def spiral_tramsform(transform_fp, template_fp, ds_factors, seq_length,
dilation):
if not osp.exists(transform_fp):
print('Generating transform matrices...')
mesh = Mesh(filename=template_fp)
# ds_factors = [3.5, 3.5, 3.5, 3.5]
_, A, D, U, F, V = mesh_sampling.generate_transform_matrices(
mesh, ds_factors)
tmp = {
'vertices': V,
'face': F,
'adj': A,
'down_transform': D,
'up_transform': U
}
with open(transform_fp, 'wb') as fp:
pickle.dump(tmp, fp)
print('Done!')
print('Transform matrices are saved in \'{}\''.format(transform_fp))
else:
with open(transform_fp, 'rb') as f:
tmp = pickle.load(f, encoding='latin1')
spiral_indices_list = [
utils.preprocess_spiral(tmp['face'][idx], seq_length[idx],
tmp['vertices'][idx],
dilation[idx]) #.to(device)
for idx in range(len(tmp['face']) - 1)
]
down_transform_list = [
utils.to_sparse(down_transform) #.to(device)
for down_transform in tmp['down_transform']
]
up_transform_list = [
utils.to_sparse(up_transform) #.to(device)
for up_transform in tmp['up_transform']
]
return spiral_indices_list, down_transform_list, up_transform_list, tmp
cudnn.deterministic = True
template_fp = osp.join(args.data_fp, 'template', 'template.obj')
# generate/load transform matrices
transform_fp = osp.join(args.data_fp, 'transform.pkl')
if not osp.exists(transform_fp):
raise (Exception('transforms should already be present in ' +
transform_fp))
else:
with open(transform_fp, 'rb') as f:
tmp = pickle.load(f, encoding='latin1')
spiral_indices_list = [
utils.preprocess_spiral(tmp['face'][idx], args.seq_length[idx],
tmp['vertices'][idx],
args.dilation[idx]).to(device)
for idx in range(len(tmp['face']) - 1)
]
del tmp['face']
del tmp['vertices']
down_transform_list = [
utils.to_sparse(down_transform).to(device)
for down_transform in tmp['down_transform']
]
del tmp['down_transform']
up_transform_list = [
utils.to_sparse(up_transform).to(device)
for up_transform in tmp['up_transform']
]
del tmp