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train.py
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import os
import cv2
import glob
import argparse
import numpy as np
from tqdm import tqdm
from random import seed, randint
import tensorflow as tf
from utils import read_image, get_name
from networks import DSRNetwork
# ======================================
# argument parser
parser = argparse.ArgumentParser(description='')
parser.add_argument('--epochs', dest='epochs', type=int, default=1000, help='number of total epoches')
parser.add_argument('--batch_size', dest='batch_size', type=int, default=3, help='number of samples in one batch')
parser.add_argument('--patch_size', dest='patch_size', type=int, default=None, help='image resolution during training')
parser.add_argument('--lr', dest='lr', type=float, default=1e-4, help='initial learning rate')
parser.add_argument('--lr_gen', dest='lr_gen', type=float, default=1e-4, help='initial learning rate for generator')
parser.add_argument('--lr_disc', dest='lr_disc', type=float, default=1e-4, help='initial learning rate for discriminator')
parser.add_argument('--eval_rate', dest='eval_rate', default=200, help='evaluating and saving checkpoints every # epoch')
parser.add_argument('--ckpt_dir', dest='ckpt_dir', default='checkpoints', help='directory for checkpoints')
args = parser.parse_args()
# ======================================
# set tensorflow to use gpu
my_devices = tf.config.experimental.list_physical_devices(device_type='GPU')
print("Available GPU Devices:", len(my_devices))
if len(my_devices) != 0:
gpu = my_devices[0] # default to last gpu
tf.config.experimental.set_visible_devices(devices=gpu, device_type='GPU')
tf.config.experimental.set_memory_growth(gpu, True)
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
tf.keras.backend.set_learning_phase (True)
tf.config.experimental_run_functions_eagerly(True)
# ======================================
# set keras tensor format to channel last
tf.keras.backend.set_image_data_format('channels_last')
# ======================================
# set training parameters
epochs = args.epochs
learning_rate_gen = args.lr_gen
learning_rate_disc = args.lr_disc
learning_rate = learning_rate_gen if learning_rate_gen == learning_rate_disc else args.lr
lr_decay_rate = 5e-5
patch_size = args.patch_size
batch_size = args.batch_size
eval_rate = args.eval_rate
ckpt_dir = args.ckpt_dir
# weights parameters
loss_weights = {
"gen_adv" : 1.0, # 0.5
"perceptual" : 50.0, # 5.0
"saliency" : 5.0, # 2.0
"hue" : 0.0, # 5.0
"gp_weight" : 10.0,
"discriminator" : 1.0
}
# ======================================
# Prepare images
train_sr_data = glob.glob('SAM_dataset/img_resize/*')
train_saliency_map = glob.glob('SAM_dataset/resize_binary_img/*')
real_image = glob.glob('SAM_dataset/resize_beauty/train/*')
test_sr_data = "SAM_dataset/testDataset/ori_img" #768,576 512,392
test_saliency_map = "SAM_dataset/testDataset/guiding_sal_noFeather"
ori_sal_path = 'SAM_dataset/ori_sal/*'
checkpoint_dir = "checkpoints/"
train_sr_data.sort()
train_saliency_map.sort()
# ======================================
# Set up training parameters
tf.random.set_seed(3) # 3324
writer_path = "logs/"
if not os.path.exists(writer_path): os.makedirs(writer_path)
writer = tf.summary.create_file_writer(writer_path)
# ==================================================================================
# get testing image
# generate random integer values
seed(1) # seed random number generator
testOriImg = [get_name(path) for path in glob.glob("%s/*"%test_sr_data)] #768,576 512,392
testGuidingSal = [get_name(path) for path in glob.glob("%s/*"%test_saliency_map)]
matches = list(set(testOriImg) & set(testGuidingSal))
# test_showcase = []
# for index,test in enumerate(testOriImg):
# ori_img = cv2.imread(test)
# ori_size = (ori_img.shape[1],ori_img.shape[0])
# ori_img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB)/255.
# ori_img = cv2.resize(ori_img,(256,192))
# ori_img = np.expand_dims(ori_img,0)
# name = test.split("/")[-1].split('\\')[-1]
# guiding_sal = cv2.imread(testGuidingSal+name)
# # guiding_sal = cv2.cvtColor(guiding_sal, cv2.COLOR_BGR2GRAY)/255.
# guiding_sal = cv2.resize(guiding_sal,(256,192))/255.
# # guiding_sal = np.expand_dims(guiding_sal,2)
# guiding_sal = np.expand_dims(guiding_sal,0)
# test_showcase.append([ori_img,guiding_sal])
# ==================================================================================
# Selecting images for training and testing
train_src = train_sr_data[:3000]
train_ref = train_saliency_map[:3000]
test_src = ["%s/%s.jpg"%(test_sr_data, path) for path in matches]
test_ref = ["%s/%s.jpg"%(test_saliency_map, path) for path in matches]
# Load images from path
print("Loading training images")
training_src = [read_image(img_path, patch_size) for img_path in tqdm(train_src)]
training_ref = [read_image(img_path, patch_size) for img_path in tqdm(train_ref)]
testing_src = [read_image(img_path, (192,256)) for img_path in tqdm(test_src)]
testing_ref = [read_image(img_path, (192,256)) for img_path in tqdm(test_ref)]
# convert image lists to numpy arrays
training_src = np.asarray(training_src)
training_ref = np.asarray(training_ref)
testing_src = np.asarray(testing_src)
testing_ref = np.asarray(testing_ref)
# Convert to tf dataset
tf_train_data = tf.data.Dataset.from_tensor_slices((training_src, training_ref))
tf_test_data = tf.data.Dataset.from_tensor_slices((testing_src, testing_ref))
# ==================================================================================
# optimizers
lr_schedule = tf.keras.optimizers.schedules.InverseTimeDecay(
learning_rate, decay_steps=1, decay_rate=lr_decay_rate
)
lr_schedule_gen = tf.keras.optimizers.schedules.InverseTimeDecay(
learning_rate_gen, decay_steps=1, decay_rate=lr_decay_rate
)
lr_schedule_disc = tf.keras.optimizers.schedules.InverseTimeDecay(
learning_rate_disc, decay_steps=1, decay_rate=lr_decay_rate
)
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)
optimizer_gen = tf.keras.optimizers.Adam(learning_rate=lr_schedule_gen)
optimizer_disc = tf.keras.optimizers.Adam(learning_rate=lr_schedule_disc)
# ==================================================================================
# set up training data iteration
tf_train_data = tf_train_data.repeat().shuffle(30).batch(batch_size).prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
train_iter = iter(tf_train_data)
tf_test_data = tf_test_data.repeat().shuffle(30).batch(batch_size).prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
test_iter = iter(tf_test_data)
# ==================================================================================
# DSRNetwork
# pass weights as parameters during init, if no values are passed,
# all values are defaulted to 1.0
model = DSRNetwork(loss_weights)
# Checkpoint
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
ckpt = tf.train.Checkpoint(step=tf.Variable(1), optimizer=optimizer, model=model)
else:
ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=model)
ckpt.restore(tf.train.latest_checkpoint(ckpt_dir))
manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=10)
# ==================================================================================
@tf.function
def train_step_1(input_images, guiding_sal, step=0):
with tf.GradientTape() as dsr_tape, tf.GradientTape() as disc_tape:
loss = model.train_batch(input_images, guiding_sal)
gen_trainables = model.get_gen_trainables()
disc_trainables = model.get_disc_trainables()
if (step+1)%5 == 0:
dsr_gradients = dsr_tape.gradient(loss[:-1], gen_trainables)
optimizer_gen.apply_gradients(zip(dsr_gradients, gen_trainables))
else:
disc_gradients = disc_tape.gradient(loss[-1], disc_trainables)
optimizer_disc.apply_gradients(zip(disc_gradients, disc_trainables))
return loss
@tf.function
def train_step_2(input_images, guiding_sal, step=0):
with tf.GradientTape() as dsr_tape, tf.GradientTape() as disc_tape:
loss = model.train_batch(input_images, guiding_sal)
dsr_gradients = dsr_tape.gradient(loss[:-1], model.generator.trainable_variables)
disc_gradients = disc_tape.gradient(loss[-1], model.discriminator.trainable_variables)
optimizer_gen.apply_gradients(zip(dsr_gradients, model.generator.trainable_variables))
optimizer_disc.apply_gradients(zip(disc_gradients, model.discriminator.trainable_variables))
return loss
@tf.function
def train_step_3(input_images, guiding_sal, step=0):
with tf.GradientTape() as tape:
loss = model.train_batch(input_images, guiding_sal)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
return loss
# ==================================================================================
# begin training
with writer.as_default():
for i in tqdm(range(epochs)):
input_images, guiding_sal = next(train_iter)
g_loss, p_loss, s_loss, h_loss, d_loss = train_step_1(input_images, guiding_sal, i)
dsr_loss = g_loss + p_loss + s_loss + h_loss
with tf.name_scope("Generator") as scope:
tf.summary.scalar("Gen Adversarial Loss", g_loss, step=i)
tf.summary.scalar("Perceptual Loss", p_loss, step=i)
tf.summary.scalar("Saliency Loss", s_loss, step=i)
tf.summary.scalar("Hue Loss", h_loss, step=i)
tf.summary.scalar("Generator Loss", dsr_loss, step=i)
with tf.name_scope("Discriminator") as scope:
tf.summary.scalar("Discriminator Loss", d_loss, step=i)
writer.flush()
ckpt.step.assign_add(1)
if (i+1) % eval_rate == 0:
print('Writing example images...')
input_images, guiding_sal = next(test_iter)
outputs = model(input_images, guiding_sal)
with tf.name_scope("Inputs") as scope:
tf.summary.image("Input Images", input_images, step=i)
tf.summary.image("Guiding Saliency Map", guiding_sal, step=i)
tf.summary.image("Output Images", outputs, step=i)
print('Writing complete')
if (i+1) % eval_rate == 0 or (i+1) == epochs:
print('Saving model checkpoint...')
ckpt_path = manager.save()
print('Saved model checkpoint to %s' % ckpt_path)