File size: 1,730 Bytes
188f311 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import numpy as np
import torch
__all__ = ["DataProvider"]
class DataProvider:
SUB_SEED = 937162211 # random seed for sampling subset
VALID_SEED = 2147483647 # random seed for the validation set
@staticmethod
def name():
"""Return name of the dataset"""
raise NotImplementedError
@property
def data_shape(self):
"""Return shape as python list of one data entry"""
raise NotImplementedError
@property
def n_classes(self):
"""Return `int` of num classes"""
raise NotImplementedError
@property
def save_path(self):
"""local path to save the data"""
raise NotImplementedError
@property
def data_url(self):
"""link to download the data"""
raise NotImplementedError
@staticmethod
def random_sample_valid_set(train_size, valid_size):
assert train_size > valid_size
g = torch.Generator()
g.manual_seed(
DataProvider.VALID_SEED
) # set random seed before sampling validation set
rand_indexes = torch.randperm(train_size, generator=g).tolist()
valid_indexes = rand_indexes[:valid_size]
train_indexes = rand_indexes[valid_size:]
return train_indexes, valid_indexes
@staticmethod
def labels_to_one_hot(n_classes, labels):
new_labels = np.zeros((labels.shape[0], n_classes), dtype=np.float32)
new_labels[range(labels.shape[0]), labels] = np.ones(labels.shape)
return new_labels |