dmlc--dgl
esang
3fef5d27d3
* publish pct * add train_cls * add readme * update opt for point transformer * update the example index * update for comments Co-authored-by: Tong He <hetong007@gmail.com>
247 行
8.5 KiB
Python
247 行
8.5 KiB
Python
import torch
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import torch.optim as optim
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from torch.utils.data import DataLoader
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import numpy as np
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import dgl
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from functools import partial
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import tqdm
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import argparse
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import time
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import provider
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from ShapeNet import ShapeNet
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from pct import PointTransformerSeg, PartSegLoss
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parser = argparse.ArgumentParser()
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parser.add_argument('--dataset-path', type=str, default='')
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parser.add_argument('--load-model-path', type=str, default='')
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parser.add_argument('--save-model-path', type=str, default='')
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parser.add_argument('--num-epochs', type=int, default=500)
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parser.add_argument('--num-workers', type=int, default=8)
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parser.add_argument('--batch-size', type=int, default=16)
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parser.add_argument('--tensorboard', action='store_true')
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args = parser.parse_args()
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num_workers = args.num_workers
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batch_size = args.batch_size
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def collate(samples):
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graphs, cat = map(list, zip(*samples))
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return dgl.batch(graphs), cat
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CustomDataLoader = partial(
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DataLoader,
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num_workers=num_workers,
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batch_size=batch_size,
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shuffle=True,
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drop_last=True)
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def train(net, opt, scheduler, train_loader, dev):
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category_list = sorted(list(shapenet.seg_classes.keys()))
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eye_mat = np.eye(16)
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net.train()
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total_loss = 0
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num_batches = 0
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total_correct = 0
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count = 0
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start = time.time()
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with tqdm.tqdm(train_loader, ascii=True) as tq:
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for data, label, cat in tq:
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num_examples = data.shape[0]
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data = data.to(dev, dtype=torch.float)
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label = label.to(dev, dtype=torch.long).view(-1)
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opt.zero_grad()
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cat_ind = [category_list.index(c) for c in cat]
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# An one-hot encoding for the object category
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cat_tensor = torch.tensor(eye_mat[cat_ind]).to(
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dev, dtype=torch.float)
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cat_tensor = cat_tensor.view(num_examples, 16, 1)
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logits = net(data, cat_tensor)
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loss = L(logits, label)
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loss.backward()
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opt.step()
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_, preds = logits.max(1)
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count += num_examples * 2048
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loss = loss.item()
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total_loss += loss
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num_batches += 1
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correct = (preds.view(-1) == label).sum().item()
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total_correct += correct
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AvgLoss = total_loss / num_batches
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AvgAcc = total_correct / count
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tq.set_postfix({
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'AvgLoss': '%.5f' % AvgLoss,
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'AvgAcc': '%.5f' % AvgAcc})
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scheduler.step()
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end = time.time()
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print("[Train] AvgLoss: {:.5}, AvgAcc: {:.5}, Time: {:.5}s".format(total_loss /
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num_batches, total_correct / count, end - start))
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return data, preds, AvgLoss, AvgAcc, end-start
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def mIoU(preds, label, cat, cat_miou, seg_classes):
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for i in range(preds.shape[0]):
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shape_iou = 0
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n = len(seg_classes[cat[i]])
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for cls in seg_classes[cat[i]]:
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pred_set = set(np.where(preds[i, :] == cls)[0])
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label_set = set(np.where(label[i, :] == cls)[0])
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union = len(pred_set.union(label_set))
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inter = len(pred_set.intersection(label_set))
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if union == 0:
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shape_iou += 1
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else:
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shape_iou += inter / union
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shape_iou /= n
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cat_miou[cat[i]][0] += shape_iou
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cat_miou[cat[i]][1] += 1
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return cat_miou
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def evaluate(net, test_loader, dev, per_cat_verbose=False):
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category_list = sorted(list(shapenet.seg_classes.keys()))
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eye_mat = np.eye(16)
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net.eval()
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cat_miou = {}
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for k in shapenet.seg_classes.keys():
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cat_miou[k] = [0, 0]
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miou = 0
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count = 0
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per_cat_miou = 0
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per_cat_count = 0
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with torch.no_grad():
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with tqdm.tqdm(test_loader, ascii=True) as tq:
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for data, label, cat in tq:
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num_examples = data.shape[0]
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data = data.to(dev, dtype=torch.float)
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label = label.to(dev, dtype=torch.long)
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cat_ind = [category_list.index(c) for c in cat]
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cat_tensor = torch.tensor(eye_mat[cat_ind]).to(
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dev, dtype=torch.float)
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cat_tensor = cat_tensor.view(
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num_examples, 16, 1)
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logits = net(data, cat_tensor)
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_, preds = logits.max(1)
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cat_miou = mIoU(preds.cpu().numpy(),
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label.view(num_examples, -1).cpu().numpy(),
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cat, cat_miou, shapenet.seg_classes)
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for _, v in cat_miou.items():
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if v[1] > 0:
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miou += v[0]
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count += v[1]
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per_cat_miou += v[0] / v[1]
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per_cat_count += 1
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tq.set_postfix({
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'mIoU': '%.5f' % (miou / count),
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'per Category mIoU': '%.5f' % (per_cat_miou / per_cat_count)})
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print("[Test] mIoU: %.5f, per Category mIoU: %.5f" %
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(miou / count, per_cat_miou / per_cat_count))
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if per_cat_verbose:
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print("-" * 60)
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print("Per-Category mIoU:")
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for k, v in cat_miou.items():
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if v[1] > 0:
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print("%s mIoU=%.5f" % (k, v[0] / v[1]))
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else:
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print("%s mIoU=%.5f" % (k, 1))
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print("-" * 60)
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return miou / count, per_cat_miou / per_cat_count
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dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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net = PointTransformerSeg()
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net = net.to(dev)
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if args.load_model_path:
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net.load_state_dict(torch.load(args.load_model_path, map_location=dev))
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opt = torch.optim.SGD(
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net.parameters(),
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lr=0.01,
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weight_decay=1e-4,
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momentum=0.9
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)
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scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
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opt, T_max=args.num_epochs)
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L = PartSegLoss()
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shapenet = ShapeNet(2048, normal_channel=False)
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train_loader = CustomDataLoader(shapenet.trainval())
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test_loader = CustomDataLoader(shapenet.test())
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# Tensorboard
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if args.tensorboard:
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import torchvision
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from torch.utils.tensorboard import SummaryWriter
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from torchvision import datasets, transforms
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writer = SummaryWriter()
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# Select 50 distinct colors for different parts
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color_map = torch.tensor([
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[47, 79, 79], [139, 69, 19], [112, 128, 144], [85, 107, 47], [139, 0, 0], [
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128, 128, 0], [72, 61, 139], [0, 128, 0], [188, 143, 143], [60, 179, 113],
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[205, 133, 63], [0, 139, 139], [70, 130, 180], [205, 92, 92], [154, 205, 50], [
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0, 0, 139], [50, 205, 50], [250, 250, 250], [218, 165, 32], [139, 0, 139],
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[10, 10, 10], [176, 48, 96], [72, 209, 204], [153, 50, 204], [255, 69, 0], [
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255, 145, 0], [0, 0, 205], [255, 255, 0], [0, 255, 0], [233, 150, 122],
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[220, 20, 60], [0, 191, 255], [160, 32, 240], [192, 192, 192], [173, 255, 47], [
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218, 112, 214], [216, 191, 216], [255, 127, 80], [255, 0, 255], [100, 149, 237],
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[128, 128, 128], [221, 160, 221], [144, 238, 144], [123, 104, 238], [255, 160, 122], [
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175, 238, 238], [238, 130, 238], [127, 255, 212], [255, 218, 185], [255, 105, 180],
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])
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# paint each point according to its pred
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def paint(batched_points):
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B, N = batched_points.shape
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colored = color_map[batched_points].squeeze(2)
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return colored
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best_test_miou = 0
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best_test_per_cat_miou = 0
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for epoch in range(args.num_epochs):
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print("Epoch #{}: ".format(epoch))
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data, preds, AvgLoss, AvgAcc, training_time = train(
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net, opt, scheduler, train_loader, dev)
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if (epoch + 1) % 5 == 0 or epoch == 0:
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test_miou, test_per_cat_miou = evaluate(
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net, test_loader, dev, True)
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if test_miou > best_test_miou:
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best_test_miou = test_miou
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best_test_per_cat_miou = test_per_cat_miou
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if args.save_model_path:
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torch.save(net.state_dict(), args.save_model_path)
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print('Current test mIoU: %.5f (best: %.5f), per-Category mIoU: %.5f (best: %.5f)' % (
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test_miou, best_test_miou, test_per_cat_miou, best_test_per_cat_miou))
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# Tensorboard
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if args.tensorboard:
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colored = paint(preds)
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writer.add_mesh('data', vertices=data,
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colors=colored, global_step=epoch)
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writer.add_scalar('training time for one epoch',
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training_time, global_step=epoch)
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writer.add_scalar('AvgLoss', AvgLoss, global_step=epoch)
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writer.add_scalar('AvgAcc', AvgAcc, global_step=epoch)
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if (epoch + 1) % 5 == 0:
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writer.add_scalar('test mIoU', test_miou, global_step=epoch)
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writer.add_scalar('best test mIoU',
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best_test_miou, global_step=epoch)
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print()
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