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)
import matplotlib.pyplot as plt import apt_expts import os import ast import apt_expts import os import pickle os.environ['CUDA_VISIBLE_DEVICES'] = '' gt_lbl = None lbl_file = '/groups/branson/bransonlab/apt/experiments/data/roian_apt_dlstripped.lbl' op_af_graph = '\(0,1\),\(0,2\),\(0,3\),\(1,2\),\(1,3\),\(2,3\)' 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' all_models = ['openpose'] gpu_model = 'GeForceRTX2080Ti' sdir = '/groups/branson/home/kabram/bransonlab/APT/deepnet/singularity_stuff' n_splits = 3 common_conf = {} common_conf['rrange'] = 10 common_conf['trange'] = 5 common_conf['mdn_use_unet_loss'] = True common_conf['dl_steps'] = 100000 common_conf['decay_steps'] = 20000