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Update AutoLC for nautilus_stack #1

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Binary file added laser_scan_matcher/models/model_conv_16.pth
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Binary file added laser_scan_matcher/models/model_match_16.pth
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Binary file removed laser_scan_matcher/scripts/__pycache__/config.cpython-38.pyc
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116 changes: 0 additions & 116 deletions laser_scan_matcher/scripts/config.py
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41 changes: 5 additions & 36 deletions laser_scan_matcher/scripts/match_laser_scans.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,49 +4,18 @@
from sensor_msgs.msg import LaserScan
from laser_scan_matcher.srv import MatchLaserScans, MatchLaserScansResponse
import sys
import os
from os import path
import torch

# TODO fix this hack
sys.path.append(path.dirname(path.dirname(path.dirname( path.dirname( path.abspath(__file__) ) ) )))
sys.path.append(path.dirname(__file__))

from model import FullNet, EmbeddingNet, LCCNet, DistanceNet, StructuredEmbeddingNet, ScanMatchNet, ScanConvNet, ScanTransformNet, ScanSingleConvNet, ScanUncertaintyNet

def create_laser_networks(model_dir, model_epoch, multi_gpu=True):
scan_conv = ScanConvNet()
if model_dir:
scan_conv.load_state_dict(torch.load(os.path.join(model_dir, 'model_conv_' + model_epoch + '.pth')))

scan_transform = ScanTransformNet()
if model_dir:
transform_path = os.path.join(model_dir, 'model_transform_' + model_epoch + '.pth')
if os.path.exists(transform_path):
scan_transform.load_state_dict(torch.load(transform_path))
else:
print("Warning: no `transform` network found for provided model_dir and epoch")

scan_match = ScanMatchNet()
if model_dir:
scan_match.load_state_dict(torch.load(os.path.join(model_dir, 'model_match_' + model_epoch + '.pth')))

if multi_gpu and torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
scan_conv = torch.nn.DataParallel(scan_conv)
scan_match = torch.nn.DataParallel(scan_match)
scan_transform = torch.nn.DataParallel(scan_transform)

scan_conv.cuda()
scan_match.cuda()
scan_transform.cuda()
return scan_conv, scan_match, scan_transform
sys.path.append(path.join(path.dirname(path.dirname(path.dirname(path.abspath(__file__)))), 'learning_loop_closure'))
from helpers import create_laser_networks

def create_scan_match_helper(scan_conv, scan_match):
def match_scans(req):

scan_np = np.array(req.scan.ranges).astype(np.float32)
alt_scan_np = np.array(req.alt_scan.ranges).astype(np.float32)
scan_np = np.minimum(np.array(req.scan.ranges).astype(np.float32), 30)
alt_scan_np = np.minimum(np.array(req.alt_scan.ranges).astype(np.float32), 30)

scan = torch.tensor(scan_np).cuda()
alt_scan = torch.tensor(alt_scan_np).cuda()
Expand Down Expand Up @@ -75,4 +44,4 @@ def service():
try:
service()
except rospy.ROSInterruptException:
pass
pass
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