[torchcodec] add get_frames_at method to SimpleVideoDecoder

src/torchcodec/decoders/__init__.py

@@ -1 +1 @@
-from ._simple_video_decoder import Frame, SimpleVideoDecoder  # noqa
+from ._simple_video_decoder import Frame, FrameBatch, SimpleVideoDecoder  # noqa

src/torchcodec/decoders/_core/VideoDecoder.cpp

@@ -124,6 +124,31 @@ VideoDecoder::VideoStreamDecoderOptions::VideoStreamDecoderOptions(
   }
 }
 
+VideoDecoder::BatchDecodedOutput::BatchDecodedOutput(
+    int64_t numFrames,
+    const VideoStreamDecoderOptions& options,
+    const StreamMetadata& metadata)
+    : ptsSeconds(torch::empty({numFrames}, {torch::kFloat})),
+      durationSeconds(torch::empty({numFrames}, {torch::kFloat})) {
+  if (options.shape == "NHWC") {
+    frames = torch::empty(
+        {numFrames,
+         options.height.value_or(*metadata.height),
+         options.width.value_or(*metadata.width),
+         3},
+        {torch::kUInt8});
+  } else if (options.shape == "NCHW") {
+    frames = torch::empty(
+        {numFrames,
+         3,
+         options.height.value_or(*metadata.height),
+         options.width.value_or(*metadata.width)},
+        {torch::kUInt8});
+  } else {
+    TORCH_CHECK(false, "Unsupported frame shape=" + options.shape)
+  }
+}
+
 VideoDecoder::VideoDecoder() {}
 
 void VideoDecoder::initializeDecoder() {
@@ -734,30 +759,6 @@ VideoDecoder::DecodedOutput VideoDecoder::convertAVFrameToDecodedOutput(
   return output;
 }
 
-torch::Tensor VideoDecoder::getEmptyTensorForBatch(
-    int64_t numFrames,
-    const VideoStreamDecoderOptions& options,
-    const StreamMetadata& metadata) {
-  if (options.shape == "NHWC") {
-    return torch::empty(
-        {numFrames,
-         options.height.value_or(*metadata.height),
-         options.width.value_or(*metadata.width),
-         3},
-        {torch::kUInt8});
-  } else if (options.shape == "NCHW") {
-    return torch::empty(
-        {numFrames,
-         3,
-         options.height.value_or(*metadata.height),
-         options.width.value_or(*metadata.width)},
-        {torch::kUInt8});
-  } else {
-    // TODO: should this be a TORCH macro of some kind?
-    throw std::runtime_error("Unsupported frame shape=" + options.shape);
-  }
-}
-
 VideoDecoder::DecodedOutput VideoDecoder::getFrameDisplayedAtTimestamp(
     double seconds) {
   for (auto& [streamIndex, stream] : streams_) {
@@ -822,11 +823,9 @@ VideoDecoder::BatchDecodedOutput VideoDecoder::getFramesAtIndexes(
         "Must scan all streams to update metadata before calling getFrameAtIndex");
   }
 
-  BatchDecodedOutput output;
   const auto& streamMetadata = containerMetadata_.streams[streamIndex];
   const auto& options = streams_[streamIndex].options;
-  output.frames =
-      getEmptyTensorForBatch(frameIndexes.size(), options, streamMetadata);
+  BatchDecodedOutput output(frameIndexes.size(), options, streamMetadata);
 
   int i = 0;
   if (streams_.count(streamIndex) == 0) {
@@ -873,16 +872,17 @@ VideoDecoder::BatchDecodedOutput VideoDecoder::getFramesInRange(
 
   int64_t numOutputFrames = std::ceil((stop - start) / double(step));
   const auto& options = stream.options;
-  BatchDecodedOutput output;
-  output.frames =
-      getEmptyTensorForBatch(numOutputFrames, options, streamMetadata);
+  BatchDecodedOutput output(numOutputFrames, options, streamMetadata);
 
   int64_t f = 0;
   for (int64_t i = start; i < stop; i += step) {
     int64_t pts = stream.allFrames[i].pts;
     setCursorPtsInSeconds(1.0 * pts / stream.timeBase.den);
-    torch::Tensor frame = getNextDecodedOutput().frame;
-    output.frames[f++] = frame;
+    DecodedOutput singleOut = getNextDecodedOutput();
+    output.frames[f] = singleOut.frame;
+    output.ptsSeconds[f] = singleOut.ptsSeconds;
+    output.durationSeconds[f] = singleOut.durationSeconds;
+    ++f;
   }
 
   return output;

src/torchcodec/decoders/_core/VideoDecoder.h

@@ -186,6 +186,13 @@ class VideoDecoder {
   DecodedOutput getFrameAtIndex(int streamIndex, int64_t frameIndex);
   struct BatchDecodedOutput {
     torch::Tensor frames;
+    torch::Tensor ptsSeconds;
+    torch::Tensor durationSeconds;
+
+    explicit BatchDecodedOutput(
+        int64_t numFrames,
+        const VideoStreamDecoderOptions& options,
+        const StreamMetadata& metadata);
   };
   // Returns frames at the given indexes for a given stream as a single stacked
   // Tensor.

src/torchcodec/decoders/_core/VideoDecoderOps.cpp

@@ -35,7 +35,7 @@ TORCH_LIBRARY(torchcodec_ns, m) {
   m.def(
       "get_frames_at_indices(Tensor(a!) decoder, *, int stream_index, int[] frame_indices) -> Tensor");
   m.def(
-      "get_frames_in_range(Tensor(a!) decoder, *, int stream_index, int start, int stop, int? step=None) -> Tensor");
+      "get_frames_in_range(Tensor(a!) decoder, *, int stream_index, int start, int stop, int? step=None) -> (Tensor, Tensor, Tensor)");
   m.def("get_json_metadata(Tensor(a!) decoder) -> str");
   m.def("get_container_json_metadata(Tensor(a!) decoder) -> str");
   m.def(
@@ -64,13 +64,17 @@ VideoDecoder* unwrapTensorToGetDecoder(at::Tensor& tensor) {
   return decoder;
 }
 
-FramePtsDuration getTensorPtsDurationFromFrame(
-    VideoDecoder::DecodedOutput& frame) {
+FramePtsDuration makeFramePtsDuration(VideoDecoder::DecodedOutput& frame) {
   return std::make_tuple(
       frame.frame,
       torch::tensor(frame.ptsSeconds),
       torch::tensor(frame.durationSeconds));
 }
+
+BatchedFramesPtsDuration makeBatchedFramesPtsDuration(
+    VideoDecoder::BatchDecodedOutput& batch) {
+  return std::make_tuple(batch.frames, batch.ptsSeconds, batch.durationSeconds);
+}
 } // namespace
 
 // ==============================
@@ -139,13 +143,13 @@ FramePtsDuration get_next_frame(at::Tensor& decoder) {
         "image_size is unexpected. Expected 3, got: " +
         std::to_string(result.frame.sizes().size()));
   }
-  return getTensorPtsDurationFromFrame(result);
+  return makeFramePtsDuration(result);
 }
 
 FramePtsDuration get_frame_at_pts(at::Tensor& decoder, double seconds) {
   auto videoDecoder = unwrapTensorToGetDecoder(decoder);
   auto result = videoDecoder->getFrameDisplayedAtTimestamp(seconds);
-  return getTensorPtsDurationFromFrame(result);
+  return makeFramePtsDuration(result);
 }
 
 FramePtsDuration get_frame_at_index(
@@ -154,7 +158,7 @@ FramePtsDuration get_frame_at_index(
     int64_t frame_index) {
   auto videoDecoder = unwrapTensorToGetDecoder(decoder);
   auto result = videoDecoder->getFrameAtIndex(stream_index, frame_index);
-  return getTensorPtsDurationFromFrame(result);
+  return makeFramePtsDuration(result);
 }
 
 at::Tensor get_frames_at_indices(
@@ -168,7 +172,7 @@ at::Tensor get_frames_at_indices(
   return result.frames;
 }
 
-at::Tensor get_frames_in_range(
+BatchedFramesPtsDuration get_frames_in_range(
     at::Tensor& decoder,
     int64_t stream_index,
     int64_t start,
@@ -177,7 +181,7 @@ at::Tensor get_frames_in_range(
   auto videoDecoder = unwrapTensorToGetDecoder(decoder);
   auto result = videoDecoder->getFramesInRange(
       stream_index, start, stop, step.value_or(1));
-  return result.frames;
+  return makeBatchedFramesPtsDuration(result);
 }
 
 std::string quoteValue(const std::string& value) {

src/torchcodec/decoders/_core/VideoDecoderOps.h

@@ -36,13 +36,25 @@ void add_video_stream(
 // Seek to a particular presentation timestamp in the video in seconds.
 void seek_to_pts(at::Tensor& decoder, double seconds);
 
-// The first element of this tuple has the frame data. The second element is a
-// Tensor that has a single float value for the PTS and the third element is a
-// Tensor that has a single value for the duration.
-// The reason we use Tensors for the second and third value is so we can run
+// The elements of this tuple are all tensors that represent a single frame:
+//   1. The frame data, which is a multidimensional tensor.
+//   2. A single float value for the pts in seconds.
+//   3. A single float value for the duration in seconds.
+// The reason we use Tensors for the second and third values is so we can run
 // under torch.compile().
 using FramePtsDuration = std::tuple<at::Tensor, at::Tensor, at::Tensor>;
 
+// All elements of this tuple are tensors of the same leading dimension. The
+// tuple represents the frames for N total frames, where N is the dimension of
+// each stacked tensor. The elments are:
+//   1. Stacked tensor of data for all N frames. Each frame is also a
+//   multidimensional tensor.
+//   2. Tensor of N pts values in seconds, where each pts is a single
+//   float.
+//   3. Tensor of N durationis in seconds, where each duration is a
+//   single float.
+using BatchedFramesPtsDuration = std::tuple<at::Tensor, at::Tensor, at::Tensor>;
+
 // Return the frame that is visible at a given timestamp in seconds. Each frame
 // in FFMPEG has a presentation timestamp and a duration. The frame visible at a
 // given timestamp T has T >= PTS and T < PTS + Duration.
@@ -67,7 +79,7 @@ at::Tensor get_frames_at_indices(
 
 // Return the frames inside a range as a single stacked Tensor. The range is
 // defined as [start, stop).
-at::Tensor get_frames_in_range(
+BatchedFramesPtsDuration get_frames_in_range(
     at::Tensor& decoder,
     int64_t stream_index,
     int64_t start,

src/torchcodec/decoders/_core/video_decoder_ops.py

@@ -175,9 +175,13 @@ def get_frames_in_range_abstract(
     start: int,
     stop: int,
     step: Optional[int] = None,
-) -> torch.Tensor:
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     image_size = [get_ctx().new_dynamic_size() for _ in range(4)]
-    return torch.empty(image_size)
+    return (
+        torch.empty(image_size),
+        torch.empty([], dtype=torch.float),
+        torch.empty([], dtype=torch.float),
+    )
 
 
 @impl_abstract("torchcodec_ns::get_json_metadata")

src/torchcodec/decoders/_simple_video_decoder.py

@@ -21,6 +21,17 @@ def __iter__(self) -> Iterator[Union[Tensor, float]]:
             yield getattr(self, field.name)
 
 
+@dataclass
+class FrameBatch(Iterable):
+    data: Tensor
+    pts_seconds: Tensor
+    duration_seconds: Tensor
+
+    def __iter__(self) -> Iterator[Union[Tensor, float]]:
+        for field in dataclasses.fields(self):
+            yield getattr(self, field.name)
+
+
 _ERROR_REPORTING_INSTRUCTIONS = """
 This should never happen. Please report an issue following the steps in <TODO>.
 """
@@ -92,13 +103,14 @@ def _getitem_slice(self, key: slice) -> Tensor:
         assert isinstance(key, slice)
 
         start, stop, step = key.indices(len(self))
-        return core.get_frames_in_range(
+        frame_data, *_ = core.get_frames_in_range(
             self._decoder,
             stream_index=self._stream_index,
             start=start,
             stop=stop,
             step=step,
         )
+        return frame_data
 
     def __getitem__(self, key: Union[int, slice]) -> Tensor:
         if isinstance(key, int):
@@ -120,6 +132,26 @@ def get_frame_at(self, index: int) -> Frame:
         )
         return Frame(*frame)
 
+    def get_frames_at(self, start: int, stop: int, step: int = 1) -> FrameBatch:
+        if not 0 <= start < self._num_frames:
+            raise IndexError(
+                f"Start index {start} is out of bounds; must be in the range [0, {self._num_frames})."
+            )
+        if stop < start:
+            raise IndexError(
+                f"Stop index ({stop}) must not be less than the start index ({start})."
+            )
+        if not step > 0:
+            raise IndexError(f"Step ({step}) must be greater than 0.")
+        frames = core.get_frames_in_range(
+            self._decoder,
+            stream_index=self._stream_index,
+            start=start,
+            stop=stop,
+            step=step,
+        )
+        return FrameBatch(*frames)
+
     def get_frame_displayed_at(self, pts_seconds: float) -> Frame:
         if not self._min_pts_seconds <= pts_seconds < self._max_pts_seconds:
             raise IndexError(