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ResidualFeat.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
# from spectral import SpectralNorm
import numpy as np
class Res2Net(nn.Module):
def __init__(self, inChannel, uPlane, scale=4):
super(Res2Net, self).__init__()
self.uPlane = uPlane
self.scale = scale
self.conv_init = nn.Conv2d(inChannel, uPlane*scale, kernel_size=1, bias=False)
self.bn_init = nn.BatchNorm2d(uPlane*scale)
convs = []
bns = []
for i in range(self.scale-1):
convs.append(nn.Conv2d(self.uPlane, self.uPlane, kernel_size=3, stride = 1, padding=1, bias=False))
bns.append(nn.BatchNorm2d(self.uPlane))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.conv_end = nn.Conv2d(uPlane*scale, inChannel, kernel_size=1, bias=False)
self.bn_end = nn.BatchNorm2d(inChannel)
self.relu = nn.ReLU(inplace=True)
def forward(self,x):
out = self.conv_init(x)
out = self.bn_init(out)
out = self.relu(out)
spx = torch.split(out, self.uPlane, 1)
for i in range(self.scale-1):
if i == 0:
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i == 0:
out = sp
else:
out = torch.cat((out, sp), 1)
out = torch.cat((out, spx[self.scale-1]),1)
out = self.conv_end(out)
out = self.bn_end(out)
return out
'''
------------------------------------------- Original Res2Net Version ---------------------------------------------
class Bottle2neck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, baseWidth=26, scale = 4, stype='normal'):
""" Constructor
Args:
inplanes: input channel dimensionality
planes: output channel dimensionality
stride: conv stride. Replaces pooling layer.
downsample: None when stride = 1
baseWidth: basic width of conv3x3
scale: number of scale.
type: 'normal': normal set. 'stage': first block of a new stage.
"""
super(Bottle2neck, self).__init__()
width = int(math.floor(planes * (baseWidth/64.0)))
self.conv1 = nn.Conv2d(inplanes, width*scale, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(width*scale)
if scale == 1:
self.nums = 1
else:
self.nums = scale -1
if stype == 'stage':
self.pool = nn.AvgPool2d(kernel_size=3, stride = stride, padding=1)
convs = []
bns = []
for i in range(self.nums):
convs.append(nn.Conv2d(width, width, kernel_size=3, stride = stride, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.conv3 = nn.Conv2d(width*scale, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stype = stype
self.scale = scale
self.width = width
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out, self.width, 1)
for i in range(self.nums):
if i==0 or self.stype=='stage':
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i==0:
out = sp
else:
out = torch.cat((out, sp), 1)
if self.scale != 1 and self.stype=='normal':
out = torch.cat((out, spx[self.nums]),1)
elif self.scale != 1 and self.stype=='stage':
out = torch.cat((out, self.pool(spx[self.nums])),1)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
'''