apple2373 · October 24, 2016 12:46 · Sep 1, 2015 · Sep 1, 2015 · Sep 1, 2015 · Sep 1, 2015
diff --git a/ArtNetGPU.py b/ArtNetGPU.py
@@ -106,9 +106,8 @@ def forward(x, p, a):
 
     with open(savedir+"/log.txt", "a") as f:
         f.write(str(epoch)+','+str(loss.data)+','+str(np.linalg.norm(grad.data))+'\n')
-    if t%500==0:
-        savename = savedir+'/imge'+str(epoch)+'.png'
-        save_x(cuda.to_cpu(x.data),savename)
+    savename = savedir+'/imge'+str(epoch)+'.png'
+    save_x(cuda.to_cpu(x.data),savename)
 
 # #optimize x(=image) with momment
 # momentum= 0.9

diff --git a/ArtNetGPU.py b/ArtNetGPU.py
@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 
 # this is a quick implementation of http://arxiv.org/abs/1508.06576
-# BUT! This is kind of dirty. Lost of hard coding.　
+# BUT! This is kind of dirty. Lots of hard coding.　
 
 import numpy as np
 import math

diff --git a/ArtNetGPU.py b/ArtNetGPU.py
@@ -1,6 +1,9 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+# this is a quick implementation of http://arxiv.org/abs/1508.06576
+# BUT! This is kind of dirty. Lost of hard coding.　
+
 import numpy as np
 import math
 from chainer import cuda, Function, FunctionSet, gradient_check, Variable, optimizers
@@ -25,7 +28,7 @@ def reshape2(conv1_1):
     pixels=conv1_1.data.shape[2]*conv1_1.data.shape[3]
     return chainer.functions.reshape(conv1_1,(k,pixels))
 
-# save the original image
+# save the image x
 def save_x(img,filename="output.png"):
     img = img.reshape((3,224,224))
     img = np.transpose(img,(1,2,0))
@@ -63,10 +66,11 @@ def forward(x, p, a):
 
 cuda.init(3)# is GPU ID!!
 
-p=readimage('satoshi_fb.png')#read content image
-a=readimage('style.png')#read style image
+p=readimage('satoshi_fb.png')#read a content image
+a=readimage('style.png')#read a style image
 
-#func = caffe.CaffeFunction('VGG_ILSVRC_19_layers.caffemodel')
+#download a pretraind caffe model from here: https://gist.github.com/ksimonyan/3785162f95cd2d5fee77#file-readme-md
+func = caffe.CaffeFunction('VGG_ILSVRC_19_layers.caffemodel')#it takes some time.
 func.to_gpu()
 
 x_data=np.random.randn(1,3,224,224).astype(np.float32)

diff --git a/ArtNetGPU.py b/ArtNetGPU.py
@@ -0,0 +1,124 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import numpy as np
+import math
+from chainer import cuda, Function, FunctionSet, gradient_check, Variable, optimizers
+import chainer.functions as Fu
+from chainer.functions import caffe
+import chainer
+
+import matplotlib.pyplot as plt
+from scipy.misc import imread, imresize, imsave
+
+def readimage(filename):
+    img = imread(filename)
+    img = imresize(img,[224, 224])
+    img = np.transpose(img,(2,0,1))
+    img = img.reshape((1,3,224,224))
+    p_data = np.ascontiguousarray(img,dtype=np.float32)
+    p = Variable(cuda.to_gpu(p_data))
+    return p
+
+def reshape2(conv1_1):
+    k=conv1_1.data.shape[1]
+    pixels=conv1_1.data.shape[2]*conv1_1.data.shape[3]
+    return chainer.functions.reshape(conv1_1,(k,pixels))
+
+# save the original image
+def save_x(img,filename="output.png"):
+    img = img.reshape((3,224,224))
+    img = np.transpose(img,(1,2,0))
+    imsave(filename,img)
+
+def forward(x, p, a):
+    conv1_1, conv2_1, conv3_1, conv4_1,conv5_1, = func(inputs={'data': x}, outputs=['conv1_1', 'conv2_1', 'conv3_1', 'conv4_1', 'conv5_1'])
+    conv1_1F,conv2_1F, conv3_1F, conv4_1F,conv5_1F, = [ reshape2(x) for x in [conv1_1,conv2_1, conv3_1, conv4_1,conv5_1]]
+    conv1_1G,conv2_1G, conv3_1G, conv4_1G,conv5_1G, = [ Fu.matmul(x, x, transa=False, transb=True) for x in [conv1_1F,conv2_1F, conv3_1F, conv4_1F,conv5_1F]]
+#
+    conv1_1,conv2_1, conv3_1, conv4_1,conv5_1, = func(inputs={'data': p}, outputs=['conv1_1', 'conv2_1', 'conv3_1', 'conv4_1', 'conv5_1'])
+    conv1_1P,conv2_1P, conv3_1P, conv4_1P,conv5_1P, = [ reshape2(x) for x in [conv1_1,conv2_1, conv3_1, conv4_1,conv5_1]]
+#
+    L_content = Fu.mean_squared_error(conv4_1F,conv4_1P)/2
+#
+    conv1_1,conv2_1, conv3_1, conv4_1,conv5_1, = func(inputs={'data': a}, outputs=['conv1_1', 'conv2_1', 'conv3_1', 'conv4_1', 'conv5_1'])
+    conv1_1A0,conv2_1A0, conv3_1A0, conv4_1A0,conv5_1A0, = [ reshape2(x) for x in [conv1_1,conv2_1, conv3_1, conv4_1,conv5_1]]
+    conv1_1A,conv2_1A, conv3_1A, conv4_1A,conv5_1A, = [ Fu.matmul(x, x, transa=False, transb=True) for x in [conv1_1A0,conv2_1A0, conv3_1A0, conv4_1A0,conv5_1A0]]
+#
+    #caution! the deviding number is hard coding!
+    #this part is correspnding to equation (4) in the original paper
+    #to check the current N and M, run the following
+    #[x.data.shape  for x in [conv1_1F,conv2_1F, conv3_1F, conv4_1F,conv5_1F]]
+    L_style = (Fu.mean_squared_error(conv1_1G,conv1_1A)/(4*64*64*50176*50176)
+    + Fu.mean_squared_error(conv2_1G,conv2_1A)/(4*128**128*12544*12544)
+    + Fu.mean_squared_error(conv3_1G,conv3_1A)/(4*256*256*3136*3136)
+    + Fu.mean_squared_error(conv4_1G,conv4_1A)/(4*512*512*784*784)\
+    )/4 # this is equal weighting of E_l
+#
+    ratio = 0.001 #alpha/beta 
+    loss = ratio*L_content + L_style
+    return loss 
+
+#main
+
+cuda.init(3)# is GPU ID!!
+
+p=readimage('satoshi_fb.png')#read content image
+a=readimage('style.png')#read style image
+
+#func = caffe.CaffeFunction('VGG_ILSVRC_19_layers.caffemodel')
+func.to_gpu()
+
+x_data=np.random.randn(1,3,224,224).astype(np.float32)
+x = Variable(cuda.to_gpu(x_data))
+
+x = readimage('imge230.png') # if you want to start from a exsiting image
+
+savedir="satoshi_fb_adam"
+
+#optimize x(=image) with adam
+#note we use numpy for optimization
+
+alpha=1
+beta1=0.9
+beta2=0.999
+eps=1e-8
+
+v=np.zeros_like(cuda.to_cpu(x.data))
+m=np.zeros_like(v)
+
+for epoch in xrange(10000):
+    t=0
+    loss=forward(x,p,a)
+    loss.backward()
+    grad_cuda=x.grad.copy()
+    grad=cuda.to_cpu(grad_cuda)
+    t +=1
+    m =  beta1*m + (1-beta1)*grad
+    v =  beta2*v + (1-beta2)*(grad*grad)
+    m_hat=m/(1-np.power(beta1,t))
+    v_hat=v/(1-np.power(beta2, t))
+    x.data -= cuda.to_gpu( alpha * m_hat / (np.sqrt(v_hat) + eps) )#back it to cuda
+
+    with open(savedir+"/log.txt", "a") as f:
+        f.write(str(epoch)+','+str(loss.data)+','+str(np.linalg.norm(grad.data))+'\n')
+    if t%500==0:
+        savename = savedir+'/imge'+str(epoch)+'.png'
+        save_x(cuda.to_cpu(x.data),savename)
+
+# #optimize x(=image) with momment
+# momentum= 0.9
+# lr=100
+# v=np.zeros_like(x.data)
+# for epoch in xrange(10000):
+#     loss=forward(x,p,a)
+#     loss.backward()
+#     grad=x.grad.copy()
+#     v *= momentum
+#     v -= lr * grad
+#     x.data += v
+
+#     with open(savedir+"/log.txt", "a") as f:
+#         f.write(str(epoch+315)+','+str(loss.data)+','+str(np.linalg.norm(x.grad))+'\n')
+#     savename = savedir+'/imge'+str(epoch+315)+'.png'
+#     save_x(x.data,savename)
No results found