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model.py
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model.py
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import tensorflow as tf
from tensorflow.contrib.framework import arg_scope
from layers import *
from utils import show_all_variables
class Model(object):
def __init__(self, config, data_loader):
self.data_loader = data_loader
self.task = config.task
self.debug = config.debug
self.config = config
self.input_height = config.input_height
self.input_width = config.input_width
self.input_channel = config.input_channel
self.reg_scale = config.reg_scale
self.learning_rate = config.learning_rate
self.max_grad_norm = config.max_grad_norm
self.batch_size = config.batch_size
self.layer_dict = {}
self._build_placeholders()
self._build_model()
self._build_steps()
self._build_optim()
show_all_variables()
def _build_placeholders(self):
image_dims = [self.input_height, self.input_width, self.input_channel]
min_after_dequeue = 5000
capacity = min_after_dequeue + 3 * self.batch_size
self.synthetic_batch_size = tf.placeholder(tf.int32, [], "synthetic_batch_size")
self.synthetic_filenames, self.synthetic_images = \
image_from_paths(self.data_loader.synthetic_data_paths,
self.data_loader.synthetic_data_dims, seed=self.config.random_seed)
self.x_filename, self.x = tf.train.shuffle_batch(
[self.synthetic_filenames, self.synthetic_images],
batch_size=self.synthetic_batch_size,
num_threads=4, capacity=capacity,
min_after_dequeue=min_after_dequeue, name='synthetic_inputs')
self.test_x_filename, self.test_x = tf.train.batch(
[self.synthetic_filenames, self.synthetic_images],
batch_size=self.synthetic_batch_size,
num_threads=1, capacity=capacity,
name='synthetic_test_inputs')
if not self.config.is_train:
self.x_filename, self.x = \
self.test_x_filename, self.test_x
self.y = tf.placeholder(
tf.uint8, [None, None, None, self.input_channel], name='real_inputs')
self.R_x_history = tf.placeholder(
tf.float32, [None, None, None, self.input_channel], 'R_x_history')
resize_dim = [self.input_height, self.input_width]
self.resized_x = tf.image.resize_images(self.x, resize_dim)
self.resized_y = tf.image.resize_images(self.y, resize_dim)
self.resized_test_x = tf.image.resize_images(self.test_x, resize_dim)
self.normalized_x = normalize(self.resized_x)
self.normalized_y = normalize(self.resized_y)
self.refiner_step = tf.Variable(0, name='refiner_step', trainable=False)
self.discrim_step = tf.Variable(0, name='discrim_step', trainable=False)
def _build_optim(self):
def minimize(loss, step, var_list):
if self.config.optimizer == "sgd":
optim = tf.train.GradientDescentOptimizer(self.learning_rate)
elif self.config.optimizer == "adam":
optim = tf.train.AdamOptimizer(self.learning_rate)
else:
raise Exception("[!] Unkown optimizer: {}".format(self.config.optimizer))
if self.max_grad_norm != None:
grads_and_vars = optim.compute_gradients(loss)
new_grads_and_vars = []
for idx, (grad, var) in enumerate(grads_and_vars):
if grad is not None and var in var_list:
new_grads_and_vars.append((tf.clip_by_norm(grad, self.max_grad_norm), var))
return optim.apply_gradients(new_grads_and_vars,
global_step=step)
else:
return optim.minimize(loss, global_step=step, var_list=var_list)
if self.task == "generative":
self.refiner_optim = minimize(
self.refiner_loss, self.refiner_step, self.refiner_vars)
self.discrim_optim = minimize(
self.discrim_loss, self.discrim_step, self.discrim_vars)
self.discrim_optim_with_history = minimize(
self.discrim_loss_with_history, self.discrim_step, self.discrim_vars)
elif self.task == "estimate":
raise Exception("[!] Not implemented yet")
def _build_model(self):
with arg_scope([resnet_block, conv2d, max_pool2d, tanh],
layer_dict=self.layer_dict):
self.R_x = self._build_refiner(self.normalized_x)
self.denormalized_R_x = denormalize(self.R_x)
self.D_y, self.D_y_logits = \
self._build_discrim(self.normalized_y, name="D_y")
self.D_R_x, self.D_R_x_logits = \
self._build_discrim(self.R_x, name="D_R_x", reuse=True)
self.D_R_x_history, self.D_R_x_history_logits = \
self._build_discrim(self.R_x_history,
name="D_R_x_history", reuse=True)
#self.estimate_outputs = self._build_estimation_network()
self._build_loss()
def _build_loss(self):
# Refiner loss
def fake_label(layer):
return tf.zeros_like(layer, dtype=tf.int32)[:,:,:,0]
def real_label(layer):
return tf.ones_like(layer, dtype=tf.int32)[:,:,:,0]
def log_loss(logits, label, name):
return tf.reduce_sum(SE_loss(logits=logits, labels=label), [1, 2], name=name)
with tf.name_scope("refiner"):
self.realism_loss = log_loss(
self.D_R_x_logits, real_label(self.D_R_x_logits), "realism_loss")
self.regularization_loss = \
self.reg_scale * tf.reduce_sum(
tf.abs(self.R_x - self.normalized_x), [1, 2, 3],
name="regularization_loss")
self.refiner_loss = tf.reduce_mean(
self.realism_loss + self.regularization_loss,
name="refiner_loss")
if self.debug:
self.refiner_loss = tf.Print(
self.refiner_loss, [self.R_x], "R_x")
self.refiner_loss = tf.Print(
self.refiner_loss, [self.D_R_x], "D_R_x")
self.refiner_loss = tf.Print(
self.refiner_loss, [self.normalized_x], "normalized_x")
self.refiner_loss = tf.Print(
self.refiner_loss, [self.denormalized_R_x], "denormalized_R_x")
self.refiner_loss = tf.Print(
self.refiner_loss, [self.regularization_loss], "reg_loss")
self.refiner_summary = tf.summary.merge([
#tf.summary.image("synthetic_images",
# self.x, max_outputs=self.config.max_image_summary),
#tf.summary.image("refined_images",
# self.denormalized_R_x, max_outputs=self.config.max_image_summary),
tf.summary.scalar("refiner/realism_loss",
tf.reduce_mean(self.realism_loss)),
tf.summary.scalar("refiner/regularization_loss",
tf.reduce_mean(self.regularization_loss)),
tf.summary.scalar("refiner/loss",
tf.reduce_mean(self.refiner_loss)),
])
# Discriminator loss
with tf.name_scope("discriminator"):
self.refiner_d_loss = log_loss(
self.D_R_x_logits, fake_label(self.D_R_x_logits), "refiner_d_loss")
self.synthetic_d_loss = log_loss(
self.D_y_logits, real_label(self.D_y_logits), "synthetic_d_loss")
self.discrim_loss = tf.reduce_mean(
self.refiner_d_loss + \
self.synthetic_d_loss, name="discrim_loss")
# with history
self.refiner_d_loss_with_history = log_loss(
self.D_R_x_history_logits,
fake_label(self.D_R_x_history_logits),
"refiner_d_loss_with_history")
self.discrim_loss_with_history = tf.reduce_mean(
tf.concat([self.refiner_d_loss, self.refiner_d_loss_with_history], axis=0) + \
self.synthetic_d_loss, name="discrim_loss_with_history")
if self.debug:
self.discrim_loss_with_history = tf.Print(
self.discrim_loss_with_history, [self.D_R_x_logits], "D_R_x_logits")
self.discrim_loss_with_history = tf.Print(
self.discrim_loss_with_history, [self.D_y_logits], "D_y_logits")
self.discrim_loss_with_history = tf.Print(
self.discrim_loss_with_history, [self.refiner_d_loss], "refiner_d_loss")
self.discrim_loss_with_history = tf.Print(
self.discrim_loss_with_history, [self.refiner_d_loss_with_history], "refiner_d_loss_with_history")
self.discrim_loss_with_history = tf.Print(
self.discrim_loss_with_history, [self.synthetic_d_loss], "synthetic_d_loss")
self.discrim_loss_with_history = tf.Print(
self.discrim_loss_with_history, [self.D_R_x_history_logits], "D_R_x_history_logits")
self.discrim_loss_with_history = tf.Print(
self.discrim_loss_with_history, [self.D_y_logits], "D_y_logits")
self.discrim_summary = tf.summary.merge([
#tf.summary.image("real_images",
# self.resized_y, max_outputs=self.config.max_image_summary),
tf.summary.scalar("synthetic_d_loss",
tf.reduce_mean(self.synthetic_d_loss)),
tf.summary.scalar("refiner_d_loss",
tf.reduce_mean(self.refiner_d_loss)),
tf.summary.scalar("discrim_loss",
tf.reduce_mean(self.discrim_loss)),
])
self.discrim_summary_with_history = tf.summary.merge([
#tf.summary.image("real_images",
# self.resized_y, max_outputs=self.config.max_image_summary),
tf.summary.scalar("synthetic_d_loss",
tf.reduce_mean(self.synthetic_d_loss)),
tf.summary.scalar("refiner_d_loss_with_history",
tf.reduce_mean(self.refiner_d_loss_with_history)),
tf.summary.scalar("discrim_loss_with_history",
tf.reduce_mean(self.discrim_loss_with_history)),
])
def _build_steps(self):
def run(sess, feed_dict, fetch,
summary_op, summary_writer, output_op=None):
if summary_writer is not None:
fetch['summary'] = summary_op
if output_op is not None:
fetch['output'] = output_op
result = sess.run(fetch, feed_dict=feed_dict)
if result.has_key('summary'):
summary_writer.add_summary(result['summary'], result['step'])
summary_writer.flush()
return result
def train_refiner(sess, feed_dict, summary_writer=None, with_output=False):
fetch = {
'loss': self.refiner_loss,
'optim': self.refiner_optim,
'step': self.refiner_step,
}
return run(sess, feed_dict, fetch,
self.refiner_summary, summary_writer,
output_op=self.R_x if with_output else None)
def test_refiner(sess, feed_dict, summary_writer=None, with_output=False):
fetch = {
'filename': self.x_filename,
'loss': self.refiner_loss,
'step': self.refiner_step,
}
return run(sess, feed_dict, fetch,
self.refiner_summary, summary_writer,
output_op=self.R_x if with_output else None)
def train_discrim(sess, feed_dict, summary_writer=None,
with_history=False, with_output=False):
fetch = {
'loss': self.discrim_loss_with_history,
'optim': self.discrim_optim_with_history,
'step': self.discrim_step,
}
return run(sess, feed_dict, fetch,
self.discrim_summary_with_history if with_history \
else self.discrim_summary, summary_writer,
output_op=self.D_R_x if with_output else None)
def test_discrim(sess, feed_dict, summary_writer=None,
with_history=False, with_output=False):
fetch = {
'loss': self.discrim_loss,
'step': self.discrim_step,
}
return run(sess, feed_dict, fetch,
self.discrim_summary_with_history if with_history \
else self.discrim_summary, summary_writer,
output_op=self.D_R_x if with_output else None)
self.train_refiner = train_refiner
self.test_refiner = test_refiner
self.train_discrim = train_discrim
self.test_discrim = test_discrim
def _build_refiner(self, layer):
with tf.variable_scope("refiner") as sc:
layer = conv2d(layer, 64, 3, 1, scope="conv_1")
layer = repeat(layer, 4, resnet_block, scope="resnet")
layer = conv2d(layer, 1, 1, 1,
activation_fn=None, scope="conv_2")
output = tanh(layer, name="tanh")
self.refiner_vars = tf.contrib.framework.get_variables(sc)
return output
def _build_discrim(self, layer, name, reuse=False):
with tf.variable_scope("discriminator", reuse=reuse) as sc:
layer = conv2d(layer, 96, 3, 2, scope="conv_1", name=name)
layer = conv2d(layer, 64, 3, 2, scope="conv_2", name=name)
layer = max_pool2d(layer, 3, 1, scope="max_1", name=name)
layer = conv2d(layer, 32, 3, 1, scope="conv_3", name=name)
layer = conv2d(layer, 32, 1, 1, scope="conv_4", name=name)
logits = conv2d(layer, 2, 1, 1, scope="conv_5", name=name)
output = tf.nn.softmax(logits, name="softmax")
self.discrim_vars = tf.contrib.framework.get_variables(sc)
return output, logits
def _build_estimation_network(self):
layer = self.normalized_x
with tf.variable_scope("estimation"):
layer = conv2d(layer, 96, 3, 2, scope="conv_1")
layer = conv2d(layer, 64, 3, 2, scope="conv_2")
layer = max_pool2d(layer, 64, 3, scope="max_1")
layer = conv2d(layer, 32, 3, 1, scope="conv_3")
layer = conv2d(layer, 32, 1, 1, scope="conv_4")
layer = conv2d(layer, 2, 1, 1, activation_fn=slim.softmax)
return layer