upgrade tensorflow version and adapt server.py

README.md

@@ -9,30 +9,38 @@ Pull request are more than welcome :)
 ## How to use
 
 ### Prerequisites
+
 * Install Python 3 + pip (for the GAN server)
+  * Avoid [SSL: CERTIFICATE_VERIFY_FAILED] error by just browse to Applications/Python 3.X and double-click Install Certificates.command.
 * Install NodeJS + npm (for the frontend)
+  * suggested node version : (v12.16.3) in order to avoid Timeout error, ref:  <https://stackoverflow.com/questions/40435315/knex-timeout-acquiring-a-connection-the-pool-is-probably-full-are-you-missing>
 * Install a PostgreSQL server
 
 ### Launch the GAN server
+
 ```bash
 cd gan_server
 # Install dependencies
 pip install -r requirements.txt
 # And go...
 python server.py
 ```
+
 Your GAN server is available at http://localhost:5000/
 
 ### Configure the frontend
 For quick hacking, if you have Docker at your disposal, you can spawn a PostgreSQL database like so:
+
 ```bash
 docker run -p 5432:5432 --name ganbreederpostgres -e POSTGRES_PASSWORD=ganbreederpostgres -d postgres
 ```
+
 With that simple scenario, the database and user would be `postgres` and the password would be `ganbreederpostgres`
 
 Copy the file `server/example_secrets.js` to `secrets.js` and modify it to fit your environment.
 
 ### Launch the frontend
+
 ```bash
 cd server
 npm install
@@ -45,24 +53,30 @@ node updatecache.js
 # And go...
 node server.js
 ```
+
 Your frontend is available at http://localhost:8888/
 
 ### docker-compose setup
 
 Make sure that [docker](https://docs.docker.com/install/) and [docker-compose](https://docs.docker.com/compose/install/) are installed.
 
 Start the containers:
+
 ```bash
 docker-compose up
 ```
+
 Your frontend is available at http://localhost:8888/, backend at http://localhost:5000/.
 Initial backend setup can take few minutes.
 
 If this is the first time you are running the project you might want to generate some random images:
+
 ```bash
 docker-compose exec server node make_randoms.js
 ```
+
 Restart only frontend server (to avoid backend initialization wait):
+
 ```bash
 docker-compose restart server
 ```

gan_server/Dockerfile

@@ -1,7 +1,7 @@
-FROM python:3.6.9-buster
+FROM python:3.9.1-buster
 ADD ./gan_server /gan_server
 WORKDIR /gan_server
 # Install dependencies
 RUN pip install -r /gan_server/requirements.txt
 # And go...
-CMD ["python", "server.py"]
+CMD ["python", "server.py"]

gan_server/requirements.txt

@@ -1,6 +1,6 @@
 Flask==1.0.2
 Flask-Cors==3.0.7
-tensorflow==1.12.0
-tensorflow_hub==0.2.0
-scipy==1.1.0
-Pillow==5.3.0
+tensorflow==2.3.1
+tensorflow_hub==0.10.0
+scipy==1.4.1
+Pillow==6.2.1

gan_server/server.py

@@ -16,10 +16,11 @@
 rand_seed = 123
 truncation = 0.5
 
-tf.reset_default_graph()
+tf.compat.v1.disable_eager_execution()
+tf.compat.v1.reset_default_graph()
 print('Loading BigGAN module from:', module_path)
 module = hub.Module(module_path)
-inputs = {k: tf.placeholder(v.dtype, v.get_shape().as_list(), k)
+inputs = {k: tf.compat.v1.placeholder(v.dtype, v.get_shape().as_list(), k)
           for k, v in module.get_input_info_dict().items()}
 output = module(inputs)
 
@@ -30,9 +31,9 @@
 dim_z = input_z.shape.as_list()[1]
 vocab_size = input_y.shape.as_list()[1]
 
-initializer = tf.global_variables_initializer()
+initializer = tf.compat.v1.global_variables_initializer()
 
-sess = tf.Session()
+sess = tf.compat.v1.Session()
 sess.run(initializer)
 
 def truncated_z_sample(batch_size):
@@ -187,3 +188,4 @@ def mix_images():
     port = int(sys.argv[1]) if len(sys.argv) > 1 else 5000
     print('port=', port)
     app.run(host='0.0.0.0', debug=True, port=port)
+