def transform_predictions(probs, dp):
i = np.argmax(probs)
b = bs[i]
if n_samples is not None:
b = sample_points(b, n_samples)
points = np.matmul(b, ps[i] + dp[i])
return dict(cloud=points, attrs=dict(template_id=example_ids[i]))
def get_cloud_dataset(cat_id, n_samples, n_resamples):
from shapenet.core.point_clouds import PointCloudAutoSavingManager
from util3d.point_cloud import sample_points
manager = PointCloudAutoSavingManager(cat_id, n_samples)
if not os.path.isfile(manager.path):
manager.save_all()
dataset = manager.get_saving_dataset(mode='r')
# get_saved_dataset seems to give issues because we don't close properly
# dataset = manager.get_saved_dataset()
return dataset.map(lambda x: sample_points(np.array(x), n_resamples))
def get_cloud_dataset(cat_ids, example_ids, n_samples=16384, n_resamples=1024):
import os
from shapenet.core.point_clouds import PointCloudAutoSavingManager
from util3d.point_cloud import sample_points
from dids.core import BiKeyDataset
if isinstance(cat_ids, str):
cat_ids = [cat_ids]
example_ids = [example_ids]
datasets = {}
for cat_id, e_ids in zip(cat_ids, example_ids):
manager = PointCloudAutoSavingManager(cat_id, n_samples)
if not os.path.isfile(manager.path):
manager.save_all()
datasets[cat_id] = manager.get_saving_dataset(
mode='r').subset(e_ids)
return BiKeyDataset(datasets).map(
lambda x: sample_points(np.array(x, dtype=np.float32), n_resamples))
def map_np(self, example_id):
points = np.array(self._dataset[example_id], dtype=np.float32)
return sample_points(points, self._n_resamples, axis=0)
def sample_fn(cloud):
return sample_points(np.array(cloud), self._n_samples)
def sample_fn(cloud):
return sample_points(np.array(cloud), self._n_samples)
def map_np(self, example_id):
points = np.array(self._dataset[example_id], dtype=np.float32)
return sample_points(points, self._n_resamples, axis=0)
