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extract time step from input data #2

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extract time step from input data #2

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11 changes: 5 additions & 6 deletions deepflame/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,15 +36,14 @@ def __init__(

# Load Dataset
self.data = torch.tensor(np.load(data_path))
# DIMENSION: n * ((T, P, Y[ns], H[ns])_in, (T, P, Y[ns], H[ns])_out)
self.time_step = 1e-7 # TODO: load from dataset
# DIMENSION: n * ((T, P, Y[ns], H[ns])_in, (T, P, Y[ns], H[ns])_out, time_step)
self.formation_enthalpies = torch.tensor(np.load(formation_enthalpies_path))
assert (
self.formation_enthalpies.shape[0] == self.n_species
), "n_species in dataset does not match formation_enthalpies"
# self.dims = (1, 2 * (2 + 2 * self.n_species))
# self.dims = (1, 2 * (2 + 2 * self.n_species) + 1)
assert (
self.data.shape[1] == 4 + 4 * self.n_species
self.data.shape[1] == 4 + 4 * self.n_species + 1
), "n_species in dataset does not match config file"
self.indices = (
1, # T_in
Expand All @@ -55,8 +54,9 @@ def __init__(
1, # P_out
self.n_species, # Y_label[ns]
self.n_species, # H_out[ns]
1, # dt
)
(T_in, P_in, Y_in, H_in, T_label, P_label, Y_label, H_label) = self.data.split(
(T_in, P_in, Y_in, H_in, T_label, P_label, Y_label, H_label, time_step) = self.data.split(
self.indices, dim=1
)
# The mass fraction calculated by cantera is not guaranteed to be positive.
Expand All @@ -83,7 +83,6 @@ def set_norm_stats(self, key: str, value=None):

set_stats(self, "lmbda", lmbda)
set_stats(self, "formation_enthalpies")
set_stats(self, "time_step")
set_norm_stats(self, "T_in", T_in)
set_norm_stats(self, "P_in", P_in)
self.Y_t_in = boxcox(Y_in, lmbda)
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4 changes: 2 additions & 2 deletions deepflame/inference.py
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Expand Up @@ -103,9 +103,9 @@ def load_lightning_model(
# Currently `inference()` is called directly from C++,
# so we have to explicitly put the model in the scope of this file.
# TODO: fix this
module: torch.nn.Module = load_lightning_model().eval()
module: torch.nn.Module = load_lightning_model().eval() # TODO: load model checkpoint from config
n_species: int = module.model.formation_enthalpies.shape[0]
time_step: float = module.model.time_step
time_step: float = settings["inferenceDeltaTime"]
lmbda: float = module.model.lmbda


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3 changes: 2 additions & 1 deletion deepflame/trainer.py
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Expand Up @@ -39,6 +39,7 @@ def training_step(self, batch, batch_idx):
P_label,
Y_label,
H_label,
time_step,
) = batch
Y_t_in = boxcox(Y_in, self.model.lmbda)
Y_t_label = boxcox(Y_label, self.model.lmbda)
Expand All @@ -60,7 +61,7 @@ def training_step(self, batch, batch_idx):
)

scale = (
self.model.time_step * 1e13
time_step * 1e13
) # prevent overflow introduced by large H and small time_step

loss3 = (
Expand Down