from deliravision.models.gans import WassersteinDivergenceGAN
from deliravision.losses import WassersteinDivergence
import os
from training.gans._basic import train, predict
import torch
if __name__ == '__main__':
img_path = os.path.abspath("~/data/")
outpath = os.path.abspath("~/GanExperiments")
num_epochs = 1000
key_mapping = {"x": "data"}
torch.autograd.set_detect_anomaly(True)
model, weight_path = train(WassersteinDivergenceGAN,
{"latent_dim": 100,
"img_shape": (1, 28, 28)},
os.path.join(outpath, "train"), img_path,
num_epochs=num_epochs,
key_mapping=key_mapping,
additional_losses={"divergence":
WassersteinDivergence()})
predict(model, weight_path, os.path.join(outpath, "preds"), num_epochs)
from deliravision.models.gans import BoundarySeekingGAN
from deliravision.losses import BoundarySeekingLoss
import os
from training.gans._basic import train, predict
if __name__ == '__main__':
img_path = os.path.abspath("~/data/")
outpath = os.path.abspath("~/GanExperiments")
num_epochs = 1000
key_mapping = {"x": "data"}
model, weight_path = train(BoundarySeekingGAN, {"latent_dim": 100,
"img_shape": (1, 28, 28)},
os.path.join(outpath, "train"), img_path,
num_epochs=num_epochs,
key_mapping=key_mapping,
additional_losses={"boundary_seeking":
BoundarySeekingLoss()})
predict(model, weight_path, os.path.join(outpath, "preds"), num_epochs)
from deliravision.models.gans import DRAGAN
from deliravision.losses import GradientPenalty
import os
from training.gans._basic import train, predict
if __name__ == '__main__':
img_path = os.path.abspath("~/data/")
outpath = os.path.abspath("~/GanExperiments")
num_epochs = 1000
key_mapping = {"x": "data"}
model, weight_path = train(
DRAGAN, {
"latent_dim": 100,
"num_channels": 1,
"img_size": 28
},
os.path.join(outpath, "train"),
img_path,
num_epochs=num_epochs,
key_mapping=key_mapping,
additional_losses={"gradient_penalty": GradientPenalty()})
predict(model, weight_path, os.path.join(outpath, "preds"), num_epochs)
from deliravision.models.gans import GenerativeAdversarialNetworks
import os
from training.gans._basic import train, predict
if __name__ == '__main__':
img_path = os.path.abspath("~/data/")
outpath = os.path.abspath("~/GanExperiments")
num_epochs = 1000
key_mapping = {"imgs": "data"}
model, weight_path = train(GenerativeAdversarialNetworks, {
"latent_dim": 100,
"img_shape": (1, 28, 28)
},
os.path.join(outpath, "train"),
img_path,
num_epochs=num_epochs,
key_mapping=key_mapping)
predict(model, weight_path, os.path.join(outpath, "preds"), num_epochs)
from deliravision.models.gans import WassersteinGradientPenaltyGAN
from deliravision.losses import GradientPenalty
import os
from training.gans._basic import train, predict
import torch
if __name__ == '__main__':
img_path = os.path.abspath("~/data/")
outpath = os.path.abspath("~/GanExperiments")
num_epochs = 1000
key_mapping = {"x": "data"}
torch.autograd.set_detect_anomaly(True)
model, weight_path = train(WassersteinGradientPenaltyGAN,
{"latent_dim": 100,
"img_shape": (1, 28, 28)},
os.path.join(outpath, "train"), img_path,
num_epochs=num_epochs,
key_mapping=key_mapping,
additional_losses={"gradient_penalty":
GradientPenalty()})
predict(model, weight_path, os.path.join(outpath, "preds"), num_epochs)
from deliravision.models.gans import WassersteinGAN
import os
from training.gans._basic import train, predict
import torch
if __name__ == '__main__':
img_path = os.path.abspath("~/data/")
outpath = os.path.abspath("~/GanExperiments")
num_epochs = 1000
key_mapping = {"x": "data"}
torch.autograd.set_detect_anomaly(True)
model, weight_path = train(WassersteinGAN, {
"latent_dim": 100,
"img_shape": (1, 28, 28)
},
os.path.join(outpath, "train"),
img_path,
num_epochs=num_epochs,
key_mapping=key_mapping)
predict(model, weight_path, os.path.join(outpath, "preds"), num_epochs)
from deliravision.models.gans import RelativisticGAN
from deliravision.losses import AdversarialLoss
import os
from training.gans._basic import train, predict
import torch
if __name__ == '__main__':
img_path = os.path.abspath("~/data/")
outpath = os.path.abspath("~/GanExperiments")
num_epochs = 1000
key_mapping = {"x": "data"}
model, weight_path = train(RelativisticGAN, {
"latent_dim": 100,
"img_size": 28,
"num_channels": 1
},
os.path.join(outpath, "train"),
img_path,
num_epochs=num_epochs,
key_mapping=key_mapping,
additional_losses={
"adversarial":
AdversarialLoss(
torch.nn.BCEWithLogitsLoss())
})
predict(model, weight_path, os.path.join(outpath, "preds"), num_epochs)
batchsize = 64
latent_dim = 100
n_classes = 10
code_dim = 2
model, weight_path = train(InfoGAN, {
"latent_dim": latent_dim,
"num_channels": 1,
"img_size": 28,
"n_classes": n_classes,
"code_dim": code_dim
},
os.path.join(outpath, "train"),
img_path,
num_epochs=num_epochs,
key_mapping=key_mapping,
additional_losses={
"categorical":
torch.nn.CrossEntropyLoss(),
"continuous":
torch.nn.MSELoss(),
"adversarial":
AdversarialLoss(torch.nn.MSELoss())
},
create_optim_fn=create_optims,
batchsize=batchsize)
predict(model,
weight_path,
os.path.join(outpath, "preds"),
num_epochs,
from deliravision.models.gans import DeepConvolutionalGAN
import os
from training.gans._basic import train, predict
if __name__ == '__main__':
img_path = os.path.abspath("~/data/")
outpath = os.path.abspath("~/GanExperiments")
num_epochs = 1000
key_mapping = {"imgs": "data"}
model, weight_path = train(DeepConvolutionalGAN, {"latent_dim": 100,
"img_size": 28,
"num_channels": 1},
os.path.join(outpath, "train"), img_path,
num_epochs=num_epochs,
key_mapping=key_mapping)
predict(model, weight_path, os.path.join(outpath, "preds"), num_epochs)
from deliravision.models.gans import BoundaryEquilibriumGAN
from deliravision.losses import BELoss
import os
from training.gans._basic import train, predict
if __name__ == '__main__':
img_path = os.path.abspath("~/data/")
outpath = os.path.abspath("~/GanExperiments")
num_epochs = 1000
key_mapping = {"x": "data"}
model, weight_path = train(BoundaryEquilibriumGAN, {
"latent_dim": 100,
"img_size": 28,
"n_channels": 1
},
os.path.join(outpath, "train"),
img_path,
num_epochs=num_epochs,
key_mapping=key_mapping,
additional_losses={"began": BELoss()})
predict(model, weight_path, os.path.join(outpath, "preds"), num_epochs)
img_path = os.path.abspath("~/data/")
outpath = os.path.abspath("~/GanExperiments")
num_epochs = 1000
key_mapping = {"real_imgs": "data", "real_labels": "label"}
latent_dim = 100
batchsize = 64
n_classes = 10
model, weight_path = train(
AuxiliaryClassifierGANPyTorch, {
"latent_dim": latent_dim,
"img_size": 28,
"n_channels": 1,
"n_classes": n_classes
},
os.path.join(outpath, "train"),
img_path,
num_epochs=num_epochs,
additional_losses={"auxiliary": torch.nn.CrossEntropyLoss()},
key_mapping=key_mapping,
batchsize=batchsize)
predict(model,
weight_path,
os.path.join(outpath, "preds"),
num_epochs,
gen_fns=[torch.randn, torch.randint],
gen_args=[(batchsize, latent_dim), (0, n_classes, (batchsize, ))],
gen_kwargs=[{}, {
"dtype": torch.long
from deliravision.models.gans import ConditionalGAN
from deliravision.losses import AdversarialLoss
import torch
import os
from training.gans._basic import train, predict
if __name__ == '__main__':
img_path = os.path.abspath("~/data/")
outpath = os.path.abspath("~/GanExperiments")
num_epochs = 1000
key_mapping = {"x": "data", "labels": "label"}
batchsize = 64
latent_dim = 100
n_classes = 10
model, weight_path = train(ConditionalGAN, {"latent_dim": latent_dim,
"n_classes": n_classes,
"img_shape": (1, 28, 28)},
os.path.join(outpath, "train"), img_path,
num_epochs=num_epochs,
key_mapping=key_mapping,
additional_losses={"adversarial":
AdversarialLoss(
torch.nn.MSELoss())},
batchsize=batchsize)
predict(model, weight_path, os.path.join(outpath, "preds"), num_epochs,
gen_fns=[torch.randn, torch.randint],
gen_args=[(batchsize, latent_dim), (0, n_classes, (batchsize, 1))],)
import os
from training.gans._basic import train, predict
import torch
if __name__ == '__main__':
img_path = os.path.abspath("~/data/")
outpath = os.path.abspath("~/GanExperiments")
num_epochs = 1000
key_mapping = {"imgs": "data"}
model, weight_path = train(EnergyBasedGAN, {
"latent_dim": 100,
"num_channels": 1,
"img_size": 28
},
os.path.join(outpath, "train"),
img_path,
num_epochs=num_epochs,
key_mapping=key_mapping,
additional_losses={
"pullaway":
PullAwayLoss(),
"pixelwise":
torch.nn.MSELoss(),
"discriminator_margin":
DiscriminatorMarginLoss()
})
predict(model, weight_path, os.path.join(outpath, "preds"), num_epochs)
from deliravision.models.gans import LeastSquareGAN
from deliravision.losses import AdversarialLoss
import os
from training.gans._basic import train, predict
import torch
if __name__ == '__main__':
img_path = os.path.abspath("~/data/")
outpath = os.path.abspath("~/GanExperiments")
num_epochs = 1000
key_mapping = {"imgs": "data"}
model, weight_path = train(
LeastSquareGAN, {
"latent_dim": 100,
"img_shape": (1, 28, 28)
},
os.path.join(outpath, "train"),
img_path,
num_epochs=num_epochs,
key_mapping=key_mapping,
additional_losses={"adversarial": AdversarialLoss(torch.nn.MSELoss())})
predict(model, weight_path, os.path.join(outpath, "preds"), num_epochs)
from deliravision.models.gans import AdversarialAutoEncoderPyTorch
import os
from training.gans._basic import train, predict
import torch
if __name__ == '__main__':
img_path = os.path.abspath("~/data/")
outpath = os.path.abspath("~/GanExperiments")
num_epochs = 1500
key_mapping = {"x": "data"}
model, weight_path = train(
AdversarialAutoEncoderPyTorch, {
"latent_dim": 100,
"img_shape": (1, 28, 28)
},
os.path.join(outpath, "train"),
img_path,
num_epochs=num_epochs,
additional_losses={"pixelwise": torch.nn.L1Loss()},
key_mapping=key_mapping)
predict(model,
weight_path,
os.path.join(outpath, "preds"),
num_epochs,
generative_network="generator.decoder")
