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srresnet.py
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import os
import time
from glob import glob
import tensorflow as tf
from scipy.misc import imresize
from generator import generator_sr
from utils import *
def doresize(x, shape):
x = np.copy((x+1.)*127.5).astype("uint8")
y = imresize(x, shape)
return y
class srresnet(object):
def __init__(self, sess, image_size=128, is_crop=True,
batch_size=64, image_shape=[128, 128, 3],
y_dim=None, z_dim=100, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024, c_dim=3, dataset_name='default',
checkpoint_dir=None):
"""
Args:
sess: TensorFlow session
batch_size: The size of batch. Should be specified before training.
y_dim: (optional) Dimension of dim for y. [None]
z_dim: (optional) Dimension of dim for Z. [100]
gf_dim: (optional) Dimension of gen filters in first conv layer. [64]
df_dim: (optional) Dimension of discrim filters in first conv layer. [64]
gfc_dim: (optional) Dimension of gen untis for for fully connected layer. [1024]
dfc_dim: (optional) Dimension of discrim units for fully connected layer. [1024]
c_dim: (optional) Dimension of image color. [3]
"""
self.sess = sess
self.is_crop = is_crop
self.batch_size = batch_size
self.image_size = image_size
self.input_size = 96
self.sample_size = batch_size
self.image_shape = image_shape
self.y_dim = y_dim
self.z_dim = z_dim
self.gf_dim = gf_dim
self.df_dim = df_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
self.c_dim = 3
self.dataset_name = dataset_name
self.checkpoint_dir = checkpoint_dir
self.build_model()
def build_model(self):
self.inputs = tf.placeholder(tf.float32, [self.batch_size, self.input_size, self.input_size, 3],
name='real_images')
try:
self.up_inputs = tf.image.resize_images(self.inputs, self.image_shape[0], self.image_shape[1], tf.image.ResizeMethod.NEAREST_NEIGHBOR)
except ValueError:
# newer versions of tensorflow
self.up_inputs = tf.image.resize_images(self.inputs, [self.image_shape[0], self.image_shape[1]], tf.image.ResizeMethod.NEAREST_NEIGHBOR)
self.images = tf.placeholder(tf.float32, [self.batch_size] + self.image_shape,
name='real_images')
self.sample_images= tf.placeholder(tf.float32, [self.sample_size] + self.image_shape,
name='sample_images')
# self.G = self.generator(self.inputs)
self.G = generator_sr(self.inputs)
self.G_sum = tf.summary.image("G", self.G)
self.g_loss = tf.reduce_mean(tf.square(self.images-self.G))
self.g_loss_sum = tf.summary.scalar("g_loss", self.g_loss)
t_vars = tf.trainable_variables()
#self.g_vars = [var for var in t_vars if 'g_' in var.name]
self.g_vars = [var for var in t_vars]
self.saver = tf.train.Saver()
def train(self, config):
# first setup validation data
data = sorted(glob(os.path.join("./data", config.dataset, "valid", "*.png")))
global_step = tf.train.create_global_step()
lr = tf.train.exponential_decay(config.learning_rate, global_step, decay_steps=config.lr_decay_step,
decay_rate=config.lr_decay_rate)
if config.optimizer == 'SGD':
g_optim = tf.train.MomentumOptimizer(learning_rate=lr, momentum=config.momentum).minimize(self.g_loss, var_list=self.g_vars)
elif config.optimizer == 'Adam':
g_optim = tf.train.AdamOptimizer(config.learning_rate, beta1=config.beta1).minimize(self.g_loss, var_list=self.g_vars)
tf.initialize_all_variables().run()
self.saver = tf.train.Saver(max_to_keep=60)
self.g_sum = tf.summary.merge([self.G_sum, self.g_loss_sum])
self.writer = tf.summary.FileWriter("./logs", self.sess.graph)
sample_files = data[0:self.sample_size]
sample = [get_image_samp(sample_file, self.image_size, is_crop=self.is_crop) for sample_file in sample_files]
sample_inputs = [doresize(xx, [self.input_size,]*2) for xx in sample]
sample_images = np.array(sample).astype(np.float32)
sample_input_images = np.array(sample_inputs).astype(np.float32)
save_images(sample_input_images, [int(self.batch_size/8), 8], './samples/inputs_small.png')
save_images(sample_images, [int(self.batch_size/8), 8], './samples/reference.png')
counter = 1
start_time = time.time()
could_load, checkpoint_counter = self.load(self.checkpoint_dir)
if could_load:
counter = checkpoint_counter
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
# we only save the validation inputs once
have_saved_inputs = False
for epoch in range(config.epoch):
print('epoch : {}'.format(epoch))
data = sorted(glob(os.path.join("./data", config.dataset, "train", "*.png")))
# batch_idxs = min(len(data), config.train_size) // config.batch_size
batch_idxs = min(len(data), config.train_size)
for idx in range(0, batch_idxs):
#batch_files = data[idx*config.batch_size:(idx+1)*config.batch_size]
#batch = [get_image(batch_file, self.image_size, config.batch_size, is_crop=self.is_crop) for batch_file in batch_files]
batch_file = data[idx]
batch = get_image(batch_file, self.image_size, config.batch_size, is_crop=self.is_crop)
input_batch = [doresize(xx, [self.input_size,]*2) for xx in batch]
batch_images = np.array(batch).astype(np.float32)
batch_inputs = np.array(input_batch).astype(np.float32)
# Update G network
_, summary_str, errG = self.sess.run([g_optim, self.g_sum, self.g_loss],
feed_dict={ self.inputs: batch_inputs, self.images: batch_images })
self.writer.add_summary(summary_str, counter)
counter += 1
print("Epoch: [%2d] [%4d/%4d] time: %4.4f, g_loss: %.8f" \
% (epoch, idx, batch_idxs,
time.time() - start_time, errG))
if np.mod(counter, 500) == 1:
samples, g_loss, up_inputs = self.sess.run(
[self.G, self.g_loss, self.up_inputs],
feed_dict={self.inputs: sample_input_images, self.images: sample_images}
)
if not have_saved_inputs:
save_images(up_inputs, [int(self.batch_size/8), 8], './samples/inputs.png')
have_saved_inputs = True
diff = (samples - sample_images)/2.
diff = np.reshape(diff, (self.batch_size, -1))
rmse = np.sqrt(np.mean(diff ** 2, 1))
psnr = 20 * np.log10(1 / rmse)
save_images(samples, [int(self.batch_size/8), 8],
'./samples/g_valid_%s_%s.png' % (epoch, idx))
print("[Sample] g_loss: %.8f, PSNR: %.8f" % (g_loss, np.mean(psnr)))
if np.mod(counter, 500) == 2:
self.save(config.checkpoint_dir, counter)
# def generator(self, z):
# # project `z` and reshape
# self.h0, self.h0_w, self.h0_b = deconv2d(z, [self.batch_size, 32, 32, self.gf_dim], k_h=1, k_w=1, d_h=1, d_w=1, name='g_h0', with_w=True)
# h0 = lrelu(self.h0)
#
# self.h1, self.h1_w, self.h1_b = deconv2d(h0, [self.batch_size, 32, 32, self.gf_dim], name='g_h1', d_h=1, d_w=1, with_w=True)
# h1 = lrelu(self.h1)
#
# h2, self.h2_w, self.h2_b = deconv2d(h1, [self.batch_size, 32, 32, 3*16], d_h=1, d_w=1, name='g_h2', with_w=True)
# h2 = PS(h2, 4, color=True)
#
# return tf.nn.tanh(h2)
def save(self, checkpoint_dir, step):
model_name = "srresnet.model"
model_dir = "%s_%s" % (self.dataset_name, self.batch_size)
checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
self.saver.save(self.sess,
os.path.join(checkpoint_dir, model_name),
global_step=step)
def load(self, checkpoint_dir):
import re
print(" [*] Reading checkpoints...")
model_dir = "%s_%s" % (self.dataset_name, self.batch_size)
checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
counter = int(next(re.finditer("(\d+)(?!.*\d)", ckpt_name)).group(0))
print(" [*] Success to read {}".format(ckpt_name))
return True, counter
else:
print(" [*] Failed to find a checkpoint")
return False, 0