def train_det_net(): # get all training sample data_info = parse_data_info(only_positive = False) # data_info = [<image-path str>,[<nonface/face int>,<pattern-id int>]] # training configuration batch = 500 size = (48,48,3) start_epoch = 0 end_epoch = 1000 train_validation_rate = 0.9 # training set / all sample # load the pretrained model , set None if you don't have pretrained = 'models/48_net_6.ckpt' # load data iterater dataset = DataSet(data_info,train_rate = train_validation_rate) _ , train_op , val_op , next_ele = dataset.get_iterator(batch,size) # load network # learning rate is great impact in training models net_12 = model.detect_12Net(lr = 0.001,size = (12,12,3)) net_24 = model.detect_24Net(lr = 0.001,size = (24,24,3)) net_48 = model.detect_48Net(lr = 0.001,size = (48,48,3)) sess = tf.InteractiveSession() saver = tf.train.Saver() if pretrained: saver.restore(sess , pretrained) else: sess.run(tf.global_variables_initializer()) for epoch in xrange(start_epoch,end_epoch): loss = 0 iteration = 0 sess.run(train_op) # get each element of the training dataset until the end is reached while True: try: # default of the size returned from data iterator is 48 inputs,clss ,pattern = sess.run(next_ele) # <ndarray> , <0/1> , <one-hot of 45-class> clss = clss.reshape(batch,2) pattern = pattern.reshape(batch,45) # resize image to fit each net inputs_12 = np.array([cv2.resize(img,(net_12.size[0],net_12.size[1])) for img in inputs]) inputs_24 = np.array([cv2.resize(img,(net_24.size[0],net_24.size[1])) for img in inputs]) inputs_48 = np.array([cv2.resize(img,(net_48.size[0],net_48.size[1])) for img in inputs]) # forward 12net net_12_fc = net_12.get_fc(inputs_12) # forward 24net net_24_fc = net_24.get_fc(inputs_24,net_12_fc) train_nets = [net_12,net_24,net_48] net_feed_dict = {net_12.inputs:inputs_12 , net_12.targets:clss,\ net_24.inputs:inputs_24 , net_24.targets:clss,net_24.from_12:net_12_fc,\ net_48.inputs:inputs_48 , net_48.targets:clss,net_48.from_24:net_24_fc} # training net sess.run([net.train_step for net in train_nets],\ feed_dict = net_feed_dict) # loss computation losses = sess.run([net.loss for net in train_nets],\ feed_dict = net_feed_dict) if iteration % 100 == 0: net_12_eva = net_12.evaluate(inputs_12,clss) net_12_acc = sum(net_12_eva)/len(net_12_eva) net_24_eva = net_24.evaluate(inputs_24,clss,net_12_fc) net_24_acc = sum(net_24_eva)/len(net_24_eva) net_48_eva = net_48.evaluate(inputs_48,clss,net_24_fc) net_48_acc = sum(net_48_eva)/len(net_48_eva) print ('Training Epoch {} --- Iter {} --- Training Accuracy: {}%,{}%,{}% --- Training Loss: {}'\ .format(epoch , iteration , net_12_acc , net_24_acc , net_48_acc , losses)) iteration += 1 except tf.errors.OutOfRangeError: # print("End of training dataset.") break # get each element of the validation dataset until the end is reached sess.run(val_op) net_12_acc = [] net_24_acc = [] net_48_acc = [] while True: try: # the size returned from data iterator is 48 inputs,clss ,pattern = sess.run(next_ele) clss = clss.reshape(batch,2) # resize image to fit each net inputs_12 = np.array([cv2.resize(img,(net_12.size[0],net_12.size[1])) for img in inputs]) inputs_24 = np.array([cv2.resize(img,(net_24.size[0],net_24.size[1])) for img in inputs]) inputs_48 = np.array([cv2.resize(img,(net_48.size[0],net_48.size[1])) for img in inputs]) # forward 12net net_12_fc = net_12.get_fc(inputs_12) # forward 24net net_24_fc = net_24.get_fc(inputs_24,net_12_fc) net_12_eva = net_12.evaluate(inputs_12,clss) net_24_eva = net_24.evaluate(inputs_24,clss,net_12_fc) net_48_eva = net_48.evaluate(inputs_48,clss,net_24_fc) for i in range(len(net_12_eva)): net_12_acc.append(net_12_eva[i]) net_24_acc.append(net_24_eva[i]) net_48_acc.append(net_48_eva[i]) except tf.errors.OutOfRangeError: # print("End of validation dataset.") break print ('Validation Epoch {} Validation Accuracy: {}%,{}%,{}%'\ .format(epoch , sum(net_12_acc)/len(net_12_acc),\ sum(net_24_acc)/len(net_24_acc),\ sum(net_48_acc)/len(net_48_acc))) saver = tf.train.Saver() save_path = saver.save(sess, "models/48_net_{}.ckpt".format(epoch)) print ("Model saved in file: ", save_path)
def train_cal_net(): # get only the positive training sample data_info = parse_data_info(only_positive=True) # training configuration batch = 500 size = (48, 48, 3) start_epoch = 0 end_epoch = 1000 train_validation_rate = 0.9 # training set / all sample # load the pretrained model , set None if you don't have pretrained = 'models/48_cal_net_18.ckpt' # load data iterater dataset = DataSet(data_info, train_rate=train_validation_rate) _, train_op, val_op, next_ele = dataset.get_iterator(batch, size) # load network net_12_c = model.calib_12Net(lr=0.001, size=(12, 12, 3)) net_24_c = model.calib_24Net(lr=0.001, size=(24, 24, 3)) net_48_c = model.calib_48Net(lr=0.001, size=(48, 48, 3)) sess = tf.InteractiveSession() saver = tf.train.Saver() if pretrained: saver.restore(sess, pretrained) else: sess.run(tf.global_variables_initializer()) for epoch in xrange(start_epoch, end_epoch): loss = 0 iteration = 0 sess.run(train_op) # get each element of the training dataset until the end is reached while True: try: # default of the size returned from data iterator is 48 inputs, clss, pattern = sess.run(next_ele) # <ndarray> , <0/1> , <one-hot of 45-class> clss = clss.reshape(batch, 2) pattern = pattern.reshape(batch, 45) # resize image to fit each net inputs_12 = np.array([ cv2.resize(img, (net_12_c.size[0], net_12_c.size[1])) for img in inputs ]) inputs_24 = np.array([ cv2.resize(img, (net_24_c.size[0], net_24_c.size[1])) for img in inputs ]) inputs_48 = np.array([ cv2.resize(img, (net_48_c.size[0], net_48_c.size[1])) for img in inputs ]) '''Put the size(48,48) into 12_cal_net and 24_cal_net ,because of the origrinal size is too small to convergence''' train_nets = [net_12_c, net_24_c, net_48_c] net_feed_dict = {net_12_c.inputs:inputs_12 , net_12_c.targets:pattern,\ net_24_c.inputs:inputs_24 , net_24_c.targets:pattern,\ net_48_c.inputs:inputs_48 , net_48_c.targets:pattern,} # training net sess.run([net.train_step for net in train_nets],\ feed_dict = net_feed_dict) # loss computation losses = sess.run([net.loss for net in train_nets],\ feed_dict = net_feed_dict) if iteration % 100 == 0: net_12_c_eva = net_12_c.evaluate(inputs_12, pattern) net_12_c_acc = sum(net_12_c_eva) / len(net_12_c_eva) net_24_c_eva = net_24_c.evaluate(inputs_24, pattern) net_24_c_acc = sum(net_24_c_eva) / len(net_24_c_eva) net_48_c_eva = net_48_c.evaluate(inputs_48, pattern) net_48_c_acc = sum(net_48_c_eva) / len(net_48_c_eva) print ('Training Epoch {} --- Iter {} --- Training Accuracy: {}%,{}%,{}% --- Training Loss: {}'\ .format(epoch , iteration , net_12_c_acc , net_24_c_acc , net_48_c_acc , losses)) iteration += 1 except tf.errors.OutOfRangeError: print("End of training dataset.") break # get each element of the validation dataset until the end is reached sess.run(val_op) net_12_c_acc = [] net_24_c_acc = [] net_48_c_acc = [] while True: try: # the size returned from data iterator is 48 inputs, clss, pattern = sess.run(next_ele) clss = clss.reshape(batch, 2) pattern = pattern.reshape(batch, 45) # resize image to fit each net inputs_12 = np.array([ cv2.resize(img, (net_12_c.size[0], net_12_c.size[1])) for img in inputs ]) inputs_24 = np.array([ cv2.resize(img, (net_24_c.size[0], net_24_c.size[1])) for img in inputs ]) inputs_48 = np.array([ cv2.resize(img, (net_48_c.size[0], net_48_c.size[1])) for img in inputs ]) net_12_c_eva = net_12_c.evaluate(inputs_12, pattern) net_24_c_eva = net_24_c.evaluate(inputs_24, pattern) net_48_c_eva = net_48_c.evaluate(inputs_48, pattern) for i in range(len(net_12_c_eva)): net_12_c_acc.append(net_12_c_eva[i]) net_24_c_acc.append(net_24_c_eva[i]) net_48_c_acc.append(net_48_c_eva[i]) except tf.errors.OutOfRangeError: print("End of validation dataset.") break print ('Validation Epoch {} Validation Accuracy: {}%,{}%,{}%'\ .format(epoch , sum(net_12_c_acc)/len(net_12_c_acc),\ sum(net_24_c_acc)/len(net_24_c_acc),\ sum(net_48_c_acc)/len(net_48_c_acc))) saver = tf.train.Saver() save_path = saver.save(sess, "models/48_cal_net_{}.ckpt".format(epoch)) print("Model saved in file: ", save_path)