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cleaned app.py
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app.py
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@@ -31,8 +31,7 @@ tf.random.set_seed(SEED)
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import gradio as gr
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import wget
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import pandas as pd
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import
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from zipfile import ZipFile
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"""## Download CelebA attributes
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@@ -42,7 +41,7 @@ We'll use face images from the CelebA dataset, resized to 64x64.
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#Download labels from public github, they have been processed in a 0,1 csv file
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os.mkdir("/content/celeba_gan")
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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")
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shutil.unpack_archive(filename="/content/celeba_gan/list_attr_celeba01.csv.zip", extract_dir="/content/celeba_gan")
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"""## Dataset preprocessing functions"""
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## Load trained GAN
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"""
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from collections import namedtuple
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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''')
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dcgan = ModelEntry('buio','t1qyadzp', 'dcgan','dcgan',
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'v24',25.64, 0, 0, (16,28), 24.29, 0,0)
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acgan2 = ModelEntry('buio','rn8xslip','acgan2_BNstdev','acgan2_BNstdev',
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'v22', 29.05, 0.9182, 0.9295, (20,31), 24.79, 0.918, 0.926)
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acgan10 = ModelEntry('buio','3ja6uvac','acgan10_nonseparBNstdev_split','acgan10_nonseparBNstdev_split',
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'v24', 26.89, 0.859, 0.785, (18,30), 24.59, 0.789,0.858)
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acgan40 = ModelEntry('buio','2ev65fpt','acgan40_BNstdev','acgan40_BNstdev',
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'v15', 28.23, 0.430, 0.842, (15,25), 27.72, 04.6, 0.851)
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acgan2_hd = ModelEntry('buio','6km6fdgr','acgan2_BNstdev_218x178','acgan2_BNstdev_218x178',
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'v11', 0,0,0 ,(0,0), 0, 0, 0)
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acgan10_hd = ModelEntry('buio','3v366skw','acgan40_BNstdev_218x178','acgan40_BNstdev_218x178',
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'v14', 0,0,0, (0,0),0, 0, 0)
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acgan40_hd = ModelEntry('buio','booicugb','acgan10_nonseparBNstdev_split_299_218x178','acgan10_nonseparBNstdev_split_299_218x178',
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'v14', 52.9, 0.410, 0.834, (12,15), 0, 0, 0)
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#1cr1a5w4 SAGAN_3 v31 buianifolli
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#2o3z6bqb SAGAN_5 v17 buianifolli
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#zscel8bz SAGAN_6 v29 buianifolli
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#wandb artifacts
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#sagan40 v18
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import gradio as gr
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import wget
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import pandas as pd
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import shutil
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"""## Download CelebA attributes
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#Download labels from public github, they have been processed in a 0,1 csv file
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os.mkdir("/content/celeba_gan")
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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")
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shutil.unpack_archive(filename="/content/celeba_gan/list_attr_celeba01.csv.zip", extract_dir="/content/celeba_gan")
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"""## Dataset preprocessing functions"""
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## Load trained GAN
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"""
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#wandb artifacts
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#sagan40 v18
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