Spaces:
Configuration error
Configuration error
File size: 5,304 Bytes
3b40f46 |
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 |
import torch
import torch.nn as nn
import numpy as np
from .downsampler import Downsampler
def add_module(self, module):
self.add_module(str(len(self) + 1), module)
torch.nn.Module.add = add_module
class Concat(nn.Module):
def __init__(self, dim, *args):
super(Concat, self).__init__()
self.dim = dim
for idx, module in enumerate(args):
self.add_module(str(idx), module)
def forward(self, input):
inputs = []
for module in self._modules.values():
inputs.append(module(input))
inputs_shapes2 = [x.shape[2] for x in inputs]
inputs_shapes3 = [x.shape[3] for x in inputs]
if np.all(np.array(inputs_shapes2) == min(inputs_shapes2)) and np.all(
np.array(inputs_shapes3) == min(inputs_shapes3)
):
inputs_ = inputs
else:
target_shape2 = min(inputs_shapes2)
target_shape3 = min(inputs_shapes3)
inputs_ = []
for inp in inputs:
diff2 = (inp.size(2) - target_shape2) // 2
diff3 = (inp.size(3) - target_shape3) // 2
inputs_.append(inp[:, :, diff2 : diff2 + target_shape2, diff3 : diff3 + target_shape3])
return torch.cat(inputs_, dim=self.dim)
def __len__(self):
return len(self._modules)
class GenNoise(nn.Module):
def __init__(self, dim2):
super(GenNoise, self).__init__()
self.dim2 = dim2
def forward(self, input):
a = list(input.size())
a[1] = self.dim2
# print (input.data.type())
b = torch.zeros(a).type_as(input.data)
b.normal_()
x = torch.autograd.Variable(b)
return x
class Swish(nn.Module):
"""
https://arxiv.org/abs/1710.05941
The hype was so huge that I could not help but try it
"""
def __init__(self):
super(Swish, self).__init__()
self.s = nn.Sigmoid()
def forward(self, x):
return x * self.s(x)
def act(act_fun="LeakyReLU"):
"""
Either string defining an activation function or module (e.g. nn.ReLU)
"""
if isinstance(act_fun, str):
if act_fun == "LeakyReLU":
return nn.LeakyReLU(0.2, inplace=True)
elif act_fun == "Swish":
return Swish()
elif act_fun == "ELU":
return nn.ELU()
elif act_fun == "none":
return nn.Sequential()
else:
assert False
else:
return act_fun()
class PixelNormLayer(nn.Module):
"""
Pixelwise feature vector normalization.
"""
def __init__(self, eps=1e-8):
super(PixelNormLayer, self).__init__()
self.eps = eps
def forward(self, x):
return x / torch.sqrt(torch.mean(x ** 2, dim=1, keepdim=True) + 1e-8)
def __repr__(self):
return self.__class__.__name__ + "(eps = %s)" % (self.eps)
def pixelnorm(num_features):
return PixelNormLayer()
def bn(num_features):
return nn.BatchNorm2d(num_features)
def conv(in_f, out_f, kernel_size, stride=1, bias=True, pad="zero", downsample_mode="stride"):
downsampler = None
if stride != 1 and downsample_mode != "stride":
if downsample_mode == "avg":
downsampler = nn.AvgPool2d(stride, stride)
elif downsample_mode == "max":
downsampler = nn.MaxPool2d(stride, stride)
elif downsample_mode in ["lanczos2", "lanczos3"]:
downsampler = Downsampler(
n_planes=out_f, factor=stride, kernel_type=downsample_mode, phase=0.5, preserve_size=True
)
else:
assert False
stride = 1
padder = None
to_pad = int((kernel_size - 1) / 2)
if pad == "reflection":
padder = nn.ReflectionPad2d(to_pad)
to_pad = 0
convolver = nn.Conv2d(in_f, out_f, kernel_size, stride, padding=to_pad, bias=bias)
layers = filter(lambda x: x is not None, [padder, convolver, downsampler])
return nn.Sequential(*layers)
class DecorrelatedColorsToRGB(nn.Module):
"""Converts from a decorrelated color space to RGB. See
https://github.com/eps696/aphantasia/blob/master/aphantasia/image.py. Usually intended
to be followed by a sigmoid.
"""
def __init__(self, inv_color_scale=1.6):
super().__init__()
color_correlation_svd_sqrt = torch.tensor([[0.26, 0.09, 0.02], [0.27, 0.00, -0.05], [0.27, -0.09, 0.03]])
color_correlation_svd_sqrt /= torch.tensor([inv_color_scale, 1.0, 1.0]) # saturate, empirical
max_norm_svd_sqrt = color_correlation_svd_sqrt.norm(dim=0).max()
color_correlation_normalized = color_correlation_svd_sqrt / max_norm_svd_sqrt
self.register_buffer("colcorr_t", color_correlation_normalized.T)
def inverse(self, image):
colcorr_t_inv = torch.linalg.inv(self.colcorr_t)
return torch.einsum("nchw,cd->ndhw", image, colcorr_t_inv)
def forward(self, image):
if image.dim() == 4:
image_rgb, alpha = image[:, :3], image[:, 3].unsqueeze(1)
image_rgb = torch.einsum("nchw,cd->ndhw", image_rgb, self.colcorr_t)
image = torch.cat([image_rgb, alpha], dim=1)
else:
image = torch.einsum("nchw,cd->ndhw", image, self.colcorr_t)
return image
|