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resnet_model.py
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import numpy as np
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from tqdm import trange
from time import sleep
use_gpu = torch.cuda.is_available()
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
### Modified
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.relu1 = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.relu2 = 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.relu1(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu2(out)
return out
class ResNet(nn.Module):
def __init__(self, n_seqs, block, layers, num_classes=2):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(n_seqs, 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)
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(8, stride=1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
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) #128x128
x = self.bn1(x)
x = self.relu(x)
#x = self.maxpool(x)
x = self.layer1(x) # 64x64x
x = self.layer2(x) # 32x32
x = self.layer3(x) # 16x16
x = self.layer4(x) # 8x8
x = self.avgpool(x) # 1x1
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
class ResNet_transfer(nn.Module):
def __init__(self, model_pre, num_classes=2, n_slfeat=20):
super(ResNet_transfer, self).__init__()
self.model_pre = model_pre
self.model_pre.fc = nn.Linear(512, 8)
self.fc_added1 = nn.Linear(8+n_slfeat, 8)
self.fc_added2 = nn.Linear(8, num_classes)
def forward(self, x, slafeat):
x = self.model_pre(x) #128x128
x = torch.cat((x, slafeat),1)
x = self.fc_added1(x)
x = self.fc_added2(x)
return x