Updated 21/Nov/2021 by Yoshihisa Nitta 
Analysis of Variational Auto Encoder for CelebA dataset with Tensorflow 2 on Google Colab¶
Train Variational Auto Encoder further on CelebA dataset. It is assumed that it is in the state after executing VAE_CelebA_Train2.ipynb.
CelebA データセットに対する Variational Auto Encoder をGoogle Colab 上の Tensorflow 2 で解析する¶
CelebA データセットに対して変分オートエンコーダを学習させた結果を解析する。 VAE_CelebA_Train2.ipynb を実行した後の状態であることを前提としている。
In [1]:
#! pip install tensorflow==2.7.0
In [2]:
%tensorflow_version 2.x
import tensorflow as tf
print(tf.__version__)
In [3]:
! nvidia-smi
! cat /proc/cpuinfo
! cat /etc/issue
! free -h
In [4]:
from google.colab import drive
drive.mount('/content/drive')
In [5]:
! ls /content/drive
Download source file from Google Drive or nw.tsuda.ac.jp¶
Download from nw.tsuda.ac.jp above only if the specifications of Google Drive change and you cannot download from Google Drive.
Google Drive または nw.tsuda.ac.jp からファイルをダウンロードする¶
Google Drive の仕様が変わってダウンロードができない場合にのみ、上の nw.tsuda.ac.jp からダウンロードすること。
In [6]:
# Download source file
nw_path = './nw'
! rm -rf {nw_path}
! mkdir -p {nw_path}
if True: # from Google Drive
url_model = 'https://drive.google.com/uc?id=1ZCihR7JkMOity4wCr66ZCp-3ZOlfwwo3'
! (cd {nw_path}; gdown {url_model})
else: # from nw.tsuda.ac.jp
URL_NW = 'https://nw.tsuda.ac.jp/lec/GoogleColab/pub'
url_model = f'{URL_NW}/models/VariationalAutoEncoder.py'
! wget -nd {url_model} -P {nw_path}
In [7]:
! cat {nw_path}/VariationalAutoEncoder.py
Preparing CelebA dataset¶
Official WWW of CelebA dataset: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
Google Drive of CelebA dataset: https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8?resourcekey=0-5BR16BdXnb8hVj6CNHKzLg
img_align_celeba.zip mirrored on my Google Drive:
https://drive.google.com/uc?id=1LFKeoI-hb96jlV0K10dO1o04iQPBoFdx
CelebA データセットを用意する¶
CelebA データセットの公式ページ: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
CelebA データセットのGoogle Drive: https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8?resourcekey=0-5BR16BdXnb8hVj6CNHKzLg
自分の Google Drive 上にミラーした img_align_celeba.zip:
https://drive.google.com/uc?id=1LFKeoI-hb96jlV0K10dO1o04iQPBoFdx
In [8]:
# Download img_align_celeba.zip from GoogleDrive
MIRRORED_URL = 'https://drive.google.com/uc?id=1LFKeoI-hb96jlV0K10dO1o04iQPBoFdx'
! gdown {MIRRORED_URL}
In [9]:
! ls -l
In [10]:
DATA_DIR = 'data'
DATA_SUBDIR = 'img_align_celeba'
In [11]:
! rm -rf {DATA_DIR}
! unzip -d {DATA_DIR} -q {DATA_SUBDIR}.zip
In [12]:
! ls -l {DATA_DIR}/{DATA_SUBDIR} | head
! ls {DATA_DIR}/{DATA_SUBDIR} | wc
In [13]:
# paths to all the image files.
import os
import glob
import numpy as np
all_file_paths = np.array(glob.glob(os.path.join(DATA_DIR, DATA_SUBDIR, '*.jpg')))
n_all_images = len(all_file_paths)
print(n_all_images)
In [14]:
# slect some image files.
n_to_show = 10
selected_indices = np.random.choice(range(n_all_images), n_to_show)
selected_paths = all_file_paths[selected_indices]
In [15]:
# Display some images.
%matplotlib inline
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, n_to_show, figsize=(1.4 * n_to_show, 1.4))
for i, path in enumerate(selected_paths):
img = tf.keras.preprocessing.image.load_img(path)
ax[i].imshow(img)
ax[i].axis('off')
plt.show()
In [16]:
TRAIN_DATA_DIR = 'train_data'
TEST_DATA_DIR = 'test_data'
In [17]:
import os
split = 0.05
indices = np.arange(n_all_images)
np.random.shuffle(indices)
train_indices = indices[: -int(n_all_images * split)]
test_indices = indices[-int(n_all_images * split):]
! rm -rf {TRAIN_DATA_DIR} {TEST_DATA_DIR}
dst=f'{TRAIN_DATA_DIR}/celeba'
if not os.path.exists(dst):
os.makedirs(dst)
for idx in train_indices:
path = all_file_paths[idx]
dpath, fname = os.path.split(path)
os.symlink(f'../../{path}', f'{dst}/{fname}')
dst=f'{TEST_DATA_DIR}/celeba'
if not os.path.exists(dst):
os.makedirs(dst)
for idx in test_indices:
path = all_file_paths[idx]
dpath, fname = os.path.split(path)
os.symlink(f'../../{path}', f'{dst}/{fname}')
In [18]:
INPUT_DIM = (128, 128, 3)
BATCH_SIZE = 32
In [19]:
data_gen = tf.keras.preprocessing.image.ImageDataGenerator(
rescale = 1.0/255
)
data_flow = data_gen.flow_from_directory(
TRAIN_DATA_DIR,
target_size = INPUT_DIM[:2],
batch_size = BATCH_SIZE,
shuffle=True,
class_mode = 'input'
)
val_data_flow = data_gen.flow_from_directory(
TEST_DATA_DIR,
target_size = INPUT_DIM[:2],
batch_size = BATCH_SIZE,
shuffle=True,
class_mode = 'input'
)
In [20]:
print(len(data_flow))
print(len(val_data_flow))
In [21]:
save_path3 = '/content/drive/MyDrive/ColabRun/VAE_CelebA03/'
In [22]:
# Load the parameters and model weights saved before
# 保存したパラメータと重みを読み込む
from nw.VariationalAutoEncoder import VariationalAutoEncoder
vae3 = VariationalAutoEncoder.load(save_path3)
print(vae3.epoch)
In [24]:
n_val_data = len(val_data_flow)
print(n_val_data)
In [25]:
data, _ = next(val_data_flow)
_, _, z = vae3.encoder.predict(data)
print(z.shape)
In [26]:
z_dim = 200
data, _ = next(val_data_flow)
_, _, z = vae3.encoder.predict(data)
z_max = z.max(axis=0)
z_min = z.min(axis=0)
print(len(z_max))
print(len(z_min))
for i in range(len(val_data_flow)):
data, _ = next(val_data_flow)
_, _, z = vae3.encoder.predict(data)
tmp_max = z.max(axis=0)
tmp_min = z.min(axis=0)
z_max = np.amax([z_max, tmp_max], axis=0)
z_min = np.amin([z_min, tmp_min], axis=0)
print(len(z_max))
print(len(z_min))
In [27]:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 1, figsize=(12, 12))
for i in range(n_val_data):
data, _ = next(val_data_flow)
_, _, z = vae3.encoder.predict(data)
ax.scatter(z[:,0], z[:,1], c='black', alpha=0.5, s=2)
plt.show()
In [28]:
# Generate 30 points in 200-dimensional latent space.
# 200次元の潜在空間の点を30個生成する。
import numpy as np
table_row = 10 # 表の横方向サイズ
table_line = 3 #表の縦方向サイズ
n_to_show = table_row * table_line
mat = np.array([ np.random.uniform(z_min[j], z_max[j], n_to_show) for j in range(z_dim)])
z_points = mat.transpose()
print(z_points.shape)
In [30]:
# Display 200-dimensional points on 2-dimensional space.
# 生成した200次元の点を2次元平面に表示する
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 1, figsize=(12, 12))
for i in range(n_val_data):
data, _ = next(val_data_flow)
_, _, z = vae3.encoder.predict(data)
ax.scatter(z[:,0], z[:,1], c='black', alpha=0.5, s=2)
ax.scatter(z_points[:, 0], z_points[:, 1], c='red', alpha=1, s=20)
for i in range(len(z_points)):
ax.text(z_points[i][0], z_points[i][1], str(i))
plt.show()
In [31]:
# Generate images from the points in 200-dimensional latent space.
# 200次元の潜在空間内の点から画像を生成する
reconst = vae3.decoder.predict(z_points)
In [33]:
# Display generated images.
# 生成した画像を表示する
%matplotlib inline
import matplotlib.pyplot as plt
VSKIP=0.5 # vertical space between subplots
fig, ax = plt.subplots(table_line, table_row, figsize=(2.8 * table_row, 2.8 * table_line * (1+VSKIP)))
plt.subplots_adjust(hspace = VSKIP)
for y in range(table_line):
for x in range(table_row):
idx = table_row * y + x
img = reconst[idx].squeeze()
ax[y][x].imshow(img, cmap='gray')
ax[y][x].text(0.5, -0.35, f'{idx} ({z_points[idx][0]:.2f}, {z_points[idx][1]:.2f})', fontsize=16, ha='center', transform=ax[y][x].transAxes)
ax[y][x].axis('off')
plt.show()
In [35]:
z_mid = np.mean([z_min, z_max], axis=0)
In [36]:
print(z_mid.shape)
In [37]:
n_grid = 20
x = np.linspace(z_min[0], z_max[0], n_grid)
y = np.linspace(z_min[1], z_max[1], n_grid)
xv, yv = np.meshgrid(x, y)
xv = xv.flatten()
yv = yv.flatten()
z_points2 = np.zeros((xv.shape[0], z_dim))
print(z_points2.shape)
for i in range(z_points2.shape[0]):
z_points2[i] = z_mid
for j in range(20):
z_points2[i,2*j+0] = xv[i]
z_points2[i,2*j+1] = yv[i]
In [38]:
reconst2 = vae3.decoder.predict(z_points2)
In [42]:
%matplotlib inline
import matplotlib.pyplot as plt
fig, ax = plt.subplots(n_grid, n_grid, figsize=(0.7*n_grid, 0.7*n_grid))
for i in range(len(reconst2)):
img = reconst2[i]
line = i // n_grid
row = i % n_grid
ax[line][row].imshow(img)
ax[line][row].axis('off')
plt.show()
In [41]:
# Display 200-dimensional points on 2-dimensional space.
# 生成した200次元の点を2次元平面に表示する
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 1, figsize=(12, 12))
for i in range(n_val_data):
data, _ = next(val_data_flow)
_, _, z = vae3.encoder.predict(data)
ax.scatter(z[:,0], z[:,1], c='black', alpha=0.5, s=2)
ax.scatter(z_points2[:, 0], z_points2[:, 1], c='red', alpha=1, s=20)
for i in range(len(z_points2)):
ax.text(z_points2[i][0], z_points2[i][1], str(i))
plt.show()
In [41]: