def shared_label():
savepath = '/research1/YOON/ECCV2016/keras/result/42x42/'
batch_size = 128
model=makenetwork.sharednet_label()
#sharednet,model1,model2,model3 = makenetwork.eccvmodel_label()
sgd =SGD(lr=0.001, decay=0, momentum=0.9, nesterov=True)
model.compile(optimizer='sgd',loss={'out':'categorical_crossentropy'})
# load trained network
trainednet = glob.glob(savepath+'*hdf5')
if len(trainednet):
trainednet.sort()
print('load pretrained net:',trainednet[-1])
model.load_weights(trainednet[-1])
h5trainpath = '/research1/YOON/ECCV2016/42x42_2/h5_train/'
h5files = glob.glob(h5trainpath+'*.h5')
h5files.sort()
nbh5files = len(h5files)
h5valpath = '/research1/YOON/ECCV2016/42x42_2/h5_test/'
h5valfiles = glob.glob(h5valpath+'*.h5')
h5valfiles.sort()
nbh5valfiles = len(h5valfiles)
for idx in range(nbh5valfiles):
f =h5py.File(h5valfiles[idx],'r')
if idx == 0:
input1_val = f['data1'][()]
input2_val = f['data2'][()]
label_val = f['label'][()]
label_val=label_val.astype('int32')
datasize = input1_val.shape[0]
label_val = np.reshape(label_val,[datasize])
label_val = np_utils.to_categorical(label_val, 13)
f.close()
else:
tmpinput1 = f['data1'][()]
tmpinput2 = f['data2'][()]
tmplabel = f['label'][()]
tmplabel = tmplabel.astype('int32')
datasize = tmpinput1.shape[0]
tmplabel = np.reshape(tmplabel,[datasize])
tmplabel = np_utils.to_categorical(tmplabel, 13)
input1_val=np.concatenate((input1_val,tmpinput1),axis=0)
input2_val=np.concatenate((input2_val,tmpinput2),axis=0)
label_val=np.concatenate((label_val,tmplabel),axis=0)
f.close()
for iter in range(nb_epoch):
s =0
total_trainacc=0.0
traincount =0
print('---------------epoch %d------------') % iter
for idx in xrange(5,nbh5files,5):
input1 =[]
input2=[]
label =[]
print (' %d/%d') % (idx,nbh5files)
traincount +=1
order = np.random.permutation(range(s,idx))
count =0
for idx2 in order:
f =h5py.File(h5files[idx2],'r')
if count == 0:
input1 = f['data1'][()]
input2 = f['data2'][()]
label = f['label'][()]
label=label.astype('int32')
datasize = input1.shape[0]
label = np.reshape(label,[datasize])
label = np_utils.to_categorical(label, 13)
count +=1
f.close()
else:
tmpinput1 = f['data1'][()]
tmpinput2 = f['data2'][()]
tmplabel = f['label'][()]
tmplabel=tmplabel.astype('int32')
datasize = tmpinput1.shape[0]
tmplabel = np.reshape(tmplabel,[datasize])
tmplabel = np_utils.to_categorical(tmplabel, 13)
input1=np.concatenate((input1,tmpinput1),axis=0)
input2=np.concatenate((input2,tmpinput2),axis=0)
label=np.concatenate((label,tmplabel),axis=0)
f.close()
count+=1
s= idx
model.fit({'input1':input1,'input2':input2,'out':label},batch_size=batch_size,nb_epoch=1,shuffle=False,verbose=1)
train_out= model.predict({'input1':input1,'input2':input2},batch_size=batch_size,verbose=1)
out = np.argmax(train_out['out'],axis=-1)
train_acc =getAP.loss(out,label)
total_trainacc +=train_acc
total_trainacc/=traincount
print('train_acc:',total_trainacc)
val_out= model.predict({'input1':input1_val,'input2':input2_val},batch_size=batch_size,verbose=1)
val_out = np.argmax(val_out['out'],axis=-1)
val_acc =getAP.loss(val_out,label_val)
print('val_acc:',val_acc)
savenum = iter + len(trainednet)
savename = "%05d" % savenum
model.save_weights(savepath+'model_'+savename+'.hdf5', overwrite=True)
gc.collect()
nbh5files = len(h5files)
print('choose a LF image:0~%d') % nbh5files
LF_img =input()
for idx in range(nbh5files):
f =h5py.File(h5files[LF_img],'r')
input1 = f['data1'][()]
input2 = f['data2'][()]
label = f['label'][()]
label=label.astype('int32')
datasize = input1.shape[0]
label = np.reshape(label,[datasize])
label = np_utils.to_categorical(label, 13)
f.close()
if LF_img >= len(h5files):
#if sub_first == sub_second or LF_img >= len(h5files) or len(sub_first) != 2 or len(sub_second) !=2:
print(' Error choose LF image carefully')
else:
print('load LF image')
ap =0.0;
model = loadnet(trainednet)
model.compile(optimizer='sgd',loss={'out':'categorical_crossentropy'} )
out =model.predict({'input1':input1,'input2':input2})
out = np.argmax(out['out'],axis=-1)
acc =getAP.loss(out,label)
print('acc:',acc)