def train(self, epochs):
self.running_loss = 0.0
for epoch in range(epochs):
for i, sample in enumerate(tqdm(self.dataloader, 0)):
poses = sample['poses']
shapes = sample['shapes']
mano_layer = ManoLayer(mano_root='mano/models',
use_pca=False,
ncomps=48,
flat_hand_mean=False)
mano_layer.to(self.device)
# Forward pass through MANO layer
_, hand_joints = mano_layer(poses, shapes)
uv_root = sample['uv_root']
scale = sample['scale']
hand_joints = hand_joints.reshape([self.batch_size, -1])
x = torch.cat((hand_joints, uv_root, scale), 1)
x = torch.cat((x, x), 1)
# print("x", x.shape)
y = sample['xyz'].reshape([self.batch_size, -1])
# print("y", y.shape)
# print("uv", uv_root.shape)
# print("sc", scale.shape)
x = x.to(self.device)
y = y.to(self.device)
self.optimizer.zero_grad()
y_ = self.model(x)
loss = self.criterion(y_, y)
loss.backward()
self.optimizer.step()
self.running_loss += loss.item()
self.g_step += 1
if self.g_step % self.save_rate == self.save_rate - 1:
self.running_loss /= self.save_rate
self.save_state()
self.writer.add_scalar('training loss',
self.running_loss / self.save_rate,
self.g_step)
print(self.running_loss / self.save_rate, self.g_step)
self.running_loss = 0.0
class ManoDatasetC(Dataset):
def __init__(self, base_path, transform, train_indices):
self.transform = transform
mano_path = os.path.join(base_path, '%s_mano.json' % 'training')
mano_list = json_load(mano_path)
mano_array = np.array(mano_list).squeeze(1)
mano_poses = mano_array[..., :51]
mano_poses = mano_poses[train_indices]
self.kde = KernelDensity(bandwidth=0.15, kernel='gaussian')
self.kde.fit(mano_poses)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.mano_layer = ManoLayer(
mano_root='mano/models', use_pca=False, ncomps=45, flat_hand_mean=False)
self.mano_layer.to(self.device)
def __len__(self):
return 32560
def __getitem__(self, idx):
sample = self.kde.sample()
pose = sample[..., :48]
shape_start = sample[..., 48:]
shape = np.ones([1, 10])
shape[..., :3] = shape_start
x = {
'p': pose,
's': shape
}
x = self.transform(x)
hand_verts, hand_joints = self.mano_layer(x['p'], x['s'])
batch_size = hand_joints.shape[0]
hand_joints = hand_joints.reshape([batch_size, 63])
sample = {
'hand_joints': torch.squeeze(hand_joints),
'hand_verts': torch.squeeze(hand_verts),
'poses': torch.squeeze(x['p']),
'shapes': torch.squeeze(x['s'])
}
return sample
def xyz_from_mano(poses, shapes):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Select number of principal components for pose space
ncomps = 45
# Initialize MANO layer
mano_layer = ManoLayer(mano_root='mano/models',
use_pca=False,
ncomps=ncomps,
flat_hand_mean=False)
mano_layer.to(device)
poses = torch.from_numpy(poses).float().to(device)
shapes = torch.from_numpy(shapes).float().to(device)
# Forward pass through MANO layer
hand_verts, hand_joints = mano_layer(poses, shapes)
return hand_verts, hand_joints
class TrainerP0(object):
def __init__(self, batch_size, dataloader, model, build_id):
self.batch_size = batch_size
self.dataloader = dataloader
self.model = model
self.save_path = f'results/{build_id}.pt'
self.writer = SummaryWriter(f'results/{build_id}')
input_example = next(iter(dataloader))['uv']
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
self.writer.add_graph(self.model, input_example)
self.writer.close()
self.criterion = nn.MSELoss()
self.optimizer = optim.SGD(self.model.parameters(),
lr=0.0001,
momentum=0.9)
self.load_state()
ncomps = 45
self.mano_layer = ManoLayer(mano_root='mano/models',
use_pca=False,
ncomps=ncomps,
flat_hand_mean=False)
self.mano_layer.to(self.device)
self.mano_layer.eval()
def train(self, epochs, save_rate):
self.running_loss = 0.0
for epoch in range(epochs):
for i, sample in enumerate(self.dataloader, 0):
uv = sample['uv']
outputs_gt = sample['mano']
uv = uv.to(self.device)
outputs_gt = outputs_gt.to(self.device)
self.optimizer.zero_grad()
outputs = self.model(uv) * 3.12
# losses = []
# losses.append(self.criterion(outputs, outputs_gt))
loss = self.criterion(outputs, outputs_gt)
# poses_pred = outputs[:, :48] # .unsqueeze(0)
# shapes_pred = outputs[:, 48:] # .unsqueeze(0)
# hand_verts_p, hand_joints_p = self.mano_layer(poses_pred, shapes_pred)
#
# poses_gt = outputs_gt[:, :48]
# shapes_gt = outputs_gt[:, 48:]
# hand_verts_gt, hand_joints_gt = self.mano_layer(poses_gt, shapes_gt)
# losses.append(self.criterion(hand_verts_p, hand_verts_gt))
# losses.append(self.criterion(hand_joints_p, hand_joints_gt))
# loss = sum(losses)
loss.backward()
self.optimizer.step()
self.running_loss += loss.item()
self.g_step += 1
self.running_loss /= 128
self.save_state()
self.writer.add_scalar('training loss', self.running_loss / 128,
self.g_step)
print(self.running_loss / 128, self.g_step)
self.running_loss = 0.0
def save_state(self):
torch.save(
{
'g_step': self.g_step,
'model_state_dict': self.model.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
'running_loss': self.running_loss / 128,
}, self.save_path)
print(
f'Model saved at step {self.g_step} with running loss {self.running_loss / 128}.'
)
def load_state(self):
if os.path.exists(self.save_path):
checkpoint = torch.load(self.save_path)
self.g_step = checkpoint['g_step'] + 1
self.running_loss = checkpoint['running_loss']
self.model.load_state_dict(checkpoint['model_state_dict'])
self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
print(
f'Model loaded. g_step: {self.g_step}; running_loss: {self.running_loss}'
)
else:
print(
f'File "{self.save_path}" does not exist. Initializing parameters from scratch.'
)
self.g_step = 0
self.running_loss = 0.0
CONFIG_M0['data_version'],
transform=ToTensor())
dataloader = DataLoader(dataset, shuffle=True)
sample = next(iter(dataloader))
random_pose = sample['poses']
random_shape = sample['shapes']
print(random_pose.shape)
# Initialize MANO layer
mano_layer = ManoLayer(mano_root='mano/models',
use_pca=False,
ncomps=48,
flat_hand_mean=False)
mano_layer.to(device)
# Forward pass through MANO layer
hand_verts, hand_joints = mano_layer(random_pose, random_shape)
# demo.display_hand({
# 'verts': hand_verts,
# 'joints': hand_joints
# },
# mano_faces=mano_layer.th_faces)
uv_root = sample['uv_root']
scale = sample['scale']
hand_joints = hand_joints.reshape([1, -1])
x = torch.cat((hand_joints, uv_root, scale), 1)
x = torch.cat((x, x), 1)
# NETWORK WAY
class pcl2mano():
def __init__(self,
device,
mano_root='mano/models',
ncomps=45,
seg_file='./face2label_sealed.npy',
visualize_interm_result=False):
self.ncomps = ncomps
self.device = device
self.mano_root = mano_root
self.mano_layer = ManoLayer(mano_root=self.mano_root,
use_pca=True,
ncomps=self.ncomps,
flat_hand_mean=False)
self.mano_layer = self.mano_layer.to(self.device)
segmentation = np.load(seg_file)
self.faces = self.mano_layer.th_faces.detach().cpu()
# assign mano vertex label according to face label:
self.vertex_label = np.zeros((self.faces.max() + 1), dtype=np.uint8)
for i in range(0, self.faces.shape[0]):
self.vertex_label[self.faces[i, 0]] = segmentation[i, 0]
self.vertex_label[self.faces[i, 1]] = segmentation[i, 0]
self.vertex_label[self.faces[i, 2]] = segmentation[i, 0]
self.label_vertex = [(np.where(self.vertex_label == 0))[0], \
(np.where(self.vertex_label == 1))[0], \
(np.where(self.vertex_label == 2))[0], \
(np.where(self.vertex_label == 3))[0], \
(np.where(self.vertex_label == 4))[0], \
(np.where(self.vertex_label == 5))[0]]
self.visualize_interm_result = visualize_interm_result
self.target_points = []
self.target_points_tree = []
self.no_label = []
def find_nearest_neighbour_index_from_hand(self, target_points_tree, hands,
no_label):
if len(hands.shape) == 3: # batch of hands
closest_indices = []
for i in range(0, hands.shape[0]):
hand = hands[i, :, :].detach().cpu().numpy()
_, closest_palm_index = target_points_tree[0].query(
hand[self.label_vertex[0], :], 1)
_, closest_thum_index = target_points_tree[1].query(
hand[self.label_vertex[1], :], 1)
_, closest_inde_index = target_points_tree[2].query(
hand[self.label_vertex[2], :], 1)
_, closest_midd_index = target_points_tree[3].query(
hand[self.label_vertex[3], :], 1)
_, closest_ring_index = target_points_tree[4].query(
hand[self.label_vertex[4], :], 1)
_, closest_pink_index = target_points_tree[5].query(
hand[self.label_vertex[5], :], 1)
closest_indices.append([closest_palm_index, closest_thum_index, closest_inde_index,\
closest_midd_index, closest_ring_index, closest_pink_index])
return closest_indices
else: # Not used
print("Fatal error in find_nearest_neighbour_index_from_hand!")
exit()
def index2points_from_hand(self, indices, closest_points):
for batch in range(0, len(indices)): # iterate over batch dimension
for label in range(0, 6): # iterate over hand parts
closest_points[
batch,
self.label_vertex[label][:], :] = self.target_points[
label][indices[batch][label][:, 0], :]
return closest_points
def find_nearest_neighbour_to_hand(self, target_points, n, hands,
no_label):
closest_batch_hand_indices = np.zeros((0, n), dtype=np.int32)
target_points_batch_rearrange = np.zeros((0, n, 3), dtype=np.float32)
for i in range(0, hands.shape[0]): # iterate over batch dimension
hand = hands[i, :, :].detach().cpu().numpy()
closest_hand_indices = np.zeros((0), dtype=np.int32)
target_points_rearrange = np.zeros((0, 3), dtype=np.float32)
for hand_part in range(0, 6):
# If there is no predicted label of that part, skip.
if no_label[hand_part]:
continue
hand_part_vertex = hand[self.label_vertex[hand_part], :]
hand_part_tree = KDTree(hand_part_vertex)
_, closest_part_index = hand_part_tree.query(
target_points[hand_part], 1)
closest_hand_indices = np.concatenate(
(closest_hand_indices,
self.label_vertex[hand_part][closest_part_index[:, 0]]),
0)
target_points_rearrange = np.concatenate(
(target_points_rearrange, target_points[hand_part]))
target_points_rearrange = np.expand_dims(target_points_rearrange,
axis=0)
target_points_batch_rearrange = np.concatenate(
(target_points_batch_rearrange, target_points_rearrange), 0)
closest_hand_indices = np.expand_dims(closest_hand_indices, axis=0)
closest_batch_hand_indices = np.concatenate(
(closest_batch_hand_indices, closest_hand_indices), 0)
return closest_batch_hand_indices, target_points_batch_rearrange
def index2points_to_hand(self, indices, hands):
hands_batch_rearrange = torch.zeros(0, indices.shape[1],
3).float().to(self.device)
for batch in range(0, indices.shape[0]):
hand_rearrange = hands[batch, indices[batch, :], :]
hand_rearrange = hand_rearrange.unsqueeze(0)
hands_batch_rearrange = torch.cat(
(hands_batch_rearrange, hand_rearrange), dim=0) # axis = 0)
return hands_batch_rearrange
def mask_no_label_from_hand(self, hand_verts, no_label):
for i in range(len(no_label)):
if no_label[i]:
hand_verts[:, self.label_vertex[i], :] = 0.
return hand_verts
def mask_no_label_to_hand(self, hand_verts, no_label):
for i in range(len(no_label)):
if no_label[i]:
hand_verts[:, self.label_vertex[i], :] = 0.
return hand_verts
def fit_mano_2_pcl(
self,
samples, # samples is a (N_v x 3) array, unit is Millimeter!
labels, # labels is a (N_v x 1) array
seeds=8,
coarse_iter=50,
fine_iter=50,
stop_loss=5.0,
verbose=0):
# classify samples according to labels
palm = samples[(np.where(labels == 0))[0], :]
thumb = samples[(np.where(labels == 1))[0], :]
index = samples[(np.where(labels == 2))[0], :]
middle = samples[(np.where(labels == 3))[0], :]
ring = samples[(np.where(labels == 4))[0], :]
pinky = samples[(np.where(labels == 5))[0], :]
for lab in [palm, thumb, index, middle, ring, pinky]:
# print(len(lab))
# print(lab.shape)
self.no_label.append(lab.shape[0] == 0)
# Add temp point (0,0,0) to part with no sample.
# The loss from these points will be masked out later when calculating loss
if palm.shape[0] == 0: palm = np.zeros([1, 3])
if thumb.shape[0] == 0: thumb = np.zeros([1, 3])
if index.shape[0] == 0: index = np.zeros([1, 3])
if middle.shape[0] == 0: middle = np.zeros([1, 3])
if ring.shape[0] == 0: ring = np.zeros([1, 3])
if pinky.shape[0] == 0: pinky = np.zeros([1, 3])
# print("No label:", self.no_label)
self.target_points_np = [palm, thumb, index, middle, ring, pinky]
self.target_points = [torch.from_numpy(palm).float().to(self.device),\
torch.from_numpy(thumb).float().to(self.device),\
torch.from_numpy(index).float().to(self.device),\
torch.from_numpy(middle).float().to(self.device),\
torch.from_numpy(ring).float().to(self.device),\
torch.from_numpy(pinky).float().to(self.device)]
self.target_points_tree = [
KDTree(palm),
KDTree(thumb),
KDTree(index),
KDTree(middle),
KDTree(ring),
KDTree(pinky)
]
# Model para initialization:
shape = torch.zeros(seeds, 10).float().to(self.device)
shape.requires_grad_()
rot = torch.zeros(seeds, 3).float().to(self.device)
rot.requires_grad_()
pose = torch.zeros(seeds, self.ncomps).float().to(self.device)
pose = (0.1 * torch.randn(seeds, self.ncomps)).float().to(self.device)
pose.requires_grad_()
trans = torch.from_numpy(samples.mean(0) /
1000.0) # trans should be in meter
trans = trans.unsqueeze(0).repeat(seeds, 1).float().to(self.device)
# trans = (0.1*torch.randn(seeds, 3)).float().to(self.device)
trans.requires_grad_()
hand_verts, hand_joints = self.mano_layer(torch.cat((rot, pose), 1),
shape, trans)
if self.visualize_interm_result:
demo.display_mosh(torch.from_numpy(samples).float().unsqueeze(0).expand(seeds, -1, -1),\
np.zeros((0,4), dtype = np.int32),
{'verts': hand_verts.detach().cpu(),
'joints': hand_joints.detach().cpu()}, \
mano_faces=self.mano_layer.th_faces.detach().cpu(), \
alpha = 0.3)
# Global optimization
criteria_loss = nn.MSELoss().to(self.device)
previous_loss = 1e8
optimizer = torch.optim.Adam([trans, rot], lr=1e-2)
print('...Optimizing global transformation...')
for i in range(0, coarse_iter):
hand_verts, hand_joints = self.mano_layer(
torch.cat((rot, pose), 1), shape, trans)
# Find closest label points:
closest_indices = self.find_nearest_neighbour_index_from_hand(
self.target_points_tree, hand_verts, self.no_label)
closest_points = self.index2points_from_hand(
closest_indices, torch.zeros_like(hand_verts))
for j in range(0, 20):
hand_verts, hand_joints = self.mano_layer(
torch.cat((rot, pose), 1), shape, trans)
hand_verts = self.mask_no_label_from_hand(
hand_verts, self.no_label)
loss = criteria_loss(hand_verts, closest_points)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss = criteria_loss(hand_verts, closest_points)
if verbose >= 1:
print(i, loss.data)
if previous_loss - loss.data < 1e-1:
break
previous_loss = loss.data.detach()
# print('After coarse alignment: %6f'%(loss.data))
if self.visualize_interm_result:
demo.display_mosh(torch.from_numpy(samples).float().unsqueeze(0).expand(seeds, -1, -1),\
np.zeros((0,4), dtype = np.int32),
{'verts': hand_verts.detach().cpu(),
'joints': hand_joints.detach().cpu()}, \
mano_faces=self.mano_layer.th_faces.detach().cpu(), \
alpha = 0.3)
# Local optimization
previous_loss = 1e8
optimizer = torch.optim.Adam([trans, rot, pose, shape], lr=1e-2)
print('...Optimizing hand pose shape and global transformation...')
for i in range(0, fine_iter):
hand_verts, hand_joints = self.mano_layer(
torch.cat((rot, pose), 1), shape, trans)
# Find closest label points:
closest_batch_hand_indices, target_points_batch_rearrange = self.find_nearest_neighbour_to_hand(
self.target_points_np, samples.shape[0], hand_verts,
self.no_label)
target_points_batch_rearrange = torch.from_numpy(
target_points_batch_rearrange).float().to(self.device)
for j in range(0, 20):
hand_verts, hand_joints = self.mano_layer(
torch.cat((rot, pose), 1), shape, trans)
hands_batch_rearrange = self.index2points_to_hand(
closest_batch_hand_indices, hand_verts)
w_pose = 100.0
loss = criteria_loss(
hands_batch_rearrange, target_points_batch_rearrange
) + w_pose * (pose * pose).mean() # pose regularizer
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss = criteria_loss(hands_batch_rearrange,
target_points_batch_rearrange)
# if previous_loss - loss.data < 1e-1:
# break
previous_loss = loss.data.detach()
# Find smallest loss in the #seeds seeds:
per_seed_error = (
(hands_batch_rearrange - target_points_batch_rearrange) *
(hands_batch_rearrange -
target_points_batch_rearrange)).mean(2).mean(1)
min_index = torch.argmin(per_seed_error).detach().cpu().numpy()
min_error = per_seed_error[min_index]
if verbose >= 1: print(i, min_error.data)
if self.visualize_interm_result and i % 40 == 0:
tmp_arange = np.expand_dims(np.arange(
target_points_batch_rearrange.shape[1]),
axis=1)
link = np.concatenate(
(tmp_arange,
tmp_arange + target_points_batch_rearrange.shape[1]), 1)
link = link[0:200, :]
visual_points = torch.cat(
(target_points_batch_rearrange[min_index, :, :],
hands_batch_rearrange[min_index, :, :]),
dim=0)
visual_points = visual_points.unsqueeze(0).expand(
seeds, -1, -1)
pass
demo.display_mosh(visual_points.detach().cpu(),\
link,
{'verts': hand_verts.detach().cpu(),
'joints': hand_joints.detach().cpu()}, \
mano_faces=self.mano_layer.th_faces.detach().cpu(), \
alpha = 0.3)
if min_error < stop_loss:
break
# print('After fine alignment: %6f'%(loss.data))
hand_verts, hand_joints = self.mano_layer(torch.cat((rot, pose), 1),
shape, trans)
hand_shape = {'vertices': hand_verts.detach().cpu().numpy()[min_index, :, :], \
'joints': hand_joints.detach().cpu().numpy()[min_index, :, :], \
'faces': self.mano_layer.th_faces.detach().cpu()}
mano_para = {'rot': rot.detach().cpu().numpy()[min_index, :], \
'pose': pose.detach().cpu().numpy()[min_index, :], \
'shape': shape.detach().cpu().numpy()[min_index, :], \
'trans': trans.detach().cpu().numpy()[min_index, :]}
return hand_shape, mano_para
