def test(model=None,
session=None,
file_list=None,
batch_size=None,
use_tfboard=True,
summary=None,
tb_writer=None,
step=0):
session.run(model.local_init)
for pfile in file_list:
logging.info('Epoch %2d, evaluating on file: %s......' % (step, pfile))
xbatch1, xbatch2, ybatch1, ybatch2 = utils.LoadNpy(pfile)
for k1 in range(0, np.shape(xbatch1)[0], batch_size):
lb = int(k1)
ub = int(np.min((lb + batch_size, np.shape(xbatch1)[0])))
#sess.run(base_model.local_init)
feed_dict = {
model.inputs_t1: xbatch1[lb:ub, :],
model.labels_t1: ybatch1[lb:ub],
model.inputs_t2: xbatch2[lb:ub, :],
model.labels_t2: ybatch2[lb:ub]
}
session.run([model.metrics_t1_op, model.metrics_t2_op],
feed_dict=feed_dict)
if args.use_tfboard is True:
tb_writer.add_summary(session.run(summary, feed_dict=feed_dict),
global_step=step)
tb_writer.flush()
acc_t1, acc_t2 = session.run([model.metrics_t1, model.metrics_t2])
return acc_t1, acc_t2
def test(sess, graph, file_list=None):
inputs_t1 = graph.get_tensor_by_name(name='inputs_t1:0')
inputs_t2 = graph.get_tensor_by_name(name='inputs_t2:0')
preds_t1 = graph.get_tensor_by_name(name='prediction_t1:0')
preds_t2 = graph.get_tensor_by_name(name='prediction_t2:0')
pred_label_t1 = None
pred_label_t2 = None
label_t1 = None
label_t2 = None
#xbatch1 = xbatch2 = np.zeros(shape=(5))
for pfile in file_list:
print('evaluating on file: %s......' % (pfile))
xbatch1, xbatch2, ybatch1, ybatch2 = utils.LoadNpy(pfile)
if label_t1 is None:
label_t1 = ybatch1
label_t2 = ybatch2
else:
label_t1 = np.concatenate((label_t1, ybatch1), axis=0)
label_t2 = np.concatenate((label_t2, ybatch2), axis=0)
for k1 in range(0, np.shape(xbatch1)[0], bs):
lb = int(k1)
ub = int(np.min((lb + bs, np.shape(xbatch1)[0])))
lt1 = np.zeros_like(label_t1).astype(np.uint8)
lt2 = np.zeros_like(label_t2).astype(np.uint8)
# sess.run(base_model.local_init)
feed_dict = {
inputs_t1: xbatch1[lb:ub, :],
inputs_t2: xbatch2[lb:ub, :]
}
tmp_pred_t1, tmp_pred_t2 = sess.run([preds_t1, preds_t2],
feed_dict=feed_dict)
if pred_label_t1 is None:
pred_label_t1 = tmp_pred_t1
pred_label_t2 = tmp_pred_t2
else:
pred_label_t1 = np.concatenate((pred_label_t1, tmp_pred_t1),
axis=0)
pred_label_t2 = np.concatenate((pred_label_t2, tmp_pred_t2),
axis=0)
pred_label_t1 = np.expand_dims(pred_label_t1, axis=1)
pred_label_t2 = np.expand_dims(pred_label_t2, axis=1)
return label_t1, label_t2, pred_label_t1, pred_label_t2
def main(trn_file=None, val_file=None, tst_file=None, args=None):
inputs_shape = [None, 200, 200, 3]
base_model = model(inputs_shape=inputs_shape)
base_model.forward(num_classes=args.num_classes)
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.Session(config=config)
#global_steps = tf.Variable(0, trainable=False)
#optimizer = tf.train.MomentumOptimizer(learning_rate=1e-3,momentum=0.9,use_nesterov=True).minimize(base_model.losses, global_step=global_steps)
global_steps = tf.Variable(0, trainable=False)
steps_per_epoch = int(11783 / args.batch_size)
boundaries = [30*steps_per_epoch, 60*steps_per_epoch, 90*steps_per_epoch]
values = [5*1e-3, 1e-3, 1e-4, 1e-5]
learning_rate = tf.train.piecewise_constant(global_steps, boundaries, values)
'''
inital_lr = 1e-3
decay_rate = 0.95
learning_rate = tf.train.exponential_decay(learning_rate=inital_lr, global_step=global_steps, decay_rate=decay_rate, decay_steps=decay_steps, staircase=True)
'''
#optimizer = tf.train.MomentumOptimizer(learning_rate=1e-3,momentum=0.9,use_nesterov=True).minimize(base_model.losses, global_step=global_steps)
tf.summary.scalar(name='lr/learning_rate', tensor=learning_rate)
tf.summary.scalar(name='lr/global_steps', tensor=global_steps)
'''
specific optimizers for DCCA loss
'''
optimizer = tf.train.MomentumOptimizer(learning_rate=1e-3,momentum=0.9).minimize(base_model.losses, global_step=global_steps)
conv_vars = tf.trainable_variables(scope='conv_layers')
dense_vars = tf.trainable_variables()[len(conv_vars):]
optimizer_dcca = tf.train.AdamOptimizer(learning_rate=1e-6).minimize(base_model.dcca_loss, var_list=dense_vars, global_step=global_steps)
optimizer = tf.group(optimizer, optimizer_dcca)
initializer = tf.global_variables_initializer()
sess.run(base_model.local_init)
sess.run(initializer)
## tensorboard logger
summary_merge = None
writer_trn = writer_tst = writer_val = None
if args.use_tfboard is True:
writer_trn = tf.summary.FileWriter(logdir=args.tb_path+'/trn', graph=sess.graph)
writer_val = tf.summary.FileWriter(logdir=args.tb_path+'/val', graph=sess.graph)
writer_tst = tf.summary.FileWriter(logdir=args.tb_path+'/tst', graph=sess.graph)
summary_merge = tf.summary.merge_all()
temp_acc_t1 = 0.
temp_acc_t2 = 0.
## storing the outputs to log.txt
f = open(args.log_path+'log.txt', 'w')
cnt = 0
for step in range(args.epoches):
### optimization
logging.info('Epoch %2d, training started......'%(step))
for trn in trn_file:
logging.info('Epoch %2d, training on file: %s......'%(step,trn))
xtrn1, xtrn2, ytrn1, ytrn2 = utils.LoadNpy(trn)
for k1 in range(0, np.shape(xtrn1)[0], args.batch_size):
lb = int(k1)
ub = int(np.min((lb + args.batch_size,np.shape(xtrn1)[0])))
feed_dict = {base_model.inputs_t1: xtrn1[lb:ub, :], base_model.labels_t1: ytrn1[lb:ub],
base_model.inputs_t2: xtrn2[lb:ub, :], base_model.labels_t2: ytrn2[lb:ub]}
sess.run([optimizer], feed_dict=feed_dict)
logging.info('Epoch %2d, training finished.....'%(step))
### training
logging.info('Epoch %2d, evaluating on training set......'%(step))
f.writelines('Epoch %2d, evaluating on training set......\n'%(step))
trn_acc_t1, trn_acc_t2 = test(model=base_model, session=sess, file_list=trn_file, batch_size=args.batch_size, use_tfboard=args.use_tfboard, summary=summary_merge, tb_writer=writer_trn, step=step)
logging.info('Epoch %2d, evaluating on training set finished, acc_t1 is: %.4f, acc_t2 is %.4f.....'%(step, trn_acc_t1, trn_acc_t2))
f.writelines('Epoch %2d, evaluating on training set finished, acc_t1 is: %.4f, acc_t2 is %.4f.....\n'%(step, trn_acc_t1, trn_acc_t2))
### validation
logging.info('Epoch %2d, evaluating on validation set......'%(step))
f.writelines('Epoch %2d, evaluating on validation set......\n'%(step))
val_acc_t1, val_acc_t2 = test(model=base_model, session=sess, file_list=val_file, batch_size=args.batch_size, use_tfboard=args.use_tfboard, summary=summary_merge, tb_writer=writer_val, step=step)
logging.info('Epoch %2d, evaluating on validation set finished, acc_t1 is: %.4f, acc_t2 is %.4f.....'%(step, val_acc_t1, val_acc_t2))
f.writelines('Epoch %2d, evaluating on validation set finished, acc_t1 is: %.4f, acc_t2 is %.4f.....\n'%(step, val_acc_t1, val_acc_t2))
### testing
logging.info('Epoch %2d, evaluating on testing set......'%(step))
f.writelines('Epoch %2d, evaluating on testing set......\n'%(step))
tst_acc_t1, tst_acc_t2 = test(model=base_model, session=sess, file_list=tst_file, batch_size=args.batch_size, use_tfboard=args.use_tfboard, summary=summary_merge, tb_writer=writer_tst, step=step)
logging.info('Epoch %2d, evaluating on testing set finished, acc_t1 is: %.4f, acc_t2 is %.4f.....'%(step, tst_acc_t1, tst_acc_t2))
f.writelines('Epoch %2d, evaluating on testing set finished, acc_t1 is: %.4f, acc_t2 is %.4f.....\n\n'%(step, tst_acc_t1, tst_acc_t2))
#### save better model
if args.save_model & ((val_acc_t1+val_acc_t2) > (temp_acc_t1+temp_acc_t2)):
model_name = 'model.ckpt'
logging.info('Saving model to %s......'%(args.model_path+'/'+model_name))
saver = tf.train.Saver(max_to_keep=3,)
saver.save(sess=sess,save_path=args.model_path+'/'+model_name)
logging.info('Model saved......\n')
temp_acc_t1 = val_acc_t1
temp_acc_t2 = val_acc_t2
else:
logging.info('Performance is worse, don\'t save model....\n')
f.close()
sess.close()
return True