Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Possible improvements #7

Open
wants to merge 5 commits into
base: main
Choose a base branch
from
Open

Possible improvements #7

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
06d31c6
switch scheduler_step with scheduler_gamma
MaxGhi8 Nov 5, 2024
643b3ce
consistently chenge the relative L^1 norm
MaxGhi8 Nov 5, 2024
92636a3
consistently change the counts of the number of parameters to handle …
MaxGhi8 Nov 5, 2024
53a3833
update unsaved files
MaxGhi8 Nov 5, 2024
e731659
forgiven parentheses
MaxGhi8 Nov 5, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion CNO1d_vanilla_torch_version/TraincCNO.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -91,7 +91,7 @@ def main():
test_relative_l2 = 0.0
for step, (input_batch, output_batch) in enumerate(testing_set):
output_pred_batch = cno(input_batch)
loss_f = (torch.mean((abs(output_pred_batch - output_batch))) / torch.mean(abs(output_batch))) ** 0.5 * 100
loss_f = torch.mean(torch.sum(abs(output_pred_batch - output_batch), dim=(1,2)) / torch.sum(abs(output_batch), dim=(1,2))) ** 0.5 * 100
test_relative_l2 += loss_f.item()
test_relative_l2 /= len(testing_set)

Expand Down
2 changes: 1 addition & 1 deletion CNO2d_original_version/CNOModule.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -548,7 +548,7 @@ def print_size(self):
nbytes = 0

for param in self.parameters():
nparams += param.numel()
nparams += param.numel()*(param.is_complex() + 1)
nbytes += param.data.element_size() * param.numel()

print(f'Total number of model parameters: {nparams} (~{format_tensor_size(nbytes)})')
Expand Down
4 changes: 2 additions & 2 deletions CNO2d_original_version/TrainCNO.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -183,7 +183,7 @@
output_pred_batch[input_batch==1] = 1
output_batch[input_batch==1] = 1

loss_f = torch.mean(abs(output_pred_batch - output_batch)) / torch.mean(abs(output_batch)) * 100
loss_f = torch.mean(torch.sum(abs(output_pred_batch - output_batch), dim=(1,2)) / torch.sum(abs(output_batch), dim=(1,2))) * 100
test_relative_l2 += loss_f.item()
test_relative_l2 /= len(val_loader)

Expand All @@ -196,7 +196,7 @@
output_pred_batch[input_batch==1] = 1
output_batch[input_batch==1] = 1

loss_f = torch.mean(abs(output_pred_batch - output_batch)) / torch.mean(abs(output_batch)) * 100
loss_f = torch.mean(torch.sum(abs(output_pred_batch - output_batch), dim=(1,2)) / torch.sum(abs(output_batch), dim=(1,2))) * 100
train_relative_l2 += loss_f.item()
train_relative_l2 /= len(train_loader)

Expand Down
2 changes: 1 addition & 1 deletion CNO2d_time_dependent_&_foundation_model/CNO_timeModule_CIN.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1327,7 +1327,7 @@ def print_size(self):
nbytes = 0

for param in self.parameters():
nparams += param.numel()
nparams += param.numel()*(param.is_complex() + 1)
nbytes += param.data.element_size() * param.numel()

print(f'Total number of model parameters: {nparams} (~{format_tensor_size(nbytes)})')
Expand Down
2 changes: 1 addition & 1 deletion CNO2d_vanilla_torch_version/TraincCNO.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -85,7 +85,7 @@ def main():
test_relative_l2 = 0.0
for step, (input_batch, output_batch) in enumerate(testing_set):
output_pred_batch = cno(input_batch)
loss_f = (torch.mean((abs(output_pred_batch - output_batch))) / torch.mean(abs(output_batch))) ** 0.5 * 100
loss_f = torch.mean(torch.sum(abs(output_pred_batch - output_batch), dim=(1,2)) / torch.sum(abs(output_batch), dim=(1,2))) ** 0.5 * 100
test_relative_l2 += loss_f.item()
test_relative_l2 /= len(testing_set)

Expand Down
6 changes: 3 additions & 3 deletions _OtherModels/BaselinesModules.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -57,7 +57,7 @@ def print_size(self):
nbytes = 0

for param in self.parameters():
nparams += param.numel()
nparams += param.numel()*(param.is_complex() + 1)
nbytes += param.data.element_size() * param.numel()

print(f'Total number of model parameters: {nparams} (~{format_tensor_size(nbytes)})')
Expand Down Expand Up @@ -229,7 +229,7 @@ def print_size(self):
nbytes = 0

for param in self.parameters():
nparams += param.numel()
nparams += param.numel()*(param.is_complex() + 1)
nbytes += param.data.element_size() * param.numel()

print(f'Total number of model parameters: {nparams} (~{format_tensor_size(nbytes)})')
Expand Down Expand Up @@ -275,7 +275,7 @@ def print_size(self):
nbytes = 0

for param in self.parameters():
nparams += param.numel()
nparams += param.numel()*(param.is_complex() + 1)
nbytes += param.data.element_size() * param.numel()

print(f'Total number of model parameters: {nparams} (~{format_tensor_size(nbytes)})')
Expand Down
4 changes: 2 additions & 2 deletions _OtherModels/CNNModules.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -188,7 +188,7 @@ def print_size(self):
nbytes = 0

for param in self.parameters():
nparams += param.numel()
nparams += param.numel()*(param.is_complex() + 1)
nbytes += param.data.element_size() * param.numel()

print(f'Total number of model parameters: {nparams} (~{format_tensor_size(nbytes)})')
Expand Down Expand Up @@ -298,7 +298,7 @@ def print_size(self):
nbytes = 0

for param in self.parameters():
nparams += param.numel()
nparams += param.numel()*(param.is_complex() + 1)
nbytes += param.data.element_size() * param.numel()

print(f'Total number of model parameters: {nparams} (~{format_tensor_size(nbytes)})')
Expand Down
4 changes: 2 additions & 2 deletions _OtherModels/DeepONetModules.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -104,7 +104,7 @@ def print_size(self):
nbytes = 0

for param in self.parameters():
nparams += param.numel()
nparams += param.numel()*(param.is_complex() + 1)
nbytes += param.data.element_size() * param.numel()

print(f'Total number of model parameters: {nparams} (~{format_tensor_size(nbytes)})')
Expand Down Expand Up @@ -145,7 +145,7 @@ def print_size(self):
nbytes = 0

for param in self.parameters():
nparams += param.numel()
nparams += param.numel()*(param.is_complex() + 1)
nbytes += param.data.element_size() * param.numel()

print(f'Total number of model parameters: {nparams} (~{format_tensor_size(nbytes)})')
Expand Down
4 changes: 2 additions & 2 deletions _OtherModels/FNOModules.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -117,7 +117,7 @@ def print_size(self):
nbytes = 0

for param in self.parameters():
nparams += param.numel()
nparams += param.numel()*(param.is_complex() + 1)
nbytes += param.data.element_size() * param.numel()

print(f'Total number of model parameters: {nparams} (~{format_tensor_size(nbytes)})')
Expand Down Expand Up @@ -264,7 +264,7 @@ def print_size(self):
nbytes = 0

for param in self.parameters():
nparams += param.numel()
nparams += param.numel()*(param.is_complex() + 1)
nbytes += param.data.element_size() * param.numel()

print(f'Total number of model parameters: {nparams} (~{format_tensor_size(nbytes)})')
Expand Down
2 changes: 1 addition & 1 deletion _OtherModels/TrainDON.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -154,7 +154,7 @@
if which_example == "airfoil":
output_pred_batch[input_batch == 1] = 1
output_batch[input_batch == 1] = 1
loss_f = torch.mean(abs(output_pred_batch - output_batch)) / torch.mean(abs(output_batch)) * 100
loss_f = torch.mean(torch.sum(abs(output_pred_batch - output_batch), dim=(1,2)) / torch.sum(abs(output_batch), dim=(1,2))) * 100
test_relative_l2 += loss_f.item()
test_relative_l2 /= len(test_loader)

Expand Down
8 changes: 4 additions & 4 deletions _OtherModels/TrainFNO.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -15,8 +15,8 @@
training_properties = {
"learning_rate": 0.001,
"weight_decay": 1e-8,
"scheduler_step": 0.97,
"scheduler_gamma": 10,
"scheduler_step": 10,
"scheduler_gamma": 0.97,
"epochs": 1000,
"batch_size": 16,
"exp": 1,
Expand Down Expand Up @@ -155,7 +155,7 @@
output_pred_batch[input_batch==1] = 1
output_batch[input_batch==1] = 1

loss_f = torch.mean(abs(output_pred_batch - output_batch)) / torch.mean(abs(output_batch)) * 100
loss_f = torch.mean(torch.sum(abs(output_pred_batch - output_batch), dim=(1,2)) / torch.sum(abs(output_batch), dim=(1,2))) * 100
test_relative_l2 += loss_f.item()
test_relative_l2 /= len(test_loader)

Expand All @@ -168,7 +168,7 @@
output_pred_batch[input_batch==1] = 1
output_batch[input_batch==1] = 1

loss_f = torch.mean(abs(output_pred_batch - output_batch)) / torch.mean(abs(output_batch)) * 100
loss_f = torch.mean(torch.sum(abs(output_pred_batch - output_batch), dim=(1,2)) / torch.sum(abs(output_batch), dim=(1,2))) * 100
train_relative_l2 += loss_f.item()
train_relative_l2 /= len(train_loader)

Expand Down
2 changes: 1 addition & 1 deletion _OtherModels/TrainResNet.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -148,7 +148,7 @@
output_pred_batch[input_batch == 1] = 1
output_batch[input_batch == 1] = 1

loss_f = torch.mean(abs(output_pred_batch - output_batch)) / torch.mean(abs(output_batch)) * 100
loss_f = torch.mean(torch.sum(abs(output_pred_batch - output_batch), dim=(1,2)) / torch.sum(abs(output_batch), dim=(1,2))) * 100
test_relative_l2 += loss_f.item()
test_relative_l2 /= len(test_loader)

Expand Down
2 changes: 1 addition & 1 deletion _OtherModels/TrainUNet.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -147,7 +147,7 @@
output_pred_batch[input_batch == 1] = 1
output_batch[input_batch == 1] = 1

loss_f = torch.mean(abs(output_pred_batch - output_batch)) / torch.mean(abs(output_batch)) * 100
loss_f = torch.mean(torch.sum(abs(output_pred_batch - output_batch), dim=(1,2)) / torch.sum(abs(output_batch), dim=(1,2))) * 100
test_relative_l2 += loss_f.item()
test_relative_l2 /= len(test_loader)

Expand Down