err.append(err1)
return err
def initialize_mu(self, K, features):
X = TensorList()
for TV in features:
Xi = np.random.randn(TV[0].shape[0], K).astype(np.float32)
Xi = torch.from_numpy(Xi).to(TV[0].device)
Xi = Xi / torch.norm(Xi, dim=0, keepdim=True)
X.append(Xi.permute(1, 0))
return X
def L2sqrList(R1_list, R2_list):
err = TensorListList()
for R1, R2 in zip(R1_list, R2_list):
M = len(R1)
err1 = TensorList()
for ind1 in range(M - 1):
for ind2 in range(ind1 + 1, M):
Rdiff = R1[ind1].permute(1, 0) @ R1[ind2] - R2[ind1].permute(
1, 0) @ R2[ind2]
err1.append(
(Rdiff * Rdiff).sum(dim=-1).sum(dim=-1).unsqueeze(0))
err.append(err1)
return err
def A(self, x):
dfdx_x = torch.autograd.grad(self.dfdxt_g,
self.g,
x,
retain_graph=True)
return TensorList(
torch.autograd.grad(self.f0, self.x, dfdx_x, retain_graph=True))
def get_init_transformation_list(pcds, mean_init=True):
if mean_init:
m_pcds = pcds.mean(dim=-1, keepdim=True)
m_target = TensorList([m[0] for m in m_pcds])
init_t = -(m_pcds - m_target)
else:
tt = []
for i in range(len(pcds)):
tt.append(TensorList([torch.zeros(3, 1).to(pcds[0][0].device) for i in range(len(pcds[i]))]))
init_t = TensorListList(tt)
rr = []
for i in range(len(pcds)):
rr.append(TensorList([torch.eye(3, 3).to(pcds[0][0].device) for i in range(len(pcds[i]))]))
init_R = TensorListList(rr)
return init_R, init_t
def run_GN_iter(self, num_cg_iter):
self.x.requires_grad_(True)
self.f0 = self.problem(self.x)
self.g = self.f0.detach()
self.g.requires_grad_(True)
self.dfdxt_g = TensorList(
torch.autograd.grad(self.f0, self.x, self.g,
create_graph=True)) # df/dx^t @ f0
self.b = -self.dfdxt_g.detach()
delta_x, res = self.run_CG(num_cg_iter, eps=self.cg_eps)
self.x.detach_()
self.x += self.step_alpha * delta_x
self.step_alpha = min(self.step_alpha * 1.2, 1.0)
def cluster_features(self, features, num_clusters):
feature_labels_LL = TensorListList()
for f in features:
feature_labels_L = TensorList()
fcat = torch.cat([fi for fi in f])
fcat = fcat.to("cpu").numpy()
kmeans = KMeans(n_clusters=num_clusters, random_state=0).fit(fcat)
labels = torch.from_numpy(kmeans.labels_)
onehot = torch.nn.functional.one_hot(
labels.long(), num_clusters).to(self.params.device)
cnt = 0
for fi in f:
feature_labels_L.append(onehot[cnt:cnt + fi.shape[0]])
cnt += fi.shape[0]
feature_labels_LL.append(feature_labels_L.permute(1, 0).float())
return feature_labels_LL
def L2sqrTransList(t1_list, t2_list, R1_list, R2_list):
err = TensorListList()
for t1, t2, R1, R2 in zip(t1_list, t2_list, R1_list, R2_list):
M = len(t1)
err1 = TensorList()
for ind1 in range(M - 1):
for ind2 in range(ind1 + 1, M):
# estimated relative translation from ind2 to ind1
test = R1[ind1].permute(1, 0) @ t1[ind2] - R1[ind1].permute(
1, 0) @ t1[ind1]
# ground truth translation from ind2 to ind1
tgt = R2[ind1].permute(1, 0) @ t2[ind2] - R2[ind1].permute(
1, 0) @ t2[ind1]
diff = test - tgt
err1.append((diff * diff).sum(dim=-2))
err.append(err1)
return err
def extract_correspondences_gpu(coords, Rs, ts, device=None):
coords = Rs @ coords + ts
M = len(coords)
ind_nns = []
dists = []
if not device is None:
coords = coords.to(device)
for ind1 in range(M - 1):
for ind2 in range(ind1 + 1, M):
point1 = coords[ind1].permute(1, 0)
point2 = coords[ind2].permute(1, 0)
inds_nn = knn(point2, point1, 1)
d = point1[inds_nn[0, :]] - point2[inds_nn[1, :]]
d = (d * d).sum(dim=1).sqrt()
dists.append(d)
ind_nns.append(inds_nn)
return dict(indices=TensorList(ind_nns), distances=TensorList(dists))
def extract_correspondences(coords, Rs, ts):
coords = Rs @ coords + ts
M = len(coords)
ind_nns = []
dists = []
for ind1 in range(M - 1):
for ind2 in range(ind1 + 1, M):
point1 = coords[ind1].cpu().numpy().T
point2 = coords[ind2].cpu().numpy().T
tree = KDTree(point1)
d, inds_nn1 = tree.query(point2, k=1)
inds_nn2 = torch.tensor([n for n in range(0, point2.shape[0])])
inds_nn1 = torch.from_numpy(inds_nn1)
ind_nns.append(torch.stack([inds_nn1, inds_nn2], dim=0))
dists.append(torch.from_numpy(d))
return dict(indices=TensorList(ind_nns).to(coords[0][0].device),
distances=TensorList(dists).to(coords[0][0].device))
def forward(self, x):
coords = x['coordinates'].clone()
t_tot = time.time()
features_LL = TensorListList()
att_LL = TensorListList()
coords_ds_LL = TensorListList()
inds_LL = []
ind_cnt = 0
for coords_b in coords:
features_L = TensorList()
att_L = TensorList()
coords_ds_L = TensorList()
inds_L = []
for coords_s in coords_b:
pcd_down, f = self.preprocess_point_cloud(
coords_s, self.params.voxel_size)
features_L.append(f)
coords_ds_L.append(pcd_down)
ind_cnt = ind_cnt + 1
features_LL.append(features_L)
att_LL.append(att_L)
coords_ds_LL.append(coords_ds_L)
inds_LL.append(inds_L)
x = dict()
x['features'] = self.cluster_features(
features_LL,
self.params.feature_distr_parameters.num_feature_clusters)
x['att'] = att_LL
x['coordinates_ds'] = coords_ds_LL.to(self.params.device)
x['indices'] = inds_LL
out = self.registration(x)
tot_time = time.time() - t_tot
print("tot time: %.1f ms" % (tot_time * 1000))
out["time"] = tot_time
out["features"] = features_LL
out["indices"] = inds_LL
out["coordinates_ds"] = coords_ds_LL
return out
def estimate_overlap(ps1, ps2, R1, R2, t1, t2, voxel_size, device):
# voxel grid downsampling
quantized_coords1 = torch.floor(ps1.permute(1, 0) / voxel_size)
inds1 = ME.utils.sparse_quantize(quantized_coords1, return_index=True)
quantized_coords2 = torch.floor(ps2.permute(1, 0) / voxel_size)
inds2 = ME.utils.sparse_quantize(quantized_coords2, return_index=True)
ps1_v = ps1[:, inds1]
ps2_v = ps2[:, inds2]
corrs = extract_correspondences_gpu(TensorList([ps1_v, ps2_v]),
TensorList([R1, R2]),
TensorList([t1, t2]), device)
d = corrs["distances"]
rates = []
thresh = voxel_size
for di in d:
rate = float((di < thresh).sum()) / di.shape[0]
rates.append(rate)
return rates
def get_init_transformation_list_dgr(pcds, dgr_init_model):
if isinstance(pcds, TensorListList):
Rs = TensorListList()
ts = TensorListList()
else:
# B, P, N, M = point_clouds.shape
# assert P == 2
Rs = []
ts = []
target_R = torch.eye(3, 3).to(pcds[0][0].device)
target_t = torch.zeros(3, 1).to(pcds[0][0].device)
for pc in pcds:
assert len(pc) == 2
R,t=dgr_init_model.register_point_sets(pc[0].permute(1,0), pc[1].permute(1,0))
if isinstance(pcds, TensorListList):
Rs.append(TensorList([R, target_R]))
ts.append(TensorList([t.unsqueeze(dim=1), target_t]))
else:
Rs.append(torch.stack([R, target_R]))
ts.append(torch.stack([t.unsqueeze(dim=1), target_t]))
return Rs, ts
def collate_tensorlist(batch):
out = dict()
info = dict()
tensorlistkeysout = []
tensorlistkeysinfo = []
for b in batch:
cb, ib = b
for k, v in cb.items():
if isinstance(v, TensorList):
if not k in out:
out[k] = TensorList()
tensorlistkeysout.append(k)
else:
if not k in out:
out[k] = []
out[k].append(v)
for k, v in ib.items():
if isinstance(v, TensorList):
if not k in info:
info[k] = TensorList()
tensorlistkeysinfo.append(k)
else:
if not k in info:
info[k] = []
info[k].append(v)
for k in tensorlistkeysout:
out[k] = TensorListList(out[k])
for k in tensorlistkeysinfo:
info[k] = TensorListList(info[k])
return out, info
def initialize(self, K, features):
init_method = self.params.get("init_method", "dirichlet")
if init_method == "uniform":
init_distr_list = []
for i in range(len(features)):
init_distr_list.append(
torch.ones(K, self.params.num_feature_clusters).float() /
self.params.num_feature_clusters)
elif init_method == "dirichlet":
init_distr_list = TensorList()
relative_distr = features.sum(dim=1).sum_list()
relative_distr = relative_distr / relative_distr.sum()
for r in relative_distr:
dir = torch.distributions.dirichlet.Dirichlet(r)
init_distr_list.append(dir.sample((K, )))
else:
init_distr_list = []
for i in range(len(features)):
init_distr_list.append(
torch.ones(K, self.params.num_feature_clusters).float() /
self.params.num_feature_clusters)
self.distr = TensorListList(init_distr_list, repeat=self.repeat_list)
def __call__(self, parameters: TensorList):
s = self.net(self.x)
s = F.interpolate(s, self.y_size, mode='bilinear', align_corners=False)
residuals = self.w * (s - self.y)
return TensorList([residuals, *(self.filter_regs * parameters)])
def init(self, x, y):
"""
:param x: Tensor of data augmented features from the first frame, shape (K, Cf, Hf, Wf),
where K is the number of augmented feature maps.
:param y: Object mask tensor, shape (K, 1, Him, Wim)
"""
pw = self.compute_pixel_weights(y)
# Run the initial optimization
memory = Memory(y.shape[0], x.shape[-3:], y.shape[-3:], self.device,
self.learning_rate)
memory.initialize(x, y, pw)
parameters = TensorList([self.project.weight, self.filter.weight])
problem = DiscriminatorLoss(x=memory.samples,
y=memory.labels,
filter_regs=self.filter_reg,
precond=self.precond,
sample_weights=memory.weights,
net=nn.Sequential(self.project,
self.filter),
pixel_weighting=memory.pixel_weights)
optimizer = GaussNewtonCG(
problem,
parameters,
fletcher_reeves=False,
standard_alpha=True,
direction_forget_factor=self.direction_forget_factor)
problem.net.train()
optimizer.run(self.init_iters)
problem.net.eval()
x = self.project(
x) # Re-project samples with the new projection matrix
# Initialize the memory
memory = Memory(self.memory_size, x.shape[-3:], y.shape[-3:],
self.device, self.learning_rate)
memory.initialize(x, y, pw)
# Build the update problem
parameters = TensorList([self.filter.weight])
problem = DiscriminatorLoss(x=memory.samples,
y=memory.labels,
filter_regs=self.filter_reg[1:],
precond=self.precond[1:],
sample_weights=memory.weights,
net=self.filter,
pixel_weighting=memory.pixel_weights)
optimizer = GaussNewtonCG(
problem,
parameters,
fletcher_reeves=False,
standard_alpha=True,
direction_forget_factor=self.direction_forget_factor)
problem.net.train()
optimizer.run(self.update_iters)
problem.net.eval()
self.memory = memory
self.update_optimizer = optimizer
def register_point_sets(self, x):
point_clouds = x["coordinates"].clone()
t_tot = time.time()
if isinstance(point_clouds, TensorListList):
Rs = TensorListList()
ts = TensorListList()
else:
#B, P, N, M = point_clouds.shape
#assert P == 2
Rs = []
ts = []
target_R = torch.eye(3, 3)
target_t = torch.zeros(3, 1)
for pcds in point_clouds:
if self.params.get("mean_init", True):
m_target = pcds[1].mean(dim=1)
m_source = pcds[0].mean(dim=1)
init_t = -(m_target - m_source).cpu().numpy()
else:
init_t = [0, 0, 0]
trans_init = np.asarray([[1., 0., 0., init_t[0]],
[0., 1., 0., init_t[1]],
[0., 0., 1., init_t[2]],
[0.0, 0.0, 0.0, 1.0]])
source = o3d.geometry.PointCloud()
target = o3d.geometry.PointCloud()
source.points = o3d.utility.Vector3dVector(pcds[0].cpu().numpy().T)
target.points = o3d.utility.Vector3dVector(pcds[1].cpu().numpy().T)
voxel_size = self.params.voxel_size
max_correspondence_distance = self.params.threshold
radius_normal = float(self.params.radius_normal)
if self.params.metric == "p2pl":
source.estimate_normals(
search_param=o3d.geometry.KDTreeSearchParamHybrid(
radius=radius_normal, max_nn=30))
target.estimate_normals(
search_param=o3d.geometry.KDTreeSearchParamHybrid(
radius=radius_normal, max_nn=30))
# o3d.geometry.estimate_normals(source)
# o3d.geometry.estimate_normals(target)
if self.params.get("downsample", True):
source = source.voxel_down_sample(voxel_size=voxel_size)
target = target.voxel_down_sample(voxel_size=voxel_size)
reg_p2p = o3d.registration.registration_icp(
source,
target,
max_correspondence_distance=max_correspondence_distance,
estimation_method=self.metric,
init=trans_init)
T = reg_p2p.transformation
R = T[0:3, 0:3]
t = T[0:3, 3:]
if isinstance(point_clouds, TensorListList):
Rs.append(TensorList([torch.from_numpy(R).float(), target_R]))
ts.append(TensorList([torch.from_numpy(t).float(), target_t]))
else:
Rs.append(torch.stack([torch.from_numpy(R).float(), target_R]))
ts.append(torch.stack([torch.from_numpy(t).float(), target_t]))
tot_time = time.time() - t_tot
print("ICP tot time: %.1f ms" % (tot_time * 1000))
if isinstance(point_clouds, TensorListList):
return dict(Rs=Rs, ts=ts, Vs=Rs @ point_clouds + ts, time=tot_time)
else:
return dict(Rs=torch.stack(Rs),
ts=torch.stack(ts),
Vs=point_clouds,
time=tot_time)
def forward(self, x_in):
coords = x_in['coordinates'].clone()
t_tot = time.time()
sinput, inds_list = self.create_sparse_tensors(coords)
resample_time = time.time() - t_tot
print("resample time: %.1f ms" % ((resample_time) * 1000))
if not self.params.feature_distr_parameters.model=='none':
features_dict = self.feature_extractor(sinput)
features = features_dict["features"]
if "attention" in features_dict.keys():
att = features_dict["attention"]
extract_time=time.time()-t_tot
print("extract time: %.1f ms" % ((extract_time) * 1000))
batch_indices = list(features.coords_man.get_batch_indices())
else:
features_dict=None
extract_time=0
time_preprocess = time.time()
features_LL = TensorListList()
att_LL = TensorListList()
coords_ds_LL = TensorListList()
inds_LL = []
ind_cnt = 0
for coords_b in coords:
features_L = TensorList()
att_L = TensorList()
coords_ds_L = TensorList()
inds_L = []
for coords_s in coords_b:
if not features_dict is None:
mask = features.C[:, 0] == batch_indices[ind_cnt]
# hacky way of finding batch channel dimension
if mask.int().sum() != inds_list[ind_cnt].shape[0]:
mask = features.C[:, -1] == batch_indices[ind_cnt]
f = features.F[mask]
assert f.shape[0] == inds_list[ind_cnt].shape[0]
if "attention" in features_dict.keys():
a = att.F[mask]
assert a.shape[0] == inds_list[ind_cnt].shape[0]
att_L.append(a)
features_L.append(f.permute(1, 0))
coords_ds_L.append(coords_s[:, inds_list[ind_cnt]])
inds_L.append(inds_list[ind_cnt])
ind_cnt = ind_cnt + 1
features_LL.append(features_L)
att_LL.append(att_L)
coords_ds_LL.append(coords_ds_L)
inds_LL.append(inds_L)
x = dict()
x['features'] = features_LL
x['att'] = att_LL
x['coordinates_ds'] = coords_ds_LL.to(self.params.device)
x['indices'] = inds_LL
x['coordinates'] = x_in['coordinates']
print("preprocess time: %.1f ms" % ((time.time()-time_preprocess) * 1000))
reg_time=time.time()
out = self.registration(x)
reg_time2 = time.time() - reg_time
print("reg time: %.1f ms" % ((time.time() - reg_time) * 1000))
tot_time = time.time() - t_tot
print("tot time: %.1f ms" % (tot_time * 1000))
out["time"] = tot_time
out["reg_time"] = reg_time2
out["extract_time"] =extract_time
out["resample_time"] = resample_time
out["coordinates_ds"] = coords_ds_LL
return out
def read_sample(self, specs):
coords = []
R_gt = []
t_gt = []
R_init = []
t_init = []
time_stamps = []
names = []
if self.parameters.with_features:
features = []
for name in specs.view_names:
names.append(specs["sequence"] + "/" + name)
data = self.loader(name, specs)
data = self.downsampler(data, self.parameters.with_features)
data = self.preprocesser(data, self.parameters.with_features)
if self.parameters.augment == True:
## generate random transformation as ground truths
r = torch.rand(1)
ax = torch.rand(3)
direction = torch.rand(3)
t_scale = torch.rand(1)
R = get_rotation_matrix(self.parameters.ang_range, r=r, ax=ax)
t = get_translation_vector(self.parameters.t_range * t_scale,
direction=direction)
# transform coordinates
data['coordinates'] = R.t() @ data['coordinates'] - R.t(
) @ t # transform with inverse augmented ground truth
R_gt.append(data['R_gt'] @ R)
t_gt.append(data['R_gt'] @ t + data['t_gt'])
else:
R_gt.append(data['R_gt'])
t_gt.append(data['t_gt'])
R_init.append(torch.eye(3, 3))
t_init.append(torch.zeros(3, 1))
coords.append(data['coordinates'])
if self.parameters.with_features:
features.append(data['features'])
if "time_stamps" in data.keys():
time_stamps.append(data["time_stamps"])
out = {'coordinates': TensorList(coords)}
info = {
'R_gt': TensorList(R_gt),
't_gt': TensorList(t_gt),
'R_init': TensorList(R_init),
't_init': TensorList(t_init),
'sequence': specs.sequence,
'names': names
}
if self.parameters.with_features:
out['features'] = TensorList(features)
if self.parameters.with_correspondences:
info['correspondences'] = self.extract_correspondences(out, info)
if len(time_stamps) > 0:
out['time_stamps'] = TensorList(time_stamps)
return out, info
class GaussNewtonCG:
def __init__(self,
problem: MinimizationProblem,
variable: TensorList,
cg_eps=0.0,
fletcher_reeves=True,
standard_alpha=True,
direction_forget_factor=0,
step_alpha=1.0):
self.fletcher_reeves = fletcher_reeves
self.standard_alpha = standard_alpha
self.direction_forget_factor = direction_forget_factor
# State
self.p = None
self.rho = torch.ones(1)
self.r_prev = None
# Right hand side
self.b = None
self.problem = problem
self.x = variable
self.cg_eps = cg_eps
self.f0 = None
self.g = None
self.dfdxt_g = None
self.residuals = torch.zeros(0)
self.external_losses = []
self.internal_losses = []
self.gradient_mags = torch.zeros(0)
self.step_alpha = step_alpha
def clear_temp(self):
self.f0 = None
self.g = None
self.dfdxt_g = None
def run(self, num_cg_iter, num_gn_iter=None):
self.problem.initialize()
if isinstance(num_cg_iter, int):
if num_gn_iter is None:
raise ValueError(
'Must specify number of GN iter if CG iter is constant')
num_cg_iter = [num_cg_iter] * num_gn_iter
num_gn_iter = len(num_cg_iter)
if num_gn_iter == 0:
return
# with torch.autograd.profiler.profile(use_cuda=True) as prof:
for cg_iter in num_cg_iter:
self.run_GN_iter(cg_iter)
self.x.detach_()
self.clear_temp()
return self.external_losses, self.internal_losses, self.residuals
def run_GN_iter(self, num_cg_iter):
self.x.requires_grad_(True)
self.f0 = self.problem(self.x)
self.g = self.f0.detach()
self.g.requires_grad_(True)
self.dfdxt_g = TensorList(
torch.autograd.grad(self.f0, self.x, self.g,
create_graph=True)) # df/dx^t @ f0
self.b = -self.dfdxt_g.detach()
delta_x, res = self.run_CG(num_cg_iter, eps=self.cg_eps)
self.x.detach_()
self.x += self.step_alpha * delta_x
self.step_alpha = min(self.step_alpha * 1.2, 1.0)
def reset_state(self):
self.p = None
self.rho = torch.ones(1)
self.r_prev = None
def run_CG(self, num_iter, x=None, eps=0.0):
"""Main conjugate gradient method"""
# Apply forgetting factor
if self.direction_forget_factor == 0:
self.reset_state()
elif self.p is not None:
self.rho /= self.direction_forget_factor
if x is None:
r = self.b.clone()
else:
r = self.b - self.A(x)
# Loop over iterations
for ii in range(num_iter):
z = self.problem.M1(r) # Preconditioner
rho1 = self.rho
self.rho = self.ip(r, z)
if self.p is None:
self.p = z.clone()
else:
if self.fletcher_reeves:
beta = self.rho / rho1
else:
rho2 = self.ip(self.r_prev, z)
beta = (self.rho - rho2) / rho1
beta = beta.clamp(0)
self.p = z + self.p * beta
q = self.A(self.p)
pq = self.ip(self.p, q)
if self.standard_alpha:
alpha = self.rho / pq
else:
alpha = self.ip(self.p, r) / pq
# Save old r for PR formula
if not self.fletcher_reeves:
self.r_prev = r.clone()
# Form new iterate
if x is None:
x = self.p * alpha
else:
x += self.p * alpha
if ii < num_iter - 1:
r -= q * alpha
return x, []
def A(self, x):
dfdx_x = torch.autograd.grad(self.dfdxt_g,
self.g,
x,
retain_graph=True)
return TensorList(
torch.autograd.grad(self.f0, self.x, dfdx_x, retain_graph=True))
def ip(self, a, b):
return self.problem.ip_input(a, b)
