ResNet学習録2 Pytorchによる実装(編集中)

はじめに

pytorchによる公式のResNetのコードはこちらに公開されている。

公式のライブラリーでは以下のresnetシリーズが実装されている。

• resnet18
• resnet34
• resnet50
• resnet101
• resnet152
• resnet50_32x4d
• resnet101_32x8d
• wide_resnet50_2
• wide_resnet101_2

その他に論文(https://arxiv.org/pdf/1512.03385.pdf)
では他にresnet20が使用されている。

本記事の目的

先ほどの実装シリーズの通り、resnet20が公式では実装されていないので、他の人の買いたpytorchの実装コードを紹介、解説していく。

注意：自分用の記事でもあるのですみません。

ResNet20の構造

引用元論文:(https://www.researchgate.net/publication/332522209_Gotta_Catch_%27Em_All_Using_Concealed_Trapdoors_to_Detect_Adversarial_Attacks_on_Neural_Networks)

ResNet20コード

残差ブロック

resnet20.py

class resblock(nn.Module):
	def __init__(self, in_channels, out_channels, return_before_act):
		super(resblock, self).__init__()
		self.return_before_act = return_before_act
		self.downsample = (in_channels != out_channels)
		if self.downsample:
			self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False)
			self.ds    = nn.Sequential(*[
							nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=2, bias=False),
							nn.BatchNorm2d(out_channels)
							])
		else:
			self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
			self.ds    = None
		self.bn1   = nn.BatchNorm2d(out_channels)
		self.relu  = nn.ReLU()
		self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
		self.bn2   = nn.BatchNorm2d(out_channels)

	def forward(self, x):
		residual = x

		pout = self.conv1(x) # pout: pre out before activation
		pout = self.bn1(pout)
		pout = self.relu(pout)

		pout = self.conv2(pout)
		pout = self.bn2(pout)

		if self.downsample:
			residual = self.ds(x)

		pout += residual
		out  = self.relu(pout)

		if not self.return_before_act:
			return out
		else:
			return pout, out

ResNet20

resnet20.py

class resnet20(nn.Module):
	def __init__(self, num_class):
		super(resnet20, self).__init__()
		self.conv1   = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
		self.bn1     = nn.BatchNorm2d(16)
		self.relu    = nn.ReLU()

		self.res1 = self.make_layer(resblock, 3, 16, 16)
		self.res2 = self.make_layer(resblock, 3, 16, 32)
		self.res3 = self.make_layer(resblock, 3, 32, 64)

		self.avgpool = nn.AvgPool2d(8)
		self.fc      = nn.Linear(64, num_class)

		for m in self.modules():
			if isinstance(m, nn.Conv2d):
				nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
				if m.bias is not None:
					nn.init.constant_(m.bias, 0)
			elif isinstance(m, nn.BatchNorm2d):
				nn.init.constant_(m.weight, 1)
				nn.init.constant_(m.bias, 0)

		self.num_class = num_class

	def make_layer(self, block, num, in_channels, out_channels): # num must >=2
		layers = [block(in_channels, out_channels, False)]
		for i in range(num-2):
			layers.append(block(out_channels, out_channels, False))
		layers.append(block(out_channels, out_channels, True))
		return nn.Sequential(*layers)

	def forward(self, x):
		pstem = self.conv1(x) # pstem: pre stem before activation
		pstem = self.bn1(pstem)
		stem  = self.relu(pstem)
		stem  = (pstem, stem)

		rb1 = self.res1(stem[1])
		rb2 = self.res2(rb1[1])
		rb3 = self.res3(rb2[1])

		feat = self.avgpool(rb3[1])
		feat = feat.view(feat.size(0), -1)
		out  = self.fc(feat)

		return stem, rb1, rb2, rb3, feat, out

	def get_channel_num(self):
		return [16, 16, 32, 64, 64, self.num_class]

	def get_chw_num(self):
		return [(16, 32, 32),
				(16, 32, 32),
				(32, 16, 16),
				(64, 8 , 8 ),
				(64,),
				(self.num_class,)]