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_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_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_lazy_dataset(self):
from template_ffd.inference.predictions import \
get_selected_template_idx_dataset
builder = get_builder(self._model_id)
template_ids = builder.template_ids
gt_ds = get_gt_voxel_dataset(
builder.cat_id, filled=self._filled, auto_save=True,
example_ids=template_ids)
gt_ds = gt_ds.map(lambda v: v.data)
with gt_ds:
template_voxels = tuple(gt_ds[tid] for tid in template_ids)
selected_ds = get_selected_template_idx_dataset(self._model_id)
selected_ds = selected_ds.map(lambda i: template_voxels[i])
return Dataset.zip(selected_ds, gt_ds).map(
lambda v: intersection_over_union(*v))
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_lazy_dataset(self):
from template_ffd.inference.predictions import \
get_selected_template_idx_dataset
builder = get_builder(self._model_id)
template_ids = builder.template_ids
gt_ds = get_gt_voxel_dataset(builder.cat_id,
filled=self._filled,
auto_save=True,
example_ids=template_ids)
gt_ds = gt_ds.map(lambda v: v.data)
with gt_ds:
template_voxels = tuple(gt_ds[tid] for tid in template_ids)
selected_ds = get_selected_template_idx_dataset(self._model_id)
selected_ds = selected_ds.map(lambda i: template_voxels[i])
return Dataset.zip(selected_ds,
gt_ds).map(lambda v: intersection_over_union(*v))
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