patch_transformer.py 9.71 KB
Newer Older
Pavlo Beylin's avatar
Pavlo Beylin committed
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
import math
from torch.nn.modules.utils import _pair, _quadruple
import torch.nn.functional as F
import numpy as np
import torch
from torch import nn

class PatchApplier(nn.Module):
    """PatchApplier: applies adversarial patches to images.

    Module providing the functionality necessary to apply a patch to all detections in all images in the batch.

    """

    def __init__(self):
        super(PatchApplier, self).__init__()

    def forward(self, img, patch):
        img = torch.where(torch.tensor(patch < 0.1), torch.tensor(img)/256, torch.tensor(patch))*256
        return img.detach().numpy()


class MedianPool2d(nn.Module):
    """ Median pool (usable as median filter when stride=1) module.

    Args:
         kernel_size: size of pooling kernel, int or 2-tuple
         stride: pool stride, int or 2-tuple
         padding: pool padding, int or 4-tuple (l, r, t, b) as in pytorch F.pad
         same: override padding and enforce same padding, boolean
    """

    def __init__(self, kernel_size=3, stride=1, padding=0, same=False):
        super(MedianPool2d, self).__init__()
        self.k = _pair(kernel_size)
        self.stride = _pair(stride)
        self.padding = _quadruple(padding)  # convert to l, r, t, b
        self.same = same

    def _padding(self, x):
        if self.same:
            ih, iw = x.size()[2:]
            if ih % self.stride[0] == 0:
                ph = max(self.k[0] - self.stride[0], 0)
            else:
                ph = max(self.k[0] - (ih % self.stride[0]), 0)
            if iw % self.stride[1] == 0:
                pw = max(self.k[1] - self.stride[1], 0)
            else:
                pw = max(self.k[1] - (iw % self.stride[1]), 0)
            pl = pw // 2
            pr = pw - pl
            pt = ph // 2
            pb = ph - pt
            padding = (pl, pr, pt, pb)
        else:
            padding = self.padding
        return padding

    def forward(self, x):
        # using existing pytorch functions and tensor ops so that we get autograd,
        # would likely be more efficient to implement from scratch at C/Cuda level
        x = F.pad(x, self._padding(x), mode='reflect')
        x = x.unfold(2, self.k[0], self.stride[0]).unfold(3, self.k[1], self.stride[1])
        x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0]
        return x

class PatchTransformer(nn.Module):
    """PatchTransformer: transforms batch of patches

    Module providing the functionality necessary to transform a batch of patches, randomly adjusting brightness and
    contrast, adding random amount of noise, and rotating randomly. Resizes patches according to as size based on the
    batch of labels, and pads them to the dimension of an image.

    """

    def __init__(self):
        super(PatchTransformer, self).__init__()
        self.min_contrast = 0.8
        self.max_contrast = 1.2
        self.min_brightness = -0.1
        self.max_brightness = 0.1
        self.noise_factor = 0.10
        self.minangle = -20 / 180 * math.pi
        self.maxangle = 20 / 180 * math.pi
        self.medianpooler = MedianPool2d(7, same=True)
        '''
        kernel = torch.cuda.FloatTensor([[0.003765, 0.015019, 0.023792, 0.015019, 0.003765],                                                                                    
                                         [0.015019, 0.059912, 0.094907, 0.059912, 0.015019],                                                                                    
                                         [0.023792, 0.094907, 0.150342, 0.094907, 0.023792],                                                                                    
                                         [0.015019, 0.059912, 0.094907, 0.059912, 0.015019],                                                                                    
                                         [0.003765, 0.015019, 0.023792, 0.015019, 0.003765]])
        self.kernel = kernel.unsqueeze(0).unsqueeze(0).expand(3,3,-1,-1)
        '''

    def forward(self, adv_patch, lab_batch, img_size_x, img_size_y, do_rotate=True, rand_loc=True):
        # adv_patch = F.conv2d(adv_patch.unsqueeze(0),self.kernel,padding=(2,2))
        adv_patch = self.medianpooler(adv_patch.unsqueeze(0))
        # Determine size of padding
        pad_x = (img_size_x - adv_patch.size(-1)) / 2
        pad_y = (img_size_y - adv_patch.size(-1)) / 2

        # Make a batch of patches
        adv_patch = adv_patch.unsqueeze(0)  # .unsqueeze(0)
        adv_batch = adv_patch.expand(lab_batch.size(0), lab_batch.size(1), -1, -1, -1)
        batch_size = torch.Size((lab_batch.size(0), lab_batch.size(1)))

        # Contrast, brightness and noise transforms

        # Create random contrast tensor
        contrast = torch.cuda.FloatTensor(batch_size).uniform_(self.min_contrast, self.max_contrast)
        contrast = contrast.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
        contrast = contrast.expand(-1, -1, adv_batch.size(-3), adv_batch.size(-2), adv_batch.size(-1))
        contrast = contrast.cuda()

        # Create random brightness tensor
        brightness = torch.cuda.FloatTensor(batch_size).uniform_(self.min_brightness, self.max_brightness)
        brightness = brightness.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
        brightness = brightness.expand(-1, -1, adv_batch.size(-3), adv_batch.size(-2), adv_batch.size(-1))
        brightness = brightness.cuda()

        # Create random noise tensor
        noise = torch.cuda.FloatTensor(adv_batch.size()).uniform_(-1, 1) * self.noise_factor

        # Apply contrast/brightness/noise, clamp
        adv_batch = adv_batch * contrast + brightness + noise

        adv_batch = torch.clamp(adv_batch, 0.000001, 0.99999)

        # TODO should that not be class 0 if we do not want to cover person? cls=1 would be bicycle ...
        # Where the label class_id is 1 we don't want a patch (padding) --> fill mask with zero's
        cls_ids = torch.narrow(lab_batch, 2, 0, 1)
        cls_mask = cls_ids.expand(-1, -1, 3)
        cls_mask = cls_mask.unsqueeze(-1)
        cls_mask = cls_mask.expand(-1, -1, -1, adv_batch.size(3))
        cls_mask = cls_mask.unsqueeze(-1)
        cls_mask = cls_mask.expand(-1, -1, -1, -1, adv_batch.size(4))
        msk_batch = torch.cuda.FloatTensor(cls_mask.size()).fill_(1) - cls_mask

        # Pad patch and mask to image dimensions
        mypad = nn.ConstantPad2d((int(pad_x + 0.5), int(pad_x), int(pad_y + 0.5), int(pad_y)), 0)
        adv_batch = mypad(adv_batch)
        msk_batch = mypad(msk_batch)

        # Rotation and rescaling transforms
        anglesize = (lab_batch.size(0) * lab_batch.size(1))
        if do_rotate:
            angle = torch.cuda.FloatTensor(anglesize).uniform_(self.minangle, self.maxangle)
        else:
            angle = torch.cuda.FloatTensor(anglesize).fill_(0)

        # Resizes and rotates
        current_patch_size = adv_patch.size(-1)
        lab_batch_scaled = torch.cuda.FloatTensor(lab_batch.size()).fill_(0)
        lab_batch_scaled[:, :, 1] = lab_batch[:, :, 1] * img_size_x
        lab_batch_scaled[:, :, 2] = lab_batch[:, :, 2] * img_size_x
        lab_batch_scaled[:, :, 3] = lab_batch[:, :, 3] * img_size_y
        lab_batch_scaled[:, :, 4] = lab_batch[:, :, 4] * img_size_y
        target_size = torch.sqrt(
            ((lab_batch_scaled[:, :, 3].mul(0.2)) ** 2) + ((lab_batch_scaled[:, :, 4].mul(0.2)) ** 2))
        target_x = lab_batch[:, :, 1].view(np.prod(batch_size)) / 2
        target_y = lab_batch[:, :, 2].view(np.prod(batch_size)) / 2
        targetoff_x = lab_batch[:, :, 3].view(np.prod(batch_size))
        targetoff_y = lab_batch[:, :, 4].view(np.prod(batch_size))
        if (rand_loc):
            off_x = targetoff_x * (torch.cuda.FloatTensor(targetoff_x.size()).uniform_(-0.4, 0.4))
            target_x = target_x + off_x
            off_y = targetoff_y * (torch.cuda.FloatTensor(targetoff_y.size()).uniform_(-0.4, 0.4))
            target_y = target_y + off_y
        target_y = target_y - 0.05
        scale = target_size / current_patch_size
        scale = scale.view(anglesize)

        s = adv_batch.size()
        adv_batch = adv_batch.view(s[0] * s[1], s[2], s[3], s[4])
        msk_batch = msk_batch.view(s[0] * s[1], s[2], s[3], s[4])

        tx = (-target_x + 0.5) * 2
        ty = (-target_y + 0.5) * 2
        sin = torch.sin(angle)
        cos = torch.cos(angle)

        # Theta = rotation,rescale matrix
        theta = torch.cuda.FloatTensor(anglesize, 2, 3).fill_(0)
        theta[:, 0, 0] = cos / scale
        theta[:, 0, 1] = sin / scale
        theta[:, 0, 2] = tx * cos / scale + ty * sin / scale
        theta[:, 1, 0] = -sin / scale
        theta[:, 1, 1] = cos / scale
        theta[:, 1, 2] = -tx * sin / scale + ty * cos / scale

        b_sh = adv_batch.shape
        grid = F.affine_grid(theta, adv_batch.shape, align_corners=False)

        adv_batch_t = F.grid_sample(adv_batch, grid, align_corners=False)
        msk_batch_t = F.grid_sample(msk_batch, grid, align_corners=False)

        '''
        # Theta2 = translation matrix
        theta2 = torch.cuda.FloatTensor(anglesize, 2, 3).fill_(0)
        theta2[:, 0, 0] = 1
        theta2[:, 0, 1] = 0
        theta2[:, 0, 2] = (-target_x + 0.5) * 2
        theta2[:, 1, 0] = 0
        theta2[:, 1, 1] = 1
        theta2[:, 1, 2] = (-target_y + 0.5) * 2

        grid2 = F.affine_grid(theta2, adv_batch.shape)
        adv_batch_t = F.grid_sample(adv_batch_t, grid2)
        msk_batch_t = F.grid_sample(msk_batch_t, grid2)

        '''
        adv_batch_t = adv_batch_t.view(s[0], s[1], s[2], s[3], s[4])
        msk_batch_t = msk_batch_t.view(s[0], s[1], s[2], s[3], s[4])

        adv_batch_t = torch.clamp(adv_batch_t, 0.000001, 0.999999)
        # img = msk_batch_t[0, 0, :, :, :].detach().cpu()
        # img = transforms.ToPILImage()(img)
        # img.show()
        # exit()

        return adv_batch_t #* msk_batch_t