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Momentum Implemented #34

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Momentum Implemented #34

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33 changes: 22 additions & 11 deletions gapt/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -411,16 +411,23 @@ def forward(self, x: Tensor, labels: Tensor = None, z: Tensor = None):
if self.use_mask:
# unnormalize the last jet label - the normalized # of particles per jet
# (between 1/``num_particles`` and 1) - to between 0 and ``num_particles`` - 1
num_jet_particles = (labels[:, -1] * self.num_particles).int() - 1
#num_jet_particles = (labels[:, -1] * self.num_particles).int() - 1
momentum_jet_particles = labels[:, -2]
# sort the particles by the first noise feature per particle, and the first
# ``num_jet_particles`` particles receive a 1-mask, the rest 0.

#mask = (
# (x[:, :, 0].argsort(1).argsort(1) <= num_jet_particles.unsqueeze(1))
# .unsqueeze(2)
# .float()
#)
mask = (
(x[:, :, 0].argsort(1).argsort(1) <= num_jet_particles.unsqueeze(1))
(x[:, :, 0].argsort(1).argsort(1) <= momentum_jet_particles.unsqueeze(1))
.unsqueeze(2)
.float()
)
logging.debug(
f"x \n {x[:2, :, 0]} \n num particles \n {num_jet_particles[:2]} \n gen mask \n {mask[:2]}"
f"x \n {x[:2, :, 0]} \n num particles \n {momentum_jet_particles[:2]} \n gen mask \n {mask[:2]}"
)
else:
mask = None
Expand All @@ -429,17 +436,19 @@ def forward(self, x: Tensor, labels: Tensor = None, z: Tensor = None):
x = self.input_embedding(x)

# Concatenate global noise and # particles depending on conditioning
if self.n_normalized:
num_jet_particles = labels[:, -1]
else:
num_jet_particles += 1
#if self.n_normalized:
# num_jet_particles = labels[:, -1]
#else:
# num_jet_particles += 1
momentum_jet_particles = labels[:, -2]
# if self.noise_conditioning or self.n_conditioning:
# if self.noise_conditioning and self.n_conditioning:
# z = torch.cat((z, num_jet_particles.unsqueeze(1)), dim=1)
# elif self.n_conditioning:
# z = num_jet_particles.unsqueeze(1).float()
if self.n_conditioning:
z = torch.cat((z, num_jet_particles.unsqueeze(1)), dim=1)
#z = torch.cat((z, num_jet_particles.unsqueeze(1)), dim=1)
z = torch.cat((z, momentum_jet_particles.unsqueeze(1)), dim=1)
z = self.global_noise_net(z)

for sab in self.sabs:
Expand Down Expand Up @@ -580,10 +589,12 @@ def forward(self, x: Tensor, labels: Tensor = None):
# Use # particles for conditioning
if self.n_conditioning:
if self.n_normalized:
num_jet_particles = labels[:, -1]
#num_jet_particles = labels[:, -1]
momentum_jet_particles = labels[:, -2]
else:
num_jet_particles = (labels[:, -1] * self.num_particles).int()
z = torch.cat([z, num_jet_particles.unsqueeze(1).float()], dim=1)
#num_jet_particles = (labels[:, -1] * self.num_particles).int()
momentum_jet_particles = labels[:, -2]
z = torch.cat([z, momentum_jet_particles.unsqueeze(1).float()], dim=1)

z = self.cond_net(z)

Expand Down
2 changes: 1 addition & 1 deletion train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,7 @@ def main():
"particle_features": JetNet.all_particle_features
if args.mask
else JetNet.all_particle_features[:-1],
"jet_features": "num_particles"
"jet_features": ["pt","num_particles"]
if (args.clabels or args.mask_c or args.gapt_mask)
else None,
"particle_normalisation": particle_norm,
Expand Down