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minor_bug_fixes related to missing keys and variables for Darcy flow … #31

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minor_bug_fixes related to missing keys and variables for Darcy flow … #31

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1 change: 1 addition & 0 deletions configs/pretrain/Darcy-pretrain.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@ model:
modes2: [20, 20, 20, 20]
fc_dim: 128
act: gelu
pad_ratio: 0.0

train:
batchsize: 20
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6 changes: 4 additions & 2 deletions train_operator.py
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Expand Up @@ -87,7 +87,7 @@ def train_2d(args, config):
nx=data_config['nx'],
sub=data_config['sub'],
pde_sub=data_config['pde_sub'],
num=data_config['n_samples'],
num=data_config['n_sample'],
offset=data_config['offset'])
train_loader = DataLoader(dataset, batch_size=config['train']['batchsize'], shuffle=True)
model = FNO2d(modes1=config['model']['modes1'],
Expand Down Expand Up @@ -121,7 +121,9 @@ def train_2d(args, config):

# parse options
parser = ArgumentParser(description='Basic paser')
parser.add_argument('--config_path', type=str, help='Path to the configuration file')
parser.add_argument('--config_path', type=str,
default='configs/pretrain/Darcy-pretrain.yaml',
help='Path to the configuration file')
parser.add_argument('--log', action='store_true', help='Turn on the wandb')
args = parser.parse_args()

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10 changes: 5 additions & 5 deletions train_utils/train_2d.py
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Expand Up @@ -74,18 +74,18 @@ def train_2d_operator(model,
if data_weight > 0:
pred = model(data_ic).squeeze(dim=-1)
pred = pred * mollifier
data_loss = myloss(pred, y)
data_loss = myloss(pred, u)

a = x[..., 0]
a = data_ic[..., 0]
f_loss = darcy_loss(pred, a)

loss = data_weight * data_loss + f_weight * f_loss
loss.backward()
optimizer.step()

loss_dict['train_loss'] += loss.item() * y.shape[0]
loss_dict['f_loss'] += f_loss.item() * y.shape[0]
loss_dict['data_loss'] += data_loss.item() * y.shape[0]
loss_dict['train_loss'] += loss.item() * u.shape[0]
loss_dict['f_loss'] += f_loss.item() * u.shape[0]
loss_dict['data_loss'] += data_loss.item() * u.shape[0]

scheduler.step()
train_loss_val = loss_dict['train_loss'] / len(train_loader.dataset)
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