9.基于cGAN的pix2pix 模型与自动上色技术 python代码实现

# pix2pix-tensorflow server Host pix2pix-tensorflow models to be used with something like the [Image-to-Image Demo](https://affinelayer.com/pixsrv/). This is a simple python server that serves models exported from `pix2pix.py --mode export`. It can serve local models or use [Cloud ML](https://cloud.google.com/ml/) to run the model. ## Exporting You can export a model to be served with `--mode export`. As with testing, you should specify the checkpoint to use with `--checkpoint`. ```sh python ../pix2pix.py \ --mode export \ --output_dir models/facades \ --checkpoint ../facades_train ``` ## Local Serving Using the [pix2pix-tensorflow Docker image](https://hub.docker.com/r/affinelayer/pix2pix-tensorflow/): ```sh # export a model to upload (if you did not export one above) python ../tools/dockrun.py python tools/export-example-model.py --output_dir models/example # process an image with the model using local tensorflow python ../tools/dockrun.py python tools/process-local.py \ --model_dir models/example \ --input_file static/facades-input.png \ --output_file output.png # run local server python ../tools/dockrun.py --port 8000 python serve.py --port 8000 --local_models_dir models # test the local server python tools/process-remote.py \ --input_file static/facades-input.png \ --url http://localhost:8000/example \ --output_file output.png ``` If you open [http://localhost:8000/](http://localhost:8000/) in a browser, you should see an interactive demo, though this expects the server to be hosting the exported models available here: - [edges2shoes](https://mega.nz/#!HtYwAZTY!5tBLYt_6HFj9u2Kxgp4-I36O4EV9r3bDP44ztX3qesI) - [edges2handbags](https://mega.nz/#!Clg3EaLA!YW2jfRHvwpJn5Elww_wM-f3eRzKiGHLw-F4A3eQCceI) - [facades](https://mega.nz/#!f1ZjmZoa!mCSxFRxt1WLBpNFsv5raoroEigxomDVpdi40aOG1KMc) Extract those to the models directory and restart the server to have it host the models. ## Cloud ML Serving For this you'll want to generate a service account JSON file from https://console.cloud.google.com/iam-admin/serviceaccounts/project (select "Furnish a new private key"). If you are already logged in with the gcloud SDK, the script will auto-detect credentials from that if you leave off the `--credentials` option. ```sh # upload model to google cloud ml python ../tools/dockrun.py python tools/upload-model.py \ --bucket your-models-bucket-name-here \ --model_name example \ --model_dir models/example \ --credentials service-account.json # process an image with the model using google cloud ml python ../tools/dockrun.py python tools/process-cloud.py \ --model example \ --input_file static/facades-input.png \ --output_file output.png \ --credentials service-account.json ``` ## Running serve.py on Google Cloud Platform Assuming you have gcloud and docker setup: ```sh export GOOGLE_PROJECT=<project name> # build image # make sure models are in a directory called "models" in the current directory sudo docker build --rm --tag us.gcr.io/$GOOGLE_PROJECT/pix2pix-server . # test image locally sudo docker run --publish 8080:8080 --rm --name server us.gcr.io/$GOOGLE_PROJECT/pix2pix-server python -u serve.py \ --port 8080 \ --local_models_dir models python tools/process-remote.py \ --input_file static/facades-input.png \ --url http://localhost:8080/example \ --output_file output.png # publish image to private google container repository python tools/upload-image.py --project $GOOGLE_PROJECT --version v1 # setup server cp terraform.tfvars.example terraform.tfvars # edit terraform.tfvars to put your cloud info in there python ../tools/dockrun.py terraform plan python ../tools/dockrun.py terraform apply ``` ## Full training + exporting + hosting commands Tested with Python 3.6, Tensorflow 1.0.0, Docker, gcloud, and Terraform (https://www.terraform.io/downloads.html) ```sh git clone https://github.com/affinelayer/pix2pix-tensorflow.git cd pix2pix-tensorflow # get some images (only 2 for testing) mkdir source curl -o source/cat1.jpg https://farm5.staticflickr.com/4032/4394955222_eea73818d9_o.jpg curl -o source/cat2.jpg http://wallpapercave.com/wp/ePMeSmp.jpg # resize source images python tools/process.py \ --input_dir source \ --operation resize \ --output_dir resized # create edges from resized images (uses docker container since compiling the dependencies is annoying) python tools/dockrun.py python tools/process.py \ --input_dir resized \ --operation edges \ --output_dir edges # combine resized with edges python tools/process.py \ --input_dir edges \ --b_dir resized \ --operation combine \ --output_dir combined # train on images (only 1 epoch for testing) python pix2pix.py \ --mode train \ --output_dir train \ --max_epochs 1 \ --input_dir combined \ --which_direction AtoB # export model (creates a version of the model that works with the server in server/serve.py as well as google hosted tensorflow) python pix2pix.py \ --mode export \ --output_dir server/models/edges2cats_AtoB \ --checkpoint train # process image locally using exported model python server/tools/process-local.py \ --model_dir server/models/edges2cats_AtoB \ --input_file edges/cat1.png \ --output_file output.png # serve model locally cd server python serve.py --port 8000 --local_models_dir models # open http://localhost:8000 in a browser, and scroll to the bottom, you should be able to process an edges2cat image and get a bunch of noise as output # serve model remotely export GOOGLE_PROJECT=<project name> # build image # make sure models are in a directory called "models" in the current directory docker build --rm --tag us.gcr.io/$GOOGLE_PROJECT/pix2pix-server . # test image locally docker run --publish 8000:8000 --rm --name server us.gcr.io/$GOOGLE_PROJECT/pix2pix-server python -u serve.py \ --port 8000 \ --local_models_dir models # run this while the above server is running python tools/process-remote.py \ --input_file static/edges2cats-input.png \ --url http://localhost:8000/edges2cats_AtoB \ --output_file output.png # publish image to private google container repository python tools/upload-image.py --project $GOOGLE_PROJECT --version v1 # create a google cloud server cp terraform.tfvars.example terraform.tfvars # edit terraform.tfvars to put your cloud info in there # get the service-account.json from the google cloud console # make sure GCE is enabled on your account as well python terraform plan python terraform apply # get name of server gcloud compute instance-groups list-instances pix2pix-manager # ssh to server gcloud compute ssh <name of instance here> # look at the logs (can take awhile to load docker image) sudo journalctl -f -u pix2pix # if you have never made an http-server before, apparently you may need this rule gcloud compute firewall-rules create http-server --allow=tcp:80 --target-tags http-server # get ip address of load balancer gcloud compute forwarding-rules list # open that in the browser, should see the same page you saw locally # to destroy the GCP resources, use this terraform destroy ```