def test_num_entries_empty(self):
path_lmdb_empty = os.path.join(self.dir_tmp, 'empty_lmdb')
db = lmdb.open(path_lmdb_empty, map_size=int(1e12))
with db.begin(write=True) as _:
_
db.close()
assert_equal(0, r.num_entries(path_lmdb_empty))
def shift_label_lmdb(path_src, path_dst):
def func_data_decrement_0to255(value):
import caffe
_, x = read_lmdb.unpack_raw_datum(value)
x[x == 0] = 256
dat = caffe.io.array_to_datum(x - 1) # 256 becomes 255
return dat.SerializeToString()
idxs_all = range(read_lmdb.num_entries(path_src))
copy_lmdb.copy_samples_lmdb(path_src, path_dst, idxs_all,
func_data=func_data_decrement_0to255)
def shift_label_lmdb(path_src, path_dst):
def func_data_decrement_0to255(value):
import caffe
_, x = read_lmdb.unpack_raw_datum(value)
x[x == 0] = 256
dat = caffe.io.array_to_datum(x - 1) # 256 becomes 255
return dat.SerializeToString()
idxs_all = range(read_lmdb.num_entries(path_src))
copy_lmdb.copy_samples_lmdb(path_src,
path_dst,
idxs_all,
func_data=func_data_decrement_0to255)
def est_min_num_fwd_passes(fpath_net, mode_str):
"""
if multiple source for same mode, base num_passes on last
fpath_net -- path to network definition
mode_str -- train or test?
return
minimum no. of forward passes to cover training set
"""
from nideep.proto.proto_utils import Parser
np = Parser().from_net_params_file(fpath_net)
num_passes = 0
for l in np.layer:
if 'data' in l.type.lower() and mode_str.lower() in l.data_param.source.lower():
num_entries = read_lmdb.num_entries(l.data_param.source)
num_passes = int(num_entries / l.data_param.batch_size)
if num_entries % l.data_param.batch_size != 0:
print("WARNING: db size not a multiple of batch size. Adding another fwd. pass.")
num_passes += 1
print("%d fwd. passes with batch size %d" % (num_passes, l.data_param.batch_size))
return num_passes
def est_min_num_fwd_passes(fpath_net, mode_str):
"""
if multiple source for same mode, base num_passes on last
fpath_net -- path to network definition
mode_str -- train or test?
return
minimum no. of forward passes to cover training set
"""
from nideep.proto.proto_utils import Parser
np = Parser().from_net_params_file(fpath_net)
num_passes = 0
for l in np.layer:
if 'data' in l.type.lower() and mode_str.lower() in l.data_param.source.lower():
num_entries = read_lmdb.num_entries(l.data_param.source)
num_passes = int(num_entries / l.data_param.batch_size)
if num_entries % l.data_param.batch_size != 0:
print("WARNING: db size not a multiple of batch size. Adding another fwd. pass.")
num_passes += 1
print("%d fwd. passes with batch size %d" % (num_passes, l.data_param.batch_size))
return num_passes
def num_entries(self):
return read_lmdb.num_entries(self.p)
fpath_net = expanduser('~/models/dark/mnist/t0/lenet_train_test.prototxt')
fpath_weights = expanduser('~/models/dark/mnist/t0/lenet_iter_10000.caffemodel')
# x = response_to_lmdb(fpath_net, fpath_weights,
# ['ip2', 'ip1'],
# expanduser('~/models/dark/mnist/t0/mnistX_'))
net = caffe.Net(fpath_net, fpath_weights, caffe.TRAIN)
keys = ['ip2', 'ip1']
x = infer_to_lmdb_cur(net, keys, 2,
expanduser('~/models/dark/mnist/t0/Xmnist_%s_train_lmdb'))
print x
import os
print [os.path.isdir(expanduser('~/models/dark/mnist/t0/Xmnist_%s_train_lmdb') % (k,)) for k in keys]
print [read_lmdb.num_entries(expanduser('~/models/dark/mnist/t0/Xmnist_%s_train_lmdb') % (k,)) for k in keys]
#print [read_lmdb.read_values(expanduser('~/models/dark/mnist/t0/Xmnist_%s_train_lmdb') % (k,)) for k in keys]
# with h5py.File(fpath, "w") as f:
#
# f['a'] = 0
#
#
# f['b'] = [1, 2]
# f['c'] = np.arange(3)
# f['d'] = [np.array([[1,2],[4,5]], dtype=float), np.array([[1,2],[4, 5]], dtype=float)+10]
#infer_to_h5(net, 1, ['accuracy'], fpath)
pass
def test_num_entries_non_num(self):
assert_equal(2, r.num_entries(self.path_lmdb_non_num))
def test_num_entries_rand_ord(self):
assert_equal(2, r.num_entries(self.path_lmdb_rand_ord))
def test_num_entries(self):
assert_equal(2, r.num_entries(self.path_lmdb))
fpath_net = expanduser('~/models/dark/mnist/t0/lenet_train_test.prototxt')
fpath_weights = expanduser('~/models/dark/mnist/t0/lenet_iter_10000.caffemodel')
# x = response_to_lmdb(fpath_net, fpath_weights,
# ['ip2', 'ip1'],
# expanduser('~/models/dark/mnist/t0/mnistX_'))
net = caffe.Net(fpath_net, fpath_weights, caffe.TRAIN)
keys = ['ip2', 'ip1']
x = infer_to_lmdb_cur(net, keys, 2,
expanduser('~/models/dark/mnist/t0/Xmnist_%s_train_lmdb'))
print x
import os
print [os.path.isdir(expanduser('~/models/dark/mnist/t0/Xmnist_%s_train_lmdb') % (k,)) for k in keys]
print [read_lmdb.num_entries(expanduser('~/models/dark/mnist/t0/Xmnist_%s_train_lmdb') % (k,)) for k in keys]
# print [read_lmdb.read_values(expanduser('~/models/dark/mnist/t0/Xmnist_%s_train_lmdb') % (k,)) for k in keys]
# with h5py.File(fpath, "w") as f:
#
# f['a'] = 0
#
#
# f['b'] = [1, 2]
# f['c'] = np.arange(3)
# f['d'] = [np.array([[1,2],[4,5]], dtype=float), np.array([[1,2],[4, 5]], dtype=float)+10]
# infer_to_h5(net, 1, ['accuracy'], fpath)
