loss += (self.ce(inputs[3], distances) * mask2d.float()).sum() / m2sum
loss += (self.ce(inputs[4], contact_angles) * mask2d.float()).sum() / m2sum
loss += (self.ce(inputs[5], contact_dihedrals) * mask2d.float()).sum() / m2sum
loss += (self.ce(inputs[6], into_dihedrals) * mask2d.float()).sum() / m2sum
loss += (self.ce(inputs[7], angle) * mask.float()).sum() / msum
loss += (self.ce(inputs[8], dihedral) * mask.float()).sum() / msum
loss += (self.kl(inputs[9].log_softmax(dim=1), pssm) * mask[:, None].float()).mean(dim=1).sum() / msum
loss += (self.ce(inputs[10], angle) * mask.float()).sum() / msum
loss += (self.ce(inputs[11], dihedral) * mask.float()).sum() / msum
loss += (self.kl(inputs[12].log_softmax(dim=1), pssm) * mask[:, None].float()).mean(dim=1).sum() / msum
return loss
if __name__ == "__main__":
num_neighbours = 15
drop = None if len(sys.argv) < 4 else float(sys.argv[3])
name = f"initial-all-wide-drop-{drop}-1" if drop else "initial-all-wide-1"
data = FoldNet(sys.argv[1], num_neighbours=num_neighbours)
valid_data = FoldNet(sys.argv[2], num_neighbours=num_neighbours)
net = SDP(DistancePredictor(pair_depth=20, seq_depth=5, size=256, drop=drop))
training = SupervisedTraining(
net, data, valid_data,
[TotalLoss()],
batch_size=8,
max_epochs=1000,
optimizer=lambda x: torch.optim.Adam(x, lr=1e-4),
device="cuda:0",
network_name=f"fold/{name}",
valid_callback=valid_callback
).load()
final_net = training.train()
step_num = torch.tensor(float(self.step_id + 1))
learning_rate *= min(torch.pow(step_num, -0.5),
step_num * torch.pow(torch.tensor(4000.0), -1.5))
self.optimizer.param_groups[0]["lr"] = learning_rate
if __name__ == "__main__":
data = CondSeqResampleNet(sys.argv[1], num_neighbours=15, n_backrub=10)
valid_data = CondTransformerNet(
sys.argv[2], num_neighbours=15,
n_backrub=0) # Validation with out augmentation
net = SDP(
ConditionalStructuredTransformer(6,
128,
10,
attention_size=128,
heads=8,
mlp_depth=2,
depth=9,
batch_norm=True))
training = ConditionalStructuredTransformerTraining(
net,
data,
valid_data,
[MaskedLoss()],
batch_size=32,
max_epochs=1000,
optimizer=lambda x: torch.optim.Adam(x), # LR scheduled
device="cuda:0",
network_name=
"cond-structured-transformer/15-9-drop-10-rub-10-pssm-bs-32-clone",
super().__init__()
self.net = net
def forward(self, *args):
inputs = []
for arg in args:
if isinstance(arg, PackedTensor):
inputs.append(arg.tensor)
else:
inputs.append(arg)
return self.net(*inputs)
if __name__ == "__main__":
data = EBMNet(sys.argv[1], num_neighbours=15)
net = SDP(ProperEnergy(depth=16, down=4, shape=64))
integrator = Langevin(rate=50.0,
noise=0.01,
steps=20,
max_norm=None,
clamp=(0, 1))
training = EBMTraining(
net,
data,
batch_size=32,
decay=1.0,
max_epochs=5000,
integrator=integrator,
buffer_probability=0.95,
buffer_size=10000,
optimizer_kwargs={
def forward(self, *args):
inputs = []
for arg in args:
if isinstance(arg, PackedTensor):
inputs.append(arg.tensor)
else:
inputs.append(arg)
return self.net(*inputs)
if __name__ == "__main__":
data = EBMNet(sys.argv[1], num_neighbours=15)
net = SDP(
StructuredEnergy(
6, 128, 10,
attention_size=32, heads=8,
mlp_depth=2, depth=3, batch_norm=False, dropout=0.1,
neighbours=15, angles=True, distance_kernels=64, connected=attention_connected
)
)
integrator = PackedLangevin(rate=10.0, noise=0.01, steps=20, max_norm=None, clamp=None)
training = EBMTraining(
net, data,
batch_size=16,
decay=1.0,
max_epochs=1000,
integrator=integrator,
buffer_probability=0.95,
buffer_size=100000,
optimizer=DiffMod,
optimizer_kwargs={"lr": 5e-4},
device="cuda:0",
inputs = []
for arg in args:
if isinstance(arg, PackedTensor):
inputs.append(arg.tensor)
else:
inputs.append(arg)
return self.net(*inputs)
if __name__ == "__main__":
data = EBMNet(sys.argv[1], num_neighbours=15)
net = SDP(
StructuredEnergy(6,
128,
10,
attention_size=128,
heads=8,
mlp_depth=2,
depth=3,
batch_norm=True,
neighbours=15))
integrator = PackedLangevin(rate=50,
noise=0.01,
steps=20,
max_norm=None,
clamp=None)
training = EBMTraining(net,
data,
batch_size=4,
decay=1.0,
max_epochs=1000,
integrator=integrator,
inputs = []
print(args)
for arg in args:
print(type(arg))
if isinstance(arg, PackedTensor):
print("yay")
inputs.append(arg.tensor)
else:
inputs.append(arg)
return self.net(*inputs)
torch.backends.cudnn.enabled = False
if __name__ == "__main__":
data = GANNet(sys.argv[1], num_neighbours=15)
gen = SDP(DistanceGenerator(128, depth=3))
disc = SDP(DistanceDiscriminator(depth=3))
training = AngleGANTraining(
gen,
disc,
data,
batch_size=4,
max_epochs=1000,
#optimizer=DiffMod,
device="cuda:0",
network_name="distance-gan/meh-2",
verbose=True,
report_interval=10)
final_net = training.train()
loss += (self.ce(inputs[5], contact_dihedrals) * mask2d.float()).sum() / m2sum
loss += (self.ce(inputs[6], into_dihedrals) * mask2d.float()).sum() / m2sum
loss += (self.ce(inputs[7], angle) * mask.float()).sum() / msum
loss += (self.ce(inputs[8], dihedral) * mask.float()).sum() / msum
loss += (self.kl(inputs[9].log_softmax(dim=1), pssm) * mask[:, None].float()).mean(dim=1).sum() / msum
loss += (self.ce(inputs[10], angle) * mask.float()).sum() / msum
loss += (self.ce(inputs[11], dihedral) * mask.float()).sum() / msum
loss += (self.kl(inputs[12].log_softmax(dim=1), pssm) * mask[:, None].float()).mean(dim=1).sum() / msum
return loss
if __name__ == "__main__":
num_neighbours = 15
drop = 0.1 if len(sys.argv) < 4 else float(sys.argv[3])
name = f"checkpointed-drop-{drop}-1"
data = FoldNet(sys.argv[1], num_neighbours=num_neighbours, pass_mask=True)
valid_data = FoldNet(sys.argv[2], num_neighbours=num_neighbours, pass_mask=True)
net = SDP(CheckpointAttentionDistancePredictor(
pair_depth=32, seq_depth=4, size=256, attention_size=64, drop=drop, split=16
))
training = SupervisedTraining(
net, data, valid_data,
[TotalLoss()],
batch_size=8,
max_epochs=1000,
optimizer=lambda x: torch.optim.Adam(x, lr=1e-4),
device="cuda:0",
network_name=f"fold/{name}",
valid_callback=valid_callback
)
final_net = training.train()
re_ter = position_lookup(
angles[(struc.indices == 0).nonzero().view(-1)],
struc.indices[(struc.indices == 0).nonzero().view(-1)])[0].view(
-1, 3).numpy()
dsttt = np.linalg.norm((re_ter[None, :] - re_ter[:, None]), axis=-1)
ax.imshow(dsttt)
trn.writer.add_figure("heat expected", fig, trn.step_id)
plt.close("all")
trn.writer.add_scalar("size", float(ter.shape[0]), trn.step_id)
if __name__ == "__main__":
data = RGNNet(sys.argv[1])
valid_data = RGNNet(sys.argv[2])
net = SDP(StructureRNN(41, distance_size=9, depth=1)) #SDP(RGN(41))
training = SupervisedTraining(
net,
data,
valid_data, [RGNLoss(), AngleMSE()],
batch_size=32,
max_epochs=1000,
optimizer=lambda x: torch.optim.Adam(x, lr=5e-6),
device="cuda:0",
network_name=
"rgn-test/stochastic-5-e-6-hopefully-fixed-attention-full-loss-more-batch-noangle-plotted",
valid_callback=valid_callback)
final_net = training.train()
ax = fig.add_subplot(111)
ax.scatter(angles[:, 1].numpy() % 6.3, angles[:, 2].numpy() % 6.3)
ax.scatter(ang[:, 1].numpy() % 6.3, ang[:, 2].numpy() % 6.3)
trn.writer.add_figure("rama", fig, trn.step_id)
plt.close("all")
if __name__ == "__main__":
with torch.autograd.detect_anomaly():
data = FragmentNet(sys.argv[1])
valid_data = FragmentNet(sys.argv[2])
net = SDP(
ProteinTransformer(6,
128,
10,
heads=8,
depth=3,
neighbours=15,
mix=20))
training = SupervisedTraining(net,
data,
valid_data, [MixtureOfVonMisesLoss()],
batch_size=16,
max_epochs=1000,
optimizer=lambda x: torch.optim.Adam(
x, lr=1e-4, weight_decay=1e-4),
device="cuda:0",
network_name="aaattention/linear-dep",
valid_callback=valid_callback).load()
final_net = training.train()
# learning_rate *= min(
# torch.pow(step_num, -0.5),
# step_num * torch.pow(torch.tensor(4000.0), -1.5)
# )
# self.optimizer.param_groups[0]["lr"] = learning_rate
if __name__ == "__main__":
data = TransformerNet(sys.argv[1], num_neighbours=15)
valid_data = TransformerNet(sys.argv[2], num_neighbours=15)
generator = SDP(
TransformerGenerator(27,
20,
128,
10,
64,
attention_size=128,
heads=8,
mlp_depth=2,
depth=3,
batch_norm=True))
discriminator = SDP(
TransformerDiscriminator(27 + 20,
20,
128,
10,
attention_size=128,
heads=8,
mlp_depth=2,
depth=6,
batch_norm=True))
def forward(self, *args):
inputs = []
for arg in args:
if isinstance(arg, PackedTensor):
inputs.append(arg.tensor)
else:
inputs.append(arg)
return self.net(*inputs)
torch.backends.cudnn.enabled = False
if __name__ == "__main__":
data = GANNet(sys.argv[1], num_neighbours=15)
gen = SDP(ModifierGenerator(1024, 128, 10, repeats=10))
disc = SDP(
StructuredDiscriminator(6,
128,
10,
attention_size=128,
heads=8,
mlp_depth=2,
depth=1,
batch_norm=True,
dropout=0.1,
neighbours=15,
angles=True,
distance_kernels=64,
connected=attention_connected))
training = AngleGANTraining(
nodes.size(-1),
dtype=torch.bool,
device=nodes.device)
for block in self.blocks:
nodes, edges = block(nodes, edges, mask)
predictions = self.predict(nodes, edges)
return predictions
if __name__ == "__main__":
data = EBMNet(sys.argv[1], num_neighbours=15, N=128)
net = SDP(
MaterializedEnergy(pair_depth=4,
size=128,
value_size=16,
kernel_size=1,
drop=0.0))
integrator = Langevin(rate=500.0,
noise=1.0,
steps=50,
max_norm=None,
clamp=None)
training = EBMTraining(net,
data,
batch_size=32,
decay=1.0,
max_epochs=5000,
integrator=integrator,
buffer_probability=0.95,
buffer_size=10000,
])
self.predict = nn.Linear(128, 1)
def forward(self, inputs):
inputs, *_ = inputs
out = self.preprocess(inputs)
for block in self.blocks:
out = out + block(out)
out = func.max_pool2d(out, 2)
out = func.adaptive_avg_pool2d(out, 1).view(-1, 128)
out = self.predict(out)
return out
if __name__ == "__main__":
data = GANNet(sys.argv[1], num_neighbours=15)
gen = SDP(StyleGenerator())
disc = SDP(Discriminator())
training = AngleGANTraining(
gen,
disc,
data,
batch_size=16,
max_epochs=1000,
#optimizer=DiffMod,
device="cuda:0",
network_name="distance-gan/simple-as-f**k-style-1",
verbose=True,
report_interval=10)
final_net = training.train()
self.predict = nn.Linear(128, 1)
def forward(self, inputs):
inputs = torch.cat(inputs, dim=1)
out = self.preprocess(inputs)
for block in self.blocks:
out = out + block(out)
out = func.avg_pool2d(out, 2)
out = func.adaptive_avg_pool2d(out, 1).view(-1, 128)
out = self.predict(func.dropout(out, 0.5))
return out
if __name__ == "__main__":
data = GANNet(sys.argv[1], num_neighbours=15, N=64)
gen = SDP(
StupidGenerator(depth=4)
)
disc = SDP(
LocalDiscriminator(depth=4)
)
training = AngleGANTraining(
gen, disc, data,
batch_size=16,
max_epochs=1000,
#optimizer=DiffMod,
device="cuda:0",
network_name="distance-gan/triangle-offset-local-combined-no-off-all-64",
verbose=True,
report_interval=10
)
final_net = training.train()
mask[:, None].float()).mean(dim=1).sum() / msum
return loss
if __name__ == "__main__":
num_neighbours = 15
drop = 0.1 if len(sys.argv) < 4 else float(sys.argv[3])
name = f"materialized-drop-{drop}-1"
data = FoldNet(sys.argv[1], num_neighbours=num_neighbours, pass_mask=True)
valid_data = FoldNet(sys.argv[2],
num_neighbours=num_neighbours,
pass_mask=True)
net = SDP(
MaterializedAttentionDistancePredictor(pair_depth=10,
seq_depth=2,
size=64,
attention_size=16,
value_size=64,
drop=drop))
training = SupervisedTraining(
net,
data,
valid_data, [TotalLoss()],
batch_size=4,
max_epochs=1000,
optimizer=lambda x: torch.optim.Adam(x, lr=1e-4),
device="cuda:0",
network_name=f"fold/{name}",
valid_callback=valid_callback).load()
final_net = training.train()
#self.optimizer.param_groups[0]["lr"] = learning_rate
if __name__ == "__main__":
data = CondTransformerNet(sys.argv[1], num_neighbours=15, n_backrub=20)
valid_data = CondTransformerNet(
sys.argv[2], num_neighbours=15,
n_backrub=0) # Validation with out augmentation
net = SDP(
ConditionalPrestructuredTransformer(
4,
128,
9,
attention_size=128,
heads=8,
connected=attention_connected,
sequence=True,
mlp_depth=2,
depth=9,
batch_norm=True,
relative=DistanceRelativeStructure,
local=False #True
))
training = ConditionalStructuredTransformerTraining(
net,
data,
valid_data,
[MaskedLoss()],
batch_size=32,
max_epochs=1000,
optimizer=lambda x: torch.optim.Adam(x), # LR scheduled
mask[:, None].float()).mean(dim=1).sum() / msum
return loss
if __name__ == "__main__":
num_neighbours = 15
drop = 0.5 if len(sys.argv) < 4 else float(sys.argv[3])
name = f"checkpointed-drop-{drop}-3"
data = FoldNet(sys.argv[1], num_neighbours=num_neighbours, pass_mask=True)
valid_data = FoldNet(sys.argv[2],
num_neighbours=num_neighbours,
pass_mask=True)
net = SDP(
CheckpointAttentionDistancePredictor(pair_depth=16,
seq_depth=2,
size=128,
attention_size=32,
value_size=16,
drop=drop,
split=16))
training = SupervisedTraining(
net,
data,
valid_data, [TotalLoss()],
batch_size=16,
accumulate=None,
max_epochs=1000,
optimizer=lambda x: torch.optim.Adam(x, lr=1e-3),
device="cuda:0",
num_workers=12,
network_name=f"fold/{name}",
valid_callback=valid_callback).load()
trn.writer.add_figure("heat expected", fig, trn.step_id)
plt.close("all")
trn.writer.add_scalar("size", float(ter.shape[0]), trn.step_id)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(angles[:, 1].numpy() % 6.3, angles[:, 2].numpy() % 6.3)
ax.scatter(ang[:, 1].numpy() % 6.3, ang[:, 2].numpy() % 6.3)
trn.writer.add_figure("rama", fig, trn.step_id)
plt.close("all")
if __name__ == "__main__":
data = GANNet(sys.argv[1])
valid_data = GANNet(sys.argv[2])
net = SDP(Recovery(128, 10, heads=8, depth=3, neighbours=15))
training = SupervisedTraining(
net,
data,
valid_data,
[AngleMSE(), StochasticRGNLoss(1000, relative=True)],
batch_size=8,
max_epochs=1000,
optimizer=lambda x: torch.optim.Adam(x, lr=1e-4),
device="cuda:0",
network_name="recovery",
valid_callback=valid_callback).load()
final_net = training.train()
if __name__ == "__main__":
num_neighbours = 15
drop = 0.1 if len(sys.argv) < 4 else float(sys.argv[3])
name = f"iterative-drop-{drop}-1"
data = DoubleFoldNet(sys.argv[1],
num_neighbours=num_neighbours,
pass_mask=True)
valid_data = DoubleFoldNet(sys.argv[2],
num_neighbours=num_neighbours,
pass_mask=True)
net = SDP(
IterativeAttentionDistancePredictor(pair_depth=4,
seq_depth=4,
size=256,
attention_size=64,
value_size=16,
drop=drop,
iterations=20))
training = SupervisedTraining(
net,
data,
valid_data, [TotalLoss(), TotalLoss()],
batch_size=4,
max_epochs=1000,
optimizer=lambda x: torch.optim.Adam(x, lr=1e-3),
device="cuda:0",
network_name=f"fold/{name}-1",
valid_callback=valid_callback)
final_net = training.train()
mask[:, None].float()).mean(dim=1).sum() / msum
return loss
if __name__ == "__main__":
num_neighbours = 15
drop = 0.5 if len(sys.argv) < 4 else float(sys.argv[3])
name = f"mixed-drop-{drop}-1"
data = FoldNet(sys.argv[1], num_neighbours=num_neighbours, pass_mask=True)
valid_data = FoldNet(sys.argv[2],
num_neighbours=num_neighbours,
pass_mask=True)
net = SDP(
MixedDistancePredictor(pair_depth=5,
seq_depth=2,
size=128,
attention_size=32,
value_size=16,
drop=drop,
split=5))
training = SupervisedTraining(
net,
data,
valid_data, [TotalLoss()],
batch_size=16,
accumulate=None,
max_epochs=1000,
optimizer=lambda x: torch.optim.Adam(x, lr=1e-3),
device="cuda:0",
num_workers=12,
network_name=f"fold/{name}",
valid_callback=valid_callback)
step_num = torch.tensor(float(self.step_id + 1))
learning_rate *= min(torch.pow(step_num, -0.5),
step_num * torch.pow(torch.tensor(4000.0), -1.5))
self.optimizer.param_groups[0]["lr"] = learning_rate
if __name__ == "__main__":
data = CondTransformerNet(sys.argv[1], num_neighbours=15, n_backrub=20)
valid_data = CondTransformerNet(
sys.argv[2], num_neighbours=15,
n_backrub=0) # Validation with out augmentation
net = SDP(
ConditionalStructuredTransformer(15,
128,
3,
attention_size=128,
heads=8,
mlp_depth=2,
depth=9,
batch_norm=True,
relative=DistanceRelativeStructure))
training = ConditionalStructuredTransformerTraining(
net,
data,
valid_data,
[MaskedLoss()],
batch_size=32,
max_epochs=1000,
optimizer=lambda x: torch.optim.Adam(x), # LR scheduled
device="cuda:0",
network_name=
"cond-structured-transformer/15-9-drop-10-rub-10-pssm-bs-32-weak-weighted-corrupted-large",
offset = offset.cumsum(dim=-1)
struc = struc + offset - struc.mean(dim=-1, keepdim=True)
if flip:
count = random.randrange(1, 10)
seq = seq.roll(count, dims=2)
else:
seq = (seq + torch.rand_like(seq)).clamp(0, 1)
return (struc, seq)
if __name__ == "__main__":
data = EBMNet(sys.argv[1], num_neighbours=15, N=100)
net = SDP(
MaterializedEnergy(pair_depth=4,
size=64,
value_size=16,
kernel_size=1,
drop=0.2,
full=True))
integrator = AugmentedLangevin(rate=(5000.0, 50.0),
noise=(1.0, 0.1),
steps=100,
transform_interval=50,
transform=transform,
max_norm=None,
clamp=(None, (0, 1)))
training = EBMTraining(
net,
data,
batch_size=12,
decay=1.0,
super().__init__()
self.net = net
def forward(self, *args):
inputs = []
for arg in args:
if isinstance(arg, PackedTensor):
inputs.append(arg.tensor)
else:
inputs.append(arg)
return self.net(*inputs)
if __name__ == "__main__":
data = EBMNet(sys.argv[1], num_neighbours=15)
net = SDP(SequenceEBM(51, 20, neighbours=15))
sampler = SDP(SequenceSampler(51, 20, neighbours=15))
training = EBMTraining(net,
sampler,
data,
batch_size=32,
sampler_steps=20,
n_sampler=100,
decay=0.0,
max_epochs=1000,
buffer_probability=0.0,
buffer_size=10000,
sampler_wrapper=Wrapper,
optimizer_kwargs={"lr": 1e-4},
device="cuda:0",
network_name="sequence-ebm/rl-sampler-full-8",
self.predict = nn.Linear(128, 1)
def forward(self, inputs):
inputs = torch.cat(inputs, dim=1)
out = self.preprocess(inputs)
for block in self.blocks:
out = out + block(out)
out = func.avg_pool2d(out, 2)
out = func.adaptive_avg_pool2d(out, 1).view(-1, 128)
out = self.predict(func.dropout(out, 0.5))
return out
if __name__ == "__main__":
data = GANNet(sys.argv[1], num_neighbours=15, N=128)
gen = SDP(
MaskedGenerator(data, depth=5)
)
disc = SDP(
MaskedDiscriminator(depth=5)
)
training = AngleGANTraining(
gen, disc, data,
batch_size=16,
max_epochs=1000,
#optimizer=DiffMod,
device="cuda:0",
network_name="distance-gan/maskenschlacht-128-i-am-f*****g-stupid-no-modulation-1",
verbose=True,
report_interval=10
)
final_net = training.train()
super().__init__()
self.net = net
def forward(self, *args):
inputs = []
for arg in args:
if isinstance(arg, PackedTensor):
inputs.append(arg.tensor)
else:
inputs.append(arg)
return self.net(*inputs)
if __name__ == "__main__":
data = EBMNet(sys.argv[1], num_neighbours=15)
net = SDP(SequenceEBM(51, 20, neighbours=15))
integrator = IndependentSampler(steps=20, scale=10, rate=1, noise=0.05)
training = EBMTraining(net,
data,
batch_size=32,
max_epochs=1000,
integrator=integrator,
buffer_probability=0.95,
decay=1.0,
buffer_size=10000,
optimizer_kwargs={"lr": 1e-4},
device="cuda:0",
network_name="sequence-ebm/independent-sampler-26",
verbose=True)
final_net = training.train()
ax = fig.add_subplot(111)
ax.scatter(angles[:, 1].numpy() % 6.3, angles[:, 2].numpy() % 6.3)
ax.scatter(mode_angles[:, 1].numpy() % 6.3,
mode_angles[:, 2].numpy() % 6.3)
trn.writer.add_figure("rama mode", fig, trn.step_id)
if __name__ == "__main__":
# with torch.autograd.detect_anomaly():
data = FragmentNet(sys.argv[1], radius=8)
valid_data = FragmentNet(sys.argv[2], radius=8)
net = SDP(
ProteinTransformer(6,
128,
10,
heads=8,
depth=6,
neighbours=15,
mix=8,
schedule=2))
training = SupervisedTraining(
net,
data,
valid_data, [MixtureOfVonMisesLoss()],
batch_size=16,
max_epochs=1000,
optimizer=lambda x: torch.optim.Adam(x, lr=1e-4, weight_decay=1e-4),
device="cuda:0",
network_name="autoregressive/scheduled-test-fixed-1/another-4",
valid_callback=valid_callback).load()
final_net = training.train()
offset = offset.cumsum(dim=-1)
struc = struc + offset - struc.mean(dim=-1, keepdim=True)
if flip:
count = random.randrange(1, 10)
seq = seq.roll(count, dims=2)
else:
seq = (seq + torch.rand_like(seq)).clamp(0, 1)
return (struc, seq)
if __name__ == "__main__":
data = EBMNet(sys.argv[1], num_neighbours=15, N=64)
net = SDP(
MaterializedEnergy(pair_depth=4,
size=128,
value_size=16,
heads=8,
kernel_size=1,
drop=0.1,
full=True))
integrator = AugmentedLangevin(rate=(5000.0, 50.0),
noise=(0.1, 0.1),
steps=50,
transform_interval=50,
transform=transform,
max_norm=None,
clamp=(None, (0, 1)))
training = EBMTraining(net,
data,
batch_size=8,
decay=1.0,
max_epochs=5000,
])
self.predict = nn.Linear(128, 1)
def forward(self, inputs):
inputs = torch.cat(inputs, dim=1)
out = self.preprocess(inputs)
for block in self.blocks:
out = out + block(out)
out = func.avg_pool2d(out, 2)
out = func.adaptive_avg_pool2d(out, 1).view(-1, 128)
out = self.predict(func.dropout(out, 0.5))
return out
if __name__ == "__main__":
data = GANNet(sys.argv[1], num_neighbours=15, N=256)
gen = SDP(StupidGenerator())
disc = SDP(Discriminator(depth=6))
training = AngleGANTraining(
gen,
disc,
data,
batch_size=8,
max_epochs=1000,
#optimizer=DiffMod,
device="cuda:0",
network_name="distance-gan/full-atom-inflate",
verbose=True,
report_interval=10)
final_net = training.train()
self.predict = nn.Linear(128, 1)
def forward(self, inputs):
inputs = torch.cat(inputs, dim=1)
out = self.preprocess(inputs)
for block in self.blocks:
out = out + block(out)
out = func.avg_pool2d(out, 2)
out = func.adaptive_avg_pool2d(out, 1).view(-1, 128)
out = self.predict(func.dropout(out, 0.5))
return out
if __name__ == "__main__":
data = GANNet(sys.argv[1], num_neighbours=15, N=64)
gen = SDP(
OrientationGenerator()
)
disc = SDP(
Discriminator()
)
training = AngleGANTraining(
gen, disc, data,
batch_size=16,
max_epochs=2000,
#optimizer=DiffMod,
device="cuda:0",
network_name="distance-gan/experiment-orientation-toss-sin-fix",
verbose=True,
report_interval=10,
)
final_net = training.train()
super().__init__()
self.net = net
def forward(self, *args):
inputs = []
for arg in args:
if isinstance(arg, PackedTensor):
inputs.append(arg.tensor)
else:
inputs.append(arg)
return self.net(*inputs)
if __name__ == "__main__":
data = EBMNet(sys.argv[1], num_neighbours=15)
net = SDP(StupidDistanceEnergy(depth=8, shape=64))
integrator = PackedLangevin(rate=10.0,
noise=0.01,
steps=5,
max_norm=None,
clamp=None)
training = EBMTraining(
net,
data,
batch_size=64,
decay=1.0,
max_epochs=1000,
integrator=integrator,
buffer_probability=0.99,
buffer_size=100000,
optimizer=DiffMod,