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Fixed small summary with model architectures until now

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This commit is contained in:
Victor Mylle
2023-11-30 21:53:35 +00:00
parent eba10c8f83
commit 120b6aa5bd
23 changed files with 402 additions and 185 deletions
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46
src/trainers/quantile_trainer.py
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@@ -60,6 +60,7 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
def __init__(
self,
model: torch.nn.Module,
input_dim: tuple,
optimizer: torch.optim.Optimizer,
data_processor: DataProcessor,
quantiles: list,
@@ -72,6 +73,7 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
criterion = PinballLoss(quantiles=quantiles)
super().__init__(
model=model,
input_dim=input_dim,
optimizer=optimizer,
criterion=criterion,
data_processor=data_processor,
@@ -192,7 +194,10 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
prev_features = prev_features.to(self.device)
targets = targets.to(self.device)
initial_sequence = prev_features[:, :96]
if len(list(prev_features.shape)) == 2:
initial_sequence = prev_features[:, :96]
else:
initial_sequence = prev_features[:, :, 0]
target_full = targets[:, 0].unsqueeze(1) # (batch_size, 1)
with torch.no_grad():
@@ -206,22 +211,37 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
predictions_full = new_predictions_full.unsqueeze(1)
for i in range(sequence_length - 1):
new_features = torch.cat(
(prev_features[:, 1:96], samples), dim=1
) # (batch_size, 96)
if len(list(prev_features.shape)) == 2:
new_features = torch.cat(
(prev_features[:, 1:96], samples), dim=1
) # (batch_size, 96)
new_features = new_features.float()
new_features = new_features.float()
other_features, new_targets = dataset.get_batch_autoregressive(
np.array(idx_batch) + i + 1
) # (batch_size, new_features)
other_features, new_targets = dataset.get_batch_autoregressive(
np.array(idx_batch) + i + 1
) # (batch_size, new_features)
if other_features is not None:
prev_features = torch.cat(
(new_features.to(self.device), other_features.to(self.device)), dim=1
) # (batch_size, 96 + new_features)
else:
prev_features = new_features
if other_features is not None:
prev_features = torch.cat(
(new_features.to(self.device), other_features.to(self.device)), dim=1
) # (batch_size, 96 + new_features)
else:
prev_features = new_features
other_features, new_targets = dataset.get_batch_autoregressive(
np.array(idx_batch) + i + 1
) # (batch_size, 1, new_features)
# change the other_features nrv based on the samples
other_features[:, 0, 0] = samples.squeeze(-1)
# make sure on same device
other_features = other_features.to(self.device)
prev_features = prev_features.to(self.device)
prev_features = torch.cat(
(prev_features[:, 1:, :], other_features), dim=1
) # (batch_size, 96, new_features)
target_full = torch.cat(
(target_full, new_targets.to(self.device)), dim=1