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| import argparse
import os
import sys
import numpy as np
from PIL import Image
import chainer
from chainer import cuda
import chainer.functions as F
import chainer.links
from chainer.links import caffe
from chainer import Variable, optimizers
import pickle
def subtract_mean(x0):
x = x0.copy()
x[0,0,:,:] -= 120
x[0,1,:,:] -= 120
x[0,2,:,:] -= 120
return x
def add_mean(x0):
x = x0.copy()
x[0,0,:,:] += 120
x[0,1,:,:] += 120
x[0,2,:,:] += 120
return x
def image_resize(img_file, width):
gogh = Image.open(img_file)
orig_w, orig_h = gogh.size[0], gogh.size[1]
if orig_w>orig_h:
new_w = width
new_h = width*orig_h//orig_w
gogh = np.asarray(gogh.resize((new_w,new_h)))[:,:,:3].transpose(2, 0, 1)[::-1].astype(np.float32)
gogh = gogh.reshape((1,3,new_h,new_w))
print("image resized to: ", gogh.shape)
hoge= np.zeros((1,3,width,width), dtype=np.float32)
hoge[0,:,width-new_h:,:] = gogh[0,:,:,:]
gogh = subtract_mean(hoge)
else:
new_w = width*orig_w//orig_h
new_h = width
gogh = np.asarray(gogh.resize((new_w,new_h)))[:,:,:3].transpose(2, 0, 1)[::-1].astype(np.float32)
gogh = gogh.reshape((1,3,new_h,new_w))
print("image resized to: ", gogh.shape)
hoge= np.zeros((1,3,width,width), dtype=np.float32)
hoge[0,:,:,width-new_w:] = gogh[0,:,:,:]
gogh = subtract_mean(hoge)
return xp.asarray(gogh), new_w, new_h
def save_image(img, width, new_w, new_h, it):
def to_img(x):
im = np.zeros((new_h,new_w,3))
im[:,:,0] = x[2,:,:]
im[:,:,1] = x[1,:,:]
im[:,:,2] = x[0,:,:]
def clip(a):
return 0 if a<0 else (255 if a>255 else a)
im = np.vectorize(clip)(im).astype(np.uint8)
Image.fromarray(im).save(args['out_dir']+"/im_%05d.png"%it)
if args['gpu']>=0:
img_cpu = add_mean(img.get())
else:
img_cpu = add_mean(img)
if width==new_w:
to_img(img_cpu[0,:,width-new_h:,:])
else:
to_img(img_cpu[0,:,:,width-new_w:])
def get_matrix(y):
ch = y.data.shape[1]
wd = y.data.shape[2]
gogh_y = F.reshape(y, (ch,wd**2))
gogh_matrix = F.matmul(gogh_y, gogh_y, transb=True)/np.float32(ch*wd**2)
return gogh_matrix
class Clip(chainer.Function):
def forward(self, x):
x = x[0]
ret = cuda.elementwise(
'T x','T ret',
'''
ret = x<-120?-120:(x>136?136:x);
''','clip')(x)
return ret
def generate_image(img_orig, img_style, width, nw, nh, max_iter, lr, img_gen=None):
mid_orig = nn.forward(Variable(img_orig))
style_mats = [get_matrix(y) for y in nn.forward(Variable(img_style))]
if img_gen is None:
if args['gpu'] >= 0:
img_gen = xp.random.uniform(-20,20,(1,3,width,width),dtype=np.float32)
else:
img_gen = np.random.uniform(-20,20,(1,3,width,width)).astype(np.float32)
img_gen = chainer.links.Parameter(img_gen)
optimizer = optimizers.Adam(alpha=lr)
optimizer.setup(img_gen)
for i in range(max_iter):
img_gen.zerograds()
x = img_gen.W
y = nn.forward(x)
L = Variable(xp.zeros((), dtype=np.float32))
for l in range(len(y)):
ch = y[l].data.shape[1]
wd = y[l].data.shape[2]
gogh_y = F.reshape(y[l], (ch,wd**2))
gogh_matrix = F.matmul(gogh_y, gogh_y, transb=True)/np.float32(ch*wd**2)
L1 = np.float32(args['lam']) * np.float32(nn.alpha[l])*F.mean_squared_error(y[l], Variable(mid_orig[l].data))
L2 = np.float32(nn.beta[l])*F.mean_squared_error(gogh_matrix, Variable(style_mats[l].data))/np.float32(len(y))
L += L1+L2
if i%100==0:
print(i,l,L1.data,L2.data)
L.backward()
img_gen.W.grad = x.grad
optimizer.update()
tmp_shape = x.data.shape
if args['gpu'] >= 0:
img_gen.W.data += Clip().forward(img_gen.W.data).reshape(tmp_shape) - img_gen.W.data
else:
def clip(x):
return -120 if x<-120 else (136 if x>136 else x)
img_gen.W.data += np.vectorize(clip)(img_gen.W.data).reshape(tmp_shape) - img_gen.W.data
if i%50==0:
save_image(img_gen.W.data, W, nw, nh, i)
args = {}
args['orig_img'] = 'cat.png'
args['style_img'] = 'style_6.png'
args['out_dir'] = 'result'
args['model'] = 'nin_imagenet.caffemodel'
args['width'] = 435
args['iter'] = 5000
args['gpu'] = -1
args['lam'] = 0.005
args['lr'] = 4.0
if args['gpu'] >= 0:
cuda.check_cuda_available()
chainer.Function.type_check_enable = False
cuda.get_device(args['gpu']).use()
xp = cuda.cupy
else:
xp = np
if 'nin' in args['model']:
nn = NIN()
elif 'vgg' == args['model']:
nn = VGG()
elif 'vgg_chainer' == args['model']:
nn = VGG_chainer()
elif 'i2v' in args['model']:
nn = I2V()
elif 'googlenet' in args['model']:
nn = GoogLeNet()
else:
print ('invalid model name. you can use (nin, vgg, vgg_chainer, i2v, googlenet)')
if args['gpu']>=0:
nn.model.to_gpu()
W = args['width']
img_content,nw,nh = image_resize(args['orig_img'], W)
img_style,_,_ = image_resize(args['style_img'], W)
generate_image(img_content, img_style, W, nw, nh, img_gen=None, max_iter=args['iter'], lr=args['lr'])
|
