Spaces:
Configuration error
Configuration error
File size: 5,156 Bytes
16d007c |
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 |
import numpy as np
import torch
import torch.nn as nn
class Downsampler(nn.Module):
"""
http://www.realitypixels.com/turk/computergraphics/ResamplingFilters.pdf
"""
def __init__(
self, n_planes, factor, kernel_type, phase=0, kernel_width=None, support=None, sigma=None, preserve_size=False
):
super(Downsampler, self).__init__()
assert phase in [0, 0.5], "phase should be 0 or 0.5"
if kernel_type == "lanczos2":
support = 2
kernel_width = 4 * factor + 1
kernel_type_ = "lanczos"
elif kernel_type == "lanczos3":
support = 3
kernel_width = 6 * factor + 1
kernel_type_ = "lanczos"
elif kernel_type == "gauss12":
kernel_width = 7
sigma = 1 / 2
kernel_type_ = "gauss"
elif kernel_type == "gauss1sq2":
kernel_width = 9
sigma = 1.0 / np.sqrt(2)
kernel_type_ = "gauss"
elif kernel_type in ["lanczos", "gauss", "box"]:
kernel_type_ = kernel_type
else:
assert False, "wrong name kernel"
# note that `kernel width` will be different to actual size for phase = 1/2
self.kernel = get_kernel(factor, kernel_type_, phase, kernel_width, support=support, sigma=sigma)
downsampler = nn.Conv2d(n_planes, n_planes, kernel_size=self.kernel.shape, stride=factor, padding=0)
downsampler.weight.data[:] = 0
downsampler.bias.data[:] = 0
kernel_torch = torch.from_numpy(self.kernel)
for i in range(n_planes):
downsampler.weight.data[i, i] = kernel_torch
self.downsampler_ = downsampler
if preserve_size:
if self.kernel.shape[0] % 2 == 1:
pad = int((self.kernel.shape[0] - 1) / 2.0)
else:
pad = int((self.kernel.shape[0] - factor) / 2.0)
self.padding = nn.ReplicationPad2d(pad)
self.preserve_size = preserve_size
def forward(self, input):
if self.preserve_size:
x = self.padding(input)
else:
x = input
self.x = x
return self.downsampler_(x)
def get_kernel(factor, kernel_type, phase, kernel_width, support=None, sigma=None):
assert kernel_type in ["lanczos", "gauss", "box"]
# factor = float(factor)
if phase == 0.5 and kernel_type != "box":
kernel = np.zeros([kernel_width - 1, kernel_width - 1])
else:
kernel = np.zeros([kernel_width, kernel_width])
if kernel_type == "box":
assert phase == 0.5, "Box filter is always half-phased"
kernel[:] = 1.0 / (kernel_width * kernel_width)
elif kernel_type == "gauss":
assert sigma, "sigma is not specified"
assert phase != 0.5, "phase 1/2 for gauss not implemented"
center = (kernel_width + 1.0) / 2.0
print(center, kernel_width)
sigma_sq = sigma * sigma
for i in range(1, kernel.shape[0] + 1):
for j in range(1, kernel.shape[1] + 1):
di = (i - center) / 2.0
dj = (j - center) / 2.0
kernel[i - 1][j - 1] = np.exp(-(di * di + dj * dj) / (2 * sigma_sq))
kernel[i - 1][j - 1] = kernel[i - 1][j - 1] / (2.0 * np.pi * sigma_sq)
elif kernel_type == "lanczos":
assert support, "support is not specified"
center = (kernel_width + 1) / 2.0
for i in range(1, kernel.shape[0] + 1):
for j in range(1, kernel.shape[1] + 1):
if phase == 0.5:
di = abs(i + 0.5 - center) / factor
dj = abs(j + 0.5 - center) / factor
else:
di = abs(i - center) / factor
dj = abs(j - center) / factor
pi_sq = np.pi * np.pi
val = 1
if di != 0:
val = val * support * np.sin(np.pi * di) * np.sin(np.pi * di / support)
val = val / (np.pi * np.pi * di * di)
if dj != 0:
val = val * support * np.sin(np.pi * dj) * np.sin(np.pi * dj / support)
val = val / (np.pi * np.pi * dj * dj)
kernel[i - 1][j - 1] = val
else:
assert False, "wrong method name"
kernel /= kernel.sum()
return kernel
# a = Downsampler(n_planes=3, factor=2, kernel_type='lanczos2', phase='1', preserve_size=True)
#################
# Learnable downsampler
# KS = 32
# dow = nn.Sequential(nn.ReplicationPad2d(int((KS - factor) / 2.)), nn.Conv2d(1,1,KS,factor))
# class Apply(nn.Module):
# def __init__(self, what, dim, *args):
# super(Apply, self).__init__()
# self.dim = dim
# self.what = what
# def forward(self, input):
# inputs = []
# for i in range(input.size(self.dim)):
# inputs.append(self.what(input.narrow(self.dim, i, 1)))
# return torch.cat(inputs, dim=self.dim)
# def __len__(self):
# return len(self._modules)
# downs = Apply(dow, 1)
# downs.type(dtype)(net_input.type(dtype)).size()
|