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import json
import logging
import os
from training.distributed import is_master
from .zero_shot import zero_shot_eval
try:
import wandb
except ImportError:
wandb = None
def evaluate_d_cls(model, data, epoch, args, tb_writer=None):
metrics = {}
if not is_master(args):
return metrics
model.eval()
zero_shot_metrics = zero_shot_eval(model, data, epoch, args)
metrics.update(zero_shot_metrics)
if not metrics:
return metrics
logging.info(
f"Eval Epoch: {epoch} "
+ "\t".join([f"{k}: {round(v, 4):.4f}" for k, v in metrics.items()])
)
if args.save_logs:
for name, val in metrics.items():
if tb_writer is not None:
tb_writer.add_scalar(f"val/d_cls/{args.val_d_cls_data[0].lower()}/{name}", val, epoch)
args.d_cls_output_dir = os.path.join(args.log_base_path, f'd_cls/{args.val_d_cls_data[0].lower()}')
os.makedirs(args.d_cls_output_dir, exist_ok=True)
with open(os.path.join(args.d_cls_output_dir, "results.jsonl"), "a+") as f:
f.write(json.dumps(metrics))
f.write("\n")
if args.wandb:
assert wandb is not None, 'Please install wandb.'
for name, val in metrics.items():
wandb.log({f"val/{name}": val, 'epoch': epoch})
return metrics
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