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changes for integrating midiImages #1

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4 changes: 2 additions & 2 deletions main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,8 @@
def main(_):

model = DTN(mode=FLAGS.mode, learning_rate=0.0003)
solver = Solver(model, batch_size=100, pretrain_iter=20000, train_iter=2000, sample_iter=100,
svhn_dir='svhn', mnist_dir='mnist', model_save_path=FLAGS.model_save_path, sample_save_path=FLAGS.sample_save_path)
solver = Solver(model, batch_size=2, pretrain_iter=20000, train_iter=2000, sample_iter=100,
svhn_dir='/home/manthan/Desktop/Classical2', mnist_dir='/home/manthan/Desktop/Metal2', model_save_path=FLAGS.model_save_path, sample_save_path=FLAGS.sample_save_path)

# create directories if not exist
if not tf.gfile.Exists(FLAGS.model_save_path):
Expand Down
45 changes: 22 additions & 23 deletions model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -131,14 +131,14 @@ def build_model(self):
self.f_train_op_src = slim.learning.create_train_op(self.f_loss_src, self.f_optimizer_src, variables_to_train=f_vars)

# summary op
d_loss_src_summary = tf.summary.scalar('src_d_loss', self.d_loss_src)
g_loss_src_summary = tf.summary.scalar('src_g_loss', self.g_loss_src)
f_loss_src_summary = tf.summary.scalar('src_f_loss', self.f_loss_src)
origin_images_summary = tf.summary.image('src_origin_images', self.src_images)
sampled_images_summary = tf.summary.image('src_sampled_images', self.fake_images)
self.summary_op_src = tf.summary.merge([d_loss_src_summary, g_loss_src_summary,
f_loss_src_summary, origin_images_summary,
sampled_images_summary])
# d_loss_src_summary = tf.summary.scalar('src_d_loss', self.d_loss_src)
# g_loss_src_summary = tf.summary.scalar('src_g_loss', self.g_loss_src)
# f_loss_src_summary = tf.summary.scalar('src_f_loss', self.f_loss_src)
# origin_images_summary = tf.summary.image('src_origin_images', self.src_images)
# sampled_images_summary = tf.summary.image('src_sampled_images', self.fake_images)
# self.summary_op_src = tf.summary.merge([d_loss_src_summary, g_loss_src_summary,
# f_loss_src_summary, origin_images_summary,
# sampled_images_summary])

# target domain (mnist)
self.fx = self.content_extractor(self.trg_images, reuse=True)
Expand All @@ -164,18 +164,17 @@ def build_model(self):
self.g_train_op_trg = slim.learning.create_train_op(self.g_loss_trg, self.g_optimizer_trg, variables_to_train=g_vars)

# summary op
d_loss_fake_trg_summary = tf.summary.scalar('trg_d_loss_fake', self.d_loss_fake_trg)
d_loss_real_trg_summary = tf.summary.scalar('trg_d_loss_real', self.d_loss_real_trg)
d_loss_trg_summary = tf.summary.scalar('trg_d_loss', self.d_loss_trg)
g_loss_fake_trg_summary = tf.summary.scalar('trg_g_loss_fake', self.g_loss_fake_trg)
g_loss_const_trg_summary = tf.summary.scalar('trg_g_loss_const', self.g_loss_const_trg)
g_loss_trg_summary = tf.summary.scalar('trg_g_loss', self.g_loss_trg)
origin_images_summary = tf.summary.image('trg_origin_images', self.trg_images)
sampled_images_summary = tf.summary.image('trg_reconstructed_images', self.reconst_images)
self.summary_op_trg = tf.summary.merge([d_loss_trg_summary, g_loss_trg_summary,
d_loss_fake_trg_summary, d_loss_real_trg_summary,
g_loss_fake_trg_summary, g_loss_const_trg_summary,
origin_images_summary, sampled_images_summary])
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)

# d_loss_fake_trg_summary = tf.summary.scalar('trg_d_loss_fake', self.d_loss_fake_trg)
# d_loss_real_trg_summary = tf.summary.scalar('trg_d_loss_real', self.d_loss_real_trg)
# d_loss_trg_summary = tf.summary.scalar('trg_d_loss', self.d_loss_trg)
# g_loss_fake_trg_summary = tf.summary.scalar('trg_g_loss_fake', self.g_loss_fake_trg)
# g_loss_const_trg_summary = tf.summary.scalar('trg_g_loss_const', self.g_loss_const_trg)
# g_loss_trg_summary = tf.summary.scalar('trg_g_loss', self.g_loss_trg)
# origin_images_summary = tf.summary.image('trg_origin_images', self.trg_images)
# sampled_images_summary = tf.summary.image('trg_reconstructed_images', self.reconst_images)
# self.summary_op_trg = tf.summary.merge([d_loss_trg_summary, g_loss_trg_summary,
# d_loss_fake_trg_summary, d_loss_real_trg_summary,
# g_loss_fake_trg_summary, g_loss_const_trg_summary,
# origin_images_summary, sampled_images_summary])
# for var in tf.trainable_variables():
# tf.summary.histogram(var.op.name, var)
56 changes: 50 additions & 6 deletions solver.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,12 +5,36 @@
import os
import scipy.io
import scipy.misc
import glob
import cv2
from numpy import array
from PIL import Image


def img_read(image_dir):
cv_img = []
for img in glob.glob(image_dir):
n = cv2.imread(img)
cv_img.append(n)

return cv_img


def resize_images(images, size=[32, 32, 3]):
# convert float type to integer
resized_image_arrays = np.zeros([len(images)] + size)
for i, image in enumerate(images):
resized_image = cv2.resize(image, (32,32), interpolation = cv2.INTER_AREA)
print type(resized_image)
resized_image_arrays[i] = resized_image

return resized_image_arrays


class Solver(object):

def __init__(self, model, batch_size=100, pretrain_iter=20000, train_iter=2000, sample_iter=100,
svhn_dir='svhn', mnist_dir='mnist', log_dir='logs', sample_save_path='sample',
svhn_dir='/home/manthan/Desktop/Classical', mnist_dir='/home/manthan/Desktop/Metal', log_dir='logs', sample_save_path='sample',
model_save_path='model', pretrained_model='model/svhn_model-20000', test_model='model/dtn-1800'):

self.model = model
Expand All @@ -30,7 +54,7 @@ def __init__(self, model, batch_size=100, pretrain_iter=20000, train_iter=2000,

def load_svhn(self, image_dir, split='train'):
print ('loading svhn image dataset..')

'''
if self.model.mode == 'pretrain':
image_file = 'extra_32x32.mat' if split=='train' else 'test_32x32.mat'
else:
Expand All @@ -41,19 +65,39 @@ def load_svhn(self, image_dir, split='train'):
images = np.transpose(svhn['X'], [3, 0, 1, 2]) / 127.5 - 1
labels = svhn['y'].reshape(-1)
labels[np.where(labels==10)] = 0
'''
image_file = '/*.jpg'
image_dir = image_dir + image_file
images = img_read(image_dir)
images = resize_images(images)
print images.shape
images = images / 127.5 - 1
print ('finished loading svhn image dataset..!')
return images, labels
return images
# return images, labels

def load_mnist(self, image_dir, split='train'):
print ('loading mnist image dataset..')
'''
image_file = 'train.pkl' if split=='train' else 'test.pkl'
image_dir = os.path.join(image_dir, image_file)
with open(image_dir, 'rb') as f:
mnist = pickle.load(f)
images = mnist['X'] / 127.5 - 1
labels = mnist['y']
'''
image_file = '/*.jpg'
image_dir = image_dir + image_file
images = img_read(image_dir)

images = np.asarray(images)
images = resize_images(images)

images = images / 127.5 - 1

print ('finished loading mnist image dataset..!')
return images, labels
return images
# return images, labels

def merge_images(self, sources, targets, k=10):
_, h, w, _ = sources.shape
Expand Down Expand Up @@ -104,8 +148,8 @@ def pretrain(self):

def train(self):
# load svhn dataset
svhn_images, _ = self.load_svhn(self.svhn_dir, split='train')
mnist_images, _ = self.load_mnist(self.mnist_dir, split='train')
svhn_images = self.load_svhn(self.svhn_dir, split='train')
mnist_images = self.load_mnist(self.mnist_dir, split='train')

# build a graph
model = self.model
Expand Down