2024-12-03 nightly release (6279faa)

Author: pytorchbot

packaging/pre_build_script.sh

@@ -32,7 +32,9 @@ else
     conda install -yq ffmpeg=4.2 libjpeg-turbo -c pytorch-nightly
   fi
 
-  yum install -y libjpeg-turbo-devel libwebp-devel freetype gnutls
+  conda install libwebp -yq
+  conda install libjpeg-turbo -c pytorch
+  yum install -y freetype gnutls
   pip install auditwheel
 fi
 

test/test_image.py

@@ -45,6 +45,8 @@
 IS_MACOS = sys.platform == "darwin"
 PILLOW_VERSION = tuple(int(x) for x in PILLOW_VERSION.split("."))
 WEBP_TEST_IMAGES_DIR = os.environ.get("WEBP_TEST_IMAGES_DIR", "")
+# See https://github.com/pytorch/vision/pull/8724#issuecomment-2503964558
+ROCM_WEBP_MESSAGE = "ROCM not built with webp support."
 
 # Hacky way of figuring out whether we compiled with libavif/libheif (those are
 # currenlty disabled by default)

torchvision/csrc/io/image/cpu/decode_webp.cpp

@@ -3,6 +3,7 @@
 
 #if WEBP_FOUND
 #include "webp/decode.h"
+#include "webp/types.h"
 #endif // WEBP_FOUND
 
 namespace vision {
@@ -44,10 +45,12 @@ torch::Tensor decode_webp(
 
   auto decoded_data =
       decoding_func(encoded_data_p, encoded_data_size, &width, &height);
+
   TORCH_CHECK(decoded_data != nullptr, "WebPDecodeRGB[A] failed.");
 
+  auto deleter = [decoded_data](void*) { WebPFree(decoded_data); };
   auto out = torch::from_blob(
-      decoded_data, {height, width, num_channels}, torch::kUInt8);
+      decoded_data, {height, width, num_channels}, deleter, torch::kUInt8);
 
   return out.permute({2, 0, 1});
 }

torchvision/io/video.py

@@ -26,6 +26,10 @@
 install PyAV on your system.
 """
         )
+    try:
+        FFmpegError = av.FFmpegError  # from av 14 https://github.com/PyAV-Org/PyAV/blob/main/CHANGELOG.rst
+    except AttributeError:
+        FFmpegError = av.AVError
 except ImportError:
     av = ImportError(
         """\
@@ -155,7 +159,13 @@ def write_video(
 
         for img in video_array:
             frame = av.VideoFrame.from_ndarray(img, format="rgb24")
-            frame.pict_type = "NONE"
+            try:
+                frame.pict_type = "NONE"
+            except TypeError:
+                from av.video.frame import PictureType  # noqa
+
+                frame.pict_type = PictureType.NONE
+
             for packet in stream.encode(frame):
                 container.mux(packet)
 
@@ -215,7 +225,7 @@ def _read_from_stream(
     try:
         # TODO check if stream needs to always be the video stream here or not
         container.seek(seek_offset, any_frame=False, backward=True, stream=stream)
-    except av.AVError:
+    except FFmpegError:
         # TODO add some warnings in this case
         # print("Corrupted file?", container.name)
         return []
@@ -228,7 +238,7 @@ def _read_from_stream(
                     buffer_count += 1
                     continue
                 break
-    except av.AVError:
+    except FFmpegError:
         # TODO add a warning
         pass
     # ensure that the results are sorted wrt the pts
@@ -350,7 +360,7 @@ def read_video(
                     )
                     info["audio_fps"] = container.streams.audio[0].rate
 
-        except av.AVError:
+        except FFmpegError:
             # TODO raise a warning?
             pass
 
@@ -441,10 +451,10 @@ def read_video_timestamps(filename: str, pts_unit: str = "pts") -> Tuple[List[in
                 video_time_base = video_stream.time_base
                 try:
                     pts = _decode_video_timestamps(container)
-                except av.AVError:
+                except FFmpegError:
                     warnings.warn(f"Failed decoding frames for file {filename}")
                 video_fps = float(video_stream.average_rate)
-    except av.AVError as e:
+    except FFmpegError as e:
         msg = f"Failed to open container for {filename}; Caught error: {e}"
         warnings.warn(msg, RuntimeWarning)
 

torchvision/utils.py

@@ -260,7 +260,7 @@ def draw_bounding_boxes(
 
         if label is not None:
             margin = width + 1
-            draw.text((bbox[0] + margin, bbox[1] + margin), label, fill=label_color, font=txt_font)
+            draw.text((bbox[0] + margin, bbox[1] + margin), label, fill=label_color, font=txt_font)  # type: ignore[arg-type]
 
     out = F.pil_to_tensor(img_to_draw)
     if original_dtype.is_floating_point: