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train.py
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train.py
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import cv2
import os
import argparse
import numpy as np
from collections import OrderedDict
import matplotlib.pyplot as plt
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from dpvo.data_readers.factory import dataset_factory
from dpvo.lietorch import SE3
from dpvo.logger import Logger
import torch.nn.functional as F
from dpvo.net import VONet
from evaluate_tartan import evaluate as validate
def show_image(image):
image = image.permute(1, 2, 0).cpu().numpy()
cv2.imshow('image', image / 255.0)
cv2.waitKey()
def image2gray(image):
image = image.mean(dim=0).cpu().numpy()
cv2.imshow('image', image / 255.0)
cv2.waitKey()
def kabsch_umeyama(A, B):
n, m = A.shape
EA = torch.mean(A, axis=0)
EB = torch.mean(B, axis=0)
VarA = torch.mean((A - EA).norm(dim=1)**2)
H = ((A - EA).T @ (B - EB)) / n
U, D, VT = torch.svd(H)
c = VarA / torch.trace(torch.diag(D))
return c
def train(args):
""" main training loop """
# legacy ddp code
rank = 0
db = dataset_factory(['tartan'], datapath="datasets/TartanAir", n_frames=args.n_frames)
train_loader = DataLoader(db, batch_size=1, shuffle=True, num_workers=4)
net = VONet()
net.train()
net.cuda()
if args.ckpt is not None:
state_dict = torch.load(args.ckpt)
new_state_dict = OrderedDict()
for k, v in state_dict.items():
new_state_dict[k.replace('module.', '')] = v
net.load_state_dict(new_state_dict, strict=False)
optimizer = torch.optim.AdamW(net.parameters(), lr=args.lr, weight_decay=1e-6)
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer,
args.lr, args.steps, pct_start=0.01, cycle_momentum=False, anneal_strategy='linear')
if rank == 0:
logger = Logger(args.name, scheduler)
total_steps = 0
while 1:
for data_blob in train_loader:
images, poses, disps, intrinsics = [x.cuda().float() for x in data_blob]
optimizer.zero_grad()
# fix poses to gt for first 1k steps
so = total_steps < 1000 and args.ckpt is None
poses = SE3(poses).inv()
traj = net(images, poses, disps, intrinsics, M=1024, STEPS=18, structure_only=so)
loss = 0.0
for i, (v, x, y, P1, P2, kl) in enumerate(traj):
e = (x - y).norm(dim=-1)
e = e.reshape(-1, net.P**2)[(v > 0.5).reshape(-1)].min(dim=-1).values
N = P1.shape[1]
ii, jj = torch.meshgrid(torch.arange(N), torch.arange(N))
ii = ii.reshape(-1).cuda()
jj = jj.reshape(-1).cuda()
k = ii != jj
ii = ii[k]
jj = jj[k]
P1 = P1.inv()
P2 = P2.inv()
t1 = P1.matrix()[...,:3,3]
t2 = P2.matrix()[...,:3,3]
s = kabsch_umeyama(t2[0], t1[0]).detach().clamp(max=10.0)
P1 = P1.scale(s.view(1, 1))
dP = P1[:,ii].inv() * P1[:,jj]
dG = P2[:,ii].inv() * P2[:,jj]
e1 = (dP * dG.inv()).log()
tr = e1[...,0:3].norm(dim=-1)
ro = e1[...,3:6].norm(dim=-1)
loss += args.flow_weight * e.mean()
if not so and i >= 2:
loss += args.pose_weight * ( tr.mean() + ro.mean() )
# kl is 0 (not longer used)
loss += kl
loss.backward()
torch.nn.utils.clip_grad_norm_(net.parameters(), args.clip)
optimizer.step()
scheduler.step()
total_steps += 1
metrics = {
"loss": loss.item(),
"kl": kl.item(),
"px1": (e < .25).float().mean().item(),
"ro": ro.float().mean().item(),
"tr": tr.float().mean().item(),
"r1": (ro < .001).float().mean().item(),
"r2": (ro < .01).float().mean().item(),
"t1": (tr < .001).float().mean().item(),
"t2": (tr < .01).float().mean().item(),
}
if rank == 0:
logger.push(metrics)
if total_steps % 10000 == 0:
torch.cuda.empty_cache()
if rank == 0:
PATH = 'checkpoints/%s_%06d.pth' % (args.name, total_steps)
torch.save(net.state_dict(), PATH)
validation_results = validate(None, net)
if rank == 0:
logger.write_dict(validation_results)
torch.cuda.empty_cache()
net.train()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--name', default='bla', help='name your experiment')
parser.add_argument('--ckpt', help='checkpoint to restore')
parser.add_argument('--steps', type=int, default=240000)
parser.add_argument('--lr', type=float, default=0.00008)
parser.add_argument('--clip', type=float, default=10.0)
parser.add_argument('--n_frames', type=int, default=15)
parser.add_argument('--pose_weight', type=float, default=10.0)
parser.add_argument('--flow_weight', type=float, default=0.1)
args = parser.parse_args()
train(args)