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train.py
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train.py
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import tensorflow as tf
import numpy as np
import model
import argparse
import pickle
from os.path import join
import h5py
from Utils import image_processing
import scipy.misc
import random
import json
import os
import shutil
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--z_dim', type=int, default=100,
help='Noise dimension')
parser.add_argument('--t_dim', type=int, default=256,
help='Text feature dimension')
parser.add_argument('--batch_size', type=int, default=64,
help='Batch Size')
parser.add_argument('--image_size', type=int, default=64,
help='Image Size a, a x a')
parser.add_argument('--gf_dim', type=int, default=64,
help='Number of conv in the first layer gen.')
parser.add_argument('--df_dim', type=int, default=64,
help='Number of conv in the first layer discr.')
parser.add_argument('--gfc_dim', type=int, default=1024,
help='Dimension of gen untis for for fully connected layer 1024')
parser.add_argument('--caption_vector_length', type=int, default=2400,
help='Caption Vector Length')
parser.add_argument('--data_dir', type=str, default="Data",
help='Data Directory')
parser.add_argument('--learning_rate', type=float, default=0.0002,
help='Learning Rate')
parser.add_argument('--beta1', type=float, default=0.5,
help='Momentum for Adam Update')
parser.add_argument('--epochs', type=int, default=600,
help='Max number of epochs')
parser.add_argument('--save_every', type=int, default=30,
help='Save Model/Samples every x iterations over batches')
parser.add_argument('--resume_model', type=str, default=None,
help='Pre-Trained Model Path, to resume from')
parser.add_argument('--data_set', type=str, default="flowers",
help='Dat set: MS-COCO, flowers')
args = parser.parse_args()
model_options = {
'z_dim' : args.z_dim,
't_dim' : args.t_dim,
'batch_size' : args.batch_size,
'image_size' : args.image_size,
'gf_dim' : args.gf_dim,
'df_dim' : args.df_dim,
'gfc_dim' : args.gfc_dim,
'caption_vector_length' : args.caption_vector_length
}
gan = model.GAN(model_options)
input_tensors, variables, loss, outputs, checks = gan.build_model()
d_optim = tf.train.AdamOptimizer(args.learning_rate, beta1 = args.beta1).minimize(loss['d_loss'], var_list=variables['d_vars'])
g_optim = tf.train.AdamOptimizer(args.learning_rate, beta1 = args.beta1).minimize(loss['g_loss'], var_list=variables['g_vars'])
sess = tf.InteractiveSession()
tf.initialize_all_variables().run()
saver = tf.train.Saver()
if args.resume_model:
saver.restore(sess, args.resume_model)
loaded_data = load_training_data(args.data_dir, args.data_set)
for i in range(args.epochs):
batch_no = 0
while batch_no*args.batch_size < loaded_data['data_length']:
real_images, wrong_images, caption_vectors, z_noise, image_files = get_training_batch(batch_no, args.batch_size,
args.image_size, args.z_dim, args.caption_vector_length, 'train', args.data_dir, args.data_set, loaded_data)
# DISCR UPDATE
check_ts = [ checks['d_loss1'] , checks['d_loss2'], checks['d_loss3']]
_, d_loss, gen, d1, d2, d3 = sess.run([d_optim, loss['d_loss'], outputs['generator']] + check_ts,
feed_dict = {
input_tensors['t_real_image'] : real_images,
input_tensors['t_wrong_image'] : wrong_images,
input_tensors['t_real_caption'] : caption_vectors,
input_tensors['t_z'] : z_noise,
})
print "d1", d1
print "d2", d2
print "d3", d3
print "D", d_loss
# GEN UPDATE
_, g_loss, gen = sess.run([g_optim, loss['g_loss'], outputs['generator']],
feed_dict = {
input_tensors['t_real_image'] : real_images,
input_tensors['t_wrong_image'] : wrong_images,
input_tensors['t_real_caption'] : caption_vectors,
input_tensors['t_z'] : z_noise,
})
# GEN UPDATE TWICE, to make sure d_loss does not go to 0
_, g_loss, gen = sess.run([g_optim, loss['g_loss'], outputs['generator']],
feed_dict = {
input_tensors['t_real_image'] : real_images,
input_tensors['t_wrong_image'] : wrong_images,
input_tensors['t_real_caption'] : caption_vectors,
input_tensors['t_z'] : z_noise,
})
print "LOSSES", d_loss, g_loss, batch_no, i, len(loaded_data['image_list'])/ args.batch_size
batch_no += 1
if (batch_no % args.save_every) == 0:
print "Saving Images, Model"
save_for_vis(args.data_dir, real_images, gen, image_files)
save_path = saver.save(sess, "Data/Models/latest_model_{}_temp.ckpt".format(args.data_set))
if i%5 == 0:
save_path = saver.save(sess, "Data/Models/model_after_{}_epoch_{}.ckpt".format(args.data_set, i))
def load_training_data(data_dir, data_set):
if data_set == 'flowers':
h = h5py.File(join(data_dir, 'flower_tv.hdf5'))
flower_captions = {}
for ds in h.iteritems():
flower_captions[ds[0]] = np.array(ds[1])
image_list = [key for key in flower_captions]
image_list.sort()
img_75 = int(len(image_list)*0.75)
training_image_list = image_list[0:img_75]
random.shuffle(training_image_list)
return {
'image_list' : training_image_list,
'captions' : flower_captions,
'data_length' : len(training_image_list)
}
else:
with open(join(data_dir, 'meta_train.pkl')) as f:
meta_data = pickle.load(f)
# No preloading for MS-COCO
return meta_data
def save_for_vis(data_dir, real_images, generated_images, image_files):
shutil.rmtree( join(data_dir, 'samples') )
os.makedirs( join(data_dir, 'samples') )
for i in range(0, real_images.shape[0]):
real_image_255 = np.zeros( (64,64,3), dtype=np.uint8)
real_images_255 = (real_images[i,:,:,:])
scipy.misc.imsave( join(data_dir, 'samples/{}_{}.jpg'.format(i, image_files[i].split('/')[-1] )) , real_images_255)
fake_image_255 = np.zeros( (64,64,3), dtype=np.uint8)
fake_images_255 = (generated_images[i,:,:,:])
scipy.misc.imsave(join(data_dir, 'samples/fake_image_{}.jpg'.format(i)), fake_images_255)
def get_training_batch(batch_no, batch_size, image_size, z_dim,
caption_vector_length, split, data_dir, data_set, loaded_data = None):
if data_set == 'mscoco':
with h5py.File( join(data_dir, 'tvs/'+split + '_tvs_' + str(batch_no))) as hf:
caption_vectors = np.array(hf.get('tv'))
caption_vectors = caption_vectors[:,0:caption_vector_length]
with h5py.File( join(data_dir, 'tvs/'+split + '_tv_image_id_' + str(batch_no))) as hf:
image_ids = np.array(hf.get('tv'))
real_images = np.zeros((batch_size, 64, 64, 3))
wrong_images = np.zeros((batch_size, 64, 64, 3))
image_files = []
for idx, image_id in enumerate(image_ids):
image_file = join(data_dir, '%s2014/COCO_%s2014_%.12d.jpg'%(split, split, image_id) )
image_array = image_processing.load_image_array(image_file, image_size)
real_images[idx,:,:,:] = image_array
image_files.append(image_file)
# TODO>> As of Now, wrong images are just shuffled real images.
first_image = real_images[0,:,:,:]
for i in range(0, batch_size):
if i < batch_size - 1:
wrong_images[i,:,:,:] = real_images[i+1,:,:,:]
else:
wrong_images[i,:,:,:] = first_image
z_noise = np.random.uniform(-1, 1, [batch_size, z_dim])
return real_images, wrong_images, caption_vectors, z_noise, image_files
if data_set == 'flowers':
real_images = np.zeros((batch_size, 64, 64, 3))
wrong_images = np.zeros((batch_size, 64, 64, 3))
captions = np.zeros((batch_size, caption_vector_length))
cnt = 0
image_files = []
for i in range(batch_no * batch_size, batch_no * batch_size + batch_size):
idx = i % len(loaded_data['image_list'])
image_file = join(data_dir, 'flowers/jpg/'+loaded_data['image_list'][idx])
image_array = image_processing.load_image_array(image_file, image_size)
real_images[cnt,:,:,:] = image_array
# Improve this selection of wrong image
wrong_image_id = random.randint(0,len(loaded_data['image_list'])-1)
wrong_image_file = join(data_dir, 'flowers/jpg/'+loaded_data['image_list'][wrong_image_id])
wrong_image_array = image_processing.load_image_array(wrong_image_file, image_size)
wrong_images[cnt, :,:,:] = wrong_image_array
random_caption = random.randint(0,4)
captions[cnt,:] = loaded_data['captions'][ loaded_data['image_list'][idx] ][ random_caption ][0:caption_vector_length]
image_files.append( image_file )
cnt += 1
z_noise = np.random.uniform(-1, 1, [batch_size, z_dim])
return real_images, wrong_images, captions, z_noise, image_files
if __name__ == '__main__':
main()