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jpegRecompress - ['--quality', 'high', '--min', '60'] in details jpegRecompress;For jpegoptim - ['--all-progressive', '-d']To use jpegoptim you must npm install jpegoptim-bin --save, this library does not work properly on some OS and platforms.from be a problems with installation and use on Win 7 x32 and maybe other OS:compress-images - issues/21Caution! if do not specify '-d' all images will be compressed in the source folder and will be replaced.For Windows x32 and x63 also, you can use Copy jpegoptim-32.exe and replace and rename in "node_modules\jpegoptim-bin\vendor\jpegoptim.exe"For tinify - ['copyright', 'creation', 'location'] In details tinify;key (type:string): Key used for engine tinify. 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Can be problems with cyrillic filename issues/317In details:pngquant andpngquant-bin - wrapperFor optipng - To use this library you need to install it manually.It does not work properly on some OS (Win 7 x32 and maybe other). npm install --save optipng-bin in details optipng-bin - wrapperand optipng;For pngout - in details pngout;For webp - ['-q', '60'] in details webp;For pngcrush (It does not work properly on some OS) - ['-reduce', '-brute'] in details pngcrush;For tinify - ['copyright', 'creation', 'location'] in details tinify;key (type:string): Key used for engine tinify. In details; tinify; Example: 1. {png: {engine: 'webp', command: ['-q', '100']}; 2. {png: {engine: 'tinify', key: "sefdfdcv335fxgfe3qw", command: ['copyright', 'creation', 'location']}}; 3. {png: {engine: 'optipng', command: false}};enginesvg (type:plainObject): Engine for compressing svg and options for compression. Key to be svg;engine (type:string): Engine for compressing svg. 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Bin/evaluate_predicts.py \$(pwd)/configs/eval2_gpu.yaml \$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \$(pwd)/inference/random_thick_512 \$(pwd)/inference/random_thick_512_metrics.csv"># Download data from Places365-Standard: Train(105GB)/Test(19GB)/Val(2.1GB) from High-resolution images sectionwget Unpack train/test/val data and create .yaml config for itbash fetch_data/places_standard_train_prepare.shbash fetch_data/places_standard_test_val_prepare.sh# Sample images for test and viz at the end of epochbash fetch_data/places_standard_test_val_sample.shbash fetch_data/places_standard_test_val_gen_masks.sh# Run trainingpython3 bin/train.py -cn lama-fourier location=places_standard# To evaluate trained model and report metrics as in our paper# we need to sample previously unseen 30k images and generate masks for thembash fetch_data/places_standard_evaluation_prepare_data.sh# Infer model on thick/thin/medium masks in 256 and 512 and run evaluation # like this:python3 bin/predict.py \model.path=$(pwd)/experiments/__lama-fourier_/ \indir=$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \outdir=$(pwd)/inference/random_thick_512 model.checkpoint=last.ckptpython3 bin/evaluate_predicts.py \$(pwd)/configs/eval2_gpu.yaml \$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \$(pwd)/inference/random_thick_512 \$(pwd)/inference/random_thick_512_metrics.csvDocker: TODOCelebAOn the host machine:__lama-fourier-celeba_/ \indir=$(pwd)/celeba-hq-dataset/visual_test_256/random_thick_256/ \outdir=$(pwd)/inference/celeba_random_thick_256 model.checkpoint=last.ckpt"># Make shure you are in lama foldercd lamaexport TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)# Download CelebA-HQ dataset# Download data256x256.zip from unzip & split into train/test/visualization & create config for itbash fetch_data/celebahq_dataset_prepare.sh# generate masks for test and visual_test at the end of epochbash fetch_data/celebahq_gen_masks.sh# Run trainingpython3 bin/train.py -cn lama-fourier-celeba data.batch_size=10# Infer model on thick/thin/medium masks in 256 and run evaluation # like this:python3 bin/predict.py \model.path=$(pwd)/experiments/__lama-fourier-celeba_/ \indir=$(pwd)/celeba-hq-dataset/visual_test_256/random_thick_256/ \outdir=$(pwd)/inference/celeba_random_thick_256 model.checkpoint=last.ckptDocker: TODOPlaces ChallengeOn the host machine:# This script downloads multiple .tar files in parallel and unpacks them# Places365-Challenge: Train(476GB) from High-resolution images (to train Big-Lama) bash places_challenge_train_download.shTODO: prepareTODO: train TODO: evalDocker: TODOCreate your dataPlease check bash scripts for data preparation and mask generation from CelebaHQ section,if you stuck at one of the following steps.On the host machine:_512.yaml \ # thick, thin, mediummy_dataset/val_source/ \my_dataset/val/random__512.yaml \# thick, thin, medium--ext jpg# So the mask generator will: # 1. resize and crop val images and save them as .png# 2. generate masksls my_dataset/val/random_medium_512/image1_crop000_mask000.pngimage1_crop000.pngimage2_crop000_mask000.pngimage2_crop000.png...# Generate thick, thin, medium masks for visual_test folder:python3 bin/gen_mask_dataset.py \$(pwd)/configs/data_gen/random__512.yaml \ #thick, thin, mediummy_dataset/visual_test_source/ \my_dataset/visual_test/random__512/ \ #thick, thin, medium--ext jpgls my_dataset/visual_test/random_thick_512/image1_crop000_mask000.pngimage1_crop000.pngimage2_crop000_mask000.pngimage2_crop000.png...# Same process for eval_source image folder:python3 bin/gen_mask_dataset.py \$(pwd)/configs/data_gen/random__512.yaml \ #thick, thin, mediummy_dataset/eval_source/ \my_dataset/eval/random__512/ \ #thick, thin, medium--ext jpg# Generate location config file which locate these folders:touch my_dataset.yamlecho "data_root_dir: $(pwd)/my_dataset/" >> my_dataset.yamlecho "out_root_dir: $(pwd)/experiments/" >> my_dataset.yamlecho "tb_dir: $(pwd)/tb_logs/" >> my_dataset.yamlmv my_dataset.yaml ${PWD}/configs/training/location/# Check data config for consistency with my_dataset folder structure:$ cat ${PWD}/configs/training/data/abl-04-256-mh-dist...train: indir: ${location.data_root_dir}/train ...val: indir: ${location.data_root_dir}/val img_suffix: .pngvisual_test: indir: ${location.data_root_dir}/visual_test img_suffix: .png# Run trainingpython3 bin/train.py -cn lama-fourier location=my_dataset data.batch_size=10# Evaluation: LaMa training procedure picks best few models according to # scores on my_dataset/val/ # To evaluate one of your best models (i.e. at epoch=32) # on previously unseen my_dataset/eval do the following # for thin, thick and medium:# infer:python3 bin/predict.py \model.path=$(pwd)/experiments/__lama-fourier_/ \indir=$(pwd)/my_dataset/eval/random__512/ \outdir=$(pwd)/inference/my_dataset/random__512 \model.checkpoint=epoch32.ckpt# metrics calculation:python3 bin/evaluate_predicts.py \$(pwd)/configs/eval2_gpu.yaml \$(pwd)/my_dataset/eval/random__512/ \$(pwd)/inference/my_dataset/random__512 \$(pwd)/inference/my_dataset/random__512_metrics.csv"># Make shure you are in lama foldercd lamaexport TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)# You need to prepare following image folders:$ ls my_datasettrainval_source # 2000 or more imagesvisual_test_source # 100 or more imageseval_source # 2000 or more images# LaMa generates random masks for the train data on the flight,# but needs fixed masks for test and visual_test for consistency of evaluation.# Suppose, we want to evaluate and pick best models # on 512x512 val dataset with thick/thin/medium masks # And your images have .jpg extention:python3 bin/gen_mask_dataset.py \$(pwd)/configs/data_gen/random__512.yaml \ # thick, thin, mediummy_dataset/val_source/ \my_dataset/val/random__512.yaml \# thick, thin, medium--ext jpg# So the mask generator will: # 1.. 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Resize and crop val images and save them as .png# 2. generate masksls my_dataset/val/random_medium_512/image1_crop000_mask000.pngimage1_crop000.pngimage2_crop000_mask000.pngimage2_crop000.png...# Generate thick, thin, medium masks for visual_test folder:python3 bin/gen_mask_dataset.py \$(pwd)/configs/data_gen/random__512.yaml \ #thick, thin, mediummy_dataset/visual_test_source/ \my_dataset/visual_test/random__512/ \ #thick, thin, medium--ext jpgls my_dataset/visual_test/random_thick_512/image1_crop000_mask000.pngimage1_crop000.pngimage2_crop000_mask000.pngimage2_crop000.png...# Same process for eval_source image folder:python3 bin/gen_mask_dataset.py \$(pwd)/configs/data_gen/random__512.yaml \ #thick, thin, mediummy_dataset/eval_source/ \my_dataset/eval/random__512/ \ #thick, thin, medium--ext jpg# Generate location config file which locate these folders:touch my_dataset.yamlecho "data_root_dir: $(pwd)/my_dataset/" >> my_dataset.yamlecho "out_root_dir: $(pwd)/experiments/" >> my_dataset.yamlecho "tb_dir: $(pwd)/tb_logs/" >> my_dataset.yamlmv my_dataset.yaml ${PWD}/configs/training/location/# Check data config for consistency with my_dataset folder structure:$ cat ${PWD}/configs/training/data/abl-04-256-mh-dist...train: indir: ${location.data_root_dir}/train ...val: indir: ${location.data_root_dir}/val img_suffix: .pngvisual_test: indir: ${location.data_root_dir}/visual_test img_suffix: .png# Run trainingpython3 bin/train.py -cn lama-fourier location=my_dataset data.batch_size=10# Evaluation: LaMa training procedure picks best few models according to # scores on my_dataset/val/ # To evaluate one of your best models (i.e. at epoch=32) # on previously unseen my_dataset/eval do the following # for thin, thick and medium:# infer:python3 bin/predict.py \model.path=$(pwd)/experiments/__lama-fourier_/ \indir=$(pwd)/my_dataset/eval/random__512/ \outdir=$(pwd)/inference/my_dataset/random__512 \model.checkpoint=epoch32.ckpt# metrics calculation:python3 bin/evaluate_predicts.py \$(pwd)/configs/eval2_gpu.yaml \$(pwd)/my_dataset/eval/random__512/ \$(pwd)/inference/my_dataset/random__512 \$(pwd)/inference/my_dataset/random__512_metrics.csvOR in the docker:HintsGenerate different kinds of masksThe following command will execute a script that generates random masks.bash docker/1_generate_masks_from_raw_images.sh \ configs/data_gen/random_medium_512.yaml \ /directory_with_input_images \ /directory_where_to_store_images_and_masks \ --ext pngThe test data generation command stores images in the format,which is suitable for prediction.The table below describes which configs we used to generate different test sets from the paper.Note that we do not fix a random seed, so the results will be slightly different each time.Places 512x512CelebA 256x256Narrowrandom_thin_512.yamlrandom_thin_256.yamlMediumrandom_medium_512.yamlrandom_medium_256.yamlWiderandom_thick_512.yamlrandom_thick_256.yamlFeel free to change the config path (argument #1) to any other config in configs/data_genor adjust config files themselves.Override parameters in configsAlso you can override parameters in config like this: data.batch_size=10 run_title=my-title">python3 bin/train.py -cn data.batch_size=10 run_title=my-titleWhere .yaml file extension is omittedModels optionsConfig names for models from paper (substitude into the training command):* big-lama* big-lama-regular* lama-fourier* lama-regular* lama_small_train_masksWhich are seated in configs/training/folderLinksAll the data (models, test images, etc.) images from the paper pre-trained models models for perceptual loss training logs are available at time & resourcesTODOAcknowledgmentsSegmentation code and models if form CSAILVision.LPIPS metric is from richzhangSSIM is from Po-Hsun-SuFID is from mseitzerCitationIf you found this code helpful, please consider citing:@article{suvorov2021resolution, title={Resolution-robust Large Mask Inpainting with Fourier Convolutions}, author={Suvorov, Roman and Logacheva, Elizaveta and Mashikhin, Anton and Remizova, Anastasia and Ashukha, Arsenii and Silvestrov, Aleksei and Kong, Naejin and Goka, Harshith and Park, Kiwoong and Lempitsky, Victor}, journal={arXiv preprint arXiv:2109.07161}, year={2021}} Related: Full Battery Full Screen Maximize Expand Full Battery Power Arrow Screen Battery Level Energy User Charge Moon Resize Trash Bin Icon Pack Waste Icon Pack Waste (Glyph) Icon Pack Mother Earth Icon Pack Containers For Sorting Waste Icon Pack Trash Icon Pack City Icon Pack City Icon Pack City Icon Pack City Icon Pack City Icon Pack City Icon Pack City Icon Pack City Icon Pack City Icon Pack Dialogue Assets Icon Pack City Icon Pack City Icon Pack City Icon Pack Miscellaneous Icon Pack Ecology Icon Pack Ecology Icon Pack Batteries Icon Pack Plastic Pollution Icon Pack Renewable Energy Icon Pack Battery And Power Icon Pack Alert Icon Pack Battery Icon Pack Battery Icon Pack Battery Icon Pack Battery Icon Pack Battery Icon Pack Arrow Icon Pack Battery Icon Pack Earth Icon Pack Earth Icon Pack Access the world's largest Design Ecosystem: Assets, Integrations, and Motion. 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Oct 28 20:19 /etc/alternatives/google-chrome -> /usr/bin/google-chrome-stable*~# /etc/apt/sources.list.d# ll /usr/bin/google-chrome-stablelrwxrwxrwx 1 root root 32 Oct 28 20:19 /usr/bin/google-chrome-stable -> /opt/google/chrome/google-chrome*~# /etc/apt/sources.list.d# ll /opt/google/chrome/google-chrome-rwxr-xr-x 1 root root 1585 Oct 28 20:19 /opt/google/chrome/google-chrome*~# /etc/apt/sources.list.d# ll /opt/google/chrome/total 292732drwxr-xr-x 8 root root 4096 Nov 2 10:52 ./drwxr-xr-x 3 root root 4096 Nov 2 10:50 ../-rwxr-xr-x 1 root root 249363672 Oct 28 20:19 chrome*-rw-r--r-- 1 root root 824033 Oct 28 20:19 chrome_100_percent.pak-rw-r--r-- 1 root root 1470792 Oct 28 20:19 chrome_200_percent.pak-rwxr-xr-x 1 root root 2444992 Oct 28 20:19 chrome_crashpad_handler*-rwxr-xr-x 1 root root 4331216 Oct 28 20:19 chrome-management-service*-rwsr-xr-x 1 root root 208000 Oct 28 20:19 chrome-sandbox*-rw-r--r-- 1 root root 7 Oct 28 20:19 CHROME_VERSION_EXTRAdrwxr-xr-x 2 root root 4096 Nov 2 10:52 cron/-rw-r--r-- 1 root root 482 Oct 28 20:19 default-app-blockdrwxr-xr-x 2 root root 4096 Nov 2 10:52 default_apps/-rwxr-xr-x 1 root root 1585 Oct 28 20:19 google-chrome*-rw-r--r-- 1 root root 10468208 Oct 28 20:19 icudtl.dat-rw-r--r-- 1 root root 240384 Oct 28 20:19 libEGL.so-rw-r--r-- 1 root root 6976728 Oct 28 20:19 libGLESv2.so-rw-r--r-- 1 root root 7691144 Oct 28 20:19 liboptimization_guide_internal.so-rw-r--r-- 1 root root 26688 Oct 28 20:19 libqt5_shim.so-rw-r--r-- 1 root root 28960 Oct 28 20:19 libqt6_shim.so-rw-r--r-- 1 root root 4683512 Oct 28 20:19 libvk_swiftshader.so-rw-r--r-- 1 root root 562344 Oct 28 20:19 libvulkan.so.1drwxr-xr-x 2 root root 4096 Nov 2 10:52 locales/drwxr-xr-x 2 root root 4096 Nov 2 10:52 MEIPreload/drwxr-xr-x 2 root root 4096 Nov 2 10:52 PrivacySandboxAttestationsPreloaded/-rw-r--r-- 1 root root 10577 Oct 28 20:19 product_logo_128.png-rw-r--r-- 1 root root 787 Oct 28 20:19 product_logo_16.png-rw-r--r-- 1 root root 1281 Oct 28 20:19 product_logo_24.png-rw-r--r-- 1 root root 38037 Oct 28 20:19 product_logo_256.png-rw-r--r-- 1 root root 1810 Oct 28 20:19 product_logo_32.png-rw-r--r-- 1 root root 7611 Oct 28 20:19 product_logo_32.xpm-rw-r--r-- 1 root root 3095 Oct 28 20:19 product_logo_48.png-rw-r--r-- 1 root root 4557 Oct 28 20:19 product_logo_64.png-rw-r--r-- 1 root root 9505137 Oct 28 20:19 resources.pak-rw-r--r-- 1 root root 687473 Oct 28 20:19 v8_context_snapshot.bin-rw-r--r-- 1 root root 107 Oct 28 20:19 vk_swiftshader_icd.jsondrwxr-xr-x 3 root root 4096 Nov 2 10:52 WidevineCdm/-rwxr-xr-x 1 root root 37394 Oct 28 20:19 xdg-mime*-rwxr-xr-x 1 root root 33273 Oct 28 20:19 xdg-settings*Interesting symbolic link path. It appears that dpkg set up the /etc/apt/sources.list.d/google-chrome.list so that future updates should work with apt.