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bd659a9 | 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 | import json
import logging
import random
from pathlib import Path
import hydra
import numpy as np
import torch
from omegaconf import OmegaConf
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, f1_score, precision_score, recall_score
from torch.utils.data import DataLoader
from dataset import MemeDataset
from models import build_model, load_tokenizer_and_llama, load_trainable_checkpoint, resolve_dtype
from train import collect_probabilities
logger = logging.getLogger(__name__)
def template_root() -> Path:
return Path(__file__).resolve().parents[1]
def resolve_template_path(raw_path: str) -> Path:
path = Path(str(raw_path))
if path.is_absolute():
return path
return (template_root() / path).resolve()
def set_seed(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def flatten_probs(probs: np.ndarray) -> np.ndarray:
return probs.reshape(-1)
@hydra.main(version_base=None, config_path="./configs", config_name="config")
def main(cfg) -> None:
logger.info(OmegaConf.to_yaml(cfg, resolve=True))
set_seed(cfg.training.seed)
checkpoint_path = resolve_template_path(cfg.dataset.finetuned_model_path)
if not checkpoint_path.exists():
raise FileNotFoundError(f"Missing checkpoint: {checkpoint_path}")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = resolve_dtype(cfg.model.dtype)
use_8bit = bool(cfg.model.use_8bit and device.type == "cuda")
checkpoint = torch.load(checkpoint_path, map_location="cpu")
checkpoint_cfg = checkpoint.get("config", {})
checkpoint_dataset_cfg = checkpoint_cfg.get("dataset", {})
checkpoint_model_cfg = checkpoint_cfg.get("model", {})
fusion_method = str(checkpoint.get("fusion_method", cfg.model.fusion_methods[0]))
threshold = cfg.training.threshold
if threshold is None:
threshold = float(checkpoint.get("best_threshold", 0.5))
text_model_name = str(checkpoint_model_cfg.get("text_model", cfg.model.text_model))
image_model_name = str(checkpoint_model_cfg.get("image_model", cfg.model.image_model))
embedding_dim = int(checkpoint_model_cfg.get("embedding_dim", cfg.model.embedding_dim))
language = str(checkpoint_dataset_cfg.get("language", cfg.dataset.language))
image_id_col = str(checkpoint_dataset_cfg.get("image_id_col", cfg.dataset.image_id_col))
text_col = str(checkpoint_dataset_cfg.get("text_col", cfg.dataset.text_col))
label_col = str(checkpoint_dataset_cfg.get("label_col", cfg.dataset.label_col))
tokenizer, llama = load_tokenizer_and_llama(text_model_name, use_8bit, dtype, device)
dataset = MemeDataset(
split=cfg.dataset.inference_split,
data_root=resolve_template_path(cfg.dataset.data_root),
language=language,
tokenizer=tokenizer,
image_id_col=image_id_col,
text_col=text_col,
label_col=label_col,
max_length=cfg.training.max_length,
oversample_positive_train=0,
)
loader = DataLoader(
dataset,
batch_size=cfg.training.batch_size,
shuffle=False,
drop_last=False,
num_workers=cfg.training.num_workers,
)
model = build_model(llama, image_model_name, fusion_method, embedding_dim).to(device)
load_trainable_checkpoint(model, checkpoint)
model.eval()
probs, labels, image_ids = collect_probabilities(model, loader, device)
probs = flatten_probs(probs)
predictions = (probs >= threshold).astype(int)
labels = labels.astype(int)
image_ids = image_ids.astype(int)
preds_path = resolve_template_path(cfg.dataset.save_preds_path)
metrics_path = resolve_template_path(cfg.dataset.save_metrics_path)
preds_path.parent.mkdir(parents=True, exist_ok=True)
metrics_path.parent.mkdir(parents=True, exist_ok=True)
np.savetxt(
preds_path,
np.column_stack([image_ids, probs, predictions, labels]),
delimiter=",",
header="image_id,probability,prediction,true_label",
comments="",
)
metrics = {
"checkpoint": str(checkpoint_path),
"split": cfg.dataset.inference_split,
"fusion_method": fusion_method,
"threshold": float(threshold),
"accuracy": accuracy_score(labels, predictions),
"macro_f1": f1_score(labels, predictions, average="macro", zero_division=0),
"macro_precision": precision_score(labels, predictions, average="macro", zero_division=0),
"macro_recall": recall_score(labels, predictions, average="macro", zero_division=0),
"confusion_matrix": confusion_matrix(labels, predictions).tolist(),
"classification_report": classification_report(labels, predictions, zero_division=0),
}
with open(metrics_path, "w", encoding="utf-8") as file:
json.dump(metrics, file, ensure_ascii=False, indent=2)
logger.info("Saved predictions: %s", preds_path)
logger.info("Saved metrics: %s", metrics_path)
if __name__ == "__main__":
main()
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