Layers, batch generator, memory
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Corentin Risselin
parent
9ab6adce7a
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5 changed files with 277 additions and 0 deletions
1
.gitignore
vendored
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.gitignore
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*.pyc
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59
layers.py
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59
layers.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .utils.logger import DummyLogger
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class LayerInfo():
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def __init__(self):
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self.memory = 0.0
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self.ops = 0.0
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self.output = 0.0
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class Layer(nn.Module):
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# Default layer arguments
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ACTIVATION = F.leaky_relu
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BATCH_NORM = True
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BATCH_NORM_TRAINING = False
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BATCH_NORM_DECAY = 0.95
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REGULARIZER = None
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PADDING = 'SAME'
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IS_TRAINING = False
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METRICS = False
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VERBOSE = 0
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LOGGER = DummyLogger()
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def __init__(self, activation, batch_norm):
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super(Layer, self).__init__()
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self.name = 'Layer'
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self.info = LayerInfo()
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# Preload default
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self.activation = Layer.ACTIVATION if activation == 0 else activation
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self.batch_norm = Layer.BATCH_NORM if batch_norm is None else batch_norm
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def forward(self, input_data: torch.Tensor) -> torch.Tensor:
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output = input_data
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if self.activation is not None:
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output = self.activation(output)
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if self.batch_norm is not None:
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output = self.batch_norm(output)
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return output
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class Conv2d(Layer):
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def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1,
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activation=0, batch_norm=None, **kwargs):
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super(Conv2d, self).__init__(activation, batch_norm)
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self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, **kwargs)
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self.batch_norm = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.01) 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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20
train.py
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train.py
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from typing import List, Tuple
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import torch
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from .utils.memory import human_size
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def parameter_summary(network: torch.nn.Module) -> List[Tuple[str, Tuple[int], str]]:
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""" Returns network parameter
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Returns a list of tuple: name, shape (tuple os ints), size (string)
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Args:
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network (torch.nn.Module): network to parse
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"""
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parameter_info = []
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for name, param in network.named_parameters():
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numpy = param.detach().cpu().numpy()
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parameter_info.append((name, numpy.shape, human_size(numpy.size * numpy.dtype.itemsize)))
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return parameter_info
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189
utils/batch_generator.py
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189
utils/batch_generator.py
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import math
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import os
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from typing import Optional, Tuple
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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,
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shuffle=True, preload=False, save=None, 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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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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if save and os.path.exists(save + '_data.npy'):
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self.data = np.load(save + '_data.npy', allow_pickle=True)
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self.label = np.load(save + '_label.npy', allow_pickle=True)
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else:
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if data_processor:
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self.data = np.asarray([data_processor(entry) for entry in data])
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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([label_processor(entry) for entry in label])
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else:
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self.label = label
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if save:
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np.save(save + '_data.npy', self.data, allow_pickle=True)
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np.save(save + '_label.npy', self.label, allow_pickle=True)
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self.step_per_epoch = math.ceil(len(self.data) / 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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self.index_list = np.arange(len(self.data))
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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 entry in self.index_list[self.step * self.batch_size:(self.step + 1) * self.batch_size]:
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self.batch_data.append(self.data_processor(self.data[entry]))
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self.batch_data = np.asarray(self.batch_data)
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else:
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self.batch_data = self.data[
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self.index_list[self.step * self.batch_size: (self.step + 1) * self.batch_size]]
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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 entry in self.index_list[self.step * self.batch_size:(self.step + 1) * self.batch_size]:
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self.batch_label.append(self.label_processor(self.label[entry]))
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self.batch_label = np.asarray(self.batch_label)
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else:
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self.batch_label = self.label[
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self.index_list[self.step * self.batch_size: (self.step + 1) * self.batch_size]]
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# print('next_batch : epoch {}, step {}/{}, data {}, label {}'.format(
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# self.epoch, self.step, self.step_per_epoch - 1, self.batch_data.shape, self.batch_label.shape))
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if self.left_right_flip and np.random.uniform() > 0.5:
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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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self.index_list = []
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for sequence_index in range(len(data)):
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start_indices = np.expand_dims(
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np.arange(len(data[sequence_index]) - sequence_size, dtype=np.uint8),
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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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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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if save and os.path.exists(save + '_data.npy'):
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self.data = np.load(save + '_data.npy', allow_pickle=True)
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self.label = np.load(save + '_label.npy', allow_pickle=True)
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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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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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else:
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self.label = label
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if save:
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np.save(save + '_data.npy', self.data, allow_pickle=True)
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np.save(save + '_label.npy', self.label, allow_pickle=True)
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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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8
utils/memory.py
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utils/memory.py
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def human_size(byte_count: int) -> str:
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"""Output byte amount in human readable format"""
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amount = float(byte_count)
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for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi', 'Yi']:
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if amount < 1024.0:
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break
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amount /= 1024.0
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return f'{amount:.2f}{unit}B'
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