def eval_resnet():
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
sess = tf.Session(config=sess_config)
config = get_config(is_train=True)
mkdir(config.result_dir)
reg = ResNet(sess, config, "DIRNet", is_train=False)
reg.restore(config.ckpt_dir)
dh = DIRNetDatahandler(config=config)
# print(reg.calc_rmse_all(dh.s_data, dh.d_data, config.result_dir + "/",save_images=False))
batch_x, batch_y, batch_labels = dh.sample_pair(config.batch_size)
# prediction = reg.deploy_with_labels(batch_x, batch_y, batch_labels)
# print(str(prediction))
amnt_pics = np.shape(dh.d_data)[0]
acc = 0
prev_x = np.empty(shape=(1, 222, 247))
amnt_eva = np.shape(dh.d_data_eval)[0]
for i in range(amnt_eva):
batch_x, batch_y, batch_labels = dh.get_eval_pair_by_idx(i)
if np.array_equal(prev_x, batch_x):
print('weird')
prev_x = batch_x
# loss = reg.fit((1, batch_x[0], batch_x[1], batch_x[2]),
# (1, batch_y[0], batch_y[1], batch_y[2]))
prediction = reg.deploy_with_labels(batch_x, batch_y, batch_labels)
print(prediction,"::", batch_labels[0])
truth = int(batch_labels[0])
# print("pred {} truth {}".format(prediction, truth))
if prediction == truth:
acc += 1
print("Acc: {0:.4f}".format(acc / amnt_eva))
def train_ResNet():
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
sess = tf.Session(config=sess_config)
config = get_config(is_train=True)
mkdir(config.tmp_dir)
mkdir(config.ckpt_dir)
reg = ResNet(sess, config, "DIRNet", is_train=True)
# reg.restore(config.ckpt_dir)
dh = DIRNetDatahandler(config=config)
amnt_pics = np.shape(dh.d_data)[0]
for epoch in range(5):
loss_sum = 0
acc = 0
for i in range(amnt_pics - 1):
batch_x, batch_y, batch_labels = dh.get_pair_by_idx(i)
# loss = reg.fit((1, batch_x[0], batch_x[1], batch_x[2]),
# (1, batch_y[0], batch_y[1], batch_y[2]))
loss, prediction = reg.fit(batch_x, batch_y, batch_labels)
loss2, prediction2 = reg.fit(batch_y, batch_x, batch_labels)
loss_sum += (loss + loss2) / 2
prediction = int(prediction[0])
truth = int(batch_labels[0])
# print("pred {} truth {}".format(prediction, truth))
if prediction == truth:
acc += 1
if prediction2[0] == truth:
acc += 1
print("epoch {0}: Loss: {1:.4f} Acc: {2:.4f}".format(
epoch, loss_sum / (amnt_pics * 2), acc / (amnt_pics * 2)))
if (epoch + 1) % 5 == 0:
# if (epoch+1) % config.checkpoint_distance == 0:
# reg.deploy(config.tmp_dir, batch_x, batch_y)
print('saving model...')
# reg.save(config.ckpt_dir)
amnt_pics = np.shape(dh.d_data)[0]
acc = 0
prev_x = np.empty(shape=(1, 222, 247))
amnt_eva = np.shape(dh.d_data_eval)[0]
for i in range(amnt_eva):
batch_x, batch_y, batch_labels = dh.get_eval_pair_by_idx(i)
if np.array_equal(prev_x, batch_x):
print('weird')
prev_x = batch_x
# loss = reg.fit((1, batch_x[0], batch_x[1], batch_x[2]),
# (1, batch_y[0], batch_y[1], batch_y[2]))
prediction = reg.deploy_with_labels(batch_x, batch_y, batch_labels)
print(prediction, "::", batch_labels[0])
truth = int(batch_labels[0])
# print("pred {} truth {}".format(prediction, truth))
if prediction == truth:
acc += 1
print("Acc: {0:.4f}".format(acc / amnt_eva))
