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""" | ||
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 | ||
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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(): | ||
"""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() | ||
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def main(): | ||
args = parse_args() | ||
print(f"args: {args}") | ||
os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" | ||
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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 | ||
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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(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']) | ||
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test_out = validate(net, test_loader, criterion, device) | ||
print(f"Vanilla out: {test_out}") | ||
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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)) | ||
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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 | ||
} | ||
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if __name__ == '__main__': | ||
main() |