def __init__(
self,
generator_and_opt: [Generator, torch.optim] = None,
discriminator_and_opt: [Discriminator, torch.optim] = None,
input_size: int = None,
hidden_channel: int = 128,
latent_dim: int = 100,
learning_rate: float = 1e-4,
):
self.generator = None
self.discriminator = None
super().__init__()
# Generator
if generator_and_opt is None:
assert input_size is None, "generator_and_opt or input_size should be given."
if self.generator is not None:
self.generator = Generator(input_size=input_size,
latent_dim=latent_dim,
hidden_channel=hidden_channel)
self.generator_opt = torch.optim.Adam(
self.generator.parameters(), learning_rate)
else:
self.generator, self.generator_opt = generator_and_opt
# Discriminator
if discriminator_and_opt is None:
assert input_size is None, "discriminator_and_opt or input_size should be given."
if self.discriminator is not None:
self.discriminator = Discriminator(
input_size=input_size, hidden_channel=hidden_channel)
self.discriminator_opt = torch.optim.Adam(
self.discriminator.parameters(), learning_rate)
else:
self.discriminator, self.discriminator_opt = discriminator_and_opt
def build_model(self):
self.G = Generator()
self.D = Discriminator()
self.G_optim = optim.Adam(self.G.parameters(), self.G_lr,
(self.beta1, 0.999))
self.D_optim = optim.Adam(self.D.parameters(), self.D_lr,
(self.beta1, 0.999))
if self.loss_type == 'BCEwL':
self.criterion = nn.BCEWithLogitsLoss()
elif self.loss_type == 'WGAN':
pass
elif self.loss_type == 'WGAN+':
pass
self.fixed_sample = None
self.fixed_noise = None
# self.true = torch.ones([self.batch_size, 1, 1, 1], requires_grad=False).to(self.device)
# self.false = torch.zeros([self.batch_size, 1, 1, 1], requires_grad=False).to(self.device)
# Change to GPU mode
print('Change CPU mode to GPU mode...')
self.G.to(self.device)
self.D.to(self.device)
print('Creating models are success...')
def __init__(self, args):
self.args = args
self.pretrained = False
self.epoch = 0
self.G = Generator()
self.D = Discriminator()
self.g_optimizer = optim.Adam(self.G.parameters(), lr=1E-4)
self.d_optimizer = optim.Adam(self.D.parameters(), lr=1E-4)
self.g_scheduler = optim.lr_scheduler.StepLR(self.g_optimizer,
step_size=40)
self.d_scheduler = optim.lr_scheduler.StepLR(self.d_optimizer,
step_size=40)
self.train_losses = []
self.val_losses = []
if args.load_model:
self._load_state(args.load_model)
# extract all layers prior to the last softmax of VGG-19
vgg19_layers = list(models.vgg19(pretrained=True).features)[:36]
self.vgg19 = nn.Sequential(*vgg19_layers).eval()
for param in self.vgg19.parameters():
param.requires_grad = False
self.mse_loss = torch.nn.MSELoss()
self.bce_loss = torch.nn.BCELoss()
def val_test(args):
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_filename = './models/{0}'.format(args.output_folder)
train_loader, valid_loader, test_loader = train_util.get_dataloaders(args)
recons_input_img = train_util.log_input_img_grid(test_loader, writer)
input_dim = 3
model = VectorQuantizedVAE(input_dim, args.hidden_size, args.k,
args.enc_type, args.dec_type)
# if torch.cuda.device_count() > 1 and args.device == "cuda":
# model = torch.nn.DataParallel(model)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
discriminators = {}
if args.recons_loss == "gan":
recons_disc = Discriminator(input_dim, args.img_res,
args.input_type).to(args.device)
recons_disc_opt = torch.optim.Adam(recons_disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
discriminators["recons_disc"] = [recons_disc, recons_disc_opt]
model.to(args.device)
for disc in discriminators:
discriminators[disc][0].to(args.device)
if args.weights == "load":
start_epoch = train_util.load_state(save_filename, model, optimizer,
discriminators)
else:
start_epoch = 0
stop_patience = args.stop_patience
best_loss = torch.tensor(np.inf)
for epoch in tqdm(range(start_epoch, 4), file=sys.stdout):
val_loss_dict, z = train_util.test(get_losses, model, valid_loader,
args, discriminators, True)
# if args.weights == "init" and epoch==1:
# epoch+=1
# break
train_util.log_recons_img_grid(recons_input_img, model, epoch + 1,
args.device, writer)
train_util.log_interp_img_grid(recons_input_img, model, epoch + 1,
args.device, writer)
train_util.log_losses("val", val_loss_dict, epoch + 1, writer)
train_util.log_latent_metrics("val", z, epoch + 1, writer)
train_util.save_state(model, optimizer, discriminators,
val_loss_dict["recons_loss"], best_loss,
args.recons_loss, epoch, save_filename)
print(val_loss_dict)
def build_model(self):
self.G = Generator(image_size=self.imsize,
z_dim=self.z_dim,
conv_dim=self.g_dim)
self.D = Discriminator(conv_dim=self.d_dim)
self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr,
[self.beta1, self.beta2])
self.d_optimizer = torch.optim.Adam(self.D.parameters(), self.d_lr,
[self.beta1, self.beta2])
self.print_network(self.G, 'G')
self.print_network(self.D, 'D')
self.G.to(self.device)
self.D.to(self.device)
def build_models(self):
self.E = Encoder(self.c64, self.rb6)
self.T_Hair = Transformer(self.hair_dim, self.c256, self.rb6)
self.T_Gender = Transformer(self.attr_dim, self.c256, self.rb6)
self.T_Smailing = Transformer(self.attr_dim, self.c256, self.rb6)
self.R = Reconstructor(self.c256)
self.D_Hair = Discriminator(self.hair_dim, self.c64)
self.D_Gender = Discriminator(self.attr_dim, self.c64)
self.D_Smailing = Discriminator(self.attr_dim, self.c64)
self.e_optim = torch.optim.Adam(self.E.parameters(), self.e_lr, [self.beta1, self.beta2])
self.th_optim = torch.optim.Adam(self.T_Hair.parameters(), self.t_lr, [self.beta1, self.beta2])
self.tg_optim = torch.optim.Adam(self.T_Gender.parameters(), self.t_lr, [self.beta1, self.beta2])
self.ts_optim = torch.optim.Adam(self.T_Smailing.parameters(), self.t_lr, [self.beta1, self.beta2])
self.r_optim = torch.optim.Adam(self.R.parameters(), self.r_lr, [self.beta1, self.beta2])
self.dh_optim = torch.optim.Adam(self.D_Hair.parameters(), self.d_lr, [self.beta1, self.beta2])
self.dg_optim = torch.optim.Adam(self.D_Gender.parameters(), self.d_lr, [self.beta1, self.beta2])
self.ds_optim = torch.optim.Adam(self.D_Smailing.parameters(), self.d_lr, [self.beta1, self.beta2])
self.print_network(self.E, 'Encoder')
self.print_network(self.T_Hair, 'Transformer for Hair Color')
self.print_network(self.T_Gender, 'Transformer for Gender')
self.print_network(self.T_Smailing, 'Transformer for Smailing')
self.print_network(self.R, 'Reconstructor')
self.print_network(self.D_Hair, 'D for Hair Color')
self.print_network(self.D_Gender, 'D for Gender')
self.print_network(self.D_Smailing, 'D for Smailing')
self.E.to(self.device)
self.T_Hair.to(self.device)
self.T_Gender.to(self.device)
self.T_Smailing.to(self.device)
self.R.to(self.device)
self.D_Gender.to(self.device)
self.D_Smailing.to(self.device)
self.D_Hair.to(self.device)
def train(dataset_dir, output_dir):
"""Train discriminator and generator"""
if torch.cuda.is_available():
torch.set_default_tensor_type('torch.cuda.FloatTensor')
device = torch.device('cuda')
else:
device = torch.device('cpu')
discriminator = Discriminator()
discriminator.to(device)
discriminator.train()
generator = Generator()
generator.to(device)
generator.train()
optimizer_discriminator = torch.optim.Adam(discriminator.parameters(), lr=0.0001)
optimizer_generator = torch.optim.Adam(generator.parameters(), lr=0.0001)
loss_func = nn.BCELoss()
loader = LFWLoader(dataset_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
for epoch in range(10):
for image in tqdm(loader):
# train discriminator on true
outputs = discriminator(torch.tensor(image, dtype=torch.float32) / 255)
loss = loss_func(outputs, torch.tensor([1.0]))
optimizer_discriminator.zero_grad()
loss.backward()
optimizer_discriminator.step()
# train discriminator on false
outputs = discriminator(generator(generate_random_seed(100)).detach())
loss = loss_func(outputs, torch.tensor([0.0]))
optimizer_discriminator.zero_grad()
loss.backward()
optimizer_discriminator.step()
# train generator
outputs = discriminator(generator(generate_random_seed(100)))
loss = loss_func(outputs, torch.tensor([1.0]))
optimizer_generator.zero_grad()
loss.backward()
optimizer_generator.step()
torch.save(generator, os.path.join(output_dir, 'G_%d.pt' % epoch))
torch.save(discriminator, os.path.join(output_dir, 'D_%d.pt' % epoch))
def __init__(self, N_FEATURES=1, N_CLASSES=2, HIDDEN_DIM=50, CELL_TYPE="LSTM",
N_LAYERS=1, DF=1., LAMBDA=1.0):
super(EARLIEST, self).__init__()
# --- Hyperparameters ---
self.CELL_TYPE = CELL_TYPE
self.HIDDEN_DIM = HIDDEN_DIM
self.DF = DF
self.LAMBDA = torch.tensor([LAMBDA], requires_grad=False)
self.N_LAYERS = N_LAYERS
self._epsilon = 1.0
self._rewards = 0
# --- Sub-networks ---
self.BaseRNN = BaseRNN(N_FEATURES,
HIDDEN_DIM,
CELL_TYPE)
self.Controller = Controller(HIDDEN_DIM+1, 1) # Add +1 for timestep input
self.BaselineNetwork = BaselineNetwork(HIDDEN_DIM, 1)
self.Discriminator = Discriminator(HIDDEN_DIM, N_CLASSES)
def __init__(self,config):
super(BiGAN,self).__init__()
self._work_type = config.work_type
self._epochs = config.epochs
self._batch_size = config.batch_size
self._encoder_lr = config.encoder_lr
self._generator_lr = config.generator_lr
self._discriminator_lr = config.discriminator_lr
self._latent_dim = config.latent_dim
self._weight_decay = config.weight_decay
self._img_shape = (config.input_size,config.input_size)
self._img_save_path = config.image_save_path
self._model_save_path = config.model_save_path
self._device = config.device
if self._work_type == 'train':
# Loss function
self._adversarial_criterion = torch.nn.MSELoss()
# Initialize generator, encoder and discriminator
self._G = Generator(self._latent_dim,self._img_shape).to(self._device)
self._E = Encoder(self._latent_dim,self._img_shape).to(self._device)
self._D = Discriminator(self._latent_dim,self._img_shape).to(self._device)
self._G.apply(self.weights_init)
self._E.apply(self.weights_init)
self._D.apply(self.discriminator_weights_init)
self._G_optimizer = torch.optim.Adam([{'params' : self._G.parameters()},{'params' : self._E.parameters()}],
lr=self._generator_lr,betas=(0.5,0.999),weight_decay=self._weight_decay)
self._D_optimizer = torch.optim.Adam(self._D.parameters(),lr=self._discriminator_lr,betas=(0.5,0.999))
self._G_scheduler = lr_scheduler.ExponentialLR(self._G_optimizer, gamma= 0.99)
self._D_scheduler = lr_scheduler.ExponentialLR(self._D_optimizer, gamma= 0.99)
def build_model(self):
"""Build generator and discriminator."""
self.generator = Generator(z_dim=self.z_dim)
print(count_parameters(self.generator))
self.discriminator = Discriminator()
print(count_parameters(self.discriminator))
self.g_optimizer = optim.Adam(self.generator.parameters(),
self.lr, (self.beta1, self.beta2))
self.d_optimizer = optim.Adam(self.discriminator.parameters(),
self.lr*1, (self.beta1, self.beta2))
if self.epoch:
g_path = os.path.join(self.model_path, 'generator-%d.pkl' % self.epoch)
d_path = os.path.join(self.model_path, 'discriminator-%d.pkl' % self.epoch)
g_optim_path = os.path.join(self.model_path, 'gen-optim-%d.pkl' % self.epoch)
d_optim_path = os.path.join(self.model_path, 'dis-optim-%d.pkl' % self.epoch)
self.generator.load_state_dict(torch.load(g_path))
self.discriminator.load_state_dict(torch.load(d_path))
self.g_optimizer.load_state_dict(torch.load(g_optim_path))
self.d_optimizer.load_state_dict(torch.load(d_optim_path))
if torch.cuda.is_available():
self.generator.cuda()
self.discriminator.cuda()
def run(argv):
global args
global model
global loader
global test_loader
global output_shape
global visualization_mode
parser = argparse.ArgumentParser()
# training
parser.add_argument('--train', action='store_true', default=False, help='Train a model')
parser.add_argument('--train_generator', type=bool, default=True, help='Include generator training')
parser.add_argument('--save_to', type=str, default='model.bin', help='Where to save the trained model')
parser.add_argument('--batch_size', type=int, default=2048, help='Training batch size')
parser.add_argument('--epochs', type=int, default=200, help='Number of epochs to train')
parser.add_argument('--lr', type=float, default=0.001, help='Learning rate')
parser.add_argument('--lr_step', type=int, default=0, help='Learning rate step size')
parser.add_argument('--lr_step_gamma', type=float, default=0.5, help='Learning rate step gamma')
parser.add_argument('--sigma', type=float, default=0.2, help='Noise standard deviation')
parser.add_argument('--reduce_sigma_every', type=int, default=0, help='Decrease sigma value every N batches')
parser.add_argument('--reduce_sigma_gamma', type=float, default=0.8, help='When decreasing sigma, sigma *= sigma_gamma')
parser.add_argument("--min_sigma", type=float, default=0, help='Lower bound of sigma')
parser.add_argument('--stop_training_real_dde_after', type=int, default=-1, help='Only train real_dde for this number of epochs')
parser.add_argument('--train_gen_every', type=int, default=10, help='Step generator every N iterations')
parser.add_argument('--visualize_every', type=int, default=50, help='Visualize model every N batches')
# loading
parser.add_argument('--load_from', type=str, default=None, help='Where to load a trained model')
# dataset
parser.add_argument('--dataset', type=str, default='ts', help='Dataset (ts, eg, cb, mnist, fashion, stacked-mnist, ncsn)')
parser.add_argument('--dataset_dir', type=str, default='/tmp/', help='Dataset location for MNIST and Fashion. Defaults to /tmp/')
parser.add_argument('--download', action='store_true', default=False, help='Download the dataset if it does not exist')
parser.add_argument('--datasize', type=int, default=51200, help='Datasize (pseudo, for epoch computation) for 2D datasets')
parser.add_argument('--dynamic', action='store_true', default=True, help='Whether 2D datasets are dynamically generated')
# model parameters
parser.add_argument('--dense_sigmoid', action='store_true', default=False, help='Add sigmoid to DenseNet generator')
parser.add_argument('--gen_activation', type=str, default='softplus', help='Generator activation function')
parser.add_argument('--dde_activation', type=str, default='softplus', help='Discriminator activation function')
parser.add_argument('--n_input', type=int, default=2, help='Number of input to generator')
parser.add_argument('--n_hidden', type=int, default=32, help='Number of hidden units in a layer')
parser.add_argument('--n_gen_hidden', type=int, default=64, help='Number of hidden units in DenseNet generator layer')
parser.add_argument('--n_layers', type=int, default=25, help='Number of layers')
parser.add_argument('--n_feat_gen', type=int, default=64, help='Number of features in DCGAN generator')
parser.add_argument('--n_feat_dsc', type=int, default=45, help='Number of features in DCGAN discriminator')
parser.add_argument('--gen_bn', type=bool, default=True, help='Use BN in DCGAN generator')
parser.add_argument('--dsc_activation', type=str, default='lrelu:0.2', help='Activation in DCGAN discriminator')
parser.add_argument('--device', type=str, default='cuda:0', help='PyTorch device')
# visualization
parser.add_argument('--visualize', action='store_true', default=False, help='Visualize a model')
parser.add_argument('--n_toy_samples', type=int, default=50000, help='Number of samples to visualize for toy datasets')
parser.add_argument('--save_toy_samples_to', type=str, default=None, help='If specified, save generated toy samples to file')
parser.add_argument('--vis_path', type=str, default='', help='Path prefix to save visualization figures.')
parser.add_argument('--n_uci_samples', type=int, default=51200, help='Number of samples to use when computing UCI ALL')
args = parser.parse_args(argv) if not argv is None else parser.parse_args()
if args.dataset == 'ts':
loader = twoSpiralsLoader(args.datasize, args.batch_size, dynamic=args.dynamic)
args.type = 'mlp'
elif args.dataset == 'eg':
loader = eightGaussiansLoader(args.datasize, args.batch_size, dynamic=args.dynamic)
args.type = 'mlp'
elif args.dataset == 'cb':
loader = checkerboardLoader(args.datasize, args.batch_size, dynamic=args.dynamic)
args.type = 'mlp'
elif args.dataset == 'mnist':
loader = mnistLoader(args.dataset_dir, args.download, args.batch_size)
args.type = 'densenet'
visualization_mode = 'images'
elif args.dataset == 'fashion':
loader = fashionLoader(args.dataset_dir, args.download, args.batch_size)
args.type = 'densenet'
visualization_mode = 'images'
elif args.dataset == 'stacked-mnist':
loader = stackedMnistLoader(args.dataset_dir, args.download, args.batch_size)
args.type = 'dcgan'
visualization_mode = 'images'
elif args.dataset == 'ncsn':
loader = stackedMnistLoader(args.dataset_dir, args.download, args.batch_size)
args.type = 'ncsn'
visualization_mode = 'images'
elif args.dataset.startswith('uci_'):
name = args.dataset[4:]
loader, test_loader = uciLoader(args.dataset_dir, name, args.batch_size)
args.type = 'mlp'
visualization_mode = 'numbers'
else:
print(f'Unknown dataset: {args.dataset}')
return
output_shape = list(loader.dataset[0][0].shape)
print(f'Output shape: {output_shape}')
output_length = loader.dataset[0][0].view(-1).shape[0]
print(f'Output length: {output_length}')
gen_activation = activation_from_name(args.gen_activation)
dde_activation = activation_from_name(args.dde_activation)
if args.type == 'mlp':
model = DdeModel(MlpModule(output_length, args.n_hidden, args.n_layers, output_length, gen_activation, True, args.device),
MlpModule(output_length, args.n_hidden, args.n_layers, 1, dde_activation, False, args.device),
MlpModule(output_length, args.n_hidden, args.n_layers, 1, dde_activation, False, args.device))
elif args.type == 'densenet':
end_activation = nn.Sigmoid if args.dense_sigmoid else None
model = DdeModel(DenseNetModule(args.n_input, args.n_gen_hidden, args.n_layers, output_length, gen_activation, end_activation, args.device),
DenseNetModule(output_length, args.n_hidden, args.n_layers, 1, dde_activation, None, args.device),
DenseNetModule(output_length, args.n_hidden, args.n_layers, 1, dde_activation, None, args.device))
elif args.type == 'dcgan':
dsc_activation = activation_from_name(args.dsc_activation)
model = DdeModel(Generator(args.n_input, args.n_feat_gen, output_shape[0], args.gen_bn, args.device),
Discriminator(args.n_feat_dsc, output_shape[0], dsc_activation, None, False, args.device),
Discriminator(args.n_feat_dsc, output_shape[0], dsc_activation, None, False, args.device))
elif args.type == 'ncsn':
model = NetModel(NCSNDde(64, gen_activation, args.device))
else:
print(f'Unknown model type: {args.type}')
if not args.load_from is None:
model.load(args.load_from)
print(f'Loaded model from {args.load_from}')
if len(args.vis_path) > 0:
os.makedirs(args.vis_path, exist_ok=True)
if args.train:
if args.type == 'ncsn':
train_ncsn()
else:
train()
if args.visualize:
visualize(None, args.min_sigma)
loss_fn = nn.BCELoss()
loss_rec = nn.MSELoss()
controller = Segmentation(args.state_dim, args.policy_hidden_1,
args.option_num)
encoder = MLPEncoder(args.option_dim, 2 * args.option_dim, 1, 0, True)
decoder = MLPDecoder(args.option_dim, 1, 2 * args.option_dim,
2 * args.option_dim, 2 * args.option_dim, 0, False)
actor = Actor_with_option(args.state_dim, args.option_dim, args.action_dim)
optimizer = optim.Adam(list(encoder.parameters()) +
list(decoder.parameters()) + list(actor.parameters()) +
list(controller.parameters()),
lr=args.lr,
betas=(0.5, 0.999))
discriminator = Discriminator(args.state_dim, args.action_dim)
optimizer_discriminator = torch.optim.Adam(discriminator.parameters(),
lr=1e-4,
betas=(0.5, 0.999))
warm_start_optimizer = optim.Adam(list(controller.parameters()),
lr=1e-2,
betas=(0.5, 0.999))
if args.CUDA:
policy.cuda()
encoder.cuda()
decoder.cuda()
controller.cuda()
meta_train_tasks = [0, 1, 2, 4]
meta_test_tasks = [5, 6]
def model_summary(model_type,
img_res,
hidden_size,
enc_type,
dec_type,
loss,
batch_size,
device=torch.device("cuda:1"),
verbose=True):
pattern = re.compile(r"Params size \(MB\):(.*)\n")
pattern2 = re.compile(r"Forward/backward pass size \(MB\):(.*)\n")
input_dim = 3
enc_input_size = (input_dim, img_res, img_res)
dec_input_size = (hidden_size, img_res // 4, img_res // 4)
pdb.set_trace()
if verbose:
print(f"model:{model_type}")
print(f"depth:{enc_type}_{dec_type}")
if model_type == "acai":
model = ACAI(img_res, input_dim, hidden_size, enc_type,
dec_type).to(device)
elif model_type == "vqvae":
model = VectorQuantizedVAE(input_dim,
hidden_size,
enc_type=enc_type,
dec_type=dec_type).to(device)
elif model_type == "vae":
model = VAE(input_dim,
hidden_size,
enc_type=enc_type,
dec_type=dec_type).to(device)
encoder_summary, _ = torchsummary.summary_string(model.encoder,
enc_input_size,
device=device,
batch_size=batch_size)
decoder_summary, _ = torchsummary.summary_string(model.decoder,
dec_input_size,
device=device,
batch_size=batch_size)
if verbose:
print(encoder_summary)
print(decoder_summary)
discriminators = {}
if model_type == "acai":
disc = Discriminator(input_dim, img_res, "image").to(device)
disc_summary, _ = torchsummary.summary_string(disc,
enc_input_size,
device=device,
batch_size=batch_size)
disc_param_size = float(re.search(pattern, disc_summary).group(1))
disc_forward_size = float(re.search(pattern2, disc_summary).group(1))
discriminators["interp_disc"] = (disc_param_size, disc_forward_size)
if loss == "gan":
disc = Discriminator(input_dim, img_res, "image").to(device)
disc_summary, _ = torchsummary.summary_string(disc,
enc_input_size,
device=device,
batch_size=batch_size)
disc_param_size = float(re.search(pattern, disc_summary).group(1))
disc_forward_size = float(re.search(pattern2, disc_summary).group(1))
discriminators["recons_disc"] = (disc_param_size,
2 * disc_forward_size)
elif loss == "comp":
disc = AnchorComparator(input_dim * 2, img_res, "image").to(device)
disc_summary, _ = torchsummary.summary_string(disc,
enc_input_size,
device=device,
batch_size=batch_size)
disc_param_size = float(re.search(pattern, disc_summary).group(1))
disc_forward_size = float(re.search(pattern2, disc_summary).group(1))
discriminators["recons_disc"] = (disc_param_size,
2 * disc_forward_size)
elif "comp_2" in loss:
disc = ClubbedPermutationComparator(input_dim * 2, img_res,
"image").to(device)
disc_summary, _ = torchsummary.summary_string(disc,
enc_input_size,
device=device,
batch_size=batch_size)
disc_param_size = float(re.search(pattern, disc_summary).group(1))
disc_forward_size = float(re.search(pattern2, disc_summary).group(1))
discriminators["recons_disc"] = (disc_param_size,
2 * disc_forward_size)
elif "comp_6" in loss:
disc = FullPermutationComparator(input_dim * 2, img_res,
"image").to(device)
disc_summary, _ = torchsummary.summary_string(disc,
enc_input_size,
device=device,
batch_size=batch_size)
disc_param_size = float(re.search(pattern, disc_summary).group(1))
disc_forward_size = float(re.search(pattern2, disc_summary).group(1))
discriminators["recons_disc"] = (disc_param_size,
2 * disc_forward_size)
encoder_param_size = float(re.search(pattern, encoder_summary).group(1))
encoder_forward_size = float(re.search(pattern2, encoder_summary).group(1))
decoder_param_size = float(re.search(pattern, decoder_summary).group(1))
decoder_forward_size = float(re.search(pattern2, decoder_summary).group(1))
if verbose:
if "ACAI" in str(type(model)):
print(
f"discriminator:\n\tparams:{disc_param_size}\n\tforward:{disc_forward_size}"
)
if loss == "gan":
print(
f"reconstruction discriminator:\n\tparams:{disc_param_size}\n\tforward:{disc_forward_size}"
)
print(
f"encoder:\n\tparams:{encoder_param_size}\n\tforward:{encoder_forward_size}"
)
print(
f"decoder:\n\tparams:{decoder_param_size}\n\tforward:{decoder_forward_size}"
)
encoder = {"params": encoder_param_size, "forward": encoder_forward_size}
decoder = {"params": decoder_param_size, "forward": decoder_forward_size}
return encoder, decoder, discriminators
EPOCHS = 20
Z_DIM = 100
D_STEPS = 1
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
t = transforms.Compose([transforms.Resize(64),
transforms.CenterCrop(64),
transforms.ToTensor(),
lambda x: x * 2 - 1]) # Scaling to -1, 1
dataset = torchvision.datasets.CelebA('G:/Datasets', download=DOWNLOAD, transform=t)
dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
G = Generator(Z_DIM).to(device)
D = Discriminator().to(device)
d_loss = D_loss().to(device)
g_loss = G_loss().to(device)
optim_D = torch.optim.Adam(D.parameters(), lr=1e-5, betas=(0.5, 0.999))
optim_G = torch.optim.Adam(G.parameters(), lr=1e-4, betas=(0.5, 0.999))
d_count = 0
for e in range(EPOCHS):
for x, _ in dataloader:
for p in D.parameters():
p.requires_grad = True
x = x.to(device)
D.zero_grad()
d_count += 1
def main(args):
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_filename = './models/{0}'.format(args.output_folder)
d_args = vars(args)
pert_types = train_util.get_perturb_types(args)
train_loader, valid_loader, test_loader = train_util.get_dataloaders(
args, pert_types)
recons_input_img = train_util.log_input_img_grid(test_loader, writer)
# print(f"nn num:{len(pert_types)}")
num_perturb_types = len(pert_types)
input_dim = 3
model = PCIE(args.img_res, input_dim, args.hidden_size, num_perturb_types,
args.enc_type, args.dec_type)
interp_disc = Discriminator(input_dim, args.img_res,
args.input_type).to(args.device)
interp_disc_opt = torch.optim.Adam(interp_disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
# if torch.cuda.device_count() > 1 and args.device == "cuda":
# model = torch.nn.DataParallel(model)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
# ae_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, "min", patience=args.lr_patience, factor=0.5,
# threshold=args.threshold, threshold_mode="abs", min_lr=1e-7)
# interp_disc_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(interp_disc_opt, "min", patience=args.lr_patience, factor=0.5,
# threshold=args.threshold, threshold_mode="abs", min_lr=1e-7)
discriminators = {"interp_disc": [interp_disc, interp_disc_opt]}
if args.recons_loss != "mse":
if args.recons_loss == "gan":
recons_disc = Discriminator(input_dim, args.img_res,
args.input_type).to(args.device)
elif args.recons_loss == "comp":
recons_disc = AnchorComparator(input_dim * 2, args.img_res,
args.input_type).to(args.device)
elif "comp_2" in args.recons_loss:
recons_disc = ClubbedPermutationComparator(
input_dim * 2, args.img_res, args.input_type).to(args.device)
elif "comp_6" in args.recons_loss:
if "color" in args.recons_loss:
recons_disc = FullPermutationColorComparator(
input_dim * 2, args.img_res,
args.input_type).to(args.device)
else:
recons_disc = FullPermutationComparator(
input_dim * 2, args.img_res,
args.input_type).to(args.device)
recons_disc_opt = torch.optim.Adam(recons_disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
recons_disc_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
recons_disc_opt,
"min",
patience=args.lr_patience,
factor=0.5,
threshold=args.threshold,
threshold_mode="abs",
min_lr=1e-7)
discriminators["recons_disc"] = [recons_disc, recons_disc_opt]
if args.prior_loss == "gan":
prior_disc = Latent2ClassDiscriminator(
args.hidden_size, args.img_res // args.scale_factor)
prior_disc_opt = torch.optim.Adam(prior_disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
# prior_disc_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(prior_disc_opt, "min", patience=args.lr_patience, factor=0.5,
# threshold=args.threshold, threshold_mode="abs", min_lr=1e-7)
discriminators["prior_disc"] = [prior_disc, prior_disc_opt]
if args.perturb_feat_gan:
for perturb_type in train_util.perturb_dict:
if d_args[perturb_type]:
num_class = train_util.perturb_dict[perturb_type].num_class
if num_class == 2:
pert_disc = Latent2ClassDiscriminator(
args.hidden_size, args.img_res // args.scale_factor)
pert_disc_opt = torch.optim.Adam(pert_disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
else:
pert_disc = LatentMultiClassDiscriminator(
args.hidden_size, args.img_res // args.scale_factor,
num_class)
pert_disc_opt = torch.optim.Adam(pert_disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
discriminators[f"{perturb_type}_disc"] = (pert_disc,
pert_disc_opt)
model.to(args.device)
for disc in discriminators:
discriminators[disc][0].to(args.device)
# Generate the samples first once
# train_util.log_recons_img_grid(recons_input_img, model, 0, args.device, writer)
if args.weights == "load":
start_epoch = train_util.load_state(save_filename, model, optimizer,
discriminators)
else:
start_epoch = 0
# stop_patience = args.stop_patience
best_loss = torch.tensor(np.inf)
for epoch in tqdm(range(start_epoch, args.num_epochs), file=sys.stdout):
# for CUDA OOM error, prevents running dependency job on slurm which is meant to run on timeout
try:
train(epoch, train_loader, model, optimizer, args, writer,
discriminators)
# pass
except RuntimeError as err:
print("".join(
traceback.TracebackException.from_exception(err).format()),
file=sys.stderr)
print("*******", file=sys.stderr)
print(err, file=sys.stderr)
exit(0)
print("out of train")
# comp = subprocess.run("nvidia-smi --query-gpu=memory.free --format=csv,noheader,nounits", text=True, stdout=subprocess.PIPE)
# print(comp.stdout, file=sys.stderr)
val_loss_dict, _ = ae_test(get_losses, model, valid_loader, args,
discriminators)
train_util.log_losses("val", val_loss_dict, epoch + 1, writer)
# print("logg loss")
# train_util.log_latent_metrics("val", z, epoch+1, writer)
# print("log metric")
train_util.save_recons_img_grid("test", recons_input_img, model,
epoch + 1, args)
# print("log recons")
train_util.save_interp_img_grid("test", recons_input_img, model,
epoch + 1, args)
# print("log interp")
train_util.save_state(model, optimizer, discriminators,
val_loss_dict["recons_loss"], best_loss,
args.recons_loss, epoch, save_filename)
def main(args):
input_dim = 3
model = VAE(input_dim, args.hidden_size, args.enc_type, args.dec_type)
opt = torch.optim.Adam(model.parameters(), lr=args.lr, amsgrad=True)
# scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(opt, "min", patience=args.lr_patience, factor=0.5,
# threshold=args.threshold, threshold_mode="abs", min_lr=1e-6)
# ae_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(opt, "min", patience=args.lr_patience, factor=0.5,
# threshold=args.threshold, threshold_mode="abs", min_lr=1e-7)
discriminators = {}
if args.recons_loss != "mse":
if args.recons_loss == "gan":
recons_disc = Discriminator(input_dim, args.img_res,
args.input_type).to(args.device)
elif args.recons_loss == "comp":
recons_disc = AnchorComparator(input_dim * 2, args.img_res,
args.input_type).to(args.device)
elif "comp_2" in args.recons_loss:
recons_disc = ClubbedPermutationComparator(
input_dim * 2, args.img_res, args.input_type).to(args.device)
elif "comp_6" in args.recons_loss:
recons_disc = FullPermutationComparator(
input_dim * 2, args.img_res, args.input_type).to(args.device)
recons_disc_opt = torch.optim.Adam(recons_disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
discriminators["recons_disc"] = [recons_disc, recons_disc_opt]
if torch.cuda.device_count() > 1:
model = train_util.ae_data_parallel(model)
for disc in discriminators:
discriminators[disc][0] = torch.nn.DataParallel(
discriminators[disc][0])
model.to(args.device)
for disc in discriminators:
discriminators[disc][0].to(args.device)
print("model built", file=sys.stderr)
#print("model created")
train_loader, val_loader, test_loader = train_util.get_dataloaders(args)
print("loaders acquired", file=sys.stderr)
#print("loaders acquired")
model_name = f"vae_{args.recons_loss}"
if args.output_folder is None:
args.output_folder = os.path.join(
model_name, args.dataset,
f"depth_{args.enc_type}_{args.dec_type}_hs_{args.img_res}_{args.hidden_size}"
)
log_save_path = os.path.join("./logs", args.output_folder)
model_save_path = os.path.join("./models", args.output_folder)
if not os.path.exists(log_save_path):
os.makedirs(log_save_path)
print(f"log:{log_save_path}", file=sys.stderr)
sys.stderr.flush()
if not os.path.exists(model_save_path):
os.makedirs(model_save_path)
writer = SummaryWriter(log_save_path)
print(f"train loader length:{len(train_loader)}", file=sys.stderr)
best_loss = torch.tensor(np.inf)
if args.weights == "load":
start_epoch = train_util.load_state(model_save_path, model, opt,
discriminators)
else:
start_epoch = 0
recons_input_img = train_util.log_input_img_grid(test_loader, writer)
train_util.log_recons_img_grid(recons_input_img, model, 0, args.device,
writer)
stop_patience = args.stop_patience
for epoch in range(start_epoch, args.num_epochs):
try:
train(model, train_loader, opt, epoch, writer, args,
discriminators)
except RuntimeError as err:
print("".join(
traceback.TracebackException.from_exception(err).format()),
file=sys.stderr)
print("*******", file=sys.stderr)
print(err, file=sys.stderr)
exit(0)
val_loss_dict, z = train_util.test(get_losses, model, val_loader, args,
discriminators)
print(f"epoch loss:{val_loss_dict['recons_loss'].item()}")
train_util.save_recons_img_grid("test", recons_input_img, model,
epoch + 1, args)
train_util.save_interp_img_grid("test", recons_input_img, model,
epoch + 1, args)
train_util.log_losses("val", val_loss_dict, epoch + 1, writer)
train_util.log_latent_metrics("val", z, epoch + 1, writer)
train_util.save_state(model, opt, discriminators,
val_loss_dict["recons_loss"], best_loss,
args.recons_loss, epoch, model_save_path)
def main(args):
train_loader, val_loader, test_loader = train_util.get_dataloaders(args)
input_dim = 3
model = VAE(input_dim, args.hidden_size, args.enc_type, args.dec_type)
opt = torch.optim.Adam(model.parameters(), lr=LR, amsgrad=True)
discriminators = {}
if args.recons_loss != "mse":
if args.recons_loss == "gan":
recons_disc = Discriminator(input_dim, args.img_res, args.input_type).to(args.device)
elif args.recons_loss == "comp":
recons_disc = AnchorComparator(input_dim*2, args.img_res, args.input_type).to(args.device)
elif "comp_2" in args.recons_loss:
recons_disc = ClubbedPermutationComparator(input_dim*2, args.img_res, args.input_type).to(args.device)
elif "comp_6" in args.recons_loss:
recons_disc = FullPermutationComparator(input_dim*2, args.img_res, args.input_type).to(args.device)
recons_disc_opt = torch.optim.Adam(recons_disc.parameters(), lr=args.disc_lr, amsgrad=True)
discriminators["recons_disc"] = [recons_disc, recons_disc_opt]
if torch.cuda.device_count() > 1:
model = train_util.ae_data_parallel(model)
for disc in discriminators:
discriminators[disc][0] = torch.nn.DataParallel(discriminators[disc][0])
model.to(args.device)
model_name = f"vae_{args.recons_loss}"
if args.output_folder is None:
args.output_folder = os.path.join(model_name, args.dataset, f"depth_{args.enc_type}_{args.dec_type}_hs_{args.img_res}_{args.hidden_size}")
log_save_path = os.path.join("./logs", args.output_folder)
model_save_path = os.path.join("./models", args.output_folder)
if not os.path.exists(log_save_path):
os.makedirs(log_save_path)
print(f"log:{log_save_path}", file=sys.stderr)
sys.stderr.flush()
if not os.path.exists(model_save_path):
os.makedirs(model_save_path)
writer = SummaryWriter(log_save_path)
print(f"train loader length:{len(train_loader)}", file=sys.stderr)
best_loss = torch.tensor(np.inf)
if args.weights == "load":
start_epoch = train_util.load_state(model_save_path, model, opt, discriminators)
else:
start_epoch = 0
recons_input_img = train_util.log_input_img_grid(test_loader, writer)
train_util.save_recons_img_grid("val", recons_input_img, model, 0, args)
for epoch in range(1, args.num_epochs):
print("Epoch {}:".format(epoch))
train(model, opt, train_loader)
curr_loss = val(model, val_loader)
# val_loss_dict, z = train_util.test(get_losses, model, val_loader, args, discriminators)
print(f"epoch val loss:{curr_loss}", file=sys.stderr)
sys.stderr.flush()
train_util.save_recons_img_grid("val", recons_input_img, model, epoch+1, args)
train_util.save_interp_img_grid("val", recons_input_img, model, epoch+1, args)
def __init__(self,
n_input: int,
n_batch: int = 0,
n_labels: int = 0,
n_hidden: int = 256,
n_latent: int = 18,
n_layers: int = 1,
dropout_rate: float = 0.,
dispersion: str = "gene",
log_variational: bool = False,
reconstruction_loss: str = "alpha-gan",
n_centroids=12,
X=None,
gan_loss='gan',
reconst_ratio=0.01,
use_cuda: bool = True,
n_batch_gan: int = 0):
super().__init__()
self.dispersion = dispersion
self.n_latent = n_latent
self.n_hidden = n_hidden
self.n_layers = n_layers
self.log_variational = log_variational
self.reconstruction_loss = reconstruction_loss
# Automatically deactivate if useless
self.n_batch = n_batch
self.n_labels = n_labels
self.n_centroids = n_centroids
self.dropout_rate = dropout_rate
self.X = X
self.gan_loss = gan_loss
self.reconst_ratio = reconst_ratio
self.use_cuda = use_cuda
self.n_batch_gan = n_batch_gan
if self.dispersion == "gene":
self.px_r = torch.nn.Parameter(torch.randn(n_input))
elif self.dispersion == "gene-batch": # batch is different times of exp
self.px_r = torch.nn.Parameter(torch.randn(n_input, n_batch))
elif self.dispersion == "gene-label":
self.px_r = torch.nn.Parameter(torch.randn(n_input, n_labels))
elif self.dispersion == "gene-cell":
pass
else:
raise ValueError("dispersion must be one of ['gene', 'gene-batch',"
" 'gene-label', 'gene-cell'], but input was "
"{}.format(self.dispersion)")
self.pi = nn.Parameter(torch.ones(n_centroids) / n_centroids) # pc
self.mu_c = nn.Parameter(torch.zeros(n_latent, n_centroids)) # mu
self.var_c = nn.Parameter(torch.ones(n_latent, n_centroids)) # sigma^2
# z encoder goes from the n_input-dimensional data to an n_latent-d
# latent space representation
self.z_encoder = Encoder(n_input,
n_latent,
n_layers=n_layers,
n_hidden=n_hidden,
dropout_rate=dropout_rate)
# l encoder goes from n_input-dimensional data to 1-d library size
self.l_encoder = Encoder(n_input,
1,
n_layers=1,
n_hidden=n_hidden,
dropout_rate=dropout_rate)
# discriminator to distinguish the generated/fake/real and batches
self.discriminator = Discriminator(n_input, n_hidden, self.n_batch_gan,
gan_loss)
# decoder goes from n_latent-dimensional space to n_input-d data
self.decoder = DecoderSCVI(
n_latent,
n_input,
n_cat_list=[n_batch],
n_layers=n_layers,
n_hidden=n_hidden,
)
def main(args):
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_filename = './models/{0}'.format(args.output_folder)
train_loader, valid_loader, test_loader = train_util.get_dataloaders(args)
num_channels = 3
model = VectorQuantizedVAE(num_channels, args.hidden_size, args.k,
args.enc_type, args.dec_type)
model.to(args.device)
# Fixed images for Tensorboard
recons_input_img = train_util.log_input_img_grid(test_loader, writer)
train_util.log_recons_img_grid(recons_input_img, model, 0, args.device,
writer)
discriminators = {}
input_dim = 3
if args.recons_loss != "mse":
if args.recons_loss == "gan":
recons_disc = Discriminator(input_dim, args.img_res,
args.input_type).to(args.device)
elif args.recons_loss == "comp":
recons_disc = AnchorComparator(input_dim * 2, args.img_res,
args.input_type).to(args.device)
elif "comp_2" in args.recons_loss:
recons_disc = ClubbedPermutationComparator(
input_dim * 2, args.img_res, args.input_type).to(args.device)
elif "comp_6" in args.recons_loss:
recons_disc = FullPermutationComparator(
input_dim * 2, args.img_res, args.input_type).to(args.device)
recons_disc_opt = torch.optim.Adam(recons_disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
recons_disc_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
recons_disc_opt,
"min",
patience=args.lr_patience,
factor=0.5,
threshold=args.threshold,
threshold_mode="abs",
min_lr=1e-7)
discriminators["recons_disc"] = [recons_disc, recons_disc_opt]
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
ae_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
"min",
patience=args.lr_patience,
factor=0.5,
threshold=args.threshold,
threshold_mode="abs",
min_lr=1e-7)
if torch.cuda.device_count() > 1:
model = train_util.ae_data_parallel(model)
for disc in discriminators:
discriminators[disc][0] = torch.nn.DataParallel(
discriminators[disc][0])
model.to(args.device)
for disc in discriminators:
discriminators[disc][0].to(args.device)
# Generate the samples first once
recons_input_img = train_util.log_input_img_grid(test_loader, writer)
train_util.log_recons_img_grid(recons_input_img, model, 0, args.device,
writer)
if args.weights == "load":
start_epoch = train_util.load_state(save_filename, model, optimizer,
discriminators)
else:
start_epoch = 0
stop_patience = args.stop_patience
best_loss = torch.tensor(np.inf)
for epoch in tqdm(range(start_epoch, args.num_epochs), file=sys.stdout):
try:
train(epoch, train_loader, model, optimizer, args, writer,
discriminators)
except RuntimeError as err:
print("".join(
traceback.TracebackException.from_exception(err).format()),
file=sys.stderr)
print("*******")
print(err, file=sys.stderr)
print(f"batch_size:{args.batch_size}", file=sys.stderr)
exit(0)
val_loss_dict, z = train_util.test(get_losses, model, valid_loader,
args, discriminators)
train_util.log_recons_img_grid(recons_input_img, model, epoch + 1,
args.device, writer)
train_util.log_interp_img_grid(recons_input_img, model, epoch + 1,
args.device, writer)
train_util.log_losses("val", val_loss_dict, epoch + 1, writer)
train_util.log_latent_metrics("val", z, epoch + 1, writer)
train_util.save_state(model, optimizer, discriminators,
val_loss_dict["recons_loss"], best_loss,
args.recons_loss, epoch, save_filename)
# early stop check
# if val_loss_dict["recons_loss"] - best_loss < args.threshold:
# stop_patience -= 1
# else:
# stop_patience = args.stop_patience
# if stop_patience == 0:
# print("training early stopped!")
# break
ae_lr_scheduler.step(val_loss_dict["recons_loss"])
if args.recons_loss != "mse":
recons_disc_lr_scheduler.step(val_loss_dict["recons_disc_loss"])
train=True,
download=True,
transform=transforms.ToTensor()),
batch_size=64,
shuffle=False,
**kwargs)
test_loader = torch.utils.data.DataLoader(datasets.MNIST(
'../data/fashion_mnist/', train=False, transform=transforms.ToTensor()),
batch_size=32,
shuffle=False,
**kwargs)
test_data = list(test_loader)
netG = Generator().cuda()
netD = Discriminator().cuda()
optimizerG = torch.optim.Adam(netG.parameters(), lr=1e-4, betas=(0.5, 0.9))
optimizerD = torch.optim.Adam(netD.parameters(), lr=1e-4, betas=(0.5, 0.9))
one = torch.cuda.FloatTensor([1])
mone = one * -1
def train(epoch):
train_loss = []
for batch_idx, (data, _) in enumerate(train_loader):
start_time = time.time()
if data.size(0) != 64:
continue
def train(rank: int, cfg: DictConfig):
print(OmegaConf.to_yaml(cfg))
if cfg.train.n_gpu > 1:
init_process_group(backend=cfg.train.dist_config['dist_backend'],
init_method=cfg.train.dist_config['dist_url'],
world_size=cfg.train.dist_config['world_size'] *
cfg.train.n_gpu,
rank=rank)
device = torch.device(
'cuda:{:d}'.format(rank) if torch.cuda.is_available() else 'cpu')
generator = Generator(sum(cfg.model.feature_dims), *cfg.model.cond_dims,
**cfg.model.generator).to(device)
discriminator = Discriminator(**cfg.model.discriminator).to(device)
if rank == 0:
print(generator)
os.makedirs(cfg.train.ckpt_dir, exist_ok=True)
print("checkpoints directory : ", cfg.train.ckpt_dir)
if os.path.isdir(cfg.train.ckpt_dir):
cp_g = scan_checkpoint(cfg.train.ckpt_dir, 'g_')
cp_do = scan_checkpoint(cfg.train.ckpt_dir, 'd_')
steps = 1
if cp_g is None or cp_do is None:
state_dict_do = None
last_epoch = -1
else:
state_dict_g = load_checkpoint(cp_g, device)
state_dict_do = load_checkpoint(cp_do, device)
generator.load_state_dict(state_dict_g['generator'])
discriminator.load_state_dict(state_dict_do['discriminator'])
steps = state_dict_do['steps'] + 1
last_epoch = state_dict_do['epoch']
if cfg.train.n_gpu > 1:
generator = DistributedDataParallel(generator,
device_ids=[rank]).to(device)
discriminator = DistributedDataParallel(discriminator,
device_ids=[rank]).to(device)
optim_g = RAdam(generator.parameters(), cfg.opt.lr, betas=cfg.opt.betas)
optim_d = RAdam(discriminator.parameters(),
cfg.opt.lr,
betas=cfg.opt.betas)
if state_dict_do is not None:
optim_g.load_state_dict(state_dict_do['optim_g'])
optim_d.load_state_dict(state_dict_do['optim_d'])
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
optim_g, gamma=cfg.opt.lr_decay, last_epoch=last_epoch)
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
optim_d, gamma=cfg.opt.lr_decay, last_epoch=last_epoch)
train_filelist = load_dataset_filelist(cfg.dataset.train_list)
trainset = FeatureDataset(cfg.dataset, train_filelist, cfg.data)
train_sampler = DistributedSampler(
trainset) if cfg.train.n_gpu > 1 else None
train_loader = DataLoader(trainset,
batch_size=cfg.train.batch_size,
num_workers=cfg.train.num_workers,
shuffle=True,
sampler=train_sampler,
pin_memory=True,
drop_last=True)
if rank == 0:
val_filelist = load_dataset_filelist(cfg.dataset.test_list)
valset = FeatureDataset(cfg.dataset,
val_filelist,
cfg.data,
segmented=False)
val_loader = DataLoader(valset,
batch_size=1,
num_workers=cfg.train.num_workers,
shuffle=False,
sampler=train_sampler,
pin_memory=True)
sw = SummaryWriter(os.path.join(cfg.train.ckpt_dir, 'logs'))
generator.train()
discriminator.train()
for epoch in range(max(0, last_epoch), cfg.train.epochs):
if rank == 0:
start = time.time()
print("Epoch: {}".format(epoch + 1))
if cfg.train.n_gpu > 1:
train_sampler.set_epoch(epoch)
for y, x_noised_features, x_noised_cond in train_loader:
if rank == 0:
start_b = time.time()
y = y.to(device, non_blocking=True)
x_noised_features = x_noised_features.transpose(1, 2).to(
device, non_blocking=True)
x_noised_cond = x_noised_cond.to(device, non_blocking=True)
z1 = torch.randn(cfg.train.batch_size,
cfg.model.cond_dims[1],
device=device)
z2 = torch.randn(cfg.train.batch_size,
cfg.model.cond_dims[1],
device=device)
y_hat1 = generator(x_noised_features, x_noised_cond, z=z1)
y_hat2 = generator(x_noised_features, x_noised_cond, z=z2)
# Discriminator
real_scores, fake_scores = discriminator(y), discriminator(
y_hat1.detach())
d_loss = discriminator_loss(real_scores, fake_scores)
optim_d.zero_grad()
d_loss.backward(retain_graph=True)
optim_d.step()
# Generator
g_stft_loss = criterion(y, y_hat1) + criterion(
y, y_hat2) - criterion(y_hat1, y_hat2)
g_adv_loss = adversarial_loss(fake_scores)
g_loss = g_adv_loss + g_stft_loss
optim_g.zero_grad()
g_loss.backward()
optim_g.step()
if rank == 0:
# STDOUT logging
if steps % cfg.train.stdout_interval == 0:
with torch.no_grad():
print(
'Steps : {:d}, Gen Loss Total : {:4.3f}, STFT Error : {:4.3f}, s/b : {:4.3f}'
.format(steps, g_loss, g_stft_loss,
time.time() - start_b))
# checkpointing
if steps % cfg.train.checkpoint_interval == 0:
ckpt_dir = "{}/g_{:08d}".format(cfg.train.ckpt_dir, steps)
save_checkpoint(
ckpt_dir, {
'generator':
(generator.module if cfg.train.n_gpu > 1 else
generator).state_dict()
})
ckpt_dir = "{}/do_{:08d}".format(cfg.train.ckpt_dir, steps)
save_checkpoint(
ckpt_dir, {
'discriminator':
(discriminator.module if cfg.train.n_gpu > 1 else
discriminator).state_dict(),
'optim_g':
optim_g.state_dict(),
'optim_d':
optim_d.state_dict(),
'steps':
steps,
'epoch':
epoch
})
# Tensorboard summary logging
if steps % cfg.train.summary_interval == 0:
sw.add_scalar("training/gen_loss_total", g_loss, steps)
sw.add_scalar("training/gen_stft_error", g_stft_loss,
steps)
# Validation
if steps % cfg.train.validation_interval == 0:
generator.eval()
torch.cuda.empty_cache()
val_err_tot = 0
with torch.no_grad():
for j, (y, x_noised_features,
x_noised_cond) in enumerate(val_loader):
y_hat = generator(
x_noised_features.transpose(1, 2).to(device),
x_noised_cond.to(device))
val_err_tot += criterion(y, y_hat).item()
if j <= 4:
# sw.add_audio('noised/y_noised_{}'.format(j), y_noised[0], steps, cfg.data.target_sample_rate)
sw.add_audio('generated/y_hat_{}'.format(j),
y_hat[0], steps,
cfg.data.sample_rate)
sw.add_audio('gt/y_{}'.format(j), y[0], steps,
cfg.data.sample_rate)
val_err = val_err_tot / (j + 1)
sw.add_scalar("validation/stft_error", val_err, steps)
generator.train()
steps += 1
scheduler_g.step()
scheduler_d.step()
if rank == 0:
print('Time taken for epoch {} is {} sec\n'.format(
epoch + 1, int(time.time() - start)))
TF2_test_data_source,
mfcc_only=False,
norm_calc=False)
test_dataset.input_meanstd = train_dataset.input_meanstd
test_dataset.output_meanstd = train_dataset.output_meanstd
train_dataset_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
test_dataset_loader = DataLoader(test_dataset,
batch_size=batch_size,
shuffle=False)
generator_A2B = Generator(24).to("cuda")
generator_B2A = Generator(24).to("cuda")
discriminator_A = Discriminator(1).to("cuda")
discriminator_B = Discriminator(1).to("cuda")
generator_params = [generator_A2B.parameters(), generator_B2A.parameters()]
generator_optimizer = torch.optim.Adam(itertools.chain(*generator_params),
lr=generator_lr)
discriminator_params = [
discriminator_A.parameters(),
discriminator_B.parameters()
]
discriminator_optimizer = torch.optim.Adam(
itertools.chain(*discriminator_params), lr=discriminator_lr)
for epoch in range(num_epochs):
print("Epoch ", epoch)
for i, sample in enumerate(train_dataset_loader):
opt = parser.parse_args()
# Write learning info
writer = SummaryWriter('runs/2d_gaussain_mixture')
np.random.seed(opt.random_seed)
torch.manual_seed(opt.random_seed)
cuda = True if torch.cuda.is_available() and opt.cuda else False
Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() and opt.cuda else torch.FloatTensor
# Loss weight for gradient penalty
lambda_gp = opt.lambda_gp
# Initialize generator and discriminator
generator = Generator(opt.latent_dim, 2)
discriminator = Discriminator(2)
gaussian = GaussianMixture(opt.grid_side_size)
train_data = gaussian.sample(opt.batch_size * opt.target_factor if opt.learning_type == 'smallgan' else opt.batch_size)
if cuda:
generator.cuda()
discriminator.cuda()
prior_batch_size = opt.batch_size * opt.prior_factor if opt.learning_type == 'smallgan' else opt.batch_size
# Valid dataset
valid_samples = np.random.uniform(-1, 1, (opt.valid_size, opt.latent_dim))
valid_dataloader = torch.utils.data.DataLoader(
valid_samples,
batch_size=opt.valid_batch_size,
shuffle=False,
def discriminator(cfg):
discriminator = Discriminator(**cfg.model.discriminator)
return discriminator
def main(args):
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_filename = './models/{0}'.format(args.output_folder)
train_loader, val_loader, test_loader = train_util.get_dataloaders(args)
recons_input_img = train_util.log_input_img_grid(test_loader, writer)
input_dim = 3
model = ACAI(args.img_res, input_dim, args.hidden_size, args.enc_type,
args.dec_type).to(args.device)
disc = Discriminator(input_dim, args.img_res,
args.input_type).to(args.device)
disc_opt = torch.optim.Adam(disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
# if torch.cuda.device_count() > 1 and args.device == "cuda":
# model = torch.nn.DataParallel(model)
opt = torch.optim.Adam(model.parameters(), lr=args.lr)
# ae_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, "min", patience=args.lr_patience, factor=0.5,
# threshold=args.threshold, threshold_mode="abs", min_lr=1e-7)
# interp_disc_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(disc_opt, "min", patience=args.lr_patience, factor=0.5,
# threshold=args.threshold, threshold_mode="abs", min_lr=1e-7)
discriminators = {"interp_disc": [disc, disc_opt]}
if args.recons_loss != "mse":
if args.recons_loss == "gan":
recons_disc = Discriminator(input_dim, args.img_res,
args.input_type).to(args.device)
elif args.recons_loss == "comp":
recons_disc = AnchorComparator(input_dim * 2, args.img_res,
args.input_type).to(args.device)
elif "comp_2" in args.recons_loss:
recons_disc = ClubbedPermutationComparator(
input_dim * 2, args.img_res, args.input_type).to(args.device)
elif "comp_6" in args.recons_loss:
recons_disc = FullPermutationComparator(
input_dim * 2, args.img_res, args.input_type).to(args.device)
recons_disc_opt = torch.optim.Adam(recons_disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
discriminators["recons_disc"] = [recons_disc, recons_disc_opt]
# Generate the samples first once
train_util.save_recons_img_grid("test", recons_input_img, model, 0, args)
if args.weights == "load":
start_epoch = train_util.load_state(save_filename, model, opt,
discriminators)
else:
start_epoch = 0
best_loss = torch.tensor(np.inf)
for epoch in range(args.num_epochs):
print("Epoch {}:".format(epoch))
train(model, opt, train_loader, args, discriminators, writer)
# curr_loss = val(model, val_loader)
# print(f"epoch val loss:{curr_loss}")
val_loss_dict, z = train_util.test(get_losses, model, val_loader, args,
discriminators)
train_util.log_losses("val", val_loss_dict, epoch + 1, writer)
train_util.log_latent_metrics("val", z, epoch + 1, writer)
# train_util.log_recons_img_grid(recons_input_img, model, epoch+1, args.device, writer)
# train_util.log_interp_img_grid(recons_input_img, model, epoch+1, args.device, writer)
train_util.save_recons_img_grid("val", recons_input_img, model,
epoch + 1, args)
train_util.save_interp_img_grid("val", recons_input_img, model,
epoch + 1, args)
train_util.save_state(model, opt, discriminators,
val_loss_dict["recons_loss"], best_loss,
args.recons_loss, epoch, save_filename)
def test_gan(self):
# models settings
lambda_gp = 0.1
Tensor = torch.FloatTensor
generator = Generator(2, 2)
discriminator = Discriminator(2)
optimizer_G = torch.optim.Adam(generator.parameters(),
lr=0.0001,
betas=(0.5, 0.999))
optimizer_D = torch.optim.Adam(discriminator.parameters(),
lr=0.0001,
betas=(0.5, 0.999))
criterion = torch.nn.BCEWithLogitsLoss()
optimizer_D.zero_grad()
# run models for test batch
real_data = Tensor(np.random.normal(0, 1, (64, 2)))
z = Tensor(np.random.normal(0, 1, (64, 2)))
real_target = Tensor(real_data.size(0), 1).fill_(1.0)
fake_target = Tensor(real_data.size(0), 1).fill_(0.0)
g_before = deepcopy(generator)
d_before = deepcopy(discriminator)
# Generate a batch of images
fake_data = generator(z)
# Real images
real_validity = discriminator(real_data)
# Fake images
fake_validity = discriminator(fake_data)
# Gradient penalty
gradient_penalty = compute_gradient_penalty(discriminator,
real_data.data,
fake_data.data, Tensor)
# Discriminator loss
d_loss = criterion(real_validity, real_target) \
+ criterion(fake_validity, fake_target) \
+ lambda_gp * gradient_penalty
d_loss.backward()
optimizer_D.step()
# Assert that D changed and G not changed
g_changed = [
torch.equal(after, before) for after, before in zip(
generator.parameters(), g_before.parameters())
]
self.assertTrue(all(g_changed))
d_changed = [
torch.equal(after, before) for after, before in zip(
discriminator.parameters(), d_before.parameters())
]
self.assertFalse(all(d_changed))
optimizer_G.zero_grad()
# Train on fake samples
g_before = deepcopy(generator)
d_before = deepcopy(discriminator)
fake_data = generator(z)
fake_validity = discriminator(fake_data)
g_loss = criterion(fake_validity, real_target)
g_loss.backward()
optimizer_G.step()
# Assert that G changed and D not changed
g_changed = [
torch.equal(after, before) for after, before in zip(
generator.parameters(), g_before.parameters())
]
self.assertFalse(all(g_changed))
d_changed = [
torch.equal(after, before) for after, before in zip(
discriminator.parameters(), d_before.parameters())
]
self.assertTrue(all(d_changed))
def val_test(args):
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_filename = './models/{0}'.format(args.output_folder)
d_args = vars(args)
num_perturb_types = 0
perturb_types = []
for perturb_type in train_util.perturb_dict:
if d_args[perturb_type]:
num_perturb_types += 1
perturb_types.append(perturb_type)
train_loader, valid_loader, test_loader = train_util.get_dataloaders(
args, perturb_types)
recons_input_img = train_util.log_input_img_grid(test_loader, writer)
input_dim = 3
model = PCIE(args.img_res, input_dim, args.hidden_size, num_perturb_types,
args.enc_type, args.dec_type)
interp_disc = Discriminator(input_dim, args.img_res,
args.input_type).to(args.device)
interp_disc_opt = torch.optim.Adam(interp_disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
# if torch.cuda.device_count() > 1 and args.device == "cuda":
# model = torch.nn.DataParallel(model)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
# ae_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, "min", patience=args.lr_patience, factor=0.5,
# threshold=args.threshold, threshold_mode="abs", min_lr=1e-7)
# interp_disc_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(interp_disc_opt, "min", patience=args.lr_patience, factor=0.5,
# threshold=args.threshold, threshold_mode="abs", min_lr=1e-7)
discriminators = {"interp_disc": [interp_disc, interp_disc_opt]}
if args.recons_loss != "mse":
if args.recons_loss == "gan":
recons_disc = Discriminator(input_dim, args.img_res,
args.input_type).to(args.device)
elif args.recons_loss == "comp":
recons_disc = AnchorComparator(input_dim * 2, args.img_res,
args.input_type).to(args.device)
elif "comp_2" in args.recons_loss:
recons_disc = ClubbedPermutationComparator(
input_dim * 2, args.img_res, args.input_type).to(args.device)
elif "comp_6" in args.recons_loss:
recons_disc = FullPermutationComparator(
input_dim * 2, args.img_res, args.input_type).to(args.device)
recons_disc_opt = torch.optim.Adam(recons_disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
recons_disc_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
recons_disc_opt,
"min",
patience=args.lr_patience,
factor=0.5,
threshold=args.threshold,
threshold_mode="abs",
min_lr=1e-7)
discriminators["recons_disc"] = [recons_disc, recons_disc_opt]
if args.prior_loss == "gan":
prior_disc = Latent2ClassDiscriminator(
args.hidden_size, args.img_res // args.scale_factor)
prior_disc_opt = torch.optim.Adam(prior_disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
# prior_disc_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(prior_disc_opt, "min", patience=args.lr_patience, factor=0.5,
# threshold=args.threshold, threshold_mode="abs", min_lr=1e-7)
discriminators["prior_disc"] = [prior_disc, prior_disc_opt]
print("pertrub gans")
if args.perturb_feat_gan:
for perturb_type in train_util.perturb_dict:
if d_args[perturb_type]:
num_classes = train_util.perturb_dict[perturb_type].num_class
pdb.set_trace()
if num_classes == 2:
print(f"perturb:{d_args[perturb_type]}\ttype: two ")
pert_disc = Latent2ClassDiscriminator(
args.hidden_size, args.img_res // args.scale_factor)
pert_disc_opt = torch.optim.Adam(pert_disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
else:
print(f"perturb:{d_args[perturb_type]}\ttype: multi ")
pert_disc = LatentMultiClassDiscriminator(
args.hidden_size, args.img_res // args.scale_factor,
num_classes)
pert_disc_opt = torch.optim.Adam(pert_disc.parameters(),
lr=args.disc_lr,
amsgrad=True)
discriminators[f"{perturb_type}_disc"] = (pert_disc,
pert_disc_opt)
print("perrturb gans set")
model.to(args.device)
for disc in discriminators:
discriminators[disc][0].to(args.device)
# Generate the samples first once
# train_util.log_recons_img_grid(recons_input_img, model, 0, args.device, writer)
if args.weights == "load":
start_epoch = train_util.load_state(save_filename, model, optimizer,
discriminators)
else:
start_epoch = 0
# stop_patience = args.stop_patience
best_loss = torch.tensor(np.inf)
for epoch in tqdm(range(start_epoch, 4), file=sys.stdout):
val_loss_dict = train_util.test(get_losses, model, valid_loader, args,
discriminators, True)
if args.weights == "init" and epoch == 1:
epoch += 1
break
train_util.log_losses("val", val_loss_dict, epoch + 1, writer)
train_util.log_recons_img_grid(recons_input_img, model, epoch, args.device,
writer)
train_util.log_interp_img_grid(recons_input_img, model, epoch + 1,
args.device, writer)
train_util.save_state(model, optimizer, discriminators,
val_loss_dict["recons_loss"], best_loss,
args.recons_loss, epoch, save_filename)
print(val_loss_dict)