def _infer_to_lmdb_cur_single_key(net, key_, n, db):
'''
Run network inference for n batches and save results to an lmdb for each key.
Higher time complexity but lower space complexity.
Takes advantage if there is only a single key
'''
idx = 0
with db.begin(write=True) as txn:
for _ in range(n):
d = forward(net, [key_])
l = []
l.extend(d[key_].astype(float))
for x in l:
x = expand_dims(x, 3)
txn.put(IDX_FMT.format(idx), caffe.io.array_to_datum(x).SerializeToString())
idx += 1
return [idx]
def _infer_to_lmdb_cur_multi_key(net, keys, n, dbs):
'''
Run network inference for n batches and save results to an lmdb for each key.
Higher time complexity but lower space complexity.
See _infer_to_lmdb_cur_single_key() if there is only a single key
'''
idxs = [0] * len(keys)
for _ in range(n):
d = forward(net, keys)
for ik, k in enumerate(keys):
with dbs[k].begin(write=True) as txn:
l = []
l.extend(d[k].astype(float))
for x in l:
x = expand_dims(x, 3)
txn.put(IDX_FMT.format(idxs[ik]), caffe.io.array_to_datum(x).SerializeToString())
idxs[ik] += 1
return idxs