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main.py
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main.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Jul 13 13:57:15 2018
@author: mtm916
"""
import numpy as np
import random
from PIL import Image
import matplotlib.pyplot as plt
from math import floor
def zero():
return np.random.uniform(0.0, 0.01, size = [1])
def one():
return np.random.uniform(0.99, 1.0, size = [1])
def noise(n):
return np.random.uniform(-1.0, 1.0, size = [n, 4096])
print("Importing Images...")
Images = []
for n in range(1, 721):
temp1 = Image.open("Sprites/im ("+str(n)+").png")
temp = np.array(temp1.convert('RGB'), dtype='float32')
Images.append(temp / 255)
Images.append(np.flip(Images[-1], 1))
from keras.layers import Conv2D, LeakyReLU, BatchNormalization, Dense, AveragePooling2D
from keras.layers import Reshape, UpSampling2D, Activation, Dropout, Flatten, Conv2DTranspose
from keras.models import model_from_json, Sequential
from keras.optimizers import Adam
class GAN(object):
def __init__(self):
#Models
self.D = None
self.G = None
self.OD = None
self.DM = None
self.AM = None
#Config
self.LR = 0.0001
self.steps = 1
def discriminator(self):
if self.D:
return self.D
self.D = Sequential()
#256x256x3 Image
self.D.add(Conv2D(filters = 8, kernel_size = 3, padding = 'same', input_shape = [256, 256, 3]))
self.D.add(LeakyReLU(0.2))
self.D.add(Dropout(0.25))
self.D.add(AveragePooling2D())
#128x128x8
self.D.add(Conv2D(filters = 16, kernel_size = 3, padding = 'same'))
self.D.add(BatchNormalization(momentum = 0.7))
self.D.add(LeakyReLU(0.2))
self.D.add(Dropout(0.25))
self.D.add(AveragePooling2D())
#64x64x16
self.D.add(Conv2D(filters = 32, kernel_size = 3, padding = 'same'))
self.D.add(BatchNormalization(momentum = 0.7))
self.D.add(LeakyReLU(0.2))
self.D.add(Dropout(0.25))
self.D.add(AveragePooling2D())
#32x32x32
self.D.add(Conv2D(filters = 64, kernel_size = 3, padding = 'same'))
self.D.add(BatchNormalization(momentum = 0.7))
self.D.add(LeakyReLU(0.2))
self.D.add(Dropout(0.25))
self.D.add(AveragePooling2D())
#16x16x64
self.D.add(Conv2D(filters = 128, kernel_size = 3, padding = 'same'))
self.D.add(BatchNormalization(momentum = 0.7))
self.D.add(LeakyReLU(0.2))
self.D.add(Dropout(0.25))
self.D.add(AveragePooling2D())
#8x8x128
self.D.add(Conv2D(filters = 256, kernel_size = 3, padding = 'same'))
self.D.add(BatchNormalization(momentum = 0.7))
self.D.add(LeakyReLU(0.2))
self.D.add(Dropout(0.25))
self.D.add(AveragePooling2D())
#4x4x256
self.D.add(Flatten())
#256
self.D.add(Dense(128))
self.D.add(LeakyReLU(0.2))
self.D.add(Dense(1, activation = 'sigmoid'))
return self.D
def generator(self):
if self.G:
return self.G
self.G = Sequential()
self.G.add(Reshape(target_shape = [1, 1, 4096], input_shape = [4096]))
#1x1x4096
self.G.add(Conv2DTranspose(filters = 512, kernel_size = 4))
self.G.add(Activation('relu'))
#4x4x256
self.G.add(Conv2D(filters = 256, kernel_size = 3, padding = 'same'))
self.G.add(BatchNormalization(momentum = 0.7))
self.G.add(Activation('relu'))
self.G.add(UpSampling2D())
#8x8x256
self.G.add(Conv2D(filters = 128, kernel_size = 3, padding = 'same'))
self.G.add(BatchNormalization(momentum = 0.7))
self.G.add(Activation('relu'))
self.G.add(UpSampling2D())
#16x16x128
self.G.add(Conv2D(filters = 64, kernel_size = 3, padding = 'same'))
self.G.add(BatchNormalization(momentum = 0.7))
self.G.add(Activation('relu'))
self.G.add(UpSampling2D())
#32x32x64
self.G.add(Conv2D(filters = 32, kernel_size = 3, padding = 'same'))
self.G.add(BatchNormalization(momentum = 0.7))
self.G.add(Activation('relu'))
self.G.add(UpSampling2D())
#64x64x32
self.G.add(Conv2D(filters = 16, kernel_size = 3, padding = 'same'))
self.G.add(BatchNormalization(momentum = 0.7))
self.G.add(Activation('relu'))
self.G.add(UpSampling2D())
#128x128x16
self.G.add(Conv2D(filters = 8, kernel_size = 3, padding = 'same'))
self.G.add(Activation('relu'))
self.G.add(UpSampling2D())
#256x256x8
self.G.add(Conv2D(filters = 3, kernel_size = 3, padding = 'same'))
self.G.add(Activation('sigmoid'))
return self.G
def DisModel(self):
if self.DM == None:
self.DM = Sequential()
self.DM.add(self.discriminator())
self.DM.compile(optimizer = Adam(lr = self.LR * (0.85 ** floor(self.steps / 10000))), loss = 'binary_crossentropy')
return self.DM
def AdModel(self):
if self.AM == None:
self.AM = Sequential()
self.AM.add(self.generator())
self.AM.add(self.discriminator())
self.AM.compile(optimizer = Adam(lr = self.LR * (0.85 ** floor(self.steps / 10000))), loss = 'binary_crossentropy')
return self.AM
def sod(self):
self.OD = self.D.get_weights()
def lod(self):
self.D.set_weights(self.OD)
class Model_GAN(object):
def __init__(self):
self.GAN = GAN()
self.DisModel = self.GAN.DisModel()
self.AdModel = self.GAN.AdModel()
self.generator = self.GAN.generator()
def train(self, batch = 16):
(a, b) = self.train_dis(batch)
c = self.train_gen(batch)
print("D Real: " + str(a))
print("D Fake: " + str(b))
print("G All:: " + str(c))
if self.GAN.steps % 50 == 0:
self.save(floor(self.GAN.steps / 1000))
self.evaluate()
if self.GAN.steps % 5000 == 0:
self.GAN.AM = None
self.GAN.DM = None
self.AdModel = self.GAN.AdModel()
self.DisModel = self.GAN.DisModel()
self.GAN.steps = self.GAN.steps + 1
def train_dis(self, batch):
#Get Real Images
im_no = random.randint(0, len(Images) - batch - 1)
train_data = Images[im_no : im_no + int(batch / 2)]
label_data = []
for i in range(int(batch / 2)):
label_data.append(one())
d_loss_real = self.DisModel.train_on_batch(np.array(train_data), np.array(label_data))
#Get Fake Images
train_data = self.generator.predict(noise(int(batch / 2)))
label_data = []
for i in range(int(batch / 2)):
label_data.append(zero())
d_loss_fake = self.DisModel.train_on_batch(train_data, np.array(label_data))
return (d_loss_real, d_loss_fake)
def train_gen(self, batch):
self.GAN.sod()
label_data = []
for i in range(int(batch)):
label_data.append(one())
g_loss = self.AdModel.train_on_batch(noise(batch), np.array(label_data))
self.GAN.lod()
return g_loss
def evaluate(self):
im_no = random.randint(0, len(Images) - 1)
im1 = Images[im_no]
im2 = self.generator.predict(noise(2))
plt.figure(1)
plt.imshow(im1)
plt.figure(2)
plt.imshow(im2[0])
plt.figure(3)
plt.imshow(im2[1])
plt.show()
def save(self, num):
gen_json = self.GAN.G.to_json()
dis_json = self.GAN.D.to_json()
with open("Models/gen.json", "w") as json_file:
json_file.write(gen_json)
with open("Models/dis.json", "w") as json_file:
json_file.write(dis_json)
self.GAN.G.save_weights("Models/gen"+str(num)+".h5")
self.GAN.D.save_weights("Models/dis"+str(num)+".h5")
#print("Saved!")
def load(self, num):
steps1 = self.GAN.steps
self.GAN = None
self.GAN = GAN()
#Generator
gen_file = open("Models/gen.json", 'r')
gen_json = gen_file.read()
gen_file.close()
self.GAN.G = model_from_json(gen_json)
self.GAN.G.load_weights("Models/gen"+str(num)+".h5")
#Discriminator
dis_file = open("Models/dis.json", 'r')
dis_json = dis_file.read()
dis_file.close()
self.GAN.D = model_from_json(dis_json)
self.GAN.D.load_weights("Models/dis"+str(num)+".h5")
#Reinitialize
self.generator = self.GAN.generator()
self.DisModel = self.GAN.DisModel()
self.AdModel = self.GAN.AdModel()
self.GAN.steps = steps1
def eval2(self, num = 0):
im2 = self.generator.predict(noise(48))
r1 = np.concatenate(im2[:8], axis = 1)
r2 = np.concatenate(im2[8:16], axis = 1)
r3 = np.concatenate(im2[16:24], axis = 1)
r4 = np.concatenate(im2[24:32], axis = 1)
r5 = np.concatenate(im2[32:40], axis = 1)
r6 = np.concatenate(im2[40:48], axis = 1)
c1 = np.concatenate([r1, r2, r3, r4, r5, r6], axis = 0)
x = Image.fromarray(np.uint8(c1*255))
x.save("Results_Pokemon/i"+str(num)+".png")
model = Model_GAN()
print("We're off! See you in a while!")
while(model.GAN.steps < 500000):
print("\n\n\n\nRound " + str(model.GAN.steps) + ":")
model.train()
if model.GAN.steps % 1000 == 0:
model.eval2(int(model.GAN.steps / 1000))