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Zero
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import numpy as np
import torch
class PoseT(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, pose):
p1 = pose[..., 0:1]
p2 = torch.sin(pose[..., 1:2])
p3 = torch.cos(pose[..., 1:2])
p4 = pose[..., 2:]
return torch.cat([p1, p2, p3, p4], dim=-1)
@torch.no_grad()
def noise_loss(model, cond_image, target_image, pose, ts_range, bsz, noise=None):
mx = ts_range[1]
mn = ts_range[0]
pose_layer = PoseT()
batch = {}
batch['image_target'] = target_image.repeat(bsz, 1, 1, 1)
batch['image_cond'] = cond_image.repeat(bsz, 1, 1, 1)
batch['T'] = pose_layer(pose.detach()).repeat(bsz, 1)
if noise is not None:
noise = torch.tensor(noise, dtype=model.dtype, device=model.device)
loss, _ = model.shared_step(batch, ts=np.arange(mn, mx, (mx-mn) / bsz), noise=noise[:bsz])
return loss.item()
@torch.no_grad()
def pairwise_loss(pose, model, cond_image, target_image, ts_range, probe_bsz, noise=None):
theta, azimuth, radius = pose
pose1 = torch.tensor([[theta, azimuth, radius]], device=model.device, dtype=torch.float32)
pose2 = torch.tensor([[-theta, np.pi*2-azimuth, -radius]], device=model.device, dtype=torch.float32)
loss1 = noise_loss(model, cond_image, target_image, pose1, ts_range, probe_bsz, noise=noise)
loss2 = noise_loss(model, target_image, cond_image, pose2, ts_range, probe_bsz, noise=noise)
return loss1 + loss2
@torch.no_grad()
def probe_pose(model, cond_image, target_image, ts_range, probe_bsz, theta_range=None, azimuth_range=None, radius_range=None, noise=None):
eps = 1e-5
if theta_range is None:
theta_range = np.arange(start=-np.pi*2/3, stop=np.pi*2/3+eps, step=np.pi/3)
if azimuth_range is None:
azimuth_range = np.arange(start=0.0, stop=np.pi*2, step=np.pi/4)
if radius_range is None:
radius_range = np.arange(start=0.0, stop=0.0+eps, step=0.1)
cands = []
for radius in radius_range:
for azimuth in azimuth_range:
for theta in theta_range:
loss = pairwise_loss([theta, azimuth, radius], model, cond_image, target_image, ts_range, probe_bsz, noise=noise)
'''convert numpy.float to float'''
cands.append((loss, [float(theta), float(azimuth), float(radius)]))
return cands
def create_random_pose():
theta = np.random.rand() * np.pi - np.pi / 2
azimuth = np.random.rand() * np.pi * 2
radius = np.random.rand() - 0.5
return [theta, azimuth, radius]
def get_inv_pose(pose):
return [-pose[0], np.pi*2 - pose[1], -pose[2]]
def add_pose(pose1, pose2):
theta = pose1[0] + pose2[0]
azimuth = pose1[1] + pose2[1]
azimuth = azimuth % (np.pi*2)
return [ theta, azimuth, (pose1[2] + pose2[2]) ]
def create_pose_params(pose, device):
theta = torch.tensor([pose[0]], requires_grad=True, device=device)
azimuth = torch.tensor([pose[1]], requires_grad=True, device=device)
radius = torch.tensor([pose[2]], requires_grad=True, device=device)
return [theta, azimuth, radius]
def find_optimal_poses(model, images, learning_rate, bsz=1, n_iter=1000, init_poses={}, ts_range=[0.02, 0.92], combinations=None, print_n=50, avg_last_n=1):
layer = PoseT()
num = len(images)
batch = {}
pose_params = { i:None for i in range(1, num)}
pose_trajs = { i:[] for i in range(1, num) }
for i in range(1, num):
if i in init_poses:
init_pose = init_poses[i]
else:
init_pose = create_random_pose()
pose = create_pose_params(init_pose, model.device)
pose_params[i] = pose
if combinations is None:
combinations = []
for i in range(0, num):
for j in range(i+1, num):
combinations.append((i, j))
combinations.append((j, i))
param_list = []
for i in pose_params:
param_list += pose_params[i]
optimizer = torch.optim.SGD(param_list, lr = learning_rate)
loss_traj = []
select_indces = set([])
for iter in range(0, n_iter):
if print_n > 0 and iter % print_n == 0 and iter > 0:
print(iter, np.mean(loss_traj[-avg_last_n:]), flush=True)
for i in range(1, num):
print(0, i, np.mean(pose_trajs[i][-avg_last_n:], axis=0).tolist())
'''record poses'''
for i in select_indces:
pose = pose_params[i]
pose_trajs[i].append([pose[0].item(), pose[1].item(), pose[2].item()])
select_indces = set([])
conds = []
targets = []
rts = []
choices = [ iter % len(combinations) ]
if bsz > 1:
choices = np.random.choice(len(combinations), size=bsz, replace=True)
for cho in choices:
i, j = combinations[cho]
conds.append(images[i])
targets.append(images[j])
if i == 0:
pose = pose_params[j]
select_indces.add(j)
elif j == 0:
pose = get_inv_pose(pose_params[i])
select_indces.add(i)
else:
pose0j = pose_params[j]
posei0 = get_inv_pose(pose_params[i])
if np.random.rand() < 0.5:
posei0 = [a.item() for a in posei0]
select_indces.add(j)
else:
pose0j = [b.item() for b in pose0j]
select_indces.add(i)
#pose = [ torch.remainder(a+b+2*np.pi, 2*np.pi) - np.pi for a, b in zip(posei0, pose0j) ]
pose = [ a+b for a, b in zip(posei0, pose0j) ]
rts.append(torch.cat(pose)[None, ...])
batch['image_cond'] = torch.cat(conds, dim=0)
batch['image_target'] = torch.cat(targets, dim=0)
batch['T'] = layer(torch.cat(rts, dim=0))
ts = np.arange(ts_range[0], ts_range[1], (ts_range[1]-ts_range[0]) / len(conds))
optimizer.zero_grad()
loss, loss_dict = model.shared_step(batch, ts=ts)
loss.backward()
optimizer.step()
loss_traj.append(loss.item())
if n_iter > 0:
result_poses = [ np.mean(pose_trajs[i][-avg_last_n:], axis=0).tolist() for i in range(1, num) ]
result_loss = np.mean(loss_traj[-avg_last_n:])
else:
result_poses = [ init_poses[i] for i in range(1, num) ]
result_loss = None
return result_poses, [ init_poses[i] for i in range(1, num) ], result_loss
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