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partially converted to M2S (multi inputs)

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wassname
2022-11-22 16:42:58 +08:00
parent 00ae1d8b8f
commit 9072613d03
7 changed files with 647 additions and 38 deletions
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experiments/forecast.py
+13 -12
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@@ -139,16 +139,16 @@ def train(model: nn.Module,
model.train()
for it, data in enumerate(train_loader):
optimizer.zero_grad()
x, y, x_time, y_time = map(to_tensor, data)
forecast = model(x, x_time, y_time)
data2 = list(map(to_tensor, data))
context_past_x, context_y, query_past_x, query_y, context_time, query_time = data2
forecast = model(*data2)
if isinstance(forecast, tuple):
# for models which require reconstruction + forecast loss
loss = training_loss_fn(forecast[0], x) + \
training_loss_fn(forecast[1], y)
loss = training_loss_fn(forecast[0], context_y) + \
training_loss_fn(forecast[1], query_y)
else:
loss = training_loss_fn(forecast, y)
loss = training_loss_fn(forecast, query_y)
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), clip)
optimizer.step()
@@ -188,22 +188,23 @@ def validate(model: nn.Module,
inps = []
total_loss = []
for it, data in enumerate(loader):
x, y, x_time, y_time = map(to_tensor, data)
data2 = list(map(to_tensor, data))
context_past_x, context_y, query_past_x, query_y, context_time, query_time = data2
if x.shape[0] == 1:
if context_past_x.shape[0] == 1:
# skip final batch if batch_size == 1
# due to bug in torch.linalg.solve which raises error when batch_size == 1
continue
forecast = model(x, x_time, y_time)
forecast = model(*data2)
if report_metrics:
preds.append(forecast)
trues.append(y)
trues.append(query_y)
if save_path is not None:
inps.append(x)
inps.append(context_y)
else:
loss = loss_fn(forecast, y, reduction='none')
loss = loss_fn(forecast, query_y, reduction='none')
total_loss.append(loss)
if report_metrics: