def cv_train_from_mat(lbl_file,
cdir,
cv_info_file,
models_run,
view=0,
skip_db=False,
create_splits=True,
dorun=False,
run_type='status'):
cv_info, in_info, label_info = read_cvinfo(lbl_file, cdir, cv_info_file,
view)
lbl = h5py.File(lbl_file, 'r')
proj_name = apt.read_string(lbl['projname'])
lbl.close()
cvifileshort = os.path.basename(cv_info_file)
cvifileshort = os.path.splitext(cvifileshort)[0]
n_splits = max(cv_info) + 1
print("{} splits, {} rows in cvi, {} rows in lbl, projname {}".format(
n_splits, len(cv_info), len(label_info), proj_name))
for sndx in range(n_splits):
val_info = [l for ndx, l in enumerate(in_info) if cv_info[ndx] == sndx]
trn_info = list(set(label_info) - set(val_info))
cur_split = [trn_info, val_info]
exp_name = '{:s}__split{}'.format(cvifileshort, sndx)
split_file = os.path.join(cdir, proj_name, exp_name) + '.json'
if not skip_db and create_splits:
assert not os.path.exists(split_file)
with open(split_file, 'w') as f:
json.dump(cur_split, f)
# create the dbs
if not skip_db:
for train_type in models_run:
conf = apt.create_conf(lbl_file, view, exp_name, cdir,
train_type)
conf.splitType = 'predefined'
if train_type == 'deeplabcut':
apt.create_deepcut_db(conf,
split=True,
split_file=split_file,
use_cache=True)
elif train_type == 'leap':
apt.create_leap_db(conf,
split=True,
split_file=split_file,
use_cache=True)
else:
apt.create_tfrecord(conf,
split=True,
split_file=split_file,
use_cache=True)
if dorun:
for train_type in models_run:
rapt.run_trainining(elblbubxp_name, train_type, view, run_type)
def train():
import PoseUNet_resnet as PoseURes
import tensorflow as tf
dstr = PoseTools.datestr()
cur_name = 'stephen_{}'.format(dstr)
for view in range(2):
conf = apt.create_conf(lbl_file,
view=view,
name=cur_name,
cache_dir=cache_dir,
net_type=model_type)
update_conf(conf)
apt.create_tfrecord(conf, False, use_cache=True)
tf.reset_default_graph()
self = PoseURes.PoseUMDN_resnet(conf, name='deepnet')
self.train_data_name = 'traindata'
self.train_umdn()
import shutil import h5py import logging reload(apt) lbl_file = '/home/mayank/work/poseTF/data/alice/multitarget_bubble_expandedbehavior_20180425_local.lbl' split_file = '/home/mayank/work/poseTF/cache/apt_interface/multitarget_bubble_view0/test_leap/splitdata.json' log = logging.getLogger() # root logger log.setLevel(logging.ERROR) import deepcut.train conf = apt.create_conf(lbl_file,0,'test_openpose_delete') conf.splitType = 'predefined' apt.create_tfrecord(conf, True, split_file=split_file) from poseConfig import config as args args.skip_db = True apt.train_openpose(conf,args) ## import deepcut.train import tensorflow as tf tf.reset_default_graph conf.batch_size = 1 pred_fn, model_file = deepcut.train.get_pred_fn(conf) rfn, n= deepcut.train.get_read_fn(conf,'/home/mayank/work/poseTF/cache/apt_interface/multitarget_bubble_view0/test_deepcut/val_data.p') A = apt.classify_db(conf, rfn, pred_fn, n) ## import socket
op_af_graph = '\(0,1\),\(0,2\),\(2,3\),\(1,3\),\(0,4\),\(1,4\)'
lbl = h5py.File(lbl_file, 'r')
proj_name = apt.read_string(lbl['projname'])
nviews = int(apt.read_entry(lbl['cfg']['NumViews']))
lbl.close()
cache_dir = '/nrs/branson/mayank/apt_cache'
train_type = 'mdn'
exp_name = 'apt_exp'
for view in range(nviews):
conf = apt.create_conf(gt_lbl, view, exp_name, cache_dir, train_type)
gt_file = os.path.join(cache_dir, proj_name, 'gtdata',
'gtdata_view{}.tfrecords'.format(view))
apt.create_tfrecord(conf, False, None, False, True, [gt_file])
##
import APT_interface as apt
import os
import glob
import apt_expts
import h5py
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
cache_dir = '/nrs/branson/mayank/apt_cache'
exp_name = 'apt_expt'
train_name = 'deepnet'
view = 0
lbl_file = '/groups/branson/bransonlab/apt/experiments/data/multitarget_bubble_expandedbehavior_20180425_FxdErrs_OptoParams20181126_dlstripped.lbl'
import APT_interface as apt
import os
# Alice's dataset
name = 'alice'
val_ratio = 0.1
lbl_file = '/home/kabram/Dropbox (HHMI)/temp/multitarget_bubble_expandedbehavior_20180425_FxdErrs_OptoParams20181126_dlstripped.lbl'
nviews = 1
for view in range(nviews):
conf = apt.create_conf(lbl_file,0,'tfds','/home/kabram/temp','mdn')
conf.cachedir = '/home/kabram/temp/tfds_{}_view{}'.format(name,view)
conf.valratio = val_ratio
os.makedirs(conf.cachedir,exist_ok=True)
apt.create_tfrecord(conf, split=True, split_file=None, use_cache=True, on_gt=False)
def compute_peformance(args):
H = h5py.File(args.lbl_file, 'r')
nviews = int(apt.read_entry(H['cfg']['NumViews']))
dir_name = args.whose
if len(args.nets) == 0:
all_nets = methods
else:
all_nets = args.nets
all_preds = {}
for view in range(nviews):
db_file = os.path.join(out_dir, args.name,
args.gt_name) + '_view{}.tfrecords'.format(view)
conf = apt.create_conf(args.lbl_file,
view,
name='a',
net_type=all_nets[0],
cache_dir=os.path.join(out_dir, args.name,
dir_name))
conf.labelfile = args.gt_lbl
if not (os.path.exists(db_file) and args.skip_gt_db):
print('Creating GT DB file {}'.format(db_file))
apt.create_tfrecord(conf,
split=False,
on_gt=True,
db_files=(db_file, ))
for curm in all_nets:
all_preds[curm] = []
for view in range(nviews):
cur_out = []
db_file = os.path.join(
out_dir, args.name,
args.gt_name) + '_view{}.tfrecords'.format(view)
if args.split_type is None:
cachedir = os.path.join(out_dir, args.name, dir_name,
'{}_view_{}'.format(curm,
view), 'full')
conf = apt.create_conf(args.lbl_file,
view,
name='a',
net_type=curm,
cache_dir=cachedir)
model_files, ts = get_model_files(conf, cachedir, curm)
for mndx, m in enumerate(model_files):
out_file = m + '_' + args.gt_name
load = False
if curm == 'unet' or curm == 'deeplabcut':
mm = m + '.index'
else:
mm = m
if os.path.exists(out_file + '.mat') and os.path.getmtime(
out_file + '.mat') > os.path.getmtime(mm):
load = True
if load:
H = sio.loadmat(out_file)
pred = H['pred_locs'] - 1
label = H['labeled_locs'] - 1
gt_list = H['list'] - 1
else:
# pred, label, gt_list = apt.classify_gt_data(conf, curm, out_file, m)
tf_iterator = multiResData.tf_reader(
conf, db_file, False)
tf_iterator.batch_size = 1
read_fn = tf_iterator.next
pred_fn, close_fn, _ = apt.get_pred_fn(curm, conf, m)
pred, label, gt_list = apt.classify_db(
conf, read_fn, pred_fn, tf_iterator.N)
close_fn()
mat_pred_locs = pred + 1
mat_labeled_locs = np.array(label) + 1
mat_list = gt_list
sio.savemat(
out_file, {
'pred_locs': mat_pred_locs,
'labeled_locs': mat_labeled_locs,
'list': mat_list
})
cur_out.append(
[pred, label, gt_list, m, out_file, view, 0, ts[mndx]])
else:
for cur_split in range(nsplits):
cachedir = os.path.join(out_dir, args.name,
'{}_view_{}'.format(curm, view),
'cv_{}'.format(cur_split))
conf = apt.create_conf(args.lbl_file,
view,
name='a',
net_type=curm,
cache_dir=cachedir)
model_files, ts = get_model_files(conf, cachedir, curm)
db_file = os.path.join(cachedir, 'val_TF.tfrecords')
for mndx, m in enumerate(model_files):
out_file = m + '.gt_data'
load = False
if curm == 'unet' or curm == 'deeplabcut':
mm = m + '.index'
else:
mm = m
if os.path.exists(
out_file + '.mat') and os.path.getmtime(
out_file + '.mat') > os.path.getmtime(mm):
load = True
if load:
H = sio.loadmat(out_file)
pred = H['pred_locs'] - 1
label = H['labeled_locs'] - 1
gt_list = H['list'] - 1
else:
tf_iterator = multiResData.tf_reader(
conf, db_file, False)
tf_iterator.batch_size = 1
read_fn = tf_iterator.next
pred_fn, close_fn, _ = apt.get_pred_fn(
curm, conf, m)
pred, label, gt_list = apt.classify_db(
conf, read_fn, pred_fn, tf_iterator.N)
close_fn()
mat_pred_locs = pred + 1
mat_labeled_locs = np.array(label) + 1
mat_list = gt_list
sio.savemat(
out_file, {
'pred_locs': mat_pred_locs,
'labeled_locs': mat_labeled_locs,
'list': mat_list
})
cur_out.append([
pred, label, gt_list, m, out_file, view, cur_split,
ts[mndx]
])
all_preds[curm].append(cur_out)
with open(
os.path.join(out_dir, args.name, dir_name,
args.gt_name + '_results.p'), 'w') as f:
pickle.dump(all_preds, f)
def create_db(args):
H = h5py.File(args.lbl_file, 'r')
nviews = int(apt.read_entry(H['cfg']['NumViews']))
all_nets = args.nets
all_split_files = []
for view in range(nviews):
if args.split_type is not None and not args.split_type.startswith(
'prog'):
cachedir = os.path.join(out_dir, args.name, 'common')
if not os.path.exists(cachedir):
os.mkdir(cachedir)
cachedir = os.path.join(out_dir, args.name, 'common',
'splits_{}'.format(view))
if not os.path.exists(cachedir):
os.mkdir(cachedir)
conf = apt.create_conf(args.lbl_file,
view,
args.name,
cache_dir=cachedir)
conf.splitType = args.split_type
print("Split type is {}".format(conf.splitType))
if args.do_split:
train_info, val_info, split_files = apt.create_cv_split_files(
conf, nsplits)
else:
split_files = [
os.path.join(conf.cachedir,
'cv_split_fold_{}.json'.format(ndx))
for ndx in range(nsplits)
]
all_split_files.append(split_files)
for curm in all_nets:
for view in range(nviews):
if args.split_type is None:
cachedir = os.path.join(out_dir, args.name, 'common',
'{}_view_{}'.format(curm,
view), 'full')
conf = apt.create_conf(args.lbl_file,
view,
args.name,
cache_dir=cachedir)
if not args.only_check:
if not os.path.exists(conf.cachedir):
os.makedirs(conf.cachedir)
if curm == 'unet' or curm == 'openpose':
apt.create_tfrecord(conf, False)
elif curm == 'leap':
apt.create_leap_db(conf, False)
elif curm == 'deeplabcut':
apt.create_deepcut_db(conf, False)
create_deepcut_cfg(conf)
else:
raise ValueError('Undefined net type: {}'.format(curm))
check_db(curm, conf)
elif args.split_type.startswith('prog'):
split_type = args.split_type[5:]
all_info = get_increasing_splits(conf, split_type)
for cur_tr in prog_thresholds:
cachedir = os.path.join(out_dir, args.name, 'common',
'{}_view_{}'.format(curm, view),
'{}'.format(cur_tr))
conf = apt.create_conf(args.lbl_file,
view,
args.name,
cache_dir=cachedir)
split_ndx = round(len(all_info) / cur_tr)
cur_train = all_info[:split_ndx]
cur_val = all_info[split_ndx:]
split_file = os.path.join(cachedir, 'splitdata.json')
with open(split_file, 'w') as f:
json.dump([cur_train, cur_val], f)
if not args.only_check:
if curm == 'unet' or curm == 'openpose':
apt.create_tfrecord(conf, True, split_file)
elif curm == 'leap':
apt.create_leap_db(conf, True, split_file)
elif curm == 'deeplabcut':
apt.create_deepcut_db(conf, True, split_file)
create_deepcut_cfg(conf)
else:
raise ValueError(
'Undefined net type: {}'.format(curm))
check_db(curm, conf)
else:
split_files = all_split_files[view]
for cur_split in range(nsplits):
conf.cachedir = os.path.join(
out_dir, args.name, 'common',
'{}_view_{}'.format(curm, view))
if not os.path.exists(conf.cachedir):
os.mkdir(conf.cachedir)
conf.cachedir = os.path.join(
out_dir, args.name, 'common',
'{}_view_{}'.format(curm,
view), 'cv_{}'.format(cur_split))
if not os.path.exists(conf.cachedir):
os.mkdir(conf.cachedir)
conf.splitType = 'predefined'
split_file = split_files[cur_split]
if not args.only_check:
if curm == 'unet' or curm == 'openpose':
apt.create_tfrecord(conf, True, split_file)
elif curm == 'leap':
apt.create_leap_db(conf, True, split_file)
elif curm == 'deeplabcut':
apt.create_deepcut_db(conf, True, split_file)
create_deepcut_cfg(conf)
else:
raise ValueError(
'Undefined net type: {}'.format(curm))
check_db(curm, conf)
base_dir = os.path.join(out_dir, args.name, 'common')
their_dir = os.path.join(out_dir, args.name, 'theirs')
our_dir = os.path.join(out_dir, args.name, 'ours')
our_default_dir = os.path.join(out_dir, args.name, 'ours_default')
cmd = 'cp -rs {} {}'.format(base_dir, their_dir)
os.system(cmd)
cmd = 'cp -rs {} {}'.format(base_dir, our_dir)
os.system(cmd)
cmd = 'cp -rs {} {}'.format(base_dir, our_default_dir)
os.system(cmd)