# APT_interface.main(cmd.split())
## stephen for multi view
lbl_file = '/groups/branson/bransonlab/mayank/PoseTF/data/apt_interface/stephen/shdl_stripped_homogeneousims_modified.lbl'
mov_files = ['/groups/branson/bransonlab/mayank/PoseTF/data/apt_interface/stephen/fly516/C001H001S0001/C001H001S0001_c.avi',
'/groups/branson/bransonlab/mayank/PoseTF/data/apt_interface/stephen/fly516/C002H001S0001/C002H001S0001_c.avi']
out_files = []
for mov_file in mov_files:
out_files.append(os.path.splitext(mov_file)[0] + '_interface_test_{}.trk'.format(dstr))
name = 'stephen_test_apt'
cmd = '{} -name {} train'.format(lbl_file,name)
#APT_interface.main(cmd.split())
cmd = '{} -name {} track -mov {} {} -out {} {}'.format(
lbl_file, name, mov_files[0], mov_files[1], out_files[0], out_files[1])
#APT_interface.main(cmd.split())
## Alice: for projects with trx files.
lbl_file = '/groups/branson/bransonlab/mayank/PoseTF/data/alice/multitarget_bubble_expandedbehavior_20180425_modified.lbl'
mov_file = '/groups/branson/bransonlab/mayank/PoseTF/data/apt_interface/alice/cx_GMR_SS00168_CsChr_RigD_20150909T111218/movie.ufmf'
trx_file = '/groups/branson/bransonlab/mayank/PoseTF/data/apt_interface/alice/cx_GMR_SS00168_CsChr_RigD_20150909T111218/registered_trx.mat'
out_file = os.path.splitext(mov_file)[0] + '_interface_test_{}.trk'.format(dstr)
name = 'alice_test_apt'
cmd = '{} -name {} train'.format(lbl_file,name)
#APT_interface.main(cmd.split())
cmd = '{} -name {} track -mov {} -trx {} -out {} -end_frame 1000'.format(lbl_file, name, mov_file, trx_file, out_file)
APT_interface.main(cmd.split())
cmd = '-name 20191206T024459 -view 1 -cache /groups/branson/home/kabram/.apt/tp0812543a_6893_40b2_befd_90409dbe6afe -err_file /groups/branson/home/kabram/.apt/tp0812543a_6893_40b2_befd_90409dbe6afe/test1/20191206T024459view0_20191206T0245171.err -type mdn /groups/branson/home/kabram/.apt/tp0812543a_6893_40b2_befd_90409dbe6afe/test1/20191206T024459_20191206T024517.lbl train -use_cache -skip_db'
import APT_interface as apt
apt.main(cmd.split())
##
import run_apt_expts as rae
reload(rae)
rae.setup('leap_fly')
rae.create_gt_db()
##
import APT_interface as apt
cmd = '/groups/branson/bransonlab/apt/experiments/data/multitarget_bubble_expandedbehavior_20180425_FxdErrs_OptoParams20181126_dlstripped.lbl -name alice_randsplit_round_4 -cache /nrs/branson/mayank/apt_cache -conf_params use_pretrained_weights False batch_size 8 trange 5 decay_steps 20000 save_step 4000 rrange 10 adjust_contrast False mdn_use_unet_loss True dl_steps 40000 normalize_img_mean False maxckpt 20 -type mdn -train_name no_pretrained -view 1 train -skip_db -use_cache'
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''
apt.main(cmd.split())
##
import run_apt_expts as rae
reload(rae)
rae.setup('romain', 0)
rae.get_cv_results(num_splits=6)
##
import PoseTools
import os
import glob
import APT_interface as apt
import apt_expts
import re
# import PoseUMDN_dataset # self = PoseUMDN_dataset.PoseUMDN(conf,name='pose_umdn_test') import PoseUNet_dataset self = PoseUNet_dataset.PoseUNet(conf,name='pose_unet_fusion') self.train_unet() ## import os os.environ['CUDA_VISIBLE_DEVICES'] = '' args = '-name pend -cache /home/mayank/temp -type unet /home/mayank/work/poseTF/data/apt/pend_1_stripped_preProcDataCache_scale4_NumChans1_v73.lbl train -use_cache' args = args.split() import APT_interface as apt apt.main(args) ## import os os.environ['CUDA_VISIBLE_DEVICES'] = '0' from poseConfig import aliceConfig as conf import tensorflow as tf import multiResData conf.trange = 5 conf.cachedir += '_dataset' import mdn_keras mdn_keras.training(conf) ## from poseConfig import aliceConfig as conf
gt_file_url = 'https://www.dropbox.com/s/71glyy7bgkry7sm/gtdata_view0.tfrecords?dl=1'
gt_file = os.path.join(tdir, 'gt_data.tfrecords')
url_lib.urlretrieve(gt_file_url, gt_file)
res_file_url = 'https://www.dropbox.com/s/cr702321rvv3htl/alice_view0_time.mat?dl=1'
res_file = os.path.join(tdir,'alice_view0_time.mat')
url_lib.urlretrieve(res_file_url,res_file)
cmd = '-cache {} -name {} -conf_params batch_size {} dl_steps {} op_affinity_graph {} -type {{}} {} train -use_cache '.format(tdir, exp_name, bsz, dl_steps,op_af_graph, lbl_file)
##
import h5py
R = h5py.File(res_file,'r')
for net in net_types:
apt.main(cmd.format(net).split())
conf = apt.create_conf(lbl_file, 0, exp_name, tdir, net)
# if data_type == 'stephen' and train_type == 'mdn':
# conf.mdn_use_unet_loss = False
if op_af_graph is not None:
conf.op_affinity_graph = ast.literal_eval(op_af_graph.replace('\\', ''))
files = glob.glob(os.path.join(conf.cachedir, "{}-[0-9]*").format('deepnet'))
files.sort(key=os.path.getmtime)
files = [f for f in files if os.path.splitext(f)[1] in ['.index', '']]
aa = [int(re.search('-(\d*)', f).groups(0)[0]) for f in files]
aa = [b - a for a, b in zip(aa[:-1], aa[1:])]
if any([a < 0 for a in aa]):
bb = int(np.where(np.array(aa) < 0)[0]) + 1
files = files[bb:]
files = files[-1:]