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Pytorch-Lightning #21

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Pytorch-Lightning #21

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e250bce
Converting metnet to pytorch-lightning
Feb 24, 2022
e8de4bd
adding links to swedish radar dataset
Feb 24, 2022
db05b09
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Feb 25, 2022
3d3ea48
Added conditional lead time solution
Feb 25, 2022
2e0b3cb
Merge branch 'lightning' of https://github.com/ValterFallenius/metnet…
Feb 25, 2022
bf31068
adding LSTM
Feb 26, 2022
d40557b
Adding wandb module
Mar 1, 2022
d4b5e5a
trying to train with smallest possible network
ValterFallenius Mar 2, 2022
ffbb020
searching for bugs
ValterFallenius Mar 4, 2022
ad9117f
bug searching with plots
ValterFallenius Mar 5, 2022
22c2f11
Bug fix positional encoding
ValterFallenius Mar 18, 2022
90c7192
new dataloader
ValterFallenius Mar 24, 2022
16bf08c
weekly backup, new sampling techinque, probabillity plots etc
ValterFallenius Apr 11, 2022
e13c39e
Bugfix with spatial aggregation, drop early return
ValterFallenius Apr 12, 2022
89bcfd9
Weekly backup
ValterFallenius Apr 21, 2022
598fa96
Implementing moving geographic point of reference
ValterFallenius Apr 26, 2022
ec77189
Adding blog post about my MetNet implementation
ValterFallenius Mar 14, 2024
24a9ddf
Full text master thesis
ValterFallenius Mar 14, 2024
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@ValterFallenius
ValterFallenius committed Apr 21, 2022
commit 89bcfd986cbd3ad591fd23fbc53943e994afd96c
34 changes: 22 additions & 12 deletions MetNet_lightning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,46 +8,56 @@
from pytorch_lightning.callbacks import DeviceStatsMonitor
from pytorch_lightning import seed_everything
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks import ModelCheckpoint
import time

wandb.login()

model = MetNetPylight(
hidden_dim=256, #384 original paper
forecast_steps=8, #240 original paper
forecast_steps=1, #240 original paper
input_channels=15, #46 original paper, hour/day/month = 3, lat/long/elevation = 3, GOES+MRMS = 40
output_channels=128, #512
input_size=112, # 112
n_samples = None, #None = All ~ 23000
num_workers = 4,
batch_size = 8,
learning_rate = 1e-2,
num_att_layers = 8,
num_att_layers = 4,
plot_every = None, #Plot every global_step
rain_step = 0.2,
momentum = 0.9,
att_heads=16,
keep_biggest = 0.15,
leadtime_spacing = 3, #1: 5 minutes, 3: 15 minutes
keep_biggest = 1,
leadtime_spacing = 12, #1: 5 minutes, 3: 15 minutes
)
#PATH_cp = "/proj/berzelius-2022-18/users/sm_valfa/metnet_pylight/metnet/epoch=430-step=22842.ckpt"
#PATH_cp = "/proj/berzelius-2022-18/users/sm_valfa/metnet_pylight/metnet/lit-wandb/jbd0j048/checkpoints/epoch=210-step=14558.ckpt"
#PATH_cp = "/proj/berzelius-2022-18/users/sm_valfa/metnet_pylight/metnet/8leads with agg.ckpt"
#PATH_cp = "/proj/berzelius-2022-18/users/sm_valfa/metnet_pylight/metnet/full_run_continue.ckpt"
#model = MetNetPylight.load_from_checkpoint(PATH_cp)
print(model)

#model.n_samples = 2000
model.testing = False
model.learning_rate = 1e-2
'''print(model)
print(model.forecast_steps)
print(model.input_channels)
print(model.output_channels)
print(model.n_samples)
print(model.file_name)
print(model.keep_biggest)
input()
model.keep_biggest = 0.9
model.n_samples = None'''
#model.printer = True
#model.plot_every = None
#MetNetPylight expects already preprocessed data. Can be change by uncommenting the preprocessing step.
#print(model)

#wandb.restore("/proj/berzelius-2022-18/users/sm_valfa/metnet_pylight/metnet/wandb/run-20220331_162434-21o2u2sj"
#wandb.init(run_id = "21o2u2sj",resume="must")
wandb_logger = WandbLogger(project="lit-wandb")

wandb_logger = WandbLogger(project="lit-wandb", log_model="all")
checkpoint_callback = ModelCheckpoint(monitor="validation/loss_epoch", mode="min", save_top_k=5)


trainer = pl.Trainer(num_sanity_val_steps=2, track_grad_norm = 2, max_epochs=1000, gpus=-1,log_every_n_steps=50, logger = wandb_logger,strategy="ddp", callbacks=[EarlyStopping(monitor="val_loss", mode="min")])
trainer = pl.Trainer(num_sanity_val_steps=2, track_grad_norm = 2, max_epochs=1000, gpus=-1,log_every_n_steps=50, logger = wandb_logger,strategy="ddp", callbacks=[checkpoint_callback])
start_time = time.time()

trainer.fit(model)
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5 changes: 4 additions & 1 deletion data_prep/metnet_dataloader.py
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Expand Up @@ -101,6 +101,7 @@ def __init__(self,ID, N=None, keep_biggest = 0.5, leadtime_spacing = 1, lead_tim
plt.title("resampling of leadtimes")
plt.show()'''


self.n_samples = len(self.file_names)

def __getitem__(self, index):
Expand All @@ -110,14 +111,16 @@ def __getitem__(self, index):
x = np.load(name_x)

y = np.load(name_y)

persistence = y[0]
y = y[self.leadtime_spacing-1::self.leadtime_spacing]

x = torch.from_numpy(x)
y = torch.from_numpy(y)
if self.ID == "train":

return x, y, self.rainy_leadtimes[index]
if self.ID == "test":
return x, y, persistence
return x, y

def __len__(self):
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