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0c717d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | import lightning as L
from lightning.pytorch.callbacks import ModelCheckpoint, TQDMProgressBar, Callback
from lightning.pytorch.loggers import TensorBoardLogger
from pathlib import Path
from torch.optim.lr_scheduler import OneCycleLR
from torch_lr_finder import LRFinder
import torch
from datamodules.imagenet_datamodule import ImageNetDataModule
from models.classifier import ImageNetClassifier
class NewLineProgressBar(Callback):
def on_train_epoch_start(self, trainer, pl_module):
print(f"\nEpoch {trainer.current_epoch}")
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
metrics = trainer.callback_metrics
train_loss = metrics.get('train_loss', 0)
train_acc = metrics.get('train_acc', 0)
print(f"\rTraining - Loss: {train_loss:.4f}, Acc: {train_acc:.4f}", end="")
def on_validation_epoch_start(self, trainer, pl_module):
print("\n\nValidation:")
def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
metrics = trainer.callback_metrics
val_loss = metrics.get('val_loss', 0)
val_acc = metrics.get('val_acc', 0)
print(f"\rValidation - Loss: {val_loss:.4f}, Acc: {val_acc:.4f}", end="")
def find_optimal_lr(model, data_module):
# Initialize LRFinder
optimizer = torch.optim.Adam(model.parameters(), lr=1e-7)
criterion = torch.nn.CrossEntropyLoss()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
lr_finder = LRFinder(model, optimizer, criterion, device=device)
# Run LR finder with stage parameter
data_module.setup(stage='fit')
lr_finder.range_test(data_module.train_dataloader(), end_lr=1, num_iter=200, step_mode="exp")
# Get the learning rate with the steepest gradient
lrs = lr_finder.history['lr']
losses = lr_finder.history['loss']
# Find the learning rate with minimum loss
optimal_lr = lrs[losses.index(min(losses))]
# You might want to pick a learning rate slightly lower than the minimum
optimal_lr = optimal_lr * 0.1 # Common practice to use 1/10th of the value
print(f"Optimal learning rate: {optimal_lr}")
# Plot the LR finder results
lr_finder.plot() # Will save the plot
lr_finder.reset() # Reset the model and optimizer
return optimal_lr
def main(chkpoint_path=None):
if chkpoint_path is not None:
model = ImageNetClassifier(lr=1e-2)
data_module = ImageNetDataModule(batch_size=256, num_workers=8)
checkpoint_callback = ModelCheckpoint(
dirpath="logs/checkpoints",
filename="{epoch}-{val_loss:.2f}",
monitor="val_loss",
save_top_k=3
)
# Initialize Trainer
trainer = L.Trainer(resume_from_checkpoint=chkpoint_path,
max_epochs=epochs,
precision="bf16-mixed",
callbacks=[
checkpoint_callback,
NewLineProgressBar(),
TQDMProgressBar(refresh_rate=1)
],
accelerator="auto",
logger=TensorBoardLogger(save_dir="logs", name="image_net_classifications"),
enable_progress_bar=True,
enable_model_summary=True,
log_every_n_steps=1,
val_check_interval=1.0,
check_val_every_n_epoch=1
)
trainer.fit(model, data_module)
else:
# Create directories
Path("logs").mkdir(exist_ok=True)
Path("data").mkdir(exist_ok=True)
# Initialize DataModule and Model
data_module = ImageNetDataModule(batch_size=256, num_workers=8)
model = ImageNetClassifier(lr=1e-2) # Initial lr will be overridden
# Find optimal learning rate
optimal_lr = find_optimal_lr(model, data_module)
#optimal_lr = 6.28E-02
# Calculate total steps for OneCycleLR
epochs = 60
data_module.setup(stage='fit')
steps_per_epoch = len(data_module.train_dataloader())
total_steps = epochs * steps_per_epoch
# # Initialize optimizer
# optimizer = torch.optim.Adam(model.parameters(), lr=optimal_lr)
# # Initialize OneCycleLR scheduler
# scheduler = OneCycleLR(
# optimizer,
# max_lr=optimal_lr,
# total_steps=total_steps,
# pct_start=0.3, # Spend 30% of time increasing LR
# div_factor=25, # Initial LR will be max_lr/25
# final_div_factor=1e4, # Final LR will be max_lr/10000
# three_phase=False, # Use one cycle policy
# anneal_strategy='cos' # Use cosine annealing
# )
model = ImageNetClassifier(lr=optimal_lr) # Initial lr will be overridden
# Initialize callbacks
checkpoint_callback = ModelCheckpoint(
dirpath="logs/checkpoints",
filename="{epoch}-{val_loss:.2f}",
monitor="val_loss",
save_top_k=3
)
# Initialize Trainer
trainer = L.Trainer(
max_epochs=epochs,
precision="bf16-mixed",
callbacks=[
checkpoint_callback,
NewLineProgressBar(),
TQDMProgressBar(refresh_rate=1)
],
accelerator="auto",
logger=TensorBoardLogger(save_dir="logs", name="image_net_classifications"),
enable_progress_bar=True,
enable_model_summary=True,
log_every_n_steps=1,
val_check_interval=1.0,
check_val_every_n_epoch=1
)
# Train the model
trainer.fit(model, data_module)
if __name__ == "__main__":
main(chkpoint_path=None)
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