ffwavesynth can produce large amounts of data relatively slowly on very small input
Fixes: Timeout (60sec -> 9sec)
Fixes: 17970/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_FFWAVESYNTH_fuzzer-5689121279836160
Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
Fixes: Timeout (110sec -> 10sec)
Fixes: 17705/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_VP7_fuzzer-5765834135306240
Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Reviewed-by: Peter Ross <pross@xvid.org>
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
The decoder is slow
Fixes: Timeout (94sec -> 7sec)
Fixes: 16417/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_MSS2_fuzzer-5711668050395136
Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
this conflicts with the current contributing guidance:
http://ffmpeg.org/developer.html#Contributing
FFmpeg is programmed in the ISO C90 language with a few additional
features from ISO C99, namely:
...
for loops with variable definition (‘for (int i = 0; i < 8; i++)’);
Signed-off-by: James Zern <jzern@google.com>
The reason to add this layer is that it is used by srcnn in vf_sr.
This layer is currently ignored in native mode. After this patch,
we can add multiple outputs support for native mode.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
Fixes: Timeout (89sec -> 7sec)
Fixes: 17035/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_BINK_fuzzer-5737222422134784
Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Reviewed-by: Peter Ross <pross@xvid.org>
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
snow allows creating large output from tiny input, like other
wavelet codecs
Fixes: Timeout (82sec -> 1.5sec)
Fixes: 9520/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_SNOW_fuzzer-6286612576075776
Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
currently, the layer number is at the beginning of the .model file,
so we have to scan twice in python script, the first scan to get the
layer number. Only one scan needed after put the layer number at the
end of .model file.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
wavelets allow significant size expansion and they are also not
very fast.
Fixes: Timeout
Fixes: 16480/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_DIRAC_fuzzer-5659892689403904 (108sec -> 17ms)
Fixes: 16480/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_DIRAC_fuzzer-5749422717140992 (big ->15sec)
Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Reviewed-by: Tomas Härdin <tjoppen@acc.umu.se>
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
conv2d with dilation > 1 generates tens of nodes in graph, it is not
easy to parse each node one by one, so we do special tricks to parse
the conv2d layer.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
Fixes: Timeout (12sec ->2sec)
Fixes: 16125/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_MSRLE_fuzzer-5650846364205056
Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Reviewed-by: Paul B Mahol <onemda@gmail.com>
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
This should reduces the number of uninteresting timeouts encountered
A single threshold for all codecs did not work
Fixes: 13979/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_QTRLE_fuzzer-5629872380051456 (14sec -> 4sec)
Fixes: 14709/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_GDV_fuzzer-5704215281795072 (179sec -> 7sec)
Fixes: 16296/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_HNM4_VIDEO_fuzzer-5756304521428992 (108sec -> 9sec)
Fixes: 15620/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_GIF_fuzzer-5657214435459072 (26sec -> 26ms)
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
This patch avoids a read past the end of the input buffer in memcpy since the size
of the received zmq message is recv_buf_size - 1.
Reviewed-by: Paul B Mahol <onemda@gmail.com>
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
since tf.pad is enabled, the conv2d(valid) changes back to its original behavior.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
Fixes: memleak
Fixes: part of 15529/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_LIBVPX_VP8_fuzzer-5140143700180992
Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
For example, given TensorFlow model file espcn.pb,
to generate native model file espcn.model, just run:
python convert.py espcn.pb
In current implementation, the native model file is generated for
specific dnn network with hard-code python scripts maintained out of ffmpeg.
For example, srcnn network used by vf_sr is generated with
https://github.com/HighVoltageRocknRoll/sr/blob/master/generate_header_and_model.py#L85
In this patch, the script is designed as a general solution which
converts general TensorFlow model .pb file into .model file. The script
now has some tricky to be compatible with current implemention, will
be refined step by step.
The script is also added into ffmpeg source tree. It is expected there
will be many more patches and community needs the ownership of it.
Another technical direction is to do the conversion in c/c++ code within
ffmpeg source tree. While .pb file is organized with protocol buffers,
it is not easy to do such work with tiny c/c++ code, see more discussion
at http://ffmpeg.org/pipermail/ffmpeg-devel/2019-May/244496.html. So,
choose the python script.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
