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basic.py
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basic.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
gks = 5
pad = [i for i in range(gks*gks)]
shift = torch.zeros(gks*gks, 4)
for i in range(gks):
for j in range(gks):
top = i
bottom = gks-1-i
left = j
right = gks-1-j
pad[i*gks + j] = torch.nn.ZeroPad2d((left, right, top, bottom))
#shift[i*gks + j, :] = torch.tensor([left, right, top, bottom])
mid_pad = torch.nn.ZeroPad2d(((gks-1)/2, (gks-1)/2, (gks-1)/2, (gks-1)/2))
zero_pad = pad[0]
gks2 = 3 #guide kernel size
pad2 = [i for i in range(gks2*gks2)]
shift = torch.zeros(gks2*gks2, 4)
for i in range(gks2):
for j in range(gks2):
top = i
bottom = gks2-1-i
left = j
right = gks2-1-j
pad2[i*gks2 + j] = torch.nn.ZeroPad2d((left, right, top, bottom))
gks3 = 7 #guide kernel size
pad3 = [i for i in range(gks3*gks3)]
shift = torch.zeros(gks3*gks3, 4)
for i in range(gks3):
for j in range(gks3):
top = i
bottom = gks3-1-i
left = j
right = gks3-1-j
pad3[i*gks3 + j] = torch.nn.ZeroPad2d((left, right, top, bottom))
def weights_init(m):
# Initialize filters with Gaussian random weights
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.ConvTranspose2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.in_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def convbnrelu(in_channels, out_channels, kernel_size=3,stride=1, padding=1):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def deconvbnrelu(in_channels, out_channels, kernel_size=5, stride=2, padding=2, output_padding=1):
return nn.Sequential(
nn.ConvTranspose2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, output_padding=output_padding, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def convbn(in_channels, out_channels, kernel_size=3,stride=1, padding=1):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=False),
nn.BatchNorm2d(out_channels)
)
def deconvbn(in_channels, out_channels, kernel_size=4, stride=2, padding=1, output_padding=0):
return nn.Sequential(
nn.ConvTranspose2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, output_padding=output_padding, bias=False),
nn.BatchNorm2d(out_channels)
)
class BasicBlock(nn.Module):
expansion = 1
__constants__ = ['downsample']
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(BasicBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
#norm_layer = encoding.nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
if stride != 1 or inplanes != planes:
downsample = nn.Sequential(
conv1x1(inplanes, planes, stride),
norm_layer(planes),
)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1, bias=False, padding=1):
"""3x3 convolution with padding"""
if padding >= 1:
padding = dilation
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=padding, groups=groups, bias=bias, dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1, groups=1, bias=False):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, groups=groups, bias=bias)
class SparseDownSampleClose(nn.Module):
def __init__(self, stride):
super(SparseDownSampleClose, self).__init__()
self.pooling = nn.MaxPool2d(stride, stride)
self.large_number = 600
def forward(self, d, mask):
encode_d = - (1-mask)*self.large_number - d
d = - self.pooling(encode_d)
mask_result = self.pooling(mask)
d_result = d - (1-mask_result)*self.large_number
return d_result, mask_result
class CSPNGenerate(nn.Module):
def __init__(self, in_channels, kernel_size):
super(CSPNGenerate, self).__init__()
self.kernel_size = kernel_size
self.generate = convbn(in_channels, self.kernel_size * self.kernel_size - 1, kernel_size=3, stride=1, padding=1)
def forward(self, feature):
guide = self.generate(feature)
#normalization
guide_sum = torch.sum(guide.abs(), dim=1).unsqueeze(1)
guide = torch.div(guide, guide_sum)
guide_mid = (1 - torch.sum(guide, dim=1)).unsqueeze(1)
#padding
weight_pad = [i for i in range(self.kernel_size * self.kernel_size)]
for t in range(self.kernel_size*self.kernel_size):
zero_pad = 0
if(self.kernel_size==3):
zero_pad = pad2[t]
elif(self.kernel_size==5):
zero_pad = pad[t]
elif(self.kernel_size==7):
zero_pad = pad3[t]
if(t < int((self.kernel_size*self.kernel_size-1)/2)):
weight_pad[t] = zero_pad(guide[:, t:t+1, :, :])
elif(t > int((self.kernel_size*self.kernel_size-1)/2)):
weight_pad[t] = zero_pad(guide[:, t-1:t, :, :])
else:
weight_pad[t] = zero_pad(guide_mid)
guide_weight = torch.cat([weight_pad[t] for t in range(self.kernel_size*self.kernel_size)], dim=1)
return guide_weight
class CSPN(nn.Module):
def __init__(self, kernel_size):
super(CSPN, self).__init__()
self.kernel_size = kernel_size
def forward(self, guide_weight, hn, h0):
#CSPN
half = int(0.5 * (self.kernel_size * self.kernel_size - 1))
result_pad = [i for i in range(self.kernel_size * self.kernel_size)]
for t in range(self.kernel_size*self.kernel_size):
zero_pad = 0
if(self.kernel_size==3):
zero_pad = pad2[t]
elif(self.kernel_size==5):
zero_pad = pad[t]
elif(self.kernel_size==7):
zero_pad = pad3[t]
if(t == half):
result_pad[t] = zero_pad(h0)
else:
result_pad[t] = zero_pad(hn)
guide_result = torch.cat([result_pad[t] for t in range(self.kernel_size*self.kernel_size)], dim=1)
#guide_result = torch.cat([result0_pad, result1_pad, result2_pad, result3_pad,result4_pad, result5_pad, result6_pad, result7_pad, result8_pad], 1)
guide_result = torch.sum((guide_weight.mul(guide_result)), dim=1)
guide_result = guide_result[:, int((self.kernel_size-1)/2):-int((self.kernel_size-1)/2), int((self.kernel_size-1)/2):-int((self.kernel_size-1)/2)]
return guide_result.unsqueeze(dim=1)
class CSPNGenerateAccelerate(nn.Module):
def __init__(self, in_channels, kernel_size):
super(CSPNGenerateAccelerate, self).__init__()
self.kernel_size = kernel_size
self.generate = convbn(in_channels, self.kernel_size * self.kernel_size - 1, kernel_size=3, stride=1, padding=1)
def forward(self, feature):
guide = self.generate(feature)
#normalization in standard CSPN
#'''
guide_sum = torch.sum(guide.abs(), dim=1).unsqueeze(1)
guide = torch.div(guide, guide_sum)
guide_mid = (1 - torch.sum(guide, dim=1)).unsqueeze(1)
#'''
#weight_pad = [i for i in range(self.kernel_size * self.kernel_size)]
half1, half2 = torch.chunk(guide, 2, dim=1)
output = torch.cat((half1, guide_mid, half2), dim=1)
return output
def kernel_trans(kernel, weight):
kernel_size = int(math.sqrt(kernel.size()[1]))
kernel = F.conv2d(kernel, weight, stride=1, padding=int((kernel_size-1)/2))
return kernel
class CSPNAccelerate(nn.Module):
def __init__(self, kernel_size, dilation=1, padding=1, stride=1):
super(CSPNAccelerate, self).__init__()
self.kernel_size = kernel_size
self.dilation = dilation
self.padding = padding
self.stride = stride
def forward(self, kernel, input, input0): #with standard CSPN, an addition input0 port is added
bs = input.size()[0]
h, w = input.size()[2], input.size()[3]
input_im2col = F.unfold(input, self.kernel_size, self.dilation, self.padding, self.stride)
kernel = kernel.reshape(bs, self.kernel_size * self.kernel_size, h * w)
# standard CSPN
input0 = input0.view(bs, 1, h * w)
mid_index = int((self.kernel_size*self.kernel_size-1)/2)
input_im2col[:, mid_index:mid_index+1, :] = input0
#print(input_im2col.size(), kernel.size())
output = torch.einsum('ijk,ijk->ik', (input_im2col, kernel))
return output.view(bs, 1, h, w)
class GeometryFeature(nn.Module):
def __init__(self):
super(GeometryFeature, self).__init__()
def forward(self, z, vnorm, unorm, h, w, ch, cw, fh, fw):
x = z*(0.5*h*(vnorm+1)-ch)/fh
y = z*(0.5*w*(unorm+1)-cw)/fw
return torch.cat((x, y, z),1)
class BasicBlockGeo(nn.Module):
expansion = 1
__constants__ = ['downsample']
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None, geoplanes=3):
super(BasicBlockGeo, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
#norm_layer = encoding.nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes + geoplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes+geoplanes, planes)
self.bn2 = norm_layer(planes)
if stride != 1 or inplanes != planes:
downsample = nn.Sequential(
conv1x1(inplanes+geoplanes, planes, stride),
norm_layer(planes),
)
self.downsample = downsample
self.stride = stride
def forward(self, x, g1=None, g2=None):
identity = x
if g1 is not None:
x = torch.cat((x, g1), 1)
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
if g2 is not None:
out = torch.cat((g2,out), 1)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out