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train_txt2im.py
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train_txt2im.py
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#! /usr/bin/python
# -*- coding: utf8 -*-
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import *
from tensorlayer.prepro import *
from tensorlayer.cost import *
import numpy as np
import scipy
from scipy.io import loadmat
import time
import os
import re
import nltk
import random
from utils import *
from model import *
is_deep = True
if is_deep:
generator_txt2img = generator_txt2img_deep
discriminator_txt2img = discriminator_txt2img_deep
os.system("mkdir samples")
os.system("mkdir checkpoint")
""" Generative Adversarial Text to Image Synthesis
Downlaod Oxford 102 flowers dataset and caption
-------------------------------------------------
Flowers : http://www.robots.ox.ac.uk/%7Evgg/data/flowers/102/
paste it in 102flowers/102flowers/*jpg
Captions : https://drive.google.com/file/d/0B0ywwgffWnLLcms2WWJQRFNSWXM/view
paste it in 102flowers/text_c10/class_*
Code References
---------------
- GAN-CLS by TensorFlow
- https://github.com/paarthneekhara/text-to-image/blob/master/train.py
- https://github.com/paarthneekhara/text-to-image/blob/master/model.py
- https://github.com/paarthneekhara/text-to-image/blob/master/Utils/ops.py
"""
###======================== PREPARE DATA ====================================###
## Load Oxford 102 flowers dataset
from data_loader import *
###======================== DEFIINE MODEL ===================================###
## you may want to see how the data augmentation work
# save_images(images[:64], [8, 8], 'temp.png')
# pre_img = threading_data(images[:64], prepro_img, mode='debug')
# save_images(pre_img, [8, 8], 'temp2.png')
# # print(images[:64].shape, np.min(images[:64]), np.max(images[:64]))
# print(pre_img.shape, np.min(pre_img), np.max(pre_img))
# exit()
## build model
t_real_image = tf.placeholder('float32', [batch_size, image_size, image_size, 3], name = 'real_image')
t_wrong_image = tf.placeholder('float32', [batch_size ,image_size, image_size, 3 ], name = 'wrong_image') # remove if DCGAN only
t_real_caption = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name='real_caption_input') # remove if DCGAN only
t_wrong_caption = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name='wrong_caption_input')
t_z = tf.placeholder(tf.float32, [batch_size, z_dim], name='z_noise')
## training inference for text-to-image mapping 2017
net_cnn = cnn_encoder(t_real_image, is_train=True, reuse=False)
x = net_cnn.outputs
v = rnn_embed(t_real_caption, is_train=True, reuse=False).outputs
x_w = cnn_encoder(t_wrong_image, is_train=True, reuse=True).outputs
v_w = rnn_embed(t_wrong_caption, is_train=True, reuse=True).outputs
alpha = 0.2 # margin alpha
e_loss = tf.reduce_mean(tf.maximum(0., alpha - cosine_similarity(x, v) + cosine_similarity(x, v_w))) + \
tf.reduce_mean(tf.maximum(0., alpha - cosine_similarity(x, v) + cosine_similarity(x_w, v)))
## training inference for training DCGAN
# from dcgan_model import *
# net_fake_image, _ = generator_dcgan(t_z, is_train=True, reuse=False)
# _, disc_fake_image_logits = discriminator_dcgan(net_fake_image.outputs, is_train=True, reuse=False)
# _, disc_real_image_logits = discriminator_dcgan(t_real_image, is_train=True, reuse=True)
## training inference for txt2img
net_rnn = rnn_embed(t_real_caption, is_train=False, reuse=True, return_embed=False) # remove if DCGAN only
net_fake_image, _ = generator_txt2img(t_z,
net_rnn, # remove if DCGAN only
is_train=True, reuse=False)
net_d, disc_fake_image_logits = discriminator_txt2img(
net_fake_image.outputs,
net_rnn, # remove if DCGAN only
is_train=True, reuse=False)
_, disc_real_image_logits = discriminator_txt2img(
t_real_image,
net_rnn, # remove if DCGAN only
is_train=True, reuse=True)
_, disc_wrong_image_logits = discriminator_txt2img( # CLS
t_wrong_image, # remove if DCGAN only
net_rnn, # remove if DCGAN only
is_train=True, reuse=True) # remove if DCGAN only
## testing inference for DCGAN
# net_g, _ = generator_dcgan(t_z, is_train=False, reuse=True)
## testing inference for txt2img
net_g, _ = generator_txt2img(t_z,
rnn_embed(t_real_caption, is_train=False, reuse=True, return_embed=False), # remove if DCGAN only
is_train=False, reuse=True)
d_loss1 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(disc_real_image_logits, tf.ones_like(disc_real_image_logits)))
d_loss2 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(disc_wrong_image_logits, tf.zeros_like(disc_wrong_image_logits))) # for CLS, if set it to zero, it is the same with normal DCGAN
d_loss3 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(disc_fake_image_logits, tf.zeros_like(disc_fake_image_logits)))
d_loss = d_loss1 + d_loss2 + d_loss3
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(disc_fake_image_logits, tf.ones_like(disc_fake_image_logits))) # real == 1, fake == 0
# net_fake_image.print_params(False)
# net_fake_image.print_layers()
# exit()
####======================== DEFINE TRAIN OPTS ==========================###
## Cost real == 1, fake == 0
lr = 0.0002
lr_decay = 0.5 # decay factor for adam, https://github.com/reedscot/icml2016/blob/master/main_cls_int.lua https://github.com/reedscot/icml2016/blob/master/scripts/train_flowers.sh
decay_every = 100 # https://github.com/reedscot/icml2016/blob/master/main_cls.lua
beta1 = 0.5
n_g_batch = 2 # update G, x time per batch
c_vars = tl.layers.get_variables_with_name('cnn', True, True)
e_vars = tl.layers.get_variables_with_name('rnn', True, True)
d_vars = tl.layers.get_variables_with_name('discriminator', True, True)
g_vars = tl.layers.get_variables_with_name('generator', True, True)
with tf.variable_scope('learning_rate'):
lr_v = tf.Variable(lr, trainable=False)
d_optim = tf.train.AdamOptimizer(lr_v, beta1=beta1).minimize(d_loss, var_list=d_vars )
g_optim = tf.train.AdamOptimizer(lr_v, beta1=beta1).minimize(g_loss, var_list=g_vars )
# e_optim = tf.train.AdamOptimizer(lr_v, beta1=beta1).minimize(e_loss, var_list=e_vars + c_vars)
grads, _ = tf.clip_by_global_norm(tf.gradients(e_loss, e_vars + c_vars), 10)
optimizer = tf.train.AdamOptimizer(lr_v, beta1=beta1)# optimizer = tf.train.GradientDescentOptimizer(lre)
e_optim = optimizer.apply_gradients(zip(grads, e_vars + c_vars))
###============================ TRAINING ====================================###
sess = tf.InteractiveSession()
# sess.run(tf.initialize_all_variables())
tl.layers.initialize_global_variables(sess)
save_dir = "checkpoint"
if not os.path.exists(save_dir):
print("[!] Folder (%s) is not exist, creating it ..." % save_dir)
os.mkdir(save_dir)
# load the latest checkpoints
net_e_name = os.path.join(save_dir, 'net_e.npz')
net_c_name = os.path.join(save_dir, 'net_c.npz')
net_g_name = os.path.join(save_dir, 'net_g.npz')
net_d_name = os.path.join(save_dir, 'net_d.npz')
if True:
if not (os.path.exists(net_e_name) and os.path.exists(net_c_name)):
print("[!] Loading RNN and CNN checkpoints failed!")
else:
net_c_loaded_params = tl.files.load_npz(name=net_c_name)
net_e_loaded_params = tl.files.load_npz(name=net_e_name)
tl.files.assign_params(sess, net_c_loaded_params, net_cnn)
tl.files.assign_params(sess, net_e_loaded_params, net_rnn)
print("[*] Loading RNN and CNN checkpoints SUCCESS!")
if not (os.path.exists(net_g_name) and os.path.exists(net_d_name)):
print("[!] Loading G and D checkpoints failed!")
else:
net_g_loaded_params = tl.files.load_npz(name=net_g_name)
net_d_loaded_params = tl.files.load_npz(name=net_d_name)
tl.files.assign_params(sess, net_g_loaded_params, net_g)
tl.files.assign_params(sess, net_d_loaded_params, net_d)
print("[*] Loading G and D checkpoints SUCCESS!")
# sess=tf.Session()
# tl.ops.set_gpu_fraction(sess=sess, gpu_fraction=0.998)
# sess.run(tf.initialize_all_variables())
## seed for generation, z and sentence ids
sample_size = batch_size
sample_seed = np.random.normal(loc=0.0, scale=1.0, size=(sample_size, z_dim)).astype(np.float32)
# sample_seed = np.random.uniform(low=-1, high=1, size=(sample_size, z_dim)).astype(np.float32) # paper said [0, 1]
# sample_sentence = ["this white and yellow flower have thin white petals and a round yellow stamen", \
# "the flower has petals that are bright pinkish purple with white stigma"] * 32
# sample_sentence = ["these flowers have petals that start off white in color and end in a dark purple towards the tips"] * 32 + \
# ["bright droopy yellow petals with burgundy streaks and a yellow stigma"] * 32
# sample_sentence = ["these white flowers have petals that start off white in color and end in a white towards the tips",
# "this yellow petals with burgundy streaks and a yellow stigma"] * 32
sample_sentence = ["the flower shown has yellow anther red pistil and bright red petals."] * int(sample_size/8) + \
["this flower has petals that are yellow, white and purple and has dark lines"] * int(sample_size/8) + \
["the petals on this flower are white with a yellow center"] * int(sample_size/8) + \
["this flower has a lot of small round pink petals."] * int(sample_size/8) + \
["this flower is orange in color, and has petals that are ruffled and rounded."] * int(sample_size/8) + \
["the flower has yellow petals and the center of it is brown."] * int(sample_size/8) + \
["this flower has petals that are blue and white."] * int(sample_size/8) +\
["these white flowers have petals that start off white in color and end in a white towards the tips."] * int(sample_size/8)
# sample_sentence = captions_ids_test[0:sample_size]
for i, sentence in enumerate(sample_sentence):
print("seed: %s" % sentence)
sample_sentence[i] = [vocab.word_to_id(word) for word in nltk.tokenize.word_tokenize(sentence)] + [vocab.end_id] # add END_ID
# sample_sentence[i] = [vocab.word_to_id(word) for word in sentence]
# print(sample_sentence[i])
sample_sentence = tl.prepro.pad_sequences(sample_sentence, padding='post')
n_epoch = 1000 # 600 when pre-trained rnn
print_freq = 1
n_batch_epoch = int(n_images / batch_size)
for epoch in range(n_epoch+1):
start_time = time.time()
if epoch !=0 and (epoch % decay_every == 0):
new_lr_decay = lr_decay ** (epoch // decay_every)
sess.run(tf.assign(lr_v, lr * new_lr_decay))
log = " ** new learning rate: %f" % (lr * new_lr_decay)
print(log)
# logging.debug(log)
elif epoch == 0:
log = " ** init lr: %f decay_every_epoch: %d, lr_decay: %f" % (lr, decay_every, lr_decay)
print(log)
for step in range(n_batch_epoch):
step_time = time.time()
## get matched text
idexs = get_random_int(min=0, max=n_captions_train-1, number=batch_size)
b_real_caption = captions_ids_train[idexs] # remove if DCGAN only
b_real_caption = tl.prepro.pad_sequences(b_real_caption, padding='post') # matched text (64, any) # remove if DCGAN only
## get real image
b_real_images = images_train[np.floor(np.asarray(idexs).astype('float')/n_captions_per_image).astype('int')] # real images (64, 64, 64, 3)
## get wrong caption
idexs = get_random_int(min=0, max=n_captions_train-1, number=batch_size)
b_wrong_caption = captions_ids[idexs]
b_wrong_caption = tl.prepro.pad_sequences(b_wrong_caption, padding='post') # mismatched text
## get wrong image
idexs2 = get_random_int(min=0, max=n_images_train-1, number=batch_size) # remove if DCGAN only
b_wrong_images = images_train[idexs2] # remove if DCGAN only
## get noise
b_z = np.random.normal(loc=0.0, scale=1.0, size=(sample_size, z_dim)).astype(np.float32)
# b_z = np.random.uniform(low=-1, high=1, size=[batch_size, z_dim]).astype(np.float32) # paper said [0, 1], but [-1, 1] is better
## check data
# print(np.min(b_real_images), np.max(b_real_images), b_real_images.shape) # [0, 1] (64, 64, 64, 3)
# for i, seq in enumerate(b_real_caption):
# # print(seq)
# print(i, " ".join([vocab.id_to_word(id) for id in seq]))
# save_images(b_real_images, [8, 8], 'real_image.png')
# exit()
## updates text-to-image mapping
if epoch < 50:
errE, _ = sess.run([e_loss, e_optim], feed_dict={
t_real_image : b_real_images,
t_wrong_image : b_wrong_images,
t_real_caption : b_real_caption,
t_wrong_caption : b_wrong_caption,
# t_z : b_z # error
})
# total_e_loss += errE
else:
errE = 0
## updates D
b_real_images = threading_data(b_real_images, prepro_img, mode='train') # [0, 255] --> [-1, 1]
b_wrong_images = threading_data(b_wrong_images, prepro_img, mode='train')
errD, _ = sess.run([d_loss, d_optim], feed_dict={
t_real_image : b_real_images,
t_wrong_image : b_wrong_images, # remove if DCGAN only
t_real_caption : b_real_caption, # remove if DCGAN only
t_z : b_z})
## updates G
for _ in range(n_g_batch):
errG, _ = sess.run([g_loss, g_optim], feed_dict={
t_real_caption : b_real_caption, # remove if DCGAN only
t_z : b_z})
print("Epoch: [%2d/%2d] [%4d/%4d] time: %4.4fs, d_loss: %.8f, g_loss: %.8f, e_loss: %.8f" \
% (epoch, n_epoch, step, n_batch_epoch, time.time() - step_time, errD, errG, errE))
# if np.isnan(errD) or np.isnan(errG):
# exit(" ** NaN error, stop training")
if (epoch + 1) % print_freq == 0:
print(" ** Epoch %d took %fs" % (epoch, time.time()-start_time))
img_gen, rnn_out = sess.run([net_g.outputs, net_rnn.outputs],
# img_gen = sess.run(net_g.outputs,
feed_dict={
t_real_caption : sample_sentence, # remove if DCGAN only
t_z : sample_seed})
# print(b_real_images[0])
print('rnn:', np.min(rnn_out[0]), np.max(rnn_out[0])) # -1.4121389, 1.4108921
print('real:', b_real_images[0].shape, np.min(b_real_images[0]), np.max(b_real_images[0]))
print('wrong:', b_wrong_images[0].shape, np.min(b_wrong_images[0]), np.max(b_wrong_images[0]))
# print(img_gen[0])
print('generate:', img_gen[0].shape, np.min(img_gen[0]), np.max(img_gen[0]))
img_gen = threading_data(img_gen, prepro_img, mode='rescale') # [-1, 1] --> [-1, 1]
# tl.visualize.frame(img_gen[0], second=0, saveable=True, name='e_%d_%s' % (epoch, " ".join([vocab.id_to_word(id) for id in sample_sentence[0]])) )
save_images(img_gen, [8, 8], '{}/train_{:02d}.png'.format('samples', epoch))
# for i, img in enumerate(img_gen):
# tl.visualize.frame(img, second=0, saveable=True, name='epoch_%d_sample_%d_%s' % (epoch, i, [vocab.id_to_word(id) for id in sample_sentence[i]]) )
# print(img_gen[:32])
# print(img_gen[32:])
# tl.visualize.images2d(images=img_gen, second=0.01, saveable=True, name='temp_generate', dtype=np.uint8)
# b_real_images = threading_data(b_real_images, prepro_img, mode='rescale')
# b_wrong_images = threading_data(b_wrong_images, prepro_img, mode='rescale')
# save_images(b_real_images, [8, 8], 'temp_real_image.png')
# save_images(b_wrong_images, [8, 8], 'temp_wrong_image.png')
if (epoch != 0) and (epoch % 100) == 0:
tl.files.save_npz(net_cnn.all_params, name=net_c_name, sess=sess)
tl.files.save_npz(net_rnn.all_params, name=net_e_name, sess=sess)
tl.files.save_npz(net_g.all_params, name=net_g_name, sess=sess)
tl.files.save_npz(net_d.all_params, name=net_d_name, sess=sess)
print("[*] Saving checkpoints SUCCESS!")
if (epoch != 0) and (epoch % 100) == 0:
net_c_name_e = os.path.join(save_dir, 'net_c_%d.npz' % epoch)
net_e_name_e = os.path.join(save_dir, 'net_e_%d.npz' % epoch)
net_g_name_e = os.path.join(save_dir, 'net_g_%d.npz' % epoch)
net_d_name_e = os.path.join(save_dir, 'net_d_%d.npz' % epoch)
tl.files.save_npz(net_cnn.all_params, name=net_c_name_e, sess=sess)
tl.files.save_npz(net_rnn.all_params, name=net_e_name_e, sess=sess)
tl.files.save_npz(net_g.all_params, name=net_g_name_e, sess=sess)
tl.files.save_npz(net_d.all_params, name=net_d_name_e, sess=sess)
# tl.visualize.images2d(images=img_gen, second=0.01, saveable=True, name='temp_generate_%d' % epoch)#, dtype=np.uint8)
#