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test_parameters.yaml
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test_parameters.yaml
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experiment:
# It contains all the about the grids and the group of runs:
name: LabelAnything # name of the logger platform experiment
group: Test # name of group of experiments for the logger platform
continue_with_errors: False # continue with other runs even if a run fails
start_from_grid: 0 # skip grids in the grid search
start_from_run: 0 # skip runs from the selected grid
search: grid
# n_trials: 5
parameters:
# Contains the parameters to build the grid.
# Each value should be a dict or a list
logger:
log_frequency: [1]
tags: [[Test]] # list of tags to attach to the run in logger platform
train_image_log_frequency: [5]
val_image_log_frequency: [5]
experiment_save_delta: [null] # save experiment every n seconds
tmp_dir: [tmp]
wandb:
offline: [False]
entity: [cilabuniba]
# resume: [True]
# run_id: [pg70tf8u]
train_params:
loss:
class_weighting: [True]
components:
# - focal:
# weight: 0.485
# dice:
# weight: 0.05
# rmi:
# weight: 0.485
- focal:
weight: 0.5
dice:
weight: 0.025
prompt_contrastive:
weight: 0.475
seed: &seed [42] # random seed to set
# substitution_threshold: [0.1] # threshold
num_points: [1] # number of points for class after the substitution
max_epochs: [2]
compile: [False]
initial_lr: [0.00001]
optimizer: [AdamW]
scheduler:
type: [reduce_lr_on_plateau]
step_moment: [epoch]
patience: [0]
# warmup_steps: [2500]
substitute: [True]
accumulate_substitution: [True] # Perform gradient accumulation over the substitution sub-batch
watch_metric: [loss] # metric to watch for early stopping and scheduler (loss or miou)
freeze_backbone: [True]
check_nan: [1] # check for nan every n batches
model:
name: [lam_b] # path to model class or model name contained in EzDL or super-gradients
checkpoint: [checkpoints/sam_vit_b_01ec64.pth] # model parameters
use_sam_checkpoint: [True]
spatial_convs: [3]
class_attention: [True]
fusion_transformer: [OneWayTransformer]
dataset: # parameters depending on the class you defined for the dataset
datasets:
# voc:
# name: [voc] #voc
# instances_path: [data/raw/instances_voc12train.json]
# img_dir: [data/raw/VOCdevkit/VOC2012/JPEGImages]
coco:
name: [coco]
instances_path: [data/annotations/instances_train2017.json]
img_dir: &img_dir [/ext/stalla/LabelAnything/images/train2017]
# lvis:
# name: [lvis]
# instances_path: [data/annotations/lvis_v1_train.json]
# img_dir: &img_dir [/ext/stalla/LabelAnything/images/train2017]
val_coco:
name: [coco]
instances_path: [data/annotations/instances_val2017.json]
img_dir: *img_dir
do_subsample: [False]
add_box_noise: [False]
# val_lvis:
# name: [lvis]
# instances_path: [data/annotations/lvis_v1_val.json]
# img_dir: *img_dir
# seed: *seed
# do_subsample: [False]
# add_box_noise: [False]
# test_coco:
# support: [coco]
# name: [coco]
# instances_path: [data/annotations/instances_val2017.json]
# img_dir: *img_dir
# seed: *seed
# add_box_noise: [False]
test_weedmap:
train_root: ["/ext/stalla/LabelAnything/WeedMap/0_rotations_processed_003_test/RedEdge/000"]
test_root: ["/ext/stalla/LabelAnything/WeedMap/0_rotations_processed_003_test/RedEdge/003"]
common:
do_subsample: [True]
add_box_noise: [True]
max_points_annotations: [50]
max_points_per_annotation: [10]
dataloader:
num_workers: [0]
possible_batch_example_nums: [[[1, 1]]]
val_possible_batch_example_nums: [[[2, 5]]]
prompt_types: [["point", "bbox", "mask"]]
val_prompt_types: [["mask"]]
other_grids: