110 lines
3.9 KiB
Python
110 lines
3.9 KiB
Python
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"""
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python test.py --model pointMLP --msg 20220209053148-404
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"""
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import argparse
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import os
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import datetime
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import torch
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import torch.nn.parallel
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import torch.backends.cudnn as cudnn
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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 torch.utils.data import DataLoader
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import models as models
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from utils import progress_bar, IOStream
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from data import ModelNet40
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import sklearn.metrics as metrics
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from helper import cal_loss
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import numpy as np
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import torch.nn.functional as F
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model_names = sorted(name for name in models.__dict__
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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('-c', '--checkpoint', type=str, metavar='PATH',
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help='path to save checkpoint (default: checkpoint)')
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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=16, help='batch size in training')
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parser.add_argument('--model', default='pointMLP', 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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return parser.parse_args()
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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 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(ModelNet40(partition='test', num_points=args.num_points), num_workers=4,
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batch_size=args.batch_size, shuffle=False, drop_last=False)
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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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test_out = validate(net, test_loader, criterion, device)
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print(f"Vanilla out: {test_out}")
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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(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
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% (test_loss / (batch_idx + 1), 100. * correct / total, correct, total))
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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. * metrics.accuracy_score(test_true, test_pred))),
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"acc_avg": float("%.3f" % (100. * metrics.balanced_accuracy_score(test_true, test_pred))),
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"time": time_cost
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}
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if __name__ == '__main__':
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main()
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