Benchmark swscale for resize transforms

benchmarks/decoders/benchmark_transforms.py

@@ -5,6 +5,7 @@
 
 import torch
 from torch import Tensor
+from torchcodec._core import _add_video_stream, create_from_file, get_frames_by_pts
 from torchcodec.decoders import VideoDecoder
 from torchvision.transforms import v2
 
@@ -93,6 +94,22 @@ def decoder_crop(
     return transformed_frames
 
 
+def decoder_resize_swscale(
+    path: Path, pts_seconds: list[float], dims: tuple[int, int], num_threads: int
+) -> Tensor:
+    height, width = dims
+    decoder = create_from_file(str(path), seek_mode="approximate")
+    _add_video_stream(
+        decoder,
+        stream_index=None,
+        num_threads=num_threads,
+        transform_specs=f"resize, {height}, {width}",
+        color_conversion_library="swscale",
+    )
+    frames, *_ = get_frames_by_pts(decoder, timestamps=pts_seconds)
+    return frames
+
+
 def main():
     parser = ArgumentParser()
     parser.add_argument("--path", type=str, help="path to file", required=True)
@@ -120,6 +137,11 @@ def main():
         type=float,
         default=[0.5, 0.25, 0.125],
     )
+    parser.add_argument(
+        "--benchmark-swscale-vs-filtergraph",
+        action="store_true",
+        help="Run swscale vs filtergraph benchmarks",
+    )
 
     args = parser.parse_args()
     path = Path(args.path)
@@ -133,6 +155,53 @@ def main():
 
     input_height = metadata.height
     input_width = metadata.width
+
+    if args.benchmark_swscale_vs_filtergraph:
+        print("\n" + "=" * 70)
+        print("SWSCALE VS FILTERGRAPH BENCHMARKS")
+        print("=" * 70)
+
+        for num_fraction in args.total_frame_fractions:
+            num_frames_to_sample = math.ceil(metadata.num_frames * num_fraction)
+            print(
+                f"\nSampling {num_fraction * 100}%, {num_frames_to_sample}, of {metadata.num_frames} frames"
+            )
+
+            # Generate timestamps for decoder API
+            uniform_timestamps = [
+                i * duration / num_frames_to_sample for i in range(num_frames_to_sample)
+            ]
+
+            # Resize at different dimensions
+            for dims_fraction in args.input_dimension_fractions:
+                dims = (
+                    int(input_height * dims_fraction),
+                    int(input_width * dims_fraction),
+                )
+
+                print(f"\n--- Resize to {dims} ---")
+                times = bench(
+                    decoder_resize_swscale,
+                    path,
+                    uniform_timestamps,
+                    dims,
+                    args.num_threads,
+                    num_exp=args.num_exp,
+                )
+                report_stats(times, prefix=f"swscale resize{dims}")
+
+                times = bench(
+                    decoder_resize,
+                    path,
+                    uniform_timestamps,
+                    dims,
+                    args.num_threads,
+                    num_exp=args.num_exp,
+                )
+                report_stats(times, prefix=f"filtergraph resize{dims}")
+
+        return
+
     for num_fraction in args.total_frame_fractions:
         num_frames_to_sample = math.ceil(metadata.num_frames * num_fraction)
         print(

src/torchcodec/_core/CpuDeviceInterface.cpp

@@ -39,15 +39,18 @@ void CpuDeviceInterface::initializeVideo(
   videoStreamOptions_ = videoStreamOptions;
   resizedOutputDims_ = resizedOutputDims;
 
-  // We can only use swscale when we have a single resize transform. Note that
-  // this means swscale will not support the case of having several,
-  // back-to-base resizes. There's no strong reason to even do that, but if
-  // someone does, it's more correct to implement that with filtergraph.
+  // We can use swscale when we have a single resize transform.
+  // With a single resize, we use swscale twice:
+  // first for color conversion (YUV->RGB24), then for resize in RGB24 space.
+  //
+  // Note that this means swscale will not support the case of having several,
+  // back-to-back resizes or other transforms.
   //
-  // We calculate this value during initilization but we don't refer to it until
-  // getColorConversionLibrary() is called. Calculating this value during
+  // We calculate this value during initialization but we don't refer to it
+  // until getColorConversionLibrary() is called. Calculating this value during
   // initialization saves us from having to save all of the transforms.
-  areTransformsSwScaleCompatible_ = transforms.empty();
+  areTransformsSwScaleCompatible_ = transforms.empty() ||
+      (transforms.size() == 1 && transforms[0]->isResize());
 
   // Note that we do not expose this capability in the public API, only through
   // the core API.
@@ -57,6 +60,16 @@ void CpuDeviceInterface::initializeVideo(
   userRequestedSwScale_ = videoStreamOptions_.colorConversionLibrary ==
       ColorConversionLibrary::SWSCALE;
 
+  // We can only use swscale when we have a single resize transform. Note that
+  // we actually decide on whether or not to actually use swscale at the last
+  // possible moment, when we actually convert the frame. This is because we
+  // need to know the actual frame dimensions.
+  if (transforms.size() == 1 && transforms[0]->isResize()) {
+    auto resize = dynamic_cast<ResizeTransform*>(transforms[0].get());
+    TORCH_CHECK(resize != nullptr, "ResizeTransform expected but not found!");
+    swsFlags_ = resize->getSwsFlags();
+  }
+
   // If we have any transforms, replace filters_ with the filter strings from
   // the transforms. As noted above, we decide between swscale and filtergraph
   // when we actually decode a frame.
@@ -238,10 +251,9 @@ int CpuDeviceInterface::convertAVFrameToTensorUsingSwScale(
   enum AVPixelFormat frameFormat =
       static_cast<enum AVPixelFormat>(avFrame->format);
 
-  TORCH_CHECK(
-      avFrame->height == outputDims.height &&
-          avFrame->width == outputDims.width,
-      "Input dimensions are not equal to output dimensions; resize for sws_scale() is not yet supported.");
+  bool needsResize =
+      (avFrame->height != outputDims.height ||
+       avFrame->width != outputDims.width);
 
   // We need to compare the current frame context with our previous frame
   // context. If they are different, then we need to re-create our colorspace
@@ -254,8 +266,8 @@ int CpuDeviceInterface::convertAVFrameToTensorUsingSwScale(
       avFrame->width,
       avFrame->height,
       frameFormat,
-      outputDims.width,
-      outputDims.height);
+      needsResize ? avFrame->width : outputDims.width,
+      needsResize ? avFrame->height : outputDims.height);
 
   if (!swsContext_ || prevSwsFrameContext_ != swsFrameContext) {
     swsContext_ = createSwsContext(
@@ -266,25 +278,97 @@ int CpuDeviceInterface::convertAVFrameToTensorUsingSwScale(
         // pixel format.
         /*outputFormat=*/AV_PIX_FMT_RGB24,
 
-        // We don't set any flags because we don't yet use sw_scale() for
-        // resizing.
+        // No flags for color conversion. When resizing is needed, we use a
+        // separate swscale context with the appropriate resize flags.
         /*swsFlags=*/0);
     prevSwsFrameContext_ = swsFrameContext;
   }
 
-  uint8_t* pointers[4] = {
-      outputTensor.data_ptr<uint8_t>(), nullptr, nullptr, nullptr};
-  int expectedOutputWidth = outputTensor.sizes()[1];
-  int linesizes[4] = {expectedOutputWidth * 3, 0, 0, 0};
-  int resultHeight = sws_scale(
-      swsContext_.get(),
-      avFrame->data,
-      avFrame->linesize,
-      0,
-      avFrame->height,
-      pointers,
-      linesizes);
-  return resultHeight;
+  if (needsResize) {
+    // Double swscale path: first convert to RGB24 at original resolution,
+    // then resize in RGB24 space. This ensures transforms happen in the
+    // output color space (RGB24) rather than the input color space (YUV).
+
+    // First pass: color conversion (YUV -> RGB24) at original resolution
+    torch::Tensor intermediateTensor = allocateEmptyHWCTensor(
+        FrameDims(avFrame->height, avFrame->width), torch::kCPU);
+
+    uint8_t* intermediatePointers[4] = {
+        intermediateTensor.data_ptr<uint8_t>(), nullptr, nullptr, nullptr};
+    int intermediateWidth = avFrame->width;
+    int intermediateLinesizes[4] = {intermediateWidth * 3, 0, 0, 0};
+
+    int intermediateHeight = sws_scale(
+        swsContext_.get(),
+        avFrame->data,
+        avFrame->linesize,
+        0,
+        avFrame->height,
+        intermediatePointers,
+        intermediateLinesizes);
+
+    TORCH_CHECK(
+        intermediateHeight == avFrame->height,
+        "First swscale pass failed: intermediateHeight != avFrame->height: ",
+        intermediateHeight,
+        " != ",
+        avFrame->height);
+
+    // Second pass: resize in RGB24 space
+    // Use cached swscale context for resizing, similar to the color conversion
+    // context caching above.
+    SwsFrameContext resizeSwsFrameContext(
+        avFrame->width,
+        avFrame->height,
+        AV_PIX_FMT_RGB24,
+        outputDims.width,
+        outputDims.height);
+
+    if (!resizeSwsContext_ ||
+        prevResizeSwsFrameContext_ != resizeSwsFrameContext) {
+      resizeSwsContext_ = createSwsContext(
+          resizeSwsFrameContext,
+          AVCOL_SPC_RGB,
+          /*outputFormat=*/AV_PIX_FMT_RGB24,
+          /*swsFlags=*/swsFlags_);
+      prevResizeSwsFrameContext_ = resizeSwsFrameContext;
+    }
+
+    uint8_t* srcPointers[4] = {
+        intermediateTensor.data_ptr<uint8_t>(), nullptr, nullptr, nullptr};
+    int srcLinesizes[4] = {avFrame->width * 3, 0, 0, 0};
+
+    uint8_t* dstPointers[4] = {
+        outputTensor.data_ptr<uint8_t>(), nullptr, nullptr, nullptr};
+    int expectedOutputWidth = outputTensor.sizes()[1];
+    int dstLinesizes[4] = {expectedOutputWidth * 3, 0, 0, 0};
+
+    int resultHeight = sws_scale(
+        resizeSwsContext_.get(),
+        srcPointers,
+        srcLinesizes,
+        0,
+        avFrame->height,
+        dstPointers,
+        dstLinesizes);
+
+    return resultHeight;
+  } else {
+    // No resize needed, just color conversion
+    uint8_t* pointers[4] = {
+        outputTensor.data_ptr<uint8_t>(), nullptr, nullptr, nullptr};
+    int expectedOutputWidth = outputTensor.sizes()[1];
+    int linesizes[4] = {expectedOutputWidth * 3, 0, 0, 0};
+    int resultHeight = sws_scale(
+        swsContext_.get(),
+        avFrame->data,
+        avFrame->linesize,
+        0,
+        avFrame->height,
+        pointers,
+        linesizes);
+    return resultHeight;
+  }
 }
 
 torch::Tensor CpuDeviceInterface::convertAVFrameToTensorUsingFilterGraph(

src/torchcodec/_core/CpuDeviceInterface.h

@@ -100,6 +100,12 @@ class CpuDeviceInterface : public DeviceInterface {
   UniqueSwsContext swsContext_;
   SwsFrameContext prevSwsFrameContext_;
 
+  // Cached swscale context for resizing in RGB24 space (used in double swscale
+  // path). Like the color conversion context above, we cache this to avoid
+  // recreating it for every frame.
+  UniqueSwsContext resizeSwsContext_;
+  SwsFrameContext prevResizeSwsFrameContext_;
+
   // We pass these filters to FFmpeg's filtergraph API. It is a simple pipeline
   // of what FFmpeg calls "filters" to apply to decoded frames before returning
   // them. In the PyTorch ecosystem, we call these "transforms". During
@@ -119,6 +125,11 @@ class CpuDeviceInterface : public DeviceInterface {
   bool areTransformsSwScaleCompatible_;
   bool userRequestedSwScale_;
 
+  // The flags we supply to the resize swscale context. The flags control the
+  // resizing algorithm. We default to bilinear. Users can override this with a
+  // ResizeTransform that specifies a different interpolation mode.
+  int swsFlags_ = SWS_BILINEAR;
+
   bool initialized_ = false;
 
   // Audio-specific members

src/torchcodec/_core/Transform.cpp

@@ -25,6 +25,18 @@ std::string toFilterGraphInterpolation(
   }
 }
 
+int toSwsInterpolation(ResizeTransform::InterpolationMode mode) {
+  switch (mode) {
+    case ResizeTransform::InterpolationMode::BILINEAR:
+      return SWS_BILINEAR;
+    default:
+      TORCH_CHECK(
+          false,
+          "Unknown interpolation mode: " +
+              std::to_string(static_cast<int>(mode)));
+  }
+}
+
 } // namespace
 
 std::string ResizeTransform::getFilterGraphCpu() const {
@@ -37,6 +49,14 @@ std::optional<FrameDims> ResizeTransform::getOutputFrameDims() const {
   return outputDims_;
 }
 
+bool ResizeTransform::isResize() const {
+  return true;
+}
+
+int ResizeTransform::getSwsFlags() const {
+  return toSwsInterpolation(interpolationMode_);
+}
+
 CropTransform::CropTransform(const FrameDims& dims) : outputDims_(dims) {}
 
 CropTransform::CropTransform(const FrameDims& dims, int x, int y)

src/torchcodec/_core/Transform.h

@@ -29,6 +29,12 @@ class Transform {
     return std::nullopt;
   }
 
+  // The ResizeTransform is special because it is the only transform
+  // that swscale can handle.
+  virtual bool isResize() const {
+    return false;
+  }
+
   // The validity of some transforms depends on the characteristics of the
   // AVStream they're being applied to. For example, some transforms will
   // specify coordinates inside a frame, we need to validate that those are
@@ -51,6 +57,9 @@ class ResizeTransform : public Transform {
 
   std::string getFilterGraphCpu() const override;
   std::optional<FrameDims> getOutputFrameDims() const override;
+  bool isResize() const override;
+
+  int getSwsFlags() const;
 
  private:
   FrameDims outputDims_;