Spaces:
Running
on
Zero
Running
on
Zero
Commit
·
0e27884
1
Parent(s):
ae9c545
add
Browse files
app.py
CHANGED
@@ -333,7 +333,7 @@ class BaseTrainer(object):
|
|
333 |
return result
|
334 |
|
335 |
|
336 |
-
@spaces.GPU(duration=
|
337 |
def _warp(args,model, batch_data,joints,joint_mask_upper,joint_mask_hands,joint_mask_lower,use_trans,mean_upper,mean_hands,mean_lower,std_upper,std_hands,std_lower,trans_mean,trans_std):
|
338 |
diffusion = create_gaussian_diffusion(use_ddim=args.use_ddim)
|
339 |
args,model,vq_model_upper,vq_model_hands,vq_model_lower,mean_upper,mean_hands,mean_lower,std_upper,std_hands,std_lower,trans_mean,trans_std,vqvae_latent_scale=_warp_create_cuda_model(args,model,mean_upper,mean_hands,mean_lower,std_upper,std_hands,std_lower,trans_mean,trans_std)
|
@@ -411,6 +411,7 @@ def _warp_g_test(loaded_data,diffusion,args,joints,joint_mask_upper,joint_mask_h
|
|
411 |
roundt = (n - args.pre_frames * vqvae_squeeze_scale) // (args.pose_length - args.pre_frames * vqvae_squeeze_scale)
|
412 |
remain = (n - args.pre_frames * vqvae_squeeze_scale) % (args.pose_length - args.pre_frames * vqvae_squeeze_scale)
|
413 |
round_l = args.pose_length - args.pre_frames * vqvae_squeeze_scale
|
|
|
414 |
|
415 |
|
416 |
for i in range(0, roundt):
|
@@ -470,6 +471,7 @@ def _warp_g_test(loaded_data,diffusion,args,joints,joint_mask_upper,joint_mask_h
|
|
470 |
rec_all_hands.append(rec_latent_hands[:, args.pre_frames:])
|
471 |
rec_all_lower.append(rec_latent_lower[:, args.pre_frames:])
|
472 |
|
|
|
473 |
rec_all_upper = torch.cat(rec_all_upper, dim=1) * vqvae_latent_scale
|
474 |
rec_all_hands = torch.cat(rec_all_hands, dim=1) * vqvae_latent_scale
|
475 |
rec_all_lower = torch.cat(rec_all_lower, dim=1) * vqvae_latent_scale
|
@@ -527,6 +529,7 @@ def _warp_g_test(loaded_data,diffusion,args,joints,joint_mask_upper,joint_mask_h
|
|
527 |
tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs*n, j, 3))
|
528 |
tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
|
529 |
|
|
|
530 |
return {
|
531 |
'rec_pose': rec_pose.detach().cpu(),
|
532 |
'rec_trans': rec_trans.detach().cpu(),
|
|
|
333 |
return result
|
334 |
|
335 |
|
336 |
+
@spaces.GPU(duration=100)
|
337 |
def _warp(args,model, batch_data,joints,joint_mask_upper,joint_mask_hands,joint_mask_lower,use_trans,mean_upper,mean_hands,mean_lower,std_upper,std_hands,std_lower,trans_mean,trans_std):
|
338 |
diffusion = create_gaussian_diffusion(use_ddim=args.use_ddim)
|
339 |
args,model,vq_model_upper,vq_model_hands,vq_model_lower,mean_upper,mean_hands,mean_lower,std_upper,std_hands,std_lower,trans_mean,trans_std,vqvae_latent_scale=_warp_create_cuda_model(args,model,mean_upper,mean_hands,mean_lower,std_upper,std_hands,std_lower,trans_mean,trans_std)
|
|
|
411 |
roundt = (n - args.pre_frames * vqvae_squeeze_scale) // (args.pose_length - args.pre_frames * vqvae_squeeze_scale)
|
412 |
remain = (n - args.pre_frames * vqvae_squeeze_scale) % (args.pose_length - args.pre_frames * vqvae_squeeze_scale)
|
413 |
round_l = args.pose_length - args.pre_frames * vqvae_squeeze_scale
|
414 |
+
print("debug3:finish it!")
|
415 |
|
416 |
|
417 |
for i in range(0, roundt):
|
|
|
471 |
rec_all_hands.append(rec_latent_hands[:, args.pre_frames:])
|
472 |
rec_all_lower.append(rec_latent_lower[:, args.pre_frames:])
|
473 |
|
474 |
+
print("debug4:finish it!")
|
475 |
rec_all_upper = torch.cat(rec_all_upper, dim=1) * vqvae_latent_scale
|
476 |
rec_all_hands = torch.cat(rec_all_hands, dim=1) * vqvae_latent_scale
|
477 |
rec_all_lower = torch.cat(rec_all_lower, dim=1) * vqvae_latent_scale
|
|
|
529 |
tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs*n, j, 3))
|
530 |
tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
|
531 |
|
532 |
+
print("debug5:finish it!")
|
533 |
return {
|
534 |
'rec_pose': rec_pose.detach().cpu(),
|
535 |
'rec_trans': rec_trans.detach().cpu(),
|