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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch.autograd import Variable | |
| from collections import OrderedDict | |
| from torch.nn import init | |
| import math | |
| import pdb # 파이썬 디버거 | |
| # Conv2D (3,3) + BatchNorm2D + ReLU | |
| def conv_bn(inp, oup, stride): | |
| return nn.Sequential( | |
| nn.Conv2d(inp, oup, 3, stride, 1, bias=False), | |
| nn.BatchNorm2d(oup), | |
| nn.ReLU(inplace=True) | |
| ) | |
| # Conv2D (1,1) + BatchNorm2D + ReLU | |
| def conv_1x1_bn(inp, oup): | |
| return nn.Sequential( | |
| nn.Conv2d(inp, oup, 1, 1, 0, bias=False), | |
| nn.BatchNorm2d(oup), | |
| nn.ReLU(inplace=True) | |
| ) | |
| # reshape -> flatten | |
| def channel_shuffle(x, groups): | |
| batchsize, num_channels, height, width = x.data.size() # data 정보 | |
| channels_per_group = num_channels // groups # 그룹당 채널 계산 | |
| # reshape | |
| x = x.view(batchsize, groups, # reshape 적용된 모양의 tensor 반환 # 원본 data 공유 | |
| channels_per_group, height, width) | |
| x = torch.transpose(x, 1, 2).contiguous() # transpose(): 2개의 차원 맞교환 # contiguous(): 원본과 다른 새로운 주소로 할당 | |
| # flatten => [batchsize, height * width] | |
| x = x.view(batchsize, -1, height, width) # reshape 적용된 모양의 tensor 반환 # 원본 data 공유 | |
| return x | |
| # Inverted Residual - 관련 모델: MobileNetV2 | |
| class InvertedResidual(nn.Module): | |
| def __init__(self, inp, oup, stride, benchmodel): | |
| super(InvertedResidual, self).__init__() | |
| self.benchmodel = benchmodel | |
| self.stride = stride | |
| # stride 가 [1,2] 인지 확인, 아니면 AssertionError 메시지를 띄움 | |
| assert stride in [1, 2] # 원하는 조건의 변수값을 보증하기 위해 사용 | |
| oup_inc = oup//2 | |
| if self.benchmodel == 1: | |
| #assert inp == oup_inc | |
| self.banch2 = nn.Sequential( | |
| # pw | |
| nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False), # Conv2D (1,1) | |
| nn.BatchNorm2d(oup_inc), # BatchNorm2D | |
| nn.ReLU(inplace=True), # ReLU | |
| # dw | |
| nn.Conv2d(oup_inc, oup_inc, 3, stride, 1, groups=oup_inc, bias=False), # Conv2D (3,3) | |
| nn.BatchNorm2d(oup_inc), # BatchNorm2D | |
| # pw-linear | |
| nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False), # Conv2D (1,1) | |
| nn.BatchNorm2d(oup_inc), # BatchNorm2D | |
| nn.ReLU(inplace=True), # ReLU | |
| ) | |
| else: | |
| self.banch1 = nn.Sequential( | |
| # dw | |
| nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False), # Conv2D (3,3) | |
| nn.BatchNorm2d(inp), # BatchNorm2D | |
| # pw-linear | |
| nn.Conv2d(inp, oup_inc, 1, 1, 0, bias=False), # Conv2D (1,1) | |
| nn.BatchNorm2d(oup_inc), # BatchNorm2D | |
| nn.ReLU(inplace=True), # ReLU | |
| ) | |
| self.banch2 = nn.Sequential( | |
| # pw | |
| nn.Conv2d(inp, oup_inc, 1, 1, 0, bias=False), # Conv2D (1,1) | |
| nn.BatchNorm2d(oup_inc), # BatchNorm2D | |
| nn.ReLU(inplace=True), # ReLU | |
| # dw | |
| nn.Conv2d(oup_inc, oup_inc, 3, stride, 1, groups=oup_inc, bias=False), # Conv2D (3,3) | |
| nn.BatchNorm2d(oup_inc), # BatchNorm2D | |
| # pw-linear | |
| nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False), # Conv2D (1,1) | |
| nn.BatchNorm2d(oup_inc), # BatchNorm2D | |
| nn.ReLU(inplace=True), # ReLU | |
| ) | |
| def _concat(x, out): | |
| # concatenate along channel axis | |
| return torch.cat((x, out), 1) # Tensor list를 한번에 tensor로 만들기 | |
| # 모델이 학습데이터를 입력받아서 forward propagation 진행 | |
| def forward(self, x): | |
| if 1==self.benchmodel: | |
| x1 = x[:, :(x.shape[1]//2), :, :] | |
| x2 = x[:, (x.shape[1]//2):, :, :] | |
| out = self._concat(x1, self.banch2(x2)) | |
| elif 2==self.benchmodel: | |
| out = self._concat(self.banch1(x), self.banch2(x)) | |
| return channel_shuffle(out, 2) # reshape -> flatten | |
| # 셔플넷 V2 | |
| class ShuffleNetV2(nn.Module): | |
| def __init__(self, n_class=1000, input_size=224, width_mult=2.): | |
| super(ShuffleNetV2, self).__init__() | |
| # 인풋사이즈 % 32 == 0 인지 확인, 아니면 AssertionError 메시지를 띄움 | |
| assert input_size % 32 == 0, "Input size needs to be divisible by 32" # 원하는 조건의 변수값을 보증하기 위해 사용 | |
| self.stage_repeats = [4, 8, 4] | |
| # index 0 is invalid and should never be called. | |
| # only used for indexing convenience. | |
| if width_mult == 0.5: | |
| self.stage_out_channels = [-1, 24, 48, 96, 192, 1024] | |
| elif width_mult == 1.0: | |
| self.stage_out_channels = [-1, 24, 116, 232, 464, 1024] | |
| elif width_mult == 1.5: | |
| self.stage_out_channels = [-1, 24, 176, 352, 704, 1024] | |
| elif width_mult == 2.0: | |
| self.stage_out_channels = [-1, 24, 244, 488, 976, 2048] | |
| else: | |
| raise ValueError( # 에러 발생시키기 | |
| """Width multiplier should be in [0.5, 1.0, 1.5, 2.0]. Current value: {}""".format(width_mult)) | |
| # building first layer | |
| input_channel = self.stage_out_channels[1] | |
| self.conv1 = conv_bn(3, input_channel, 2) # Conv2D (3,3) + BatchNorm2D + ReLU | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) # MaxPool2D | |
| self.features = [] | |
| # building inverted residual blocks | |
| for idxstage in range(len(self.stage_repeats)): | |
| numrepeat = self.stage_repeats[idxstage] | |
| output_channel = self.stage_out_channels[idxstage+2] | |
| for i in range(numrepeat): | |
| if i == 0: | |
| #inp, oup, stride, benchmodel): | |
| self.features.append(InvertedResidual(input_channel, output_channel, 2, 2)) | |
| else: | |
| self.features.append(InvertedResidual(input_channel, output_channel, 1, 1)) | |
| input_channel = output_channel | |
| # make it nn.Sequential | |
| self.features = nn.Sequential(*self.features) | |
| # building last several layers | |
| self.conv_last = conv_1x1_bn(input_channel, self.stage_out_channels[-1]) # Conv2D (1,1) + BatchNorm2D + ReLU | |
| self.globalpool = nn.Sequential(nn.AvgPool2d(int(input_size/32))) # AvgPool2D | |
| # building classifier # 선형 회귀 모델 | |
| self.classifier = nn.Sequential(nn.Linear(self.stage_out_channels[-1], n_class)) | |
| # 모델이 학습데이터를 입력받아서 forward propagation 진행 | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.maxpool(x) | |
| x = self.features(x) | |
| x = self.conv_last(x) | |
| x = self.globalpool(x) | |
| x = x.view(-1, self.stage_out_channels[-1]) | |
| x = self.classifier(x) | |
| return x | |