fix bug in output format for pyav (#6672) (#6703)

* fix bug in output format for pyav

* add read from memory with constructor overload

* Revert "add read from memory with constructor overload"

This reverts commit 14cbbab.

* run ufmt

Author: jdsgomes

torchvision/io/video.py

@@ -273,72 +273,74 @@ def read_video(
         raise RuntimeError(f"File not found: {filename}")
 
     if get_video_backend() != "pyav":
-        return _video_opt._read_video(filename, start_pts, end_pts, pts_unit)
-
-    _check_av_available()
-
-    if end_pts is None:
-        end_pts = float("inf")
-
-    if end_pts < start_pts:
-        raise ValueError(f"end_pts should be larger than start_pts, got start_pts={start_pts} and end_pts={end_pts}")
-
-    info = {}
-    video_frames = []
-    audio_frames = []
-    audio_timebase = _video_opt.default_timebase
-
-    try:
-        with av.open(filename, metadata_errors="ignore") as container:
-            if container.streams.audio:
-                audio_timebase = container.streams.audio[0].time_base
-            if container.streams.video:
-                video_frames = _read_from_stream(
-                    container,
-                    start_pts,
-                    end_pts,
-                    pts_unit,
-                    container.streams.video[0],
-                    {"video": 0},
-                )
-                video_fps = container.streams.video[0].average_rate
-                # guard against potentially corrupted files
-                if video_fps is not None:
-                    info["video_fps"] = float(video_fps)
-
-            if container.streams.audio:
-                audio_frames = _read_from_stream(
-                    container,
-                    start_pts,
-                    end_pts,
-                    pts_unit,
-                    container.streams.audio[0],
-                    {"audio": 0},
-                )
-                info["audio_fps"] = container.streams.audio[0].rate
-
-    except av.AVError:
-        # TODO raise a warning?
-        pass
-
-    vframes_list = [frame.to_rgb().to_ndarray() for frame in video_frames]
-    aframes_list = [frame.to_ndarray() for frame in audio_frames]
-
-    if vframes_list:
-        vframes = torch.as_tensor(np.stack(vframes_list))
-    else:
-        vframes = torch.empty((0, 1, 1, 3), dtype=torch.uint8)
-
-    if aframes_list:
-        aframes = np.concatenate(aframes_list, 1)
-        aframes = torch.as_tensor(aframes)
-        if pts_unit == "sec":
-            start_pts = int(math.floor(start_pts * (1 / audio_timebase)))
-            if end_pts != float("inf"):
-                end_pts = int(math.ceil(end_pts * (1 / audio_timebase)))
-        aframes = _align_audio_frames(aframes, audio_frames, start_pts, end_pts)
+        vframes, aframes, info = _video_opt._read_video(filename, start_pts, end_pts, pts_unit)
     else:
-        aframes = torch.empty((1, 0), dtype=torch.float32)
+        _check_av_available()
+
+        if end_pts is None:
+            end_pts = float("inf")
+
+        if end_pts < start_pts:
+            raise ValueError(
+                f"end_pts should be larger than start_pts, got start_pts={start_pts} and end_pts={end_pts}"
+            )
+
+        info = {}
+        video_frames = []
+        audio_frames = []
+        audio_timebase = _video_opt.default_timebase
+
+        try:
+            with av.open(filename, metadata_errors="ignore") as container:
+                if container.streams.audio:
+                    audio_timebase = container.streams.audio[0].time_base
+                if container.streams.video:
+                    video_frames = _read_from_stream(
+                        container,
+                        start_pts,
+                        end_pts,
+                        pts_unit,
+                        container.streams.video[0],
+                        {"video": 0},
+                    )
+                    video_fps = container.streams.video[0].average_rate
+                    # guard against potentially corrupted files
+                    if video_fps is not None:
+                        info["video_fps"] = float(video_fps)
+
+                if container.streams.audio:
+                    audio_frames = _read_from_stream(
+                        container,
+                        start_pts,
+                        end_pts,
+                        pts_unit,
+                        container.streams.audio[0],
+                        {"audio": 0},
+                    )
+                    info["audio_fps"] = container.streams.audio[0].rate
+
+        except av.AVError:
+            # TODO raise a warning?
+            pass
+
+        vframes_list = [frame.to_rgb().to_ndarray() for frame in video_frames]
+        aframes_list = [frame.to_ndarray() for frame in audio_frames]
+
+        if vframes_list:
+            vframes = torch.as_tensor(np.stack(vframes_list))
+        else:
+            vframes = torch.empty((0, 1, 1, 3), dtype=torch.uint8)
+
+        if aframes_list:
+            aframes = np.concatenate(aframes_list, 1)
+            aframes = torch.as_tensor(aframes)
+            if pts_unit == "sec":
+                start_pts = int(math.floor(start_pts * (1 / audio_timebase)))
+                if end_pts != float("inf"):
+                    end_pts = int(math.ceil(end_pts * (1 / audio_timebase)))
+            aframes = _align_audio_frames(aframes, audio_frames, start_pts, end_pts)
+        else:
+            aframes = torch.empty((1, 0), dtype=torch.float32)
 
     if output_format == "TCHW":
         # [T,H,W,C] --> [T,C,H,W]