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ccyclegan_t22.py
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from __future__ import print_function, division
import scipy
from keras.datasets import mnist
from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, Concatenate
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D
from keras.models import Sequential, Model
from keras.optimizers import Adam
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply
from keras.layers import Reshape
import datetime
import matplotlib.pyplot as plt
import sys
from data_loader import DataLoader
from keras.layers import Concatenate, Dense, LSTM, Input, concatenate
import numpy as np
import os
import random
import tensorflow as tf
from keras.utils import to_categorical
from sklearn.metrics import accuracy_score
from models import *
class CCycleGAN():
def __init__(self,img_rows = 48,img_cols = 48,channels = 1, num_classes=7, latent_dim=100):
# Input shape
self.img_rows = img_rows
self.img_cols = img_cols
self.channels = channels
self.img_shape = (self.img_rows, self.img_cols, self.channels)
self.num_classes = num_classes
self.latent_dim = latent_dim
## dict
self.lab_dict = {0: "Angry", 1: "Disgust" , 2: "Fear" , 3: "Happy" , 4: "Sad" , 5: "Surprise" , 6: "Neutral"}
# Configure data loader
self.dataset_name = 'fer2013'
self.data_loader = DataLoader(dataset_name=self.dataset_name,img_res=self.img_shape)
# Calculate output shape of D (PatchGAN)
patch = int(self.img_rows / 2**4)
self.disc_patch = (patch, patch, 1)
# Number of filters in the first layer of G and D
self.gf = 32
self.df = 64
# Loss weights
self.lambda_cycle = 1 # Cycle-consistency loss
self.lambda_id = 0.1 * self.lambda_cycle # Identity loss
optimizer = Adam(0.0002, 0.5)
# Build and compile the discriminators
self.d = build_discriminator(img_shape=self.img_shape,df=64,num_classes=self.num_classes,act_multi_label='sigmoid')
print("******** Discriminator/Classifier ********")
self.d.summary()
self.d.compile(loss=[
'binary_crossentropy', # gan
'binary_crossentropy' # 0 - Angry
# 'binary_crossentropy' # 1 - Disgust
# 'binary_crossentropy' # 2 - Fear
# 'binary_crossentropy' # 3 - Happy
# 'binary_crossentropy' # 4 - Sad
# 'binary_crossentropy' # 5 - Surprise
# 'binary_crossentropy' # 6 - Neutral
],
optimizer=optimizer,
metrics=['accuracy'],
loss_weights=[
1 , # gan
3 # 0 - Angry
# 3 , # 1 - Disgust
# 3 , # 2 - Fear
# 3 , # 3 - Happy
# 3 , # 4 - Sad
# 3 , # 5 - Surprise
# 3 , # 6 - Neutral
])
#-------------------------
# Construct Computational
# Graph of Generators
#-------------------------
# Build the generators
#self.g = self.build_generator()
self.g_enc , self.g_dec = build_generator_enc_dec(img_shape=(48,48,1),gf=64,num_classes=7,channels=1,tranform_layer=True)
print("******** Generator_ENC ********")
self.g_enc.summary()
print("******** Generator_DEC ********")
self.g_dec.summary()
# Input images from both domains
img = Input(shape=self.img_shape)
label0 = Input(shape=(self.num_classes,))
label1 = Input(shape=(self.num_classes,))
# Translate images to the other domain
#fake = self.g([label1,img])
z1,z2,z3,z4 = self.g_enc(img)
fake = self.g_dec([z1,z2,z3,z4,label1])
# Translate images back to original domain
#reconstr = self.g([label0,fake])
reconstr = self.g_dec([z1,z2,z3,z4,label0])
# Identity mapping of images
#img_id = self.g([label0,img])
# For the combined model we will only train the generators
self.d.trainable = False
# Discriminators determines validity of translated images gan_prob,class_prob [label,img], [gan_prob,class_prob]
gan_valid , class_valid = self.d(fake)
# Combined model trains generators to fool discriminators
self.combined = Model(inputs=[img,label0,label1],
outputs=[ gan_valid, class_valid,
reconstr])
self.combined.compile(loss=['binary_crossentropy','categorical_crossentropy',
'mae'],
loss_weights=[ 1 , 3, 1],
optimizer=optimizer)
def generate_new_labels(self,labels0):
labels1 = []
for i in range(len(labels0)):
allowed_values = list(range(0, self.num_classes))
allowed_values.remove(labels0[i])
labels1.append(random.choice(allowed_values))
return np.array(labels1,'int32')
def train(self, epochs, batch_size=1, sample_interval=50 , d_g_ratio=5):
start_time = datetime.datetime.now()
# Adversarial loss ground truths
#valid = np.ones((batch_size,) + self.disc_patch)
#fake = np.zeros((batch_size,) + self.disc_patch)
valid = np.ones((batch_size,1) )
fake = np.zeros((batch_size,1) )
null_labels = np.zeros((batch_size,7) )
for epoch in range(epochs):
for batch_i, (labels0 , imgs) in enumerate(self.data_loader.load_batch(batch_size=batch_size)):
labels1 = self.generate_new_labels(labels0)
#labels01 = self.generate_new_labels(labels0)
labels0_cat = to_categorical(labels0, num_classes=self.num_classes)
labels1_cat = to_categorical(labels1, num_classes=self.num_classes)
# ----------------------
# Train Discriminators
# ----------------------
# Translate images to opposite domain
#fakes = self.g.predict([labels1,imgs])
zs1,zs2,zs3,zs4 = self.g_enc.predict(imgs)
fakes = self.g_dec.predict([zs1,zs2,zs3,zs4,labels1_cat])
#print("fake",str(fake.shape))
# Train the discriminators (original images = real / translated = Fake)
#d_loss_real = self.d.train_on_batch([labels0,imgs], valid)
#d_loss_real_fake = self.d.train_on_batch([labels01,imgs], fake)
#d_loss_fake = self.d.train_on_batch([labels1,fakes], fake)
#d_loss = (1/2) * np.add(d_loss_real, d_loss_fake)
#d_loss_real = self.d.train_on_batch(imgs, [valid,labels0_cat])
#d_loss_fake = self.d.train_on_batch(fakes, [fake,labels01_cat])
#print("d_loss_real:",d_loss_real)
#print("d_loss_fake:",d_loss_fake)
#d_loss_gan = (1/2) * np.add(d_loss_real[0], d_loss_fake[0])
#d_loss_class = (1/2) * np.add(d_loss_real[1], d_loss_fake[1])
idx = np.random.permutation(2*labels1.shape[0])
_labels_cat = np.concatenate([labels0_cat,null_labels])
_imgs = np.concatenate([imgs,fakes])
_vf = np.concatenate([valid,fake])
_labels_cat = _labels_cat[idx]
_imgs = _imgs[idx]
_vf = _vf[idx]
d_loss = self.d.train_on_batch(_imgs, [_vf,_labels_cat])
#d_loss = self.d.train_on_batch(_imgs, [_vf,_labels])
if batch_i % d_g_ratio == 0:
# ------------------
# Train Generators
# ------------------
# Train the generators
g_loss = self.combined.train_on_batch([imgs, labels0_cat, labels1_cat],
[valid, labels1_cat, imgs])
elapsed_time = datetime.datetime.now() - start_time
# Plot the progress
# print ("[Epoch %d/%d] [Batch %d/%d] [D loss: %f, acc: %3d%%] [G loss: %05f, adv: %05f, recon: %05f, id: %05f] time: %s " \
# % ( epoch, epochs,
# batch_i, self.data_loader.n_batches,
# d_loss[0], 100*d_loss[1],
# g_loss[0],
# np.mean(g_loss[1:2]),
# np.mean(g_loss[2:3]),
# np.mean(g_loss[3:4]),
# elapsed_time))
# print("[Epoch %d/%d]"% ( epoch, epochs),
# "[d_loss_gan",d_loss_gan,"]",
# "[d_loss_class:", d_loss_class,"]",
# "[g_loss_gan:",g_loss[0],"]",
# "[g_loss_class:", g_loss[1],"]",
# "[recon_loss:",g_loss[2],"]",
# "[time:",elapsed_time,"]")
print ("[Epoch %d/%d] [Batch %d/%d] [D_gan loss: %f, acc_gan: %3d%%] [D_cl loss: %f, acc_cl: %3d%%] [G_gan loss: %05f, G_cl: %05f, recon: %05f] time: %s " \
% ( epoch, epochs,
batch_i, self.data_loader.n_batches,
d_loss[1],100*d_loss[3],d_loss[2],100*d_loss[4],
g_loss[1],g_loss[2],g_loss[3],
elapsed_time))
# If at save interval => save generated image samples
if batch_i % sample_interval == 0:
self.sample_images(epoch, batch_i)
def sample_images(self, epoch, batch_i):
#os.makedirs('images/%s' % self.dataset_name, exist_ok=True)
r, c = 1, 3
## disc
labels0_d , imgs_d = self.data_loader.load_data(batch_size=64, is_testing=True)
gan_pred_prob,class_pred_prob = self.d.predict(imgs_d)
#print("gan_pred_prob:",gan_pred_prob.shape,gan_pred_prob) #64x1
gan_pred = (gan_pred_prob > 0.5)*1.0
gan_pred = gan_pred.reshape((64,))
#print("gan_pred:",gan_pred.shape,gan_pred)
#print("class_pred_prob:",class_pred_prob.shape,class_pred_prob) #64x7
class_pred = np.argmax(class_pred_prob,axis=1)
#print("class_pred:",class_pred.shape,class_pred)
#print("labels0_d:",labels0_d)
gan_test_accuracy = accuracy_score(y_true=np.ones(64), y_pred=gan_pred)
class_test_accuracy = accuracy_score(y_true=labels0_d, y_pred=class_pred)
print("*** gan_test_accuracy :",gan_test_accuracy,"***")
print("*** class_test_accuracy :",class_test_accuracy,"***")
## gen
labels0_ , imgs_ = self.data_loader.load_data(batch_size=1, is_testing=True)
labels1_ = self.generate_new_labels(labels0_)
labels0_cat = to_categorical(labels0_, num_classes=self.num_classes)
labels1_cat = to_categorical(labels1_, num_classes=self.num_classes)
# Demo (for GIF)
#imgs_A = self.data_loader.load_img('datasets/apple2orange/testA/n07740461_1541.jpg')
#imgs_B = self.data_loader.load_img('datasets/apple2orange/testB/n07749192_4241.jpg')
# Translate images to the other domain
#fake_ = self.g.predict([labels1_,imgs_])
zs1_,zs2_,zs3_,zs4_ = self.g_enc.predict(imgs_)
fake_ = self.g_dec.predict([zs1_,zs2_,zs3_,zs4_,labels1_cat])
# Translate back to original domain
reconstr_ = self.g_dec.predict([zs1_,zs2_,zs3_,zs4_,labels0_cat])
gen_imgs = np.concatenate([imgs_, fake_, reconstr_])
# Rescale images 0 - 1
gen_imgs = 0.5 * gen_imgs + 0.5
titles = ['Orig-l0:'+str(self.lab_dict[labels0_.item(0)]), 'Trans-l1:'+str(self.lab_dict[labels1_.item(0)]), 'Reconstr.']
fig, axs = plt.subplots(r, c)
cnt = 0
if not os.path.exists( "images/%s/"% (self.dataset_name)):
os.makedirs( "images/%s/"% (self.dataset_name) )
for j in range(c):
axs[j].imshow(gen_imgs[cnt].reshape((self.img_rows,self.img_cols)),cmap='gray')
axs[j].set_title(titles[j])
axs[j].axis('off')
cnt += 1
fig.savefig("images/%s/%d_%d.png" % (self.dataset_name, epoch, batch_i))
plt.close()
if __name__ == '__main__':
gan = CCycleGAN()
gan.train(epochs=400, batch_size=64, sample_interval=200 , d_g_ratio=2)