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base.yaml
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base.yaml
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model:
ckpt_path: null
encoder_cfg:
model_name: "resnet18"
backend: "timm"
in_chans: 3
d_model: 1024
nhead: 8
num_encoder_layers: 1
num_decoder_layers: 1
dropout: 0.1
activation: "relu"
return_intermediate_dec: False
norm: False
num_layers_attn_head: 2
dropout_attn_head: 0.1
embedding_meta:
pos:
mode: "fixed"
normalize: true
temp:
mode: "fixed"
return_embedding: False
decoder_self_attn: False
loss:
neg_unmatched: false
epsilon: 1e-4
asso_weight: 1.0
#currently assumes adam. TODO adapt logic for other optimizers like sgd
optimizer:
name: "Adam"
lr: 0.001
betas: [0.9, 0.999]
eps: 1e-8
weight_decay: 0.01
#currently assumes reduce lr on plateau
scheduler:
name: "ReduceLROnPlateau"
mode: "min"
factor: 0.5
patience: 10
threshold: 1e-4
threshold_mode: "rel"
tracker:
window_size: 8
use_vis_feats: true
overlap_thresh: 0.01
mult_thresh: true
decay_time: null
iou: null
max_center_dist: null
runner:
metrics:
train: ['num_switches']
val: ['num_switches']
test: ['num_switches']
persistent_tracking:
train: false
val: true
test: true
dataset:
train_dataset:
slp_files: ["../../tests/data/sleap/two_flies.slp"]
video_files: ["../../tests/data/sleap/two_flies.mp4"]
padding: 5
crop_size: 128
chunk: true
clip_length: 32
val_dataset:
slp_files: ["../../tests/data/sleap/two_flies.slp"]
video_files: ["../../tests/data/sleap/two_flies.mp4"]
padding: 5
crop_size: 128
chunk: True
clip_length: 32
test_dataset:
slp_files: ["../../tests/data/sleap/two_flies.slp"]
video_files: ["../../tests/data/sleap/two_flies.mp4"]
padding: 5
crop_size: 128
chunk: True
clip_length: 32
dataloader:
train_dataloader:
shuffle: true
num_workers: 0
val_dataloader:
shuffle: false
num_workers: 0
test_dataloader:
shuffle: false
num_workers: 0
logging:
logger_type: null
name: "example_train"
entity: null
job_type: "train"
notes: "Example train job"
dir: "./logs"
group: "example"
save_dir: './logs'
project: "GTR"
log_model: "all"
early_stopping:
monitor: "val_loss"
min_delta: 0.1
patience: 10
mode: "min"
check_finite: true
stopping_threshold: 1e-8
divergence_threshold: 30
checkpointing:
monitor: ["val_loss","val_num_switches"]
verbose: true
save_last: true
dirpath: null
auto_insert_metric_name: true
every_n_epochs: 10
trainer:
check_val_every_n_epoch: 1
enable_checkpointing: true
gradient_clip_val: null
limit_train_batches: 1.0
limit_test_batches: 1.0
limit_val_batches: 1.0
log_every_n_steps: 1
max_epochs: 100
min_epochs: 10
view_batch:
enable: False
num_frames: 0
no_train: False