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872b0a0 | 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 | ## Portions of Code from, copyright 2018 Jochen Gast
from __future__ import absolute_import, division, print_function
import torch
import torch.nn.functional as tf
from torch import nn
def _bchw2bhwc(tensor):
return tensor.transpose(1, 2).transpose(2, 3)
def _bhwc2bchw(tensor):
return tensor.transpose(2, 3).transpose(1, 2)
class Meshgrid(nn.Module):
def __init__(self):
super(Meshgrid, self).__init__()
self.width = 0
self.height = 0
self.register_buffer("xx", torch.zeros(1, 1))
self.register_buffer("yy", torch.zeros(1, 1))
self.register_buffer("rangex", torch.zeros(1, 1))
self.register_buffer("rangey", torch.zeros(1, 1))
def _compute_meshgrid(self, width, height):
torch.arange(0, width, out=self.rangex)
torch.arange(0, height, out=self.rangey)
self.xx = self.rangex.repeat(height, 1).contiguous()
self.yy = self.rangey.repeat(width, 1).t().contiguous()
def forward(self, width, height):
if self.width != width or self.height != height:
self._compute_meshgrid(width=width, height=height)
self.width = width
self.height = height
return self.xx, self.yy
class BatchSub2Ind(nn.Module):
def __init__(self):
super(BatchSub2Ind, self).__init__()
self.register_buffer("_offsets", torch.LongTensor())
def forward(self, shape, row_sub, col_sub, out=None):
batch_size = row_sub.size(0)
height, width = shape
ind = row_sub * width + col_sub
torch.arange(batch_size, out=self._offsets)
self._offsets *= height * width
if out is None:
return torch.add(ind, self._offsets.view(-1, 1, 1))
else:
torch.add(ind, self._offsets.view(-1, 1, 1), out=out)
class Interp2(nn.Module):
def __init__(self, clamp=False):
super(Interp2, self).__init__()
self._clamp = clamp
self._batch_sub2ind = BatchSub2Ind()
self.register_buffer("_x0", torch.LongTensor())
self.register_buffer("_x1", torch.LongTensor())
self.register_buffer("_y0", torch.LongTensor())
self.register_buffer("_y1", torch.LongTensor())
self.register_buffer("_i00", torch.LongTensor())
self.register_buffer("_i01", torch.LongTensor())
self.register_buffer("_i10", torch.LongTensor())
self.register_buffer("_i11", torch.LongTensor())
self.register_buffer("_v00", torch.FloatTensor())
self.register_buffer("_v01", torch.FloatTensor())
self.register_buffer("_v10", torch.FloatTensor())
self.register_buffer("_v11", torch.FloatTensor())
self.register_buffer("_x", torch.FloatTensor())
self.register_buffer("_y", torch.FloatTensor())
def forward(self, v, xq, yq):
batch_size, channels, height, width = v.size()
# clamp if wanted
if self._clamp:
xq.clamp_(0, width - 1)
yq.clamp_(0, height - 1)
# ------------------------------------------------------------------
# Find neighbors
#
# x0 = torch.floor(xq).long(), x0.clamp_(0, width - 1)
# x1 = x0 + 1, x1.clamp_(0, width - 1)
# y0 = torch.floor(yq).long(), y0.clamp_(0, height - 1)
# y1 = y0 + 1, y1.clamp_(0, height - 1)
#
# ------------------------------------------------------------------
self._x0 = torch.floor(xq).long().clamp(0, width - 1)
self._y0 = torch.floor(yq).long().clamp(0, height - 1)
self._x1 = torch.add(self._x0, 1).clamp(0, width - 1)
self._y1 = torch.add(self._y0, 1).clamp(0, height - 1)
# batch_sub2ind
self._batch_sub2ind([height, width], self._y0, self._x0, out=self._i00)
self._batch_sub2ind([height, width], self._y0, self._x1, out=self._i01)
self._batch_sub2ind([height, width], self._y1, self._x0, out=self._i10)
self._batch_sub2ind([height, width], self._y1, self._x1, out=self._i11)
# reshape
v_flat = _bchw2bhwc(v).contiguous().view(-1, channels)
torch.index_select(v_flat, dim=0, index=self._i00.view(-1), out=self._v00)
torch.index_select(v_flat, dim=0, index=self._i01.view(-1), out=self._v01)
torch.index_select(v_flat, dim=0, index=self._i10.view(-1), out=self._v10)
torch.index_select(v_flat, dim=0, index=self._i11.view(-1), out=self._v11)
# local_coords
torch.add(xq, -self._x0.float(), out=self._x)
torch.add(yq, -self._y0.float(), out=self._y)
# weights
w00 = torch.unsqueeze((1.0 - self._y) * (1.0 - self._x), dim=1)
w01 = torch.unsqueeze((1.0 - self._y) * self._x, dim=1)
w10 = torch.unsqueeze(self._y * (1.0 - self._x), dim=1)
w11 = torch.unsqueeze(self._y * self._x, dim=1)
def _reshape(u):
return _bhwc2bchw(u.view(batch_size, height, width, channels))
# values
values = (
_reshape(self._v00) * w00
+ _reshape(self._v01) * w01
+ _reshape(self._v10) * w10
+ _reshape(self._v11) * w11
)
if self._clamp:
return values
else:
# find_invalid
invalid = (
((xq < 0) | (xq >= width) | (yq < 0) | (yq >= height))
.unsqueeze(dim=1)
.float()
)
# maskout invalid
transformed = invalid * torch.zeros_like(values) + (1.0 - invalid) * values
return transformed
def resize2D(inputs, size_targets, mode="bilinear"):
size_inputs = [inputs.size(2), inputs.size(3)]
if all([size_inputs == size_targets]):
return inputs # nothing to do
elif any([size_targets < size_inputs]):
resized = tf.adaptive_avg_pool2d(inputs, size_targets) # downscaling
else:
resized = tf.upsample(inputs, size=size_targets, mode=mode) # upsampling
# correct scaling
return resized
def resize2D_as(inputs, output_as, mode="bilinear"):
size_targets = [output_as.size(2), output_as.size(3)]
return resize2D(inputs, size_targets, mode=mode)
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