def __init__(self,
model,
pruning_rate,
previous_masks,
current_dataset_idx,
previous_samples=None,
unrelated_tasks=None):
self.model = model
self.pruning_rate = pruning_rate
self.pruner = pruner.Pruner(self.model, self.pruning_rate,
previous_masks, current_dataset_idx)
self.unrelated_tasks = unrelated_tasks
self.discriminator = discriminator.Discriminator(
previous_samples, unrelated_tasks)
self.criterion = nn.CrossEntropyLoss()
self.optimizer = optim.SGD(self.model.parameters(),
lr=1e-3,
momentum=0.9)
self.decay = [10]
self.scheduler = MultiStepLR(self.optimizer,
milestones=self.decay,
gamma=0.2)
def __init__(self,
channels,
width,
heigth,
num_classes: int,
embed_size: int,
latent_dim: int = 100,
lr: float = 0.002,
b1: float = 0.5,
b2: float = 0.999,
batch_size: int = 1024,
**kwargs):
super().__init__()
self.save_hyperparameters()
# networks
data_shape = (channels, width, heigth)
self.generator = generator.Generator(
num_classes=num_classes,
embed_size=embed_size,
latent_dim=self.hparams.latent_dim,
img_shape=data_shape,
output_dim=int(np.prod(data_shape)))
self.discriminator = discriminator.Discriminator(
img_shape=data_shape,
output_dim=int(np.prod(data_shape)),
num_classes=num_classes,
)
self.validation_z = torch.rand(batch_size, self.hparams.latent_dim)
self.example_input_array = torch.zeros(2, self.hparams.latent_dim)
def build_discriminator(self):
#with tf.variable_scope("discriminator"):
self.discriminator = discriminator.Discriminator(
n_node=self.n_node,
n_relation=self.n_relation,
node_emd_init=self.node_embed_init_d,
relation_emd_init=None)
def __init__(self, latent_spaces, batch_size):
super(ANVAE, self).__init__()
self.batch_size = batch_size
self.latent_spaces = 3
self.level_sizes = [1, 1, 1]
self.input_s = [32, 32, 1]
self.latent_channels = 20
self.h_dim = 1000
self.encoder = encoder.Encoder(self.latent_spaces, self.input_s)
self.decoder = decoder.Decoder(self.encoder(
tf.zeros([self.batch_size, 32, 32, 1]), False),
latent_channels=self.latent_channels,
level_sizes=self.level_sizes)
self.discriminator = discriminator.Discriminator(
self.latent_spaces, self.input_s, self.h_dim)
self.lr_ae = .0001
self.lr_dc = .0001
self.lr_gen = .0001
self.ae_optimizer = tf.keras.optimizers.Adamax(self.lr_ae, clipnorm=2)
self.gen_optimizer = tf.keras.optimizers.Adamax(self.lr_gen,
clipnorm=2)
self.dc_optimizer = tf.keras.optimizers.Adamax(self.lr_dc, clipnorm=2)
self.ae_loss_weight = 1.
self.gen_loss_weight = 6.
self.dc_loss_weight = 6.
self.lastEncVars = []
self.lastDecVars = []
self.lastDiscVars = []
self.debugCount = 0
self.counter = 1
self.log_writer = tf.summary.create_file_writer(logdir='./tf_summary')
self.step_count = 0
self.conv_layers = []
self.sr_u = {}
self.sr_v = {}
self.num_power_iter = 4
for layer in self.encoder.layers:
if isinstance(layer, tf.keras.layers.Conv2D) or isinstance(
layer, tf.keras.layers.DepthwiseConv2D):
self.conv_layers.append(layer)
for layer in self.decoder.layers:
if isinstance(layer, tf.keras.layers.Conv2D) or isinstance(
layer, tf.keras.layers.DepthwiseConv2D):
self.conv_layers.append(layer)
for layer in self.discriminator.layers:
if isinstance(layer, tf.keras.layers.Conv2D) or isinstance(
layer, tf.keras.layers.DepthwiseConv2D):
self.conv_layers.append(layer)
def __init__(self, vocab_size, batch_size, pre_gen_epochs, pre_dis_epochs,
gan_epochs, generate_sum, sequence_len, lr, real_file,
fake_file, eval_file, update_rate):
super(Solver, self).__init__()
self.vocal_size = vocab_size
self.batch_size = batch_size
self.pre_gen_epochs = pre_gen_epochs
self.pre_dis_epochs = pre_dis_epochs
self.gan_epochs = gan_epochs
self.generate_sum = generate_sum
self.sequence_len = sequence_len
self.lr = lr
self.real_file = real_file
self.fake_file = fake_file
self.eval_file = eval_file
self.update_rate = update_rate
self.discriminator = discriminator.Discriminator(
sequence_len, vocab_size, DisParams.emb_dim,
DisParams.filter_sizes, DisParams.num_filters, DisParams.dropout)
self.generator = generator.Generator(vocab_size, GenParams.emb_dim,
GenParams.hidden_dim,
GenParams.num_layers)
self.target_lstm = target_lstm.TargetLSTM(vocab_size,
GenParams.emb_dim,
GenParams.hidden_dim,
GenParams.num_layers)
self.discriminator = util.to_cuda(self.discriminator)
self.generator = util.to_cuda(self.generator)
self.target_lstm = util.to_cuda(self.target_lstm)
def build_discriminator(self):
"""initialize the discriminator"""
with tf.variable_scope("discriminator"):
self.discriminator = discriminator.Discriminator(
n_node=self.n_node,
node_emd_init=self.node_embed_init_d,
node_features=self.node_feature_init)
def __init__(self, prn, receiver, param_list=_M_ARRAY_DATA_NAMES):
"""
Constructs a Channel object with specified prn and measurement length.
"""
self.prn = prn
self.receiver = receiver
self.rawfile = self.receiver.rawfile
self._measurement_logs_on = False
self.init_measurement_logs(param_list=param_list)
self._cpcount = 0 # internal codeperiod counter
self.cp[0] = 0
# Initialize correlator.
self.correlator = correlator.Correlator(self.prn, channel=self)
# Initialize discriminators and loopfilters.
self.cdiscriminator = discriminator.Discriminator(flavor='DLL',
channel=self)
self.idiscriminator = discriminator.Discriminator(flavor='PLL',
channel=self)
self.cloopfilter = loopfilter.LoopFilter(self.rawfile.T,
Bnp=3.0,
channel=self)
self.iloopfilter = loopfilter.LoopFilter(self.rawfile.T,
Bnp=40.0,
channel=self)
# Initialize lockdetector and snrmeter.
self.lockdetector = lockdetector.LockDetector(N=20,
k=1.5,
lossthreshold=50,
lockthreshold=240)
self.snrmeter = snrmeter.SignalNoiseMeter(N=20, T=self.rawfile.T)
def trainer(model_params):
datasets = load_dataset('dataset', model_params)
train_set = datasets[0][0] + datasets[0][1]
valid_set = datasets[1][0] + datasets[1][1]
test_set = datasets[2][0] + datasets[2][1]
labels = {dataset: i for (i, dataset) in enumerate(model_params.data_set)}
train_generator = DataGenerator(train_set,
labels,
batch_size=model_params.batch_size,
shuffle=True)
valid_generator = DataGenerator(valid_set,
labels,
batch_size=model_params.batch_size,
shuffle=True)
model = discriminator.Discriminator(model_params)
model.train(train_generator, valid_generator)
model.save()
print('Done!')
def init_GAN():
# models
generator = g.Generator()
discriminator = d.Discriminator()
# Loss functions
adversarial_loss = torch.nn.MSELoss()
categorical_loss = torch.nn.CrossEntropyLoss()
continuous_loss = torch.nn.MSELoss()
if cuda:
generator.cuda()
discriminator.cuda()
adversarial_loss.cuda()
categorical_loss.cuda()
continuous_loss.cuda()
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
return generator, discriminator, adversarial_loss, categorical_loss, continuous_loss
def create_model(actions, sampling=False):
policy_net = seq2seq_model.Seq2SeqModel(
FLAGS.architecture,
FLAGS.seq_length_in if not sampling else 50,
FLAGS.seq_length_out if not sampling else 25,
FLAGS.size, # hidden layer size
FLAGS.num_layers,
FLAGS.batch_size,
summaries_dir,
FLAGS.loss_to_use if not sampling else "sampling_based",
len( actions ),
device,
not FLAGS.omit_one_hot,
FLAGS.residual_velocities,
stochastic = True,
dtype=torch.float32)
discrim_net = discriminator.Discriminator(
not FLAGS.omit_one_hot,
FLAGS.discrim_hidden_size,
FLAGS.batch_size,
FLAGS.discrim_num_layers,
len( actions )
)
#initalize a new model
if FLAGS.load <= 0 and FLAGS.discrim_load <=0:
print("Creating model with fresh parameters.")
#TODO: Initial parameter here
return policy_net, discrim_net
#Load model from iteration
if os.path.isfile(os.path.join(train_dir, 'pretrain-policy-checkpoint-{0}.pt'.format(FLAGS.load))):
policy_net.load_state_dict(torch.load(os.path.join(train_dir, 'pretrain-policy-checkpoint-{0}.pt'.format(FLAGS.load))))
elif FLAGS.load > 0:
raise ValueError("Asked to load pretrain policy checkpoint {0}, but it does not seem to exist".format(FLAGS.load))
if os.path.isfile(os.path.join(train_dir, 'pretrain-discrim-checkpoint-{0}.pt'.format(FLAGS.discrim_load))):
policy_net.load_state_dict(torch.load(os.path.join(train_dir, 'discrim-checkpoint-{0}.pt'.format(FLAGS.load))))
elif FLAGS.discrim_load > 0:
raise ValueError("Asked to load pretrain discrim checkpoint {0}, but it does not seem to exist".format(FLAGS.discrim_load))
return policy_net, discrim_net
def train(train_dataset, test_dataset, epochs, tr_urls):
gen = generator.Generator()
generator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discr = discriminator.Discriminator()
discriminator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
checkpoint = tf.train.Checkpoint(
generator_optimizer=generator_optimizer,
discriminator_optimizer=discriminator_optimizer,
generator=gen,
discriminator=discr)
summary_writer = tf.summary.create_file_writer(
variables.LOG_DIR + '/' +
datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
for epoch in range(epochs):
start = time.time()
imgi = 0
for input_image, target in train_dataset:
print('epoch ' + str(epoch) + ' - train: ' + str(imgi) + '/' +
str(len(tr_urls)))
imgi += 1
train_step(input_image, target, gen, generator_optimizer, discr,
discriminator_optimizer, summary_writer, epoch)
if (epochs + 1) % 3 == 0:
checkpoint.save(file_prefix=variables.CHECK_DIR + 'ckpt')
print('Time taken for epoch {} is {} sec\n'.format(
epoch + 1,
time.time() - start))
checkpoint.save(file_prefix=variables.CHECK_DIR + 'ckpt')
checkpoint.restore(tf.train.latest_checkpoint(variables.CHECK_DIR))
# clear
imgi = 0
for inp, tar in test_dataset.take(5):
generate_images(gen, inp, tar)
imgi += 1
def main(config: str):
config = load_config(config)
parameters = config['parameters']
train_data = load_data(
data_path=config['data']['directory'],
img_size=parameters['img_size'],
batch_size=parameters['batch_size'],
use_rgb=parameters["rgb"],
)
gen = generator.Generator(
generator.GeneratorConfig.from_config_dict(parameters))
disc = discriminator.Discriminator(
discriminator.DiscriminatorConfig.from_config_dict(parameters))
trainer = Trainer(gen,
disc,
noise_dim=parameters['generator']['noise_dim'],
rgb=parameters["rgb"])
trainer.train(dataset=train_data,
epochs=config['train']['epochs'],
save_interval=config['train']['save_interval'])
gen.save("saved_model")
tfjs.converters.save_keras_model(gen, "saved_js_model")
def __init__(self,
z_dim=512,
resolution=1024,
randomize_noise=True,
gpu_id=0):
self.z_dim = z_dim
self.gpu = gpu_id
torch.cuda.set_device(self.gpu)
# loss function : non-saturating loss with R1 regularization
self.loss_function_G = loss.G_logistic_nonsaturating()
self.loss_function_D = loss.D_logistic()
self.G = generator.Generator(self.z_dim,
resolution=resolution,
randomize_noise=randomize_noise).cuda(
self.gpu)
self.D = discriminator.Discriminator(resolution=resolution).cuda(
self.gpu)
self.train_G_losses = []
self.train_D_losses = []
VOCAB_SIZE,
MAX_SEQ_LEN,
gpu=CUDA)
oracle.load_state_dict(torch.load(oracle_state_dict_path))
oracle_samples = torch.load(oracle_samples_path).type(torch.LongTensor)
# a new oracle can be generated by passing oracle_init=True in the generator constructor
# samples for the new oracle can be generated using helpers.batchwise_sample()
gen = generator.Generator(GEN_EMBEDDING_DIM,
GEN_HIDDEN_DIM,
VOCAB_SIZE,
MAX_SEQ_LEN,
gpu=CUDA)
dis = discriminator.Discriminator(DIS_EMBEDDING_DIM,
DIS_HIDDEN_DIM,
VOCAB_SIZE,
MAX_SEQ_LEN,
gpu=CUDA)
if CUDA:
oracle = oracle.cuda()
gen = gen.cuda()
dis = dis.cuda()
oracle_samples = oracle_samples.cuda()
#训练GENERATOR MLE
print('Starting Generator MLE Training...')
gen_optimizer = optim.Adam(gen.parameters(), lr=1e-2)
train_generator_MLE(gen, gen_optimizer, oracle, oracle_samples,
MLE_TRAIN_EPOCHS)
return minibatch, rescaled
with tf.device('/cpu:0'):
minibatch, rescaled = read(filenames)
resnet = srResNet.srResNet(rescaled)
result = (resnet.conv5 + 1) * 127.5
gen_var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
dbatch = tf.concat([tf.cast(minibatch, tf.float32), result], 0)
vgg = vgg19.Vgg19()
vgg.build(dbatch)
fmap = tf.split(vgg.conv5_4, 2)
content_loss = tf.losses.mean_squared_error(fmap[0], fmap[1])
disc = discriminator.Discriminator(dbatch)
D_x, D_G_z = tf.split(tf.squeeze(disc.dense2), 2)
adv_loss = tf.reduce_mean(tf.square(D_G_z - 1.0))
gen_loss = (adv_loss + content_loss)
disc_loss = (tf.reduce_mean(tf.square(D_x - 1.0) + tf.square(D_G_z)))
disc_var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
for x in gen_var_list:
disc_var_list.remove(x)
global_step = tf.Variable(0, trainable=0, name='global_step')
gen_train_step1 = tf.train.AdamOptimizer(learn_rate1).minimize(
gen_loss, global_step, gen_var_list)
#gen_train_step2=tf.train.AdamOptimizer(learn_rate2).minimize(gen_loss,global_step)
disc_train_step1 = tf.train.AdamOptimizer(learn_rate1).minimize(
disc_loss, global_step, disc_var_list)
#disc_train_step2=tf.train.AdamOptimizer(learn_rate2).minimize(disc_loss,global_step)
def build_discriminator(self):
"""initializing the discriminator"""
with tf.compat.v1.variable_scope("discriminator"):
self.discriminator = discriminator.Discriminator(
self.graph.n_node, self.node_embed_init_d, config)
def parse_args():
"""
Creates command line arguments with the same name and default values as those in the global opts variable
Then updates opts using their respective argument values
"""
# Parse command line arguments to assign to the global opt variable
parser = argparse.ArgumentParser(
description=
'colorize images using conditional generative adversarial networks')
for opt_name, value in opts.items():
parser.add_argument("--%s" % opt_name, default=value)
# Update global opts variable using flag values
args = parser.parse_args()
for opt_name, _ in opts.items():
opts[opt_name] = getattr(args, opt_name)
parse_args()
with tf.Session() as sess:
# Initialize networks
gen = generator.Generator()
disc = discriminator.Discriminator()
# Train them
t = Trainer(sess, gen, disc, opts)
t.train()
args.vocab_size,
args.max_seq_len,
gpu=args.cuda,
oracle_init=True)
gen = generator.Generator(
args.g_emb_dim,
args.g_hid_dim,
args.vocab_size,
args.max_seq_len,
gpu=args.cuda,
)
dis = discriminator.Discriminator(args.d_emb_dim,
args.d_hid_dim,
args.vocab_size,
args.max_seq_len,
gpu=args.cuda)
if args.cuda:
oracle = oracle.cuda()
gen = gen.cuda()
dis = dis.cuda()
oracle_samples = helpers.batchwise_sample(oracle, args.num_data)
if args.oracle_save is not None:
torch.save(oracle.state_dict(), args.oracle_save)
logger = logger.Logger(args.log_dir)
# GENERATOR MLE TRAINING
gen_optimizer = optim.Adam(gen.parameters(), lr=1e-2)
def __init__(self):
# self.IsTraining = tf.placeholder(tf.bool)
"""
This is placeholder for the input for the Generator it is a4 channnel input
"""
self.GeneratorInput=tf.placeholder(tf.float32, [None, None, None, 4])
"""
The DiscriminatorLabelsFake is always an array of zeros expanded
of the same sized as batch size
For the DiscriminatorLabelsReal are an array of ones expanded
of the same sized as batch size
The GeneratorLabels shall take the same labels as that of DiscriminatorRealLabels
The TargetImagePlaceholder is for when we need to feed the target image for the discriminnator
"""
self.DiscriminatorLabelsFake=tf.placeholder(tf.float32, [None, 1])
self.DiscriminatorLabelsReal=tf.placeholder(tf.float32, [None, 1])
self.GeneratorLabels=tf.placeholder(tf.float32, [None, 1])
self.TargetImagePlaceholder=tf.placeholder(tf.float32, [None, None, None, 3])
self.GeneratedImage=generator.network(self.GeneratorInput)
"""
The discriminator generates 2 outputs , the activation and the logits = sigmoid(activation) =P(Input=FakeImage)
This is generated for both when fake image is fed(logit expected to be 1),and realimage is fed(logit expected to be 0)
"""
self.DiscriminatorOutReal,self.DiscriminatorLogitsReal=discriminator.Discriminator(Target=self.TargetImagePlaceholder,DarkInput=self.GeneratorInput)
self.DiscriminatorOutFake,self.DiscriminatorLogitsFake=discriminator.Discriminator(Target=self.GeneratedImage,DarkInput=self.GeneratorInput,reuse=True)
"""
As the target and the computation graph are different when the generated image is fed or the true target
is, we compute 2 sigmoid crossentropy losses and the sum of both is the Final loss for the discriminnator
Discriminatorloss=SigLoss(P(Discriminator thinks True target is correct),logit=1)+SigLoss(P(Discriminator thinks Generated Image is correct),logit =0)
the first term is the RealLoss and the second term is FakeLoss
"""
self.DiscriminatorRealLoss=tf.reduce_mean(self.cross_entropy(logits=self.DiscriminatorLogitsReal, labels=self.DiscriminatorLabelsReal))
self.DiscriminatorFakeLoss=tf.reduce_mean(self.cross_entropy(logits=self.DiscriminatorLogitsFake, labels=self.DiscriminatorLabelsFake))
self.DiscriminatorLoss=tf.reduce_mean(self.DiscriminatorRealLoss+self.DiscriminatorFakeLoss)
"""
GeneratorLoss=SigLoss(P(Discriminator thinks Generated Image is correct),logit =1)
"""
self.GeneratorLoss=tf.reduce_mean(self.cross_entropy(logits=self.DiscriminatorLogitsFake, labels=self.GeneratorLabels))
"""
This is for the user to observe how well with time is the model able to generate images simillar to the target
"""
self.GeneratorABS=tf.losses.absolute_difference(self.GeneratedImage,self.TargetImagePlaceholder)
self.TrainableVars=tf.trainable_variables()
"""
d_vars are the weights of the discriminator as all of them is under the scope of "Discriminator"
This is necessary for allowing the optimizer to minimize the loss wrt discriminator weights
g_vars are the weights of the generator as all of them is under the scope of "Generator"
This is necessary for allowing the optimizer to minimize the loss wrt generator weights
"""
self.d_vars=[var for var in self.TrainableVars if 'Discriminator' in var.name]
self.g_vars=[var for var in self.TrainableVars if 'Generator' in var.name]
"""
In Soumith Chintala's GAN hacks tutorial, he sugest to feed all Generated and Real Targets be fed
in exclusicve batches the following 2 optimizers allows us to do the same
They optimize Discriminator weights only
"""
self.DiscriminatorOptimizerReal=tf.train.AdamOptimizer(0.00001).minimize(self.DiscriminatorRealLoss,var_list=self.d_vars)
self.DiscriminatorOptimizerFake=tf.train.AdamOptimizer(0.00001).minimize(self.DiscriminatorFakeLoss,var_list=self.d_vars)
"""
The followinng Optimizers are for minizing the Generator and Discriminator Loss wrt their weights
"""
self.gen_loss_lambda1 = 1.0
self.GeneratorOptimizer=tf.train.AdamOptimizer(0.00001).minimize(self.GeneratorLoss+self.gen_loss_lambda1*self.GeneratorABS,var_list=self.g_vars)
self.GeneratorGradients=tf.train.AdamOptimizer(0.00001).compute_gradients(self.GeneratorLoss,var_list=self.g_vars)
self.DiscriminatorOptimizer=tf.train.AdamOptimizer(0.00001).minimize(self.DiscriminatorLoss,var_list=self.d_vars)
self.Session = tf.Session()
self.saver=tf.train.Saver()
self.init_op= tf.initialize_all_variables()
self.Session.run(self.init_op)
self.BatchReplayBool=True
self.BatchSize=1
self.TrainSize=10
self.HmEpochs=10
self.PatchSize=64
self.save_path="./models/"
with open('DoDtrain_exposures.json') as f:
self.TrainDict = json.load(f)
with open('DoDval_exposures.json') as f:
self.ValidDict = json.load(f)
if tf.train.latest_checkpoint(self.save_path) is not None:
self.checkpoint = tf.train.latest_checkpoint(self.save_path)
self.saver.restore(self.Session,self.checkpoint)
def main(_argv):
weight_decay = FLAGS.weight_decay
learning_rate = FLAGS.learning_rate
batch_size = FLAGS.batch_size
epoch = FLAGS.epoch
log_dir = FLAGS.logdir
use_lsgan = FLAGS.use_lsgan
norm = FLAGS.norm
lambda1 = FLAGS.lambda1
lambda2 = FLAGS.lambda2
beta1 = FLAGS.beta1
ngf = FLAGS.ngf
G = generator.Generator('G',ngf,weight_decay,norm=norm,more=True)
F = generator.Generator('F',ngf,weight_decay,norm=norm,more=True)
D_Y = discriminator.Discriminator('D_Y',reg=weight_decay,norm=norm)
D_X = discriminator.Discriminator('D_X',reg=weight_decay,norm=norm)
forbuild=np.random.rand(1,768,768,3).astype(np.float32)
built=G(forbuild)
built=F(forbuild)
built=D_Y(forbuild)
built=D_X(forbuild)
source_data=data.source_data(batch_size)
target_data=data.target_data(batch_size)
train_loss_G = tf.keras.metrics.Mean('train_loss_G', dtype=tf.float32)
train_loss_F = tf.keras.metrics.Mean('train_loss_F', dtype=tf.float32)
train_loss_DX = tf.keras.metrics.Mean('train_loss_DX', dtype=tf.float32)
train_loss_DY = tf.keras.metrics.Mean('train_loss_DY', dtype=tf.float32)
train_loss = [train_loss_G,train_loss_F,train_loss_DX,train_loss_DY]
generator_g_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
generator_f_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_x_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_y_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
train_summary_writer = tf.summary.create_file_writer(log_dir)
ckpt = tf.train.Checkpoint(G=G,F=F,D_X=D_X,D_Y=D_Y,generator_g_optimizer=generator_g_optimizer,generator_f_optimizer=generator_f_optimizer,discriminator_x_optimizer=discriminator_x_optimizer,discriminator_y_optimizer=discriminator_y_optimizer)
ckpt_manager = tf.train.CheckpointManager(ckpt, log_dir, max_to_keep=10)
start =0
lr = lr_sch(start)
if ckpt_manager.latest_checkpoint:
ckpt.restore(ckpt_manager.latest_checkpoint)
start=int(ckpt_manager.latest_checkpoint.split('-')[-1])
lr = lr_sch(start)
print ('Latest checkpoint restored!!')
for ep in range(start,epoch,1):
print('Epoch:'+str(ep+1))
for step, source in enumerate(source_data):
target = next(iter(target_data))
fake_y, cycled_x,fake_x,cycled_y,same_x,same_y, total_gen_g_loss,total_gen_f_loss,disc_x_loss,disc_y_loss,steps = train_step(G,F,D_Y,D_X,source,target,generator_g_optimizer,generator_f_optimizer,discriminator_x_optimizer,discriminator_y_optimizer,train_loss,lambda1,lambda2)
print('Step: '+str(steps.numpy())+' , G loss: '+str(total_gen_g_loss.numpy())+' , F loss: '+str(total_gen_f_loss.numpy())+' , D_X loss: '+str(disc_x_loss.numpy())+' , D_Y loss: '+str(disc_y_loss.numpy()))
if (steps.numpy()-1)%10==0:
source=tf.image.convert_image_dtype((source+1)/2,dtype = tf.uint8)
target=tf.image.convert_image_dtype((target+1)/2,dtype = tf.uint8)
fake_y=tf.image.convert_image_dtype((fake_y+1)/2,dtype = tf.uint8)
cycled_x=tf.image.convert_image_dtype((cycled_x+1)/2,dtype = tf.uint8)
same_x=tf.image.convert_image_dtype((same_x+1)/2,dtype = tf.uint8)
fake_x=tf.image.convert_image_dtype((fake_x+1)/2,dtype = tf.uint8)
cycled_y=tf.image.convert_image_dtype((cycled_y+1)/2,dtype = tf.uint8)
same_y=tf.image.convert_image_dtype((same_y+1)/2,dtype = tf.uint8)
with train_summary_writer.as_default():
tf.summary.scalar('Learning_rate', lr, step=steps)
tf.summary.scalar('Loss/G_loss', train_loss[0].result(), step=steps)
tf.summary.scalar('Loss/F_loss', train_loss[1].result(), step=steps)
tf.summary.scalar('Loss/DX_loss', train_loss[2].result(), step=steps)
tf.summary.scalar('Loss/DY_loss', train_loss[3].result(), step=steps)
tf.summary.image('image_source/source',source,max_outputs=1,step=steps)
tf.summary.image('image_source/fake_target',fake_y,max_outputs=1, step=steps)
tf.summary.image('image_source/cycle_source',cycled_x,max_outputs=1,step=steps)
tf.summary.image('image_source/same_source',same_x,max_outputs=1, step=steps)
tf.summary.image('image_target/target',target,max_outputs=1,step=steps)
tf.summary.image('image_target/fake_source',fake_x,max_outputs=1, step=steps)
tf.summary.image('image_target/cycle_target',cycled_y,max_outputs=1,step=steps)
tf.summary.image('image_target/same_target',same_y,max_outputs=1, step=steps)
train_loss[0].reset_states()
train_loss[1].reset_states()
train_loss[2].reset_states()
train_loss[3].reset_states()
lr = lr_sch(ep)
generator_g_optimizer.learning_rate = lr
generator_f_optimizer.learning_rate = lr
discriminator_x_optimizer.learning_rate = lr
discriminator_y_optimizer.learning_rate = lr
ckpt_save_path = ckpt_manager.save()
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
# =============== GENEREATOR =============>>>
n_gen = generator.Generator(args.ngpu).to(device)
if (device.type == 'cuda') and (args.ngpu > 1):
n_gen = nn.DataParallel(n_gen, list(range(args.ngpu)))
n_gen.apply(weights_init)
# ============ DISCRIMINATOR =====>>>
n_disc = discriminator.Discriminator(args.ngpu).to(device)
if (device.type == 'cuda') and (args.ngpu > 1):
n_disc = nn.DataParallel(n_disc, list(range(args.ngpu)))
n_disc.apply(weights_init)
# ====== LOSS ==========>>>
loss = nn.BCELoss()
# Establish convention for real and fake labels during training
real_label = 1
fake_label = 0
# ============== OPTIMISERS ===============>>>
optimizerD = optim.Adam(n_disc.parameters(),
def GAN(data_name = "circle"):
# hyperparameters
G_learning_rate = 2e-4
D_learning_rate = 2e-4
batch_size = 1000
epoc_num = 20000
d_steps = 1
g_steps = 1
dim = 0
ori_z = None
if data_name == "circle":
dim = 2
z_size = dim
input_size = dim
output_size = input_size
img_dir = "imgs_circle"
sample_true_data_func = sample_true_data_circle
plot_true_data_and_noise(img_dir, sample_true_data_func, dim)
g_dim_list = [z_size, 128, 128, output_size]
g_act_list = [tf.nn.leaky_relu, tf.nn.leaky_relu, tf.nn.tanh]
elif data_name == "Gaussian":
dim = 2
z_size = dim
input_size = dim
output_size = input_size
img_dir = "imgs_Gaussian"
sample_true_data_func = sample_true_data_Gaussian
plot_true_data_and_noise(img_dir, sample_true_data_func, dim)
g_dim_list = [z_size, 128, 128, output_size]
g_act_list = [tf.nn.leaky_relu, tf.nn.leaky_relu, None]
elif data_name == "mnist":
# increase epoc_num for mnist
batch_size = 250
epoc_num = 120000
img_height = 28
img_width = 28
dim = img_height * img_width
z_size = dim
input_size = dim
output_size = input_size
img_dir = "imgs_mnist"
global mnist
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
sample_true_data_func = sample_true_data_mnist
g_dim_list = [z_size, 128, 128, output_size]
g_act_list = [tf.nn.leaky_relu, tf.nn.leaky_relu, tf.sigmoid]
ori_z = sample_noise(batch_size, dim)
d_dim_list = [input_size, 128, 128, 1]
d_act_list = [tf.nn.leaky_relu, tf.nn.leaky_relu, tf.nn.sigmoid]
# build graph
Generator = generator.Generator(batch_size, g_dim_list, g_act_list, scope_name = "Generator")
Discriminator = discriminator.Discriminator(batch_size, d_dim_list, d_act_list, scope_name = "Discriminator")
generator_output = Generator.build()
data_preds, gen_preds = Discriminator.build(generator_output)
G_loss = -tf.reduce_mean(tf.log(gen_preds))
D_loss = -tf.reduce_mean((tf.log(data_preds) + tf.log(1 - gen_preds)))
# G trainer
G_trainer = tf.train.AdamOptimizer(G_learning_rate)
G_step = G_trainer.minimize(G_loss, var_list = Generator.get_var_list())
# D trainer
D_trainer = tf.train.AdamOptimizer(D_learning_rate)
D_step = D_trainer.minimize(D_loss, var_list = Discriminator.get_var_list())
G_loss_list = []
D_loss_list = []
with tf.Session() as sess:
#graph_writer = tf.summary.FileWriter("logs/", sess.graph)
sess.run(tf.global_variables_initializer())
m = batch_size
# train proces
for i in range(epoc_num):
# 1. d steps for optimize D
for d in range(d_steps):
# sample m noise samples {z_1, z_2, ..., z_m} from p_z(z)
z = sample_noise(m, dim)
# sample m examples {x_1, x_2, ..., x_m} from p_data(x) (true_data)
true_samples = sample_true_data_func(num = m)
# update D
d_loss, _, d_probs, g_probs = sess.run([D_loss, D_step, data_preds, gen_preds],
feed_dict = {
Generator.get_input_layer_tensor() : z, # p_z
Discriminator.get_input_layer_tensor() : true_samples, # true data
})
G_loss_list.append(d_loss)
# 2. g steps for optimize G
for g in range(g_steps):
# sample m noise samples {z_1, z_2, ..., z_m} from p_z(z)
z = sample_noise(m, dim)
# sample m examples {x_1, x_2, ..., x_m} from p_data(x) (true_data)
true_samples = sample_true_data_func(num = m)
zero_data_for_G = np.zeros((m, dim))
# update G
g_loss, _, g_data = sess.run([G_loss, G_step, generator_output],
feed_dict = {
Generator.get_input_layer_tensor() : z, # p_z
Discriminator.get_input_layer_tensor() : zero_data_for_G, # zero data or true data?
})
D_loss_list.append(g_loss)
if i % 100 == 0:
print(str(i) + " of " + str(epoc_num) + ", " + "{:.2f}".format(100.0 * i / epoc_num) + "%")
if (i + 1) % 1000 == 0 or i == 0:
z = sample_noise(m, dim)
if data_name == "mnist":
z = ori_z
gen_data = sess.run([generator_output],
feed_dict = {
Generator.get_input_layer_tensor() : z, # p_z
})
if data_name == "mnist":
save_gen_img_data(img_dir, gen_data, i + 1, img_height, img_width)
else:
plot_gen_data(img_dir, gen_data, i + 1)
plot_loss_fig(img_dir, D_loss_list, G_loss_list)
batch_size=config.batch_size,
shuffle=True,
num_workers=workers,
drop_last=True)
device = torch.device("cuda" if (
torch.cuda.is_available() and config.gpus > 0) else "cpu")
# Create discriminator and generator
netG = generator.Generator(config.channels_noise, config.channels_img,
config.features_g, config.gpus,
config.classes).to(device)
netG.apply(weights_initialize)
netD = discriminator.Discriminator(config.channels_img, config.features_d,
config.gpus, config.classes).to(device)
netD.apply(weights_initialize)
# Setup Optimizer for G and D
optimizerD = optim.Adam(netD.parameters(),
lr=config.lrD,
betas=(config.beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(),
lr=config.lrG,
betas=(config.beta1, 0.999))
netG.train()
netD.train()
criterion = nn.BCELoss()
real_label = 1
import discriminator
from tensorboardX import SummaryWriter
data = np.load('expert_data.npy')
expert_data = collections.deque()
for x in data:
expert_data.append(x)
sess = tf.Session()
state_size = 4
action_size = 2
n_step = 128
agent = ppo.PPO(sess, state_size, action_size)
dis = discriminator.Discriminator(sess, state_size, action_size)
env = gym.make('CartPole-v0')
score = 0
episode = 0
p = 0
gail = True
writer = SummaryWriter()
state = env.reset()
while True:
values_list, states_list, actions_list, dones_list, logp_ts_list, rewards_list = \
[], [], [], [], [], []
def __init__(self,
in_dim=512,
embed_dim=16,
latent_std=1.0,
strt_dim=3,
n_heads=4,
init_knl=3,
max_pos=1000,
pos_conv_knl=3,
chstl_fc_layers=4,
chstl_activ=tf.keras.layers.LeakyReLU(alpha=0.1),
chsyn_fc_activation=tf.keras.layers.LeakyReLU(alpha=0.1),
chsyn_encoder_layers=3,
chsyn_decoder_layers=3,
chsyn_fc_layers=3,
chsyn_norm_epsilon=1e-6,
chsyn_transformer_dropout_rate=0.2,
chsyn_noise_std=0.5,
time_features=3,
tmstl_fc_layers=4,
tmstl_activ=tf.keras.layers.LeakyReLU(alpha=0.1),
tmsyn_encoder_layers=3,
tmsyn_decoder_layers=3,
tmsyn_fc_layers=3,
tmsyn_norm_epsilon=1e-6,
tmsyn_transformer_dropout_rate=0.2,
tmsyn_fc_activation=tf.keras.layers.LeakyReLU(alpha=0.1),
tmsyn_noise_std=0.5,
d_kernel_size=3,
d_encoder_layers=1,
d_decoder_layers=1,
d_fc_layers=3,
d_norm_epsilon=1e-6,
d_transformer_dropout_rate=0.2,
d_fc_activation=tf.keras.activations.tanh,
d_out_dropout=0.3,
d_recycle_fc_activ=tf.keras.activations.elu,
mode_='comb'):
super(WGAN, self).__init__()
if mode_ in ['chords', 'comb']:
assert embed_dim % n_heads == 0, 'make sure: embed_dim % chsyn_n_heads == 0'
# ---------------------------------- settings ----------------------------------
self.mode_ = mode_
self.embed_dim = embed_dim
self.time_features = time_features # 3 for [velocity, velocity, time since last start, chords duration]
self.in_dim = in_dim
self.strt_dim = strt_dim
self.latent_std = latent_std
# callback settings
self.ckpts = dict()
self.ckpt_managers = dict()
# optimisers
self.optimizer_gen = tf.keras.optimizers.Adam(0.01,
beta_1=0.9,
beta_2=0.98,
epsilon=1e-9)
self.optimizer_disc = tf.keras.optimizers.Adam(0.0001,
beta_1=0.9,
beta_2=0.98,
epsilon=1e-9)
# losses
self.train_loss_gen = tf.keras.metrics.Mean(name='train_loss_gen')
self.train_loss_disc = tf.keras.metrics.Mean(name='train_loss_disc')
# ---------------------------------- layers ----------------------------------
# generators
if mode_ != 'time':
self.chords_style = generator.Mapping(fc_layers=chstl_fc_layers,
activ=chstl_activ)
self.chords_syn = generator.ChordsSynthesis(
embed_dim=embed_dim,
init_knl=init_knl,
strt_dim=strt_dim,
n_heads=n_heads,
fc_activation=chsyn_fc_activation,
encoder_layers=chsyn_encoder_layers,
decoder_layers=chsyn_decoder_layers,
fc_layers=chsyn_fc_layers,
norm_epsilon=chsyn_norm_epsilon,
transformer_dropout_rate=chsyn_transformer_dropout_rate,
noise_std=chsyn_noise_std)
if mode_ != 'chords':
self.time_style = generator.Mapping(fc_layers=tmstl_fc_layers,
activ=tmstl_activ)
self.time_syn = generator.TimeSynthesis(
time_features=time_features,
init_knl=init_knl,
strt_dim=strt_dim,
fc_activation=tmsyn_fc_activation,
encoder_layers=tmsyn_encoder_layers,
decoder_layers=tmsyn_decoder_layers,
fc_layers=tmsyn_fc_layers,
norm_epsilon=tmsyn_norm_epsilon,
transformer_dropout_rate=tmsyn_transformer_dropout_rate,
noise_std=tmsyn_noise_std,
max_pos=max_pos,
pos_conv_knl=pos_conv_knl)
# discriminator
if mode_ == 'chords':
self.disc = discriminator.ChordsDiscriminator(
embed_dim=embed_dim,
n_heads=n_heads,
fc_activation=d_fc_activation,
encoder_layers=d_encoder_layers,
decoder_layers=d_decoder_layers,
fc_layers=d_fc_layers,
norm_epsilon=d_norm_epsilon,
transformer_dropout_rate=d_transformer_dropout_rate,
pre_out_dim=in_dim,
out_dropout=d_out_dropout,
recycle_fc_activ=d_recycle_fc_activ)
elif mode_ == 'time':
self.disc = discriminator.TimeDiscriminator(
time_features=time_features,
fc_activation=d_fc_activation,
encoder_layers=d_encoder_layers,
decoder_layers=d_decoder_layers,
fc_layers=d_fc_layers,
norm_epsilon=d_norm_epsilon,
transformer_dropout_rate=d_transformer_dropout_rate,
pre_out_dim=in_dim,
out_dropout=d_out_dropout,
recycle_fc_activ=d_recycle_fc_activ)
else: # mode_ == 'comb'
self.disc = discriminator.Discriminator(
embed_dim=embed_dim,
n_heads=n_heads,
kernel_size=d_kernel_size,
fc_activation=d_fc_activation,
encoder_layers=d_encoder_layers,
decoder_layers=d_decoder_layers,
fc_layers=d_fc_layers,
norm_epsilon=d_norm_epsilon,
transformer_dropout_rate=d_transformer_dropout_rate,
pre_out_dim=in_dim,
out_dropout=d_out_dropout,
recycle_fc_activ=d_recycle_fc_activ)
def build_discriminator(self):
self.discriminator = discriminator.Discriminator(n_node = self.n_node, node_emd_init=self.node_embed_init_d)
#Define Models
AEncoder = ABot_Encoder.ABotEncoder(params)
ADecoder = ABot_Decoder.ABotDecoder(params)
QEncoder = QBot_Encoder.QBotEncoder(params)
QDecoder = QBot_Decoder.QBotDecoder(params)
embedding_weights = np.random.random(
(params['vocab_size'], params['embed_size']))
embedding_weights[0, :] = np.zeros((1, params['embed_size']))
ABot_embedding_layer = ABot.EmbeddingLayer(embedding_weights)
QBot_embedding_layer = QBot.EmbeddingLayer(embedding_weights)
sampler = ABot.GumbelSampler()
embedding_weights_discr = np.random.random(
(params['vocab_size'], params['embed_size']))
embedding_weights_discr[0, :] = np.zeros((1, params['embed_size']))
print(embedding_weights_discr)
discriminator = Discriminator.Discriminator(params, embedding_weights_discr)
#Criterion
criterion = {}
criterion['CrossEntropyLoss'] = nn.CrossEntropyLoss(reduce=False)
criterion['HingeEmbeddingLoss'] = nn.HingeEmbeddingLoss(margin=0.0,
size_average=False)
criterion['MSELoss'] = nn.MSELoss(size_average=False)
criterion['BCELoss'] = nn.BCELoss(size_average=False)
#Optimizer
ABot_optimizer = torch.optim.Adam([{
'params': AEncoder.parameters()
}, {
'params': ADecoder.parameters()
}, {
GEN_HIDDEN_DIM,
GEN_EMBEDDING_DIM,
MAX_SEQ_LEN,
device=DEVICE)
gen_optimizer = optim.Adam(gen.parameters(), lr=1e-2)
if DISCRIMINATOR_LM:
dis = discriminator_LM.Discriminator(DIS_EMBEDDING_DIM,
DIS_HIDDEN_DIM,
VOCAB_SIZE,
MAX_SEQ_LEN,
device=DEVICE)
else:
dis = discriminator.Discriminator(DIS_EMBEDDING_DIM,
DIS_HIDDEN_DIM,
VOCAB_SIZE,
MAX_SEQ_LEN,
device=DEVICE)
dis_optimizer = optim.Adagrad(dis.parameters()) ## ADAGRAD ??
if CUDA:
dis = dis.cuda()
# OPTIONAL: Pretrain generator
# checkpoint = torch.load('generator_checkpoint.pth.tar')
print('Starting Generator MLE Training...')
train_generator_MLE(gen, gen_optimizer, train_data_loader,
MLE_TRAIN_EPOCHS)
# # OPTIONAL: Pretrain discriminator
# print('\nStarting Discriminator Training...')
gen_optimizer = Ranger(itertools.chain(state_encoder.parameters(),reason_decoder.parameters()), lr=1e-5,weight_decay= 1e-5)
# train_generator_MLE(state_encoder, reason_decoder, gen_optimizer, PRE_TRAIN_GEN)
print('\nStarting Generator Evaluating...')
best_epoch = dev_generator(state_encoder, reason_decoder, gen_optimizer, name="pretrain_gen")
pretrained_gen_path = "./model/pretrain_gen"+str(best_epoch)+".pth"
state_encoder.load_state_dict(torch.load(pretrained_gen_path)["state_encoder"])
reason_decoder.load_state_dict(torch.load(pretrained_gen_path)["reason_decoder"])
embedding.load_state_dict(torch.load(pretrained_gen_path)["embed"])
state = {"state_encoder": state_encoder.state_dict(), "reason_decoder": reason_decoder.state_dict(),
'embed': embedding.state_dict()}
pretrained_gen_path = "./model/pretrain_gen.pth"
torch.save(state, pretrained_gen_path)
# PRETRAIN DISCRIMINATOR
dis = discriminator.Discriminator(hidden_size,DIS_HIDDEN_DIM).to(
device) # use directly as classifier
dis_option_encoder = EncoderRNN(VOCAB_SIZE, EMBED_DIM, hidden_size=int(hidden_size), embedding=embedding, bidirectional= False).to(device)
print('\nStarting Discriminator Training...')
dis_optimizer = Ranger(itertools.chain(dis_option_encoder.parameters(),dis.parameters()), lr=1e-5, weight_decay= 1e-4)
# train_discriminator(dis, dis_option_encoder, dis_optimizer, PRE_TRAIN_DIS)
print('\nStarting Discriminator Evaluating...')
best_epoch = dev_discriminator(dis, dis_option_encoder, name="pretrain_dis")
pretrained_dis_path = "./model/pretrain_dis"+str(best_epoch)+".pth"
dis_option_encoder.load_state_dict(torch.load(pretrained_dis_path)["dis_option_encoder"])
dis.load_state_dict(torch.load(pretrained_dis_path)["dis"])
embedding.load_state_dict(torch.load(pretrained_dis_path)["embed"])
state = {"dis_option_encoder": dis_option_encoder.state_dict(), "dis": dis.state_dict(),'embed': embedding.state_dict()}
pretrained_dis_path = "./model/pretrain_dis.pth"
torch.save(state, pretrained_dis_path)
def train(device=device):
batch_size = 1024
image_size = 64
G_out_D_in = 3
G_in = 100
G_hidden = 64
D_hidden = 64
epochs = 5
lr = 0.001
betal = 0.5
dataset = torchvision.datasets.ImageFolder(root='./dcgan/pic',transform=transforms.Compose(
[
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
]
))
dataloader = torch.utils.data.DataLoader(dataset=dataset,batch_size=batch_size,shuffle=True)
netg = generator.Generator(G_in,G_hidden,G_out_D_in).to(device)
print(netg)
netd = discriminator.Discriminator(G_out_D_in,D_hidden).to(device)
print(netd)
criterion = nn.BCELoss()
fixed_noise = torch.randn(64,G_in,1,1,device=device)
real_label = 1
fake_label = 0
optimizerd = optim.Adam(netd.parameters(),lr=lr,betas=(betal,0.999))
optimizerg = optim.Adam(netg.parameters(),lr=lr,betas=(betal,0.999))
img_list = []
g_losses = []
d_losses = []
iters = 0
print('start')
for epoch in range(epochs):
for i,data in enumerate(dataloader,0):
netd.zero_grad()
real_cpu = data[0].to(device)
b_size =real_cpu.size(0)
label = torch.full((b_size,),real_label,device=device)
output = netd(real_cpu).view(-1)
errD_real = criterion(output,label)
errD_real.backward()
D_x = output.mean().item()
noise = torch.randn(b_size,G_in,1,1,device=device)
fake = netg(noise)
label.fill_(fake_label)
output = netd(fake.detach()).view(-1)
errd_fake = criterion(output,label)
errd_fake.backward()
d_g_z1 = output.mean().item()
errd = errD_real+errd_fake
optimizerd.step()
netg.zero_grad()
label.fill_(real_label)
output = netd(fake).view(-1)
errg = criterion(output,label)
errg.backwar()
d_g_z2 = output.mean().item()
optimizerg.step()
if(i%50 == 0):
print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f' % (epoch, epochs, i, len(dataloader), errd.item(), errg.item(), D_x, d_g_z1, d_g_z2))
g_losses.append(errg.item())
d_losses.append(errd.item())
if (iters%500 == 0) or((epoch == epochs-1)and(i == len(dataloader)-1)):
with torch.no_grad():
fake = netg(fixed_noise).detach().cpu()
img_list.append(utils.make_grid(fake,padding=2,normalize=True))
iters = iters+1
torch.save(netd,'netd.pkl')
torch.save(netd.state_dict(),'netd_parameters.pkl')
torch.save(netg,'net.pkl')
torch.save(netg.state_dict(),'net_parameters.pkl')
return g_losses,d_losses,img_list
