conditional sagan 10 attributes

app.py

@@ -1,7 +1,270 @@
+# -*- coding: utf-8 -*-
+"""evaluate_gan_gradio.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1ckZU76dq3XWcpa5PpQF8a6qJwkTttg8v
+
+# ⚙️ Setup
+
+## 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"""
+
+!pip install gradio -q
+
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+from scipy import linalg
+import matplotlib.pyplot as plt
+import pandas as pd
+
+from tensorflow import keras
+from tensorflow.keras import layers
+from keras.applications.inception_v3 import InceptionV3, preprocess_input
+
+from tensorflow.keras.layers import Layer, Input, Dense, Reshape, Flatten
+from tensorflow.keras.layers import Conv2D, Conv2DTranspose, ReLU, LeakyReLU
+from tensorflow.keras.layers import Dropout, Embedding, Concatenate, Add, Activation
+from tensorflow.keras.layers import GlobalAveragePooling2D, UpSampling2D, BatchNormalization
+import tensorflow.keras.backend as K
+
+from tensorflow.python.keras.utils import conv_utils
+from tensorflow.keras.initializers import RandomNormal
+from tensorflow.keras.optimizers import Adam
+
+!pip install tensorflow_addons
+import tensorflow_addons as tfa
+from tensorflow_addons.layers import SpectralNormalization
+
+import gdown
+from zipfile import ZipFile
+
+from tqdm.notebook import tqdm
+
+"""## 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
+!mkdir "/content/celeba_gan"
+!wget -q -O "/content/celeba_gan/list_attr_celeba01.csv.zip" "https://github.com/buoi/conditional-face-GAN/blob/main/list_attr_celeba01.csv.zip?raw=true" 
+!unzip -o "/content/celeba_gan/list_attr_celeba01.csv.zip" -d "/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
+
+#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)
+
+
+!pip install wget -q
+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(debug=True)
+
+def sentence_builder(quantity, animal, place, activity_list, morning):
+    return f"""The {quantity} {animal}s went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""
+
+def generate_image(attributes)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface = gr.Interface(
+    sentence_builder,
+    [
+        gr.inputs.Slider(2, 20),
+        gr.inputs.Dropdown(["cat", "dog", "bird"]),
+        gr.inputs.Radio(["park", "zoo", "road"]),
+        gr.inputs.CheckboxGroup(["ran", "swam", "ate", "slept"]),
+        gr.inputs.Checkbox(label="Is it the morning?"),
+    ],
+    "text",
+    examples=[
+        [2, "cat", "park", ["ran", "swam"], True],
+        [4, "dog", "zoo", ["ate", "swam"], False],
+        [10, "bird", "road", ["ran"], False],
+        [8, "cat", "zoo", ["ate"], True],
+    ],
+)
 iface.launch()