368 lines
16 KiB
Python
368 lines
16 KiB
Python
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"""
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Usage:
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python main.py --model CurveNet --exp_name=demo1
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@Author: An Tao
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@Contact: ta19@mails.tsinghua.edu.cn
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@File: main_partseg.py
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@Time: 2019/12/31 11:17 AM
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Modified by
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@Author: Tiange Xiang
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@Contact: txia7609@uni.sydney.edu.au
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@Time: 2021/01/21 3:10 PM
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"""
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from __future__ import print_function
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import os
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import datetime
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import argparse
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from torch.optim.lr_scheduler import CosineAnnealingLR, StepLR, MultiStepLR
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from data import ShapeNetPart
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import models as models
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import numpy as np
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from torch.utils.data import DataLoader
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from util import cal_loss, IOStream
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import sklearn.metrics as metrics
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seg_num = [4, 2, 2, 4, 4, 3, 3, 2, 4, 2, 6, 2, 3, 3, 3, 3]
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index_start = [0, 4, 6, 8, 12, 16, 19, 22, 24, 28, 30, 36, 38, 41, 44, 47]
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def _init_():
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# fix random seed
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if args.seed is not None:
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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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# prepare file structures
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if not os.path.exists('checkpoints'):
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os.makedirs('checkpoints')
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if not os.path.exists('checkpoints/'+args.exp_name):
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os.makedirs('checkpoints/'+args.exp_name)
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if not os.path.exists('checkpoints/'+args.exp_name+'/'+'models'):
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os.makedirs('checkpoints/'+args.exp_name+'/'+'models')
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def calculate_shape_IoU(pred_np, seg_np, label, class_choice, eva=False):
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label = label.squeeze()
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shape_ious = []
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category = {}
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for shape_idx in range(seg_np.shape[0]):
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if not class_choice:
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start_index = index_start[label[shape_idx]]
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num = seg_num[label[shape_idx]]
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parts = range(start_index, start_index + num)
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else:
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parts = range(seg_num[label[0]])
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part_ious = []
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for part in parts:
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I = np.sum(np.logical_and(pred_np[shape_idx] == part, seg_np[shape_idx] == part))
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U = np.sum(np.logical_or(pred_np[shape_idx] == part, seg_np[shape_idx] == part))
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if U == 0:
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iou = 1 # If the union of groundtruth and prediction points is empty, then count part IoU as 1
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else:
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iou = I / float(U)
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part_ious.append(iou)
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shape_ious.append(np.mean(part_ious))
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if label[shape_idx] not in category:
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category[label[shape_idx]] = [shape_ious[-1]]
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else:
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category[label[shape_idx]].append(shape_ious[-1])
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if eva:
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return shape_ious, category
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else:
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return shape_ious
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def train(args, io):
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train_dataset = ShapeNetPart(partition='trainval', num_points=args.num_points, class_choice=args.class_choice)
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if (len(train_dataset) < 100):
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drop_last = False
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else:
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drop_last = True
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train_loader = DataLoader(train_dataset, num_workers=8, batch_size=args.batch_size, shuffle=True, drop_last=drop_last, pin_memory=True)
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test_loader = DataLoader(ShapeNetPart(partition='test', num_points=args.num_points, class_choice=args.class_choice),
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num_workers=8, batch_size=args.test_batch_size, shuffle=False, drop_last=False, pin_memory=True)
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device = torch.device("cuda" if args.cuda else "cpu")
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io.cprint("Let's use " + str(torch.cuda.device_count()) + " GPUs!")
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seg_num_all = train_loader.dataset.seg_num_all
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seg_start_index = train_loader.dataset.seg_start_index
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# create model
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model = models.__dict__[args.model]().to(device)
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io.cprint(str(model))
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model = nn.DataParallel(model)
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if args.use_sgd:
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print("Use SGD")
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opt = optim.SGD(model.parameters(), lr=args.lr*100, momentum=args.momentum, weight_decay=1e-4)
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else:
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print("Use Adam")
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opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4)
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if args.scheduler == 'cos':
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if args.use_sgd:
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eta_min = args.lr/5.0
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else:
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eta_min = args.lr/100.0
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scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=eta_min)
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elif args.scheduler == 'step':
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scheduler = MultiStepLR(opt, [140, 180], gamma=0.1)
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criterion = cal_loss
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best_test_iou = 0
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for epoch in range(args.epochs):
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####################
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# Train
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####################
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train_time_cost = datetime.datetime.now()
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train_loss = 0.0
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count = 0.0
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model.train()
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train_true_cls = []
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train_pred_cls = []
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train_true_seg = []
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train_pred_seg = []
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train_label_seg = []
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for data, label, seg in train_loader:
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seg = seg - seg_start_index
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label_one_hot = np.zeros((label.shape[0], 16))
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for idx in range(label.shape[0]):
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label_one_hot[idx, label[idx]] = 1
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label_one_hot = torch.from_numpy(label_one_hot.astype(np.float32))
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data, label_one_hot, seg = data.to(device), label_one_hot.to(device), seg.to(device)
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data = data.permute(0, 2, 1)
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batch_size = data.size()[0]
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opt.zero_grad()
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seg_pred = model(data, label_one_hot)
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seg_pred = seg_pred.permute(0, 2, 1).contiguous()
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loss = criterion(seg_pred.view(-1, seg_num_all), seg.view(-1,1).squeeze())
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), 1)
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opt.step()
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pred = seg_pred.max(dim=2)[1] # (batch_size, num_points)
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count += batch_size
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train_loss += loss.item() * batch_size
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seg_np = seg.cpu().numpy() # (batch_size, num_points)
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pred_np = pred.detach().cpu().numpy() # (batch_size, num_points)
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train_true_cls.append(seg_np.reshape(-1)) # (batch_size * num_points)
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train_pred_cls.append(pred_np.reshape(-1)) # (batch_size * num_points)
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train_true_seg.append(seg_np)
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train_pred_seg.append(pred_np)
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train_label_seg.append(label.reshape(-1))
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if args.scheduler == 'cos':
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scheduler.step()
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elif args.scheduler == 'step':
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if opt.param_groups[0]['lr'] > 1e-5:
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scheduler.step()
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if opt.param_groups[0]['lr'] < 1e-5:
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for param_group in opt.param_groups:
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param_group['lr'] = 1e-5
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train_true_cls = np.concatenate(train_true_cls)
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train_pred_cls = np.concatenate(train_pred_cls)
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train_acc = metrics.accuracy_score(train_true_cls, train_pred_cls)
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avg_per_class_acc = metrics.balanced_accuracy_score(train_true_cls, train_pred_cls)
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train_true_seg = np.concatenate(train_true_seg, axis=0)
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train_pred_seg = np.concatenate(train_pred_seg, axis=0)
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train_label_seg = np.concatenate(train_label_seg)
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train_ious = calculate_shape_IoU(train_pred_seg, train_true_seg, train_label_seg, args.class_choice)
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train_time_cost = int((datetime.datetime.now() - train_time_cost).total_seconds())
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outstr = 'Train %d, loss: %.6f, train acc: %.6f, train avg acc: %.6f, train iou: %.6f' % (epoch,
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train_loss*1.0/count,
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train_acc,
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avg_per_class_acc,
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np.mean(train_ious))
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io.cprint(outstr)
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io.cprint(f"Training time: {train_time_cost} seconds.")
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####################
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# Test
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####################
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test_time_cost = datetime.datetime.now()
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test_loss = 0.0
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count = 0.0
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model.eval()
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test_true_cls = []
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test_pred_cls = []
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test_true_seg = []
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test_pred_seg = []
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test_label_seg = []
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for data, label, seg in test_loader:
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seg = seg - seg_start_index
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label_one_hot = np.zeros((label.shape[0], 16))
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for idx in range(label.shape[0]):
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label_one_hot[idx, label[idx]] = 1
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label_one_hot = torch.from_numpy(label_one_hot.astype(np.float32))
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data, label_one_hot, seg = data.to(device), label_one_hot.to(device), seg.to(device)
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data = data.permute(0, 2, 1)
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batch_size = data.size()[0]
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seg_pred = model(data, label_one_hot)
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seg_pred = seg_pred.permute(0, 2, 1).contiguous()
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loss = criterion(seg_pred.view(-1, seg_num_all), seg.view(-1,1).squeeze())
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pred = seg_pred.max(dim=2)[1]
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count += batch_size
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test_loss += loss.item() * batch_size
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seg_np = seg.cpu().numpy()
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pred_np = pred.detach().cpu().numpy()
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test_true_cls.append(seg_np.reshape(-1))
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test_pred_cls.append(pred_np.reshape(-1))
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test_true_seg.append(seg_np)
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test_pred_seg.append(pred_np)
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test_label_seg.append(label.reshape(-1))
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test_true_cls = np.concatenate(test_true_cls)
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test_pred_cls = np.concatenate(test_pred_cls)
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test_acc = metrics.accuracy_score(test_true_cls, test_pred_cls)
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avg_per_class_acc = metrics.balanced_accuracy_score(test_true_cls, test_pred_cls)
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test_true_seg = np.concatenate(test_true_seg, axis=0)
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test_pred_seg = np.concatenate(test_pred_seg, axis=0)
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test_label_seg = np.concatenate(test_label_seg)
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test_ious = calculate_shape_IoU(test_pred_seg, test_true_seg, test_label_seg, args.class_choice)
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test_time_cost = int((datetime.datetime.now() - test_time_cost).total_seconds())
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outstr = 'Test %d, loss: %.6f, test acc: %.6f, test avg acc: %.6f, test iou: %.6f, best iou %.6f' % (epoch,
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test_loss*1.0/count,
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test_acc,
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avg_per_class_acc,
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np.mean(test_ious), best_test_iou)
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io.cprint(outstr)
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io.cprint(f"Testing time: {test_time_cost} seconds.")
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if np.mean(test_ious) >= best_test_iou:
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best_test_iou = np.mean(test_ious)
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torch.save(model.state_dict(), 'checkpoints/%s/models/model.t7' % args.exp_name)
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def test(args, io):
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test_loader = DataLoader(ShapeNetPart(partition='test', num_points=args.num_points, class_choice=args.class_choice),
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batch_size=args.test_batch_size, shuffle=True, drop_last=False)
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device = torch.device("cuda" if args.cuda else "cpu")
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#Try to load models
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seg_start_index = test_loader.dataset.seg_start_index
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model = models.__dict__[args.model]().to(device)
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model = nn.DataParallel(model)
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model.load_state_dict(torch.load(args.model_path))
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model = model.eval()
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test_acc = 0.0
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test_true_cls = []
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test_pred_cls = []
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test_true_seg = []
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test_pred_seg = []
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test_label_seg = []
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category = {}
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for data, label, seg in test_loader:
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seg = seg - seg_start_index
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label_one_hot = np.zeros((label.shape[0], 16))
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for idx in range(label.shape[0]):
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label_one_hot[idx, label[idx]] = 1
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label_one_hot = torch.from_numpy(label_one_hot.astype(np.float32))
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data, label_one_hot, seg = data.to(device), label_one_hot.to(device), seg.to(device)
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data = data.permute(0, 2, 1)
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seg_pred = model(data, label_one_hot)
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seg_pred = seg_pred.permute(0, 2, 1).contiguous()
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pred = seg_pred.max(dim=2)[1]
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seg_np = seg.cpu().numpy()
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pred_np = pred.detach().cpu().numpy()
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test_true_cls.append(seg_np.reshape(-1))
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test_pred_cls.append(pred_np.reshape(-1))
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test_true_seg.append(seg_np)
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test_pred_seg.append(pred_np)
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test_label_seg.append(label.reshape(-1))
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test_true_cls = np.concatenate(test_true_cls)
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test_pred_cls = np.concatenate(test_pred_cls)
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test_acc = metrics.accuracy_score(test_true_cls, test_pred_cls)
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avg_per_class_acc = metrics.balanced_accuracy_score(test_true_cls, test_pred_cls)
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test_true_seg = np.concatenate(test_true_seg, axis=0)
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test_pred_seg = np.concatenate(test_pred_seg, axis=0)
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test_label_seg = np.concatenate(test_label_seg)
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test_ious,category = calculate_shape_IoU(test_pred_seg, test_true_seg, test_label_seg, args.class_choice, eva=True)
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outstr = 'Test :: test acc: %.6f, test avg acc: %.6f, test iou: %.6f' % (test_acc,
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avg_per_class_acc,
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np.mean(test_ious))
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io.cprint(outstr)
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results = []
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for key in category.keys():
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results.append((int(key), np.mean(category[key]), len(category[key])))
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results.sort(key=lambda x:x[0])
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for re in results:
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io.cprint('idx: %d mIoU: %.3f num: %d' % (re[0], re[1], re[2]))
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if __name__ == "__main__":
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# Training settings
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parser = argparse.ArgumentParser(description='Point Cloud Part Segmentation')
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parser.add_argument('--model', type=str, default='CurveNet')
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parser.add_argument('--exp_name', type=str, default='exp', metavar='N',
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help='Name of the experiment')
|
||
|
|
parser.add_argument('--dataset', type=str, default='shapenetpart', metavar='N',
|
||
|
|
choices=['shapenetpart'])
|
||
|
|
parser.add_argument('--class_choice', type=str, default=None, metavar='N',
|
||
|
|
choices=['airplane', 'bag', 'cap', 'car', 'chair',
|
||
|
|
'earphone', 'guitar', 'knife', 'lamp', 'laptop',
|
||
|
|
'motor', 'mug', 'pistol', 'rocket', 'skateboard', 'table'])
|
||
|
|
parser.add_argument('--batch_size', type=int, default=32, metavar='batch_size',
|
||
|
|
help='Size of batch)')
|
||
|
|
parser.add_argument('--test_batch_size', type=int, default=16, metavar='batch_size',
|
||
|
|
help='Size of batch)')
|
||
|
|
parser.add_argument('--epochs', type=int, default=200, metavar='N',
|
||
|
|
help='number of episode to train ')
|
||
|
|
parser.add_argument('--seed', type=int)
|
||
|
|
parser.add_argument('--use_sgd', type=bool, default=True,
|
||
|
|
help='Use SGD')
|
||
|
|
parser.add_argument('--lr', type=float, default=0.0005, metavar='LR',
|
||
|
|
help='learning rate (default: 0.001, 0.1 if using sgd)')
|
||
|
|
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
|
||
|
|
help='SGD momentum (default: 0.9)')
|
||
|
|
parser.add_argument('--scheduler', type=str, default='step', metavar='N',
|
||
|
|
choices=['cos', 'step'],
|
||
|
|
help='Scheduler to use, [cos, step]')
|
||
|
|
parser.add_argument('--no_cuda', type=bool, default=False,
|
||
|
|
help='enables CUDA training')
|
||
|
|
parser.add_argument('--eval', type=bool, default=False,
|
||
|
|
help='evaluate the model')
|
||
|
|
parser.add_argument('--num_points', type=int, default=2048,
|
||
|
|
help='num of points to use')
|
||
|
|
parser.add_argument('--model_path', type=str, default='', metavar='N',
|
||
|
|
help='Pretrained model path')
|
||
|
|
args = parser.parse_args()
|
||
|
|
time_str = str(datetime.datetime.now().strftime('-%Y%m%d%H%M%S'))
|
||
|
|
if args.exp_name is None:
|
||
|
|
args.exp_name = time_str
|
||
|
|
args.exp_name = args.model+"_"+args.exp_name
|
||
|
|
|
||
|
|
_init_()
|
||
|
|
|
||
|
|
if args.eval:
|
||
|
|
io = IOStream('checkpoints/' + args.exp_name + '/eval.log')
|
||
|
|
else:
|
||
|
|
io = IOStream('checkpoints/' + args.exp_name + '/run.log')
|
||
|
|
io.cprint(str(args))
|
||
|
|
io.cprint('random seed is: ' + str(args.seed))
|
||
|
|
|
||
|
|
args.cuda = not args.no_cuda and torch.cuda.is_available()
|
||
|
|
|
||
|
|
if args.cuda:
|
||
|
|
io.cprint(
|
||
|
|
'Using GPU : ' + str(torch.cuda.current_device()) + ' from ' + str(torch.cuda.device_count()) + ' devices')
|
||
|
|
else:
|
||
|
|
io.cprint('Using CPU')
|
||
|
|
|
||
|
|
if not args.eval:
|
||
|
|
train(args, io)
|
||
|
|
else:
|
||
|
|
with torch.no_grad():
|
||
|
|
test(args, io)
|