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Some linear probe experiments with dinov3_vith16plus to avoid overfitting on BACH #25

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114 changes: 114 additions & 0 deletions ...s/vision/pathology/offline/classification/bach-linear-experiments/eval_dinov3_custom.yaml
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---
trainer:
class_path: eva.Trainer
init_args:
n_runs: &N_RUNS ${oc.env:N_RUNS, 5}
default_root_dir: &OUTPUT_ROOT ${oc.env:OUTPUT_ROOT, logs/${oc.env:MODEL_NAME, dino_vits16}/offline/bach}
max_steps: &MAX_STEPS ${oc.env:MAX_STEPS, 6000} #usually 12500
checkpoint_type: ${oc.env:CHECKPOINT_TYPE, best}
callbacks:
- class_path: eva.callbacks.ConfigurationLogger
- class_path: lightning.pytorch.callbacks.TQDMProgressBar
init_args:
refresh_rate: ${oc.env:TQDM_REFRESH_RATE, 1}
- class_path: lightning.pytorch.callbacks.LearningRateMonitor
init_args:
logging_interval: epoch
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
filename: best
save_last: ${oc.env:SAVE_LAST, false}
save_top_k: 1
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/MulticlassAccuracy}
mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
- class_path: lightning.pytorch.callbacks.EarlyStopping
init_args:
min_delta: 0
patience: ${oc.env:PATIENCE, 1250}
monitor: *MONITOR_METRIC
mode: *MONITOR_METRIC_MODE
- class_path: eva.callbacks.ClassificationEmbeddingsWriter
init_args:
output_dir: &DATASET_EMBEDDINGS_ROOT ${oc.env:EMBEDDINGS_ROOT, ./data/embeddings}/${oc.env:MODEL_NAME, dino_vits16}/bach
dataloader_idx_map:
0: train
1: val
backbone:
class_path: eva.core.models.wrappers.ModelFromLocal
init_args:
local_repo_path: "./dinov3"
model_name: "dinov3_vith16plus" #The model you want to load
checkpoint_path: "./dinov3_vith16plus_pretrain_lvd1689m-7c1da9a5.pth" #Path to the model checkpoint


overwrite: true
logger:
- class_path: lightning.pytorch.loggers.TensorBoardLogger
init_args:
save_dir: *OUTPUT_ROOT
name: ""
model:
class_path: eva.HeadModule
init_args:
head:
class_path: eva.core.models.networks.batch_linear.batch_linear
init_args:
in_features: ${oc.env:IN_FEATURES, 1280} #MODIFY ME
out_features: &NUM_CLASSES 4
criterion: torch.nn.CrossEntropyLoss
optimizer:
class_path: torch.optim.AdamW
init_args:
lr: ${oc.env:LR_VALUE, 0.0001}
metrics:
common:
- class_path: eva.metrics.AverageLoss
- class_path: eva.metrics.MulticlassClassificationMetrics
init_args:
num_classes: *NUM_CLASSES
data:
class_path: eva.DataModule
init_args:
datasets:
train:
class_path: eva.datasets.EmbeddingsClassificationDataset
init_args: &DATASET_ARGS
root: *DATASET_EMBEDDINGS_ROOT
manifest_file: manifest.csv
split: train
val:
class_path: eva.datasets.EmbeddingsClassificationDataset
init_args:
<<: *DATASET_ARGS
split: val
predict:
- class_path: eva.vision.datasets.BACH
init_args: &PREDICT_DATASET_ARGS
root: ${oc.env:DATA_ROOT, ./data/bach}
split: train
download: ${oc.env:DOWNLOAD_DATA, false}
# Set `download: true` to download the dataset from https://zenodo.org/records/3632035
# The BACH dataset is distributed under the following license
# Attribution-NonCommercial-NoDerivs 4.0 International license
# (see: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode)
transforms:
class_path: eva.vision.data.transforms.common.ResizeAndCrop
init_args:
size: ${oc.env:RESIZE_DIM, 224}
mean: ${oc.env:NORMALIZE_MEAN, [0.485, 0.456, 0.406]} #Flagged for maybe needs fixing
std: ${oc.env:NORMALIZE_STD, [0.229, 0.224, 0.225]}
- class_path: eva.vision.datasets.BACH
init_args:
<<: *PREDICT_DATASET_ARGS
split: val
dataloaders:
train:
batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 256}
num_workers: &N_DATA_WORKERS ${oc.env:N_DATA_WORKERS, 4}
shuffle: true
val:
batch_size: *BATCH_SIZE
num_workers: *N_DATA_WORKERS
predict:
batch_size: &PREDICT_BATCH_SIZE ${oc.env:PREDICT_BATCH_SIZE, 64}
num_workers: *N_DATA_WORKERS
114 changes: 114 additions & 0 deletions .../offline/classification/bach-linear-experiments/eval_dinov3_custom_adj_pooled_linear.yaml
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---
trainer:
class_path: eva.Trainer
init_args:
n_runs: &N_RUNS ${oc.env:N_RUNS, 5}
default_root_dir: &OUTPUT_ROOT ${oc.env:OUTPUT_ROOT, logs/${oc.env:MODEL_NAME, dino_vits16}/offline/bach}
max_steps: &MAX_STEPS ${oc.env:MAX_STEPS, 6000} #usually 12500
checkpoint_type: ${oc.env:CHECKPOINT_TYPE, best}
callbacks:
- class_path: eva.callbacks.ConfigurationLogger
- class_path: lightning.pytorch.callbacks.TQDMProgressBar
init_args:
refresh_rate: ${oc.env:TQDM_REFRESH_RATE, 1}
- class_path: lightning.pytorch.callbacks.LearningRateMonitor
init_args:
logging_interval: epoch
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
filename: best
save_last: ${oc.env:SAVE_LAST, false}
save_top_k: 1
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/MulticlassAccuracy}
mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
- class_path: lightning.pytorch.callbacks.EarlyStopping
init_args:
min_delta: 0
patience: ${oc.env:PATIENCE, 1250}
monitor: *MONITOR_METRIC
mode: *MONITOR_METRIC_MODE
- class_path: eva.callbacks.ClassificationEmbeddingsWriter
init_args:
output_dir: &DATASET_EMBEDDINGS_ROOT ${oc.env:EMBEDDINGS_ROOT, ./data/embeddings}/${oc.env:MODEL_NAME, dino_vits16}/bach
dataloader_idx_map:
0: train
1: val
backbone:
class_path: eva.core.models.wrappers.ModelFromLocal
init_args:
local_repo_path: "./dinov3"
model_name: "dinov3_vith16plus" #The model you want to load
checkpoint_path: "./dinov3_vith16plus_pretrain_lvd1689m-7c1da9a5.pth" #Path to the model checkpoint


overwrite: true
logger:
- class_path: lightning.pytorch.loggers.TensorBoardLogger
init_args:
save_dir: *OUTPUT_ROOT
name: ""
model:
class_path: eva.HeadModule
init_args:
head:
class_path: eva.core.models.networks.adj_pooled_linear.adj_pooled_linear
init_args:
in_features: ${oc.env:IN_FEATURES, 1280} #MODIFY ME
out_features: &NUM_CLASSES 4
criterion: torch.nn.CrossEntropyLoss
optimizer:
class_path: torch.optim.AdamW
init_args:
lr: ${oc.env:LR_VALUE, 0.0001}
metrics:
common:
- class_path: eva.metrics.AverageLoss
- class_path: eva.metrics.MulticlassClassificationMetrics
init_args:
num_classes: *NUM_CLASSES
data:
class_path: eva.DataModule
init_args:
datasets:
train:
class_path: eva.datasets.EmbeddingsClassificationDataset
init_args: &DATASET_ARGS
root: *DATASET_EMBEDDINGS_ROOT
manifest_file: manifest.csv
split: train
val:
class_path: eva.datasets.EmbeddingsClassificationDataset
init_args:
<<: *DATASET_ARGS
split: val
predict:
- class_path: eva.vision.datasets.BACH
init_args: &PREDICT_DATASET_ARGS
root: ${oc.env:DATA_ROOT, ./data/bach}
split: train
download: ${oc.env:DOWNLOAD_DATA, false}
# Set `download: true` to download the dataset from https://zenodo.org/records/3632035
# The BACH dataset is distributed under the following license
# Attribution-NonCommercial-NoDerivs 4.0 International license
# (see: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode)
transforms:
class_path: eva.vision.data.transforms.common.ResizeAndCrop
init_args:
size: ${oc.env:RESIZE_DIM, 224}
mean: ${oc.env:NORMALIZE_MEAN, [0.485, 0.456, 0.406]} #Flagged for maybe needs fixing
std: ${oc.env:NORMALIZE_STD, [0.229, 0.224, 0.225]}
- class_path: eva.vision.datasets.BACH
init_args:
<<: *PREDICT_DATASET_ARGS
split: val
dataloaders:
train:
batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 256}
num_workers: &N_DATA_WORKERS ${oc.env:N_DATA_WORKERS, 4}
shuffle: true
val:
batch_size: *BATCH_SIZE
num_workers: *N_DATA_WORKERS
predict:
batch_size: &PREDICT_BATCH_SIZE ${oc.env:PREDICT_BATCH_SIZE, 64}
num_workers: *N_DATA_WORKERS
114 changes: 114 additions & 0 deletions ...ne/classification/bach-linear-experiments/eval_dinov3_custom_adj_pooled_linear_noreg.yaml
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---
trainer:
class_path: eva.Trainer
init_args:
n_runs: &N_RUNS ${oc.env:N_RUNS, 5}
default_root_dir: &OUTPUT_ROOT ${oc.env:OUTPUT_ROOT, logs/${oc.env:MODEL_NAME, dino_vits16}/offline/bach}
max_steps: &MAX_STEPS ${oc.env:MAX_STEPS, 6000} #usually 12500
checkpoint_type: ${oc.env:CHECKPOINT_TYPE, best}
callbacks:
- class_path: eva.callbacks.ConfigurationLogger
- class_path: lightning.pytorch.callbacks.TQDMProgressBar
init_args:
refresh_rate: ${oc.env:TQDM_REFRESH_RATE, 1}
- class_path: lightning.pytorch.callbacks.LearningRateMonitor
init_args:
logging_interval: epoch
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
filename: best
save_last: ${oc.env:SAVE_LAST, false}
save_top_k: 1
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/MulticlassAccuracy}
mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
- class_path: lightning.pytorch.callbacks.EarlyStopping
init_args:
min_delta: 0
patience: ${oc.env:PATIENCE, 1250}
monitor: *MONITOR_METRIC
mode: *MONITOR_METRIC_MODE
- class_path: eva.callbacks.ClassificationEmbeddingsWriter
init_args:
output_dir: &DATASET_EMBEDDINGS_ROOT ${oc.env:EMBEDDINGS_ROOT, ./data/embeddings}/${oc.env:MODEL_NAME, dino_vits16}/bach
dataloader_idx_map:
0: train
1: val
backbone:
class_path: eva.core.models.wrappers.ModelFromLocal
init_args:
local_repo_path: "./dinov3"
model_name: "dinov3_vith16plus" #The model you want to load
checkpoint_path: "./dinov3_vith16plus_pretrain_lvd1689m-7c1da9a5.pth" #Path to the model checkpoint


overwrite: true
logger:
- class_path: lightning.pytorch.loggers.TensorBoardLogger
init_args:
save_dir: *OUTPUT_ROOT
name: ""
model:
class_path: eva.HeadModule
init_args:
head:
class_path: eva.core.models.networks.adj_pooled_linear_noreg.adj_pooled_linear_noreg
init_args:
in_features: ${oc.env:IN_FEATURES, 1280} #MODIFY ME
out_features: &NUM_CLASSES 4
criterion: torch.nn.CrossEntropyLoss
optimizer:
class_path: torch.optim.AdamW
init_args:
lr: ${oc.env:LR_VALUE, 0.0001}
metrics:
common:
- class_path: eva.metrics.AverageLoss
- class_path: eva.metrics.MulticlassClassificationMetrics
init_args:
num_classes: *NUM_CLASSES
data:
class_path: eva.DataModule
init_args:
datasets:
train:
class_path: eva.datasets.EmbeddingsClassificationDataset
init_args: &DATASET_ARGS
root: *DATASET_EMBEDDINGS_ROOT
manifest_file: manifest.csv
split: train
val:
class_path: eva.datasets.EmbeddingsClassificationDataset
init_args:
<<: *DATASET_ARGS
split: val
predict:
- class_path: eva.vision.datasets.BACH
init_args: &PREDICT_DATASET_ARGS
root: ${oc.env:DATA_ROOT, ./data/bach}
split: train
download: ${oc.env:DOWNLOAD_DATA, false}
# Set `download: true` to download the dataset from https://zenodo.org/records/3632035
# The BACH dataset is distributed under the following license
# Attribution-NonCommercial-NoDerivs 4.0 International license
# (see: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode)
transforms:
class_path: eva.vision.data.transforms.common.ResizeAndCrop
init_args:
size: ${oc.env:RESIZE_DIM, 224}
mean: ${oc.env:NORMALIZE_MEAN, [0.485, 0.456, 0.406]} #Flagged for maybe needs fixing
std: ${oc.env:NORMALIZE_STD, [0.229, 0.224, 0.225]}
- class_path: eva.vision.datasets.BACH
init_args:
<<: *PREDICT_DATASET_ARGS
split: val
dataloaders:
train:
batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 256}
num_workers: &N_DATA_WORKERS ${oc.env:N_DATA_WORKERS, 4}
shuffle: true
val:
batch_size: *BATCH_SIZE
num_workers: *N_DATA_WORKERS
predict:
batch_size: &PREDICT_BATCH_SIZE ${oc.env:PREDICT_BATCH_SIZE, 64}
num_workers: *N_DATA_WORKERS
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