Spaces:
Sleeping
Sleeping
File size: 7,201 Bytes
b8f5ef0 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 | # Adapted from
# https://github.com/arnaghosh/Auto-Encoder/blob/master/resnet.py
import torch
from torch.autograd import Variable
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, models,transforms
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import os
import matplotlib.pyplot as plt
from torch.autograd import Function
from collections import OrderedDict
import torch.nn as nn
import math
import torchvision.models as models
zsize=48
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = 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:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
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
class Encoder(nn.Module):
def __init__(self, block, layers, num_classes=23):
self.inplanes = 64
super (Encoder, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)#, return_indices = True)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(512 * block.expansion, 1000)
#self.fc = nn.Linear(num_classes,16)
for m in self.modules():
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))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
encoder = Encoder(Bottleneck, [3, 4, 6, 3])
encoder_state_dict = torch.hub.load_state_dict_from_url('https://download.pytorch.org/models/resnet50-19c8e357.pth')
encoder.load_state_dict(encoder_state_dict)
encoder.fc = nn.Linear(2048, 48)
encoder=encoder.to(device)
class Binary(Function):
@staticmethod
def forward(ctx, input):
return F.relu(Variable(input.sign())).data
@staticmethod
def backward(ctx, grad_output):
return grad_output
class Decoder(nn.Module):
def __init__(self):
super(Decoder,self).__init__()
self.dfc3 = nn.Linear(zsize, 4096)
self.bn3 = nn.BatchNorm1d(4096)
self.dfc2 = nn.Linear(4096, 4096)
self.bn2 = nn.BatchNorm1d(4096)
self.dfc1 = nn.Linear(4096,256 * 6 * 6)
self.bn1 = nn.BatchNorm1d(256*6*6)
self.upsample1=nn.Upsample(scale_factor=2)
self.dconv5 = nn.ConvTranspose2d(256, 256, 3, padding = 0)
self.dconv4 = nn.ConvTranspose2d(256, 384, 3, padding = 1)
self.dconv3 = nn.ConvTranspose2d(384, 192, 3, padding = 1)
self.dconv2 = nn.ConvTranspose2d(192, 64, 5, padding = 2)
self.dconv1 = nn.ConvTranspose2d(64, 3, 12, stride = 4, padding = 4)
def forward(self,x):#,i1,i2,i3):
x = self.dfc3(x)
#x = F.relu(x)
x = F.relu(self.bn3(x))
x = self.dfc2(x)
x = F.relu(self.bn2(x))
#x = F.relu(x)
x = self.dfc1(x)
x = F.relu(self.bn1(x))
#x = F.relu(x)
#print(x.size())
x = x.view(x.shape[0],256,6,6)
#print (x.size())
x=self.upsample1(x)
#print x.size()
x = self.dconv5(x)
#print x.size()
x = F.relu(x)
#print x.size()
x = F.relu(self.dconv4(x))
#print x.size()
x = F.relu(self.dconv3(x))
#print x.size()
x=self.upsample1(x)
#print x.size()
x = self.dconv2(x)
#print x.size()
x = F.relu(x)
x=self.upsample1(x)
#print x.size()
x = self.dconv1(x)
#print x.size()
x = torch.sigmoid(x)
#print x
return x
class Autoencoder(nn.Module):
def __init__(self):
super(Autoencoder,self).__init__()
self.encoder = encoder
self.binary = Binary()
self.decoder = Decoder()
def forward(self,x):
#x=Encoder(x)
x = self.encoder(x)
x = self.binary.apply(x)
#print x
#x,i2,i1 = self.binary(x)
#x=Variable(x)
x = self.decoder(x)
return x
|