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model.py
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model.py
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import os
import time
import cv2
import vgg19
import numpy as np
import tensorflow as tf
import os.path as ops
import math
from skimage.measure import compare_psnr
from skimage.measure import compare_ssim
from skimage.measure import compare_mse
from skimage.measure import compare_nrmse
from operations import TransposeConv, DropOut
from operations import Conv, ReLU, LeakyReLU, AvgPool, BatchNorm
from PIL import Image
from numpy import average, linalg, dot
class GAN():
def __init__(self, args):
self.num_discriminator_filters = args.D_filters
self.layers = args.layers
self.growth_rate = args.growth_rate
self.gan_wt = args.gan_wt
self.l1_wt = args.l1_wt
self.vgg_wt = args.vgg_wt
self.restore = args.restore
self.batch_size = args.batch_size
self.epochs = args.epochs
self.lr = args.lr
self.model_name = args.model_name
self.decay = args.decay
self.save_samples = args.save_samples
self.sample_image_dir = args.sample_image_dir
self.A_dir = args.A_dir
self.B_dir = args.B_dir
self.custom_data = args.custom_data
self.val_fraction = args.val_fraction
self.val_threshold = args.val_threshold
self.val_frequency = args.val_frequency
self.logger_frequency = args.logger_frequency
self.EPS = 10e-12
self.score_best = -1
self.ckpt_dir = os.path.join(os.getcwd(), self.model_name, 'checkpoint')
#self.ckpt_dir = args.weights_path
self.tensorboard_dir = os.path.join(os.getcwd(), self.model_name, 'tensorboard')
#self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')
def Layer(self, input_):
"""
This function creates the components inside a composite layer
of a Dense Block.
"""
with tf.variable_scope("Composite"):
next_layer = BatchNorm(input_, isTrain = self.isTrain)
next_layer = ReLU(next_layer)
next_layer = Conv(next_layer, kernel_size = 3, stride = 1, output_channels = self.growth_rate)
next_layer = DropOut(next_layer, isTrain = self.isTrain, rate = 0.2)
return next_layer
def TransitionDown(self, input_, name):
with tf.variable_scope(name):
reduction = 0.5
reduced_output_size = int(int(input_.get_shape()[-1]) * reduction)
next_layer = BatchNorm(input_, isTrain = self.isTrain, decay = self.decay)
next_layer = Conv(next_layer, kernel_size = 1, stride = 1, output_channels = reduced_output_size)
next_layer = DropOut(next_layer, isTrain = self.isTrain, rate = 0.2)
next_layer = AvgPool(next_layer)
return next_layer
def TransitionUp(self, input_, output_channels, name):
with tf.variable_scope(name):
next_layer = TransposeConv(input_, output_channels = output_channels, kernel_size = 3)
return next_layer
def DenseBlock(self, input_, name, layers = 4):
with tf.variable_scope(name):
for i in range(layers):
with tf.variable_scope("Layer" + str(i + 1)) as scope:
output = self.Layer(input_)
output = tf.concat([input_, output], axis=3)
input_ = output
return output
def generator(self, input_):
with tf.variable_scope("ORG", reuse=tf.AUTO_REUSE):
"""
54 Layer Tiramisu
"""
with tf.variable_scope('InputConv') as scope:
input_ = Conv(input_, kernel_size = 3, stride=1, output_channels = self.growth_rate * 4)
collect_conv = []
for i in range(1, 6):
input_ = self.DenseBlock(input_, name = 'Encoder' + str(i), layers = self.layers)
collect_conv.append(input_)
input_ = self.TransitionDown(input_, name = 'TD' + str(i))
input_ = self.DenseBlock(input_, name = 'BottleNeck', layers = 15)
for i in range(1, 6):
input_ = self.TransitionUp(input_, output_channels = self.growth_rate * 4, name = 'TU' + str(6 - i))
input_ = tf.concat([input_, collect_conv[6 - i - 1]], axis = 3, name = 'Decoder' + str(6 - i) + '/Concat')
input_ = self.DenseBlock(input_, name = 'Decoder' + str(6 - i), layers = self.layers)
with tf.variable_scope('OutputConv') as scope:
output = Conv(input_, kernel_size = 1, stride = 1, output_channels = 3)
return tf.nn.tanh(output)
def generator_i(self, input_):
with tf.variable_scope("NEW", reuse=tf.AUTO_REUSE):
"""
54 Layer Tiramisu
"""
with tf.variable_scope('InputConv') as scope:
input_ = Conv(input_, kernel_size = 3, stride=1, output_channels = self.growth_rate * 4)
collect_conv = []
for i in range(1, 6):
input_ = self.DenseBlock(input_, name = 'Encoder' + str(i), layers = self.layers)
collect_conv.append(input_)
input_ = self.TransitionDown(input_, name = 'TD' + str(i))
input_ = self.DenseBlock(input_, name = 'BottleNeck', layers = 15)
for i in range(1, 6):
input_ = self.TransitionUp(input_, output_channels = self.growth_rate * 4, name = 'TU' + str(6 - i))
input_ = tf.concat([input_, collect_conv[6 - i - 1]], axis = 3, name = 'Decoder' + str(6 - i) + '/Concat')
input_ = self.DenseBlock(input_, name = 'Decoder' + str(6 - i), layers = self.layers)
with tf.variable_scope('OutputConv') as scope:
output = Conv(input_, kernel_size = 1, stride = 1, output_channels = 3)
return tf.nn.tanh(output)
def discriminator(self, input_, target, stride = 2, layer_count = 4):
"""
Using the PatchGAN as a discriminator
"""
input_ = tf.concat([input_, target], axis=3, name='Concat')
layer_specs = self.num_discriminator_filters * np.array([1, 2, 4, 8])
for i, output_channels in enumerate(layer_specs, 1):
with tf.variable_scope('Layer' + str(i)) as scope:
if i != 1:
input_ = BatchNorm(input_, isTrain = self.isTrain)
if i == layer_count:
stride = 1
input_ = LeakyReLU(input_)
input_ = Conv(input_, output_channels = output_channels, kernel_size = 4, stride = stride, padding = 'VALID', mode = 'discriminator')
with tf.variable_scope('Final_Layer') as scope:
output = Conv(input_, output_channels = 1, kernel_size = 4, stride = 1, padding = 'VALID', mode = 'discriminator')
return tf.sigmoid(output)
def build_vgg(self, img):
model = vgg19.Vgg19()
img = tf.image.resize_images(img, [224, 224])
layer = model.feature_map(img)
return layer
def build_model(self):
with tf.variable_scope('Placeholders') as scope:
self.RealA = tf.placeholder(name='A', shape=[None, 256, 256, 3], dtype=tf.float32)
self.RealB = tf.placeholder(name='B', shape=[None, 256, 256, 3], dtype=tf.float32)
self.isTrain = tf.placeholder(name = "isTrain", shape = None, dtype = tf.bool)
self.step = tf.train.get_or_create_global_step()
with tf.variable_scope('Generator', reuse=tf.AUTO_REUSE) as scope:
self.FakeB = self.generator(self.RealA)
self.illum_gt = tf.abs(self.RealA - self.RealB)
#self.illum_pred = tf.abs(self.RealA - self.FakeB)
self.Fake_illum = self.generator_i(self.RealA)
self.Fake_C = self.Fake_illum + self.RealA
self.i_gt = tf.divide(self.RealA, self.RealB)
self.Fake_i = self.generator_i(self.RealA)
self.Fake_Ci = tf.divide(self.RealA, self.Fake_i)
with tf.name_scope('Real_Discriminator'):
with tf.variable_scope('Discriminator') as scope:
self.predict_real = self.discriminator(self.RealA, self.RealB)
#self.predict_realillum = self.discriminator(self.illum_pred, self.illum_gt)
with tf.name_scope('Fake_Discriminator'):
with tf.variable_scope('Discriminator', reuse=True) as scope:
self.predict_fake = self.discriminator(self.RealA, self.FakeB)
#self.predict_fakeillum = self.discriminator(self.illum_gt, self.illum_pred)
with tf.name_scope('Real_VGG'):
with tf.variable_scope('VGG') as scope:
self.RealB_VGG = self.build_vgg(self.RealB)
with tf.name_scope('Fake_VGG'):
with tf.variable_scope('VGG', reuse=True) as scope:
self.FakeB_VGG = self.build_vgg(self.FakeB)
with tf.name_scope('DiscriminatorLoss'):
self.D_loss = tf.reduce_mean(-(tf.log(self.predict_real + self.EPS) + tf.log(1 - self.predict_fake + self.EPS)))
with tf.name_scope('GeneratorLoss'):
self.gan_loss = tf.reduce_mean(-tf.log(self.predict_fake + self.EPS))
self.l1_loss = tf.reduce_mean(tf.abs(self.RealB - self.FakeB))
self.l1_e_loss = tf.reduce_mean(tf.abs(self.RealB - self.Fake_C))
self.e_loss = tf.reduce_mean(tf.abs(self.illum_gt - self.Fake_illum))
self.l1_e_loss1 = tf.reduce_mean(tf.abs(self.RealB - self.Fake_Ci))
self.e_loss1 = tf.reduce_mean(tf.abs(self.i_gt - self.Fake_i))
#self.l1_loss = tf.reduce_mean(tf.abs(self.RealB - self.FakeB))
self.vgg_loss = (1e-5) * tf.losses.mean_squared_error(self.RealB_VGG, self.FakeB_VGG)
#self.illum_gt = tf.abs(self.RealA - self.RealB)
#self.illum_pred = tf.abs(self.RealA - self.FakeB)
#self.loss_G_Ang = tf.reduce_mean(tf.abs(self.illum_gt - self.illum_pred))
#print(self.loss_G_Ang.shape)
#assert False, "Pause"
self.G_loss = self.l1_e_loss * 1 + self.e_loss * 10 + self.l1_e_loss1 * 0.2 + self.e_loss1 * 1 + self.gan_wt * self.gan_loss + self.l1_wt * self.l1_loss + self.vgg_wt * self.vgg_loss
with tf.name_scope('Summary'):
D_loss_sum = tf.summary.scalar('Discriminator Loss', self.D_loss)
G_loss_sum = tf.summary.scalar('Generator Loss', self.G_loss)
gan_loss_sum = tf.summary.scalar('GAN Loss', self.gan_loss)
l1_loss_sum = tf.summary.scalar('L1 Loss', self.l1_loss)
l1_e_loss_sum = tf.summary.scalar('Ang Loss', self.l1_e_loss)
e_loss_sum = tf.summary.scalar('Ang Loss', self.e_loss)
vgg_loss_sum = tf.summary.scalar('VGG Loss', self.gan_loss)
output_img = tf.summary.image('Output', self.FakeB, max_outputs = 1)
target_img = tf.summary.image('Target', self.RealB, max_outputs = 1)
input_img = tf.summary.image('Input', self.RealA, max_outputs = 1)
self.image_summary = tf.summary.merge([output_img, target_img, input_img])
self.G_summary = tf.summary.merge([gan_loss_sum, l1_loss_sum, l1_e_loss_sum, e_loss_sum, vgg_loss_sum, G_loss_sum])
self.D_summary = D_loss_sum
with tf.name_scope('Variables'):
self.G_vars = [var for var in tf.trainable_variables() if var.name.startswith("Generator")]
self.D_vars = [var for var in tf.trainable_variables() if var.name.startswith("Discriminator")]
with tf.name_scope('Save'):
self.saver = tf.train.Saver(max_to_keep=3)
with tf.name_scope('Optimizer'):
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
with tf.name_scope("Discriminator_Train"):
D_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0.5)
self.D_grads_and_vars = D_optimizer.compute_gradients(self.D_loss, var_list = self.D_vars)
self.D_train = D_optimizer.apply_gradients(self.D_grads_and_vars, global_step = self.step)
with tf.name_scope("Generator_Train"):
G_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0.5)
self.G_grads_and_vars = G_optimizer.compute_gradients(self.G_loss, var_list = self.G_vars)
self.G_train = G_optimizer.apply_gradients(self.G_grads_and_vars, global_step = self.step)
def train(self):
start_epoch = 0
logger_frequency = self.logger_frequency
val_frequency = self.val_frequency
val_threshold = self.val_threshold
if not os.path.exists(self.model_name):
os.mkdir(self.model_name)
print('Loading Model')
self.build_model()
print('Model Loaded')
print('Loading Data')
if self.custom_data:
# Please ensure that the input images and target images have
# the same filename.
data = sorted(os.listdir(self.A_dir))
total_image_count = int(len(data) * (1 - self.val_fraction))
batches = total_image_count // self.batch_size
train_data = data[: total_image_count]
val_data = data[total_image_count: ]
val_image_count = len(val_data)
self.A_train = np.zeros((total_image_count, 256, 256, 3))
self.B_train = np.zeros((total_image_count, 256, 256, 3))
self.A_val = np.zeros((val_image_count, 256, 256, 3))
self.B_val = np.zeros((val_image_count, 256, 256, 3))
print(self.A_train.shape, self.A_val.shape)
for i, file in enumerate(train_data):
self.A_train[i] = cv2.resize(cv2.imread(os.path.join(os.getcwd(), self.A_dir, file), 1).astype(np.float32),(256,256))
self.B_train[i] = cv2.resize(cv2.imread(os.path.join(os.getcwd(), self.B_dir, file), 1).astype(np.float32),(256,256))
for i, file in enumerate(val_data):
self.A_val[i] = cv2.resize(cv2.imread(os.path.join(os.getcwd(), self.A_dir, file), 1).astype(np.float32),(256,256))
self.B_val[i] = cv2.resize(cv2.imread(os.path.join(os.getcwd(), self.B_dir, file), 1).astype(np.float32),(256,256))
else:
self.A_train = np.load('A_train.npy').astype(np.float32)
self.B_train = np.load('B_train.npy').astype(np.float32)
self.A_val = np.load('A_val.npy').astype(np.float32) # Valset 2
self.B_val = np.load('B_val.npy').astype(np.float32)
total_image_count = len(self.A_train)
val_image_count = len(self.A_val)
batches = total_image_count // self.batch_size
self.A_val = (self.A_val / 255) * 2 - 1
self.B_val = (self.B_val / 255) * 2 - 1
self.A_train = (self.A_train / 255) * 2 - 1
self.B_train = (self.B_train / 255) * 2 - 1
print('Data Loaded')
with tf.Session() as self.sess:
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
if self.restore:
print('Loading Checkpoint')
ckpt = tf.train.latest_checkpoint(self.ckpt_dir)
self.saver.restore(self.sess, ckpt)
self.step = tf.train.get_or_create_global_step()
print('Checkpoint Loaded')
self.writer = tf.summary.FileWriter(self.tensorboard_dir, tf.get_default_graph())
total_parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables()])
G_parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables() if v.name.startswith("Generator")])
D_parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables() if v.name.startswith("Discriminator")])
loss_operations = [self.D_loss, self.G_loss, self.gan_loss, self.l1_loss, self.l1_e_loss, self.e_loss, self.vgg_loss]
counts = self.sess.run([G_parameter_count, D_parameter_count, total_parameter_count])
print('Generator parameter count:', counts[0])
print('Discriminator parameter count:', counts[1])
print('Total parameter count:', counts[2])
# The variable below is divided by 2 since both the Generator
# and the Discriminator increases step count by 1
start = self.step.eval() // (batches * 2)
for i in range(start, self.epochs):
print('Epoch:', i)
shuffle = np.random.permutation(total_image_count)
for j in range(batches):
if j != batches - 1:
current_batch = shuffle[j * self.batch_size: (j + 1) * self.batch_size]
else:
current_batch = shuffle[j * self.batch_size: ]
a = self.A_train[current_batch]
b = self.B_train[current_batch]
feed_dict = {self.RealA: a, self.RealB: b, self.isTrain: True}
begin = time.time()
step = self.step.eval()
_, D_summary = self.sess.run([self.D_train, self.D_summary], feed_dict = feed_dict)
self.writer.add_summary(D_summary, step)
_, G_summary = self.sess.run([self.G_train, self.G_summary], feed_dict = feed_dict)
self.writer.add_summary(G_summary, step)
print('Time Per Step: ', format(time.time() - begin, '.3f'), end='\r')
if j % logger_frequency == 0:
D_loss, G_loss, GAN_loss, L1_loss, l1_e_loss, e_loss, VGG_loss = self.sess.run(loss_operations, feed_dict=feed_dict)
GAN_loss = GAN_loss * self.gan_wt
L1_loss = L1_loss * self.l1_wt
VGG_loss = VGG_loss * self.vgg_wt
trial_image_idx = np.random.randint(total_image_count)
a = self.A_train[trial_image_idx]
b = self.B_train[trial_image_idx]
if a.ndim == 3:
a = np.expand_dims(a, axis = 0)
if b.ndim == 3:
b = np.expand_dims(b, axis = 0)
feed_dict = {self.RealA: a, self.RealB: b, self.isTrain: False}
img_summary = self.sess.run(self.image_summary, feed_dict=feed_dict)
self.writer.add_summary(img_summary, step)
line = 'Batch: %d, G_Loss: %.3f, GAN: %.3f, L1: %.3f, L1e: %.3f, e: %.3f, P: %.3f' % (
j, G_loss, GAN_loss, L1_loss, l1_e_loss, e_loss, VGG_loss)
print(line)
print(self.RealA)
print('B')
print(self.FakeB)
print('C')
print(self.Fake_illum)
# The variable `step` counts both D and G updates as individual steps.
# The variable `G_D_step` counts one D update followed by a G update
# as a single step.
G_D_step = step // 2
print('GD', G_D_step, 'val', val_threshold)
if (val_threshold > G_D_step) and (j % val_frequency == 0):
self.validate()
if not os.path.exists(self.ckpt_dir):
os.makedirs(self.ckpt_dir)
if i%30 == 0 & i<self.epochs:
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',time.localtime(time.time()))
model_name = 'deshadow_gan_{:s}.ckpt'.format(str(train_start_time))
model_save_path=os.path.join(self.ckpt_dir,model_name)
self.saver.save(sess = self.sess, save_path = model_save_path, global_step =i)
#self.saver(self.sess,)
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',time.localtime(time.time()))
model_name = 'deshadow_gan_{:s}.ckpt'.format(str(train_start_time))
model_save_path=os.path.join(self.ckpt_dir,model_name)
self.saver.save(sess = self.sess, save_path = model_save_path, global_step =self.epochs)
"""
model_save_dir = 'model'
if not ops.exists(model_save_dir):
os.makedirs(model_save_dir)
if i%30==0&i<self.epochs:
train_start_time = time.strftime(
'%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))
model_name = 'deshadow_gan_{:s}.ckpt'.format(str(train_start_time))
model_save_path = ops.join(model_save_dir, model_name)
self.saver.save(sess=self.sess, save_path=model_save_path,
global_step=i)
#model_save_dir = 'model/deshadow_gan_tensorflow10'
train_start_time = time.strftime(
'%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))
model_name = 'deshadow_gan_{:s}.ckpt'.format(str(train_start_time))
model_save_path = ops.join(model_save_dir, model_name)
self.saver.save(sess=self.sess, save_path=model_save_path,
global_step=self.epochs)
"""
def validate(self):
total_ssim = 0
total_psnr = 0
psnr_weight = 1/20
ssim_weight = 1
val_image_count = len(self.A_val)
for i in range(val_image_count):
x = np.expand_dims(self.A_val[i], axis = 0)
feed_dict = {self.RealA: x ,self.isTrain: False}
generated_B = self.FakeB.eval(feed_dict = feed_dict)
print('Validation Image', i, end = '\r')
generated_B = (((generated_B[0] + 1)/2) * 255).astype(np.uint8)
real_B = (((self.B_val[i] + 1)/2)*255).astype(np.uint8)
psnr = compare_psnr(real_B, generated_B)
ssim = compare_ssim(real_B, generated_B, multichannel = True)
total_psnr = total_psnr + psnr
total_ssim = total_ssim + ssim
average_psnr = total_psnr / val_image_count
average_ssim = total_ssim / val_image_count
score = average_psnr * psnr_weight + average_ssim * ssim_weight
if(score > self.score_best):
self.score_best = score
self.saver.save(self.sess, os.path.join(self.ckpt_dir, 'gan'), global_step = self.step.eval())
line = 'Better Score: %.6f, PSNR: %.6f, SSIM: %.6f' %(score, average_psnr, average_ssim)
print(line)
with open(os.path.join(self.ckpt_dir, 'logs.txt'),'a') as f:
line += '\n'
f.write(line)
if self.save_samples:
try:
image_list = os.listdir(self.sample_image_dir)
except:
print('Sample images not found. Terminating program')
exit(0)
for i, file in enumerate(image_list, 1):
print('Sample Image', i, end = '\r')
x = cv2.imread(os.path.join(self.sample_image_dir, file), 1)
x = (x/255)*2 - 1
x = np.reshape(x,(1,256,256,3))
feed_dict = {self.RealA: x, self.isTrain: False}
img = self.FakeB.eval(feed_dict = feed_dict)
img = img[0,:,:,:]
img = (((img + 1)/2) * 255).astype(np.uint8)
cv2.imwrite(os.path.join(self.ckpt_dir, file), img)
def inference(self, input_dir, result_dir):
input_list = os.listdir(input_dir)
if not os.path.exists(result_dir):
os.mkdir(result_dir)
print('Loading Model')
self.build_model()
print('Model Loaded')
with tf.Session() as self.sess:
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
print('Loading Checkpoint')
ckpt = tf.train.latest_checkpoint(self.ckpt_dir)
print(self.ckpt_dir)
#self.saver.restore(self.sess, save_path=self.ckpt_dir)
self.saver.restore(self.sess, ckpt)
self.step = tf.train.get_or_create_global_step()
print('Checkpoint Loaded')
for i, img_file in enumerate(input_list, 1):
img = cv2.imread(os.path.join(input_dir, img_file), 1)
img=cv2.resize(img,(256,256))
print('Processing image', i, end = '\r')
img = ((np.expand_dims(img, axis = 0) / 255) * 2) - 1
feed_dict = {self.RealA: img, self.isTrain: False}
generated_B = self.FakeB.eval(feed_dict = feed_dict)
generated_B = (((generated_B[0] + 1)/2) * 255).astype(np.uint8)
cv2.imwrite(os.path.join(result_dir, img_file), generated_B)
print('Done.')