def fkl_torch(angles, parent, offset, rotInd, expmapInd):
"""
pytorch version of fkl.
convert joint angles to joint locations
batch pytorch version of the fkl() method above
:param angles: N*99
:param parent:
:param offset:
:param rotInd:
:param expmapInd:
:return: N*joint_n*3
"""
n = angles.data.shape[0]
j_n = offset.shape[0]
p3d = Variable(torch.from_numpy(offset)).float()
if torch.cuda.is_available():
p3d = p3d.cuda()
p3d = p3d.unsqueeze(0).repeat(n, 1, 1)
angles = angles[:, 3:].contiguous().view(-1, 3)
R = data_utils.expmap2rotmat_torch(angles).view(n, j_n, 3, 3)
for i in np.arange(1, j_n):
if parent[i] > 0:
R[:, i, :, :] = torch.matmul(R[:, i, :, :],
R[:, parent[i], :, :]).clone()
p3d[:, i, :] = torch.matmul(
p3d[0, i, :], R[:, parent[i], :, :]) + p3d[:, parent[i], :]
return p3d
def euler_error(outputs, all_seq, input_n, dim_used, N):
"""
:param outputs:
:param all_seq:
:param input_n:
:param dim_used:
:return:
"""
n, seq_len, dim_full_len = all_seq.data.shape
dim_used_len = len(dim_used)
t = np.arange(1, N + 1, 1)
A = chebyshev.chebvander(t, N - 1)
A = Variable(torch.from_numpy(A)).float().cuda()
outputs_t = torch.mm(A, outputs.view(-1, N).transpose(0, 1))
outputs_exp = outputs_t.transpose(0, 1).view(-1, dim_used_len,
seq_len).transpose(1, 2)
pred_expmap = all_seq.clone()
dim_used = np.array(dim_used)
pred_expmap[:, :, dim_used] = outputs_exp
pred_expmap = pred_expmap[:,
input_n:, :].contiguous().view(-1, dim_full_len)
targ_expmap = all_seq[:, input_n:, :].clone().contiguous().view(
-1, dim_full_len)
# pred_expmap[:, 0:6] = 0
# targ_expmap[:, 0:6] = 0
pred_expmap = pred_expmap.view(-1, 3)
targ_expmap = targ_expmap.view(-1, 3)
pred_eul = data_utils.rotmat2euler_torch(
data_utils.expmap2rotmat_torch(pred_expmap))
pred_eul = pred_eul.view(-1, dim_full_len)
targ_eul = data_utils.rotmat2euler_torch(
data_utils.expmap2rotmat_torch(targ_expmap))
targ_eul = targ_eul.view(-1, dim_full_len)
mean_errors = torch.mean(torch.norm(pred_eul - targ_eul, 2, 1))
return mean_errors
def euler_error(outputs, all_seq, input_n, dim_used, dct_n):
"""
:param outputs:
:param all_seq:
:param input_n:
:param dim_used:
:return:
"""
n, seq_len, dim_full_len = all_seq.data.shape
dim_used_len = len(dim_used)
_, idct_m = data_utils.get_dct_matrix(seq_len)
idct_m = Variable(torch.from_numpy(idct_m)).float().cuda()
outputs_t = outputs.view(-1, dct_n).transpose(0, 1)
outputs_exp = torch.matmul(idct_m[:, :dct_n],
outputs_t).transpose(0, 1).contiguous().view(
-1, dim_used_len, seq_len).transpose(1, 2)
pred_expmap = all_seq.clone()
dim_used = np.array(dim_used)
pred_expmap[:, :, dim_used] = outputs_exp
pred_expmap = pred_expmap[:,
input_n:, :].contiguous().view(-1, dim_full_len)
targ_expmap = all_seq[:, input_n:, :].clone().contiguous().view(
-1, dim_full_len)
# pred_expmap[:, 0:6] = 0
# targ_expmap[:, 0:6] = 0
pred_expmap = pred_expmap.view(-1, 3)
targ_expmap = targ_expmap.view(-1, 3)
pred_eul = data_utils.rotmat2euler_torch(
data_utils.expmap2rotmat_torch(pred_expmap))
pred_eul = pred_eul.view(-1, dim_full_len)
targ_eul = data_utils.rotmat2euler_torch(
data_utils.expmap2rotmat_torch(targ_expmap))
targ_eul = targ_eul.view(-1, dim_full_len)
mean_errors = torch.mean(torch.norm(pred_eul - targ_eul, 2, 1))
return mean_errors
def run_model(net_pred,
optimizer=None,
is_train=0,
data_loader=None,
epo=1,
opt=None):
if is_train == 0:
net_pred.train()
else:
net_pred.eval()
l_ang = 0
if is_train <= 1:
m_ang_seq = 0
else:
titles = np.array(range(opt.output_n)) + 1
m_ang_seq = np.zeros([opt.output_n])
n = 0
in_n = opt.input_n
out_n = opt.output_n
dim_used = np.array([
6, 7, 8, 9, 12, 13, 14, 15, 21, 22, 23, 24, 27, 28, 29, 30, 36, 37, 38,
39, 40, 41, 42, 43, 44, 45, 46, 47, 51, 52, 53, 54, 55, 56, 57, 60, 61,
62, 75, 76, 77, 78, 79, 80, 81, 84, 85, 86
])
seq_in = opt.kernel_size
itera = 1
idx = np.expand_dims(np.arange(seq_in + out_n), axis=1) + (
out_n - seq_in + np.expand_dims(np.arange(itera), axis=0))
st = time.time()
for i, (ang_h36) in enumerate(data_loader):
batch_size, seq_n, _ = ang_h36.shape
# when only one sample in this batch
if batch_size == 1 and is_train == 0:
continue
n += batch_size
bt = time.time()
ang_h36 = ang_h36.float().cuda()
ang_sup = ang_h36.clone()[:, :, dim_used][:, -out_n - seq_in:]
ang_src = ang_h36.clone()[:, :, dim_used]
ang_out_all = net_pred(ang_src,
output_n=out_n,
itera=itera,
input_n=in_n)
ang_out = ang_h36.clone()[:, in_n:in_n + out_n]
ang_out[:, :, dim_used] = ang_out_all[:, seq_in:, 0]
# 2d joint loss:
grad_norm = 0
if is_train == 0:
loss_ang = torch.mean(
torch.sum(torch.abs(ang_out_all[:, :, 0] - ang_sup), dim=2))
loss_all = loss_ang
optimizer.zero_grad()
loss_all.backward()
grad_norm = nn.utils.clip_grad_norm_(list(net_pred.parameters()),
max_norm=opt.max_norm)
optimizer.step()
# update log values
l_ang += loss_ang.cpu().data.numpy() * batch_size
if is_train <= 1: # if is validation or train simply output the overall mean error
with torch.no_grad():
ang_out_euler = ang_out.reshape([-1, 99]).reshape([-1, 3])
ang_gt_euler = ang_h36[:, in_n:in_n + out_n].reshape(
[-1, 99]).reshape([-1, 3])
import utils.data_utils as data_utils
ang_out_euler = data_utils.rotmat2euler_torch(
data_utils.expmap2rotmat_torch(ang_out_euler))
ang_out_euler = ang_out_euler.view(-1, 99)
ang_gt_euler = data_utils.rotmat2euler_torch(
data_utils.expmap2rotmat_torch(ang_gt_euler))
ang_gt_euler = ang_gt_euler.view(-1, 99)
eulererr_ang_seq = torch.mean(
torch.norm(ang_out_euler - ang_gt_euler, dim=1))
m_ang_seq += eulererr_ang_seq.cpu().data.numpy() * batch_size
else:
with torch.no_grad():
ang_out_euler = ang_out.reshape([-1, 99]).reshape([-1, 3])
ang_gt_euler = ang_h36[:, in_n:in_n + out_n].reshape(
[-1, 99]).reshape([-1, 3])
import utils.data_utils as data_utils
ang_out_euler = data_utils.rotmat2euler_torch(
data_utils.expmap2rotmat_torch(ang_out_euler))
ang_out_euler = ang_out_euler.view(-1, out_n, 99)
ang_gt_euler = data_utils.rotmat2euler_torch(
data_utils.expmap2rotmat_torch(ang_gt_euler))
ang_gt_euler = ang_gt_euler.view(-1, out_n, 99)
eulererr_ang_seq = torch.sum(torch.norm(ang_out_euler -
ang_gt_euler,
dim=2),
dim=0)
m_ang_seq += eulererr_ang_seq.cpu().data.numpy()
if i % 1000 == 0:
print('{}/{}|bt {:.3f}s|tt{:.0f}s|gn{}'.format(
i + 1, len(data_loader),
time.time() - bt,
time.time() - st, grad_norm))
ret = {}
if is_train == 0:
ret["l_ang"] = l_ang / n
if is_train <= 1:
ret["m_ang_h36"] = m_ang_seq / n
else:
m_ang_h36 = m_ang_seq / n
for j in range(out_n):
ret["#{:d}".format(titles[j])] = m_ang_h36[j]
return ret
def test(self, train_loader, dataset='h3.6m', input_n=20, output_n=50, dct_n=20, cartesian=False,
dim_used=[]):
N = 0
# t_l = 0
if output_n >= 25:
eval_frame = [1, 3, 7, 9, 13, 24]
elif output_n == 10:
eval_frame = [1, 3, 7, 9]
t_e = np.zeros(len(eval_frame))
t_3d = np.zeros(len(eval_frame))
self.model.eval()
st = time.time()
bar = Bar('>>>', fill='>', max=len(train_loader))
for i, (inputs, targets, all_seq) in enumerate(train_loader):
bt = time.time()
if self.is_cuda:
inputs = Variable(inputs.cuda()).float()
all_seq = Variable(all_seq.cuda(non_blocking=True)).float()
outputs, reconstructions, log_var, z = self.model(inputs.float())
n = outputs.shape[0]
n, seq_len, dim_full_len = all_seq.data.shape
dim_used_len = len(dim_used)
all_seq[:, :, 0:6] = 0
# inverse dct transformation
_, idct_m = data_utils.get_dct_matrix(seq_len)
idct_m = Variable(torch.from_numpy(idct_m)).float().cuda()
outputs_t = outputs.view(-1, dct_n).transpose(0, 1)
if cartesian == False:
outputs_exp = torch.matmul(idct_m[:, :dct_n], outputs_t).transpose(0, 1).contiguous().view(-1,
dim_used_len,
seq_len).transpose(
1, 2)
pred_expmap = all_seq.clone()
dim_used = np.array(dim_used)
pred_expmap[:, :, dim_used] = outputs_exp
pred_expmap = pred_expmap[:, input_n:, :].contiguous().view(-1, dim_full_len)
targ_expmap = all_seq[:, input_n:, :].clone().contiguous().view(-1, dim_full_len)
pred_expmap[:, 0:6] = 0
targ_expmap[:, 0:6] = 0
pred_expmap = pred_expmap.view(-1, 3)
targ_expmap = targ_expmap.view(-1, 3)
# get euler angles from expmap
pred_eul = data_utils.rotmat2euler_torch(data_utils.expmap2rotmat_torch(pred_expmap))
pred_eul = pred_eul.view(-1, dim_full_len).view(-1, output_n, dim_full_len)
targ_eul = data_utils.rotmat2euler_torch(data_utils.expmap2rotmat_torch(targ_expmap))
targ_eul = targ_eul.view(-1, dim_full_len).view(-1, output_n, dim_full_len)
if dataset == 'h3.6m':
# get 3d coordinates
targ_p3d = data_utils.expmap2xyz_torch(targ_expmap.view(-1, dim_full_len)).view(n, output_n, -1, 3)
pred_p3d = data_utils.expmap2xyz_torch(pred_expmap.view(-1, dim_full_len)).view(n, output_n, -1, 3)
elif dataset == 'cmu_mocap':
# get 3d coordinates
targ_p3d = data_utils.expmap2xyz_torch_cmu(targ_expmap.view(-1, dim_full_len)).view(n, output_n, -1,
3)
pred_p3d = data_utils.expmap2xyz_torch_cmu(pred_expmap.view(-1, dim_full_len)).view(n, output_n, -1,
3)
for k in np.arange(0, len(eval_frame)):
j = eval_frame[k]
t_e[k] += torch.mean(torch.norm(pred_eul[:, j, :] - targ_eul[:, j, :], 2, 1)).cpu().data.numpy() * n
t_3d[k] += torch.mean(torch.norm(
targ_p3d[:, j, :, :].contiguous().view(-1, 3) - pred_p3d[:, j, :, :].contiguous().view(-1, 3),
2, 1)).cpu().data.numpy() * n
elif cartesian:
outputs_3d = torch.matmul(idct_m[:, :dct_n], outputs_t).transpose(0, 1).contiguous().view(-1,
dim_used_len,
seq_len).transpose(
1, 2)
pred_3d = all_seq.clone()
dim_used = np.array(dim_used)
# deal with joints at same position
joint_to_ignore = np.array([16, 20, 29, 24, 27, 33, 36])
index_to_ignore = np.concatenate(
(joint_to_ignore * 3, joint_to_ignore * 3 + 1, joint_to_ignore * 3 + 2))
joint_equal = np.array([15, 15, 15, 23, 23, 32, 32])
index_to_equal = np.concatenate((joint_equal * 3, joint_equal * 3 + 1, joint_equal * 3 + 2))
pred_3d[:, :, dim_used] = outputs_3d
pred_3d[:, :, index_to_ignore] = pred_3d[:, :, index_to_equal]
pred_p3d = pred_3d.contiguous().view(n, seq_len, -1, 3)[:, input_n:, :, :]
targ_p3d = all_seq.contiguous().view(n, seq_len, -1, 3)[:, input_n:, :, :]
for k in np.arange(0, len(eval_frame)):
j = eval_frame[k]
t_e[k] += torch.mean(torch.norm(
targ_p3d[:, j, :, :].contiguous().view(-1, 3) - pred_p3d[:, j, :, :].contiguous().view(-1, 3),
2, 1)).cpu().data.numpy()[0] * n
t_3d[k] += torch.mean(torch.norm(
targ_p3d[:, j, :, :].contiguous().view(-1, 3) - pred_p3d[:, j, :, :].contiguous().view(-1, 3),
2, 1)).cpu().data.numpy()[0] * n
N += n
bar.suffix = '{}/{}|batch time {:.4f}s|total time{:.2f}s'.format(i + 1, len(train_loader), time.time() - bt,
time.time() - st)
bar.next()
bar.finish()
return t_e / N, t_3d / N
def test(train_loader,
model,
stepsize,
input_n=20,
output_n=50,
dct_n=20,
is_cuda=False,
dim_used=[]):
N = 0
# t_l = 0
if output_n == 25:
eval_frame = [1, 3, 7, 9, 13, 24]
elif output_n == 50:
eval_frame = [1, 3, 7, 9, 13, 24, 35]
elif output_n == 100:
eval_frame = [1, 3, 7, 9, 13, 24, 35, 49]
elif output_n == 10:
eval_frame = [1, 3, 7, 9]
t_e = np.zeros(len(eval_frame))
t_3d = np.zeros(len(eval_frame))
model.eval()
# calculate no of iterations in auto regression to perform
iterations = int(output_n / stepsize)
print('iterations: {}'.format(iterations))
st = time.time()
bar = Bar('>>>', fill='>', max=len(train_loader))
for i, (inputs, targets, all_seq) in enumerate(train_loader):
bt = time.time()
all_seq = Variable(all_seq).float()
dim_used_len = len(dim_used)
if is_cuda:
all_seq = all_seq.cuda()
dct_m_in, _ = data_utils.get_dct_matrix(dct_n)
dct_m_in = Variable(torch.from_numpy(dct_m_in)).float().cuda()
_, idct_m = data_utils.get_dct_matrix(dct_n)
idct_m = Variable(torch.from_numpy(idct_m)).float().cuda()
n, seq_len, dim_full_len = all_seq.data.shape
y_hat = None
# Auto regression
for idx in range(iterations):
# start index of the input sequence
start = input_n + idx * stepsize
# end index of the input sequence
stop = start + stepsize
if y_hat is None:
# slice the sequence of length = (input_n + output_n) in iteration 1
input_seq = all_seq[:, :dct_n, dim_used]
else:
# stack output from prev iteration and next frames to form the next input seq
input_seq = torch.cat(
(y_hat, all_seq[:, start:stop, dim_used]), 1)
# calculate DCT of the input seq
input_dct_seq = torch.matmul(dct_m_in, input_seq).transpose(1, 2)
if is_cuda:
input_dct_seq = input_dct_seq.cuda()
y = model(input_dct_seq)
y_t = y.view(-1, dct_n).transpose(0, 1)
y_exp = torch.matmul(idct_m,
y_t).transpose(0, 1).contiguous().view(
-1, dim_used_len, dct_n).transpose(1, 2)
y_hat = y_exp[:, stepsize:, :]
# accumulate the output frames in a single tensor
if idx == 0:
outputs = y_exp
else:
outputs = torch.cat((outputs, y_exp[:, input_n:, :]), 1)
pred_expmap = all_seq.clone()
dim_used = np.array(dim_used)
pred_expmap[:, :, dim_used] = outputs
pred_expmap = pred_expmap[:, input_n:, :].contiguous().view(
-1, dim_full_len)
targ_expmap = all_seq[:, input_n:, :].clone().contiguous().view(
-1, dim_full_len)
pred_expmap[:, 0:6] = 0
targ_expmap[:, 0:6] = 0
pred_expmap = pred_expmap.view(-1, 3)
targ_expmap = targ_expmap.view(-1, 3)
# get euler angles from expmap
pred_eul = data_utils.rotmat2euler_torch(
data_utils.expmap2rotmat_torch(pred_expmap))
pred_eul = pred_eul.view(-1,
dim_full_len).view(-1, output_n, dim_full_len)
targ_eul = data_utils.rotmat2euler_torch(
data_utils.expmap2rotmat_torch(targ_expmap))
targ_eul = targ_eul.view(-1,
dim_full_len).view(-1, output_n, dim_full_len)
# get 3d coordinates
targ_p3d = data_utils.expmap2xyz_torch(
targ_expmap.view(-1, dim_full_len)).view(n, output_n, -1, 3)
pred_p3d = data_utils.expmap2xyz_torch(
pred_expmap.view(-1, dim_full_len)).view(n, output_n, -1, 3)
# update loss and testing errors
for k in np.arange(0, len(eval_frame)):
j = eval_frame[k]
'''
t_e[k] += torch.mean(torch.norm(pred_eul[:, j, :] - targ_eul[:, j, :], 2, 1)).cpu().data.numpy()[0] * n
t_3d[k] += torch.mean(torch.norm(
targ_p3d[:, j, :, :].contiguous().view(-1, 3) - pred_p3d[:, j, :, :].contiguous().view(-1, 3), 2,
1)).cpu().data.numpy()[0] * n
'''
t_e[k] += torch.mean(
torch.norm(pred_eul[:, j, :] - targ_eul[:, j, :], 2,
1)).item() * n
t_3d[k] += torch.mean(
torch.norm(
targ_p3d[:, j, :, :].contiguous().view(-1, 3) -
pred_p3d[:, j, :, :].contiguous().view(-1, 3), 2,
1)).item() * n
# t_l += loss.cpu().data.numpy()[0] * n
N += n
bar.suffix = '{}/{}|batch time {:.4f}s|total time{:.2f}s'.format(
i + 1, len(train_loader),
time.time() - bt,
time.time() - st)
bar.next()
bar.finish()
return t_e / N, t_3d / N
def test(train_loader,
model,
input_n=20,
output_n=50,
is_cuda=False,
dim_used=[],
dct_n=20):
N = 0
if output_n == 25:
eval_frame = [1, 3, 7, 9, 13, 24]
elif output_n == 10:
eval_frame = [1, 3, 7, 9]
t_e = np.zeros(len(eval_frame))
t_3d = np.zeros(len(eval_frame))
model.eval()
st = time.time()
bar = Bar('>>>', fill='>', max=len(train_loader))
for i, (inputs, targets, all_seq) in enumerate(train_loader):
bt = time.time()
if is_cuda:
inputs = Variable(inputs.cuda()).float()
all_seq = Variable(all_seq.cuda(async=True)).float()
outputs = model(inputs)
n, seq_len, dim_full_len = all_seq.data.shape
dim_used_len = len(dim_used)
all_seq[:, :, 0:6] = 0
_, idct_m = data_utils.get_dct_matrix(seq_len)
idct_m = Variable(torch.from_numpy(idct_m)).float().cuda()
outputs_t = outputs.view(-1, seq_len).transpose(0, 1)
outputs_exp = torch.matmul(idct_m[:, :dct_n], outputs_t).transpose(0, 1).contiguous().view \
(-1, dim_used_len, seq_len).transpose(1, 2)
pred_expmap = all_seq.clone()
dim_used = np.array(dim_used)
pred_expmap[:, :, dim_used] = outputs_exp
pred_expmap = pred_expmap[:, input_n:, :].contiguous().view(
-1, dim_full_len)
targ_expmap = all_seq[:, input_n:, :].clone().contiguous().view(
-1, dim_full_len)
pred_expmap = pred_expmap.view(-1, 3)
targ_expmap = targ_expmap.view(-1, 3)
pred_eul = data_utils.rotmat2euler_torch(
data_utils.expmap2rotmat_torch(pred_expmap))
pred_eul = pred_eul.view(-1, dim_full_len).view(
-1, output_n, dim_full_len) # [:, :, dim_used]
targ_eul = data_utils.rotmat2euler_torch(
data_utils.expmap2rotmat_torch(targ_expmap))
targ_eul = targ_eul.view(-1, dim_full_len).view(
-1, output_n, dim_full_len) # [:, :, dim_used]
targ_p3d = data_utils.expmap2xyz_torch_cmu(
targ_expmap.view(-1, dim_full_len)).view(n, output_n, -1, 3)
pred_p3d = data_utils.expmap2xyz_torch_cmu(
pred_expmap.view(-1, dim_full_len)).view(n, output_n, -1, 3)
for k in np.arange(0, len(eval_frame)):
j = eval_frame[k]
t_e[k] += torch.mean(
torch.norm(pred_eul[:, j, :] - targ_eul[:, j, :], 2,
1)).cpu().data.numpy()[0] * n
t_3d[k] += torch.mean(
torch.norm(
targ_p3d[:, j, :, :].contiguous().view(-1, 3) -
pred_p3d[:, j, :, :].contiguous().view(-1, 3), 2,
1)).cpu().data.numpy()[0] * n
# update the training loss
N += n
bar.suffix = '{}/{}|batch time {:.4f}s|total time{:.2f}s'.format(
i + 1, len(train_loader),
time.time() - bt,
time.time() - st)
bar.next()
bar.finish()
return t_e / N, t_3d / N
def test(train_loader,
model,
input_n=20,
output_n=50,
is_cuda=False,
dim_used=[]):
N = 0
# t_l = 0
if output_n >= 25:
eval_frame = [1, 3, 7, 9, 13, 24]
elif output_n == 10:
eval_frame = [1, 3, 7, 9]
else:
eval_frame = [1, 2, 3, 4]
t_e = np.zeros(len(eval_frame))
t_3d = np.zeros(len(eval_frame))
model.eval()
st = time.time()
bar = Bar('>>>', fill='>', max=len(train_loader))
for i, (inputs, targets, all_seq) in enumerate(train_loader):
bt = time.time()
if is_cuda:
inputs = Variable(inputs.cuda()).float()
# targets = Variable(targets.cuda(async=True)).float()
all_seq = Variable(all_seq.cuda()).float()
outputs = model(inputs)
# inverse dct transformation
n, seq_len, dim_full_len = all_seq.data.shape
dim_used_len = len(dim_used)
N = input_n + output_n
t = np.arange(1, N + 1, 1)
A = chebyshev.chebvander(t, N - 1)
A = Variable(torch.from_numpy(A)).float().cuda()
outputs_t = torch.matmul(A, outputs.view(-1, N).transpose(0, 1))
outputs_expmap = outputs_t.transpose(0,
1).view(-1, dim_used_len,
seq_len).transpose(1, 2)
pred_expmap = all_seq.clone()
dim_used = np.array(dim_used)
pred_expmap[:, :, dim_used] = outputs_expmap
pred_expmap = pred_expmap[:, input_n:, :].contiguous().view(
-1, dim_full_len)
targ_expmap = all_seq[:, input_n:, :].clone().contiguous().view(
-1, dim_full_len)
pred_expmap[:, 0:6] = 0
targ_expmap[:, 0:6] = 0
pred_expmap = pred_expmap.view(-1, 3)
targ_expmap = targ_expmap.view(-1, 3)
# get euler angles from expmap
pred_eul = data_utils.rotmat2euler_torch(
data_utils.expmap2rotmat_torch(pred_expmap))
pred_eul = pred_eul.view(-1,
dim_full_len).view(-1, output_n, dim_full_len)
targ_eul = data_utils.rotmat2euler_torch(
data_utils.expmap2rotmat_torch(targ_expmap))
targ_eul = targ_eul.view(-1,
dim_full_len).view(-1, output_n, dim_full_len)
# get 3d coordinates
targ_p3d = data_utils.expmap2xyz_torch(
targ_expmap.view(-1, dim_full_len)).view(n, output_n, -1, 3)
pred_p3d = data_utils.expmap2xyz_torch(
pred_expmap.view(-1, dim_full_len)).view(n, output_n, -1, 3)
# update loss and testing errors
for k in np.arange(0, len(eval_frame)):
j = eval_frame[k]
t_e[k] += torch.mean(
torch.norm(pred_eul[:, j, :] - targ_eul[:, j, :], 2,
1)).cpu().data.numpy() * n
t_3d[k] += torch.mean(
torch.norm(
targ_p3d[:, j, :, :].contiguous().view(-1, 3) -
pred_p3d[:, j, :, :].contiguous().view(-1, 3), 2,
1)).cpu().data.numpy() * n
# t_l += loss.cpu().data.numpy()[0] * n
N += n
bar.suffix = '{}/{}|batch time {:.4f}s|total time{:.2f}s'.format(
i + 1, len(train_loader),
time.time() - bt,
time.time() - st)
bar.next()
bar.finish()
return t_e / N, t_3d / N