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fix UNet training code #91

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fix UNet training code #91

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6d5488c
fix indentation
till-m Oct 13, 2025
08a769d
fix kwargs
till-m Oct 13, 2025
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44 changes: 19 additions & 25 deletions pdebench/models/unet/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -88,16 +88,10 @@ def run_training(

train_data = UNetDatasetMult(
flnm,
reduced_resolution=reduced_resolution,
reduced_resolution_t=reduced_resolution_t,
reduced_batch=reduced_batch,
saved_folder=base_path,
)
val_data = UNetDatasetMult(
flnm,
reduced_resolution=reduced_resolution,
reduced_resolution_t=reduced_resolution_t,
reduced_batch=reduced_batch,
if_test=True,
saved_folder=base_path,
)
Expand Down Expand Up @@ -312,19 +306,19 @@ def run_training(
loss.backward()
optimizer.step()

if training_type in ["single"]:
x = xx[..., 0, :]
y = yy[..., t_train - 1 : t_train, :]
pred = model(x.permute([0, 2, 1])).permute([0, 2, 1])
_batch = yy.size(0)
loss += loss_fn(pred.reshape(_batch, -1), y.reshape(_batch, -1))
if training_type in ["single"]:
x = xx[..., 0, :]
y = yy[..., t_train - 1 : t_train, :]
pred = model(x.permute([0, 2, 1])).permute([0, 2, 1])
_batch = yy.size(0)
loss += loss_fn(pred.reshape(_batch, -1), y.reshape(_batch, -1))

train_l2_step += loss.item()
train_l2_full += loss.item()
train_l2_step += loss.item()
train_l2_full += loss.item()

optimizer.zero_grad()
loss.backward()
optimizer.step()
optimizer.zero_grad()
loss.backward()
optimizer.step()

if ep % model_update == 0:
val_l2_step = 0
Expand Down Expand Up @@ -370,15 +364,15 @@ def run_training(
_pred.reshape(_batch, -1), _yy.reshape(_batch, -1)
).item()

if training_type in ["single"]:
x = xx[..., 0, :]
y = yy[..., t_train - 1 : t_train, :]
pred = model(x.permute([0, 2, 1])).permute([0, 2, 1])
_batch = yy.size(0)
loss += loss_fn(pred.reshape(_batch, -1), y.reshape(_batch, -1))
if training_type in ["single"]:
x = xx[..., 0, :]
y = yy[..., t_train - 1 : t_train, :]
pred = model(x.permute([0, 2, 1])).permute([0, 2, 1])
_batch = yy.size(0)
loss += loss_fn(pred.reshape(_batch, -1), y.reshape(_batch, -1))

val_l2_step += loss.item()
val_l2_full += loss.item()
val_l2_step += loss.item()
val_l2_full += loss.item()

if val_l2_full < loss_val_min:
loss_val_min = val_l2_full
Expand Down
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