File size: 15,038 Bytes
681fa96
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
""" Mixup and Cutmix



Papers:

mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412)



CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (https://arxiv.org/abs/1905.04899)



Code Reference:

CutMix: https://github.com/clovaai/CutMix-PyTorch



Hacked together by / Copyright 2019, Ross Wightman

"""
import numpy as np
import torch


def one_hot(x, num_classes, on_value=1., off_value=0., device='cuda'):
    x = x.long().view(-1, 1)
    return torch.full((x.size()[0], num_classes), off_value, device=device).scatter_(1, x, on_value)


def mixup_target(target, num_classes, lam=1., smoothing=0.0, device='cuda'):
    off_value = smoothing / num_classes
    on_value = 1. - smoothing + off_value
    y1 = one_hot(target, num_classes, on_value=on_value, off_value=off_value, device=device)
    y2 = one_hot(target.flip(0), num_classes, on_value=on_value, off_value=off_value, device=device)
    return y1 * lam + y2 * (1. - lam)


def rand_bbox(img_shape, lam, margin=0., count=None):
    """ Standard CutMix bounding-box

    Generates a random square bbox based on lambda value. This impl includes

    support for enforcing a border margin as percent of bbox dimensions.



    Args:

        img_shape (tuple): Image shape as tuple

        lam (float): Cutmix lambda value

        margin (float): Percentage of bbox dimension to enforce as margin (reduce amount of box outside image)

        count (int): Number of bbox to generate

    """
    ratio = np.sqrt(1 - lam)
    img_h, img_w = img_shape[-2:]
    cut_h, cut_w = int(img_h * ratio), int(img_w * ratio)
    margin_y, margin_x = int(margin * cut_h), int(margin * cut_w)
    cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count)
    cx = np.random.randint(0 + margin_x, img_w - margin_x, size=count)
    yl = np.clip(cy - cut_h // 2, 0, img_h)
    yh = np.clip(cy + cut_h // 2, 0, img_h)
    xl = np.clip(cx - cut_w // 2, 0, img_w)
    xh = np.clip(cx + cut_w // 2, 0, img_w)
    return yl, yh, xl, xh


def rand_bbox_minmax(img_shape, minmax, count=None):
    """ Min-Max CutMix bounding-box

    Inspired by Darknet cutmix impl, generates a random rectangular bbox

    based on min/max percent values applied to each dimension of the input image.



    Typical defaults for minmax are usually in the  .2-.3 for min and .8-.9 range for max.



    Args:

        img_shape (tuple): Image shape as tuple

        minmax (tuple or list): Min and max bbox ratios (as percent of image size)

        count (int): Number of bbox to generate

    """
    assert len(minmax) == 2
    img_h, img_w = img_shape[-2:]
    cut_h = np.random.randint(int(img_h * minmax[0]), int(img_h * minmax[1]), size=count)
    cut_w = np.random.randint(int(img_w * minmax[0]), int(img_w * minmax[1]), size=count)
    yl = np.random.randint(0, img_h - cut_h, size=count)
    xl = np.random.randint(0, img_w - cut_w, size=count)
    yu = yl + cut_h
    xu = xl + cut_w
    return yl, yu, xl, xu


def cutmix_bbox_and_lam(img_shape, lam, ratio_minmax=None, correct_lam=True, count=None):
    """ Generate bbox and apply lambda correction.

    """
    if ratio_minmax is not None:
        yl, yu, xl, xu = rand_bbox_minmax(img_shape, ratio_minmax, count=count)
    else:
        yl, yu, xl, xu = rand_bbox(img_shape, lam, count=count)
    if correct_lam or ratio_minmax is not None:
        bbox_area = (yu - yl) * (xu - xl)
        lam = 1. - bbox_area / float(img_shape[-2] * img_shape[-1])
    return (yl, yu, xl, xu), lam


class Mixup:
    """ Mixup/Cutmix that applies different params to each element or whole batch



    Args:

        mixup_alpha (float): mixup alpha value, mixup is active if > 0.

        cutmix_alpha (float): cutmix alpha value, cutmix is active if > 0.

        cutmix_minmax (List[float]): cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None.

        prob (float): probability of applying mixup or cutmix per batch or element

        switch_prob (float): probability of switching to cutmix instead of mixup when both are active

        mode (str): how to apply mixup/cutmix params (per 'batch', 'pair' (pair of elements), 'elem' (element)

        correct_lam (bool): apply lambda correction when cutmix bbox clipped by image borders

        label_smoothing (float): apply label smoothing to the mixed target tensor

        num_classes (int): number of classes for target

    """
    def __init__(self, mixup_alpha=1., cutmix_alpha=0., cutmix_minmax=None, prob=1.0, switch_prob=0.5,

                 mode='batch', correct_lam=True, label_smoothing=0.1, num_classes=1000):
        self.mixup_alpha = mixup_alpha
        self.cutmix_alpha = cutmix_alpha
        self.cutmix_minmax = cutmix_minmax
        if self.cutmix_minmax is not None:
            assert len(self.cutmix_minmax) == 2
            # force cutmix alpha == 1.0 when minmax active to keep logic simple & safe
            self.cutmix_alpha = 1.0
        self.mix_prob = prob
        self.switch_prob = switch_prob
        self.label_smoothing = label_smoothing
        self.num_classes = num_classes
        self.mode = mode
        self.correct_lam = correct_lam  # correct lambda based on clipped area for cutmix
        self.mixup_enabled = True  # set to false to disable mixing (intended tp be set by train loop)

    def _params_per_elem(self, batch_size):
        lam = np.ones(batch_size, dtype=np.float32)
        use_cutmix = np.zeros(batch_size, dtype=np.bool)
        if self.mixup_enabled:
            if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
                use_cutmix = np.random.rand(batch_size) < self.switch_prob
                lam_mix = np.where(
                    use_cutmix,
                    np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size),
                    np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size))
            elif self.mixup_alpha > 0.:
                lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size)
            elif self.cutmix_alpha > 0.:
                use_cutmix = np.ones(batch_size, dtype=np.bool)
                lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size)
            else:
                assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
            lam = np.where(np.random.rand(batch_size) < self.mix_prob, lam_mix.astype(np.float32), lam)
        return lam, use_cutmix

    def _params_per_batch(self):
        lam = 1.
        use_cutmix = False
        if self.mixup_enabled and np.random.rand() < self.mix_prob:
            if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
                use_cutmix = np.random.rand() < self.switch_prob
                lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) if use_cutmix else \
                    np.random.beta(self.mixup_alpha, self.mixup_alpha)
            elif self.mixup_alpha > 0.:
                lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha)
            elif self.cutmix_alpha > 0.:
                use_cutmix = True
                lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha)
            else:
                assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
            lam = float(lam_mix)
        return lam, use_cutmix

    def _mix_elem(self, x):
        batch_size = len(x)
        lam_batch, use_cutmix = self._params_per_elem(batch_size)
        x_orig = x.clone()  # need to keep an unmodified original for mixing source
        for i in range(batch_size):
            j = batch_size - i - 1
            lam = lam_batch[i]
            if lam != 1.:
                if use_cutmix[i]:
                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
                        x[i].shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
                    x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
                    lam_batch[i] = lam
                else:
                    x[i] = x[i] * lam + x_orig[j] * (1 - lam)
        return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1)

    def _mix_pair(self, x):
        batch_size = len(x)
        lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)
        x_orig = x.clone()  # need to keep an unmodified original for mixing source
        for i in range(batch_size // 2):
            j = batch_size - i - 1
            lam = lam_batch[i]
            if lam != 1.:
                if use_cutmix[i]:
                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
                        x[i].shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
                    x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
                    x[j][:, yl:yh, xl:xh] = x_orig[i][:, yl:yh, xl:xh]
                    lam_batch[i] = lam
                else:
                    x[i] = x[i] * lam + x_orig[j] * (1 - lam)
                    x[j] = x[j] * lam + x_orig[i] * (1 - lam)
        lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))
        return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1)

    def _mix_batch(self, x):
        lam, use_cutmix = self._params_per_batch()
        if lam == 1.:
            return 1.
        if use_cutmix:
            (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
                x.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
            x[:, :, yl:yh, xl:xh] = x.flip(0)[:, :, yl:yh, xl:xh]
        else:
            x_flipped = x.flip(0).mul_(1. - lam)
            x.mul_(lam).add_(x_flipped)
        return lam

    def __call__(self, x, target):
        assert len(x) % 2 == 0, 'Batch size should be even when using this'
        if self.mode == 'elem':
            lam = self._mix_elem(x)
        elif self.mode == 'pair':
            lam = self._mix_pair(x)
        else:
            lam = self._mix_batch(x)
        target = mixup_target(target, self.num_classes, lam, self.label_smoothing, x.device)
        return x, target


class FastCollateMixup(Mixup):
    """ Fast Collate w/ Mixup/Cutmix that applies different params to each element or whole batch



    A Mixup impl that's performed while collating the batches.

    """

    def _mix_elem_collate(self, output, batch, half=False):
        batch_size = len(batch)
        num_elem = batch_size // 2 if half else batch_size
        assert len(output) == num_elem
        lam_batch, use_cutmix = self._params_per_elem(num_elem)
        for i in range(num_elem):
            j = batch_size - i - 1
            lam = lam_batch[i]
            mixed = batch[i][0]
            if lam != 1.:
                if use_cutmix[i]:
                    if not half:
                        mixed = mixed.copy()
                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
                        output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
                    mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
                    lam_batch[i] = lam
                else:
                    mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
                    np.rint(mixed, out=mixed)
            output[i] += torch.from_numpy(mixed.astype(np.uint8))
        if half:
            lam_batch = np.concatenate((lam_batch, np.ones(num_elem)))
        return torch.tensor(lam_batch).unsqueeze(1)

    def _mix_pair_collate(self, output, batch):
        batch_size = len(batch)
        lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)
        for i in range(batch_size // 2):
            j = batch_size - i - 1
            lam = lam_batch[i]
            mixed_i = batch[i][0]
            mixed_j = batch[j][0]
            assert 0 <= lam <= 1.0
            if lam < 1.:
                if use_cutmix[i]:
                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
                        output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
                    patch_i = mixed_i[:, yl:yh, xl:xh].copy()
                    mixed_i[:, yl:yh, xl:xh] = mixed_j[:, yl:yh, xl:xh]
                    mixed_j[:, yl:yh, xl:xh] = patch_i
                    lam_batch[i] = lam
                else:
                    mixed_temp = mixed_i.astype(np.float32) * lam + mixed_j.astype(np.float32) * (1 - lam)
                    mixed_j = mixed_j.astype(np.float32) * lam + mixed_i.astype(np.float32) * (1 - lam)
                    mixed_i = mixed_temp
                    np.rint(mixed_j, out=mixed_j)
                    np.rint(mixed_i, out=mixed_i)
            output[i] += torch.from_numpy(mixed_i.astype(np.uint8))
            output[j] += torch.from_numpy(mixed_j.astype(np.uint8))
        lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))
        return torch.tensor(lam_batch).unsqueeze(1)

    def _mix_batch_collate(self, output, batch):
        batch_size = len(batch)
        lam, use_cutmix = self._params_per_batch()
        if use_cutmix:
            (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
                output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
        for i in range(batch_size):
            j = batch_size - i - 1
            mixed = batch[i][0]
            if lam != 1.:
                if use_cutmix:
                    mixed = mixed.copy()  # don't want to modify the original while iterating
                    mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
                else:
                    mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
                    np.rint(mixed, out=mixed)
            output[i] += torch.from_numpy(mixed.astype(np.uint8))
        return lam

    def __call__(self, batch, _=None):
        batch_size = len(batch)
        assert batch_size % 2 == 0, 'Batch size should be even when using this'
        half = 'half' in self.mode
        if half:
            batch_size //= 2
        output = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
        if self.mode == 'elem' or self.mode == 'half':
            lam = self._mix_elem_collate(output, batch, half=half)
        elif self.mode == 'pair':
            lam = self._mix_pair_collate(output, batch)
        else:
            lam = self._mix_batch_collate(output, batch)
        target = torch.tensor([b[1] for b in batch], dtype=torch.int64)
        target = mixup_target(target, self.num_classes, lam, self.label_smoothing, device='cpu')
        target = target[:batch_size]
        return output, target