219 lines
8 KiB
Python
219 lines
8 KiB
Python
import argparse
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import datetime
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import os
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import models as models
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import numpy as np
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import sklearn.metrics as metrics
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import torch
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import torch.backends.cudnn as cudnn
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import torch.nn.functional as F
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import torch.nn.parallel
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import torch.optim
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import torch.utils.data
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import torch.utils.data.distributed
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from data import ModelNet40
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from helper import cal_loss
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from torch.utils.data import DataLoader
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from utils import IOStream, progress_bar
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model_names = sorted(name for name in models.__dict__ if callable(models.__dict__[name]))
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def parse_args():
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"""Parameters"""
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parser = argparse.ArgumentParser("training")
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parser.add_argument(
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"-c",
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"--checkpoint",
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type=str,
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metavar="PATH",
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help="path to save checkpoint (default: checkpoint)",
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)
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parser.add_argument("--msg", type=str, help="message after checkpoint")
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parser.add_argument("--batch_size", type=int, default=32, help="batch size in training")
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parser.add_argument("--model", default="model31A", help="model name [default: pointnet_cls]")
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parser.add_argument("--num_classes", default=40, type=int, choices=[10, 40], help="training on ModelNet10/40")
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parser.add_argument("--num_points", type=int, default=1024, help="Point Number")
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parser.add_argument("--seed", type=int, help="random seed (default: 1)")
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# Voting evaluation, referring: https://github.com/CVMI-Lab/PAConv/blob/main/obj_cls/eval_voting.py
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parser.add_argument("--NUM_PEPEAT", type=int, default=300)
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parser.add_argument("--NUM_VOTE", type=int, default=10)
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parser.add_argument("--validate", action="store_true", help="Validate the original testing result.")
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return parser.parse_args()
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class PointcloudScale: # input random scaling
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def __init__(self, scale_low=2.0 / 3.0, scale_high=3.0 / 2.0):
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self.scale_low = scale_low
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self.scale_high = scale_high
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def __call__(self, pc):
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bsize = pc.size()[0]
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for i in range(bsize):
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xyz1 = np.random.uniform(low=self.scale_low, high=self.scale_high, size=[3])
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pc[i, :, 0:3] = torch.mul(pc[i, :, 0:3], torch.from_numpy(xyz1).float().cuda())
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return pc
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def main():
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args = parse_args()
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print(f"args: {args}")
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os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
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if args.seed is None:
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args.seed = np.random.randint(1, 10000)
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print(f"random seed is set to {args.seed}, the speed will slow down.")
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torch.manual_seed(args.seed)
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np.random.seed(args.seed)
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torch.cuda.manual_seed_all(args.seed)
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torch.cuda.manual_seed(args.seed)
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torch.set_printoptions(10)
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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os.environ["PYTHONHASHSEED"] = str(args.seed)
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if torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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print(f"==> Using device: {device}")
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if args.msg is None:
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message = str(datetime.datetime.now().strftime("-%Y%m%d%H%M%S"))
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else:
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message = "-" + args.msg
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args.checkpoint = "checkpoints/" + args.model + message
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print("==> Preparing data..")
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test_loader = DataLoader(
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ModelNet40(partition="test", num_points=args.num_points),
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num_workers=4,
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batch_size=args.batch_size // 2,
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shuffle=False,
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drop_last=False,
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)
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# Model
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print("==> Building model..")
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net = models.__dict__[args.model]()
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criterion = cal_loss
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net = net.to(device)
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checkpoint_path = os.path.join(args.checkpoint, "best_checkpoint.pth")
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checkpoint = torch.load(checkpoint_path, map_location=torch.device("cpu"))
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# criterion = criterion.to(device)
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if device == "cuda":
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net = torch.nn.DataParallel(net)
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cudnn.benchmark = True
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net.load_state_dict(checkpoint["net"])
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if args.validate:
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test_out = validate(net, test_loader, criterion, device)
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print(f"Vanilla out: {test_out}")
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print(
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"Note 1: Please also load the random seed parameter (if forgot, see out.txt).\n"
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"Note 2: This result may vary little on different GPUs (and number of GPUs), we tested 2080Ti, P100, and V100.\n"
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"[note : Original result is achieved with V100 GPUs.]\n\n\n",
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)
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# Interestingly, we get original best_test_acc on 4 V100 gpus, but this model is trained on one V100 gpu.
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# On different GPUs, and different number of GPUs, both OA and mean_acc vary a little.
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# Also, the batch size also affect the testing results, could not understand.
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print("===> start voting evaluation...")
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voting(net, test_loader, device, args)
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def validate(net, testloader, criterion, device):
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net.eval()
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test_loss = 0
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correct = 0
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total = 0
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test_true = []
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test_pred = []
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time_cost = datetime.datetime.now()
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with torch.no_grad():
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for batch_idx, (data, label) in enumerate(testloader):
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data, label = data.to(device), label.to(device).squeeze()
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data = data.permute(0, 2, 1)
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logits = net(data)
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loss = criterion(logits, label)
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test_loss += loss.item()
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preds = logits.max(dim=1)[1]
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test_true.append(label.cpu().numpy())
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test_pred.append(preds.detach().cpu().numpy())
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total += label.size(0)
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correct += preds.eq(label).sum().item()
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progress_bar(
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batch_idx,
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len(testloader),
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"Loss: %.3f | Acc: %.3f%% (%d/%d)"
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% (test_loss / (batch_idx + 1), 100.0 * correct / total, correct, total),
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)
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time_cost = int((datetime.datetime.now() - time_cost).total_seconds())
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test_true = np.concatenate(test_true)
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test_pred = np.concatenate(test_pred)
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return {
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"loss": float("%.3f" % (test_loss / (batch_idx + 1))),
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"acc": float("%.3f" % (100.0 * metrics.accuracy_score(test_true, test_pred))),
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"acc_avg": float("%.3f" % (100.0 * metrics.balanced_accuracy_score(test_true, test_pred))),
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"time": time_cost,
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}
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def voting(net, testloader, device, args):
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name = (
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"/evaluate_voting" + str(datetime.datetime.now().strftime("-%Y%m%d%H%M%S")) + "seed_" + str(args.seed) + ".log"
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)
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io = IOStream(args.checkpoint + name)
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io.cprint(str(args))
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net.eval()
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best_acc = 0
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best_mean_acc = 0
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# pointscale = PointcloudScale(scale_low=0.8, scale_high=1.18) # set the range of scaling
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# pointscale = PointcloudScale()
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pointscale = PointcloudScale(scale_low=0.85, scale_high=1.15)
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for i in range(args.NUM_PEPEAT):
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test_true = []
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test_pred = []
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for _batch_idx, (data, label) in enumerate(testloader):
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data, label = data.to(device), label.to(device).squeeze()
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pred = 0
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for v in range(args.NUM_VOTE):
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new_data = data
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# batch_size = data.size()[0]
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if v > 0:
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new_data.data = pointscale(new_data.data)
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with torch.no_grad():
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pred += F.softmax(net(new_data.permute(0, 2, 1)), dim=1) # sum 10 preds
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pred /= args.NUM_VOTE # avg the preds!
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label = label.view(-1)
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pred_choice = pred.max(dim=1)[1]
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test_true.append(label.cpu().numpy())
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test_pred.append(pred_choice.detach().cpu().numpy())
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test_true = np.concatenate(test_true)
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test_pred = np.concatenate(test_pred)
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test_acc = 100.0 * metrics.accuracy_score(test_true, test_pred)
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test_mean_acc = 100.0 * metrics.balanced_accuracy_score(test_true, test_pred)
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if test_acc > best_acc:
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best_acc = test_acc
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if test_mean_acc > best_mean_acc:
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best_mean_acc = test_mean_acc
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outstr = "Voting %d, test acc: %.3f, test mean acc: %.3f, [current best(all_acc: %.3f mean_acc: %.3f)]" % (
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i,
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test_acc,
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test_mean_acc,
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best_acc,
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best_mean_acc,
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)
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io.cprint(outstr)
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final_outstr = "Final voting test acc: %.6f," % (best_acc * 100)
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io.cprint(final_outstr)
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if __name__ == "__main__":
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main()
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