def prepare_data_loaders():
if not os.path.exists(BPS_CACHE_FILE):
# load modelnet point clouds
xtr, ytr, xte, yte = load_modelnet40(root_data_dir=DATA_PATH)
# this will normalise your point clouds and return scaler parameters for inverse operation
xtr_normalized = bps.normalize(xtr)
xte_normalized = bps.normalize(xte)
# this will encode your normalised point clouds with random basis of 512 points,
# each BPS cell containing l2-distance to closest point
print("converting data to BPS representation..")
print("number of basis points: %d" % N_BPS_POINTS)
print("BPS sampling radius: %f" % BPS_RADIUS)
print("converting train..")
xtr_bps = bps.encode(xtr_normalized,
bps_arrangement='grid',
n_bps_points=N_BPS_POINTS,
radius=BPS_RADIUS,
bps_cell_type='deltas')
xtr_bps = xtr_bps.reshape([-1, 32, 32, 32, 3])
print("converting test..")
xte_bps = bps.encode(xte_normalized,
bps_arrangement='grid',
n_bps_points=N_BPS_POINTS,
radius=BPS_RADIUS,
bps_cell_type='deltas')
xte_bps = xte_bps.reshape([-1, 32, 32, 32, 3])
print("saving cache file for future runs..")
np.savez(BPS_CACHE_FILE, xtr=xtr_bps, ytr=ytr, xte=xte_bps, yte=yte)
else:
print("loading converted data from cache..")
data = np.load(BPS_CACHE_FILE)
xtr_bps = data['xtr']
ytr = data['ytr']
xte_bps = data['xte']
yte = data['yte']
xtr_bps = xtr_bps.transpose(0, 4, 2, 3, 1)
dataset_tr = pt.utils.data.TensorDataset(pt.Tensor(xtr_bps),
pt.Tensor(ytr[:, 0]).long())
tr_loader = pt.utils.data.DataLoader(dataset_tr,
batch_size=64,
shuffle=True)
xte_bps = xte_bps.transpose(0, 4, 2, 3, 1)
dataset_te = pt.utils.data.TensorDataset(pt.Tensor(xte_bps),
pt.Tensor(yte[:, 0]).long())
te_loader = pt.utils.data.DataLoader(dataset_te,
batch_size=64,
shuffle=True)
return tr_loader, te_loader
def prepare_data_loaders(n_parts=2):
if n_parts == 1:
APTBPS_CACHE_FILE = os.path.join(DATA_PATH, 'aptbps_mlp_data.npz')
else:
file_name = str(n_parts) + 'aptbps_mlp_data.npz'
APTBPS_CACHE_FILE = os.path.join(DATA_PATH, file_name)
if not os.path.exists(BPS_CACHE_FILE):
# load modelnet point clouds
xtr, ytr, xte, yte = load_modelnet40(root_data_dir=DATA_PATH)
# this will normalise your point clouds and return scaler parameters for inverse operation
xtr_normalized = bps.normalize(xtr)
xte_normalized = bps.normalize(xte)
# this will encode your normalised point clouds with random basis of 512 points,
# each BPS cell containing l2-distance to closest point
print("converting data to BPS representation..")
print("number of basis points: %d" % N_BPS_POINTS)
print("BPS sampling radius: %f" % BPS_RADIUS)
print("converting train..")
xtr_bps = aptbps.adaptive_encode(xtr_normalized,
n_parts=n_parts,
n_bps_points=N_BPS_POINTS,
bps_cell_type='dists',
radius=BPS_RADIUS)
print("converting test..")
xte_bps = aptbps.adaptive_encode(xte_normalized,
n_parts=n_parts,
n_bps_points=N_BPS_POINTS,
bps_cell_type='dists',
radius=BPS_RADIUS)
print("conversion finished. ")
print("saving cache file for future runs..")
np.savez(APTBPS_CACHE_FILE, xtr=xtr_bps, ytr=ytr, xte=xte_bps, yte=yte)
else:
print("loading converted data from cache..")
data = np.load(APTBPS_CACHE_FILE)
xtr_bps = data['xtr']
ytr = data['ytr']
xte_bps = data['xte']
yte = data['yte']
dataset_tr = pt.utils.data.TensorDataset(pt.Tensor(xtr_bps),
pt.Tensor(ytr[:, 0]).long())
print("----")
print(dataset_tr)
train_loader = pt.utils.data.DataLoader(dataset_tr,
batch_size=512,
shuffle=True)
print("---")
print(train_loader)
dataset_te = pt.utils.data.TensorDataset(pt.Tensor(xte_bps),
pt.Tensor(yte[:, 0]).long())
test_loader = pt.utils.data.DataLoader(dataset_te,
batch_size=512,
shuffle=True)
return train_loader, test_loader
# the effect is that the model 'rotates' slightly each frame.
def animate(i):
ax.view_init(elev=ELEV, azim=i * ANIMATION_SPEED)
ax.set_xlim3d(-0.8, 0.8)
ax.set_ylim3d(-0.8, 0.8)
ax.set_zlim3d(-0.7, 0.7)
return fig,
# Generate reference set
basis_set = aptbps.generate_random_basis(N_BPS_POINTS,
n_dims=3,
radius=BPS_RADIUS,
random_seed=RANDOM_SEED)
xtr, ytr, xte, yte = load_modelnet40(root_data_dir=DATA_PATH)
# Normalize
xtr_normalized = aptbps.normalize(xtr)
# Point cloud to animate
XTR_ID = 31
input_cloud = xtr_normalized[XTR_ID]
values = input_cloud.T
x, y, z = values
# 1. NORMALIZED INPUT POINT CLOUD
# Create a figure and a 3D Axes