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535fb25 | 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 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import importlib.util
import json
import os
import sys
from pathlib import Path
from typing import Any
import numpy as np
from PIL import Image, ImageDraw
REPO_ROOT = Path(__file__).resolve().parents[1]
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
from flow_grpo.server_profiles import apply_server_profile_defaults
apply_server_profile_defaults()
OUT_DIR = REPO_ROOT / "analysis_outputs" / "h20_eval_corruption" / "single_sample_eval"
DEFAULT_OLD_RL_LORA = REPO_ROOT / "logs/radiomics/img-only-r32-a64-bs32-evalbs24-kl-beta0p005-scratch-15k/checkpoints/checkpoint-190/lora"
def load_config(entry: str):
module_path, function_name = entry.split(":", 1)
spec = importlib.util.spec_from_file_location("debug_h20_eval_config", Path(module_path).resolve())
if spec is None or spec.loader is None:
raise RuntimeError(f"Could not load config module {module_path}")
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return getattr(module, function_name)()
def stats(name: str, value: Any) -> dict[str, Any]:
try:
import torch
if isinstance(value, torch.Tensor):
arr = value.detach().float().cpu().numpy()
else:
arr = np.asarray(value)
except Exception:
arr = np.asarray(value)
return {
"name": name,
"shape": list(arr.shape),
"dtype": str(arr.dtype),
"min": float(np.nanmin(arr)),
"max": float(np.nanmax(arr)),
"mean": float(np.nanmean(arr)),
"std": float(np.nanstd(arr)),
}
def side_by_side(paths: list[tuple[str, Image.Image]], out_path: Path) -> None:
cell_w, cell_h = 256, 286
sheet = Image.new("RGB", (cell_w * len(paths), cell_h), "white")
draw = ImageDraw.Draw(sheet)
for i, (label, image) in enumerate(paths):
img = image.convert("RGB")
img.thumbnail((cell_w, cell_h - 30), Image.Resampling.BILINEAR)
x = i * cell_w + (cell_w - img.width) // 2
y = 28 + (cell_h - 30 - img.height) // 2
draw.text((i * cell_w + 4, 6), label[:34], fill=(0, 0, 0))
sheet.paste(img, (x, y))
sheet.save(out_path)
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--config", default=os.environ.get("CONFIG_ENTRY", "config/grpo.py:general_radiomics_omnigen_4gpu_kl"))
parser.add_argument("--sample_index", type=int, default=0)
parser.add_argument("--max_cases", type=int, default=4)
parser.add_argument("--old_rl_lora_path", default=os.environ.get("OLD_RL_LORA_PATH") or os.environ.get("EVAL_LORA_PATH") or str(DEFAULT_OLD_RL_LORA))
parser.add_argument("--output_dir", default=str(OUT_DIR))
args = parser.parse_args()
out_dir = Path(args.output_dir)
out_dir.mkdir(parents=True, exist_ok=True)
import torch
from peft import PeftModel
from scripts.train_omnigen import RadiomicsEditDataset, _to_rgb_pil, load_omnigen_components, merge_lora_into_base_model, requires_grad
from flow_grpo.omnigen_patch.omnigen_pipeline_with_logprob import pipeline_with_logprob
config = load_config(args.config)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if device.type != "cuda":
raise RuntimeError("CUDA is not available; single-sample eval needs the real H20 GPU environment.")
weight_dtype = torch.bfloat16 if getattr(config, "mixed_precision", "bf16") == "bf16" else torch.float16
dataset = RadiomicsEditDataset(config.dataset, "test")
sample = dataset[args.sample_index]
input_image = Image.open(sample["input_image_paths"][0]).convert("RGB")
gt_image = Image.open(sample["metadata"].get("output_image") or sample["metadata"].get("gt_image")).convert("RGB")
model, vae, processor = load_omnigen_components(config, device, weight_dtype)
requires_grad(vae, False)
if getattr(config, "use_lora", False) and getattr(config.train, "merge_lora_path", None):
model = merge_lora_into_base_model(model, config.train.merge_lora_path, weight_dtype, trainable=False)
requires_grad(model, False)
model.eval()
cases = [("sft_only", None, 0.0), ("sft_only", None, 0.01), ("sft_plus_old190", args.old_rl_lora_path, 0.0), ("sft_plus_old190", args.old_rl_lora_path, 0.01)]
results = {
"config": args.config,
"device": str(device),
"weight_dtype": str(weight_dtype),
"sample_index": args.sample_index,
"instruction": sample["instruction"],
"input_path": sample["input_image_paths"][0],
"gt_path": sample["metadata"].get("output_image") or sample["metadata"].get("gt_image"),
"cases": [],
}
active_model = model
for case_index, (label, lora_path, noise_level) in enumerate(cases[: args.max_cases]):
current_model = active_model
if lora_path:
if not Path(lora_path).exists():
results["cases"].append({"label": label, "noise_level": noise_level, "error": f"missing lora path: {lora_path}"})
continue
current_model = PeftModel.from_pretrained(active_model, lora_path, is_trainable=False).to(dtype=weight_dtype)
if hasattr(current_model, "set_adapter"):
current_model.set_adapter("default")
current_model.eval()
generator = torch.Generator(device=device).manual_seed(int(getattr(config, "seed", 0)))
with torch.no_grad():
collected = pipeline_with_logprob(
current_model,
vae,
processor,
[sample["instruction"]],
[sample["input_image_paths"]],
height=config.resolution,
width=config.resolution,
num_inference_steps=config.sample.eval_num_steps,
guidance_scale=config.sample.eval_guidance_scale,
img_guidance_scale=config.sample.eval_img_guidance_scale,
max_input_image_size=config.sample.max_input_image_size,
use_img_guidance=config.sample.use_img_guidance,
use_input_image_size_as_output=config.sample.use_input_image_size_as_output,
dtype=weight_dtype,
generator=generator,
output_type="pt",
noise_level=noise_level,
sde_type=config.sample.sde_type,
)
output_tensor = collected["images"][0]
output_pil = _to_rgb_pil(output_tensor)
case_name = f"{case_index}_{label}_noise{str(noise_level).replace('.', 'p')}"
output_pil.save(out_dir / f"{case_name}.png")
side_by_side([("input", input_image), ("generated", output_pil), ("gt", gt_image)], out_dir / f"{case_name}_panel.png")
results["cases"].append({
"label": label,
"lora_path": lora_path,
"noise_level": noise_level,
"initial_latent": stats("initial_latent", collected["all_latents"][0]),
"final_latent": stats("final_latent", collected["all_latents"][-1]),
"decoded_tensor": stats("decoded_tensor", output_tensor),
"final_pil": stats("final_pil", np.asarray(output_pil)),
"output": str(out_dir / f"{case_name}.png"),
})
(out_dir / "single_sample_eval_stats.json").write_text(json.dumps(results, indent=2, sort_keys=True) + "\n", encoding="utf-8")
print(json.dumps(results, indent=2, sort_keys=True))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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