Add Conv1d layer, update SequenceBatchGenerator
This commit is contained in:
Corentin
parent
beeb4c1898
commit
5dee78b729
3 changed files with 259 additions and 121 deletions
16
layers.py
16
layers.py
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@ -45,6 +45,22 @@ class Layer(nn.Module):
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return output
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class Conv1d(Layer):
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def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3,
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stride: Union[int, Tuple[int, int]] = 1, activation=0, batch_norm=None, **kwargs):
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super().__init__(activation, batch_norm)
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self.conv = nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
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bias=not self.batch_norm, **kwargs)
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self.batch_norm = nn.BatchNorm1d(
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out_channels,
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momentum=Layer.BATCH_NORM_MOMENTUM,
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track_running_stats=not Layer.BATCH_NORM_TRAINING) if self.batch_norm else None
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def forward(self, input_data: torch.Tensor) -> torch.Tensor:
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return super().forward(self.conv(input_data))
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class Conv2d(Layer):
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def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3,
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stride: Union[int, Tuple[int, int]] = 1, activation=0, batch_norm=None, **kwargs):
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@ -11,7 +11,7 @@ import numpy as np
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class BatchGenerator:
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def __init__(self, data, label, batch_size, data_processor=None, label_processor=None, precache=True,
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shuffle=True, preload=False, save=None, left_right_flip=False):
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shuffle=True, preload=False, save=None, initial_shuffle=False, left_right_flip=False):
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self.batch_size = batch_size
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self.shuffle = shuffle
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self.left_right_flip = left_right_flip
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@ -60,7 +60,7 @@ class BatchGenerator:
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self.batch_label: Optional[np.ndarray] = None
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self.index_list = np.arange(len(self.data))
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if shuffle:
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if shuffle or initial_shuffle:
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np.random.shuffle(self.index_list)
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if self.precache:
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@ -83,6 +83,8 @@ class BatchGenerator:
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self.cache_pipe_parent, self.cache_pipe_child = mp.Pipe()
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self.cache_stop = shared_memory.SharedMemory(create=True, size=1)
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self.cache_stop.buf[0] = 0
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self.cache_skip = shared_memory.SharedMemory(create=True, size=1)
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self.cache_skip.buf[0] = 0
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self.cache_process = mp.Process(target=self.cache_worker)
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self.cache_process.start()
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@ -94,6 +96,8 @@ class BatchGenerator:
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self.cache_stop.close()
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self.cache_stop.unlink()
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self.cache_skip.close()
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self.cache_skip.unlink()
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self.cache_memory_data[0].close()
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self.cache_memory_data[0].unlink()
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self.cache_memory_data[1].close()
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@ -114,6 +118,9 @@ class BatchGenerator:
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try:
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self.cache_pipe_child.recv()
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self.cache_pipe_child.send(current_cache)
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if self.cache_skip.buf[0]:
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self.cache_skip.buf[0] = 0
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self.step = self.step_per_epoch
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self.next_batch()
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current_cache = 1 - current_cache
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self.cache_data[current_cache][:len(self.batch_data)] = self.batch_data[:]
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@ -121,6 +128,12 @@ class BatchGenerator:
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except KeyboardInterrupt:
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break
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def skip_epoch(self):
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if self.precache:
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self.cache_skip.buf[0] = 1
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self.step = self.step_per_epoch
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self.next_batch()
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def next_batch(self) -> Tuple[np.ndarray, np.ndarray]:
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if self.step >= self.step_per_epoch - 1: # step start at 0
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self.step = 0
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@ -161,122 +174,3 @@ class BatchGenerator:
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self.batch_data = self.batch_data[:, :, ::-1]
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self.batch_label = self.batch_label[:, :, ::-1]
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return self.batch_data, self.batch_label
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class SequenceGenerator:
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def __init__(self, data, label, sequence_size, batch_size, data_processor=None, label_processor=None,
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preload=False, shuffle=True, save=None):
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self.sequence_size = sequence_size
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self.batch_size = batch_size
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self.shuffle = shuffle
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if not preload:
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self.data_processor = data_processor
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self.label_processor = label_processor
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self.data = data
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self.label = label
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else:
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self.data_processor = None
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self.label_processor = None
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save_path = save
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if save is not None:
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if '.' not in os.path.basename(save_path):
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save_path += '.hdf5'
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if not os.path.exists(os.path.dirname(save_path)):
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os.makedirs(os.path.dirname(save_path))
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if save and os.path.exists(save_path):
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with h5py.File(save_path, 'r') as h5_file:
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data_len = np.asarray(h5_file['data_len'])
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self.data = []
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self.label = []
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for sequence_index in range(data_len):
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self.data.append(np.asarray(h5_file[f'data_{sequence_index}']))
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self.label.append(np.asarray(h5_file[f'label_{sequence_index}']))
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self.data = np.asarray(self.data)
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self.label = np.asarray(self.label)
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else:
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if data_processor:
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self.data = np.asarray(
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[np.asarray([data_processor(entry) for entry in serie]) for serie in data],
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dtype=np.object if len(data) > 1 else None)
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else:
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self.data = data
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if label_processor:
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self.label = np.asarray(
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[np.asarray([label_processor(entry) for entry in serie]) for serie in label],
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dtype=np.object if len(label) > 1 else None)
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else:
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self.label = label
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if save:
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with h5py.File(save_path, 'w') as h5_file:
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h5_file.create_dataset(f'data_len', data=len(self.data))
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for sequence_index in range(len(self.data)):
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h5_file.create_dataset(f'data_{sequence_index}', data=self.data[sequence_index])
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h5_file.create_dataset(f'label_{sequence_index}', data=self.label[sequence_index])
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self.index_list = []
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for sequence_index in range(len(self.data)):
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start_indices = np.expand_dims(
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np.arange(len(self.data[sequence_index]) - sequence_size, dtype=np.uint32),
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axis=-1)
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start_indices = np.insert(start_indices, 0, sequence_index, axis=1)
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self.index_list.append(start_indices)
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self.index_list = np.concatenate(self.index_list, axis=0)
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self.step_per_epoch = math.ceil(len(self.index_list) / batch_size)
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self.epoch = 0
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self.global_step = -1
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self.step = -1
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self.batch_data: Optional[np.ndarray] = None
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self.batch_label: Optional[np.ndarray] = None
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if shuffle:
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np.random.shuffle(self.index_list)
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def next_batch(self) -> Tuple[np.ndarray, np.ndarray]:
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if self.step >= self.step_per_epoch - 1: # step start at 0
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self.step = 0
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self.epoch += 1
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if self.shuffle:
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np.random.shuffle(self.index_list)
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else:
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self.step += 1
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self.global_step += 1
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# Loading data
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if self.data_processor is not None:
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self.batch_data = []
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for sequence_index, start_index in self.index_list[
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self.step * self.batch_size:(self.step + 1) * self.batch_size]:
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self.batch_data.append(
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[self.data_processor(input_data)
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for input_data in self.data[sequence_index][start_index: start_index + self.sequence_size]])
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self.batch_data = np.asarray(self.batch_data)
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else:
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self.batch_data = []
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for sequence_index, start_index in self.index_list[
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self.step * self.batch_size:(self.step + 1) * self.batch_size]:
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self.batch_data.append(
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self.data[sequence_index][start_index: start_index + self.sequence_size])
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self.batch_data = np.asarray(self.batch_data)
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# Loading label
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if self.label_processor is not None:
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self.batch_label = []
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for sequence_index, start_index in self.index_list[
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self.step * self.batch_size:(self.step + 1) * self.batch_size]:
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self.batch_label.append(
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[self.label_processor(input_data)
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for input_data in self.label[sequence_index][start_index: start_index + self.sequence_size]])
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self.batch_label = np.asarray(self.batch_label)
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else:
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self.batch_label = []
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for sequence_index, start_index in self.index_list[
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self.step * self.batch_size:(self.step + 1) * self.batch_size]:
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self.batch_label.append(
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self.label[sequence_index][start_index: start_index + self.sequence_size])
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self.batch_label = np.asarray(self.batch_label)
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return self.batch_data, self.batch_label
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228
utils/sequence_batch_generator.py
Normal file
228
utils/sequence_batch_generator.py
Normal file
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@ -0,0 +1,228 @@
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import math
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import multiprocessing as mp
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from multiprocessing import shared_memory
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import os
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from typing import Optional, Tuple
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import h5py
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import numpy as np
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class SequenceGenerator:
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def __init__(self, data, label, sequence_size, batch_size, data_processor=None, label_processor=None,
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precache=True, preload=False, shuffle=True, initial_shuffle=False, save=None):
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self.batch_size = batch_size
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self.sequence_size = sequence_size
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self.shuffle = shuffle
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self.precache = precache and not preload
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if not preload:
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self.data_processor = data_processor
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self.label_processor = label_processor
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self.data = data
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self.label = label
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else:
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self.data_processor = None
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self.label_processor = None
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save_path = save
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if save is not None:
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if '.' not in os.path.basename(save_path):
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save_path += '.hdf5'
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if not os.path.exists(os.path.dirname(save_path)):
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os.makedirs(os.path.dirname(save_path))
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if save and os.path.exists(save_path):
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with h5py.File(save_path, 'r') as h5_file:
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data_len = np.asarray(h5_file['data_len'])
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self.data = []
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self.label = []
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for sequence_index in range(data_len):
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self.data.append(np.asarray(h5_file[f'data_{sequence_index}']))
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self.label.append(np.asarray(h5_file[f'label_{sequence_index}']))
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self.data = np.asarray(self.data)
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self.label = np.asarray(self.label)
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else:
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if data_processor:
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self.data = np.asarray(
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[np.asarray([data_processor(entry) for entry in serie]) for serie in data],
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dtype=np.object if len(data) > 1 else None)
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else:
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self.data = data
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if label_processor:
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self.label = np.asarray(
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[np.asarray([label_processor(entry) for entry in serie]) for serie in label],
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dtype=np.object if len(label) > 1 else None)
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else:
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self.label = label
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if save:
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with h5py.File(save_path, 'w') as h5_file:
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h5_file.create_dataset('data_len', data=len(self.data))
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for sequence_index in range(len(self.data)):
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h5_file.create_dataset(f'data_{sequence_index}', data=self.data[sequence_index])
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h5_file.create_dataset(f'label_{sequence_index}', data=self.label[sequence_index])
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self.index_list = []
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for sequence_index in range(len(self.data)):
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start_indices = np.expand_dims(
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np.arange(len(self.data[sequence_index]) - sequence_size, dtype=np.uint32),
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axis=-1)
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start_indices = np.insert(start_indices, 0, sequence_index, axis=1)
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self.index_list.append(start_indices)
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self.index_list = np.concatenate(self.index_list, axis=0)
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self.step_per_epoch = math.ceil(len(self.index_list) / batch_size)
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self.epoch = 0
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self.global_step = -1
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self.step = -1
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self.batch_data: Optional[np.ndarray] = None
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self.batch_label: Optional[np.ndarray] = None
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if shuffle or initial_shuffle:
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np.random.shuffle(self.index_list)
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if self.precache:
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if data_processor:
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data_sample = []
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for sequence_index, start_index in self.index_list[:batch_size]:
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data_sample.append(
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[data_processor(input_data)
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for input_data in self.data[sequence_index][start_index: start_index + self.sequence_size]])
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data_sample = np.asarray(data_sample)
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else:
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data_sample = []
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for sequence_index, start_index in self.index_list[:batch_size]:
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data_sample.append(
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self.data[sequence_index][start_index: start_index + self.sequence_size])
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data_sample = np.asarray(data_sample)
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if label_processor:
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label_sample = []
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for sequence_index, start_index in self.index_list[:batch_size]:
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label_sample.append(
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[label_processor(input_label)
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for input_label in self.label[sequence_index][start_index: start_index + self.sequence_size]])
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label_sample = np.asarray(label_sample)
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else:
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label_sample = []
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for sequence_index, start_index in self.index_list[:batch_size]:
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label_sample.append(
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self.label[sequence_index][start_index: start_index + self.sequence_size])
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label_sample = np.asarray(label_sample)
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self.cache_memory_data = [
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shared_memory.SharedMemory(create=True, size=data_sample.nbytes),
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shared_memory.SharedMemory(create=True, size=data_sample.nbytes)]
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self.cache_data = [
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np.ndarray(data_sample.shape, dtype=data_sample.dtype, buffer=self.cache_memory_data[0].buf),
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np.ndarray(data_sample.shape, dtype=data_sample.dtype, buffer=self.cache_memory_data[1].buf)]
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self.cache_memory_label = [
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shared_memory.SharedMemory(create=True, size=label_sample.nbytes),
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shared_memory.SharedMemory(create=True, size=label_sample.nbytes)]
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self.cache_label = [
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np.ndarray(label_sample.shape, dtype=label_sample.dtype, buffer=self.cache_memory_label[0].buf),
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np.ndarray(label_sample.shape, dtype=label_sample.dtype, buffer=self.cache_memory_label[1].buf)]
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self.cache_pipe_parent, self.cache_pipe_child = mp.Pipe()
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self.cache_stop = shared_memory.SharedMemory(create=True, size=1)
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self.cache_stop.buf[0] = 0
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self.cache_skip = shared_memory.SharedMemory(create=True, size=1)
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self.cache_skip.buf[0] = 0
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self.cache_process = mp.Process(target=self.cache_worker)
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self.cache_process.start()
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def __del__(self):
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if self.precache:
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self.cache_stop.buf[0] = 1
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self.cache_pipe_parent.send(True)
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self.cache_process.join()
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self.cache_stop.close()
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self.cache_stop.unlink()
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self.cache_skip.close()
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self.cache_skip.unlink()
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self.cache_memory_data[0].close()
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self.cache_memory_data[0].unlink()
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self.cache_memory_data[1].close()
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self.cache_memory_data[1].unlink()
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self.cache_memory_label[0].close()
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self.cache_memory_label[0].unlink()
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self.cache_memory_label[1].close()
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self.cache_memory_label[1].unlink()
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def cache_worker(self):
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self.precache = False
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self.next_batch()
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self.cache_data[0][:] = self.batch_data[:]
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self.cache_label[0][:] = self.batch_label[:]
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current_cache = 0
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while not self.cache_stop.buf[0]:
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try:
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self.cache_pipe_child.recv()
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self.cache_pipe_child.send(current_cache)
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if self.cache_skip.buf[0]:
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self.cache_skip.buf[0] = 0
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self.step = self.step_per_epoch
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self.next_batch()
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current_cache = 1 - current_cache
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self.cache_data[current_cache][:len(self.batch_data)] = self.batch_data[:]
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self.cache_label[current_cache][:len(self.batch_label)] = self.batch_label[:]
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except KeyboardInterrupt:
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break
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def skip_epoch(self):
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if self.precache:
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self.cache_skip.buf[0] = 1
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self.step = self.step_per_epoch
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self.next_batch()
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def next_batch(self) -> Tuple[np.ndarray, np.ndarray]:
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if self.step >= self.step_per_epoch - 1: # step start at 0
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self.step = 0
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self.epoch += 1
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if self.shuffle:
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np.random.shuffle(self.index_list)
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else:
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self.step += 1
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self.global_step += 1
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# Loading data
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if self.precache:
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self.cache_pipe_parent.send(True)
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current_cache = self.cache_pipe_parent.recv()
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self.batch_data = self.cache_data[current_cache].copy()
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elif self.data_processor is not None:
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self.batch_data = []
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for sequence_index, start_index in self.index_list[
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self.step * self.batch_size:(self.step + 1) * self.batch_size]:
|
||||
self.batch_data.append(
|
||||
[self.data_processor(input_data)
|
||||
for input_data in self.data[sequence_index][start_index: start_index + self.sequence_size]])
|
||||
self.batch_data = np.asarray(self.batch_data)
|
||||
else:
|
||||
self.batch_data = []
|
||||
for sequence_index, start_index in self.index_list[
|
||||
self.step * self.batch_size:(self.step + 1) * self.batch_size]:
|
||||
self.batch_data.append(
|
||||
self.data[sequence_index][start_index: start_index + self.sequence_size])
|
||||
self.batch_data = np.asarray(self.batch_data)
|
||||
# Loading label
|
||||
if self.precache:
|
||||
self.batch_label = self.cache_label[current_cache].copy()
|
||||
elif self.label_processor is not None:
|
||||
self.batch_label = []
|
||||
for sequence_index, start_index in self.index_list[
|
||||
self.step * self.batch_size:(self.step + 1) * self.batch_size]:
|
||||
self.batch_label.append(
|
||||
[self.label_processor(input_data)
|
||||
for input_data in self.label[sequence_index][start_index: start_index + self.sequence_size]])
|
||||
self.batch_label = np.asarray(self.batch_label)
|
||||
else:
|
||||
self.batch_label = []
|
||||
for sequence_index, start_index in self.index_list[
|
||||
self.step * self.batch_size:(self.step + 1) * self.batch_size]:
|
||||
self.batch_label.append(
|
||||
self.label[sequence_index][start_index: start_index + self.sequence_size])
|
||||
self.batch_label = np.asarray(self.batch_label)
|
||||
|
||||
return self.batch_data, self.batch_label
|
||||
Loading…
Add table
Add a link
Reference in a new issue