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Running on Zero
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143710c 7b4e05b 143710c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | """Config loading utility.
Every experiment (architecture choice, hyperparameters, paths) is described
by a YAML file under configs/. This module is the single place that reads
those files into a plain dict, so every script (split, vocab, features,
train, evaluate, predict) loads config the same way.
"""
from __future__ import annotations
import copy
from pathlib import Path
from typing import Any
import yaml
DEFAULT_CONFIG: dict[str, Any] = {
"seed": 42,
"paths": {
"raw_images_dir": "data/raw/images",
"captions_file": "data/raw/captions.txt",
"processed_dir": "data/processed",
"features_dir": "data/features",
"models_dir": "models",
},
"split": {
"train_ratio": 0.8,
"val_ratio": 0.1,
"test_ratio": 0.1,
},
"vocab": {
"min_freq": 5,
"max_len": 35,
},
"encoder": {
"type": "resnet50",
"feature_dim": 2048,
"freeze": True,
},
"decoder": {
"type": "lstm",
"embed_dim": 256,
"hidden_dim": 512,
"num_layers": 1,
"dropout": 0.0,
},
"training": {
"batch_size": 32,
"epochs": 30,
"lr": 1e-3,
"optimizer": "adam",
"weight_decay": 0.0,
"grad_clip_norm": 0.0,
"lr_scheduler_patience": 2,
"lr_scheduler_factor": 0.5,
"early_stop_patience": 5,
"num_workers": 2,
"device": "auto", # "auto" | "cpu" | "cuda"
},
"inference": {
"decoding": "greedy", # "greedy" | "beam"
"beam_width": 3,
},
"run_name": "base_resnet_lstm",
}
def _deep_update(base: dict, override: dict) -> dict:
"""Recursively merge override into base (override wins), without mutating inputs."""
result = copy.deepcopy(base)
for key, value in override.items():
if isinstance(value, dict) and isinstance(result.get(key), dict):
result[key] = _deep_update(result[key], value)
else:
result[key] = value
return result
def load_config(path: str | Path) -> dict[str, Any]:
"""Load a YAML config and merge it on top of DEFAULT_CONFIG.
Any key not specified in the YAML file falls back to DEFAULT_CONFIG,
so config files only need to declare what they change from baseline.
"""
path = Path(path)
if not path.exists():
raise FileNotFoundError(f"Config file not found: {path}")
with open(path, "r") as f:
user_config = yaml.safe_load(f) or {}
config = _deep_update(DEFAULT_CONFIG, user_config)
config["_config_path"] = str(path)
return config
def save_config(config: dict[str, Any], path: str | Path) -> None:
"""Persist a resolved config dict to YAML (e.g. alongside a checkpoint)."""
path = Path(path)
path.parent.mkdir(parents=True, exist_ok=True)
clean = {k: v for k, v in config.items() if not k.startswith("_")}
with open(path, "w") as f:
yaml.safe_dump(clean, f, sort_keys=False) |