validate.py

import yaml
import torch
import argparse
import os

from tqdm import tqdm
from models import get_model
from loaders import get_loader
from losses import get_loss_fn
from metrics import AverageMeter
from utils import convert_state_dict, accuracy

torch.backends.cudnn.benchmark = True


def validate(cfg, model_path):
    assert model_path is not None, 'Not assert model path'
    use_cuda = False
    if cfg.get("cuda", None) is not None:
        if cfg.get("cuda", None) != "all":
            os.environ["CUDA_VISIBLE_DEVICES"] = cfg.get("cuda", None)
        use_cuda = torch.cuda.is_available()

    # Setup Dataloader
    train_loader, val_loader = get_loader(cfg)

    loss_fn = get_loss_fn(cfg)

    # Load Model
    model = get_model(cfg)
    if use_cuda:
        model.cuda()
        loss_fn.cuda()
        checkpoint = torch.load(model_path)
        if torch.cuda.device_count() > 1:  # multi gpus
            model = torch.nn.DataParallel(model, device_ids=list(range(torch.cuda.device_count())))
            state = checkpoint["state_dict"]
        else:  # 1 gpu
            state = convert_state_dict(checkpoint["state_dict"])
    else:  # cpu
        checkpoint = torch.load(model_path, map_location='cpu')
        state = convert_state_dict(checkpoint["state_dict"])
    model.load_state_dict(state)

    validate_epoch(val_loader, model, loss_fn, use_cuda)


def validate_epoch(val_loader, model, loss_fn, use_cuda):
    top1 = AverageMeter()
    top5 = AverageMeter()
    model.eval()
    for i, (input, label) in enumerate(tqdm(val_loader)):
        with torch.no_grad():
            if use_cuda:
                label = label.cuda()
                input = input.cuda()
            input_var = torch.autograd.Variable(input)
            label_var = torch.autograd.Variable(label)
            output = model(input_var)
            loss = loss_fn(output, label_var)
        prec1, prec5 = accuracy(output.data, label, topk=(1, 5))
        top1.update(prec1, input.size(0))
        top5.update(prec5, input.size(0))
    print('  **Test** Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'.format(top1=top1, top5=top5))
    return top1.avg


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="config")
    parser.add_argument(
        "--config",
        nargs="?",
        type=str,
        default="configs/resnet_imagenet.yml",
        help="Configuration file to use",
    )
    args = parser.parse_args()
    with open(args.config) as fp:
        cfg = yaml.load(fp)

    run_id = cfg["training"].get("runid", None)
    if run_id is None:
        raise Exception('In validate mode, the \033[1;35mrunid\033[0m of the model directory in configs file must be specified')
    logdir = os.path.join("runs", os.path.basename(args.config)[:-4], str(run_id))
    model_path = os.path.join(logdir, cfg["training"]["best_model"])

    validate(cfg, model_path=model_path)