import matchmmd from gen_deepart import read_lfw_attributes,attr_pairs from gen_deepart import deepart_reconstruct ## Read P, Q, X, weights and layers colorization=False attr=10 source_k=2000 target_k=2000 test_indices=sorted([6005, 3659, 8499, 12217, 9982, 4322, 10449, 10969, 4245, 7028]) test_indices=[0,176] _,_,lfwattr=read_lfw_attributes() if attr>=0: target_indices,source_indices=attr_pairs(lfwattr,attr,target_k,source_k) else: source_indices,target_indices=attr_pairs(lfwattr,-attr,source_k,target_k) if colorization: source_indices=list(numpy.random.choice(range(len(lfwattr)),source_k,replace=False)) target_indices=list(numpy.random.choice(range(len(lfwattr)),target_k,replace=False)) P=sorted(source_indices) # list of indices (source distribution) Q=sorted(target_indices) # list of indices (target distribution) X=test_indices # list of indices (test images) weights=[8e-8,4e-8,2e-8,1e-8] ## Form F (first N rows are P, next M rows are Q, last row is x) prefix='data'
mu=data['mu'] shape=data['shape'].item() del data print 'U',U.shape,U.dtype,U.min(),U.max() print 'T',T.shape,T.dtype,T.min(),T.max() print 'mu',mu.shape,mu.dtype,mu.min(),mu.max() ## Read attributes from gen_deepart import read_lfw_attributes from gen_deepart import attr_pairs _,_,lfwattr=read_lfw_attributes() # 8 is Youth # 10 is Senior target_indices,source_indices=attr_pairs(lfwattr,10,2000,2000) print 'source',source_indices[:5] print 'target',target_indices[:5] weights=[5e-6] test_indices=[0,1] print 'feat_img[:5]',T[0,:5] #P=T[source_indices].astype(np.float64) #Q=T[target_indices].astype(np.float64) P=T[source_indices] Q=T[target_indices] print 'P',P.shape,P.dtype print 'Q',Q.shape,Q.dtype ## Match distributions