From ac20576ae198dbb56173fb57200090de82432e1e Mon Sep 17 00:00:00 2001 From: hgkim Date: Wed, 17 Dec 2025 11:00:36 +0900 Subject: [PATCH] clear up my project --- core/model_song.py | 243 --------------------------------------------- 1 file changed, 243 deletions(-) delete mode 100755 core/model_song.py diff --git a/core/model_song.py b/core/model_song.py deleted file mode 100755 index 5ec9648..0000000 --- a/core/model_song.py +++ /dev/null @@ -1,243 +0,0 @@ -from torch import nn -import torch -import torch.nn.functional as F -from torch.autograd import Variable -import math -from torch.nn import Parameter - -class Bottleneck(nn.Module): - def __init__(self, inp, oup, stride, expansion): - super(Bottleneck, self).__init__() - self.connect = stride == 1 and inp == oup - self.conv = nn.Sequential( - #pw - nn.Conv2d(inp, inp * expansion, 1, 1, 0, bias=False), - nn.BatchNorm2d(inp * expansion), - nn.ReLU(inplace=True), - - #dw - nn.Conv2d(inp * expansion, inp * expansion, 3, stride, 1, groups=inp * expansion, bias=False), - nn.BatchNorm2d(inp * expansion), - nn.ReLU(inplace=True), - - #pw-linear - nn.Conv2d(inp * expansion, oup, 1, 1, 0, bias=False), - nn.BatchNorm2d(oup), - ) - - def forward(self, x): - if self.connect: - return x + self.conv(x) - else: - return self.conv(x) - -# class ConvBlock(nn.Module): # prelu 버전 -# def __init__(self, inp, oup, k, s, p, dw=False, linear=False): -# super(ConvBlock, self).__init__() -# self.linear = linear -# if dw: -# self.conv = nn.Conv2d(inp, oup, k, s, p, groups=inp, bias=False) -# else: -# self.conv = nn.Conv2d(inp, oup, k, s, p, bias=False) -# self.bn = nn.BatchNorm2d(oup) -# if not linear: -# self.prelu = nn.PReLU(oup) -# def forward(self, x): -# x = self.conv(x) -# x = self.bn(x) -# if self.linear: -# return x -# else: -# return self.prelu(x) - -class ConvBlock(nn.Module): - def __init__(self, inp, oup, k, s, p, dw=False, linear=False): - super(ConvBlock, self).__init__() - self.linear = linear - if dw: - self.conv = nn.Conv2d(inp, oup, k, s, p, groups=inp, bias=False) - else: - self.conv = nn.Conv2d(inp, oup, k, s, p, bias=False) - self.bn = nn.BatchNorm2d(oup) - if not linear: - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - x = self.conv(x) - x = self.bn(x) - if self.linear: - return x - else: - return self.relu(x) - -Mobilefacenet_bottleneck_setting = [ - # t, c , n ,s - [2, 64, 5, 2], - [4, 128, 1, 2], - [2, 128, 6, 1], - [4, 128, 1, 2], - [2, 128, 2, 1] -] - -Mobilenetv2_bottleneck_setting = [ - # t, c, n, s - [1, 16, 1, 1], - [6, 24, 2, 2], - [6, 32, 3, 2], - [6, 64, 4, 2], - [6, 96, 3, 1], - [6, 160, 3, 2], - [6, 320, 1, 1], -] - -class MobileFacenet(nn.Module): - def __init__(self, bottleneck_setting=Mobilefacenet_bottleneck_setting): - super(MobileFacenet, self).__init__() - - self.conv1 = ConvBlock(3, 64, 3, 2, 1) - - self.dw_conv1 = ConvBlock(64, 64, 3, 1, 1, dw=True) - - self.inplanes = 64 - block = Bottleneck - self.blocks = self._make_layer(block, bottleneck_setting) - - self.conv2 = ConvBlock(128, 512, 1, 1, 0) - - self.linear7 = nn.Sequential( - nn.Conv2d(512, 512, kernel_size=1, stride=1, padding=0, bias=False), - nn.BatchNorm2d(512), - nn.ReLU(inplace=True), - ) - - self.gap = nn.AvgPool2d(kernel_size=7) - - self.linear1 = ConvBlock(512, 128, 1, 1, 0, linear=True) - - 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, setting): - layers = [] - for t, c, n, s in setting: - for i in range(n): - if i == 0: - layers.append(block(self.inplanes, c, s, t)) - else: - layers.append(block(self.inplanes, c, 1, t)) - self.inplanes = c - - return nn.Sequential(*layers) - - def forward(self, x): - x = x[:, :, 8:120, 8:120] - x = self.conv1(x) - x = self.dw_conv1(x) - x = self.blocks(x) - x = self.conv2(x) - x = self.linear7(x) - x = self.gap(x) - x = self.linear1(x) - x = x.view(x.size(0), -1) - - return x - - - -# class MobileFacenet(nn.Module): -# def __init__(self, bottleneck_setting=Mobilefacenet_bottleneck_setting): -# super(MobileFacenet, self).__init__() - -# self.conv1 = ConvBlock(3, 64, 3, 2, 1) - -# self.dw_conv1 = ConvBlock(64, 64, 3, 1, 1, dw=True) - -# self.inplanes = 64 -# block = Bottleneck -# self.blocks = self._make_layer(block, bottleneck_setting) - -# self.conv2 = ConvBlock(128, 512, 1, 1, 0) - -# self.linear7 = ConvBlock(512, 512, (7, 6), 1, 0, dw=True, linear=True) - -# self.linear1 = ConvBlock(512, 128, 1, 1, 0, linear=True) - -# 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, setting): -# layers = [] -# for t, c, n, s in setting: -# for i in range(n): -# if i == 0: -# layers.append(block(self.inplanes, c, s, t)) -# else: -# layers.append(block(self.inplanes, c, 1, t)) -# self.inplanes = c - -# return nn.Sequential(*layers) - -# def forward(self, x): -# x = self.conv1(x) -# x = self.dw_conv1(x) -# x = self.blocks(x) -# x = self.conv2(x) -# x = self.linear7(x) -# x = self.linear1(x) -# x = x.view(x.size(0), -1) - -# return x - - -class ArcMarginProduct(nn.Module): - def __init__(self, in_features=128, out_features=200, s=32.0, m=0.50, easy_margin=False): - super(ArcMarginProduct, self).__init__() - self.in_features = in_features - self.out_features = out_features - self.s = s - self.m = m - self.weight = Parameter(torch.Tensor(out_features, in_features)) - nn.init.xavier_uniform_(self.weight) - # init.kaiming_uniform_() - # self.weight.data.normal_(std=0.001) - - self.easy_margin = easy_margin - self.cos_m = math.cos(m) - self.sin_m = math.sin(m) - # make the function cos(theta+m) monotonic decreasing while theta in [0°,180°] - self.th = math.cos(math.pi - m) - self.mm = math.sin(math.pi - m) * m - - def forward(self, x, label): - cosine = F.linear(F.normalize(x), F.normalize(self.weight)) - sine = torch.sqrt(1.0 - torch.pow(cosine, 2)) - phi = cosine * self.cos_m - sine * self.sin_m - if self.easy_margin: - phi = torch.where(cosine > 0, phi, cosine) - else: - phi = torch.where((cosine - self.th) > 0, phi, cosine - self.mm) - - one_hot = torch.zeros(cosine.size(), device='cuda') - one_hot.scatter_(1, label.view(-1, 1).long(), 1) - output = (one_hot * phi) + ((1.0 - one_hot) * cosine) - output *= self.s - return output - - -if __name__ == "__main__": - # input = Variable(torch.FloatTensor(2, 3, 112, 96)) - input = Variable(torch.FloatTensor(2, 3, 128, 128)) # 해상도 128x128 수정 진행. - net = MobileFacenet() - print(net) - x = net(input) - print(x.shape)