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train.py
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train.py
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import os
import math
import tensorflow as tf
import numpy as np
from network import inference
from dataset import get_train_loader
from tqdm import tqdm
os.environ["CUDA_VISIBLE_DEVICES"]="0"
def log10(x):
numerator = tf.log(x)
denominator = tf.log(tf.constant(10, dtype=numerator.dtype))
return numerator / denominator
def color_loss(image, label, len_reg=0):
vec1 = tf.reshape(image, [-1, 3])
vec2 = tf.reshape(label, [-1, 3])
clip_value = 0.999999
norm_vec1 = tf.nn.l2_normalize(vec1, 1)
norm_vec2 = tf.nn.l2_normalize(vec2, 1)
dot = tf.reduce_sum(norm_vec1*norm_vec2, 1)
dot = tf.clip_by_value(dot, -clip_value, clip_value)
angle = tf.acos(dot) * (180/math.pi)
return tf.reduce_mean(angle)
def smoothness_loss(image):
clip_low, clip_high = 0.000001, 0.999999
image = tf.clip_by_value(image, clip_low, clip_high)
image_h, image_w = tf.shape(image)[1], tf.shape(image)[2]
tv_x = tf.reduce_mean((image[:, 1:, :, :]-image[:, :image_h-1, :, :])**2)
tv_y = tf.reduce_mean((image[:, :, 1:, :]-image[:, :, :image_w-1, :])**2)
total_loss = (tv_x + tv_y)/2
'''
log_image = tf.log(image)
log_tv_x = tf.reduce_mean((log_image[:, 1:, :, :]-
log_image[:, :image_h-1, :, :])**1.2)
log_tv_y = tf.reduce_mean((log_image[:, :, 1:, :]-
log_image[:, :, :image_w-1, :])**1.2)
total_loss = tv_x / (log_tv_x + 1e-4) + tv_y / (log_tv_y + 1e-4)
'''
return total_loss
def reconstruct_loss(image, label):
l2_loss = tf.reduce_mean(tf.square(label-image))
return l2_loss
def cal_psnr(pred, label):
label_tmp, pred_tmp = label*255, pred*255
mse = tf.reduce_mean(tf.squared_difference(label_tmp, pred_tmp))
mse = tf.cast(mse, tf.float32)
train_psnr = tf.constant(10, dtype=tf.float32)*\
log10(tf.constant(255**2, dtype=tf.float32)/mse)
return train_psnr
def train():
total_epoch, total_iter = 100, 0
best_loss, init_lr = 1e10, 5e-5
batch_size, image_h, image_w = 8, 512, 512
image = tf.placeholder(tf.float32, [None, image_h, image_w, 3])
label = tf.placeholder(tf.float32, [None, image_h, image_w, 3])
lr = tf.placeholder(tf.float32)
pred = inference(image, width=0.75, is_training=True)
c_loss = color_loss(pred, label)
s_loss = smoothness_loss(pred)
r_loss = reconstruct_loss(pred, label)
total_loss = 1e-2*c_loss + 1e2*s_loss + r_loss
#total_loss = c_loss + r_loss
all_vars = tf.trainable_variables()
backbone_vars = [var for var in all_vars if 'backbone' in var.name]
train_psnr = cal_psnr(pred, label)
tf.summary.scalar('loss', total_loss)
tf.summary.scalar('color_loss', c_loss)
tf.summary.scalar('smoothness_loss', s_loss)
tf.summary.scalar('reconstruct_loss', r_loss)
tf.summary.scalar('psnr', train_psnr)
optimizer = tf.train.AdamOptimizer(learning_rate=lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
train_op = optimizer.minimize(total_loss)
train_op = tf.group([train_op, update_ops])
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
'''
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.75)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
'''
train_writer = tf.summary.FileWriter('train_log', sess.graph)
summary_op = tf.summary.merge_all()
saver = tf.train.Saver()
with tf.device('/device:GPU:0'):
sess.run(tf.global_variables_initializer())
weight = np.load('mobilenetv2_075.npy', allow_pickle=True)
assign_ops = []
for var, para in zip(backbone_vars, weight):
assign_ops.append(var.assign(para))
sess.run(assign_ops)
data_dir = 'data_loacation_in_your_computer'
dataloader = get_train_loader((image_h, image_w), batch_size, data_dir)
for epoch in range(total_epoch):
for batch in tqdm(dataloader):
total_iter += 1
_, train_info, loss = sess.run([train_op, summary_op, total_loss],
feed_dict={image: batch[0],
label: batch[1],
lr: init_lr})
train_writer.add_summary(train_info, total_iter)
if np.mod(total_iter, 20) == 0:
print('{}th epoch, {}th iter, loss: {}'.format(epoch, total_iter, loss))
if loss < best_loss:
best_loss = loss
saver.save(sess, 'saved_models/model', global_step=total_iter)
if __name__ == '__main__':
train()
#test()