Note
Go to the end to download the full example code.
Tutorial 7: Finetuning Lightly Checkpoints
LightlySSL provides pre-trained models on various datasets such as ImageNet1k, ImageNet100, Imagenette, and CIFAR-10. All these models’ weights along with their hyperparameter configurations are available here Benchmarks.
In this tutorial, we will learn how to use these pre-trained model checkpoints to fine-tune an image classification model for the Food-101 dataset using PyTorch Lightning.
Imports
Import the Python frameworks we need for this tutorial. Make sure you have the necessary packages installed.
pip install lightly torchmetrics
import pytorch_lightning as pl
import torch
import torch.nn as nn
from pytorch_lightning.loggers import TensorBoardLogger
from torchvision import transforms
from lightly.transforms.utils import IMAGENET_NORMALIZE
Downloading Model Checkpoint
Let’s use the resnet50 model pre-trained on ImageNet1k using the SimCLR method.
You can browse other model checkpoints at Benchmarks.
checkpoint_url = "https://lightly-ssl-checkpoints.s3.amazonaws.com/imagenet_resnet50_simclr_2023-06-22_09-11-13/pretrain/version_0/checkpoints/epoch%3D99-step%3D500400.ckpt"
Tensorboard Logger
tb_logger = TensorBoardLogger("tb_logs", name="lightly_finetuning")
Configuration
Let’s set the configuration parameters for our experiments.
We use a batch size of 32 and an input size of 128.
We only train for 5 epochs because the focus of this tutorial is on finetuning lightly checkpoints.
learning_rate = 0.001
num_workers = 8
batch_size = 3
input_size = 128
seed = 42
num_train_epochs = 5
# use cuda if possible
device = "cuda" if torch.cuda.is_available() else "cpu"
Setup data augmentations and loaders
For this tutorial, we’ll use the Food-101 dataset of 101 food categories with 101,000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.
We will also use some minimal augmentations for the train and test subsets. To learn more about data pipelines in LightlySSL you can refer to Tutorial 1: Structure Your Input and to learn more about the different augmentations and learned invariances please refer to Advanced Concepts in Self-Supervised Learning.
from torch.utils.data import DataLoader
from torchvision.datasets import Food101
num_classes = 101
# Training Transformations
train_transform = transforms.Compose(
[
transforms.RandomResizedCrop(input_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(
mean=IMAGENET_NORMALIZE["mean"],
std=IMAGENET_NORMALIZE["std"],
),
]
)
train_dataset = Food101(
"datasets/food101", split="train", download=True, transform=train_transform
)
train_dataloader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True,
num_workers=num_workers,
)
# Validation Transformations
val_transform = transforms.Compose(
[
transforms.Resize(input_size),
transforms.CenterCrop(input_size),
transforms.ToTensor(),
transforms.Normalize(
mean=IMAGENET_NORMALIZE["mean"],
std=IMAGENET_NORMALIZE["std"],
),
]
)
val_dataset = Food101(
"datasets/food101", split="test", download=True, transform=val_transform
)
val_dataloader = DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True,
num_workers=num_workers,
)
Downloading https://data.vision.ee.ethz.ch/cvl/food-101.tar.gz to datasets/food101/food-101.tar.gz
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Extracting datasets/food101/food-101.tar.gz to datasets/food101
Load Model Checkpoint
Having chosen a model checkpoint to load, we will now create a PyTorch Module using the pre-trained weights.
from torchvision.models import resnet50
# Initialize the ResNet-50 model
model = resnet50()
num_feats = model.fc.in_features
# Load the checkpoint
checkpoint = torch.hub.load_state_dict_from_url(url=checkpoint_url, map_location=device)
# Remove 'backbone.' prefix from keys in state_dict
backbone_state_dict = {
k.replace("backbone.", ""): v
for k, v in checkpoint["state_dict"].items()
if k.startswith("backbone.")
}
# Load the state_dict into the model
model.load_state_dict(backbone_state_dict, strict=False)
# Add a classification head
model.fc = nn.Linear(num_feats, num_classes)
Create a FineTuning Lightning Module
Now let’s create a PyTorch Lightning Module to finetune our pre-trained model.
from torch.optim import AdamW
from torchmetrics import Accuracy
class FinetuningModel(pl.LightningModule):
def __init__(self, model, num_classes: int, lr: float):
super().__init__()
self.save_hyperparameters()
self.model = model
self.train_accuracy = Accuracy(task="multiclass", num_classes=num_classes)
self.val_accuracy = Accuracy(task="multiclass", num_classes=num_classes)
self.loss_fn = nn.CrossEntropyLoss()
def forward(self, x):
return self.model(x)
def training_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = self.loss_fn(logits, y)
self.log("train_loss", loss)
preds = torch.argmax(logits, dim=1)
acc = self.train_accuracy(preds, y)
self.log("train_accuracy", acc)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = self.loss_fn(logits, y)
self.log("val_loss", loss)
preds = torch.argmax(logits, dim=1)
acc = self.val_accuracy(preds, y)
self.log("val_accuracy", acc)
return loss
def configure_optimizers(self):
optimizer = AdamW(self.parameters(), lr=self.hparams.lr)
return optimizer
Training
Let’s instantiate our Lightning Module and train it using the Lightning Trainer.
finetuning_model = FinetuningModel(model, lr=learning_rate, num_classes=num_classes)
trainer = pl.Trainer(
max_epochs=num_train_epochs, devices=1, logger=tb_logger, accelerator=device
)
trainer.fit(
model=finetuning_model,
train_dataloaders=train_dataloader,
val_dataloaders=val_dataloader,
)
/datasets/actions-runner/core_gpu_runner_03/_work/lightly-core/lightly-core/.venv/lib/python3.10/site-packages/pytorch_lightning/utilities/parsing.py:269: UserWarning: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.
rank_zero_warn(
/datasets/actions-runner/core_gpu_runner_03/_work/lightly-core/lightly-core/.venv/lib/python3.10/site-packages/lightning_fabric/plugins/environments/slurm.py:165: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python3 /datasets/actions-runner/core_gpu_runner_03/_work/l ...
rank_zero_warn(
Sanity Checking: 0it [00:00, ?it/s]/datasets/actions-runner/core_gpu_runner_03/_work/lightly-core/lightly-core/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:488: PossibleUserWarning: Your `val_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test/predict dataloaders.
rank_zero_warn(
Sanity Checking: 0%| | 0/2 [00:00<?, ?it/s]
Sanity Checking DataLoader 0: 0%| | 0/2 [00:00<?, ?it/s]
Sanity Checking DataLoader 0: 50%|█████ | 1/2 [00:01<00:01, 1.90s/it]
Sanity Checking DataLoader 0: 100%|██████████| 2/2 [00:01<00:00, 1.05it/s]
Training: 0it [00:00, ?it/s]
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Epoch 0: 0%| | 136/33666 [00:05<24:11, 23.10it/s, loss=4.71, v_num=0]
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Epoch 0: 0%| | 137/33666 [00:05<24:09, 23.13it/s, loss=4.69, v_num=0]
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Epoch 0: 0%| | 138/33666 [00:05<24:08, 23.15it/s, loss=4.7, v_num=0]
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Epoch 0: 0%| | 139/33666 [00:05<24:06, 23.18it/s, loss=4.7, v_num=0]
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Epoch 0: 0%| | 149/33666 [00:06<23:49, 23.45it/s, loss=4.65, v_num=0]
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Epoch 0: 0%| | 150/33666 [00:06<23:48, 23.47it/s, loss=4.65, v_num=0]
Epoch 0: 0%| | 151/33666 [00:06<23:46, 23.50it/s, loss=4.65, v_num=0]
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Epoch 0: 0%| | 152/33666 [00:06<23:45, 23.51it/s, loss=4.66, v_num=0]
Epoch 0: 0%| | 152/33666 [00:06<23:45, 23.51it/s, loss=4.66, v_num=0]
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Epoch 0: 0%| | 153/33666 [00:06<23:44, 23.53it/s, loss=4.67, v_num=0]
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Epoch 0: 0%| | 154/33666 [00:06<23:44, 23.52it/s, loss=4.67, v_num=0]
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Epoch 0: 0%| | 155/33666 [00:06<23:42, 23.55it/s, loss=4.66, v_num=0]
Epoch 0: 0%| | 156/33666 [00:06<23:41, 23.58it/s, loss=4.66, v_num=0]
Epoch 0: 0%| | 156/33666 [00:06<23:41, 23.58it/s, loss=4.66, v_num=0]
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Epoch 0: 0%| | 157/33666 [00:06<23:39, 23.60it/s, loss=4.65, v_num=0]
Epoch 0: 0%| | 158/33666 [00:06<23:38, 23.62it/s, loss=4.65, v_num=0]
Epoch 0: 0%| | 158/33666 [00:06<23:38, 23.62it/s, loss=4.65, v_num=0]
Epoch 0: 0%| | 159/33666 [00:06<23:38, 23.62it/s, loss=4.65, v_num=0]
Epoch 0: 0%| | 159/33666 [00:06<23:38, 23.62it/s, loss=4.65, v_num=0]
Epoch 0: 0%| | 160/33666 [00:06<23:36, 23.65it/s, loss=4.65, v_num=0]
Epoch 0: 0%| | 160/33666 [00:06<23:37, 23.65it/s, loss=4.64, v_num=0]
Epoch 0: 0%| | 161/33666 [00:06<23:35, 23.67it/s, loss=4.64, v_num=0]
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Epoch 0: 0%| | 162/33666 [00:06<23:33, 23.70it/s, loss=4.66, v_num=0]
Epoch 0: 0%| | 163/33666 [00:06<23:33, 23.71it/s, loss=4.66, v_num=0]
Epoch 0: 0%| | 163/33666 [00:06<23:33, 23.71it/s, loss=4.65, v_num=0]
Epoch 0: 0%| | 164/33666 [00:06<23:31, 23.73it/s, loss=4.65, v_num=0]
Epoch 0: 0%| | 164/33666 [00:06<23:31, 23.73it/s, loss=4.65, v_num=0]
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Epoch 0: 0%| | 165/33666 [00:06<23:30, 23.75it/s, loss=4.65, v_num=0]
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Epoch 0: 1%| | 169/33666 [00:07<23:26, 23.82it/s, loss=4.64, v_num=0]
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Epoch 0: 1%| | 170/33666 [00:07<23:24, 23.85it/s, loss=4.63, v_num=0]
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Epoch 0: 1%| | 171/33666 [00:07<23:24, 23.86it/s, loss=4.63, v_num=0]
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Epoch 0: 1%| | 172/33666 [00:07<23:23, 23.86it/s, loss=4.62, v_num=0]
Epoch 0: 1%| | 173/33666 [00:07<23:22, 23.88it/s, loss=4.62, v_num=0]
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Epoch 0: 1%| | 174/33666 [00:07<23:22, 23.88it/s, loss=4.62, v_num=0]
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Epoch 0: 1%| | 175/33666 [00:07<23:22, 23.88it/s, loss=4.61, v_num=0]
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Epoch 0: 1%| | 177/33666 [00:07<23:21, 23.89it/s, loss=4.63, v_num=0]
Epoch 0: 1%| | 178/33666 [00:07<23:20, 23.91it/s, loss=4.63, v_num=0]
Epoch 0: 1%| | 178/33666 [00:07<23:20, 23.90it/s, loss=4.63, v_num=0]
Epoch 0: 1%| | 179/33666 [00:07<23:20, 23.92it/s, loss=4.63, v_num=0]
Epoch 0: 1%| | 179/33666 [00:07<23:20, 23.92it/s, loss=4.62, v_num=0]
Epoch 0: 1%| | 180/33666 [00:07<23:18, 23.94it/s, loss=4.62, v_num=0]
Epoch 0: 1%| | 180/33666 [00:07<23:18, 23.94it/s, loss=4.62, v_num=0]
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Epoch 0: 1%| | 181/33666 [00:07<23:18, 23.94it/s, loss=4.62, v_num=0]
Epoch 0: 1%| | 182/33666 [00:07<23:17, 23.96it/s, loss=4.62, v_num=0]
Epoch 0: 1%| | 182/33666 [00:07<23:17, 23.96it/s, loss=4.63, v_num=0]
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