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arch.py
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arch.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# con1x1 (shrink the dimension)
def conv1x1(in_channels, out_channels, stride=1):
return nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False)
# cov3x3
def conv3x3(in_channels, out_channels, stride=1, dilation=1):
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=dilation, groups=1, bias=False, dilation=dilation)
# custom resnet34 module (final layer is global average pooling)
# Conv blocks
class ConvBlock(nn.Module):
expansion = 1
def __init__(self, in_channels, out_channels, stride=1, downsample=None, base_width=64):
super(ConvBlock, self).__init__()
self.conv1 = conv3x3(in_channels, out_channels, stride)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(out_channels, out_channels)
self.bn2 = nn.BatchNorm2d(out_channels)
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
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_channels, out_channels, stride=1, downsample=None, base_width=64):
super(Bottleneck, self).__init__()
width = int(in_channels * (base_width / 64.0))
self.conv1 = conv1x1(in_channels, width)
self.bn1 = nn.BatchNorm2d(width)
self.conv2 = conv3x3(width, width, stride)
self.bn2 = nn.BatchNorm2d(width)
self.conv3 = conv1x1(width, out_channels * 4)
self.bn3 = nn.BatchNorm2d(out_channels * 4)
self.relu = nn.ReLU(inplace=True)
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)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Resnet(nn.Module):
def __init__(self, block, layers, num_classes=47, width=64):
super(Resnet, self).__init__()
self.in_channels = 64
self.width = 64
self.conv1 = nn.Conv2d(1, self.in_channels, kernel_size=7, stride=2, padding=3, bias=False) # why padding is 3
self.bn1 = nn.BatchNorm2d(self.in_channels)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=3, 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, num_classes, layers[2], stride=2) # actually 256 in pure ResNet
self.layer4 = self._make_layer(block, 512, layers[3], stride=2) # this layer will be discarded
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes) # should be modified as Global Average pooling (will be discared)
def _make_layer(self, block, out_channels, blocks, stride=1):
downsample = None
if stride != 1 or self.in_channels != out_channels * block.expansion:
downsample = nn.Sequential(
conv1x1(self.in_channels, out_channels * block.expansion, stride),
nn.BatchNorm2d(out_channels * block.expansion)
)
layers = []
layers.append(block(self.in_channels, out_channels, stride, downsample, self.width))
self.in_channels = out_channels * block.expansion
for _ in range(1, blocks):
layers.append(block(self.in_channels, out_channels, base_width=self.width))
return nn.Sequential(*layers)
# make global average pool
def GlobalAvgPool(self, x):
x = self.avgpool(x)
x = torch.mean(x, dim=2)
return x
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.GlobalAvgPool(x)
x = x.view((x.shape[0], x.shape[1]))
return x
def _resnet(arch, block, layers, progress, **kwargs):
model = Resnet(block, layers, **kwargs)
return model
def custom_resnet34(progress=True, **kwargs):
return _resnet('resnet34_custom', ConvBlock, [3, 4, 6, 3], progress, **kwargs)
def custom_resnet18(progress=True, **kwargs):
return _resnet('resnet18_custom', ConvBlock, [2, 2, 2, 2], progress, **kwargs)
def custom_resnet50(progress=True, **kwargs):
return _resnet('resnet50_custom', Bottleneck, [3, 4, 6, 3], progress, **kwargs)
def custom_resnet152(progress=True, **kwargs):
return _resnet('resnet152_custom', Bottleneck, [3, 8, 36, 3], progress, **kwargs)
class simple_block(nn.Module):
def __init__(self, num_classes=47, transform=None):
super(simple_block, self).__init__()
self.num_classes = num_classes
self.transform = transform
self.conv = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=(3, 3), stride=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.fc1 = nn.Sequential(
nn.Linear(32 *13 * 13, 512),
nn.ReLU()
)
self.fc2 = nn.Sequential(
nn.Linear(512, num_classes),
)
def forward(self, x):
if self.transform is not None:
x = self.transform(x)
x = self.conv(x)
x = torch.flatten(x)
x = self.fc1(x)
x = self.fc2(x)
return x
def simple(num_classes=47, transform=None):
return simple_block(num_classes, transform)
'''class simple_lowdim(nn.Module):
def __init__(self, num_classes=47, transform=None):
super(simple_lowdim, self).__init__()
self.num_classes = num_classes
self.transform = transform
'''