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utils.py
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utils.py
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"""
Some codes from https://github.com/Newmu/dcgan_code
"""
from __future__ import division
import math
import json
import random
import pprint
import os
import scipy.misc
import numpy as np
from tqdm import tqdm
from time import gmtime, strftime
import pdb
pp = pprint.PrettyPrinter()
get_stddev = lambda x, k_h, k_w: 1/math.sqrt(k_w*k_h*x.get_shape()[-1])
class celebA():
def __init__(self, batch_size, sample_size, is_crop, image_size, resize_w):
self.batch_size = batch_size
self.sample_size = sample_size
self.data_dir = os.path.join(os.path.join("./data", 'celeba'))
split = np.load(os.path.join(self.data_dir, 'split_img.npz'))
w_attr_name = split['w_attr']
wo_attr_name = split['wo_attr']
i = len(w_attr_name) // self.batch_size
self.w_attr = w_attr_name[:i*self.batch_size]
i = len(wo_attr_name) // self.batch_size
self.wo_attr = wo_attr_name[:500*self.batch_size]
self.top_number = len(self.w_attr)
self.bot_number = len(self.wo_attr)
self.top_current = 0
self.bot_current = 0
def get_list(self):
return self.w_attr, self.wo_attr
def sequential_sample(self):
top_end = (self.top_current+self.batch_size)%self.top_number
bot_end = (self.bot_current+self.batch_size)%self.bot_number
if top_end == 0:
#top = range(self.top_current, self.top_number)
top = self.w_attr[self.top_current:]
else:
#top = range(self.top_current,top_end)
top = self.w_attr[self.top_current:(self.top_current+self.batch_size)%self.top_number]
if bot_end == 0:
#bot = range(self.bot_current, self.bot_number)
bot = self.wo_attr[self.bot_current:]
else:
#bot = range(self.bot_current,bot_end)
bot = self.wo_attr[self.bot_current:(self.bot_current+self.batch_size)%self.bot_number]
self.top_current = top_end
self.bot_current = bot_end
return top, bot
#return np.asarray(self.w_attr_imgs)[top], np.asarray(self.wo_attr_imgs)[bot]
def get_image(image_path, image_size, is_crop=True, resize_w=64, is_grayscale = False):
return transform(imread(image_path, is_grayscale), image_size, is_crop, resize_w)
def save_images(images, size, image_path, type='RGB'):
if type == 'RGB':
return imsave(inverse_transform(images), size, image_path)
elif type == 'BW':
return imsave(images, size, image_path)
def imread(path, is_grayscale = False):
if (is_grayscale):
return scipy.misc.imread(path, flatten = True).astype(np.float)
else:
return scipy.misc.imread(path).astype(np.float)
def merge_images(images, size):
return inverse_transform(images)
def merge(images, size):
h, w = images.shape[1], images.shape[2]
img = np.zeros((h * size[0], w * size[1], 3))
for idx, image in enumerate(images):
i = idx % size[1]
j = idx // size[1]
img[j*h:j*h+h, i*w:i*w+w, :] = image
return img
def imsave(images, size, path):
return scipy.misc.imsave(path, merge(images, size))
def center_crop(x, crop_h, crop_w=None, resize_w=64):
if crop_w is None:
crop_w = crop_h
h, w = x.shape[:2]
j = int(round((h - crop_h)/2.))
i = int(round((w - crop_w)/2.))
return scipy.misc.imresize(x[j:j+crop_h, i:i+crop_w],
[resize_w, resize_w])
def transform(image, npx=64, is_crop=True, resize_w=64):
# npx : # of pixels width/height of image
if is_crop:
cropped_image = center_crop(image, npx, resize_w=resize_w)
else:
cropped_image = image
# resize the image
cropped_image = scipy.misc.imresize(cropped_image, (resize_w,resize_w))
# -1~1
return (np.array(cropped_image)/255.-0.5)*2
def inverse_transform(images):
# -1~1 -> 0~1
return images/2.+0.5
def to_json(output_path, *layers):
with open(output_path, "w") as layer_f:
lines = ""
for w, b, bn in layers:
layer_idx = w.name.split('/')[0].split('h')[1]
B = b.eval()
if "lin/" in w.name:
W = w.eval()
depth = W.shape[1]
else:
W = np.rollaxis(w.eval(), 2, 0)
depth = W.shape[0]
biases = {"sy": 1, "sx": 1, "depth": depth, "w": ['%.2f' % elem for elem in list(B)]}
if bn != None:
gamma = bn.gamma.eval()
beta = bn.beta.eval()
gamma = {"sy": 1, "sx": 1, "depth": depth, "w": ['%.2f' % elem for elem in list(gamma)]}
beta = {"sy": 1, "sx": 1, "depth": depth, "w": ['%.2f' % elem for elem in list(beta)]}
else:
gamma = {"sy": 1, "sx": 1, "depth": 0, "w": []}
beta = {"sy": 1, "sx": 1, "depth": 0, "w": []}
if "lin/" in w.name:
fs = []
for w in W.T:
fs.append({"sy": 1, "sx": 1, "depth": W.shape[0], "w": ['%.2f' % elem for elem in list(w)]})
lines += """
var layer_%s = {
"layer_type": "fc",
"sy": 1, "sx": 1,
"out_sx": 1, "out_sy": 1,
"stride": 1, "pad": 0,
"out_depth": %s, "in_depth": %s,
"biases": %s,
"gamma": %s,
"beta": %s,
"filters": %s
};""" % (layer_idx.split('_')[0], W.shape[1], W.shape[0], biases, gamma, beta, fs)
else:
fs = []
for w_ in W:
fs.append({"sy": 5, "sx": 5, "depth": W.shape[3], "w": ['%.2f' % elem for elem in list(w_.flatten())]})
lines += """
var layer_%s = {
"layer_type": "deconv",
"sy": 5, "sx": 5,
"out_sx": %s, "out_sy": %s,
"stride": 2, "pad": 1,
"out_depth": %s, "in_depth": %s,
"biases": %s,
"gamma": %s,
"beta": %s,
"filters": %s
};""" % (layer_idx, 2**(int(layer_idx)+2), 2**(int(layer_idx)+2),
W.shape[0], W.shape[3], biases, gamma, beta, fs)
layer_f.write(" ".join(lines.replace("'","").split()))
def make_gif(images, fname, duration=2, true_image=False):
import moviepy.editor as mpy
def make_frame(t):
try:
x = images[int(len(images)/duration*t)]
except:
x = images[-1]
if true_image:
return x.astype(np.uint8)
else:
return ((x+1)/2*255).astype(np.uint8)
clip = mpy.VideoClip(make_frame, duration=duration)
clip.write_gif(fname, fps = len(images) / duration)
def visualize(sess, dcgan, config, option):
if option == 0:
z_sample = np.random.uniform(-0.5, 0.5, size=(config.batch_size, dcgan.z_dim))
samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
save_images(samples, [8, 8], './samples/test_%s.png' % strftime("%Y-%m-%d %H:%M:%S", gmtime()))
elif option == 1:
values = np.arange(0, 1, 1./config.batch_size)
for idx in xrange(100):
print(" [*] %d" % idx)
z_sample = np.zeros([config.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample):
z[idx] = values[kdx]
samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
save_images(samples, [8, 8], './samples/test_arange_%s.png' % (idx))
elif option == 2:
values = np.arange(0, 1, 1./config.batch_size)
for idx in [random.randint(0, 99) for _ in xrange(100)]:
print(" [*] %d" % idx)
z = np.random.uniform(-0.2, 0.2, size=(dcgan.z_dim))
z_sample = np.tile(z, (config.batch_size, 1))
#z_sample = np.zeros([config.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample):
z[idx] = values[kdx]
samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
make_gif(samples, './samples/test_gif_%s.gif' % (idx))
elif option == 3:
values = np.arange(0, 1, 1./config.batch_size)
for idx in xrange(100):
print(" [*] %d" % idx)
z_sample = np.zeros([config.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample):
z[idx] = values[kdx]
samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
make_gif(samples, './samples/test_gif_%s.gif' % (idx))
elif option == 4:
image_set = []
values = np.arange(0, 1, 1./config.batch_size)
for idx in xrange(100):
print(" [*] %d" % idx)
z_sample = np.zeros([config.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample): z[idx] = values[kdx]
image_set.append(sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample}))
make_gif(image_set[-1], './samples/test_gif_%s.gif' % (idx))
new_image_set = [merge(np.array([images[idx] for images in image_set]), [10, 10]) \
for idx in range(64) + range(63, -1, -1)]
make_gif(new_image_set, './samples/test_gif_merged.gif', duration=8)