def visualize_meta_features(self,pic_name=None,color=False):
# W is a [H,F+1] matrix
meta_features = self._input.W[:,1:].transpose() # [F,H] matrix
# display_patch will treat each column as a single image patch
if color:
display.display_color_patches(meta_features,pic_name)
else:
display.display_patches(meta_features,pic_name)
def display(self,save_string=None,save=True,normalize_A=True,max_factors=64,zerophasewhiten=True):
if self.patch_sz is None:
raise NotImplemented
from display import display_color_patches
output = {}
if hasattr(self.A,'get_value'):
A = self.A.get_value()
else:
A = self.A
if self.whiten:
A = np.dot(self.dewhitenmatrix,A)
if zerophasewhiten:
A = np.dot(self.zerophasewhitenmatrix,A)
# plot the vectors in A
NN = min(self.NN,2*max_factors)
patches = np.zeros((self.patch_sz,self.patch_sz,3,2*NN))
count = 0
for nn in range(NN):
if normalize_A:
Ann = A[...,nn]
Ann /= np.abs(Ann).max() / 127.5
Ann += 127.5
else:
Ann = A[...,nn]
patches[...,count ] = Ann.reshape(6,self.patch_sz,self.patch_sz)[:3,...].T
patches[...,count+1] = Ann.reshape(6,self.patch_sz,self.patch_sz)[3:,...].T
count += 2
patches = patches.reshape((self.patch_sz*self.patch_sz*3,2*NN))
array = display_color_patches(patches,self.patch_sz,fig_num=1,normalize=False)
output['A'] = array
plt.draw()
if save:
from config import state_dir, tstring
savepath = os.path.join(state_dir,self.model_name + '_' + self.tstring)
if not os.path.isdir(savepath): os.makedirs(savepath)
if save_string is None:
save_string = tstring()
plt.figure(1)
fname = os.path.join(savepath, 'A_' + save_string + '.png')
plt.savefig(fname)
return output
def display_whitening(self,save_string=None,save=True,normalize_A=True,max_factors=64,zerophasewhiten=True):
if self.patch_sz is None:
raise NotImplemented
from display import display_color_patches
output = {}
if zerophasewhiten:
A = self.zerophasewhitenmatrix
else:
A = self.whitenmatrix
if hasattr(A,'get_value'):
A = A.get_value()
A = A.T # make the columns the filters
# plot the vectors in A
NN = min(A.shape[1],2*max_factors)
patches = np.zeros((self.patch_sz,self.patch_sz,3,2*NN))
count = 0
for nn in range(NN):
patches[...,count ] = A[...,nn].reshape(6,self.patch_sz,self.patch_sz)[:3,...].T
patches[...,count+1] = A[...,nn].reshape(6,self.patch_sz,self.patch_sz)[3:,...].T
count += 2
patches = patches.reshape((self.patch_sz*self.patch_sz*3,2*NN))
array = display_color_patches(patches,self.patch_sz,fig_num=1,normalize=normalize_A)
output['whitenmatrix'] = array
plt.draw()
if save:
from config import state_dir, tstring
savepath = os.path.join(state_dir,self.model_name + '_' + self.tstring)
if not os.path.isdir(savepath): os.makedirs(savepath)
if save_string is None:
save_string = tstring()
plt.figure(1)
fname = os.path.join(savepath, 'whitenmatrix_' + save_string + '.png')
plt.savefig(fname)
return output
def plot_patches(imageset,topK,filename=None):
# imageset is organized by row, but display_color_patches expect image on column
display.display_color_patches(imageset[:topK].T,filename)
