Prepare 0.0.2

benchmarks/decoders/BenchmarkDecodersMain.cpp

@@ -63,7 +63,7 @@ void runNDecodeIterations(
     decoder->addVideoStreamDecoder(-1);
     for (double pts : ptsList) {
       decoder->setCursorPtsInSeconds(pts);
-      torch::Tensor tensor = decoder->getNextDecodedOutputNoDemux().frame;
+      torch::Tensor tensor = decoder->getNextDecodedOutput().frame;
     }
     if (i + 1 == warmupIterations) {
       start = std::chrono::high_resolution_clock::now();
@@ -95,7 +95,7 @@ void runNdecodeIterationsGrabbingConsecutiveFrames(
         VideoDecoder::createFromFilePath(videoPath);
     decoder->addVideoStreamDecoder(-1);
     for (int j = 0; j < consecutiveFrameCount; ++j) {
-      torch::Tensor tensor = decoder->getNextDecodedOutputNoDemux().frame;
+      torch::Tensor tensor = decoder->getNextDecodedOutput().frame;
     }
     if (i + 1 == warmupIterations) {
       start = std::chrono::high_resolution_clock::now();

benchmarks/decoders/gpu_benchmark.py

@@ -5,59 +5,43 @@
 import torch.utils.benchmark as benchmark
 
 import torchcodec
-import torchvision.transforms.v2.functional as F
+from torchvision.transforms import Resize
 
-RESIZED_WIDTH = 256
-RESIZED_HEIGHT = 256
 
-
-def transfer_and_resize_frame(frame, resize_device_string):
-    # This should be a no-op if the frame is already on the target device.
-    frame = frame.to(resize_device_string)
-    frame = F.resize(frame, (RESIZED_HEIGHT, RESIZED_WIDTH))
+def transfer_and_resize_frame(frame, device):
+    # This should be a no-op if the frame is already on the device.
+    frame = frame.to(device)
+    frame = Resize((256, 256))(frame)
     return frame
 
 
-def decode_full_video(video_path, decode_device_string, resize_device_string):
-    # We use the core API instead of SimpleVideoDecoder because the core API
-    # allows us to natively resize as part of the decode step.
-    print(f"{decode_device_string=} {resize_device_string=}")
+def decode_full_video(video_path, decode_device):
     decoder = torchcodec.decoders._core.create_from_file(video_path)
     num_threads = None
-    if "cuda" in decode_device_string:
+    if "cuda" in decode_device:
         num_threads = 1
-    width = None
-    height = None
-    if "native" in resize_device_string:
-        width = RESIZED_WIDTH
-        height = RESIZED_HEIGHT
     torchcodec.decoders._core.add_video_stream(
-        decoder,
-        stream_index=-1,
-        device_string=decode_device_string,
-        num_threads=num_threads,
-        width=width,
-        height=height,
+        decoder, stream_index=0, device_string=decode_device, num_threads=num_threads
     )
-
     start_time = time.time()
     frame_count = 0
     while True:
         try:
             frame, *_ = torchcodec.decoders._core.get_next_frame(decoder)
-            if resize_device_string != "none" and "native" not in resize_device_string:
-                frame = transfer_and_resize_frame(frame, resize_device_string)
+            # You can do a resize to simulate extra preproc work that happens
+            # on the GPU by uncommenting the following line:
+            # frame = transfer_and_resize_frame(frame, decode_device)
 
             frame_count += 1
         except Exception as e:
             print("EXCEPTION", e)
             break
-
+        # print(f"current {frame_count=}", flush=True)
     end_time = time.time()
     elapsed = end_time - start_time
     fps = frame_count / (end_time - start_time)
     print(
-        f"****** DECODED full video {decode_device_string=} {frame_count=} {elapsed=} {fps=}"
+        f"****** DECODED full video {decode_device=} {frame_count=} {elapsed=} {fps=}"
     )
     return frame_count, end_time - start_time
 
@@ -70,12 +54,6 @@ def main():
         type=str,
         help="Comma-separated devices to test decoding on.",
     )
-    parser.add_argument(
-        "--resize_devices",
-        default="cuda:0,cpu,native,none",
-        type=str,
-        help="Comma-separated devices to test preroc (resize) on. Use 'none' to specify no resize.",
-    )
     parser.add_argument(
         "--video",
         type=str,
@@ -100,44 +78,23 @@ def main():
             decode_full_video(video_path, device)
         return
 
-    resize_devices = args.resize_devices.split(",")
-    resize_devices = [d for d in resize_devices if d != ""]
-    if len(resize_devices) == 0:
-        resize_devices.append("none")
-
-    label = "Decode+Resize Time"
-
     results = []
-    for decode_device_string in args.devices.split(","):
-        for resize_device_string in resize_devices:
-            decode_label = decode_device_string
-            if "cuda" in decode_label:
-                # Shorten "cuda:0" to "cuda"
-                decode_label = "cuda"
-            resize_label = resize_device_string
-            if "cuda" in resize_device_string:
-                # Shorten "cuda:0" to "cuda"
-                resize_label = "cuda"
-            print("decode_device", decode_device_string)
-            print("resize_device", resize_device_string)
-            t = benchmark.Timer(
-                stmt="decode_full_video(video_path, decode_device_string, resize_device_string)",
-                globals={
-                    "decode_device_string": decode_device_string,
-                    "video_path": video_path,
-                    "decode_full_video": decode_full_video,
-                    "resize_device_string": resize_device_string,
-                },
-                label=label,
-                description=f"video={os.path.basename(video_path)}",
-                sub_label=f"D={decode_label} R={resize_label}",
-            ).blocked_autorange()
-            results.append(t)
+    for device in args.devices.split(","):
+        print("device", device)
+        t = benchmark.Timer(
+            stmt="decode_full_video(video_path, device)",
+            globals={
+                "device": device,
+                "video_path": video_path,
+                "decode_full_video": decode_full_video,
+            },
+            label="Decode+Resize Time",
+            sub_label=f"video={os.path.basename(video_path)}",
+            description=f"decode_device={device}",
+        ).blocked_autorange()
+        results.append(t)
     compare = benchmark.Compare(results)
     compare.print()
-    print("Key: D=Decode, R=Resize")
-    print("Native resize is done as part of the decode step")
-    print("none resize means there is no resize step -- native or otherwise")
 
 
 if __name__ == "__main__":

examples/basic_example.py

@@ -171,14 +171,3 @@ def plot(frames: torch.Tensor, title : Optional[str] = None):
 # %%
 plot(frame_at_2_seconds.data, "Frame displayed at 2 seconds")
 plot(first_two_seconds.data, "Frames displayed during [0, 2) seconds")
-
-# %%
-# Using a CUDA GPU to accelerate decoding
-# ---------------------------------------
-#
-# If you have a CUDA GPU that has NVDEC, you can decode on the GPU.
-if torch.cuda.is_available():
-    cuda_decoder = SimpleVideoDecoder(raw_video_bytes, device="cuda:0")
-    cuda_frame = cuda_decoder.get_frame_displayed_at(seconds=2)
-    print(cuda_frame.data.device)  # should be cuda:0
-    plot(cuda_frame.data.to("cpu"), "Frame displayed at 2 seconds on CUDA")

src/torchcodec/decoders/_core/VideoDecoder.cpp

@@ -759,7 +759,7 @@ VideoDecoder::DecodedOutput VideoDecoder::getDecodedOutputWithFilter(
   if (activeStreamIndices_.size() == 0) {
     throw std::runtime_error("No active streams configured.");
   }
-  VLOG(9) << "Starting getNextDecodedOutputNoDemux()";
+  VLOG(9) << "Starting getNextDecodedOutput()";
   resetDecodeStats();
   if (maybeDesiredPts_.has_value()) {
     VLOG(9) << "maybeDesiredPts_=" << *maybeDesiredPts_;
@@ -920,7 +920,7 @@ VideoDecoder::DecodedOutput VideoDecoder::convertAVFrameToDecodedOutput(
   return output;
 }
 
-VideoDecoder::DecodedOutput VideoDecoder::getFrameDisplayedAtTimestampNoDemux(
+VideoDecoder::DecodedOutput VideoDecoder::getFrameDisplayedAtTimestamp(
     double seconds) {
   for (auto& [streamIndex, stream] : streams_) {
     double frameStartTime = ptsToSeconds(stream.currentPts, stream.timeBase);
@@ -985,7 +985,7 @@ VideoDecoder::DecodedOutput VideoDecoder::getFrameAtIndex(
 
   int64_t pts = stream.allFrames[frameIndex].pts;
   setCursorPtsInSeconds(ptsToSeconds(pts, stream.timeBase));
-  return getNextDecodedOutputNoDemux();
+  return getNextDecodedOutput();
 }
 
 VideoDecoder::BatchDecodedOutput VideoDecoder::getFramesAtIndexes(
@@ -1138,7 +1138,7 @@ VideoDecoder::getFramesDisplayedByTimestampInRange(
   return output;
 }
 
-VideoDecoder::DecodedOutput VideoDecoder::getNextDecodedOutputNoDemux() {
+VideoDecoder::DecodedOutput VideoDecoder::getNextDecodedOutput() {
   return getDecodedOutputWithFilter(
       [this](int frameStreamIndex, AVFrame* frame) {
         StreamInfo& activeStream = streams_[frameStreamIndex];

src/torchcodec/decoders/_core/VideoDecoder.h

@@ -153,8 +153,8 @@ class VideoDecoder {
 
   // ---- SINGLE FRAME SEEK AND DECODING API ----
   // Places the cursor at the first frame on or after the position in seconds.
-  // Calling getNextDecodedOutputNoDemux() will return the first frame at or
-  // after this position.
+  // Calling getNextFrameAsTensor() will return the first frame at or after this
+  // position.
   void setCursorPtsInSeconds(double seconds);
   struct DecodedOutput {
     // The actual decoded output as a Tensor.
@@ -180,14 +180,13 @@ class VideoDecoder {
   };
   // Decodes the frame where the current cursor position is. It also advances
   // the cursor to the next frame.
-  DecodedOutput getNextDecodedOutputNoDemux();
-  // Decodes the first frame in any added stream that is visible at a given
-  // timestamp. Frames in the video have a presentation timestamp and a
-  // duration. For example, if a frame has presentation timestamp of 5.0s and a
-  // duration of 1.0s, it will be visible in the timestamp range [5.0, 6.0).
-  // i.e. it will be returned when this function is called with seconds=5.0 or
-  // seconds=5.999, etc.
-  DecodedOutput getFrameDisplayedAtTimestampNoDemux(double seconds);
+  DecodedOutput getNextDecodedOutput();
+  // Decodes the frame that is visible at a given timestamp. Frames in the video
+  // have a presentation timestamp and a duration. For example, if a frame has
+  // presentation timestamp of 5.0s and a duration of 1.0s, it will be visible
+  // in the timestamp range [5.0, 6.0). i.e. it will be returned when this
+  // function is called with seconds=5.0 or seconds=5.999, etc.
+  DecodedOutput getFrameDisplayedAtTimestamp(double seconds);
   DecodedOutput getFrameAtIndex(int streamIndex, int64_t frameIndex);
   struct BatchDecodedOutput {
     torch::Tensor frames;

src/torchcodec/decoders/_core/VideoDecoderOps.cpp

@@ -147,7 +147,7 @@ OpsDecodedOutput get_next_frame(at::Tensor& decoder) {
   auto videoDecoder = unwrapTensorToGetDecoder(decoder);
   VideoDecoder::DecodedOutput result;
   try {
-    result = videoDecoder->getNextDecodedOutputNoDemux();
+    result = videoDecoder->getNextDecodedOutput();
   } catch (const VideoDecoder::EndOfFileException& e) {
     throw pybind11::stop_iteration(e.what());
   }
@@ -161,7 +161,7 @@ OpsDecodedOutput get_next_frame(at::Tensor& decoder) {
 
 OpsDecodedOutput get_frame_at_pts(at::Tensor& decoder, double seconds) {
   auto videoDecoder = unwrapTensorToGetDecoder(decoder);
-  auto result = videoDecoder->getFrameDisplayedAtTimestampNoDemux(seconds);
+  auto result = videoDecoder->getFrameDisplayedAtTimestamp(seconds);
   return makeOpsDecodedOutput(result);
 }
 

src/torchcodec/decoders/_simple_video_decoder.py

@@ -9,7 +9,7 @@
 from pathlib import Path
 from typing import Iterable, Iterator, Literal, Tuple, Union
 
-from torch import device as torch_device, Tensor
+from torch import Tensor
 
 from torchcodec.decoders import _core as core
 
@@ -89,14 +89,6 @@ class SimpleVideoDecoder:
             This can be either "NCHW" (default) or "NHWC", where N is the batch
             size, C is the number of channels, H is the height, and W is the
             width of the frames.
-        device (torch.device, optional): The device to use for decoding.
-            Currently we only support CPU and CUDA devices. If CUDA is used,
-            we use NVDEC and CUDA to do decoding and color-conversion
-            respectively. The resulting frame is left on the GPU for further
-            processing.
-            You can either pass in a string like "cpu" or "cuda:0" or a
-            torch.device like torch.device("cuda:0").
-            Default: ``torch.device("cpu")``.
 
             .. note::
 
@@ -114,7 +106,6 @@ def __init__(
         self,
         source: Union[str, Path, bytes, Tensor],
         dimension_order: Literal["NCHW", "NHWC"] = "NCHW",
-        device: Union[str, torch_device] = torch_device("cpu"),
     ):
         if isinstance(source, str):
             self._decoder = core.create_from_file(source)
@@ -138,20 +129,7 @@ def __init__(
             )
 
         core.scan_all_streams_to_update_metadata(self._decoder)
-        num_threads = None
-        if isinstance(device, str):
-            device = torch_device(device)
-        if device.type == "cuda":
-            # Using multiple CPU threads seems to slow down decoding on CUDA.
-            # CUDA internally uses dedicated hardware to do decoding so we
-            # don't need CPU software threads here.
-            num_threads = 1
-        core.add_video_stream(
-            self._decoder,
-            dimension_order=dimension_order,
-            device_string=str(device),
-            num_threads=num_threads,
-        )
+        core.add_video_stream(self._decoder, dimension_order=dimension_order)
 
         self.metadata, self._stream_index = _get_and_validate_stream_metadata(
             self._decoder