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| import math | |
| import torch.nn as nn | |
| import pdb # 파이썬 디버거 | |
| # Conv1D (3,3) | |
| def conv3x3(in_planes, out_planes, stride=1): | |
| return nn.Conv1d(in_planes, out_planes, kernel_size=3, stride=stride, | |
| padding=1, bias=False) | |
| # Conv1D (1,1) + BatchNorm1D | |
| def downsample_basic_block( inplanes, outplanes, stride ): | |
| return nn.Sequential( | |
| nn.Conv1d(inplanes, outplanes, kernel_size=1, stride=stride, bias=False), | |
| nn.BatchNorm1d(outplanes), | |
| ) | |
| # AvgPool1D + Conv1D (1,1) + BatchNorm1D | |
| def downsample_basic_block_v2( inplanes, outplanes, stride ): | |
| return nn.Sequential( | |
| nn.AvgPool1d(kernel_size=stride, stride=stride, ceil_mode=True, count_include_pad=False), | |
| nn.Conv1d(inplanes, outplanes, kernel_size=1, stride=1, bias=False), | |
| nn.BatchNorm1d(outplanes), | |
| ) | |
| # 기본 블럭 1D | |
| class BasicBlock1D(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None, relu_type = 'relu' ): | |
| super(BasicBlock1D, self).__init__() | |
| # relu_type 변수 값이 'relu','prelu' 인지 확인, 아니면 AssertionError 메시지를 띄움 | |
| assert relu_type in ['relu','prelu'] # 원하는 조건의 변수값을 보증하기 위해 사용 | |
| self.conv1 = conv3x3(inplanes, planes, stride) # Conv1D (3,3) | |
| self.bn1 = nn.BatchNorm1d(planes) # BatchNorm1D | |
| # type of ReLU is an input option | |
| if relu_type == 'relu': # ReLU | |
| self.relu1 = nn.ReLU(inplace=True) | |
| self.relu2 = nn.ReLU(inplace=True) | |
| elif relu_type == 'prelu': # PReLU | |
| self.relu1 = nn.PReLU(num_parameters=planes) | |
| self.relu2 = nn.PReLU(num_parameters=planes) | |
| else: | |
| raise Exception('relu type not implemented') # 에러 발생시키기 | |
| # -------- | |
| self.conv2 = conv3x3(planes, planes) # Conv1D (3,3) | |
| self.bn2 = nn.BatchNorm1d(planes) # BatchNorm1D | |
| self.downsample = downsample | |
| self.stride = stride | |
| # 모델이 학습데이터를 입력받아서 forward propagation 진행 | |
| 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 | |
| # 레즈넷1D | |
| class ResNet1D(nn.Module): | |
| def __init__(self, block, layers, relu_type = 'relu'): | |
| super(ResNet1D, self).__init__() | |
| self.inplanes = 64 | |
| self.relu_type = relu_type | |
| self.downsample_block = downsample_basic_block | |
| self.conv1 = nn.Conv1d(1, self.inplanes, kernel_size=80, stride=4, padding=38, | |
| bias=False) # Conv1D | |
| self.bn1 = nn.BatchNorm1d(self.inplanes) # BatchNorm1D | |
| # type of ReLU is an input option | |
| if relu_type == 'relu': # ReLU | |
| self.relu = nn.ReLU(inplace=True) | |
| elif relu_type == 'prelu': # PReLU | |
| self.relu = nn.PReLU(num_parameters=self.inplanes) | |
| 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) | |
| # For LRW, we downsample the sampling rate to 25fps | |
| self.avgpool = nn.AvgPool1d(kernel_size=21, padding=1) | |
| ''' | |
| # The following pooling setting is the general configuration # 일반 구성 AvgPool1D | |
| self.avgpool = nn.AvgPool1d(kernel_size=20, stride=20) | |
| ''' | |
| # default init | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv1d): # Conv1D 인스턴스인가 | |
| n = m.kernel_size[0] * m.out_channels | |
| m.weight.data.normal_(0, math.sqrt(2. / n)) | |
| elif isinstance(m, nn.BatchNorm1d): # BatchNrom1D 인스턴스인가 | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| # 레이어 생성 | |
| def _make_layer(self, block, planes, blocks, stride=1): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| downsample = self.downsample_block( inplanes = self.inplanes, | |
| outplanes = planes * block.expansion, | |
| stride = stride ) # (AvgPool1D) + Conv1D (1,1) + BatchNorm1D | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, stride, downsample, relu_type = self.relu_type)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes, relu_type = self.relu_type)) | |
| return nn.Sequential(*layers) # 설정한 레이어 반환 | |
| # 모델이 학습데이터를 입력받아서 forward propagation 진행 | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = self.avgpool(x) | |
| return x | |