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trainer.py
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trainer.py
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from __future__ import print_function
import os
import StringIO
import scipy.misc
import numpy as np
from glob import glob
from tqdm import trange
from itertools import chain
from collections import deque
from models import *
from utils import save_image
def next(loader):
return loader.next()[0].data.numpy()
def to_nhwc(image, data_format):
if data_format == 'NCHW':
new_image = nchw_to_nhwc(image)
else:
new_image = image
return new_image
def to_nchw_numpy(image):
if image.shape[3] in [1, 3]:
new_image = image.transpose([0, 3, 1, 2])
else:
new_image = image
return new_image
def norm_img(image, data_format=None):
image = image/127.5 - 1.
if data_format:
image = to_nhwc(image, data_format)
return image
def denorm_img(norm, data_format):
return tf.clip_by_value(to_nhwc((norm + 1)*127.5, data_format), 0, 255)
def slerp(val, low, high):
"""Code from https://github.com/soumith/dcgan.torch/issues/14"""
omega = np.arccos(np.clip(np.dot(low/np.linalg.norm(low), high/np.linalg.norm(high)), -1, 1))
so = np.sin(omega)
if so == 0:
return (1.0-val) * low + val * high # L'Hopital's rule/LERP
return np.sin((1.0-val)*omega) / so * low + np.sin(val*omega) / so * high
class Trainer(object):
def __init__(self, config, data_loader):
self.config = config
self.data_loader = data_loader
self.dataset = config.dataset
self.beta1 = config.beta1
self.beta2 = config.beta2
self.optimizer = config.optimizer
self.batch_size = config.batch_size
self.step = tf.Variable(0, name='step', trainable=False)
self.g_lr = tf.Variable(config.g_lr, name='g_lr')
self.d_lr = tf.Variable(config.d_lr, name='d_lr')
self.g_lr_update = tf.assign(self.g_lr, tf.maximum(self.g_lr * 0.5, config.lr_lower_boundary), name='g_lr_update')
self.d_lr_update = tf.assign(self.d_lr, tf.maximum(self.d_lr * 0.5, config.lr_lower_boundary), name='d_lr_update')
self.gamma = config.gamma
self.lambda_k = config.lambda_k
self.z_num = config.z_num
self.conv_hidden_num = config.conv_hidden_num
self.input_scale_size = config.input_scale_size
self.model_dir = config.model_dir
self.load_path = config.load_path
self.use_gpu = config.use_gpu
self.data_format = config.data_format
_, height, width, self.channel = \
get_conv_shape(self.data_loader, self.data_format)
self.repeat_num = int(np.log2(height)) - 2
self.start_step = 0
self.log_step = config.log_step
self.max_step = config.max_step
self.save_step = config.save_step
self.lr_update_step = config.lr_update_step
self.is_train = config.is_train
self.build_model()
self.saver = tf.train.Saver()
self.summary_writer = tf.summary.FileWriter(self.model_dir)
sv = tf.train.Supervisor(logdir=self.model_dir,
is_chief=True,
saver=self.saver,
summary_op=None,
summary_writer=self.summary_writer,
save_model_secs=300,
global_step=self.step,
ready_for_local_init_op=None)
gpu_options = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
self.sess = sv.prepare_or_wait_for_session(config=sess_config)
if not self.is_train:
# dirty way to bypass graph finilization error
g = tf.get_default_graph()
g._finalized = False
self.build_test_model()
def train(self):
z_fixed = np.random.uniform(-1, 1, size=(self.batch_size, self.z_num))
x_fixed = self.get_image_from_loader()
save_image(x_fixed, '{}/x_fixed.png'.format(self.model_dir))
prev_measure = 1
measure_history = deque([0]*self.lr_update_step, self.lr_update_step)
for step in trange(self.start_step, self.max_step):
fetch_dict = {
"k_update": self.k_update,
"measure": self.measure,
}
if step % self.log_step == 0:
fetch_dict.update({
"summary": self.summary_op,
"g_loss": self.g_loss,
"d_loss": self.d_loss,
"k_t": self.k_t,
})
result = self.sess.run(fetch_dict)
measure = result['measure']
measure_history.append(measure)
if step % self.log_step == 0:
self.summary_writer.add_summary(result['summary'], step)
self.summary_writer.flush()
g_loss = result['g_loss']
d_loss = result['d_loss']
k_t = result['k_t']
print("[{}/{}] Loss_D: {:.6f} Loss_G: {:.6f} measure: {:.4f}, k_t: {:.4f}". \
format(step, self.max_step, d_loss, g_loss, measure, k_t))
if step % (self.log_step * 10) == 0:
x_fake = self.generate(z_fixed, self.model_dir, idx=step)
self.autoencode(x_fixed, self.model_dir, idx=step, x_fake=x_fake)
if step % self.lr_update_step == self.lr_update_step - 1:
self.sess.run([self.g_lr_update, self.d_lr_update])
#cur_measure = np.mean(measure_history)
#if cur_measure > prev_measure * 0.99:
#prev_measure = cur_measure
def build_model(self):
self.x = self.data_loader
x = norm_img(self.x)
self.z = tf.random_uniform(
(tf.shape(x)[0], self.z_num), minval=-1.0, maxval=1.0)
self.k_t = tf.Variable(0., trainable=False, name='k_t')
G, self.G_var = GeneratorCNN(
self.z, self.conv_hidden_num, self.channel,
self.repeat_num, self.data_format, reuse=False)
d_out, self.D_z, self.D_var = DiscriminatorCNN(
tf.concat([G, x], 0), self.channel, self.z_num, self.repeat_num,
self.conv_hidden_num, self.data_format)
AE_G, AE_x = tf.split(d_out, 2)
self.G = denorm_img(G, self.data_format)
self.AE_G, self.AE_x = denorm_img(AE_G, self.data_format), denorm_img(AE_x, self.data_format)
if self.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer
else:
raise Exception("[!] Caution! Paper didn't use {} opimizer other than Adam".format(config.optimizer))
g_optimizer, d_optimizer = optimizer(self.g_lr), optimizer(self.d_lr)
self.d_loss_real = tf.reduce_mean(tf.abs(AE_x - x))
self.d_loss_fake = tf.reduce_mean(tf.abs(AE_G - G))
self.d_loss = self.d_loss_real - self.k_t * self.d_loss_fake
self.g_loss = tf.reduce_mean(tf.abs(AE_G - G))
d_optim = d_optimizer.minimize(self.d_loss, var_list=self.D_var)
g_optim = g_optimizer.minimize(self.g_loss, global_step=self.step, var_list=self.G_var)
self.balance = self.gamma * self.d_loss_real - self.g_loss
self.measure = self.d_loss_real + tf.abs(self.balance)
with tf.control_dependencies([d_optim, g_optim]):
self.k_update = tf.assign(
self.k_t, tf.clip_by_value(self.k_t + self.lambda_k * self.balance, 0, 1))
self.summary_op = tf.summary.merge([
tf.summary.image("G", self.G),
tf.summary.image("AE_G", self.AE_G),
tf.summary.image("AE_x", self.AE_x),
tf.summary.scalar("loss/d_loss", self.d_loss),
tf.summary.scalar("loss/d_loss_real", self.d_loss_real),
tf.summary.scalar("loss/d_loss_fake", self.d_loss_fake),
tf.summary.scalar("loss/g_loss", self.g_loss),
tf.summary.scalar("misc/measure", self.measure),
tf.summary.scalar("misc/k_t", self.k_t),
tf.summary.scalar("misc/d_lr", self.d_lr),
tf.summary.scalar("misc/g_lr", self.g_lr),
tf.summary.scalar("misc/balance", self.balance),
])
def build_test_model(self):
with tf.variable_scope("test") as vs:
# Extra ops for interpolation
z_optimizer = tf.train.AdamOptimizer(0.0001)
self.z_r = tf.get_variable("z_r", [self.batch_size, self.z_num], tf.float32)
self.z_r_update = tf.assign(self.z_r, self.z)
G_z_r, _ = GeneratorCNN(
self.z_r, self.conv_hidden_num, self.channel, self.repeat_num, self.data_format, reuse=True)
with tf.variable_scope("test") as vs:
self.z_r_loss = tf.reduce_mean(tf.abs(self.x - G_z_r))
self.z_r_optim = z_optimizer.minimize(self.z_r_loss, var_list=[self.z_r])
test_variables = tf.contrib.framework.get_variables(vs)
self.sess.run(tf.variables_initializer(test_variables))
def generate(self, inputs, root_path=None, path=None, idx=None, save=True):
x = self.sess.run(self.G, {self.z: inputs})
if path is None and save:
path = os.path.join(root_path, '{}_G.png'.format(idx))
save_image(x, path)
print("[*] Samples saved: {}".format(path))
return x
def autoencode(self, inputs, path, idx=None, x_fake=None):
items = {
'real': inputs,
'fake': x_fake,
}
for key, img in items.items():
if img is None:
continue
if img.shape[3] in [1, 3]:
img = img.transpose([0, 3, 1, 2])
x_path = os.path.join(path, '{}_D_{}.png'.format(idx, key))
x = self.sess.run(self.AE_x, {self.x: img})
save_image(x, x_path)
print("[*] Samples saved: {}".format(x_path))
def encode(self, inputs):
if inputs.shape[3] in [1, 3]:
inputs = inputs.transpose([0, 3, 1, 2])
return self.sess.run(self.D_z, {self.x: inputs})
def decode(self, z):
return self.sess.run(self.AE_x, {self.D_z: z})
def interpolate_G(self, real_batch, step=0, root_path='.', train_epoch=0):
batch_size = len(real_batch)
half_batch_size = int(batch_size/2)
self.sess.run(self.z_r_update)
tf_real_batch = to_nchw_numpy(real_batch)
for i in trange(train_epoch):
z_r_loss, _ = self.sess.run([self.z_r_loss, self.z_r_optim], {self.x: tf_real_batch})
z = self.sess.run(self.z_r)
z1, z2 = z[:half_batch_size], z[half_batch_size:]
real1_batch, real2_batch = real_batch[:half_batch_size], real_batch[half_batch_size:]
generated = []
for idx, ratio in enumerate(np.linspace(0, 1, 10)):
z = np.stack([slerp(ratio, r1, r2) for r1, r2 in zip(z1, z2)])
z_decode = self.generate(z, save=False)
generated.append(z_decode)
generated = np.stack(generated).transpose([1, 0, 2, 3, 4])
for idx, img in enumerate(generated):
save_image(img, os.path.join(root_path, 'test{}_interp_G_{}.png'.format(step, idx)), nrow=10)
all_img_num = np.prod(generated.shape[:2])
batch_generated = np.reshape(generated, [all_img_num] + list(generated.shape[2:]))
save_image(batch_generated, os.path.join(root_path, 'test{}_interp_G.png'.format(step)), nrow=10)
def interpolate_D(self, real1_batch, real2_batch, step=0, root_path="."):
real1_encode = self.encode(real1_batch)
real2_encode = self.encode(real2_batch)
decodes = []
for idx, ratio in enumerate(np.linspace(0, 1, 10)):
z = np.stack([slerp(ratio, r1, r2) for r1, r2 in zip(real1_encode, real2_encode)])
z_decode = self.decode(z)
decodes.append(z_decode)
decodes = np.stack(decodes).transpose([1, 0, 2, 3, 4])
for idx, img in enumerate(decodes):
img = np.concatenate([[real1_batch[idx]], img, [real2_batch[idx]]], 0)
save_image(img, os.path.join(root_path, 'test{}_interp_D_{}.png'.format(step, idx)), nrow=10 + 2)
def test(self):
root_path = "./"#self.model_dir
all_G_z = None
for step in range(3):
real1_batch = self.get_image_from_loader()
real2_batch = self.get_image_from_loader()
save_image(real1_batch, os.path.join(root_path, 'test{}_real1.png'.format(step)))
save_image(real2_batch, os.path.join(root_path, 'test{}_real2.png'.format(step)))
self.autoencode(
real1_batch, self.model_dir, idx=os.path.join(root_path, "test{}_real1".format(step)))
self.autoencode(
real2_batch, self.model_dir, idx=os.path.join(root_path, "test{}_real2".format(step)))
self.interpolate_G(real1_batch, step, root_path)
#self.interpolate_D(real1_batch, real2_batch, step, root_path)
z_fixed = np.random.uniform(-1, 1, size=(self.batch_size, self.z_num))
G_z = self.generate(z_fixed, path=os.path.join(root_path, "test{}_G_z.png".format(step)))
if all_G_z is None:
all_G_z = G_z
else:
all_G_z = np.concatenate([all_G_z, G_z])
save_image(all_G_z, '{}/G_z{}.png'.format(root_path, step))
save_image(all_G_z, '{}/all_G_z.png'.format(root_path), nrow=16)
def get_image_from_loader(self):
x = self.data_loader.eval(session=self.sess)
if self.data_format == 'NCHW':
x = x.transpose([0, 2, 3, 1])
return x