Updates to Implicitron dataset, metrics and tools

Summary: Update Pytorch3D to be able to run assetgen (see later diffs in the stack)

Reviewed By: shapovalov

Differential Revision: D65942513

fbshipit-source-id: 1d01141c9f7e106608fa591be6e0d3262cb5944f

pytorch3d/implicitron/dataset/frame_data.py

@@ -48,6 +48,7 @@ from pytorch3d.implicitron.dataset.utils import (
 from pytorch3d.implicitron.tools.config import registry, ReplaceableBase
 from pytorch3d.renderer.camera_utils import join_cameras_as_batch
 from pytorch3d.renderer.cameras import CamerasBase, PerspectiveCameras
+from pytorch3d.structures.meshes import join_meshes_as_batch, Meshes
 from pytorch3d.structures.pointclouds import join_pointclouds_as_batch, Pointclouds
 
 FrameAnnotationT = types.FrameAnnotation | orm_types.SqlFrameAnnotation
@@ -158,7 +159,7 @@ class FrameData(Mapping[str, Any]):
         new_params = {}
         for field_name in iter(self):
             value = getattr(self, field_name)
-            if isinstance(value, (torch.Tensor, Pointclouds, CamerasBase)):
+            if isinstance(value, (torch.Tensor, Pointclouds, CamerasBase, Meshes)):
                 new_params[field_name] = value.to(*args, **kwargs)
             else:
                 new_params[field_name] = value
@@ -420,7 +421,6 @@ class FrameData(Mapping[str, Any]):
             for f in fields(elem):
                 if not f.init:
                     continue
-
                 list_values = override_fields.get(
                     f.name, [getattr(d, f.name) for d in batch]
                 )
@@ -429,7 +429,7 @@ class FrameData(Mapping[str, Any]):
                     if all(list_value is not None for list_value in list_values)
                     else None
                 )
-            return cls(**collated)
+            return type(elem)(**collated)
 
         elif isinstance(elem, Pointclouds):
             return join_pointclouds_as_batch(batch)
@@ -437,6 +437,8 @@ class FrameData(Mapping[str, Any]):
         elif isinstance(elem, CamerasBase):
             # TODO: don't store K; enforce working in NDC space
             return join_cameras_as_batch(batch)
+        elif isinstance(elem, Meshes):
+            return join_meshes_as_batch(batch)
         else:
             return torch.utils.data.dataloader.default_collate(batch)
 
@@ -592,6 +594,7 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
         fg_mask_np: np.ndarray | None = None
         bbox_xywh: tuple[float, float, float, float] | None = None
         mask_annotation = frame_annotation.mask
+
         if mask_annotation is not None:
             if load_blobs and self.load_masks:
                 fg_mask_np, mask_path = self._load_fg_probability(frame_annotation)

pytorch3d/implicitron/dataset/sql_dataset.py

@@ -10,6 +10,7 @@ import hashlib
 import json
 import logging
 import os
+
 import urllib
 from dataclasses import dataclass, Field, field
 from typing import (
@@ -37,12 +38,13 @@ from pytorch3d.implicitron.dataset.frame_data import (
     FrameDataBuilder,  # noqa
     FrameDataBuilderBase,
 )
+
 from pytorch3d.implicitron.tools.config import (
     registry,
     ReplaceableBase,
     run_auto_creation,
 )
-from sqlalchemy.orm import Session
+from sqlalchemy.orm import scoped_session, Session, sessionmaker
 
 from .orm_types import SqlFrameAnnotation, SqlSequenceAnnotation
 
@@ -91,6 +93,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
             engine verbatim. Don’t expose it to end users of your application!
         pick_categories: Restrict the dataset to the given list of categories.
         pick_sequences: A Sequence of sequence names to restrict the dataset to.
+        pick_sequences_sql_clause: Custom SQL WHERE clause to constrain sequence annotations.
         exclude_sequences: A Sequence of the names of the sequences to exclude.
         limit_sequences_per_category_to: Limit the dataset to the first up to N
             sequences within each category (applies after all other sequence filters
@@ -105,6 +108,10 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
             more frames than that; applied after other frame-level filters.
         seed: The seed of the random generator sampling `n_frames_per_sequence`
             random frames per sequence.
+        preload_metadata: If True, the metadata is preloaded into memory.
+        precompute_seq_to_idx: If True, precomputes the mapping from sequence name to indices.
+        scoped_session: If True, allows different parts of the code to share
+            a global session to access the database.
     """
 
     frame_annotations_type: ClassVar[Type[SqlFrameAnnotation]] = SqlFrameAnnotation
@@ -123,6 +130,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
     pick_categories: Tuple[str, ...] = ()
 
     pick_sequences: Tuple[str, ...] = ()
+    pick_sequences_sql_clause: Optional[str] = None
     exclude_sequences: Tuple[str, ...] = ()
     limit_sequences_per_category_to: int = 0
     limit_sequences_to: int = 0
@@ -130,6 +138,8 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
     n_frames_per_sequence: int = -1
     seed: int = 0
     remove_empty_masks_poll_whole_table_threshold: int = 300_000
+    preload_metadata: bool = False
+    precompute_seq_to_idx: bool = False
     # we set it manually in the constructor
     _index: pd.DataFrame = field(init=False, metadata={"omegaconf_ignore": True})
     _sql_engine: sa.engine.Engine = field(
@@ -142,6 +152,8 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
     frame_data_builder: FrameDataBuilderBase  # pyre-ignore[13]
     frame_data_builder_class_type: str = "FrameDataBuilder"
 
+    scoped_session: bool = False
+
     def __post_init__(self) -> None:
         if sa.__version__ < "2.0":
             raise ImportError("This class requires SQL Alchemy 2.0 or later")
@@ -169,6 +181,9 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
             f"sqlite:///file:{urllib.parse.quote(self.sqlite_metadata_file)}?mode=ro&uri=true"
         )
 
+        if self.preload_metadata:
+            self._sql_engine = self._preload_database(self._sql_engine)
+
         sequences = self._get_filtered_sequences_if_any()
 
         if self.subsets:
@@ -192,6 +207,20 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
 
         logger.info(str(self))
 
+        if self.scoped_session:
+            self._session_factory = sessionmaker(bind=self._sql_engine)  # pyre-ignore
+
+        if self.precompute_seq_to_idx:
+            # This is deprecated and will be removed in the future.
+            # After we backport https://github.com/facebookresearch/uco3d/pull/3
+            logger.warning(
+                "Using precompute_seq_to_idx is deprecated and will be removed in the future."
+            )
+            self._index["rowid"] = np.arange(len(self._index))
+            groupby = self._index.groupby("sequence_name", sort=False)["rowid"]
+            self._seq_to_indices = dict(groupby.apply(list))  # pyre-ignore
+            del self._index["rowid"]
+
     def __len__(self) -> int:
         return len(self._index)
 
@@ -252,9 +281,15 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
         seq_stmt = sa.select(self.sequence_annotations_type).where(
             self.sequence_annotations_type.sequence_name == seq
         )
-        with Session(self._sql_engine) as session:
-            entry = session.scalars(stmt).one()
-            seq_metadata = session.scalars(seq_stmt).one()
+        if self.scoped_session:
+            # pyre-ignore
+            with scoped_session(self._session_factory)() as session:
+                entry = session.scalars(stmt).one()
+                seq_metadata = session.scalars(seq_stmt).one()
+        else:
+            with Session(self._sql_engine) as session:
+                entry = session.scalars(stmt).one()
+                seq_metadata = session.scalars(seq_stmt).one()
 
         assert entry.image.path == self._index.loc[(seq, frame), "_image_path"]
 
@@ -363,6 +398,20 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
 
         yield from index_slice.itertuples(index=False)
 
+    # override
+    def sequence_indices_in_order(
+        self, seq_name: str, subset_filter: Optional[Sequence[str]] = None
+    ) -> Iterator[int]:
+        """Same as `sequence_frames_in_order` but returns the iterator over
+        only dataset indices.
+        """
+        if self.precompute_seq_to_idx and subset_filter is None:
+            # pyre-ignore
+            yield from self._seq_to_indices[seq_name]
+        else:
+            for _, _, idx in self.sequence_frames_in_order(seq_name, subset_filter):
+                yield idx
+
     # override
     def get_eval_batches(self) -> Optional[List[Any]]:
         """
@@ -396,11 +445,35 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
             or self.limit_sequences_to > 0
             or self.limit_sequences_per_category_to > 0
             or len(self.pick_sequences) > 0
+            or self.pick_sequences_sql_clause is not None
             or len(self.exclude_sequences) > 0
             or len(self.pick_categories) > 0
             or self.n_frames_per_sequence > 0
         )
 
+    def _preload_database(
+        self, source_engine: sa.engine.base.Engine
+    ) -> sa.engine.base.Engine:
+        destination_engine = sa.create_engine("sqlite:///:memory:")
+        metadata = sa.MetaData()
+        metadata.reflect(bind=source_engine)
+        metadata.create_all(bind=destination_engine)
+
+        with source_engine.connect() as source_conn:
+            with destination_engine.connect() as destination_conn:
+                for table_obj in metadata.tables.values():
+                    # Select all rows from the source table
+                    source_rows = source_conn.execute(table_obj.select())
+
+                    # Insert rows into the destination table
+                    for row in source_rows:
+                        destination_conn.execute(table_obj.insert().values(row))
+
+                    # Commit the changes for each table
+                    destination_conn.commit()
+
+        return destination_engine
+
     def _get_filtered_sequences_if_any(self) -> Optional[pd.Series]:
         # maximum possible filter (if limit_sequences_per_category_to == 0):
         # WHERE category IN 'self.pick_categories'
@@ -413,6 +486,9 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
             *self._get_pick_filters(),
             *self._get_exclude_filters(),
         ]
+        if self.pick_sequences_sql_clause:
+            print("Applying the custom SQL clause.")
+            where_conditions.append(sa.text(self.pick_sequences_sql_clause))
 
         def add_where(stmt):
             return stmt.where(*where_conditions) if where_conditions else stmt
@@ -749,9 +825,15 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
             self.frame_annotations_type.sequence_name == seq_name,
             self.frame_annotations_type.frame_number.in_(frames),
         )
+        frame_no_ts = None
 
-        with self._sql_engine.connect() as connection:
-            frame_no_ts = pd.read_sql_query(stmt, connection)
+        if self.scoped_session:
+            stmt_text = str(stmt.compile(compile_kwargs={"literal_binds": True}))
+            with scoped_session(self._session_factory)() as session:  # pyre-ignore
+                frame_no_ts = pd.read_sql_query(stmt_text, session.connection())
+        else:
+            with self._sql_engine.connect() as connection:
+                frame_no_ts = pd.read_sql_query(stmt, connection)
 
         if len(frame_no_ts) != len(index_slice):
             raise ValueError(

pytorch3d/implicitron/dataset/sql_dataset_provider.py

@@ -284,8 +284,14 @@ class SqlIndexDatasetMapProvider(DatasetMapProviderBase):
                 logger.info(f"Val dataset: {str(val_dataset)}")
 
             logger.debug("Extracting test dataset.")
-            eval_batches_file = self._get_lists_file("eval_batches")
-            del common_dataset_kwargs["eval_batches_file"]
+            if self.eval_batches_path is None:
+                eval_batches_file = None
+            else:
+                eval_batches_file = self._get_lists_file("eval_batches")
+
+            if "eval_batches_file" in common_dataset_kwargs:
+                common_dataset_kwargs.pop("eval_batches_file", None)
+
             test_dataset = dataset_type(
                 **common_dataset_kwargs,
                 subsets=self._get_subsets(self.test_subsets, True),

pytorch3d/implicitron/dataset/utils.py

@@ -211,14 +211,21 @@ def resize_image(
     if isinstance(image, np.ndarray):
         image = torch.from_numpy(image)
 
-    if image_height is None or image_width is None:
+    if (
+        image_height is None
+        or image_width is None
+        or image.shape[-2] == 0
+        or image.shape[-1] == 0
+    ):
         # skip the resizing
         return image, 1.0, torch.ones_like(image[:1])
+
     # takes numpy array or tensor, returns pytorch tensor
     minscale = min(
         image_height / image.shape[-2],
         image_width / image.shape[-1],
     )
+
     imre = torch.nn.functional.interpolate(
         image[None],
         scale_factor=minscale,
@@ -226,6 +233,7 @@ def resize_image(
         align_corners=False if mode == "bilinear" else None,
         recompute_scale_factor=True,
     )[0]
+
     imre_ = torch.zeros(image.shape[0], image_height, image_width)
     imre_[:, 0 : imre.shape[1], 0 : imre.shape[2]] = imre
     mask = torch.zeros(1, image_height, image_width)
@@ -238,20 +246,21 @@ def transpose_normalize_image(image: np.ndarray) -> np.ndarray:
     return im.astype(np.float32) / 255.0
 
 
-def load_image(path: str, try_read_alpha: bool = False) -> np.ndarray:
+def load_image(
+    path: str, try_read_alpha: bool = False, pil_format: str = "RGB"
+) -> np.ndarray:
     """
     Load an image from a path and return it as a numpy array.
     If try_read_alpha is True, the image is read as RGBA and the alpha channel is
     returned as the fourth channel.
     Otherwise, the image is read as RGB and a three-channel image is returned.
     """
-
     with Image.open(path) as pil_im:
         # Check if the image has an alpha channel
         if try_read_alpha and pil_im.mode == "RGBA":
             im = np.array(pil_im)
         else:
-            im = np.array(pil_im.convert("RGB"))
+            im = np.array(pil_im.convert(pil_format))
 
     return transpose_normalize_image(im)
 
@@ -389,7 +398,7 @@ def adjust_camera_to_image_scale_(
     )
     camera.focal_length = focal_length_scaled[None]
     # pyre-fixme[16]: `PerspectiveCameras` has no attribute `principal_point`.
-    camera.principal_point = principal_point_scaled[None]
+    camera.principal_point = principal_point_scaled[None]  # pyre-ignore[16]
 
 
 # NOTE this cache is per-worker; they are implemented as processes.

pytorch3d/implicitron/models/metrics.py

@@ -6,7 +6,6 @@
 
 # pyre-unsafe
 
-
 import warnings
 from typing import Any, Dict, Optional
 
@@ -298,9 +297,8 @@ class ViewMetrics(ViewMetricsBase):
                 _rgb_metrics(
                     image_rgb,
                     image_rgb_pred,
-                    fg_probability,
-                    fg_probability_pred,
-                    mask_crop,
+                    masks=fg_probability,
+                    masks_crop=mask_crop,
                 )
             )
 
@@ -310,9 +308,21 @@ class ViewMetrics(ViewMetricsBase):
             metrics["mask_neg_iou"] = utils.neg_iou_loss(
                 fg_probability_pred, fg_probability, mask=mask_crop
             )
-            metrics["mask_bce"] = utils.calc_bce(
-                fg_probability_pred, fg_probability, mask=mask_crop
-            )
+            if torch.is_autocast_enabled():
+                # To avoid issues with mixed precision
+                metrics["mask_bce"] = utils.calc_bce(
+                    fg_probability_pred.logit(),
+                    fg_probability,
+                    mask=mask_crop,
+                    pred_logits=True,
+                )
+            else:
+                metrics["mask_bce"] = utils.calc_bce(
+                    fg_probability_pred,
+                    fg_probability,
+                    mask=mask_crop,
+                    pred_logits=False,
+                )
 
         if depth_map is not None and depth_map_pred is not None:
             assert mask_crop is not None
@@ -324,7 +334,11 @@ class ViewMetrics(ViewMetricsBase):
             if fg_probability is not None:
                 mask = fg_probability * mask_crop
                 _, abs_ = utils.eval_depth(
-                    depth_map_pred, depth_map, get_best_scale=True, mask=mask, crop=0
+                    depth_map_pred,
+                    depth_map,
+                    get_best_scale=True,
+                    mask=mask,
+                    crop=0,
                 )
                 metrics["depth_abs_fg"] = abs_.mean()
 
@@ -346,18 +360,26 @@ class ViewMetrics(ViewMetricsBase):
         return metrics
 
 
-def _rgb_metrics(images, images_pred, masks, masks_pred, masks_crop):
+def _rgb_metrics(
+    images,
+    images_pred,
+    masks=None,
+    masks_crop=None,
+    huber_scaling: float = 0.03,
+):
     assert masks_crop is not None
     if images.shape[1] != images_pred.shape[1]:
         raise ValueError(
             f"Network output's RGB images had {images_pred.shape[1]} "
             f"channels. {images.shape[1]} expected."
         )
+    rgb_abs = ((images_pred - images).abs()).mean(dim=1, keepdim=True)
     rgb_squared = ((images_pred - images) ** 2).mean(dim=1, keepdim=True)
-    rgb_loss = utils.huber(rgb_squared, scaling=0.03)
+    rgb_loss = utils.huber(rgb_squared, scaling=huber_scaling)
     crop_mass = masks_crop.sum().clamp(1.0)
     results = {
         "rgb_huber": (rgb_loss * masks_crop).sum() / crop_mass,
+        "rgb_l1": (rgb_abs * masks_crop).sum() / crop_mass,
         "rgb_mse": (rgb_squared * masks_crop).sum() / crop_mass,
         "rgb_psnr": utils.calc_psnr(images_pred, images, mask=masks_crop),
     }

pytorch3d/implicitron/tools/metric_utils.py

@@ -6,12 +6,15 @@
 
 # pyre-unsafe
 
+import logging
 import math
 from typing import Optional, Tuple
 
 import torch
 from torch.nn import functional as F
 
+logger = logging.getLogger(__name__)
+
 
 def eval_depth(
     pred: torch.Tensor,
@@ -21,6 +24,8 @@ def eval_depth(
     get_best_scale: bool = True,
     mask_thr: float = 0.5,
     best_scale_clamp_thr: float = 1e-4,
+    use_disparity: bool = False,
+    disparity_eps: float = 1e-4,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     """
     Evaluate the depth error between the prediction `pred` and the ground
@@ -64,6 +69,13 @@ def eval_depth(
         # 	s.t. we get best possible mse error
         scale_best = estimate_depth_scale_factor(pred, gt, dmask, best_scale_clamp_thr)
         pred = pred * scale_best[:, None, None, None]
+    if use_disparity:
+        gt = torch.div(1.0, (gt + disparity_eps))
+        pred = torch.div(1.0, (pred + disparity_eps))
+        scale_best = estimate_depth_scale_factor(
+            pred, gt, dmask, best_scale_clamp_thr
+        ).detach()
+        pred = pred * scale_best[:, None, None, None]
 
     df = gt - pred
 
@@ -117,6 +129,7 @@ def calc_bce(
     pred_eps: float = 0.01,
     mask: Optional[torch.Tensor] = None,
     lerp_bound: Optional[float] = None,
+    pred_logits: bool = False,
 ) -> torch.Tensor:
     """
     Calculates the binary cross entropy.
@@ -139,9 +152,23 @@ def calc_bce(
         weight = torch.ones_like(gt) * mask
 
     if lerp_bound is not None:
+        # binary_cross_entropy_lerp requires pred to be in [0, 1]
+        if pred_logits:
+            pred = F.sigmoid(pred)
+
         return binary_cross_entropy_lerp(pred, gt, weight, lerp_bound)
     else:
-        return F.binary_cross_entropy(pred, gt, reduction="mean", weight=weight)
+        if pred_logits:
+            loss = F.binary_cross_entropy_with_logits(
+                pred,
+                gt,
+                reduction="none",
+                weight=weight,
+            )
+        else:
+            loss = F.binary_cross_entropy(pred, gt, reduction="none", weight=weight)
+
+        return loss.mean()
 
 
 def binary_cross_entropy_lerp(

pytorch3d/implicitron/tools/video_writer.py

@@ -16,8 +16,17 @@ from typing import Optional, Tuple, Union
 import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
+import torch
+
 from PIL import Image
 
+_NO_TORCHVISION = False
+try:
+    import torchvision
+except ImportError:
+    _NO_TORCHVISION = True
+
+
 _DEFAULT_FFMPEG = os.environ.get("FFMPEG", "ffmpeg")
 
 matplotlib.use("Agg")
@@ -36,6 +45,7 @@ class VideoWriter:
         fps: int = 20,
         output_format: str = "visdom",
         rmdir_allowed: bool = False,
+        use_torchvision_video_writer: bool = False,
         **kwargs,
     ) -> None:
         """
@@ -49,6 +59,8 @@ class VideoWriter:
                 is supported.
             rmdir_allowed: If `True` delete and create `cache_dir` in case
                 it is not empty.
+            use_torchvision_video_writer: If `True` use `torchvision.io.write_video`
+            to write the video
         """
         self.rmdir_allowed = rmdir_allowed
         self.output_format = output_format
@@ -56,10 +68,14 @@ class VideoWriter:
         self.out_path = out_path
         self.cache_dir = cache_dir
         self.ffmpeg_bin = ffmpeg_bin
+        self.use_torchvision_video_writer = use_torchvision_video_writer
         self.frames = []
         self.regexp = "frame_%08d.png"
         self.frame_num = 0
 
+        if self.use_torchvision_video_writer:
+            assert not _NO_TORCHVISION, "torchvision not available"
+
         if self.cache_dir is not None:
             self.tmp_dir = None
             if os.path.isdir(self.cache_dir):
@@ -114,7 +130,7 @@ class VideoWriter:
                 resize = im.size
             # make sure size is divisible by 2
             resize = tuple([resize[i] + resize[i] % 2 for i in (0, 1)])
-            # pyre-fixme[16]: Module `Image` has no attribute `ANTIALIAS`.
+
             im = im.resize(resize, Image.ANTIALIAS)
             im.save(outfile)
 
@@ -139,38 +155,56 @@ class VideoWriter:
         #  got `Optional[str]`.
         regexp = os.path.join(self.cache_dir, self.regexp)
 
-        if shutil.which(self.ffmpeg_bin) is None:
-            raise ValueError(
-                f"Cannot find ffmpeg as `{self.ffmpeg_bin}`. "
-                + "Please set FFMPEG in the environment or ffmpeg_bin on this class."
-            )
-
         if self.output_format == "visdom":  # works for ppt too
-            args = [
-                self.ffmpeg_bin,
-                "-r",
-                str(self.fps),
-                "-i",
-                regexp,
-                "-vcodec",
-                "h264",
-                "-f",
-                "mp4",
-                "-y",
-                "-crf",
-                "18",
-                "-b",
-                "2000k",
-                "-pix_fmt",
-                "yuv420p",
-                self.out_path,
-            ]
-            if quiet:
-                subprocess.check_call(
-                    args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL
+            # Video codec parameters
+            video_codec = "h264"
+            crf = "18"
+            b = "2000k"
+            pix_fmt = "yuv420p"
+
+            if self.use_torchvision_video_writer:
+                torchvision.io.write_video(
+                    self.out_path,
+                    torch.stack(
+                        [torch.from_numpy(np.array(Image.open(f))) for f in self.frames]
+                    ),
+                    fps=self.fps,
+                    video_codec=video_codec,
+                    options={"crf": crf, "b": b, "pix_fmt": pix_fmt},
                 )
+
             else:
-                subprocess.check_call(args)
+                if shutil.which(self.ffmpeg_bin) is None:
+                    raise ValueError(
+                        f"Cannot find ffmpeg as `{self.ffmpeg_bin}`. "
+                        + "Please set FFMPEG in the environment or ffmpeg_bin on this class."
+                    )
+
+                args = [
+                    self.ffmpeg_bin,
+                    "-r",
+                    str(self.fps),
+                    "-i",
+                    regexp,
+                    "-vcodec",
+                    video_codec,
+                    "-f",
+                    "mp4",
+                    "-y",
+                    "-crf",
+                    crf,
+                    "-b",
+                    b,
+                    "-pix_fmt",
+                    pix_fmt,
+                    self.out_path,
+                ]
+                if quiet:
+                    subprocess.check_call(
+                        args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL
+                    )
+                else:
+                    subprocess.check_call(args)
         else:
             raise ValueError("no such output type %s" % str(self.output_format))