PointMLP/classification_ModelNet40/test.py

"""
python test.py --model pointMLP --msg 20220209053148-404
"""
import argparse
import os
import datetime
import torch
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torch.utils.data import DataLoader
import models as models
from utils import progress_bar, IOStream
from data import ModelNet40
import sklearn.metrics as metrics
from helper import cal_loss
import numpy as np
import torch.nn.functional as F

model_names = sorted(name for name in models.__dict__
                     if callable(models.__dict__[name]))


def parse_args():
    """Parameters"""
    parser = argparse.ArgumentParser('training')
    parser.add_argument('-c', '--checkpoint', type=str, metavar='PATH',
                        help='path to save checkpoint (default: checkpoint)')
    parser.add_argument('--msg', type=str, help='message after checkpoint')
    parser.add_argument('--batch_size', type=int, default=16, help='batch size in training')
    parser.add_argument('--model', default='pointMLP', help='model name [default: pointnet_cls]')
    parser.add_argument('--num_classes', default=40, type=int, choices=[10, 40], help='training on ModelNet10/40')
    parser.add_argument('--num_points', type=int, default=1024, help='Point Number')
    return parser.parse_args()

def main():
    args = parse_args()
    print(f"args: {args}")
    os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"

    if torch.cuda.is_available():
        device = 'cuda'
    else:
        device = 'cpu'
    print(f"==> Using device: {device}")
    # if args.msg is None:
    #     message = str(datetime.datetime.now().strftime('-%Y%m%d%H%M%S'))
    # else:
    #     message = "-"+args.msg
    # args.checkpoint = 'checkpoints/' + args.model + message
    if args.checkpoint is not None:
        print(f"==> Using checkpoint: {args.checkpoint}")

    print('==> Preparing data..')
    test_loader = DataLoader(
        ModelNet40(partition='test', num_points=args.num_points),
        num_workers=4,
        batch_size=args.batch_size,
        shuffle=False,
        drop_last=False
    )
    # Model
    print('==> Building model..')
    net = models.__dict__[args.model]()
    criterion = cal_loss
    net = net.to(device)
    # checkpoint_path = os.path.join(args.checkpoint, 'best_checkpoint.pth')
    checkpoint = torch.load(args.checkpoint, map_location=torch.device('cpu'))
    # criterion = criterion.to(device)
    if device == 'cuda':
        net = torch.nn.DataParallel(net)
        cudnn.benchmark = True
    net.load_state_dict(checkpoint['net'])

    test_out = validate(net, test_loader, criterion, device)
    print(f"Vanilla out: {test_out}")


def validate(net, testloader, criterion, device):
    net.eval()
    test_loss = 0
    correct = 0
    total = 0
    test_true = []
    test_pred = []
    time_cost = datetime.datetime.now()
    with torch.no_grad():
        for batch_idx, (data, label) in enumerate(testloader):
            data, label = data.to(device), label.to(device).squeeze()
            data = data.permute(0, 2, 1)
            logits = net(data)
            loss = criterion(logits, label)
            test_loss += loss.item()
            preds = logits.max(dim=1)[1]
            test_true.append(label.cpu().numpy())
            test_pred.append(preds.detach().cpu().numpy())
            total += label.size(0)
            correct += preds.eq(label).sum().item()
            progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
                         % (test_loss / (batch_idx + 1), 100. * correct / total, correct, total))

    time_cost = int((datetime.datetime.now() - time_cost).total_seconds())
    test_true = np.concatenate(test_true)
    test_pred = np.concatenate(test_pred)
    return {
        "loss": float("%.3f" % (test_loss / (batch_idx + 1))),
        "acc": float("%.3f" % (100. * metrics.accuracy_score(test_true, test_pred))),
        "acc_avg": float("%.3f" % (100. * metrics.balanced_accuracy_score(test_true, test_pred))),
        "time": time_cost
    }


if __name__ == '__main__':
    main()