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# # # import os
# # # import pytorch_lightning as L
# # # from dataloader import AerialImageDataset
# # # from train5 import deeplabv3_encoder_decoder
# # # from torch.utils.data import DataLoader
# # # from torchvision.transforms import transforms
# # # import torch
# # # train_path = r"C:\Users\User\Downloads\Nishant\train"
# # # val_path = r"C:\Users\User\Downloads\Nishant\val"
# # # data_transform = transforms.Compose([
# # # transforms.Resize((512, 512)),
# # # transforms.ToTensor()
# # # ])
# # # train_dataset = AerialImageDataset(os.path.join(train_path, 'images'), os.path.join(train_path, 'masks'), transform=data_transform)
# # # val_dataset = AerialImageDataset(os.path.join(val_path, 'images'), os.path.join(val_path, 'masks'), transform=data_transform)
# # # train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True)
# # # val_loader = DataLoader(val_dataset, batch_size=2, shuffle=False)
# # # model = deeplabv3_encoder_decoder()
# # # # Adjust the refresh rate of the progress bar
# # # trainer = L.Trainer(max_epochs=100, progress_bar_refresh_rate=20) # Adjust the refresh rate as needed
# # # trainer.fit(model, train_loader, val_loader)
# # # torch.save(model.state_dict(), r"C:\Users\User\Downloads\Nishant\main.py\model.pth")
# # import os
# # import pytorch_lightning as pl
# # from dataloader import AerialImageDataset
# # from train5 import deeplabv3_encoder_decoder
# # from torch.utils.data import DataLoader
# # from torchvision.transforms import transforms
# # import torch
# # train_path = r"C:\Users\User\Downloads\Nishant\train"
# # val_path = r"C:\Users\User\Downloads\Nishant\val"
# # data_transform = transforms.Compose([
# # transforms.Resize((512, 512)),
# # transforms.ToTensor()
# # ])
# # train_dataset = AerialImageDataset(os.path.join(train_path, 'images'), os.path.join(train_path, 'masks'), transform=data_transform)
# # val_dataset = AerialImageDataset(os.path.join(val_path, 'images'), os.path.join(val_path, 'masks'), transform=data_transform)
# # train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True)
# # val_loader = DataLoader(val_dataset, batch_size=2, shuffle=False)
# # model = deeplabv3_encoder_decoder()
# # # Adjust other trainer parameters as needed
# # trainer = pl.Trainer(max_epochs=100)
# # trainer.fit(model, train_loader, val_loader)
# # torch.save(model.state_dict(), r"C:\Users\User\Downloads\Nishant\main.py\model.pth")
# #running code
# # import os
# # import pytorch_lightning as pl
# # from dataloader import AerialImageDataset
# # from train5 import deeplabv3_encoder_decoder
# # from torch.utils.data import DataLoader
# # from torchvision.transforms import transforms
# # import torch
# # train_path = r"C:\Users\User\Downloads\Nishant\train"
# # val_path = r"C:\Users\User\Downloads\Nishant\val"
# # data_transform = transforms.Compose([
# # transforms.Resize((512, 512)),
# # transforms.ToTensor()
# # ])
# # train_dataset = AerialImageDataset(os.path.join(train_path, 'images'), os.path.join(train_path, 'masks'), transform=data_transform)
# # val_dataset = AerialImageDataset(os.path.join(val_path, 'images'), os.path.join(val_path, 'masks'), transform=data_transform)
# # train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True)
# # val_loader = DataLoader(val_dataset, batch_size=2, shuffle=False)
# # model = deeplabv3_encoder_decoder()
# # # Adjust other trainer parameters as needed
# # trainer = pl.Trainer(num_sanity_val_steps=0, max_epochs=100)
# # trainer.fit(model, train_loader, val_loader)
# # torch.save(model.state_dict(), r"C:\Users\User\Downloads\Nishant\main.py\model.pth")
# import os
# import pytorch_lightning as pl
# from dataloader import AerialImageDataset
# from train5 import deeplabv3_encoder_decoder
# from torch.utils.data import DataLoader
# from torchvision.transforms import transforms
# import torch
# from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
# train_path = r"C:\Users\User\Downloads\Nishant\train"
# val_path = r"C:\Users\User\Downloads\Nishant\val"
# data_transform = transforms.Compose([
# transforms.Resize((512, 512)),
# transforms.ToTensor()
# ])
# train_dataset = AerialImageDataset(os.path.join(train_path, 'images'), os.path.join(train_path, 'masks'), transform=data_transform)
# val_dataset = AerialImageDataset(os.path.join(val_path, 'images'), os.path.join(val_path, 'masks'), transform=data_transform)
# train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True)
# val_loader = DataLoader(val_dataset, batch_size=2, shuffle=False)
# model = deeplabv3_encoder_decoder()
# checkpoint_callback = ModelCheckpoint(
# monitor='val_loss',
# dirpath='checkpoints',
# filename='best_model',
# save_top_k=1,
# mode='min'
# )
# early_stop_callback = EarlyStopping(
# monitor='val_loss',
# patience=20,
# verbose=True,
# mode='min'
# )
# trainer = pl.Trainer(
# num_sanity_val_steps=0,
# max_epochs=100,
# callbacks=[checkpoint_callback, early_stop_callback] # Pass both callbacks
# )
# trainer.fit(model, train_loader, val_loader)
# torch.save(model.state_dict(), r"C:\Users\User\Downloads\Nishant\main.py\model.pth")
import os
import pytorch_lightning as pl
from dataloader import AerialImageDataset
from train5 import deeplabv3_encoder_decoder
from torch.utils.data import DataLoader
from torchvision.transforms import transforms
import torch
from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
train_path = r"/teamspace/studios/this_studio/Segmentation/train"
val_path = r"/teamspace/studios/this_studio/Segmentation/val"
data_transform = transforms.Compose([
transforms.Resize((512, 512)),
transforms.ToTensor()
])
train_dataset = AerialImageDataset(os.path.join(train_path, 'images'), os.path.join(train_path, 'masks'), transform=data_transform)
val_dataset = AerialImageDataset(os.path.join(val_path, 'images'), os.path.join(val_path, 'masks'), transform=data_transform)
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=16, shuffle=False)
model = deeplabv3_encoder_decoder()
checkpoint_callback = ModelCheckpoint(
monitor='val_loss',
dirpath='checkpoints1',
filename='best_model',
save_top_k=1,
mode='min' # Save the model based on minimizing validation loss
)
early_stop_callback = EarlyStopping(
monitor='val_loss',
patience=20,
verbose=True,
mode='min'
)
trainer = pl.Trainer(
num_sanity_val_steps=0,
max_epochs=1000,
callbacks=[checkpoint_callback, early_stop_callback] # Pass both callbacks
)
trainer.fit(model, train_loader, val_loader)
torch.save(model.state_dict(), r"/teamspace/studios/this_studio/Segmentation/model.pth")
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