<gh_stars>10-100
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():
	print('lol')
	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=4800,
					   help='Caption Vector Length')

	parser.add_argument('--data_dir', type=str, default="/media/ssd_working_space/osaid/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=200,
					   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="face",
                       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.global_variables_initializer().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)
		

			# 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,
				})

			_, g_loss, fake_img = 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,
                                })


			# DISCR UPDATE
			if(batch_no%4==0):
				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'] : fake_img,
						input_tensors['t_wrong_image'] : wrong_images,
						input_tensors['t_real_caption'] : caption_vectors,
						input_tensors['t_z'] : z_noise,
					})
			else:
				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('here')

			print("d1", d1)
			print("d2", d2)
			print("d3", d3)
			print("D", d_loss)		
			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(i,batch_no,args.data_dir, real_images, gen, image_files)
				save_path = saver.save(sess, "/media/ssd_working_space/osaid/Data/Models/latest_model_{}_temp.ckpt".format(args.data_set))
		if i%5 == 0:
			save_path = saver.save(sess, "/media/ssd_working_space/osaid/Data/Models/model_after_{}_epoch_{}.ckpt".format(args.data_set, i))

def load_training_data(data_dir, data_set):
	if data_set == 'face':
		#h = h5py.File(join(data_dir, 'face_tv.hdf5'))
		h = pickle.load(open(data_dir+'/train_encoding','rb'))
		face_captions = {}
		for key in h.keys():
			face_captions[key] = h[key]

		training_image_list = [key for key in face_captions]
		print("training image list",training_image_list)
		random.shuffle(training_image_list)
		return {
			'image_list' : training_image_list,
			'captions' : face_captions,
			'data_length' : len(training_image_list)
		}
	
	else:
		with open(join(data_dir, 'meta_train.pkl')) as f:
			meta_data = pickle.load(f)
		return meta_data

def save_for_vis(epoch,batch_no,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/'+str(epoch)+'_'+str(batch_no)+'_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 == 'face':
		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, 'face/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, 'face/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

			captions[cnt,:] = loaded_data['captions'][ loaded_data['image_list'][idx] ][0][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()
