def classify_db_all(conf,
db_file,
model_files,
model_type,
name='deepnet',
distort=False,
return_hm=False,
hm_dec=1,
hm_floor=0.1,
hm_nclustermax=1):
cur_out = []
extra_str = ''
if model_type not in ['leap', 'openpose']:
extra_str = '.index'
# else:
# extra_str = '.h5'
ts = [os.path.getmtime(f + extra_str) for f in model_files]
for mndx, m in enumerate(model_files):
# 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(model_type,
conf,
m,
name=name,
distort=distort)
ret_list = apt.classify_db(conf,
read_fn,
pred_fn,
tf_iterator.N,
return_hm=return_hm,
hm_dec=hm_dec,
hm_floor=hm_floor,
hm_nclustermax=hm_nclustermax)
pred, label, gt_list = ret_list[:3]
if model_type == 'mdn':
extra_stuff = ret_list[3:]
else:
extra_stuff = 0
close_fn()
gt_list = np.array(gt_list)
cur_out.append([pred, label, gt_list, m, extra_stuff, ts[mndx]])
return cur_out
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 import APT_interface as apt import os import shutil import h5py import logging lbl_file = '/home/mayank/work/poseTF/data/alice/multitarget_bubble_expandedbehavior_20180425_local.lbl' conf = apt.create_conf(lbl_file,view=0,name='test_openpose') graph = [ [1,2],[1,3],[2,5],[3,4],[1,6],[6,7],[6,8],[6,10],[8,9],[10,11],[5,12],[9,13],[6,14],[6,15],[11,16],[4,17]] graph = [[g1-1, g2-1] for g1, g2 in graph] conf.op_affinity_graph = graph
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)