PointMLP/classification_ModelNet40/voting.py

import argparse
import datetime
import os

import models as models
import numpy as np
import sklearn.metrics as metrics
import torch
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from data import ModelNet40
from helper import cal_loss
from torch.utils.data import DataLoader
from utils import IOStream, progress_bar

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=32, help="batch size in training")
    parser.add_argument("--model", default="model31A", 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")
    parser.add_argument("--seed", type=int, help="random seed (default: 1)")

    # Voting evaluation, referring: https://github.com/CVMI-Lab/PAConv/blob/main/obj_cls/eval_voting.py
    parser.add_argument("--NUM_PEPEAT", type=int, default=300)
    parser.add_argument("--NUM_VOTE", type=int, default=10)

    parser.add_argument("--validate", action="store_true", help="Validate the original testing result.")
    return parser.parse_args()


class PointcloudScale:  # input random scaling
    def __init__(self, scale_low=2.0 / 3.0, scale_high=3.0 / 2.0):
        self.scale_low = scale_low
        self.scale_high = scale_high

    def __call__(self, pc):
        bsize = pc.size()[0]
        for i in range(bsize):
            xyz1 = np.random.uniform(low=self.scale_low, high=self.scale_high, size=[3])
            pc[i, :, 0:3] = torch.mul(pc[i, :, 0:3], torch.from_numpy(xyz1).float().cuda())

        return pc


def main():
    args = parse_args()
    print(f"args: {args}")
    os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
    if args.seed is None:
        args.seed = np.random.randint(1, 10000)
    print(f"random seed is set to {args.seed}, the speed will slow down.")
    torch.manual_seed(args.seed)
    np.random.seed(args.seed)
    torch.cuda.manual_seed_all(args.seed)
    torch.cuda.manual_seed(args.seed)
    torch.set_printoptions(10)
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True
    os.environ["PYTHONHASHSEED"] = str(args.seed)
    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

    print("==> Preparing data..")
    test_loader = DataLoader(
        ModelNet40(partition="test", num_points=args.num_points),
        num_workers=4,
        batch_size=args.batch_size // 2,
        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(checkpoint_path, 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"])

    if args.validate:
        test_out = validate(net, test_loader, criterion, device)
        print(f"Vanilla out: {test_out}")
        print(
            "Note 1: Please also load the random seed parameter (if forgot, see out.txt).\n"
            "Note 2: This result may vary little on different GPUs (and number of GPUs), we tested 2080Ti, P100, and V100.\n"
            "[note : Original result is achieved with V100 GPUs.]\n\n\n",
        )
        # Interestingly, we get original best_test_acc on 4 V100 gpus, but this model is trained on one V100 gpu.
        # On different GPUs, and different number of GPUs, both OA and mean_acc vary a little.
        # Also, the batch size also affect the testing results, could not understand.

    print("===> start voting evaluation...")
    voting(net, test_loader, device, args)


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), 100.0 * 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" % (100.0 * metrics.accuracy_score(test_true, test_pred))),
        "acc_avg": float("%.3f" % (100.0 * metrics.balanced_accuracy_score(test_true, test_pred))),
        "time": time_cost,
    }


def voting(net, testloader, device, args):
    name = (
        "/evaluate_voting" + str(datetime.datetime.now().strftime("-%Y%m%d%H%M%S")) + "seed_" + str(args.seed) + ".log"
    )
    io = IOStream(args.checkpoint + name)
    io.cprint(str(args))

    net.eval()
    best_acc = 0
    best_mean_acc = 0
    # pointscale = PointcloudScale(scale_low=0.8, scale_high=1.18)  # set the range of scaling
    # pointscale = PointcloudScale()
    pointscale = PointcloudScale(scale_low=0.85, scale_high=1.15)

    for i in range(args.NUM_PEPEAT):
        test_true = []
        test_pred = []

        for _batch_idx, (data, label) in enumerate(testloader):
            data, label = data.to(device), label.to(device).squeeze()
            pred = 0
            for v in range(args.NUM_VOTE):
                new_data = data
                # batch_size = data.size()[0]
                if v > 0:
                    new_data.data = pointscale(new_data.data)
                with torch.no_grad():
                    pred += F.softmax(net(new_data.permute(0, 2, 1)), dim=1)  # sum 10 preds
            pred /= args.NUM_VOTE  # avg the preds!
            label = label.view(-1)
            pred_choice = pred.max(dim=1)[1]
            test_true.append(label.cpu().numpy())
            test_pred.append(pred_choice.detach().cpu().numpy())
        test_true = np.concatenate(test_true)
        test_pred = np.concatenate(test_pred)
        test_acc = 100.0 * metrics.accuracy_score(test_true, test_pred)
        test_mean_acc = 100.0 * metrics.balanced_accuracy_score(test_true, test_pred)
        if test_acc > best_acc:
            best_acc = test_acc
        if test_mean_acc > best_mean_acc:
            best_mean_acc = test_mean_acc
        outstr = "Voting %d, test acc: %.3f, test mean acc: %.3f,  [current best(all_acc: %.3f mean_acc: %.3f)]" % (
            i,
            test_acc,
            test_mean_acc,
            best_acc,
            best_mean_acc,
        )
        io.cprint(outstr)

    final_outstr = "Final voting test acc: %.6f," % (best_acc * 100)
    io.cprint(final_outstr)


if __name__ == "__main__":
    main()