model_params:
model: &model CNNFinetuneModels
model_name: &model_name densenet169
num_classes: 6
args:
expdir: "src"
logdir: &logdir "./logs/rsna"
baselogdir: "./logs/rsna"
distributed_params:
opt_level: O1
stages:
state_params:
main_metric: &reduce_metric loss
minimize_metric: True
criterion_params:
criterion: &criterion LogLoss
weight: [1,1,1,1,1,2]
data_params:
batch_size: 32
num_workers: 4
drop_last: False
image_size: &image_size [512, 512]
train_csv: "./csv/stratified_kfold/train_0.csv.gz"
valid_csv: "./csv/stratified_kfold/valid_0.csv.gz"
# dataset_type: "RSNAMultiWindowsDataset"
with_any: True
root: "../stage_1_train_images_jpg_preprocessing/"
image_type: "jpg"
warmup:
optimizer_params:
optimizer: AdamW
lr: 0.001
scheduler_params:
scheduler: MultiStepLR
milestones: [10]
gamma: 0.3
state_params:
num_epochs: 3
callbacks_params: &callbacks_params
loss:
callback: CriterionCallback
optimizer:
callback: OptimizerCallback
accumulation_steps: 1
scheduler:
callback: SchedulerCallback
reduce_metric: *reduce_metric
saver:
callback: CheckpointCallback
save_n_best: 5
stage1:
optimizer_params:
optimizer: AdamW
lr: 0.0001
scheduler_params:
scheduler: ReduceLROnPlateau
patience: 1
min_lr: 0.00001
verbose: True
# scheduler: OneCycleLR
# num_steps: &num_epochs 25
# lr_range: [0.0005, 0.00001]
# warmup_steps: 5
# momentum_range: [0.85, 0.95]
state_params:
num_epochs: 20
callbacks_params:
loss:
callback: CriterionCallback
optimizer:
callback: OptimizerCallback
accumulation_steps: 1
scheduler:
callback: SchedulerCallback
reduce_metric: *reduce_metric
saver:
callback: CheckpointCallback
save_n_best: 5
early_stoping:
callback: EarlyStoppingCallback
patience: 2
monitoring_params:
project: "Kaggle-RSNA"
tags: [*model, *model_name, *criterion]