Initial commit
fbshipit-source-id: da6be2f26e3a1202f4bffde8cb980e2dcb851294
This commit is contained in:
facebook-github-bot
279
sam3/train/configs/eval_base.yaml
Normal file
279
sam3/train/configs/eval_base.yaml
Normal file
@@ -0,0 +1,279 @@
|
||||
# @package _global_
|
||||
defaults:
|
||||
- _self_
|
||||
|
||||
# This config is the base configuration for all evaluations. Amongst other things, it defines:
|
||||
# - the model
|
||||
# - the image transforms
|
||||
# - the post processors
|
||||
# - cluster configuration (only relevant for slurm-based evals, ignored otherwise)
|
||||
#
|
||||
# Most of the parameters should be kept as-is. The main modifications you may want to make are:
|
||||
# - the cluster configuration, to adjust partitions/qos to your system
|
||||
# - the flag gather_pred_via_filesys if you ram is tight
|
||||
# - num_val_workers if your number of cores is small (should be roughly number of cores / number of gpus)
|
||||
# - the paths below
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Paths Configuration (Chage this to your own paths)
|
||||
# ============================================================================
|
||||
paths:
|
||||
# If you leave the checkpoint path to null, the model will be downloaded from hugging-face. Otherwise provide a path
|
||||
checkpoint_path: null
|
||||
# the experiments will be subfolders of this
|
||||
base_experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>
|
||||
|
||||
# base path to the annotation folder for gold (refer to the readmes on how to download)
|
||||
base_annotation_path: <YOUR_GOLD_GT_DIR>
|
||||
|
||||
# base path to the annotation folder for silver (refer to the readmes on how to download)
|
||||
base_annotation_path_silver: <YOUR_SILVER_GT_DIR>
|
||||
|
||||
# path to the metaclip images, used for SA-Co gold (refer to the readme for instructions). Can be null if you don't intend on evaluating on this dataset.
|
||||
metaclip_img_path: <YOUR_METACLIP_IMG_DIR>
|
||||
|
||||
# path to the sa1b images, used for SA-Co gold (refer to the readme for instructions). Can be null if you don't intend on evaluating on this dataset.
|
||||
sa1b_img_path: <YOUR_SA1B_IMG_DIR>
|
||||
|
||||
# path to the SA-Co/silver images
|
||||
silver_img_path: <YOUR_SILVER_IMG_DIR>
|
||||
|
||||
bpe_path: <BPE_PATH> # This should be under assets/bpe_simple_vocab_16e6.txt.gz
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Different helper parameters and functions
|
||||
# ============================================================================
|
||||
scratch:
|
||||
|
||||
use_presence_eval: True
|
||||
|
||||
base_val_transform:
|
||||
- _target_: sam3.train.transforms.basic_for_api.ComposeAPI
|
||||
transforms:
|
||||
######## transforms for validation (begin) ########
|
||||
- _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI
|
||||
sizes: ${scratch.resolution} # originally `resolution: 1024`
|
||||
max_size:
|
||||
_target_: sam3.train.transforms.basic.get_random_resize_max_size
|
||||
size: ${scratch.resolution} # originally `resolution: 1024`
|
||||
square: true
|
||||
consistent_transform: False
|
||||
######## transforms for validation (end) ########
|
||||
- _target_: sam3.train.transforms.basic_for_api.ToTensorAPI
|
||||
- _target_: sam3.train.transforms.basic_for_api.NormalizeAPI
|
||||
mean: ${scratch.val_norm_mean}
|
||||
std: ${scratch.val_norm_std}
|
||||
|
||||
loss: null
|
||||
|
||||
# Model parameters
|
||||
d_model: 256
|
||||
input_box_embedding_dim: ${add:${scratch.d_model},2}
|
||||
|
||||
# Box processing
|
||||
original_box_postprocessor:
|
||||
_target_: sam3.eval.postprocessors.PostProcessImage
|
||||
max_dets_per_img: -1 # infinite detections
|
||||
use_original_ids: true
|
||||
use_original_sizes_box: true
|
||||
use_presence: ${scratch.use_presence_eval}
|
||||
|
||||
box_postprocessor:
|
||||
_target_: sam3.eval.postprocessors.PostProcessImage
|
||||
max_dets_per_img: -1 #infinite detections
|
||||
use_original_ids: false
|
||||
use_original_sizes_box: false
|
||||
use_presence: ${scratch.use_presence_eval}
|
||||
|
||||
box_postprocessor_thresholded:
|
||||
_target_: sam3.eval.postprocessors.PostProcessImage
|
||||
max_dets_per_img: -1 #infinite detections
|
||||
use_original_ids: false
|
||||
use_original_sizes_box: false
|
||||
detection_threshold: 0.3
|
||||
use_presence: ${scratch.use_presence_eval}
|
||||
|
||||
mask_postprocessor_thresholded:
|
||||
_target_: sam3.eval.postprocessors.PostProcessImage
|
||||
max_dets_per_img: -1 #infinite detections
|
||||
iou_type: "segm"
|
||||
use_original_ids: false
|
||||
use_original_sizes_box: false
|
||||
use_original_sizes_mask: true
|
||||
convert_mask_to_rle: True
|
||||
detection_threshold: 0.3
|
||||
use_presence: ${scratch.use_presence_eval}
|
||||
|
||||
# Image processing parameters
|
||||
resolution: 1008
|
||||
max_ann_per_img: 200
|
||||
|
||||
# Normalization parameters
|
||||
train_norm_mean: [0.5, 0.5, 0.5]
|
||||
train_norm_std: [0.5, 0.5, 0.5]
|
||||
val_norm_mean: [0.5, 0.5, 0.5]
|
||||
val_norm_std: [0.5, 0.5, 0.5]
|
||||
|
||||
# Training parameters
|
||||
train_batch_size: 1
|
||||
val_batch_size: 1
|
||||
num_train_workers: 0
|
||||
num_val_workers: 10 # change this depending on the number of cpu cores available
|
||||
max_data_epochs: 20
|
||||
target_epoch_size: 1500
|
||||
hybrid_repeats: 1
|
||||
context_length: 2
|
||||
|
||||
# All reduce - this controls how the predictions are sent back to node 0.
|
||||
# If you have a lot of ram, CPU gather is faster. Otherwise, we provide a fallback through filesystem (eg NFS)
|
||||
# Switch to true if you get cpu ooms during gather.
|
||||
gather_pred_via_filesys: false
|
||||
|
||||
# Learning rate and scheduler parameters (unused for eval)
|
||||
lr_scale: 0.1
|
||||
lr_transformer: ${times:8e-4,${scratch.lr_scale}}
|
||||
lr_vision_backbone: ${times:2.5e-4,${scratch.lr_scale}}
|
||||
lr_language_backbone: ${times:5e-5,${scratch.lr_scale}}
|
||||
lrd_vision_backbone: 0.9 # (lower for in-domain adn higher for ood)
|
||||
wd: 0.1
|
||||
scheduler_timescale: 20
|
||||
scheduler_warmup: 20
|
||||
scheduler_cooldown: 20
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Trainer Configuration
|
||||
# ============================================================================
|
||||
|
||||
trainer:
|
||||
_target_: sam3.train.trainer.Trainer
|
||||
skip_saving_ckpts: true
|
||||
empty_gpu_mem_cache_after_eval: True
|
||||
skip_first_val: True
|
||||
max_epochs: ${scratch.max_data_epochs}
|
||||
accelerator: cuda
|
||||
seed_value: 123
|
||||
val_epoch_freq: 10
|
||||
mode: val
|
||||
|
||||
distributed:
|
||||
backend: nccl
|
||||
find_unused_parameters: True
|
||||
gradient_as_bucket_view: True
|
||||
|
||||
loss:
|
||||
all:
|
||||
_target_: sam3.train.loss.sam3_loss.DummyLoss
|
||||
default:
|
||||
_target_: sam3.train.loss.sam3_loss.DummyLoss
|
||||
|
||||
data:
|
||||
train: null
|
||||
val: null
|
||||
|
||||
model:
|
||||
_target_: sam3.model_builder.build_sam3_image_model
|
||||
bpe_path: ${paths.bpe_path}
|
||||
device: cpus
|
||||
eval_mode: true
|
||||
enable_segmentation: true # Warning: Enable this if using segmentation.
|
||||
checkpoint_path: ${paths.checkpoint_path}
|
||||
|
||||
meters:
|
||||
val: null
|
||||
|
||||
optim:
|
||||
amp:
|
||||
enabled: True
|
||||
amp_dtype: bfloat16
|
||||
|
||||
optimizer:
|
||||
_target_: torch.optim.AdamW
|
||||
|
||||
gradient_clip:
|
||||
_target_: sam3.train.optim.optimizer.GradientClipper
|
||||
max_norm: 0.1
|
||||
norm_type: 2
|
||||
|
||||
param_group_modifiers:
|
||||
- _target_: sam3.train.optim.optimizer.layer_decay_param_modifier
|
||||
_partial_: True
|
||||
layer_decay_value: ${scratch.lrd_vision_backbone}
|
||||
apply_to: 'backbone.vision_backbone.trunk'
|
||||
overrides:
|
||||
- pattern: '*pos_embed*'
|
||||
value: 1.0
|
||||
|
||||
options:
|
||||
lr:
|
||||
- scheduler: # transformer and class_embed
|
||||
_target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler
|
||||
base_lr: ${scratch.lr_transformer}
|
||||
timescale: ${scratch.scheduler_timescale}
|
||||
warmup_steps: ${scratch.scheduler_warmup}
|
||||
cooldown_steps: ${scratch.scheduler_cooldown}
|
||||
- scheduler:
|
||||
_target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler
|
||||
base_lr: ${scratch.lr_vision_backbone}
|
||||
timescale: ${scratch.scheduler_timescale}
|
||||
warmup_steps: ${scratch.scheduler_warmup}
|
||||
cooldown_steps: ${scratch.scheduler_cooldown}
|
||||
param_names:
|
||||
- 'backbone.vision_backbone.*'
|
||||
- scheduler:
|
||||
_target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler
|
||||
base_lr: ${scratch.lr_language_backbone}
|
||||
timescale: ${scratch.scheduler_timescale}
|
||||
warmup_steps: ${scratch.scheduler_warmup}
|
||||
cooldown_steps: ${scratch.scheduler_cooldown}
|
||||
param_names:
|
||||
- 'backbone.language_backbone.*'
|
||||
|
||||
weight_decay:
|
||||
- scheduler:
|
||||
_target_: fvcore.common.param_scheduler.ConstantParamScheduler
|
||||
value: ${scratch.wd}
|
||||
- scheduler:
|
||||
_target_: fvcore.common.param_scheduler.ConstantParamScheduler
|
||||
value: 0.0
|
||||
param_names:
|
||||
- '*bias*'
|
||||
module_cls_names: ['torch.nn.LayerNorm']
|
||||
|
||||
checkpoint:
|
||||
save_dir: ${launcher.experiment_log_dir}/checkpoints
|
||||
save_freq: 0 # 0 only last checkpoint is saved.
|
||||
|
||||
|
||||
logging:
|
||||
tensorboard_writer:
|
||||
_target_: sam3.train.utils.logger.make_tensorboard_logger
|
||||
log_dir: ${launcher.experiment_log_dir}/tensorboard
|
||||
flush_secs: 120
|
||||
should_log: True
|
||||
wandb_writer: null
|
||||
log_dir: ${launcher.experiment_log_dir}/logs/
|
||||
log_freq: 10
|
||||
|
||||
# ============================================================================
|
||||
# Launcher and Submitit Configuration
|
||||
# ============================================================================
|
||||
|
||||
launcher:
|
||||
num_nodes: 4
|
||||
gpus_per_node: 8
|
||||
experiment_log_dir: ${paths.experiment_log_dir}
|
||||
multiprocessing_context: forkserver
|
||||
|
||||
|
||||
submitit:
|
||||
account: null # Add your SLURM account if use_cluster == 1
|
||||
partition: null
|
||||
qos: null # Add your QoS if use_cluster == 1
|
||||
timeout_hour: 72
|
||||
use_cluster: True
|
||||
cpus_per_task: 10
|
||||
port_range: [10000, 65000]
|
||||
constraint: null
|
||||
Reference in New Issue
Block a user