corentin/torch_utils
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Fix SequenceGenerator for single sequence data

Browse source
This commit is contained in:
corentin 2020-07-06 13:20:31 +09:00
commit 95f2e52ff3
1 changed files with 35 additions and 18 deletions
Download patch file Download diff file Expand all files Collapse all files

53
utils/batch_generator.py
View file

@ -104,16 +104,6 @@ class SequenceGenerator:
self.batch_size = batch_size
self.shuffle = shuffle
self.index_list = []
for sequence_index in range(len(data)):
start_indices = np.expand_dims(
np.arange(len(data[sequence_index]) - sequence_size, dtype=np.uint8),
axis=-1)
start_indices = np.insert(start_indices, 0, sequence_index, axis=1)
self.index_list.append(start_indices)
self.index_list = np.concatenate(self.index_list, axis=0)
self.step_per_epoch = math.ceil(len(self.index_list) / batch_size)
if not preload:
self.data_processor = data_processor
self.label_processor = label_processor
@ -122,25 +112,52 @@ class SequenceGenerator:
else:
self.data_processor = None
self.label_processor = None
save_path = save
if save is not None:
if '.' not in os.path.basename(save_path):
save_path += '.hdf5'
if not os.path.exists(os.path.dirname(save_path)):
os.makedirs(os.path.dirname(save_path))
if save and os.path.exists(save + '_data.npy'):
self.data = np.load(save + '_data.npy', allow_pickle=True)
self.label = np.load(save + '_label.npy', allow_pickle=True)
if save and os.path.exists(save_path):
with h5py.File(save_path, 'r') as h5_file:
data_len = np.asarray(h5_file['data_len'])
self.data = []
self.label = []
for sequence_index in range(data_len):
self.data.append(np.asarray(h5_file[f'data_{sequence_index}']))
self.label.append(np.asarray(h5_file[f'label_{sequence_index}']))
self.data = np.asarray(self.data)
self.label = np.asarray(self.label)
else:
if data_processor:
self.data = np.asarray(
[np.asarray([data_processor(entry) for entry in serie]) for serie in data])
[np.asarray([data_processor(entry) for entry in serie]) for serie in data],
dtype=np.object if len(data) > 1 else None)
else:
self.data = data
if label_processor:
self.label = np.asarray(
[np.asarray([label_processor(entry) for entry in serie]) for serie in label])
[np.asarray([label_processor(entry) for entry in serie]) for serie in label],
dtype=np.object if len(label) > 1 else None)
else:
self.label = label
if save:
np.save(save + '_data.npy', self.data, allow_pickle=True)
np.save(save + '_label.npy', self.label, allow_pickle=True)
with h5py.File(save_path, 'w') as h5_file:
h5_file.create_dataset(f'data_len', data=len(self.data))
for sequence_index in range(len(self.data)):
h5_file.create_dataset(f'data_{sequence_index}', data=self.data[sequence_index])
h5_file.create_dataset(f'label_{sequence_index}', data=self.label[sequence_index])
self.index_list = []
for sequence_index in range(len(self.data)):
start_indices = np.expand_dims(
np.arange(len(self.data[sequence_index]) - sequence_size, dtype=np.uint32),
axis=-1)
start_indices = np.insert(start_indices, 0, sequence_index, axis=1)
self.index_list.append(start_indices)
self.index_list = np.concatenate(self.index_list, axis=0)
self.step_per_epoch = math.ceil(len(self.index_list) / batch_size)
self.epoch = 0
self.global_step = -1