def __init__(self, epochs, batch_size):
self.epochs = epochs
self.batch_size = batch_size
# Generator
filter_kernal = [[32, 128, 128, 128, 256],
[32, 128, 128, 128, 128, 256, 512],
[32, 128, 128, 128, 128, 128, 128, 512, 512]]
self.g_1 = generator(3, filter_kernal[2], in_1_size, 'Adam',
'mse').generator_model()
self.g_2 = generator(2, filter_kernal[1], in_2_size, 'Adam',
'mse').generator_model()
self.g_3 = generator(1, filter_kernal[0], in_3_size, 'Adam',
'mse').generator_model()
# Discriminator
self.D = discriminator(out).modified_vgg()
self.D.compile(loss='mse', optimizer='Adam', metrics=['accuracy'])
# Images
lr = Input(shape=in_1_size)
hr_1 = Input(shape=in_2_size)
hr_2 = Input(shape=in_3_size)
hr_3 = Input(shape=out)
fake_hr_image_1 = self.g_1(lr)
fake_hr_image_2 = self.g_2(fake_hr_image_1)
fake_hr_image_3 = self.g_3(fake_hr_image_2)
self.vgg_1 = vgg(in_2_size).vgg_loss_model()
self.vgg_2 = vgg(in_3_size).vgg_loss_model()
self.vgg_3 = vgg(out).vgg_loss_model()
fake_hr_feat_1 = self.vgg_1(fake_hr_image_1)
fake_hr_feat_2 = self.vgg_2(fake_hr_image_2)
fake_hr_feat_3 = self.vgg_3(fake_hr_image_3)
self.D.trainable = False
valid = self.D(fake_hr_image_3)
self.merged_net = Model(
inputs=[lr, hr_1, hr_2, hr_3],
outputs=[valid, fake_hr_feat_1, fake_hr_feat_2, fake_hr_feat_3])
self.merged_net.compile(
loss=['binary_crossentropy', 'mse', 'mse', 'mse'],
loss_weights=[1e-3, .3, .3, .3],
optimizer='Adam')
self.merged_net.summary()
def gen_model(param_object):
x_train, y_train = load_data(flag=0)
_G = generator(param_object.gen_type,
param_object.filter_kernal[param_object.filter_kernal_no],
param_object.input_shape, 'Adam', mean_squared_error,
tf.zeros(shape=param_object.inflow_shape,
dtype=tf.float32)).generator_model()
opti_trick = ReduceLROnPlateau(monitor='val_loss',
factor=0.2,
patience=100,
verbose=1)
ckpt = ModelCheckpoint(ckpt_path,
monitor='val_loss',
verbose=1,
save_best_only=True,
save_weights_only=True,
period=1)
_G.fit(x_train,
y_train,
epochs=param_object.epochs,
batch_size=param_object.batch_size,
callbacks=[opti_trick, ckpt],
validation_split=0.2,
shuffle=True)
return _
def feed_forward(self):
in_1 = [256,256,3]
in_2 = [512,512,3]
in_3 = [1024,1024,3]
out_ = [128,128,3]
#/home/mdo2/sid_codes/newew/ee2.jpg
#/home/mdo2/sid_codes/datasets_big/qhd_no_pad/train/1.png
img = cv2.resize(cv2.imread('/home/mdo2/sid_codes/datasets_big/qhd_no_pad/train/1.png',1),(512*2,512*2))
img = img.reshape(1,512*2,512*2,3)
filter_kernal = [[32,128,128,128,256],[32,128,128,128,128,256,512],[32,128,128,128,128,128,128,128,512]]
g3 = generator(3,filter_kernal[2],in_3,'Adam',path = '/home/mdo2/sid_codes/new_codes/gen_1.h5').generator_model()
#g2 = generator(2,filter_kernal[1],in_2,'Adam',path = '/home/mdo2/sid_codes/new_codes/new_save/gen_2.h5').generator_model()
#g1 = generator(1,filter_kernal[0],in_1,'Adam',path = '/home/mdo2/sid_codes/new_codes/new_save/gen_3.h5').generator_model()
img = g3.predict(img)
#img = g2.predict(img)
img = np.array(img).astype('uint8')
img = img.reshape(1024*2,1024*2,3)
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.imwrite('ee3.png',img)
im1 = tf.io.decode_image('/home/mdo2/sid_codes/newew/ee3.png')
#im1 = tf.image.convert_image_dtype(im1, tf.float32)
im2 = tf.io.decode_image('/home/mdo2/sid_codes/datasets_big/qhd_no_pad/train/1.png')
#im2 = tf.image.convert_image_dtype(im2, tf.float32)
psnr1 = tf.image.psnr(im2, im2, max_val=1.0)
print(psnr1)
def feed_forward(self):
in_1 = [64, 64, 3]
in_2 = [64, 64, 3]
in_3 = [64 * 4, 64 * 4, 3]
out_ = [128, 128, 3]
img = cv2.resize(
cv2.imread(
'/home/mdo2/sid_codes/datasets_big/qhd_no_pad/train/1.png', 1),
(64, 64))
img = img.reshape(1, 64, 64, 3)
filter_kernal = [[32, 128, 128, 128, 256],
[32, 128, 128, 128, 128, 256, 512],
[32, 128, 128, 128, 128, 128, 128, 128, 512]]
g3 = generator(
3,
filter_kernal[2],
in_1,
'Adam',
path='/home/mdo2/sid_codes/new_codes/gen_1.h5').generator_model()
#g2 = generator(2,filter_kernal[1],in_2,'Adam',path = '/home/mdo2/sid_codes/new_codes/gen_2.h5').generator_model()
#g1 = generator(1,filter_kernal[0],in_1,'Adam',path = '/home/mdo2/sid_codes/new_codes/gen_3.h5').generator_model()
img = g3.predict(img)
#img = g2.predict(img)
img = np.array(img).astype('uint8')
img = img.reshape(128, 128, 3)
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.imwrite('ee.jpg', img)
def __init__(self,epochs,batch_size):
self.epochs = epochs
self.batch_size = batch_size
# Generator
filter_kernal = [[32,128,128,128,256],[32,128,128,128,128,256,512],[32,128,128,128,128,128,128,128,512]]
#self.g_1 = generator(3,filter_kernal[2],in_1_size,'Adam','mse',path = '/home/mdo2/sid_codes/new_codes/gen_1.h5').generator_model()
#self.g_2 = generator(2,filter_kernal[1],in_2_size,'Adam','mse',path = '/home/mdo2/sid_codes/new_codes/gen_2.h5').generator_model()
self.g_3 = generator(1,filter_kernal[0],in_3_size,'Adam','mse',path = '/home/mdo2/sid_codes/new_codes/gen_3.h5').generator_model()
# Discriminator
#self.D1 = discriminator(in_2_size).modified_vgg()
#self.D1.compile(loss='mse',optimizer='Adam',metrics=['accuracy'])
#self.D2 = discriminator(in_3_size).modified_vgg()
#self.D2.compile(loss='mse',optimizer='Adam',metrics=['accuracy'])
self.D = discriminator(out,'/home/mdo2/sid_codes/new_codes/D.h5').modified_vgg()
self.D.compile(loss='mse',optimizer='Adam',metrics=['accuracy'])
self.g = 0
# Images
lr = Input(shape=in_1_size)
hr_1 = Input(shape=in_2_size)
hr_2 = Input(shape=in_2_size)
hr_3 = Input(shape=out)
#fake_hr_image_1 = self.g_1(lr)
#fake_hr_image_2 = self.g_2(lr)
fake_hr_image_3 = self.g_3(lr)
#self.vgg_1 = vgg(in_2_size).vgg_loss_model()
#self.vgg_2 = vgg(in_2_size).vgg_loss_model()
self.vgg_3 = vgg(in_2_size).vgg_loss_model()
#fake_hr_feat_1 = self.vgg_1(fake_hr_image_1)
#fake_hr_feat_2 = self.vgg_2(fake_hr_image_2)
fake_hr_feat_3 = self.vgg_3(fake_hr_image_3)
self.D.trainable = False
#self.D1.trainable = False
#self.D2.trainable = False
validity = self.D(fake_hr_image_3)
#self.D.load_weights('/media/arjun/119GB/attachments/D_best.h5')
self.merged_net = Model(inputs=[lr,hr_2],outputs=[validity,fake_hr_feat_3])
self.merged_net.compile(loss=['binary_crossentropy','mse'],loss_weights=[1e-3,.3],optimizer='Adam')
self.merged_net.summary()
#self.merged_net.load_weights('/home/mdo2/sid_codes/newew/my_model.h5')
print('loaded')