Clean temp... segment parameters: 12.4M netG parameters: 11.38M Clean Mosaic: C:\Users\OneRains\Desktop\mosaic.png --------------------ERROR-------------------- --------------Environment-------------- DeepMosaics: 0.4.0 Python: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 14:00:49) [MSC v.1915 64 bit (AMD64)] Pytorch: 0.4.1 OpenCV: 4.1.0 Platform: Windows-10-10.0.18362-SP0 --------------BUG-------------- Error Type: <class 'cv2.error'> OpenCV(4.1.0) c:\projects\opencv-python\opencv\modules\imgproc\src\filterengine.hpp:363: error: (-215:Assertion failed) anchor.inside(Rect(0, 0, ksize.width, ksize.height)) in function 'cv::normalizeAnchor'

<FrameSummary file deepmosaic.py, line 64 in > <FrameSummary file deepmosaic.py, line 40 in main> <FrameSummary file cores\core.py, line 169 in cleanmosaic_img> <FrameSummary file models\runmodel.py, line 75 in get_mosaic_position> <FrameSummary file util\image_processing.py, line 201 in mask_threshold> Please press any key to exit.

1. Update readme.md to include:
   • How to install dependencies
   • Troubleshoot issue Please check mosaic_position_model_path!
   • Troubleshoot issue when building scikit-image
2. Bump version of numpy to 1.19.3 to solve conflict with opencv_python (openvc_python require numpy >=1.19.3)

--------------------ERROR-------------------- Error Type: <class 'UnboundLocalError'> local variable 'img_fake' referenced before assignment <FrameSummary file deepmosaic.py, line 66 in > <FrameSummary file deepmosaic.py, line 45 in main> <FrameSummary file D:\DeepMosaics-master\cores\core.py, line 142 in cleanmosaic_video_byframe> Please press any key to exit.

i run the program on windows 10 with newest python and got this error.

i don't know how to fix it.

Hi, I'm getting following error when running on python 3.6. Do you know what could cause the issue? Thanks

frame=26508 fps=194 q=0.0 Lsize=N/A time=00:14:43.60 bitrate=N/A dup=4 drop=0 speed=6.45x
video:1418716kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
Find mosaic location:
--------------------ERROR--------------------
Error Type: <class 'ValueError'>
too many values to unpack (expected 2)
<FrameSummary file deepmosaic.py, line 64 in <module>>
<FrameSummary file deepmosaic.py, line 43 in main>
<FrameSummary file C:\temp\DeepMosaics-master\cores\core.py, line 233 in cleanmosaic_video_fusion>
<FrameSummary file C:\temp\DeepMosaics-master\cores\core.py, line 144 in get_mosaic_positions>
<FrameSummary file C:\temp\DeepMosaics-master\models\runmodel.py, line 77 in get_mosaic_position>
<FrameSummary file C:\temp\DeepMosaics-master\util\image_processing.py, line 149 in find_mostlikely_ROI>
Please press any key to exit.

I just tested out on Google Colab with image sample on readme (white cloth girl), and result is still the same (blurred face)

Tested on: Google Colab
Runtime: GPU
Cuda: True

More information & screenshot here

After attempting to change the ffmpeg parameters in the pre build version, unsuccessful, i am now trying to run DeepMasaics from source.

So far i stuck to the installation guide on the front page, i cloned the repository and installed all the dependencies. When im trying to run it from the command line (i'm using Power Shell) i get this error: --------------BUG-------------- Error Type: <class 'json.decoder.JSONDecodeError'> Expecting value: line 1 column 1 (char 0) <FrameSummary file V:\DeepMosaics\deepmosaic.py, line 77 in <module>> <FrameSummary file V:\DeepMosaics\deepmosaic.py, line 51 in main> <FrameSummary file V:\DeepMosaics\cores\clean.py, line 169 in cleanmosaic_video_fusion> <FrameSummary file V:\DeepMosaics\cores\init.py, line 8 in video_init> <FrameSummary file V:\DeepMosaics\util\ffmpeg.py, line 63 in get_video_infos> <FrameSummary file C:\Users\First User\AppData\Local\Programs\Python\Python39\lib\json\__init__.py, line 346 in loads> <FrameSummary file C:\Users\First User\AppData\Local\Programs\Python\Python39\lib\json\decoder.py, line 337 in decode> <FrameSummary file C:\Users\First User\AppData\Local\Programs\Python\Python39\lib\json\decoder.py, line 355 in raw_decode> Please press any key to exit.

This is what i have installed: (im Running Windows 10) --------------Environment-------------- DeepMosaics: 0.5.1 Python: 3.9.4 (tags/v3.9.4:1f2e308, Apr 6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)] Pytorch: 1.10.0+cu102 OpenCV: 4.5.5

This is the command im running from within the DeepMosaics Folder: python deepmosaic.py --mode clean --media_path V:\test.mp4 --model_path V:\DeepMosaics\pretrained_models\clean_youknow_video.pth --fps 0 --gpu_id 0 -ss 00:00:00 -t 00:00:00 --result_dir V:\DeepResult\ --mask_threshold 64 --tempimage_type png

To me it looks like it cant get useful data from the video, but its a bog standard h264 encoded .mp4 (SD resolution, constant frame rate)

Type of Generator error! Please press any key to exit.

If set --netG to be HD, the error is as follows: segment parameters: 12.4M --------------------ERROR-------------------- --------------Environment-------------- DeepMosaics: 0.5.0 Python: 3.7.5 (default, Nov 7 2019, 10:50:52) [GCC 8.3.0] Pytorch: 1.6.0 OpenCV: 4.5.1 Platform: Linux-5.4.0-65-generic-x86_64-with-Ubuntu-18.04-bionic --------------BUG-------------- Error Type: <class 'RuntimeError'> Error(s) in loading state_dict for BiSeNet: Missing key(s) in state_dict: "saptial_path.convblock1.conv1.weight", "saptial_path.convblock1.bn.weight", "saptial_path.convblock1.bn.bias", "saptial_path.convblock1.bn.running_mean", "saptial_path.convblock1.bn.running_var", "saptial_path.convblock2.conv1.weight", "saptial_path.convblock2.bn.weight", "saptial_path.convblock2.bn.bias", "saptial_path.convblock2.bn.running_mean", "saptial_path.convblock2.bn.running_var", "saptial_path.convblock3.conv1.weight", "saptial_path.convblock3.bn.weight", "saptial_path.convblock3.bn.bias", "saptial_path.convblock3.bn.running_mean", "saptial_path.convblock3.bn.running_var", "context_path.features.conv1.weight", "context_path.features.bn1.weight", "context_path.features.bn1.bias", "context_path.features.bn1.running_mean", "context_path.features.bn1.running_var", "context_path.features.layer1.0.conv1.weight", "context_path.features.layer1.0.bn1.weight", "context_path.features.layer1.0.bn1.bias", "context_path.features.layer1.0.bn1.running_mean", "context_path.features.layer1.0.bn1.running_var", "context_path.features.layer1.0.conv2.weight", "context_path.features.layer1.0.bn2.weight", "context_path.features.layer1.0.bn2.bias", "context_path.features.layer1.0.bn2.running_mean", "context_path.features.layer1.0.bn2.running_var", "context_path.features.layer1.1.conv1.weight", "context_path.features.layer1.1.bn1.weight", "context_path.features.layer1.1.bn1.bias", "context_path.features.layer1.1.bn1.running_mean", "context_path.features.layer1.1.bn1.running_var", "context_path.features.layer1.1.conv2.weight", "context_path.features.layer1.1.bn2.weight", "context_path.features.layer1.1.bn2.bias", "context_path.features.layer1.1.bn2.running_mean", "context_path.features.layer1.1.bn2.running_var", "context_path.features.layer2.0.conv1.weight", "context_path.features.layer2.0.bn1.weight", "context_path.features.layer2.0.bn1.bias", "context_path.features.layer2.0.bn1.running_mean", "context_path.features.layer2.0.bn1.running_var", "context_path.features.layer2.0.conv2.weight", "context_path.features.layer2.0.bn2.weight", "context_path.features.layer2.0.bn2.bias", "context_path.features.layer2.0.bn2.running_mean", "context_path.features.layer2.0.bn2.running_var", "context_path.features.layer2.0.downsample.0.weight", "context_path.features.layer2.0.downsample.1.weight", "context_path.features.layer2.0.downsample.1.bias", "context_path.features.layer2.0.downsample.1.running_mean", "context_path.features.layer2.0.downsample.1.running_var", "context_path.features.layer2.1.conv1.weight", "context_path.features.layer2.1.bn1.weight", "context_path.features.layer2.1.bn1.bias", "context_path.features.layer2.1.bn1.running_mean", "context_path.features.layer2.1.bn1.running_var", "context_path.features.layer2.1.conv2.weight", "context_path.features.layer2.1.bn2.weight", "context_path.features.layer2.1.bn2.bias", "context_path.features.layer2.1.bn2.running_mean", "context_path.features.layer2.1.bn2.running_var", "context_path.features.layer3.0.conv1.weight", "context_path.features.layer3.0.bn1.weight", "context_path.features.layer3.0.bn1.bias", "context_path.features.layer3.0.bn1.running_mean", "context_path.features.layer3.0.bn1.running_var", "context_path.features.layer3.0.conv2.weight", "context_path.features.layer3.0.bn2.weight", "context_path.features.layer3.0.bn2.bias", "context_path.features.layer3.0.bn2.running_mean", "context_path.features.layer3.0.bn2.running_var", "context_path.features.layer3.0.downsample.0.weight", "context_path.features.layer3.0.downsample.1.weight", "context_path.features.layer3.0.downsample.1.bias", "context_path.features.layer3.0.downsample.1.running_mean", "context_path.features.layer3.0.downsample.1.running_var", "context_path.features.layer3.1.conv1.weight", "context_path.features.layer3.1.bn1.weight", "context_path.features.layer3.1.bn1.bias", "context_path.features.layer3.1.bn1.running_mean", "context_path.features.layer3.1.bn1.running_var", "context_path.features.layer3.1.conv2.weight", "context_path.features.layer3.1.bn2.weight", "context_path.features.layer3.1.bn2.bias", "context_path.features.layer3.1.bn2.running_mean", "context_path.features.layer3.1.bn2.running_var", "context_path.features.layer4.0.conv1.weight", "context_path.features.layer4.0.bn1.weight", "context_path.features.layer4.0.bn1.bias", "context_path.features.layer4.0.bn1.running_mean", "context_path.features.layer4.0.bn1.running_var", "context_path.features.layer4.0.conv2.weight", "context_path.features.layer4.0.bn2.weight", "context_path.features.layer4.0.bn2.bias", "context_path.features.layer4.0.bn2.running_mean", "context_path.features.layer4.0.bn2.running_var", "context_path.features.layer4.0.downsample.0.weight", "context_path.features.layer4.0.downsample.1.weight", "context_path.features.layer4.0.downsample.1.bias", "context_path.features.layer4.0.downsample.1.running_mean", "context_path.features.layer4.0.downsample.1.running_var", "context_path.features.layer4.1.conv1.weight", "context_path.features.layer4.1.bn1.weight", "context_path.features.layer4.1.bn1.bias", "context_path.features.layer4.1.bn1.running_mean", "context_path.features.layer4.1.bn1.running_var", "context_path.features.layer4.1.conv2.weight", "context_path.features.layer4.1.bn2.weight", "context_path.features.layer4.1.bn2.bias", "context_path.features.layer4.1.bn2.running_mean", "context_path.features.layer4.1.bn2.running_var", "context_path.features.fc.weight", "context_path.features.fc.bias", "context_path.conv1.weight", "context_path.bn1.weight", "context_path.bn1.bias", "context_path.bn1.running_mean", "context_path.bn1.running_var", "context_path.layer1.0.conv1.weight", "context_path.layer1.0.bn1.weight", "context_path.layer1.0.bn1.bias", "context_path.layer1.0.bn1.running_mean", "context_path.layer1.0.bn1.running_var", "context_path.layer1.0.conv2.weight", "context_path.layer1.0.bn2.weight", "context_path.layer1.0.bn2.bias", "context_path.layer1.0.bn2.running_mean", "context_path.layer1.0.bn2.running_var", "context_path.layer1.1.conv1.weight", "context_path.layer1.1.bn1.weight", "context_path.layer1.1.bn1.bias", "context_path.layer1.1.bn1.running_mean", "context_path.layer1.1.bn1.running_var", "context_path.layer1.1.conv2.weight", "context_path.layer1.1.bn2.weight", "context_path.layer1.1.bn2.bias", "context_path.layer1.1.bn2.running_mean", "context_path.layer1.1.bn2.running_var", "context_path.layer2.0.conv1.weight", "context_path.layer2.0.bn1.weight", "context_path.layer2.0.bn1.bias", "context_path.layer2.0.bn1.running_mean", "context_path.layer2.0.bn1.running_var", "context_path.layer2.0.conv2.weight", "context_path.layer2.0.bn2.weight", "context_path.layer2.0.bn2.bias", "context_path.layer2.0.bn2.running_mean", "context_path.layer2.0.bn2.running_var", "context_path.layer2.0.downsample.0.weight", "context_path.layer2.0.downsample.1.weight", "context_path.layer2.0.downsample.1.bias", "context_path.layer2.0.downsample.1.running_mean", "context_path.layer2.0.downsample.1.running_var", "context_path.layer2.1.conv1.weight", "context_path.layer2.1.bn1.weight", "context_path.layer2.1.bn1.bias", "context_path.layer2.1.bn1.running_mean", "context_path.layer2.1.bn1.running_var", "context_path.layer2.1.conv2.weight", "context_path.layer2.1.bn2.weight", "context_path.layer2.1.bn2.bias", "context_path.layer2.1.bn2.running_mean", "context_path.layer2.1.bn2.running_var", "context_path.layer3.0.conv1.weight", "context_path.layer3.0.bn1.weight", "context_path.layer3.0.bn1.bias", "context_path.layer3.0.bn1.running_mean", "context_path.layer3.0.bn1.running_var", "context_path.layer3.0.conv2.weight", "context_path.layer3.0.bn2.weight", "context_path.layer3.0.bn2.bias", "context_path.layer3.0.bn2.running_mean", "context_path.layer3.0.bn2.running_var", "context_path.layer3.0.downsample.0.weight", "context_path.layer3.0.downsample.1.weight", "context_path.layer3.0.downsample.1.bias", "context_path.layer3.0.downsample.1.running_mean", "context_path.layer3.0.downsample.1.running_var", "context_path.layer3.1.conv1.weight", "context_path.layer3.1.bn1.weight", "context_path.layer3.1.bn1.bias", "context_path.layer3.1.bn1.running_mean", "context_path.layer3.1.bn1.running_var", "context_path.layer3.1.conv2.weight", "context_path.layer3.1.bn2.weight", "context_path.layer3.1.bn2.bias", "context_path.layer3.1.bn2.running_mean", "context_path.layer3.1.bn2.running_var", "context_path.layer4.0.conv1.weight", "context_path.layer4.0.bn1.weight", "context_path.layer4.0.bn1.bias", "context_path.layer4.0.bn1.running_mean", "context_path.layer4.0.bn1.running_var", "context_path.layer4.0.conv2.weight", "context_path.layer4.0.bn2.weight", "context_path.layer4.0.bn2.bias", "context_path.layer4.0.bn2.running_mean", "context_path.layer4.0.bn2.running_var", "context_path.layer4.0.downsample.0.weight", "context_path.layer4.0.downsample.1.weight", "context_path.layer4.0.downsample.1.bias", "context_path.layer4.0.downsample.1.running_mean", "context_path.layer4.0.downsample.1.running_var", "context_path.layer4.1.conv1.weight", "context_path.layer4.1.bn1.weight", "context_path.layer4.1.bn1.bias", "context_path.layer4.1.bn1.running_mean", "context_path.layer4.1.bn1.running_var", "context_path.layer4.1.conv2.weight", "context_path.layer4.1.bn2.weight", "context_path.layer4.1.bn2.bias", "context_path.layer4.1.bn2.running_mean", "context_path.layer4.1.bn2.running_var", "attention_refinement_module1.conv.weight", "attention_refinement_module1.conv.bias", "attention_refinement_module1.bn.weight", "attention_refinement_module1.bn.bias", "attention_refinement_module1.bn.running_mean", "attention_refinement_module1.bn.running_var", "attention_refinement_module2.conv.weight", "attention_refinement_module2.conv.bias", "attention_refinement_module2.bn.weight", "attention_refinement_module2.bn.bias", "attention_refinement_module2.bn.running_mean", "attention_refinement_module2.bn.running_var", "supervision1.weight", "supervision1.bias", "supervision2.weight", "supervision2.bias", "feature_fusion_module.convblock.conv1.weight", "feature_fusion_module.convblock.bn.weight", "feature_fusion_module.convblock.bn.bias", "feature_fusion_module.convblock.bn.running_mean", "feature_fusion_module.convblock.bn.running_var", "feature_fusion_module.conv1.weight", "feature_fusion_module.conv1.bias", "feature_fusion_module.conv2.weight", "feature_fusion_module.conv2.bias", "conv.weight", "conv.bias". Unexpected key(s) in state_dict: "model.1.weight", "model.1.bias", "model.4.weight", "model.4.bias", "model.7.weight", "model.7.bias", "model.10.weight", "model.10.bias", "model.13.weight", "model.13.bias", "model.16.conv_block.1.weight", "model.16.conv_block.1.bias", "model.16.conv_block.5.weight", "model.16.conv_block.5.bias", "model.17.conv_block.1.weight", "model.17.conv_block.1.bias", "model.17.conv_block.5.weight", "model.17.conv_block.5.bias", "model.18.conv_block.1.weight", "model.18.conv_block.1.bias", "model.18.conv_block.5.weight", "model.18.conv_block.5.bias", "model.19.conv_block.1.weight", "model.19.conv_block.1.bias", "model.19.conv_block.5.weight", "model.19.conv_block.5.bias", "model.20.conv_block.1.weight", "model.20.conv_block.1.bias", "model.20.conv_block.5.weight", "model.20.conv_block.5.bias", "model.21.conv_block.1.weight", "model.21.conv_block.1.bias", "model.21.conv_block.5.weight", "model.21.conv_block.5.bias", "model.22.conv_block.1.weight", "model.22.conv_block.1.bias", "model.22.conv_block.5.weight", "model.22.conv_block.5.bias", "model.23.conv_block.1.weight", "model.23.conv_block.1.bias", "model.23.conv_block.5.weight", "model.23.conv_block.5.bias", "model.24.conv_block.1.weight", "model.24.conv_block.1.bias", "model.24.conv_block.5.weight", "model.24.conv_block.5.bias", "model.25.weight", "model.25.bias", "model.28.weight", "model.28.bias", "model.31.weight", "model.31.bias", "model.34.weight", "model.34.bias", "model.38.weight", "model.38.bias". <FrameSummary file /mnt/diskc/even/DeepMosaic/deepmosaic.py, line 81 in > <FrameSummary file /mnt/diskc/even/DeepMosaic/deepmosaic.py, line 40 in run> <FrameSummary file /mnt/diskc/even/DeepMosaic/models/loadmodel.py, line 86 in bisenet> <FrameSummary file /home/jaya/.local/lib/python3.7/site-packages/torch/nn/modules/module.py, line 1045 in load_state_dict> Please press any key to exit.

I'm running into a CUDA out of memory error. Currently, I'm using i3-6100 with 16GB of memory and a Nvidia GTX 750 that only has 1GB of memory. Is there a way to adjust the parameter for low end GPU? If not, what's the minimum memory required for the GPU?

I run this in python 3.6.8 and 3.7.10. Got the following error. Do you know why? Error Type: <class 'RuntimeError'> Given groups=1, weight of size [64, 3, 3, 3], expected input[1, 4, 500, 360] to have 3 channels, but got 4 channels instead <FrameSummary file deepmosaic.py, line 66 in > <FrameSummary file deepmosaic.py, line 40 in main> <FrameSummary file cores\core.py, line 112 in cleanmosaic_img> <FrameSummary file models\runmodel.py, line 73 in get_mosaic_position> <FrameSummary file models\runmodel.py, line 13 in run_segment> <FrameSummary file site-packages\torch\nn\modules\module.py, line 477 in call> <FrameSummary file models\BiSeNet_model.py, line 236 in forward> <FrameSummary file site-packages\torch\nn\modules\module.py, line 477 in call> <FrameSummary file models\BiSeNet_model.py, line 122 in forward> <FrameSummary file site-packages\torch\nn\modules\module.py, line 477 in call> <FrameSummary file models\BiSeNet_model.py, line 111 in forward> <FrameSummary file site-packages\torch\nn\modules\module.py, line 477 in call> <FrameSummary file site-packages\torch\nn\modules\conv.py, line 301 in forward> Please press any key to exit.

deep mosiac exits early when rebuilding the video when using google colab. The video being processed has been cut using the windows photo app which might be an issue. The input command is !python3 deepmosaic.py --media_path="/content/DeepMosaics/imgs/MIDD-817(1a).mp4" --model_path="/content/DeepMosaics/pretrained_models/clean_youknow_video.pth" --mode="clean" --no_preview --gpu_id=0 --fps 0 -ss 00:00:00 -t 00:00:00 --mask_threshold 64 and the output is attached below.

makedir: ./result segment parameters: 12.4M netG parameters: 26.65M Step:1/4 -- Convert video to images Clean temp... ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04) configuration: --prefix=/usr --extra-version=0ubuntu0.2 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared libavutil 55. 78.100 / 55. 78.100 libavcodec 57.107.100 / 57.107.100 libavformat 57. 83.100 / 57. 83.100 libavdevice 57. 10.100 / 57. 10.100 libavfilter 6.107.100 / 6.107.100 libavresample 3. 7. 0 / 3. 7. 0 libswscale 4. 8.100 / 4. 8.100 libswresample 2. 9.100 / 2. 9.100 libpostproc 54. 7.100 / 54. 7.100 Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/content/DeepMosaics/imgs/MIDD-817(1a).mp4': Metadata: major_brand : mp42 minor_version : 0 compatible_brands: mp41isom creation_time : 2020-02-12T18:31:23.000000Z Duration: 00:29:00.67, start: 0.000000, bitrate: 3902 kb/s Stream #0:0(und): Video: h264 (Main) (avc1 / 0x31637661), yuv420p(tv), 1920x1080 [SAR 1:1 DAR 16:9], 3707 kb/s, 30 fps, 30 tbr, 30k tbn, 60 tbc (default) Metadata: creation_time : 2021-07-27T15:07:05.000000Z handler_name : VideoHandler encoder : AVC Coding Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 192 kb/s (default) Metadata: creation_time : 2021-07-27T15:07:05.000000Z handler_name : SoundHandler Stream mapping: Stream #0:1 -> #0:0 (aac (native) -> mp3 (libmp3lame)) Press [q] to stop, [?] for help -async is forwarded to lavfi similarly to -af aresample=async=1:min_hard_comp=0.100000:first_pts=0. Output #0, mp3, to './tmp/DeepMosaics_temp/voice_tmp.mp3': Metadata: major_brand : mp42 minor_version : 0 compatible_brands: mp41isom TSSE : Lavf57.83.100 Stream #0:0(und): Audio: mp3 (libmp3lame), 48000 Hz, stereo, fltp, 320 kb/s (default) Metadata: creation_time : 2021-07-27T15:07:05.000000Z handler_name : SoundHandler encoder : Lavc57.107.100 libmp3lame [aac @ 0x560c0eb5f900] decode_band_types: Input buffer exhausted before END element found Error while decoding stream #0:1: Invalid data found when processing input size= 67997kB time=00:29:00.67 bitrate= 320.0kbits/s speed=41.7x
video:0kB audio:67996kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.001577% ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04) configuration: --prefix=/usr --extra-version=0ubuntu0.2 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared libavutil 55. 78.100 / 55. 78.100 libavcodec 57.107.100 / 57.107.100 libavformat 57. 83.100 / 57. 83.100 libavdevice 57. 10.100 / 57. 10.100 libavfilter 6.107.100 / 6.107.100 libavresample 3. 7. 0 / 3. 7. 0 libswscale 4. 8.100 / 4. 8.100 libswresample 2. 9.100 / 2. 9.100 libpostproc 54. 7.100 / 54. 7.100 Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/content/DeepMosaics/imgs/MIDD-817(1a).mp4': Metadata: major_brand : mp42 minor_version : 0 compatible_brands: mp41isom creation_time : 2020-02-12T18:31:23.000000Z Duration: 00:29:00.67, start: 0.000000, bitrate: 3902 kb/s Stream #0:0(und): Video: h264 (Main) (avc1 / 0x31637661), yuv420p(tv), 1920x1080 [SAR 1:1 DAR 16:9], 3707 kb/s, 30 fps, 30 tbr, 30k tbn, 60 tbc (default) Metadata: creation_time : 2021-07-27T15:07:05.000000Z handler_name : VideoHandler encoder : AVC Coding Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 192 kb/s (default) Metadata: creation_time : 2021-07-27T15:07:05.000000Z handler_name : SoundHandler Stream mapping: Stream #0:0 -> #0:0 (h264 (native) -> mjpeg (native)) Press [q] to stop, [?] for help [swscaler @ 0x55a76142c000] deprecated pixel format used, make sure you did set range correctly Output #0, image2, to './tmp/DeepMosaics_temp/video2image/output_%06d.jpg': Metadata: major_brand : mp42 minor_version : 0 compatible_brands: mp41isom encoder : Lavf57.83.100 Stream #0:0(und): Video: mjpeg, yuvj420p(pc), 1920x1080 [SAR 1:1 DAR 16:9], q=2-31, 200 kb/s, 30 fps, 30 tbn, 30 tbc (default) Metadata: creation_time : 2021-07-27T15:07:05.000000Z handler_name : VideoHandler encoder : Lavc57.107.100 mjpeg Side data: cpb: bitrate max/min/avg: 0/0/200000 buffer size: 0 vbv_delay: -1 frame=52220 fps= 52 q=0.0 Lsize=N/A time=00:29:00.66 bitrate=N/A speed=1.73x
video:5994909kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown Step:2/4 -- Find mosaic location 52220/52220 [###################################] 100.00% 00:35:41/00:35:41 Optimize mosaic locations... Step:3/4 -- Clean Mosaic: 52220/52220 [###################################] 100.00% 01:22:43/01:22:43 Step:4/4 -- Convert images to video Traceback (most recent call last): File "/usr/lib/python3.7/multiprocessing/queues.py", line 242, in _feed send_bytes(obj) File "/usr/lib/python3.7/multiprocessing/connection.py", line 200, in send_bytes self._send_bytes(m[offset:offset + size]) File "/usr/lib/python3.7/multiprocessing/connection.py", line 397, in _send_bytes self._send(header) File "/usr/lib/python3.7/multiprocessing/connection.py", line 368, in _send n = write(self._handle, buf) BrokenPipeError: [Errno 32] Broken pipe Traceback (most recent call last): File "/usr/lib/python3.7/multiprocessing/queues.py", line 242, in _feed send_bytes(obj) File "/usr/lib/python3.7/multiprocessing/connection.py", line 200, in send_bytes self._send_bytes(m[offset:offset + size]) File "/usr/lib/python3.7/multiprocessing/connection.py", line 397, in _send_bytes self._send(header) File "/usr/lib/python3.7/multiprocessing/connection.py", line 368, in _send n = write(self._handle, buf) BrokenPipeError: [Errno 32] Broken pipe Traceback (most recent call last): File "/usr/lib/python3.7/multiprocessing/queues.py", line 242, in _feed send_bytes(obj) File "/usr/lib/python3.7/multiprocessing/connection.py", line 200, in send_bytes self._send_bytes(m[offset:offset + size]) File "/usr/lib/python3.7/multiprocessing/connection.py", line 397, in _send_bytes self._send(header) File "/usr/lib/python3.7/multiprocessing/connection.py", line 368, in _send n = write(self._handle, buf) BrokenPipeError: [Errno 32] Broken pipe Traceback (most recent call last): File "/usr/lib/python3.7/multiprocessing/queues.py", line 242, in _feed send_bytes(obj) File "/usr/lib/python3.7/multiprocessing/connection.py", line 200, in send_bytes self._send_bytes(m[offset:offset + size]) File "/usr/lib/python3.7/multiprocessing/connection.py", line 397, in _send_bytes self._send(header) File "/usr/lib/python3.7/multiprocessing/connection.py", line 368, in _send n = write(self._handle, buf) BrokenPipeError: [Errno 32] Broken pipe Exception in thread Thread-52222: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "/content/DeepMosaics/cores/clean.py", line 183, in write_result save_ori,imagepath,img_origin,img_fake,x,y,size = write_pool.get() File "/usr/lib/python3.7/multiprocessing/queues.py", line 94, in get res = self._recv_bytes() File "/usr/lib/python3.7/multiprocessing/connection.py", line 212, in recv_bytes self._check_closed() File "/usr/lib/python3.7/multiprocessing/connection.py", line 136, in _check_closed raise OSError("handle is closed") OSError: handle is closed

ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04) configuration: --prefix=/usr --extra-version=0ubuntu0.2 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared libavutil 55. 78.100 / 55. 78.100 libavcodec 57.107.100 / 57.107.100 libavformat 57. 83.100 / 57. 83.100 libavdevice 57. 10.100 / 57. 10.100 libavfilter 6.107.100 / 6.107.100 libavresample 3. 7. 0 / 3. 7. 0 libswscale 4. 8.100 / 4. 8.100 libswresample 2. 9.100 / 2. 9.100 libpostproc 54. 7.100 / 54. 7.100 [mjpeg @ 0x55bac588ca00] EOI missing, emulating Input #0, image2, from './tmp/DeepMosaics_temp/replace_mosaic/output_%06d.jpg': Duration: 00:34:48.64, start: 0.000000, bitrate: N/A Stream #0:0: Video: mjpeg, yuvj420p(pc, bt470bg/unknown/unknown), 1920x1080 [SAR 1:1 DAR 16:9], 25 fps, 25 tbr, 25 tbn, 25 tbc Stream mapping: Stream #0:0 -> #0:0 (mjpeg (native) -> h264 (libx264)) Press [q] to stop, [?] for help [libx264 @ 0x55bac588de00] using SAR=1/1 [libx264 @ 0x55bac588de00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 [libx264 @ 0x55bac588de00] profile High, level 4.0 [libx264 @ 0x55bac588de00] 264 - core 152 r2854 e9a5903 - H.264/MPEG-4 AVC codec - Copyleft 2003-2017 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=3 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00 Output #0, mp4, to './tmp/DeepMosaics_temp/video_tmp.mp4': Metadata: encoder : Lavf57.83.100 Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuvj420p(pc), 1920x1080 [SAR 1:1 DAR 16:9], q=-1--1, 30 fps, 15360 tbn, 30 tbc Metadata: encoder : Lavc57.107.100 libx264 Side data: cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1 frame=52216 fps=6.8 q=-1.0 Lsize= 953084kB time=00:29:00.43 bitrate=4486.0kbits/s speed=0.228x
video:952465kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.064936% [libx264 @ 0x55bac588de00] frame I:272 Avg QP:19.24 size: 58788 [libx264 @ 0x55bac588de00] frame P:13709 Avg QP:22.37 size: 33376 [libx264 @ 0x55bac588de00] frame B:38235 Avg QP:24.08 size: 13124 [libx264 @ 0x55bac588de00] consecutive B-frames: 1.8% 0.5% 3.4% 94.3% [libx264 @ 0x55bac588de00] mb I I16..4: 31.2% 64.6% 4.2% [libx264 @ 0x55bac588de00] mb P I16..4: 19.8% 31.7% 0.7% P16..4: 27.7% 7.5% 1.8% 0.0% 0.0% skip:10.9% [libx264 @ 0x55bac588de00] mb B I16..4: 4.3% 6.8% 0.1% B16..8: 40.9% 4.0% 0.3% direct: 2.0% skip:41.6% L0:55.9% L1:41.1% BI: 3.0% [libx264 @ 0x55bac588de00] 8x8 transform intra:60.8% inter:90.6% [libx264 @ 0x55bac588de00] coded y,uvDC,uvAC intra: 30.0% 61.4% 4.8% inter: 8.4% 19.9% 0.2% [libx264 @ 0x55bac588de00] i16 v,h,dc,p: 29% 26% 15% 30% [libx264 @ 0x55bac588de00] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 32% 24% 31% 2% 2% 2% 2% 2% 2% [libx264 @ 0x55bac588de00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 36% 20% 13% 3% 8% 8% 6% 4% 2% [libx264 @ 0x55bac588de00] i8c dc,h,v,p: 42% 25% 27% 5% [libx264 @ 0x55bac588de00] Weighted P-Frames: Y:0.8% UV:0.7% [libx264 @ 0x55bac588de00] ref P L0: 68.0% 8.8% 17.5% 5.7% 0.0% [libx264 @ 0x55bac588de00] ref B L0: 90.9% 7.4% 1.6% [libx264 @ 0x55bac588de00] ref B L1: 96.0% 4.0% [libx264 @ 0x55bac588de00] kb/s:4482.87 sh: 1: Syntax error: "(" unexpected Clean temp... Finished!

when I tese my own videos,the error occur in the last step:

./tmp/DeepMosaics_temp/voice_tmp.mp3: No such file or directory

and this error only occured in several of videos! I want to konw the reasons! thank you!

On x64 win10 and 11, I got the follow error even if I only try the CPU version. I have installed VC_redist.x64.exe while win 11 is a clean system.

[WinError 126] The specified module could not be found. Error loading "C:\Users\vm\Downloads\DeepMosaics_0.5.1_gpu\core\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies.

Please refer the log as following. I've tried both in GUI and shell, given the same error. Other models without 'video' in filename could run properly.

D:\DeepMosaics_0.5.1_gpu\core\torchvision\__init__.py:26: UserWarning: You are importing torchvision within its own root folder (D:\DeepMosaics_0.5.1_gpu\core). This is not expected to work and may give errors. Please exit the torchvision project source and relaunch your python interpreter.
segment parameters: 12.4M
netG parameters: 26.65M
--------------------ERROR--------------------
--------------Environment--------------
DeepMosaics: 0.5.1
Python: 3.7.3 (default, Apr 24 2019, 15:29:51) [MSC v.1915 64 bit (AMD64)]
Pytorch: 1.7.1
OpenCV: 4.1.2
Platform: Windows-10-10.0.22621-SP0
--------------BUG--------------
Error Type: <class 'RuntimeError'>
Error(s) in loading state_dict for BVDNet:
        Missing key(s) in state_dict: "encoder3d.model.0.weight_orig", "encoder3d.model.0.weight", "encoder3d.model.0.weight_u", "encoder3d.model.0.bias", "encoder3d.model.0.weight_orig", "encoder3d.model.0.weight_u", "encoder3d.model.0.weight_v", "encoder3d.model.2.weight_orig", "encoder3d.model.2.weight", "encoder3d.model.2.weight_u", "encoder3d.model.2.bias", "encoder3d.model.2.weight_orig", "encoder3d.model.2.weight_u", "encoder3d.model.2.weight_v", "encoder3d.model.4.weight_orig", "encoder3d.model.4.weight", "encoder3d.model.4.weight_u", "encoder3d.model.4.bias", "encoder3d.model.4.weight_orig", "encoder3d.model.4.weight_u", "encoder3d.model.4.weight_v", "encoder3d.model.6.weight_orig", "encoder3d.model.6.weight", "encoder3d.model.6.weight_u", "encoder3d.model.6.bias", "encoder3d.model.6.weight_orig", "encoder3d.model.6.weight_u", "encoder3d.model.6.weight_v", "encoder2d.model.1.weight_orig", "encoder2d.model.1.weight", "encoder2d.model.1.weight_u", "encoder2d.model.1.bias", "encoder2d.model.1.weight_orig", "encoder2d.model.1.weight_u", "encoder2d.model.1.weight_v", "encoder2d.model.4.weight_orig", "encoder2d.model.4.weight", "encoder2d.model.4.weight_u", "encoder2d.model.4.bias", "encoder2d.model.4.weight_orig", "encoder2d.model.4.weight_u", "encoder2d.model.4.weight_v", "encoder2d.model.7.weight_orig", "encoder2d.model.7.weight", "encoder2d.model.7.weight_u", "encoder2d.model.7.bias", "encoder2d.model.7.weight_orig", "encoder2d.model.7.weight_u", "encoder2d.model.7.weight_v", "encoder2d.model.10.weight_orig", "encoder2d.model.10.weight", "encoder2d.model.10.weight_u", "encoder2d.model.10.bias", "encoder2d.model.10.weight_orig", "encoder2d.model.10.weight_u", "encoder2d.model.10.weight_v", "blocks.0.conv_block.1.weight_orig", "blocks.0.conv_block.1.weight", "blocks.0.conv_block.1.weight_u", "blocks.0.conv_block.1.bias", "blocks.0.conv_block.1.weight_orig", "blocks.0.conv_block.1.weight_u", "blocks.0.conv_block.1.weight_v", "blocks.0.conv_block.4.weight_orig", "blocks.0.conv_block.4.weight", "blocks.0.conv_block.4.weight_u", "blocks.0.conv_block.4.bias", "blocks.0.conv_block.4.weight_orig", "blocks.0.conv_block.4.weight_u", "blocks.0.conv_block.4.weight_v", "blocks.1.conv_block.1.weight_orig", "blocks.1.conv_block.1.weight", "blocks.1.conv_block.1.weight_u", "blocks.1.conv_block.1.bias", "blocks.1.conv_block.1.weight_orig", "blocks.1.conv_block.1.weight_u", "blocks.1.conv_block.1.weight_v", "blocks.1.conv_block.4.weight_orig", "blocks.1.conv_block.4.weight", "blocks.1.conv_block.4.weight_u", "blocks.1.conv_block.4.bias", "blocks.1.conv_block.4.weight_orig", "blocks.1.conv_block.4.weight_u", "blocks.1.conv_block.4.weight_v", "blocks.2.conv_block.1.weight_orig", "blocks.2.conv_block.1.weight", "blocks.2.conv_block.1.weight_u", "blocks.2.conv_block.1.bias", "blocks.2.conv_block.1.weight_orig", "blocks.2.conv_block.1.weight_u", "blocks.2.conv_block.1.weight_v", "blocks.2.conv_block.4.weight_orig", "blocks.2.conv_block.4.weight", "blocks.2.conv_block.4.weight_u", "blocks.2.conv_block.4.bias", "blocks.2.conv_block.4.weight_orig", "blocks.2.conv_block.4.weight_u", "blocks.2.conv_block.4.weight_v", "blocks.3.conv_block.1.weight_orig", "blocks.3.conv_block.1.weight", "blocks.3.conv_block.1.weight_u", "blocks.3.conv_block.1.bias", "blocks.3.conv_block.1.weight_orig", "blocks.3.conv_block.1.weight_u", "blocks.3.conv_block.1.weight_v", "blocks.3.conv_block.4.weight_orig", "blocks.3.conv_block.4.weight", "blocks.3.conv_block.4.weight_u", "blocks.3.conv_block.4.bias", "blocks.3.conv_block.4.weight_orig", "blocks.3.conv_block.4.weight_u", "blocks.3.conv_block.4.weight_v", "decoder.0.convup.2.weight_orig", "decoder.0.convup.2.weight", "decoder.0.convup.2.weight_u", "decoder.0.convup.2.bias", "decoder.0.convup.2.weight_orig", "decoder.0.convup.2.weight_u", "decoder.0.convup.2.weight_v", "decoder.1.convup.2.weight_orig", "decoder.1.convup.2.weight", "decoder.1.convup.2.weight_u", "decoder.1.convup.2.bias", "decoder.1.convup.2.weight_orig", "decoder.1.convup.2.weight_u", "decoder.1.convup.2.weight_v", "decoder.2.convup.2.weight_orig", "decoder.2.convup.2.weight", "decoder.2.convup.2.weight_u", "decoder.2.convup.2.bias", "decoder.2.convup.2.weight_orig", "decoder.2.convup.2.weight_u", "decoder.2.convup.2.weight_v", "decoder.4.weight", "decoder.4.bias".
        Unexpected key(s) in state_dict: "encoder_2d.model.1.weight", "encoder_2d.model.1.bias", "encoder_2d.model.5.weight", "encoder_2d.model.5.bias", "encoder_2d.model.9.weight", "encoder_2d.model.9.bias", "encoder_2d.model.13.weight", "encoder_2d.model.13.bias", "encoder_2d.model.17.weight", "encoder_2d.model.17.bias", "encoder_3d.inconv.conv.0.weight", "encoder_3d.inconv.conv.0.bias", "encoder_3d.down1.conv.0.weight", "encoder_3d.down1.conv.0.bias", "encoder_3d.down2.conv.0.weight", "encoder_3d.down2.conv.0.bias", "encoder_3d.down3.conv.0.weight", "encoder_3d.down3.conv.0.bias", "encoder_3d.down4.conv.0.weight", "encoder_3d.down4.conv.0.bias", "decoder_2d.model.0.conv_block.1.weight", "decoder_2d.model.0.conv_block.1.bias", "decoder_2d.model.0.conv_block.5.weight", "decoder_2d.model.0.conv_block.5.bias", "decoder_2d.model.1.conv_block.1.weight", "decoder_2d.model.1.conv_block.1.bias", "decoder_2d.model.1.conv_block.5.weight", "decoder_2d.model.1.conv_block.5.bias", "decoder_2d.model.2.conv_block.1.weight", "decoder_2d.model.2.conv_block.1.bias", "decoder_2d.model.2.conv_block.5.weight", "decoder_2d.model.2.conv_block.5.bias", "decoder_2d.model.3.conv_block.1.weight", "decoder_2d.model.3.conv_block.1.bias", "decoder_2d.model.3.conv_block.5.weight", "decoder_2d.model.3.conv_block.5.bias", "decoder_2d.model.4.conv_block.1.weight", "decoder_2d.model.4.conv_block.1.bias", "decoder_2d.model.4.conv_block.5.weight", "decoder_2d.model.4.conv_block.5.bias", "decoder_2d.model.5.conv_block.1.weight", "decoder_2d.model.5.conv_block.1.bias", "decoder_2d.model.5.conv_block.5.weight", "decoder_2d.model.5.conv_block.5.bias", "decoder_2d.model.6.conv_block.1.weight", "decoder_2d.model.6.conv_block.1.bias", "decoder_2d.model.6.conv_block.5.weight", "decoder_2d.model.6.conv_block.5.bias", "decoder_2d.model.7.conv_block.1.weight", "decoder_2d.model.7.conv_block.1.bias", "decoder_2d.model.7.conv_block.5.weight", "decoder_2d.model.7.conv_block.5.bias", "decoder_2d.model.8.conv_block.1.weight", "decoder_2d.model.8.conv_block.1.bias", "decoder_2d.model.8.conv_block.5.weight", "decoder_2d.model.8.conv_block.5.bias", "decoder_2d.model.9.weight", "decoder_2d.model.9.bias", "decoder_2d.model.12.weight", "decoder_2d.model.12.bias", "decoder_2d.model.15.weight", "decoder_2d.model.15.bias", "decoder_2d.model.18.weight", "decoder_2d.model.18.bias", "decoder_2d.model.22.weight", "decoder_2d.model.22.bias", "merge1.conv.1.weight", "merge1.conv.1.bias".
<FrameSummary file deepmosaic.py, line 77 in <module>>
<FrameSummary file deepmosaic.py, line 41 in main>
<FrameSummary file models\loadmodel.py, line 56 in video>
<FrameSummary file torch\nn\modules\module.py, line 1052 in load_state_dict>
Please press any key to exit.

I have using the CPU GUI version .exe file that works, but so slow. I have wanted to get the GPU command prompt version to work as the GUI version.exe kept getting errors that DeepMosaics said where too complicated on another post. After getting the command prompt version to work, the video output is the same as the input. There is no error while running the source code.

While using source code GPU version when step two starts the python program goes to not responding. After 15 minutes it will work again, but never finds any mosaic. The program runs without any errors. The only thing I can think off is my CPU is too weak and is causing the not responding on start of step two.

I have using the CPU GUI version .exe file that works, but so slow. I have wanted to get the GPU command prompt version to work as the GUI version.exe kept getting errors that DeepMosaics said where too complicated on another post. After getting the command prompt version to work, the video output is the same as the input. There is no error while running the source code.

While using source code GPU version when step two starts the python program goes to not responding. After 15 minutes it will work again but never finds the mosaic. The only thing I can think off is my CPU is too weak and is causing the not responding on start of step two.

make_pix2pix_dataset.pyを実行すると発生しました。このpyファイルを調べてみると、108行目から152行目まで余分なインデントが1つあることがわかりました。これをインデント1つ分削除して修正します。

また、私の場合、最初はインデントを消しても画像が生成されなかったので、品質オプションを20にしたところ、train_Aフォルダにモザイクをかけた画像が、train_Bフォルダに元画像が格納されるようになりました。

おそらく、私の動画の画質が悪すぎて、フィルタリングされていたのでしょう。やっと解決しましたー。 ちなみにimage_processing.pyを見ると、(quality)>20が正常で、argオプションのデフォルト設定は40と書いてあります。