File size: 7,242 Bytes
ce209f5 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 | from __future__ import annotations
import json
import os
import shutil
from pathlib import Path
import numpy as np
from PIL import Image
from utils.dataset_cache import dataset_runtime_summary
ROOT = Path(__file__).resolve().parents[1]
IMG_EXTS = {".png", ".jpg", ".jpeg", ".tif", ".tiff", ".bmp"}
def _safe_link_or_copy(src: Path, dst: Path) -> None:
if dst.is_symlink():
try:
if Path(os.readlink(dst)) == src:
return
except OSError:
pass
dst.unlink()
if dst.exists():
return
dst.parent.mkdir(parents=True, exist_ok=True)
try:
os.symlink(src, dst, target_is_directory=src.is_dir())
except OSError:
if src.is_dir():
shutil.copytree(src, dst, dirs_exist_ok=True)
else:
shutil.copy2(src, dst)
def _safe_mask_link_or_copy(src: Path, dst: Path) -> None:
dst.parent.mkdir(parents=True, exist_ok=True)
if not src.exists():
return
try:
arr = np.asarray(Image.open(src))
if arr.ndim == 3:
arr = arr[..., 0]
if arr.size and int(arr.max()) <= 1:
if dst.exists() or dst.is_symlink():
dst.unlink()
Image.fromarray((arr > 0).astype(np.uint8) * 255).save(dst, format="PNG")
return
except Exception as exc:
print(f"[DATASET-VIEW] Could not inspect mask {src}: {exc}")
_safe_link_or_copy(src, dst)
def _split_dir(dataset_cfg: dict, split: str) -> Path:
return Path(dataset_cfg["data_root"]) / dataset_cfg.get("splits", {}).get(split, split)
def _scan(folder: Path) -> dict[str, Path]:
if not folder.is_dir():
return {}
return {
p.stem: p
for p in sorted(folder.iterdir())
if p.is_file() and not p.name.startswith(".") and p.suffix.lower() in IMG_EXTS
}
def prepare_legacy_list_view(dataset_cfg: dict) -> Path:
view = ROOT / "generated_dataset_views" / dataset_cfg["name"] / "legacy_list"
list_dir = view / "list"
for folder in ("A", "B", "label", "list"):
(view / folder).mkdir(parents=True, exist_ok=True)
for split in ("train", "val", "test"):
split_root = _split_dir(dataset_cfg, split)
a_files = _scan(split_root / dataset_cfg.get("image_a_folder", "A"))
b_files = _scan(split_root / dataset_cfg.get("image_b_folder", "B"))
m_files = _scan(split_root / dataset_cfg.get("mask_folder", "label"))
names = []
for stem in sorted(set(a_files) & set(b_files) & set(m_files)):
target_name = f"{split}__{stem}{a_files[stem].suffix}"
_safe_link_or_copy(a_files[stem], view / "A" / target_name)
_safe_link_or_copy(b_files[stem], view / "B" / target_name)
_safe_mask_link_or_copy(m_files[stem], view / "label" / target_name)
label_name = f"{split}__{stem}{m_files[stem].suffix}"
if label_name != target_name:
_safe_mask_link_or_copy(m_files[stem], view / "label" / label_name)
names.append(target_name)
(list_dir / f"{split}.txt").write_text("\n".join(names) + ("\n" if names else ""), encoding="utf-8")
counts = {}
for split in ("train", "val", "test"):
list_path = list_dir / f"{split}.txt"
counts[split] = len(list_path.read_text(encoding="utf-8").splitlines()) if list_path.exists() else 0
print(f"[DATASET] {dataset_runtime_summary(dataset_cfg)}")
print(f"[DATASET] generated view path: {view}")
print(f"[DATASET] train/val/test counts: {counts}")
return view
def _base_config(model_name: str, dataset_cfg: dict, model_cfg: dict, prepare_view: bool = True) -> dict:
root_path = prepare_legacy_list_view(dataset_cfg) if prepare_view else ROOT / "generated_dataset_views" / dataset_cfg["name"] / "legacy_list"
root = str(root_path)
img_size = int(dataset_cfg.get("img_size", model_cfg.get("img_size", 256)))
batch_size = int(dataset_cfg.get("batch_size", 8))
num_workers = int(dataset_cfg.get("num_workers", 4))
epochs = int(model_cfg.get("num_epochs", 200))
lr = float(model_cfg.get("lr", 1e-4))
optimizer = str(model_cfg.get("optimizer", "adam"))
loss = str(model_cfg.get("loss", "ce_dice"))
dataset_name = dataset_cfg.get("source_name", dataset_cfg.get("name", "CD"))
return {
"name": f"{dataset_cfg['name']}-train-{model_name}",
"phase": "train",
"gpu_ids": [0],
"path_cd": {
"log": "logs",
"result": "results",
"checkpoint": "checkpoint",
"resume_state": None,
},
"datasets": {
"train": {
"name": dataset_name,
"datasetroot": root,
"resolution": img_size,
"num_workers": num_workers,
"batch_size": batch_size,
"use_shuffle": True,
"data_len": -1,
},
"val": {
"name": dataset_name,
"datasetroot": root,
"resolution": img_size,
"num_workers": num_workers,
"batch_size": batch_size,
"use_shuffle": False,
"data_len": -1,
},
"test": {
"name": dataset_name,
"datasetroot": root,
"resolution": img_size,
"num_workers": num_workers,
"batch_size": batch_size,
"use_shuffle": False,
"data_len": -1,
},
},
"model": {"name": model_name, "loss": loss},
"train": {
"n_epoch": epochs,
"train_print_iter": 50,
"val_freq": 1,
"val_print_iter": 20,
"optimizer": {"type": optimizer, "lr": lr},
"sheduler": {"lr_policy": model_cfg.get("scheduler", "linear"), "n_step": 3, "gamma": 0.1},
},
}
def _cdmamba_model_block() -> dict:
return {
"name": "cdmamba",
"loss": "ce_dice",
"init_filters": 16,
"n_classes": 2,
"mode": "AGLGF",
"conv_mode": "orignal_dinner",
"local_query_model": "orignal_dinner",
"up_mode": "SRCM",
"up_conv_mode": "deepwise",
"spatial_dims": 2,
"in_channels": 3,
"resdiual": False,
"blocks_down": [1, 2, 2, 4],
"blocks_up": [1, 1, 1],
"diff_abs": "later",
"stage": 2,
"mamba_act": "relu",
"norm": ["GROUP", {"num_groups": 8}],
}
def write_bifa_or_cdmamba_config(
model_name: str,
dataset_cfg: dict,
model_cfg: dict,
prepare_view: bool = True,
) -> Path:
if model_name not in {"bifa", "cdmamba"}:
raise ValueError(f"Unsupported generated legacy JSON model: {model_name}")
cfg = _base_config(model_name, dataset_cfg, model_cfg, prepare_view=prepare_view)
if model_name == "cdmamba":
cfg["model"] = _cdmamba_model_block()
out = ROOT / "generated_configs" / f"{dataset_cfg['name']}__{model_name}.json"
out.parent.mkdir(parents=True, exist_ok=True)
with out.open("w", encoding="utf-8") as f:
json.dump(cfg, f, indent=2)
return out
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