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97aa5af | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import os
import gc
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import MultiStepLR
from learning3d.models import FlowNet3D
from learning3d.data_utils import SceneflowDataset
import numpy as np
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from tqdm import tqdm
class IOStream:
def __init__(self, path):
self.f = open(path, 'a')
def cprint(self, text):
print(text)
self.f.write(text + '\n')
self.f.flush()
def close(self):
self.f.close()
def _init_(args):
if not os.path.exists('checkpoints'):
os.makedirs('checkpoints')
if not os.path.exists('checkpoints/' + args.exp_name):
os.makedirs('checkpoints/' + args.exp_name)
if not os.path.exists('checkpoints/' + args.exp_name + '/' + 'models'):
os.makedirs('checkpoints/' + args.exp_name + '/' + 'models')
def weights_init(m):
classname=m.__class__.__name__
if classname.find('Conv2d') != -1:
nn.init.kaiming_normal_(m.weight.data)
if classname.find('Conv1d') != -1:
nn.init.kaiming_normal_(m.weight.data)
def test_one_epoch(args, net, test_loader):
net.eval()
total_loss = 0
num_examples = 0
for i, data in tqdm(enumerate(test_loader), total=len(test_loader), smoothing=0.9):
pc1, pc2, color1, color2, flow, mask1 = data
pc1 = pc1.cuda().transpose(2,1).contiguous()
pc2 = pc2.cuda().transpose(2,1).contiguous()
color1 = color1.cuda().transpose(2,1).contiguous()
color2 = color2.cuda().transpose(2,1).contiguous()
flow = flow.cuda()
mask1 = mask1.cuda().float()
batch_size = pc1.size(0)
num_examples += batch_size
flow_pred = net(pc1, pc2, color1, color2).permute(0,2,1)
loss_1 = torch.mean(mask1 * torch.sum((flow_pred - flow) * (flow_pred - flow), -1) / 2.0)
pc1, pc2 = pc1.permute(0,2,1), pc2.permute(0,2,1)
pc1_ = pc1 + flow_pred
total_loss += loss_1.item() * batch_size
return total_loss * 1.0 / num_examples
def train_one_epoch(args, net, train_loader, opt):
net.train()
num_examples = 0
total_loss = 0
for i, data in tqdm(enumerate(train_loader), total=len(train_loader), smoothing=0.9):
pc1, pc2, color1, color2, flow, mask1 = data
pc1 = pc1.cuda().transpose(2,1).contiguous()
pc2 = pc2.cuda().transpose(2,1).contiguous()
color1 = color1.cuda().transpose(2,1).contiguous()
color2 = color2.cuda().transpose(2,1).contiguous()
flow = flow.cuda().transpose(2,1).contiguous()
mask1 = mask1.cuda().float()
batch_size = pc1.size(0)
opt.zero_grad()
num_examples += batch_size
flow_pred = net(pc1, pc2, color1, color2)
loss_1 = torch.mean(mask1 * torch.sum((flow_pred - flow) ** 2, 1) / 2.0)
pc1, pc2, flow_pred = pc1.permute(0,2,1), pc2.permute(0,2,1), flow_pred.permute(0,2,1)
pc1_ = pc1 + flow_pred
loss_1.backward()
opt.step()
total_loss += loss_1.item() * batch_size
# if (i+1) % 100 == 0:
# print("batch: %d, mean loss: %f" % (i, total_loss / 100 / batch_size))
# total_loss = 0
return total_loss * 1.0 / num_examples
def test(args, net, test_loader, boardio, textio):
test_loss = test_one_epoch(args, net, test_loader)
textio.cprint('==FINAL TEST==')
textio.cprint('mean test loss: %f'%test_loss)
def train(args, net, train_loader, test_loader, boardio, textio):
if args.use_sgd:
print("Use SGD")
opt = optim.SGD(net.parameters(), lr=args.lr * 100, momentum=args.momentum, weight_decay=1e-4)
else:
print("Use Adam")
opt = optim.Adam(net.parameters(), lr=args.lr, weight_decay=1e-4)
scheduler = MultiStepLR(opt, milestones=[75, 150, 200], gamma=0.1)
best_test_loss = np.inf
for epoch in range(args.epochs):
scheduler.step()
textio.cprint('==epoch: %d=='%epoch)
train_loss = train_one_epoch(args, net, train_loader, opt)
textio.cprint('mean train EPE loss: %f'%train_loss)
test_loss = test_one_epoch(args, net, test_loader)
textio.cprint('mean test EPE loss: %f'%test_loss)
if best_test_loss >= test_loss:
best_test_loss = test_loss
textio.cprint('best test loss till now: %f'%test_loss)
if torch.cuda.device_count() > 1:
torch.save(net.module.state_dict(), 'checkpoints/%s/models/model.best.t7' % args.exp_name)
else:
torch.save(net.state_dict(), 'checkpoints/%s/models/model.best.t7' % args.exp_name)
boardio.add_scalar('Train Loss', train_loss, epoch+1)
boardio.add_scalar('Test Loss', test_loss, epoch+1)
boardio.add_scalar('Best Test Loss', best_test_loss, epoch+1)
if torch.cuda.device_count() > 1:
torch.save(net.module.state_dict(), 'checkpoints/%s/models/model.%d.t7' % (args.exp_name, epoch))
else:
torch.save(net.state_dict(), 'checkpoints/%s/models/model.%d.t7' % (args.exp_name, epoch))
gc.collect()
def main():
parser = argparse.ArgumentParser(description='Point Cloud Registration')
parser.add_argument('--exp_name', type=str, default='exp_flownet', metavar='N',
help='Name of the experiment')
parser.add_argument('--model', type=str, default='flownet', metavar='N',
choices=['flownet'],
help='Model to use, [flownet]')
parser.add_argument('--emb_dims', type=int, default=512, metavar='N',
help='Dimension of embeddings')
parser.add_argument('--num_points', type=int, default=2048,
help='Point Number [default: 2048]')
parser.add_argument('--dropout', type=float, default=0.5, metavar='N',
help='Dropout ratio in transformer')
parser.add_argument('--batch_size', type=int, default=16, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--test_batch_size', type=int, default=10, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--epochs', type=int, default=250, metavar='N',
help='number of episode to train ')
parser.add_argument('--use_sgd', action='store_true', default=True,
help='Use SGD')
parser.add_argument('--lr', type=float, default=0.001, 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('--no_cuda', action='store_true', default=False,
help='enables CUDA training')
parser.add_argument('--seed', type=int, default=1234, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--eval', action='store_true', default=False,
help='evaluate the model')
parser.add_argument('--cycle', type=bool, default=False, metavar='N',
help='Whether to use cycle consistency')
parser.add_argument('--gaussian_noise', type=bool, default=False, metavar='N',
help='Wheter to add gaussian noise')
parser.add_argument('--unseen', type=bool, default=False, metavar='N',
help='Whether to test on unseen category')
parser.add_argument('--dataset', type=str, default='SceneflowDataset',
choices=['SceneflowDataset'], metavar='N',
help='dataset to use')
parser.add_argument('--dataset_path', type=str, default='data_processed_maxcut_35_20k_2k_8192', metavar='N',
help='dataset to use')
parser.add_argument('--model_path', type=str, default='', metavar='N',
help='Pretrained model path')
parser.add_argument('--pretrained', type=str, default='', metavar='N',
help='Pretrained model path')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
# CUDA settings
torch.backends.cudnn.deterministic = True
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
boardio = SummaryWriter(log_dir='checkpoints/' + args.exp_name)
_init_(args)
textio = IOStream('checkpoints/' + args.exp_name + '/run.log')
textio.cprint(str(args))
if args.dataset == 'SceneflowDataset':
train_loader = DataLoader(
SceneflowDataset(npoints=args.num_points, partition='train'),
batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(
SceneflowDataset(npoints=args.num_points, partition='test'),
batch_size=args.test_batch_size, shuffle=False, drop_last=False)
else:
raise Exception("not implemented")
if args.model == 'flownet':
net = FlowNet3D().cuda()
net.apply(weights_init)
if args.pretrained:
net.load_state_dict(torch.load(args.pretrained), strict=False)
print("Pretrained Model Loaded Successfully!")
if args.eval:
if args.model_path is '':
model_path = 'checkpoints' + '/' + args.exp_name + '/models/model.best.t7'
else:
model_path = args.model_path
print(model_path)
if not os.path.exists(model_path):
print("can't find pretrained model")
return
net.load_state_dict(torch.load(model_path), strict=False)
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
print("Let's use", torch.cuda.device_count(), "GPUs!")
else:
raise Exception('Not implemented')
if args.eval:
test(args, net, test_loader, boardio, textio)
else:
train(args, net, train_loader, test_loader, boardio, textio)
print('FINISH')
# boardio.close()
if __name__ == '__main__':
main() |