UKD/utils.py at master · yuanmao1/UKD · GitHub

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
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
import time
from datetime import datetime

import numpy as np
from timm.data import ImageDataset
from torchvision.datasets import CIFAR100


class ImageNetInstanceSample(ImageDataset):
    """: Folder datasets which returns (img, label, index, contrast_index):
    """

    def __init__(self, root, name, class_map, load_bytes, is_sample=False, k=4096, **kwargs):
        super().__init__(root, parser=name, class_map=class_map, load_bytes=load_bytes, **kwargs)
        self.k = k
        self.is_sample = is_sample
        if self.is_sample:
            print('preparing contrastive data...')
            num_classes = 1000
            num_samples = len(self.parser)
            label = np.zeros(num_samples, dtype=np.int32)
            for i in range(num_samples):
                _, target = self.parser[i]
                label[i] = target

            self.cls_positive = [[] for _ in range(num_classes)]
            for i in range(num_samples):
                self.cls_positive[label[i]].append(i)

            self.cls_negative = [[] for _ in range(num_classes)]
            for i in range(num_classes):
                for j in range(num_classes):
                    if j == i:
                        continue
                    self.cls_negative[i].extend(self.cls_positive[j])

            self.cls_positive = [np.asarray(self.cls_positive[i], dtype=np.int32) for i in range(num_classes)]
            self.cls_negative = [np.asarray(self.cls_negative[i], dtype=np.int32) for i in range(num_classes)]
            print('done.')

    def __getitem__(self, index):
        """
        Args:
            index (int): Index
        Returns:
            tuple: (image, target) where target is class_index of the target class.
        """
        img, target = super().__getitem__(index)

        if self.is_sample:
            # sample contrastive examples
            pos_idx = index
            neg_idx = np.random.choice(self.cls_negative[target], self.k, replace=True)
            sample_idx = np.hstack((np.asarray([pos_idx]), neg_idx))
            return img, target, index, sample_idx
        else:
            return img, target, index


class CIFAR100InstanceSample(CIFAR100, ImageNetInstanceSample):
    """: Folder datasets which returns (img, label, index, contrast_index):
    """

    def __init__(self, root, train, is_sample=False, k=4096, **kwargs):
        CIFAR100.__init__(self, root, train, **kwargs)
        self.k = k
        self.is_sample = is_sample
        if self.is_sample:
            print('preparing contrastive data...')
            num_classes = 100
            num_samples = len(self.data)

            self.cls_positive = [[] for _ in range(num_classes)]
            for i in range(num_samples):
                self.cls_positive[self.targets[i]].append(i)

            self.cls_negative = [[] for _ in range(num_classes)]
            for i in range(num_classes):
                for j in range(num_classes):
                    if j == i:
                        continue
                    self.cls_negative[i].extend(self.cls_positive[j])

            self.cls_positive = [np.asarray(self.cls_positive[i], dtype=np.int32) for i in range(num_classes)]
            self.cls_negative = [np.asarray(self.cls_negative[i], dtype=np.int32) for i in range(num_classes)]
            print('done.')

    def __getitem__(self, index):
        img, target = CIFAR100.__getitem__(self, index)

        if self.is_sample:
            # sample contrastive examples
            pos_idx = index
            neg_idx = np.random.choice(self.cls_negative[target], self.k, replace=True)
            sample_idx = np.hstack((np.asarray([pos_idx]), neg_idx))
            return img, target, index, sample_idx
        else:
            return img, target, index


class TimePredictor:
    def __init__(self, steps, most_recent=30, drop_first=True):
        self.steps = steps
        self.most_recent = most_recent
        self.drop_first = drop_first  # drop iter 0
        self.cur_step = 0

        self.time_list = []
        self.temp_time = time.time()

    def update(self):
        self.cur_step += 1
        time_interval = time.time() - self.temp_time
        self.time_list.append(time_interval)

        if self.drop_first and len(self.time_list) > 1:
            self.time_list = self.time_list[1:]
            self.drop_first = False

        self.time_list = self.time_list[-self.most_recent:]
        self.temp_time = time.time()

    def get_pred_text(self):
        single_step_time = np.mean(self.time_list)
        end_timestamp = time.time() + single_step_time * (self.steps - self.cur_step)
        return datetime.fromtimestamp(end_timestamp).strftime('%Y-%m-%d %H:%M:%S')