DL之BigGAN：利用BigGAN算法实现超强炸天效果——画风的确skr、skr、skr，太特么的skr了
导读 本博主刚刚利用代码进行测试，结果的确吊(不)炸(可)天(思议)！BigGAN的效果的确不可思议，但是关于GAN到底学了什么，仍需要理论研究支持，这个可视化的过渡阶段，倒是给了一个好的想象空间。还有，BigGAN的以假乱真，到底应该如何应用，这也是一个亟待解决的问题。
目录
输出效果
实现代码
相关文章Paper之BigGAN：ICLR 2019最新论文《LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS》论文研究中
输出效果一、Explore BigGAN samples of a particular category Try varying the truncation value.
1、ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus PS：保护北极熊，阻止全球气候变暖，爱护环境，人人有责；绿色出行，低碳生活，从我做起！
2、sloth bear, Melursus ursinus, Ursus ursinus
3、ant, emmet, pismire
4、great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
5、house finch, linnet, Carpodacus mexicanus
6、tiger cat
二、Interpolate between BigGAN samples Try setting different categorys with the same noise_seeds, or the same categorys with different noise_seeds. Or go wild and set both any way you like!
1、pug, pug-dog→Persian cat
2、pug, pug-dogjian→lion, king of beasts, Panthera leo
3、pug, pug-dog → tiger, Panthera tigris
4、pug, pug-dog →brown bear, bruin, Ursus arctos
5、 golden retriever→coral fungus
6、golden retriever→toilet tissue, toilet paper, bathroom tissue
7、golden retriever→pineapple, ananas
8、golden retriever→banana
9、 goldfish, Carassius auratus→red wine
10、golf ball → starfish, sea star
实现代码
import cStringIO
import IPython.display
import numpy as np
import PIL.Image
from scipy.stats import truncnorm
import tensorflow as tf
import tensorflow_hub as hub
input_z = inputs['z']
input_y = inputs['y']
input_trunc = inputs['truncation']
dim_z = input_z.shape.as_list()[1]
vocab_size = input_y.shape.as_list()[1]
def truncated_z_sample(batch_size, truncation=1., seed=None):
state = None if seed is None else np.random.RandomState(seed)
values = truncnorm.rvs(-2, 2, size=(batch_size, dim_z), random_state=state)
return truncation * values
def one_hot(index, vocab_size=vocab_size):
index = np.asarray(index)
if len(index.shape) == 0:
index = np.asarray([index])
assert len(index.shape) == 1
num = index.shape[0]
output = np.zeros((num, vocab_size), dtype=np.float32)
output[np.arange(num), index] = 1
return output
def one_hot_if_needed(label, vocab_size=vocab_size):
label = np.asarray(label)
if len(label.shape) = 0
cols = int(cols)
assert cols >= 1
N, H, W, C = imarray.shape
rows = int(np.ceil(N / float(cols)))
batch_pad = rows * cols - N
assert batch_pad >= 0
post_pad = [batch_pad, pad, pad, 0]
pad_arg = [[0, p] for p in post_pad]
imarray = np.pad(imarray, pad_arg, 'constant', constant_values=255)
H += pad
W += pad
grid = (imarray
.reshape(rows, cols, H, W, C)
.transpose(0, 2, 1, 3, 4)
.reshape(rows*H, cols*W, C))
if pad:
grid = grid[:-pad, :-pad]
return grid
def imshow(a, format='png', jpeg_fallback=True):
a = np.asarray(a, dtype=np.uint8)
str_file = cStringIO.StringIO()
PIL.Image.fromarray(a).save(str_file, format)
png_data = str_file.getvalue()
try:
disp = IPython.display.display(IPython.display.Image(png_data))
except IOError:
if jpeg_fallback and format != 'jpeg':
print ('Warning: image was too large to display in format "{}"; '
'trying jpeg instead.').format(format)
return imshow(a, format='jpeg')
else:
raise
return disp
tf.reset_default_graph()
print 'Loading BigGAN module from:', module_path
module = hub.Module(module_path)
inputs = {k: tf.placeholder(v.dtype, v.get_shape().as_list(), k)
for k, v in module.get_input_info_dict().iteritems()}
output = module(inputs)
print
print 'Inputs:\n', '\n'.join(
' {}: {}'.format(*kv) for kv in inputs.iteritems())
print
print 'Output:', output
tf.reset_default_graph()
print 'Loading BigGAN module from:', module_path
module = hub.Module(module_path)
inputs = {k: tf.placeholder(v.dtype, v.get_shape().as_list(), k)
for k, v in module.get_input_info_dict().iteritems()}
output = module(inputs)
print
print 'Inputs:\n', '\n'.join(
' {}: {}'.format(*kv) for kv in inputs.iteritems())
print
print 'Output:', output
