def get_slice_data_from_file(dataset, split, class_idx, gmm, nr_pos, nr_neg):
samples = _get_samples(dataset, class_idx, data_type=split, nr_pos=nr_pos,
nr_neg=nr_neg)[0]
len_descs = gmm.k + 2 * gmm.d * gmm.k
sstats, labels, info = SstatsMap(
os.path.join(dataset.SSTATS_DIR, 'stats.tmp')).get_merged(
samples, len_descs)
sstats = sstats.reshape((-1, len_descs))
binary_labels = tuple_labels_to_list_labels(labels, class_idx)
return SliceData(sstats, binary_labels, info)
def master(src_cfg, suffix_in, suffix_out, K, N, nr_processes, double_norm):
D = 64
dataset = Dataset(src_cfg, nr_clusters=K)
samples = [
str(sample) for sample in dataset.get_data('train')[0] +
dataset.get_data('test')[0]
]
if double_norm:
worker = double_normalization
suffix = '.double_norm'
gmm = load_gmm(
os.path.join(dataset.FEAT_DIR + suffix_in, 'gmm', 'gmm_%d' % K))
else:
worker = merge
suffix = ''
gmm = None
path_in = os.path.join(dataset.FEAT_DIR + suffix_in,
'statistics_k_%d' % dataset.VOC_SIZE, 'stats.tmp')
path_out = os.path.join(dataset.FEAT_DIR + suffix_out,
'statistics_k_%d' % dataset.VOC_SIZE,
'stats.tmp' + suffix)
sstats_in = SstatsMap(path_in)
sstats_out = SstatsMap(path_out)
len_sstats = dataset.VOC_SIZE + 2 * D * dataset.VOC_SIZE
kwargs = {
'N': N,
'sstats_in': sstats_in,
'sstats_out': sstats_out,
'len_sstats': len_sstats,
'gmm': gmm
}
if nr_processes > 1:
nr_samples_per_process = len(samples) / nr_processes + 1
for ii in xrange(nr_processes):
mp.Process(target=worker,
args=(samples[ii * nr_samples_per_process:(ii + 1) *
nr_samples_per_process], ),
kwargs=kwargs).start()
else:
worker(samples, **kwargs)
def setup(self):
self.nr_dims = 10
np.random.seed(1)
self.data = SstatsMap('expected_results/test_data')
# Generate some data.
self.xx = np.random.randn(3, self.nr_dims)
self.info = {
'label': 4,
'nr_slices': 3,
'begin_frames': [1, 3, 10],
'end_frames': [2, 9, 15],
'nr_descs_per_slice': [10, 9, 13]}
class TestData():
def setup(self):
self.nr_dims = 10
np.random.seed(1)
self.data = SstatsMap('expected_results/test_data')
# Generate some data.
self.xx = np.random.randn(3, self.nr_dims)
self.info = {
'label': 4,
'nr_slices': 3,
'begin_frames': [1, 3, 10],
'end_frames': [2, 9, 15],
'nr_descs_per_slice': [10, 9, 13]}
def test_write_data(self):
self.data.write('test_1', self.xx[0])
self.data.write('test_2', self.xx[1], info=self.info)
self.data.write('test_3', self.xx[2], info=dict(label=10))
def test_read_data(self):
# Nose tests in random order.
self.data.write('test_1', self.xx[0])
self.data.write('test_2', self.xx[1], info=self.info)
test_1_data = self.data.read('test_1')
assert_array_almost_equal(self.xx[0], test_1_data)
test_2_data = self.data.read('test_2')
assert_array_almost_equal(self.xx[1], test_2_data)
test_2_info = self.data.read_info('test_2')
assert test_2_info == self.info
@raises(Exception)
def test_read_data_exception(self):
self.data.read('file_that_does_not_exist')
def test_check_data(self):
self.data.write('test_1', self.xx)
self.data.write('test_2', [])
assert self.data.check(['test_1'], self.nr_dims, verbose=False) == True
assert self.data.check(
['test_1'], self.nr_dims + 1, verbose=False) == False
assert self.data.check(
['test_1', 'test_2'], self.nr_dims, verbose=False) == False
