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e14b469 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | from __future__ import annotations
import copy
from pathlib import Path
from typing import Any
import yaml
from .paths import project_root, resolve_path
DEFAULT_CONFIG: dict[str, Any] = {
"project": {"name": "egg-damage-classification"},
"paths": {
"data_dir": "../Eggs Classification",
"output_dir": "outputs",
"model_dir": "models",
"split_csv": "outputs/splits.csv",
},
"kaggle": {
"enabled": False,
"dataset": "abdullahkhanuet22/eggs-images-classification-damaged-or-not",
"download_dir": "data/raw",
},
"seed": 42,
"data": {
"image_extensions": [".jpg", ".jpeg", ".png", ".bmp", ".webp"],
"train_size": 0.70,
"val_size": 0.15,
"test_size": 0.15,
"class_names": ["Not Damaged", "Damaged"],
"positive_class": "Damaged",
"imbalance_threshold": 1.20,
},
"preprocessing": {"image_size": 224},
"balance": {
"enabled": True,
"strategy": "augment_minority",
"max_augmented_train_samples": 3000,
},
"features": {
"hog": {
"orientations": 9,
"pixels_per_cell": [16, 16],
"cells_per_block": [2, 2],
"block_norm": "L2-Hys",
},
"lbp": {"radius": 2, "n_points": 16, "method": "uniform"},
},
"classical": {
"svm": {
"kernel": ["rbf"],
"C": [1.0, 3.0],
"gamma": ["scale"],
"class_weight": "balanced",
}
},
"models": {
"enabled": {
"hog_svm": True,
"lbp_svm": True,
"mobilenet_v3": True,
"resnet50": True,
"efficientnet_b0": True,
"densenet121": False,
"xception": False,
"vit_small": False,
},
"pretrained": True,
},
"training": {
"batch_size": 16,
"epochs": 3,
"learning_rate": 3e-4,
"weight_decay": 1e-4,
"optimizer": "adamw",
"scheduler_patience": 1,
"early_stopping_patience": 2,
"num_workers": 0,
"freeze_backbone": True,
"mixed_precision": True,
"pin_memory": True,
"max_grad_norm": 5.0,
},
"augmentation": {
"enabled": True,
"horizontal_flip": True,
"rotation_degrees": 10,
"translate": 0.03,
"scale_min": 0.95,
"scale_max": 1.05,
"color_jitter": {
"enabled": True,
"brightness": 0.12,
"contrast": 0.12,
"saturation": 0.08,
"hue": 0.02,
},
},
"evaluation": {
"threshold": 0.5,
"save_precision_recall_curve": True,
"save_calibration_plot": False,
"sample_grid_count": 12,
},
"explainability": {"enabled": True, "max_images": 8},
"gradio": {
"host": "127.0.0.1",
"port": 7860,
"share": False,
"low_confidence_threshold": 0.65,
},
}
def deep_update(base: dict[str, Any], override: dict[str, Any]) -> dict[str, Any]:
merged = copy.deepcopy(base)
for key, value in (override or {}).items():
if isinstance(value, dict) and isinstance(merged.get(key), dict):
merged[key] = deep_update(merged[key], value)
else:
merged[key] = value
return merged
def load_config(config_path: str | Path | None = None, overrides: dict[str, Any] | None = None) -> dict[str, Any]:
root = project_root()
path = Path(config_path).expanduser().resolve() if config_path else root / "configs" / "default.yaml"
user_config: dict[str, Any] = {}
if path.exists():
with path.open("r", encoding="utf-8") as f:
user_config = yaml.safe_load(f) or {}
config = deep_update(DEFAULT_CONFIG, user_config)
if overrides:
config = deep_update(config, overrides)
config["_config_path"] = str(path)
config["_project_root"] = str(root)
normalize_paths(config)
return config
def normalize_paths(config: dict[str, Any]) -> None:
root = Path(config["_project_root"])
for key in ("data_dir", "output_dir", "model_dir", "split_csv"):
value = config.get("paths", {}).get(key)
resolved = resolve_path(value, root)
if resolved is not None:
config["paths"][key] = str(resolved)
kaggle_dir = config.get("kaggle", {}).get("download_dir")
resolved_kaggle = resolve_path(kaggle_dir, root)
if resolved_kaggle is not None:
config["kaggle"]["download_dir"] = str(resolved_kaggle)
def save_config_snapshot(config: dict[str, Any], output_dir: str | Path) -> Path:
path = Path(output_dir) / "config_resolved.yaml"
path.parent.mkdir(parents=True, exist_ok=True)
safe = copy.deepcopy(config)
with path.open("w", encoding="utf-8") as f:
yaml.safe_dump(safe, f, sort_keys=False)
return path
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