def train():
data = get_data()
train_data, ground_data = data.get_train_data(down_size=8)
loss_file = open(r'resmcnn_train_loss.txt', 'a')
gpu_options = tf.GPUOptions(allow_growth=True)
resnet_ = get_resnet()
data_x1, data_x2, data_x3 = resnet_.get_resnet_output(train_data)
#raise Exception("whatever")
with tf.Graph().as_default() as g2:
input_x1 = tf.placeholder(tf.float32,
shape=[None, size_x // 2, size_y // 2, 64])
input_x2 = tf.placeholder(tf.float32,
shape=[None, size_x // 4, size_y // 4, 256])
input_x3 = tf.placeholder(tf.float32,
shape=[None, size_x // 8, size_y // 8, 512])
gt = tf.placeholder(tf.float32, shape=[None, size_x // 8, size_y // 8])
net = resnet_mcnn()
pred = net.model(input_x1, input_x2, input_x3)
loss_ = net.loss_layer(pred, gt)
gloabal_step_ = tf.Variable(0, trainable=False)
learning_rate = tf.train.inverse_time_decay(
starting_learning_rate,
global_step=gloabal_step_,
decay_steps=decay_step_,
decay_rate=learning_rate_decay,
staircase=False)
train_opt = tf.train.AdamOptimizer(learning_rate)
add_global = gloabal_step_.assign_add(1)
with tf.control_dependencies([add_global]):
train_step = train_opt.minimize(loss_)
saver2 = tf.train.Saver(max_to_keep=20)
with tf.Session(graph=g2) as sess:
sess.run(tf.global_variables_initializer())
for i in range(ts_num):
print('training step:' + str(i) + ' / ' + str(ts_num) + '\n')
feed_dict = {
input_x1: data_x1,
input_x2: data_x2,
input_x3: data_x3,
gt: ground_data
}
_, train_loss, pred_, gt_ = sess.run([train_step, loss_, pred, gt],
feed_dict=feed_dict)
#print(pred_)
#print('\npred_')
#print(gt_)
#print('\ngt_')
print('train loss: %d' % (train_loss))
loss_file.write(str(train_loss) + '\n')
train_data, ground_data = data.get_train_data(down_size=8)
data_x1, data_x2, data_x3 = resnet_.get_resnet_output(train_data)
if i % 100 == 0:
if os.path.isdir('./res_mcnn_save'):
saver2.save(sess, './res_mcnn_save/session2.ckpt')
else:
os.mkdir('./res_mcnn_save')
loss_file.close()
def test():
data = get_data()
resnet_ = get_resnet()
gpu_options = tf.GPUOptions(allow_growth=True)
for img_num_ in range(1001, 1002):
test_data, test_gronud, test_x, test_y = data.get_test_data(
down_size=8, img_num=img_num_)
data_x1, data_x2, data_x3 = resnet_.get_resnet_output(test_data)
with tf.Graph().as_default() as g2:
input_x1 = tf.placeholder(
tf.float32, shape=[None, test_x // 2, test_y // 2, 64])
input_x2 = tf.placeholder(
tf.float32, shape=[None, test_x // 4, test_y // 4, 256])
input_x3 = tf.placeholder(
tf.float32, shape=[None, test_x // 8, test_y // 8, 512])
gt = tf.placeholder(tf.float32,
shape=[None, test_x // 8, test_y // 8])
net = resnet_mcnn()
pred = net.model(input_x1, input_x2, input_x3)
pred = tf.reshape(pred, [test_x // 8, test_y // 8])
#saver2 = tf.train.Saver(max_to_keep=20)
with tf.Session(graph=g2) as sess:
#sess.run(tf.global_variables_initializer())
if os.path.exists('./res_mcnn_save/session2.ckpt.index'):
saver = tf.train.Saver()
saver.restore(sess, './res_mcnn_save/session.ckpt')
feed_dict = {
input_x1: data_x1,
input_x2: data_x2,
input_x3: data_x3
}
pred_img = sess.run(pred, feed_dict=feed_dict)
print(type(pred_img))
tmin, tmax = pred_img.min(), pred_img.max()
img_show = (pred_img - tmin) / (tmax - tmin)
img_show = img_as_ubyte(img_show)
io.imsave('./res_test_mall' + str(img_num_) + '.jpg', img_show)
print(img_show)
scio.savemat('./res_test_mall' + str(img_num_),
{'anno_img': pred_img})
else:
raise Exception("**@liang : ckpt file not found")
area = data_cap_map[i]
test_batch[i, :, :, :] = img[area[2]:area[3], area[0]:area[1], :]
ground_batch[i, :, :] = ground[area[2]:area[3], area[0]:area[1]]
frame1_batch[i, :, :, :] = frame_1[area[2]:area[3], area[0]:area[1], :]
frame2_batch[i, :, :, :] = frame_2[area[2]:area[3], area[0]:area[1], :]
return test_batch, ground_batch, frame1_batch, frame2_batch
########################################################
sequence = random.sample(range(1, sample_for_train + 1), sample_for_train)
batch_times = 1
Data = Source_Data(batch_times, sequence)
train_batch, ground_batch, frame1_batch, frame2_batch = Data.get_train_data()
resnet_ = get_resnet()
data_x1, data_x2, data_x3 = resnet_.get_resnet_output(train_batch)
with tf.Graph().as_default() as g2:
net = Model_net()
prediction_img = net.prediction #tf.reshape(net.prediction,[-1,ground_size_x//2,ground_size_y//2,1])
gloabal_step_ = tf.Variable(0, trainable=False)
learning_rate = tf.train.inverse_time_decay(starting_learning_rate,
global_step=gloabal_step_,
decay_steps=decay_step_,
decay_rate=learning_rate_decay,
staircase=False)
