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"""
File: model.py
Author: Elena Ryumina and Dmitry Ryumin
Description: This module provides model architectures.
License: MIT License
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_channels, out_channels, i_downsample=None, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, padding=0, bias=False)
self.batch_norm1 = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.99)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding='same', bias=False)
self.batch_norm2 = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.99)
self.conv3 = nn.Conv2d(out_channels, out_channels*self.expansion, kernel_size=1, stride=1, padding=0, bias=False)
self.batch_norm3 = nn.BatchNorm2d(out_channels*self.expansion, eps=0.001, momentum=0.99)
self.i_downsample = i_downsample
self.stride = stride
self.relu = nn.ReLU()
def forward(self, x):
identity = x.clone()
x = self.relu(self.batch_norm1(self.conv1(x)))
x = self.relu(self.batch_norm2(self.conv2(x)))
x = self.conv3(x)
x = self.batch_norm3(x)
#downsample if needed
if self.i_downsample is not None:
identity = self.i_downsample(identity)
#add identity
x+=identity
x=self.relu(x)
return x
class Conv2dSame(torch.nn.Conv2d):
def calc_same_pad(self, i: int, k: int, s: int, d: int) -> int:
return max((math.ceil(i / s) - 1) * s + (k - 1) * d + 1 - i, 0)
def forward(self, x: torch.Tensor) -> torch.Tensor:
ih, iw = x.size()[-2:]
pad_h = self.calc_same_pad(i=ih, k=self.kernel_size[0], s=self.stride[0], d=self.dilation[0])
pad_w = self.calc_same_pad(i=iw, k=self.kernel_size[1], s=self.stride[1], d=self.dilation[1])
if pad_h > 0 or pad_w > 0:
x = F.pad(
x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]
)
return F.conv2d(
x,
self.weight,
self.bias,
self.stride,
self.padding,
self.dilation,
self.groups,
)
class ResNet(nn.Module):
def __init__(self, ResBlock, layer_list, num_classes, num_channels=3):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv_layer_s2_same = Conv2dSame(num_channels, 64, 7, stride=2, groups=1, bias=False)
self.batch_norm1 = nn.BatchNorm2d(64, eps=0.001, momentum=0.99)
self.relu = nn.ReLU()
self.max_pool = nn.MaxPool2d(kernel_size = 3, stride=2)
self.layer1 = self._make_layer(ResBlock, layer_list[0], planes=64, stride=1)
self.layer2 = self._make_layer(ResBlock, layer_list[1], planes=128, stride=2)
self.layer3 = self._make_layer(ResBlock, layer_list[2], planes=256, stride=2)
self.layer4 = self._make_layer(ResBlock, layer_list[3], planes=512, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1,1))
self.fc1 = nn.Linear(512*ResBlock.expansion, 512)
self.relu1 = nn.ReLU()
self.fc2 = nn.Linear(512, num_classes)
def extract_features(self, x):
x = self.relu(self.batch_norm1(self.conv_layer_s2_same(x)))
x = self.max_pool(x)
# print(x.shape)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.reshape(x.shape[0], -1)
x = self.fc1(x)
return x
def forward(self, x):
x = self.extract_features(x)
x = self.relu1(x)
x = self.fc2(x)
return x
def _make_layer(self, ResBlock, blocks, planes, stride=1):
ii_downsample = None
layers = []
if stride != 1 or self.in_channels != planes*ResBlock.expansion:
ii_downsample = nn.Sequential(
nn.Conv2d(self.in_channels, planes*ResBlock.expansion, kernel_size=1, stride=stride, bias=False, padding=0),
nn.BatchNorm2d(planes*ResBlock.expansion, eps=0.001, momentum=0.99)
)
layers.append(ResBlock(self.in_channels, planes, i_downsample=ii_downsample, stride=stride))
self.in_channels = planes*ResBlock.expansion
for i in range(blocks-1):
layers.append(ResBlock(self.in_channels, planes))
return nn.Sequential(*layers)
def ResNet50(num_classes, channels=3):
return ResNet(Bottleneck, [3,4,6,3], num_classes, channels)
class LSTMPyTorch(nn.Module):
def __init__(self):
super(LSTMPyTorch, self).__init__()
self.lstm1 = nn.LSTM(input_size=512, hidden_size=512, batch_first=True, bidirectional=False)
self.lstm2 = nn.LSTM(input_size=512, hidden_size=256, batch_first=True, bidirectional=False)
self.fc = nn.Linear(256, 7)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
x, _ = self.lstm1(x)
x, _ = self.lstm2(x)
x = self.fc(x[:, -1, :])
x = self.softmax(x)
return x |