def get_ds(cat_desc, regime='e'): view_index = 5 edge_length_threshold = 0.02 cat_id = cat_desc_to_id(cat_desc) model_id = '%s_%s' % (regime, cat_desc) image_ds = RenderConfig().get_dataset(cat_id, view_index) cloud_ds = get_cloud_manager( model_id, pre_sampled=True, n_samples=n_samples).get_lazy_dataset() mesh_ds = get_inferred_mesh_dataset( model_id, edge_length_threshold=edge_length_threshold) gt_mesh_ds = get_mesh_dataset(cat_id) voxel_ds = get_voxel_dataset( model_id, edge_length_threshold=edge_length_threshold, filled=False) selected_template_ds = get_selected_template_idx_dataset(model_id) template_meshes = [] with gt_mesh_ds: for template_id in get_template_ids(cat_id): mesh = gt_mesh_ds[template_id] template_meshes.append( {k: np.array(mesh[k]) for k in ('vertices', 'faces')}) template_mesh_ds = selected_template_ds.map(lambda i: template_meshes[i]) return Dataset.zip( image_ds, gt_mesh_ds, cloud_ds, mesh_ds, voxel_ds, template_mesh_ds)
def get_ds(cat_desc, regime='e'): view_index = 5 edge_length_threshold = 0.02 cat_id = cat_desc_to_id(cat_desc) model_id = '%s_%s' % (regime, cat_desc) image_ds = RenderConfig().get_dataset(cat_id, view_index) cloud_ds = get_cloud_manager( model_id, pre_sampled=True, n_samples=n_samples).get_lazy_dataset() mesh_ds = get_inferred_mesh_dataset( model_id, edge_length_threshold=edge_length_threshold) gt_mesh_ds = get_mesh_dataset(cat_id) voxel_ds = get_voxel_dataset( model_id, edge_length_threshold=edge_length_threshold, filled=False) selected_template_ds = get_selected_template_idx_dataset(model_id) template_meshes = [] with gt_mesh_ds: for template_id in get_template_ids(cat_id): mesh = gt_mesh_ds[template_id] template_meshes.append( {k: np.array(mesh[k]) for k in ('vertices', 'faces')}) template_mesh_ds = selected_template_ds.map(lambda i: template_meshes[i]) return Dataset.zip( image_ds, gt_mesh_ds, cloud_ds, mesh_ds, voxel_ds, template_mesh_ds)
def create_split_mesh( cat_desc, edge_length_threshold, overwrite=False, start_threshold=None): """Create split mesh data for templates.""" from shapenet.core import cat_desc_to_id from shapenet.core.meshes.config import get_mesh_config from template_ffd.templates.ids import get_template_ids cat_id = cat_desc_to_id(cat_desc) example_ids = get_template_ids(cat_id) config = get_mesh_config(edge_length_threshold) init = None if start_threshold is None else get_mesh_config( start_threshold) config.create_cat_data(cat_id, example_ids, overwrite, init)
def create_split_mesh(cat_desc, edge_length_threshold, overwrite=False, start_threshold=None): """Create split mesh data for templates.""" from shapenet.core import cat_desc_to_id from shapenet.core.meshes.config import get_mesh_config from template_ffd.templates.ids import get_template_ids cat_id = cat_desc_to_id(cat_desc) example_ids = get_template_ids(cat_id) config = get_mesh_config(edge_length_threshold) init = None if start_threshold is None else get_mesh_config( start_threshold) config.create_cat_data(cat_id, example_ids, overwrite, init)
def create_split(self, cat_id, overwrite=False): import random from shapenet.core import get_example_ids from template_ffd.templates.ids import get_template_ids if not overwrite and self.has_split(cat_id): return template_ids = set(get_template_ids(cat_id)) example_ids = get_example_ids(cat_id) example_ids = [i for i in example_ids if i not in template_ids] example_ids.sort() random.seed(self._seed) random.shuffle(example_ids) train_ids, eval_ids = _train_eval_partition(example_ids, self._train_prop) train_ids.sort() eval_ids.sort() for mode, ids in (('train', train_ids), ('eval', eval_ids)): with open(self.get_txt_path(cat_id, mode), 'w') as fp: fp.writelines(('%s\n' % i for i in ids))
def create_split(self, cat_id, overwrite=False): import random from shapenet.core import get_example_ids from template_ffd.templates.ids import get_template_ids if not overwrite and self.has_split(cat_id): return template_ids = set(get_template_ids(cat_id)) example_ids = get_example_ids(cat_id) example_ids = [i for i in example_ids if i not in template_ids] example_ids.sort() random.seed(self._seed) random.shuffle(example_ids) train_ids, eval_ids = _train_eval_partition( example_ids, self._train_prop) train_ids.sort() eval_ids.sort() for mode, ids in (('train', train_ids), ('eval', eval_ids)): with open(self.get_txt_path(cat_id, mode), 'w') as fp: fp.writelines(('%s\n' % i for i in ids))
def get_hist_data(model_id, n_bins, mode): from shapenet.core import cat_desc_to_id from template_ffd.templates.ids import get_template_ids from template_ffd.model import load_params from template_ffd.inference.predictions import get_predictions_dataset cat_id = cat_desc_to_id(load_params(model_id)['cat_desc']) n_templates = len(get_template_ids(cat_id)) counts = np.zeros((n_bins, ), dtype=np.int32) argmax_counts = np.zeros((n_templates, ), dtype=np.int32) with get_predictions_dataset(model_id) as dataset: for example_id in dataset: probs = np.array(dataset[example_id]['probs']) counts[int(np.max(probs) * n_bins)] += 1 # prob_indices = np.array(0.999*probs * n_bins, dtype=np.int32) # for pi in prob_indices: # counts[pi] += 1 argmax_counts[np.argmax(probs)] += 1 counts = counts / np.sum(counts) argmax_counts = argmax_counts / np.sum(argmax_counts) return counts, argmax_counts
def template_ids(self): template_ids = get_template_ids(self.cat_id) idxs = self.params.get('template_idxs') if idxs: template_ids = tuple(template_ids[i] for i in idxs) return template_ids
def _get_cat_template_ids(cat_id, template_idxs): from template_ffd.templates.ids import get_template_ids template_ids = get_template_ids(cat_id) if template_idxs is not None: template_ids = tuple(template_ids[i] for i in template_idxs) return tuple((cat_id, t) for t in template_ids)