wehub-resource-sync
331 lines
9.8 KiB
Python
331 lines
9.8 KiB
Python
import argparse
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import time
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from functools import partial
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import dgl
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import numpy as np
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import torch
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import torch.optim as optim
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import tqdm
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from point_transformer import PartSegLoss, PointTransformerSeg
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from ShapeNet import ShapeNet
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from torch.utils.data import DataLoader
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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=250)
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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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parser.add_argument("--opt", type=str, default="adam")
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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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)
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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 = (
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torch.tensor(eye_mat[cat_ind])
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.to(dev, dtype=torch.float)
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.repeat(1, 2048)
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)
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cat_tensor = cat_tensor.view(num_examples, -1, 16)
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logits = net(data, cat_tensor).permute(0, 2, 1)
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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, "AvgAcc": "%.5f" % AvgAcc}
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)
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scheduler.step()
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end = time.time()
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print(
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"[Train] AvgLoss: {:.5}, AvgAcc: {:.5}, Time: {:.5}s".format(
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total_loss / num_batches, total_correct / count, end - start
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)
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)
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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 = (
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torch.tensor(eye_mat[cat_ind])
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.to(dev, dtype=torch.float)
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.repeat(1, 2048)
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)
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cat_tensor = cat_tensor.view(num_examples, -1, 16)
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logits = net(data, cat_tensor).permute(0, 2, 1)
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_, preds = logits.max(1)
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cat_miou = mIoU(
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preds.cpu().numpy(),
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label.view(num_examples, -1).cpu().numpy(),
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cat,
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cat_miou,
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shapenet.seg_classes,
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)
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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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{
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"mIoU": "%.5f" % (miou / count),
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"per Category mIoU": "%.5f"
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% (per_cat_miou / per_cat_count),
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}
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)
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print(
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"[Test] mIoU: %.5f, per Category mIoU: %.5f"
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% (miou / count, per_cat_miou / per_cat_count)
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)
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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(50, batch_size)
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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(
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torch.load(args.load_model_path, weights_only=False, map_location=dev)
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)
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if args.opt == "sgd":
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# The optimizer strategy described in paper:
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opt = torch.optim.SGD(
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net.parameters(), lr=0.01, momentum=0.9, weight_decay=1e-4
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)
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scheduler = torch.optim.lr_scheduler.MultiStepLR(
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opt, milestones=[120, 160], gamma=0.1
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)
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elif args.opt == "adam":
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# The optimizer strategy proposed by
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# https://github.com/qq456cvb/Point-Transformers:
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opt = torch.optim.Adam(
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net.parameters(),
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lr=1e-3,
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betas=(0.9, 0.999),
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eps=1e-08,
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weight_decay=1e-4,
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)
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scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=50, gamma=0.3)
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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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[
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[47, 79, 79],
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[139, 69, 19],
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[112, 128, 144],
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[85, 107, 47],
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[139, 0, 0],
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[128, 128, 0],
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[72, 61, 139],
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[0, 128, 0],
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[188, 143, 143],
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[60, 179, 113],
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[205, 133, 63],
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[0, 139, 139],
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[70, 130, 180],
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[205, 92, 92],
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[154, 205, 50],
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[0, 0, 139],
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[50, 205, 50],
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[250, 250, 250],
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[218, 165, 32],
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[139, 0, 139],
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[10, 10, 10],
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[176, 48, 96],
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[72, 209, 204],
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[153, 50, 204],
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[255, 69, 0],
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[255, 145, 0],
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[0, 0, 205],
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[255, 255, 0],
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[0, 255, 0],
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[233, 150, 122],
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[220, 20, 60],
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[0, 191, 255],
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[160, 32, 240],
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[192, 192, 192],
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[173, 255, 47],
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[218, 112, 214],
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[216, 191, 216],
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[255, 127, 80],
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[255, 0, 255],
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[100, 149, 237],
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[128, 128, 128],
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[221, 160, 221],
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[144, 238, 144],
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[123, 104, 238],
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[255, 160, 122],
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[175, 238, 238],
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[238, 130, 238],
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[127, 255, 212],
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[255, 218, 185],
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[255, 105, 180],
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]
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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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)
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if (epoch + 1) % 5 == 0 or epoch == 0:
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test_miou, test_per_cat_miou = evaluate(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(
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"Current test mIoU: %.5f (best: %.5f), per-Category mIoU: %.5f (best: %.5f)"
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% (
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test_miou,
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best_test_miou,
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test_per_cat_miou,
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best_test_per_cat_miou,
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)
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)
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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(
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"data", vertices=data, colors=colored, global_step=epoch
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)
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writer.add_scalar(
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"training time for one epoch", training_time, global_step=epoch
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)
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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(
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"best test mIoU", best_test_miou, global_step=epoch
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)
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print()
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