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e170a8e | 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 | from typing import Any
import numpy as np
import torch
import lightning as L
from lightglue import SuperPoint, DISK, SIFT, ALIKED
import time
from utils import rotation_angular_error, translation_angular_error, error_auc
from .relpose import RelPose
keypoint_dict = {
'superpoint': SuperPoint,
'disk': DISK,
'sift': SIFT,
'aliked': ALIKED,
}
class PL_RelPose(L.LightningModule):
def __init__(
self,
task,
lr,
epochs,
pct_start,
num_keypoints,
n_layers,
num_heads,
features='superpoint',
):
super().__init__()
self.extractor = keypoint_dict[features](max_num_keypoints=num_keypoints, detection_threshold=0.0).eval()
self.module = RelPose(features=features, task=task, n_layers=n_layers, num_heads=num_heads)
self.criterion = torch.nn.HuberLoss()
self.s_r = torch.nn.Parameter(torch.zeros(1))
# self.s_ta = torch.nn.Parameter(torch. zeros(1))
self.s_t = torch.nn.Parameter(torch.zeros(1))
self.r_errors = {k:[] for k in ['train', 'valid', 'test']}
self.ta_errors = {k:[] for k in ['train', 'valid', 'test']}
self.t_errors = {k:[] for k in ['train', 'valid', 'test']}
self.save_hyperparameters()
def _shared_log(self, mode, loss, loss_r, loss_t, loss_ta, loss_tn):
self.log_dict({
f'{mode}_loss/sum': loss,
f'{mode}_loss/r': loss_r,
f'{mode}_loss/t': loss_t,
f'{mode}_loss/ta': loss_ta,
f'{mode}_loss/tn': loss_tn,
}, on_epoch=True, sync_dist=True)
def training_step(self, batch, batch_idx):
loss, loss_r, loss_ta, loss_t, loss_tn, r_err, ta_err, t_err = self._shared_forward_step(batch, batch_idx)
self.r_errors['train'].append(r_err)
self.ta_errors['train'].append(ta_err)
self.t_errors['train'].append(t_err)
self._shared_log('train', loss, loss_r, loss_t, loss_ta, loss_tn)
return loss
def validation_step(self, batch, batch_idx):
loss, loss_r, loss_ta, loss_t, loss_tn, r_err, ta_err, t_err = self._shared_forward_step(batch, batch_idx)
self.r_errors['valid'].append(r_err)
self.ta_errors['valid'].append(ta_err)
self.t_errors['valid'].append(t_err)
self._shared_log('valid', loss, loss_r, loss_t, loss_ta, loss_tn)
def test_step(self, batch, batch_idx):
loss, loss_r, loss_ta, loss_t, loss_tn, r_err, ta_err, t_err = self._shared_forward_step(batch, batch_idx)
self.r_errors['test'].append(r_err)
self.ta_errors['test'].append(ta_err)
self.t_errors['test'].append(t_err)
self._shared_log('test', loss, loss_r, loss_t, loss_ta, loss_tn)
def _shared_forward_step(self, batch, batch_idx):
images = batch['images']
rotation = batch['rotation']
translation = batch['translation']
intrinsics = batch['intrinsics']
image0 = images[:, 0, ...]
image1 = images[:, 1, ...]
with torch.no_grad():
feats0 = self.extractor({'image': image0})
feats1 = self.extractor({'image': image1})
if 'scales' in batch:
scales = batch['scales']
feats0['keypoints'] *= scales[:, 0].unsqueeze(1)
feats1['keypoints'] *= scales[:, 1].unsqueeze(1)
if self.hparams.task == 'scene':
pred_r, pred_t = self.module({'image0': {**feats0, 'intrinsics': intrinsics[:, 0]}, 'image1': {**feats1, 'intrinsics': intrinsics[:, 1]}})
elif self.hparams.task == 'object':
bboxes = batch['bboxes']
pred_r, pred_t = self.module({'image0': {**feats0, 'intrinsics': intrinsics[:, 0], 'bbox': bboxes[:, 0]}, 'image1': {**feats1, 'intrinsics': intrinsics[:, 1]}})
r_err = rotation_angular_error(pred_r, rotation)
ta_err = translation_angular_error(pred_t, translation)
loss_r = self.criterion(r_err, torch.zeros_like(r_err))
loss_ta = self.criterion(ta_err, torch.zeros_like(ta_err))
loss_tn = self.criterion(pred_t / pred_t.norm(2, dim=-1, keepdim=True), translation / translation.norm(2, dim=-1, keepdim=True))
loss_t = self.criterion(pred_t, translation)
# loss = loss_r * torch.exp(-self.s_r) + loss_t * torch.exp(-self.s_t) + loss_ta * torch.exp(-self.s_ta) + self.s_r + self.s_t + self.s_ta
loss = loss_r + loss_ta + loss_t + loss_tn
r_err = r_err.detach()
ta_err = ta_err.detach()
t_err = (pred_t.detach() - translation).norm(2, dim=1)
return loss, loss_r, loss_ta, loss_t, loss_tn, r_err, ta_err, t_err
def predict_one_data(self, data, device='cuda'):
st_time = time.time()
images = data['images'].to(device)
intrinsics = data['intrinsics'].to(device)
image0 = images[:, 0, ...]
image1 = images[:, 1, ...]
preprocess = time.time()
with torch.no_grad():
feats0 = self.extractor({'image': image0})
feats1 = self.extractor({'image': image1})
extract_time = time.time()
if 'scales' in data:
scales = data['scales'].to(device)
feats0['keypoints'] *= scales[:, 0].unsqueeze(1)
feats1['keypoints'] *= scales[:, 1].unsqueeze(1)
if self.hparams.task == 'scene':
pred_r, pred_t = self.module({'image0': {**feats0, 'intrinsics': intrinsics[:, 0]}, 'image1': {**feats1, 'intrinsics': intrinsics[:, 1]}})
elif self.hparams.task == 'object':
bboxes = data['bboxes'].to(device)
pred_r, pred_t = self.module({'image0': {**feats0, 'intrinsics': intrinsics[:, 0], 'bbox': bboxes[:, 0]}, 'image1': {**feats1, 'intrinsics': intrinsics[:, 1]}})
regress_time = time.time()
return pred_r[0], pred_t[0], preprocess-st_time, extract_time-preprocess, regress_time-extract_time
def _shared_on_epoch_end(self, mode):
r_errors = torch.hstack(self.r_errors[mode]).rad2deg()
ta_errors = torch.hstack(self.ta_errors[mode]).rad2deg()
ta_errors = torch.minimum(ta_errors, 180-ta_errors)
auc = error_auc(torch.maximum(r_errors, ta_errors).cpu(), [5, 10, 20], mode)
t_errors = torch.hstack(self.t_errors[mode])
self.log_dict({
**auc,
f'{mode}_Rot./Avg. Error': r_errors.mean(),
f'{mode}_Rot./Med. Error': r_errors.median(),
f'{mode}_Rot./@30° ACC': (r_errors < 30).float().mean(),
f'{mode}_Rot./@15° ACC': (r_errors < 15).float().mean(),
# f'{mode}_ta/avg': ta_errors.mean(),
# f'{mode}_ta/med': ta_errors.median(),
f'{mode}_Trans./Avg. Error': t_errors.mean(),
f'{mode}_Trans./Med. Error': t_errors.median(),
f'{mode}_Trans./@10cm ACC': (t_errors < 0.1).float().mean(),
f'{mode}_Trans./@1m ACC': (t_errors < 1.0).float().mean(),
}, sync_dist=True)
self.r_errors[mode].clear()
self.ta_errors[mode].clear()
self.t_errors[mode].clear()
def on_train_epoch_end(self):
self._shared_on_epoch_end('train')
def on_validation_epoch_end(self):
self._shared_on_epoch_end('valid')
def on_test_epoch_end(self):
self._shared_on_epoch_end('test')
def configure_optimizers(self):
optimizer = torch.optim.AdamW(self.module.parameters(), lr=self.hparams.lr)
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=self.hparams.lr, steps_per_epoch=1, epochs=self.hparams.epochs, pct_start=self.hparams.pct_start)
return {
'optimizer': optimizer,
'lr_scheduler': scheduler
}
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