app.py

# -*- coding: utf-8 -*-
"""evaluate_gan_gradio.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1ckZU76dq3XWcpa5PpQF8a6qJwkTttg8v

# ⚙️ Setup
"""

#!pip install gradio -q
#!pip install wget -q
#!pip install tensorflow_addons -q

"""## Fix random seeds"""

SEED = 11
import os
os.environ['PYTHONHASHSEED']=str(SEED)
import random
import numpy as np
import tensorflow as tf

random.seed(SEED)
np.random.seed(SEED)
tf.random.set_seed(SEED)

"""## Imports"""

import gradio as gr
import wget
import pandas as pd
import gdown
from zipfile import ZipFile

"""## Download CelebA attributes

We'll use face images from the CelebA dataset, resized to 64x64.
"""

#Download labels from public github, they have been processed in a 0,1 csv file
os.mkdir("/content/celeba_gan")
wget.download(url="https://github.com/buoi/conditional-face-GAN/blob/main/list_attr_celeba01.csv.zip?raw=true", out="/content/celeba_gan/list_attr_celeba01.csv.zip")
import shutil
shutil.unpack_archive(filename="/content/celeba_gan/list_attr_celeba01.csv.zip", extract_dir="/content/celeba_gan")

"""## Dataset preprocessing functions"""

# image utils functions

def conv_range(in_range=(-1,1), out_range=(0,255)):
    """ Returns range conversion function"""

    # compute means and spans once
    in_mean, out_mean = np.mean(in_range), np.mean(out_range)
    in_span, out_span = np.ptp(in_range), np.ptp(out_range)

    # return function
    def convert_img_range(in_img):
        out_img = (in_img - in_mean) / in_span
        out_img = out_img * out_span + out_mean
        return out_img

    return convert_img_range

def crop128(img):
    #return img[:, 77:141, 57:121]# 64,64 center crop
    return img[:, 45:173, 25:153] # 128,128 center crop

def resize64(img):
    return tf.image.resize(img, (64,64), antialias=True, method='bilinear')

"""# 📉 Evaluate model

## Load trained GAN
"""

from collections import namedtuple
ModelEntry = namedtuple('ModelEntry', '''entity, resume_id, model_name, exp_name, best_epoch, expected_fid, expected_f1, expected_acc, epoch_range, exp_avg_fid, exp_avg_f1, exp_avg_acc''')

dcgan = ModelEntry('buio','t1qyadzp', 'dcgan','dcgan',
                   'v24',25.64, 0, 0, (16,28), 24.29, 0,0)

acgan2 = ModelEntry('buio','rn8xslip','acgan2_BNstdev','acgan2_BNstdev', 
                    'v22', 29.05, 0.9182, 0.9295, (20,31), 24.79, 0.918, 0.926)

acgan10 = ModelEntry('buio','3ja6uvac','acgan10_nonseparBNstdev_split','acgan10_nonseparBNstdev_split',
                     'v24', 26.89, 0.859, 0.785, (18,30), 24.59, 0.789,0.858)

acgan40 = ModelEntry('buio','2ev65fpt','acgan40_BNstdev','acgan40_BNstdev',
                     'v15', 28.23, 0.430, 0.842, (15,25), 27.72, 04.6, 0.851)


acgan2_hd = ModelEntry('buio','6km6fdgr','acgan2_BNstdev_218x178','acgan2_BNstdev_218x178',
                     'v11', 0,0,0 ,(0,0), 0, 0, 0)

acgan10_hd = ModelEntry('buio','3v366skw','acgan40_BNstdev_218x178','acgan40_BNstdev_218x178',
                     'v14', 0,0,0, (0,0),0, 0, 0)

acgan40_hd = ModelEntry('buio','booicugb','acgan10_nonseparBNstdev_split_299_218x178','acgan10_nonseparBNstdev_split_299_218x178',
                     'v14', 52.9, 0.410, 0.834, (12,15), 0, 0, 0)



#1cr1a5w4 SAGAN_3 v31 buianifolli
#2o3z6bqb SAGAN_5 v17 buianifolli
#zscel8bz SAGAN_6 v29 buianifolli

#wandb artifacts
#sagan40 v18

#keras_metadata_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6Mjg3MzA4NTY=/5f09f68e9bb5b09efbc37ad76cdcdbb0"
#saved_model_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6Mjg3NDY1OTU=/2676cd88ef1866d6e572916e413a933e"
#variables_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6Mjg3NDY1OTU=/5cab1cb7351f0732ea137fb2d2e0d4ec"
#index_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6Mjg3NDY1OTU=/480b55762c3358f868b8cce53984736b"

#sagan10 v16

keras_metadata_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6MjYxMDQwMDE=/392d036bf91d3648eb5a2fa74c1eb716"
saved_model_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6MjYxMzQ0Mjg=/a5f8608efcc5dafbe780babcffbc79a9"
variables_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6MjYxMzQ0Mjg=/a62bf0c4bf7047c0a31df7d2cfdb54f0"
index_url = "https://api.wandb.ai/artifactsV2/gcp-us/buianifolli/QXJ0aWZhY3Q6MjYxMzQ0Mjg=/de6539a7f0909d1dafa89571c7df43d1"

#download model
gan_path = "/content/gan_model/"
try:
    os.remove(gan_path+"keras_metadata.pb")
    os.remove(gan_path+"saved_model.pb")
    os.remove(gan_path+"variables/variables.data-00000-of-00001")
    os.remove(gan_path+"variables/variables.index")
except FileNotFoundError:
    pass
os.makedirs(gan_path,exist_ok =True)
os.makedirs(gan_path+"/variables",exist_ok =True)



import wget
wget.download(keras_metadata_url, gan_path+"keras_metadata.pb",)
wget.download(saved_model_url, gan_path+"saved_model.pb")
wget.download(variables_url, gan_path+"variables/variables.data-00000-of-00001")
wget.download(index_url, gan_path+"variables/variables.index")

gan = tf.keras.models.load_model(gan_path)

IMAGE_RANGE='11'
IMAGE_SIZE = gan.discriminator.input_shape[1]
if IMAGE_SIZE == 64:
    IMAGE_SHAPE = (64,64,3)
elif IMAGE_SIZE == 218:
    IMAGE_SHAPE = (218,178,3)

try:
    LATENT_DIM = gan.generator.input_shape[0][1]
    N_ATTRIBUTES = gan.generator.input_shape[1][1]
except TypeError:
    LATENT_DIM = gan.generator.input_shape[1]
    N_ATTRIBUTES =0

"""## 💾 Dataset"""

#@title Select Attributes {form-width: "50%", display-mode: "both" }

#NUMBER_OF_ATTRIBUTES = "10" #@param [0, 2, 10, 12, 40]
#N_ATTRIBUTES = int(NUMBER_OF_ATTRIBUTES)

IMAGE_RANGE = '11'

BATCH_SIZE =  64 #@param {type: "number"}
if N_ATTRIBUTES == 2:
    LABELS = ["Male", "Smiling"]

elif N_ATTRIBUTES == 10:
    LABELS = [
          "Mouth_Slightly_Open", "Wearing_Lipstick", "High_Cheekbones", "Male", "Smiling", 
          "Heavy_Makeup", "Wavy_Hair", "Oval_Face", "Pointy_Nose", "Arched_Eyebrows"]

elif N_ATTRIBUTES == 12:
    LABELS = ['Wearing_Lipstick','Mouth_Slightly_Open','Male','Smiling',
              'High_Cheekbones','Heavy_Makeup','Attractive','Young',
              'No_Beard','Black_Hair','Arched_Eyebrows','Big_Nose']
elif N_ATTRIBUTES == 40:
    LABELS = [
            '5_o_Clock_Shadow', 'Arched_Eyebrows', 'Attractive',
            'Bags_Under_Eyes', 'Bald', 'Bangs', 'Big_Lips', 'Big_Nose',
            'Black_Hair', 'Blond_Hair', 'Blurry', 'Brown_Hair', 'Bushy_Eyebrows',
            'Chubby', 'Double_Chin', 'Eyeglasses', 'Goatee', 'Gray_Hair',
            'Heavy_Makeup', 'High_Cheekbones', 'Male', 'Mouth_Slightly_Open',
            'Mustache', 'Narrow_Eyes', 'No_Beard', 'Oval_Face', 'Pale_Skin',
            'Pointy_Nose', 'Receding_Hairline', 'Rosy_Cheeks', 'Sideburns',
            'Smiling', 'Straight_Hair', 'Wavy_Hair', 'Wearing_Earrings',
            'Wearing_Hat', 'Wearing_Lipstick', 'Wearing_Necklace',
            'Wearing_Necktie', 'Young']


else:
    LABELS = ["Male", "Smiling"]# just for dataset creation
 

# Take labels and a list of image locations in memory
df = pd.read_csv(r"/content/celeba_gan/list_attr_celeba01.csv")
attr_list = df[LABELS].values.tolist()

def gen_img(attributes):

    attr = np.zeros((1,N_ATTRIBUTES))
    for a in attributes:
        attr[0,int(a)] = 1
    num_img = 1
    random_latent_vectors = tf.random.normal(shape=(num_img, LATENT_DIM))

    generated_images = gan.generator((random_latent_vectors, attr))
    generated_images = (generated_images*0.5+0.5).numpy()
    print(generated_images[0].shape)
    return generated_images[0]

iface = gr.Interface(
    gen_img,
    gr.inputs.CheckboxGroup([LABELS[i] for i in range(N_ATTRIBUTES)], type='index'),
    "image",
    layout='unaligned'
)
iface.launch()