from rankutils.utilities import safe_create_dir
import numpy as np
ranks_to_convert = OrderedDict(places365=[1, 2],
vggfaces=[1, 2],
imagenet=[1, 3],
unicamp=[1, 2, 4])
if __name__ == "__main__":
fvname = sys.argv[1]
outfvname = sys.argv[2]
nk = int(sys.argv[3])
pathcfg = cfgloader("../source/path_2.cfg")
for key, values in ranks_to_convert.items():
for v in values:
expkey = "{0:s}_{1:03d}".format(key, v)
print("converting: {0:s} - {1:s}".format(expkey, fvname))
sfvdir = pathcfg.get(expkey, 'seqfeature')
# pdb.set_trace()
in_fvpath = glob.glob("{0:s}*{1:s}*".format(sfvdir, fvname))[0]
out_sfvpath = "{0:s}{1:s}.{2:s}".format(sfvdir, expkey, outfvname)
#!/usr/bin/env python
#-*- coding: utf-8 -*-
import sys, os
sys.path.append('../source/')
import glob
from rankutils.cfgloader import cfgloader
import argparse
import numpy as np
completedir = lambda d: d if d[-1] == '/' else d+'/'
pathtcfg = cfgloader('../source/path_2.cfg')
def gen_fold_assign(n):
idx = np.arange(n)
fold_idx = np.zeros((n, 1), dtype=np.uint8)
np.random.shuffle(idx)
aux = idx[0:(n//2)]
fold_idx[aux] = 1
return fold_idx
for section in sys.argv[1:]:
def merge_topk(dataset, desc_a, desc_b, method, outdir):
print(rk_dtype)
round = 0
pathcfg = cfgloader("path_2.cfg")
key_a = "{0:s}_desc{1:d}".format(dataset, desc_a)
key_b = "{0:s}_desc{1:d}".format(dataset, desc_b)
rkdir_a = pathcfg['rank'][key_a]
rkdir_b = pathcfg['rank'][key_b]
folds_a = np.load(glob.glob(rkdir_a + "*.folds.npy")[0])[:, round]
folds_b = np.load(glob.glob(rkdir_b + "*.folds.npy")[0])[:, round]
#print('\n' + pathcfg['output'][key_a] + method + "/rel-prediction/*r{0:03d}*".format(round))
#print('\n' + pathcfg['output'][key_b] + method + "/rel-prediction/*r{0:03d}*".format(round))
ppath_a = glob.glob(pathcfg['output'][key_a] + method +
"/rel-prediction/*r{0:03d}*.npy".format(round))
ppath_a.sort()
ppath_b = glob.glob(pathcfg['output'][key_b] + method +
"/rel-prediction/*r{0:03d}*.npy".format(round))
ppath_b.sort()
assert len(ppath_a) == 2, "Could not find predictions for {0:s}".format(
key_a)
assert len(ppath_b) == 2, "Could not find predictions for {0:s}".format(
key_b)
pred_pair_a = [np.load(ppath_a[0]), np.load(ppath_a[1])]
pred_pair_b = [np.load(ppath_b[0]), np.load(ppath_b[1])]
fullpred_a = []
fullpred_b = []
n0 = 0
n1 = 0
for fa in folds_a:
if fa == 0:
fullpred_a.append(pred_pair_a[fa][n0, :])
n0 += 1
if fa == 1:
fullpred_a.append(pred_pair_a[fa][n1, :])
n1 += 1
n0 = 0
n1 = 0
for fb in folds_b:
if fb == 0:
fullpred_b.append(pred_pair_b[fb][n0:n0 + 1, :])
n0 += 1
if fb == 1:
fullpred_b.append(pred_pair_b[fb][n1:n1 + 1, :])
n1 += 1
rkfiles_a = glob.glob(rkdir_a + "/*.rk")
rkfiles_a.sort()
rkfiles_b = glob.glob(rkdir_b + "/*.rk")
rkfiles_b.sort()
assert len(rkfiles_a) == len(rkfiles_b), "Inconsistent number of ranks for rank directory a <{0:d}> and b <{1:d}>"\
.format(len(rkfiles_a), len(rkfiles_b))
pos = 0
for rkfpath_a, rkfpath_b in zip(rkfiles_a, rkfiles_b):
assert os.path.basename(rkfpath_a) == os.path.basename(rkfpath_b),\
"Rank files <{0:s}> and <{1:s}> diverge!".format(os.path.basename(rkfpath_a), os.path.basename(rkfpath_b))
print("->" + os.path.basename(rkfpath_a))
rank_a = read_rank(rkfpath_a)
rank_b = read_rank(rkfpath_b)
pred_a = fullpred_a[pos]
pred_b = fullpred_b[pos]
rank_merge = merge(rank_a, pred_a, rank_b, pred_b)
rankoutname = "{0:s}{1:s}".format(outdir, os.path.basename(rkfpath_a))
write_rank(rankoutname, rank_merge)