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model.py
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"""
Author: Vignesh Gokul
Code structure inspired from https://github.com/carpedm20/DCGAN-tensorflow
"""
import tensorflow as tf
import numpy as np
import glob
from utils import *
import sys
class VideoGAN():
def __init__(self,sess,video_dim,zdim,batch_size,epochs,checkpoint_file,lambd):
self.bv1 = batch_norm(name = "genb1")
self.bv2 = batch_norm(name = "genb2")
self.bv3 = batch_norm(name = "genb3")
self.bv4 = batch_norm(name = "genb4")
self.bs1 = batch_norm(name = "genb5")
self.bs2 = batch_norm(name = "genb6")
self.bs3 = batch_norm(name = "genb7")
self.bs4 = batch_norm(name = "genb8")
self.bd1 = batch_norm(name = "dis1")
self.bd2 = batch_norm(name = "dis2")
self.bd3 = batch_norm(name = "dis3")
self.video_dim = video_dim
self.zdim = zdim
self.batch_size = batch_size
self.epochs = epochs
self.checkpoint_file = checkpoint_file
self.lambd = lambd
self.sess = sess
def build_model(self):
self.z = tf.placeholder(tf.float32, [None,self.zdim])
self.zsample = tf.placeholder(tf.float32,[None,self.zdim])
self.real_video = tf.placeholder(tf.float32, [None] +self.video_dim)
self.fake_video,self.foreground,self.background,self.mask = self.generator(self.z)
self.genvideo,self.bg= self.visualize_videos()
prob_real, logits_real = self.discriminator(self.real_video)
prob_fake, logits_fake = self.discriminator(self.fake_video,reuse = True)
d_real_cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = logits_real, labels = tf.ones_like(prob_real)))
d_fake_cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = logits_fake, labels = tf.zeros_like(prob_fake)))
self.g_cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = logits_fake, labels = tf.ones_like(prob_fake))) + self.lambd*tf.norm(self.mask,1)
self.d_cost = d_real_cost + d_fake_cost
def train(self):
gen_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,scope="generator")
dis_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,scope="discriminator")
self.g_opt = tf.train.AdamOptimizer(learning_rate = 0.0002, beta1 = 0.5).minimize(self.g_cost, var_list = gen_var)
self.d_opt = tf.train.AdamOptimizer(learning_rate = 0.0002, beta1 = 0.5).minimize(self.d_cost, var_list = dis_var)
visualize_count = 1
saver = tf.train.Saver()
if self.checkpoint_file == "None":
self.ckpt_file = None
if self.checkpoint_file:
saver_ = tf.train.import_meta_graph('./checkpoints/' + self.checkpoint_file + '.meta')
saver_.restore(self.sess,tf.train.latest_checkpoint('./checkpoints/'))
print "Restored model"
else:
tf.global_variables_initializer().run()
data_files = glob.glob("./trainvideos/*")
print_count = len(data_files)/self.batch_size
for epoch in range(self.epochs):
for counter in range(len(data_files)/self.batch_size):
noise_sample = np.random.normal(-1, 1, size = [visualize_count, self.zdim]).astype(np.float32)
noise = np.random.normal(-1, 1, size = [self.batch_size, self.zdim]).astype(np.float32)
print("....Iteration....:", counter)
batch_files = data_files[counter*self.batch_size:(counter+1)*self.batch_size]
videos = read_and_process_video(batch_files,self.batch_size,32)
#print videos.shape
#process_and_write_video(videos,"true_video" + str(counter))
_, dloss = self.sess.run([self.d_opt, self.d_cost], feed_dict = {self.z : noise, self.real_video: videos})
_, gloss = self.sess.run([self.g_opt, self.g_cost], feed_dict = {self.z : noise, self.real_video: videos})
# _, gloss = self.sess.run([self.g_opt, self.g_cost], feed_dict = {self.z : noise, self.real_video: videos})
print("Discriminator Loss: ", dloss)
print("Generator Loss", gloss)
if np.mod(counter + 1, print_count) == 0:
gen_videos,bg = self.sess.run([self.genvideo,self.bg], feed_dict = {self.zsample : noise_sample})
process_and_write_video(gen_videos,"video" + str(counter))
process_and_write_image(bg,"bg" + str(counter))
print (".....Writing sample generated videos......")
saver.save(self.sess,'./checkpoints/VideoGAN_{}_{}_{}.ckpt'.format(self.batch_size,epoch,counter))
print 'Saved {}'.format(counter)
def generator(self,z,reuse = False):
with tf.variable_scope("generator") as scope:
#Background
z = tf.reshape(z,[-1,1,1,self.zdim])
deconvb1 = tf.layers.conv2d_transpose(z,512,kernel_size=[4,4],strides =[1,1],name="gen1")
deconvb1 = tf.nn.relu(self.bs1(deconvb1))
deconvb2 = tf.layers.conv2d_transpose(deconvb1,256,kernel_size=[2,2],strides =[2,2],padding="VALID",name="gen2")
deconvb2 = tf.nn.relu(self.bs2(deconvb2))
deconvb3 = tf.layers.conv2d_transpose(deconvb2,128,kernel_size=[2,2],strides =[2,2],padding="VALID",name="gen3")
deconvb3 = tf.nn.relu(self.bs3(deconvb3))
deconvb4 = tf.layers.conv2d_transpose(deconvb3,64,kernel_size=[2,2],strides =[2,2],padding="VALID",name="gen4")
deconvb4 = tf.nn.relu(self.bs4(deconvb4))
deconvb5 = tf.layers.conv2d_transpose(deconvb4,3,kernel_size=[2,2],strides =[2,2],padding="VALID",name="gen5")
background = tf.nn.tanh(deconvb5)
#Foreground
#z = tf.expand_dims(z,1)
z = tf.reshape(z,[-1,1,1,1,self.zdim])
deconv1 = tf.layers.conv3d_transpose(z,filters = 512,kernel_size = [2,4,4],strides = [1,1,1], use_bias = False,name="gen6")
deconv1 = tf.nn.relu(self.bv1(deconv1))
deconv2 = tf.layers.conv3d_transpose(deconv1,filters= 256,kernel_size=[4,4,4],strides=[2,2,2],padding = "SAME",use_bias = False,name="gen7")
deconv2 = tf.nn.relu(self.bv2(deconv2))
deconv3 = tf.layers.conv3d_transpose(deconv2,filters= 128,kernel_size =[4,4,4],strides = [2,2,2], padding ="SAME",use_bias = False,name="gen8")
deconv3 = tf.nn.relu(self.bv3(deconv3))
deconv4 = tf.layers.conv3d_transpose(deconv3,filters=64,kernel_size=[4,4,4],strides=[2,2,2],padding ="SAME",use_bias = False,name="gen9")
deconv4 = tf.nn.relu(self.bv4(deconv4))
#Mask
mask = tf.layers.conv3d_transpose(deconv4,filters= 1, kernel_size=[4,4,4], strides =[2,2,2],padding ="SAME",use_bias = False,name="gen10")
mask = tf.nn.sigmoid(mask)
#Video
foreground = tf.layers.conv3d_transpose(deconv4,filters = 3, kernel_size = [4,4,4], strides = [2,2,2], padding ="SAME",use_bias = False,name="gen11")
foreground = tf.nn.tanh(foreground)
#Replicate background and mask
background = tf.expand_dims(background,1)
backreplicate = tf.tile(background,[-1,32,1,1,1])
maskreplicate = tf.tile(mask,[-1,1,1,1,3])
#Incorporate mask
video = tf.add(tf.multiply(mask,foreground),tf.multiply(1-mask,background))
print("Video Shape")
print video.get_shape()
return video,foreground,background,mask
def discriminator(self,vid,reuse = False):
with tf.variable_scope("discriminator") as scope:
if reuse:
scope.reuse_variables()
conv1 = tf.layers.conv3d(vid,64,kernel_size=[4,4,4],strides=[2,2,2],padding="SAME",reuse=reuse,name="dis1")
conv1 = lrelu(conv1)
conv2 = tf.layers.conv3d(conv1,128,kernel_size=[4,4,4],strides=[2,2,2],padding="SAME",reuse=reuse,name="dis2")
conv2 = lrelu(self.bd1(conv2))
conv3 = tf.layers.conv3d(conv2,256,kernel_size=[4,4,4],strides=[2,2,2],padding="SAME",reuse=reuse,name="dis3")
conv3 = lrelu(self.bd2(conv3))
conv4 = tf.layers.conv3d(conv3,512,kernel_size=[4,4,4],strides=[2,2,2],padding="SAME",reuse=reuse,name="dis4")
conv4 = lrelu(self.bd3(conv4))
conv5 = tf.layers.conv3d(conv4,1,kernel_size=[2,4,4],strides=[1,1,1],padding="VALID",reuse=reuse,name="dis5")
conv5 = tf.reshape(conv5, [-1,1])
conv5sigmoid = tf.nn.sigmoid(conv5)
return conv5sigmoid,conv5
def visualize_videos(self):
with tf.variable_scope("generator") as scope:
scope.reuse_variables()
#Background
z = tf.reshape(self.zsample,[-1,1,1,self.zdim])
deconvb1 = tf.layers.conv2d_transpose(z,512,kernel_size=[4,4],strides =[1,1],name="gen1")
deconvb1 = tf.nn.relu(self.bs1(deconvb1))
deconvb2 = tf.layers.conv2d_transpose(deconvb1,256,kernel_size=[2,2],strides =[2,2],padding="VALID",name="gen2")
deconvb2 = tf.nn.relu(self.bs2(deconvb2))
deconvb3 = tf.layers.conv2d_transpose(deconvb2,128,kernel_size=[2,2],strides =[2,2],padding="VALID",name="gen3")
deconvb3 = tf.nn.relu(self.bs3(deconvb3))
deconvb4 = tf.layers.conv2d_transpose(deconvb3,64,kernel_size=[2,2],strides =[2,2],padding="VALID",name="gen4")
deconvb4 = tf.nn.relu(self.bs4(deconvb4))
deconvb5 = tf.layers.conv2d_transpose(deconvb4,3,kernel_size=[2,2],strides =[2,2],padding="VALID",name="gen5")
background = tf.nn.tanh(deconvb5)
#Foreground
#z = tf.expand_dims(z,1)
z = tf.reshape(z,[-1,1,1,1,self.zdim])
deconv1 = tf.layers.conv3d_transpose(z,filters = 512,kernel_size = [2,4,4],strides = [1,1,1], use_bias = False,name="gen6")
deconv1 = tf.nn.relu(self.bv1(deconv1))
deconv2 = tf.layers.conv3d_transpose(deconv1,filters= 256,kernel_size=[4,4,4],strides=[2,2,2],padding = "SAME",use_bias = False,name="gen7")
deconv2 = tf.nn.relu(self.bv2(deconv2))
deconv3 = tf.layers.conv3d_transpose(deconv2,filters= 128,kernel_size =[4,4,4],strides = [2,2,2], padding ="SAME",use_bias = False,name="gen8")
deconv3 = tf.nn.relu(self.bv3(deconv3))
deconv4 = tf.layers.conv3d_transpose(deconv3,filters=64,kernel_size=[4,4,4],strides=[2,2,2],padding ="SAME",use_bias = False,name="gen9")
deconv4 = tf.nn.relu(self.bv4(deconv4))
#Mask
mask = tf.layers.conv3d_transpose(deconv4,filters= 1, kernel_size=[4,4,4], strides =[2,2,2],padding ="SAME",use_bias = False,name="gen10")
mask = tf.nn.sigmoid(mask)
#Video
foreground = tf.layers.conv3d_transpose(deconv4,filters = 3, kernel_size = [4,4,4], strides = [2,2,2], padding ="SAME",use_bias = False,name="gen11")
foreground = tf.nn.tanh(foreground)
#Replicate background and mask
background = tf.expand_dims(background,1)
backreplicate = tf.tile(background,[-1,32,1,1,1])
maskreplicate = tf.tile(mask,[-1,1,1,1,3])
#Incorporate mask
video = tf.add(tf.multiply(mask,foreground),tf.multiply(1-mask,background))
print("Video Shape")
print video.get_shape()
return video,background