def eval_step(self, data):
''' Performs an evaluation step.
Args:
data (dict): data dictionary
'''
self.model.eval()
device = self.device
threshold = self.threshold
occ = data.get('voxels').to(device)
inputs = data.get('inputs').to(device)
points = data.get('points_iou')
points_occ = data.get('points_iou.occ')
with torch.no_grad():
occ_logits = self.model(inputs).squeeze(1)
eval_dict = {}
# Compute loss
occ_hat = torch.sigmoid(occ_logits)
loss = F.binary_cross_entropy_with_logits(occ_logits, occ)
eval_dict['loss'] = loss.item()
# Compute discretized IOU
occ_np = (occ >= 0.5).cpu().numpy()
occ_hat_np = (occ_hat >= threshold).cpu().numpy()
iou_voxels = compute_iou(occ_np, occ_hat_np).mean()
eval_dict['iou_voxels'] = iou_voxels
# Compute continuous IOU (if possible)
if points is not None:
voxel_grids = [
VoxelGrid(occ_hat_np_i) for occ_hat_np_i in occ_hat_np
]
points_np = points.cpu().numpy()
points_occ_np = (points_occ >= 0.5).cpu().numpy()
points_occ_hat_np = np.stack(
[vg.contains(p) for p, vg in zip(points_np, voxel_grids)])
iou = compute_iou(points_occ_np, points_occ_hat_np).mean()
eval_dict['iou'] = iou
return eval_dict
pointcloud_out_file = os.path.join(pointcloud_dir,
"%s.ply" % modelname)
export_pointcloud(pointcloud, pointcloud_out_file)
out_file_dict["pointcloud"] = pointcloud_out_file
if cfg["generation"]["copy_input"]:
# Save inputs
if input_type == "img":
inputs_path = os.path.join(in_dir, "%s.jpg" % modelname)
inputs = tf.squeeze(data["inputs"], axis=0)
visualize_data(inputs, "img", inputs_path)
out_file_dict["in"] = inputs_path
elif input_type == "voxels":
inputs_path = os.path.join(in_dir, "%s.off" % modelname)
inputs = tf.squeeze(data["inputs"], axis=0)
voxel_mesh = VoxelGrid(inputs).to_mesh()
voxel_mesh.export(inputs_path)
out_file_dict["in"] = inputs_path
elif input_type == "pointcloud":
inputs_path = os.path.join(in_dir, "%s.ply" % modelname)
inputs = tf.squeeze(data["inputs"], axis=0).numpy()
export_pointcloud(inputs, inputs_path, False)
out_file_dict["in"] = inputs_path
# Copy to visualization directory for first vis_n_output samples
c_it = model_counter[category_id]
if c_it < vis_n_outputs:
# Save output files
img_name = "%02d.off" % c_it
for k, filepath in out_file_dict.items():
ext = os.path.splitext(filepath)[1]