import netdef_slim as nd
import os
schedule = nd.FixedStepSchedule('S_custom',
max_iter=200000,
steps=[50000, 90000, 150000],
base_lr=1e-05)
nd.evo_manager.set_training_dir(
os.path.join(os.path.dirname(__file__), 'training'))
nd.add_evo(nd.Evolution('kitti.train', [], schedule))
import netdef_slim as nd
import os
nd.evo_manager.set_training_dir(os.path.join(os.path.dirname(__file__), 'training'))
nd.add_evo(nd.Evolution('SDMixture_no_hom', [], 'S_refinement'))
import netdef_slim as nd
import os
import json
from collections import namedtuple
from model_genotype import model_func
from netdef_slim.schedules.named_schedule import NamedSchedule
nd.evo_manager.set_training_dir(
os.path.join(os.path.dirname(__file__), 'training'))
# add evolution
max_steps = 150000
evo = nd.Evolution('FT3D', [], NamedSchedule('genotype', max_steps))
nd.add_evo(evo)
nd.config['model_fn'] = model_func
nd.config['num_gpus'] = 1
nd.config['test_batch_size'] = 1
nd.config['no_bn'] = True
Genotype = namedtuple('Genotype', 'normal reduce upsample')
genotype = Genotype(normal=[('skip_connect', 0), ('sep_conv_5x5', 1),
('sep_conv_3x3', 2), ('skip_connect', 0),
('dil_conv_3x3', 2), ('dil_conv_3x3', 0)],
reduce=[('sep_conv_5x5', 1), ('sep_conv_3x3', 0),
('sep_conv_5x5', 2), ('skip_connect', 0),
('skip_connect', 1), ('sep_conv_5x5', 3)],
upsample=[('sep_conv_3x3', 3), ('dil_conv_3x3', 2),
('sep_conv_3x3', 1), ('skip_connect', 0),
('sep_conv_3x3', 3), ('sep_conv_3x3', 4),
import netdef_slim as nd
import os
nd.evo_manager.set_training_dir(
os.path.join(os.path.dirname(__file__), 'training'))
nd.add_evo(
nd.Evolution('flyingThings3D.train', ['flyingThings3D.test'], 'S_short'))
import netdef_slim as nd
import os
nd.evo_manager.set_training_dir(os.path.join(os.path.dirname(__file__), 'training'))
schedule = nd.FixedStepSchedule('S_custom', max_iter=200000, steps=[50000, 150000], base_lr=1e-05)
nd.add_evo(nd.Evolution('kitti_sceneflow_ft', [], schedule))
import netdef_slim as nd
import os
nd.evo_manager.set_training_dir(
os.path.join(os.path.dirname(__file__), 'training'))
schedule = nd.FixedStepSchedule('S_custom',
max_iter=250000,
steps=[5000, 10000, 20000],
base_lr=1e-05)
nd.add_evo(nd.Evolution('sintel_mixture', [], schedule))
import netdef_slim as nd
import os
nd.evo_manager.set_training_dir(
os.path.join(os.path.dirname(__file__), 'training'))
nd.add_evo(
nd.Evolution('flyingThings3D.train', ['flyingThings3D.test'],
'S_fine_half'))
import netdef_slim as nd
import os
nd.evo_manager.set_training_dir(
os.path.join(os.path.dirname(__file__), 'training'))
nd.add_evo(nd.Evolution('kitti.all.train', ['kitti.all.val'], 'S_short'))
