def vis_voxels(model_id, edge_length_threshold, filled, shuffle=False):
from mayavi import mlab
from util3d.mayavi_vis import vis_voxels
from shapenet.core import cat_desc_to_id
from template_ffd.inference.voxels import get_voxel_dataset
from template_ffd.data.voxels import get_gt_voxel_dataset
from template_ffd.model import load_params
from template_ffd.data.ids import get_example_ids
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
gt_ds = get_gt_voxel_dataset(cat_id, filled)
inf_ds = get_voxel_dataset(model_id, edge_length_threshold)
example_ids = get_example_ids(cat_id, 'eval')
if shuffle:
example_ids = list(example_ids)
example_ids.shuffle
with gt_ds:
with inf_ds:
for example_id in example_ids:
gt = gt_ds[example_id].data
inf = inf_ds[example_id].data
vis_voxels(gt, color=(0, 0, 1))
mlab.figure()
vis_voxels(inf, color=(0, 1, 0))
mlab.show()
def vis_mesh(model_id, edge_length_threshold, shuffle=False, wireframe=False):
import numpy as np
from mayavi import mlab
from shapenet.core.meshes import get_mesh_dataset
from shapenet.core import cat_desc_to_id
from util3d.mayavi_vis import vis_mesh
from template_ffd.inference.meshes import get_inferred_mesh_dataset
from template_ffd.model import load_params
import random
def vis(mesh, **kwargs):
v, f = (np.array(mesh[k]) for k in ('vertices', 'faces'))
vis_mesh(v, f, include_wireframe=wireframe, **kwargs)
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
inf_mesh_dataset = get_inferred_mesh_dataset(
model_id, edge_length_threshold)
with inf_mesh_dataset:
with get_mesh_dataset(cat_id) as gt_mesh_dataset:
example_ids = list(inf_mesh_dataset.keys())
if shuffle:
random.shuffle(example_ids)
for example_id in example_ids:
inf = inf_mesh_dataset[example_id]
gt = gt_mesh_dataset[example_id]
mlab.figure()
vis(inf, color=(0, 1, 0), opacity=0.2)
mlab.figure()
vis(gt, opacity=0.2)
mlab.show()
def vis_mesh(model_id, edge_length_threshold, shuffle=False, wireframe=False):
import numpy as np
from mayavi import mlab
from shapenet.core.meshes import get_mesh_dataset
from shapenet.core import cat_desc_to_id
from util3d.mayavi_vis import vis_mesh
from template_ffd.inference.meshes import get_inferred_mesh_dataset
from template_ffd.model import load_params
import random
def vis(mesh, **kwargs):
v, f = (np.array(mesh[k]) for k in ('vertices', 'faces'))
vis_mesh(v, f, include_wireframe=wireframe, **kwargs)
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
inf_mesh_dataset = get_inferred_mesh_dataset(model_id,
edge_length_threshold)
with inf_mesh_dataset:
with get_mesh_dataset(cat_id) as gt_mesh_dataset:
example_ids = list(inf_mesh_dataset.keys())
if shuffle:
random.shuffle(example_ids)
for example_id in example_ids:
inf = inf_mesh_dataset[example_id]
gt = gt_mesh_dataset[example_id]
mlab.figure()
vis(inf, color=(0, 1, 0), opacity=0.2)
mlab.figure()
vis(gt, opacity=0.2)
mlab.show()
def vis_voxels(model_id, edge_length_threshold, filled, shuffle=False):
from mayavi import mlab
from util3d.mayavi_vis import vis_voxels
from shapenet.core import cat_desc_to_id
from template_ffd.inference.voxels import get_voxel_dataset
from template_ffd.data.voxels import get_gt_voxel_dataset
from template_ffd.model import load_params
from template_ffd.data.ids import get_example_ids
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
gt_ds = get_gt_voxel_dataset(cat_id, filled)
inf_ds = get_voxel_dataset(model_id, edge_length_threshold)
example_ids = get_example_ids(cat_id, 'eval')
if shuffle:
example_ids = list(example_ids)
example_ids.shuffle
with gt_ds:
with inf_ds:
for example_id in example_ids:
gt = gt_ds[example_id].data
inf = inf_ds[example_id].data
vis_voxels(gt, color=(0, 0, 1))
mlab.figure()
vis_voxels(inf, color=(0, 1, 0))
mlab.show()
def get_iou_dataset(model_id, edge_length_threshold=0.1, filled=False):
from shapenet.core import cat_desc_to_id
from template_ffd.data.ids import get_example_ids
from template_ffd.model import load_params
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
n_eval = len(get_example_ids(cat_id, 'eval'))
manager = IouAutoSavingManager(model_id=model_id,
edge_length_threshold=edge_length_threshold,
filled=filled)
with manager.get_saving_dataset() as ds:
needs_calc = len(ds) < n_eval
if needs_calc:
manager.save_all()
return manager.get_saving_dataset()
def report_emd_average(model_id, pre_sampled=True, **kwargs):
from shapenet.core import cat_desc_to_id
from template_ffd.data.ids import get_example_ids
from template_ffd.model import load_params
manager = get_emd_manager(model_id, pre_sampled, **kwargs)
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
n_eval = len(get_example_ids(cat_id, 'eval'))
with manager.get_saving_dataset('r') as ds:
if len(ds) == n_eval:
values = np.array([ds.values()])
else:
values = None
if values is None:
with manager.get_saved_dataset() as ds:
values = np.array([ds.values()])
print(np.mean(values))
def get_lazy_dataset(self):
cat_id = cat_desc_to_id(load_params(self._model_id)['cat_desc'])
example_ids = get_example_ids(cat_id, 'eval')
inferred_dataset = get_voxel_dataset(
self._model_id, self._edge_length_threshold, self._voxel_config,
filled=self._filled, example_ids=example_ids)
gt_dataset = get_gt_voxel_dataset(cat_id, filled=self._filled)
voxel_datasets = Dataset.zip(inferred_dataset, gt_dataset)
voxel_datasets = voxel_datasets.subset(example_ids)
def map_fn(v):
return intersection_over_union(v[0].data, v[1].data)
iou_dataset = voxel_datasets.map(map_fn)
return iou_dataset
def report_emd_average(model_id, pre_sampled=True, **kwargs):
import os
from shapenet.core import cat_desc_to_id
from template_ffd.data.ids import get_example_ids
from template_ffd.model import load_params
manager = get_emd_manager(model_id, pre_sampled, **kwargs)
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
n_eval = len(get_example_ids(cat_id, 'eval'))
values = None
if os.path.isfile(manager.path):
with manager.get_saving_dataset('r') as ds:
if len(ds) == n_eval:
values = np.array(tuple(ds.values()))
if values is None:
manager.save_all()
with manager.get_saving_dataset('r') as ds:
values = np.array(tuple(ds.values()))
print(np.mean(values))
def report_chamfer_average(model_id, pre_sampled=True, **kwargs):
import os
from shapenet.core import cat_desc_to_id
from template_ffd.data.ids import get_example_ids
from template_ffd.model import load_params
manager = get_chamfer_manager(model_id, pre_sampled, **kwargs)
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
n_eval = len(get_example_ids(cat_id, 'eval'))
values = None
if os.path.isfile(manager.path):
with manager.get_saving_dataset('r') as ds:
if len(ds) == n_eval:
values = np.array(tuple(ds.values()))
if values is None:
manager.save_all()
with manager.get_saving_dataset('r') as ds:
values = np.array(tuple(ds.values()))
print(np.mean(values))
def get_lazy_dataset(self):
cat_id = cat_desc_to_id(load_params(self._model_id)['cat_desc'])
example_ids = get_example_ids(cat_id, 'eval')
inferred_dataset = get_voxel_dataset(self._model_id,
self._edge_length_threshold,
self._voxel_config,
filled=self._filled,
example_ids=example_ids)
gt_dataset = get_gt_voxel_dataset(cat_id, filled=self._filled)
voxel_datasets = Dataset.zip(inferred_dataset, gt_dataset)
voxel_datasets = voxel_datasets.subset(example_ids)
def map_fn(v):
return intersection_over_union(v[0].data, v[1].data)
iou_dataset = voxel_datasets.map(map_fn)
return iou_dataset
def get_iou_dataset(
model_id, edge_length_threshold=0.1, filled=False, recalc=False):
from shapenet.core import cat_desc_to_id
from template_ffd.data.ids import get_example_ids
from template_ffd.model import load_params
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
n_eval = len(get_example_ids(cat_id, 'eval'))
manager = IouAutoSavingManager(
model_id=model_id,
edge_length_threshold=edge_length_threshold,
filled=filled
)
if not recalc:
with manager.get_saving_dataset() as ds:
recalc = len(ds) < n_eval
if recalc:
manager.save_all()
return manager.get_saving_dataset('r')
def _create_unfilled_voxel_data(
model_id, edge_length_threshold=0.1, voxel_config=None,
overwrite=False, example_ids=None):
from template_ffd.data.ids import get_example_ids
from shapenet.core import cat_desc_to_id
from template_ffd.model import load_params
import numpy as np
from progress.bar import IncrementalBar
if voxel_config is None:
voxel_config = _default_config
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
if example_ids is None:
example_ids = get_example_ids(cat_id, 'eval')
mesh_dataset = get_inferred_mesh_dataset(model_id, edge_length_threshold)
voxel_dataset = get_voxel_dataset(
model_id, edge_length_threshold, voxel_config, filled=False,
auto_save=False)
if not overwrite:
example_ids = [i for i in example_ids if i not in voxel_dataset]
if len(example_ids) == 0:
return
print('Creating %d voxels for model %s' % (len(example_ids), model_id))
kwargs = dict(
voxel_dim=voxel_config.voxel_dim,
exact=voxel_config.exact,
dc=voxel_config.dc,
aw=voxel_config.aw)
with mesh_dataset:
bar = IncrementalBar(max=len(example_ids))
for example_id in example_ids:
bar.next()
mesh = mesh_dataset[example_id]
vertices, faces = (
np.array(mesh[k]) for k in ('vertices', 'faces'))
binvox_path = voxel_dataset.path(example_id)
# x, z, y = vertices.T
# vertices = np.stack([x, y, z], axis=1)
bio.mesh_to_binvox(
vertices, faces, binvox_path, **kwargs)
bar.finish()
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 get_lazy_dataset(self):
cat_id = cat_desc_to_id(load_params(self._model_id)['cat_desc'])
if not isinstance(cat_id, (list, tuple)):
cat_id = [cat_id]
inferred_dataset = get_voxel_dataset(self._model_id,
self._edge_length_threshold,
self._voxel_config,
filled=self._filled)
gt_dataset = get_gt_voxel_dataset(cat_id, filled=self._filled)
gt_dataset = gt_dataset.map_keys(lambda key: key[:2])
with inferred_dataset:
keys = tuple(inferred_dataset.keys())
voxel_datasets = Dataset.zip(inferred_dataset, gt_dataset)
voxel_datasets = voxel_datasets.subset(keys)
def map_fn(v):
return intersection_over_union(v[0].dense_data(),
v[1].dense_data())
iou_dataset = voxel_datasets.map(map_fn)
return iou_dataset
print(ret)
return list(ret)
descs = (
'plane',
'bench',
'car',
'chair',
'sofa',
'cabinet',
'monitor',
'lamp',
'speaker',
'pistol',
'table',
'cellphone',
'watercraft',
)
model_ids = tuple('e_%s_v8' % c for c in descs)
template_idx = tuple(get_top_k(get_template_counts(m), 2) for m in model_ids)
params = load_params(model_ids[0])
params['cat_desc'] = descs
params['template_idxs'] = template_idx
params['use_bn_bugged_version'] = False
with open(path, 'w') as fp:
json.dump(params, fp)