总结几个好用的CNN模块（Pytorch）
【摘要】
总结几个比较好的CNN模块。
SEBlock
代码：
class SEBlock(nn.Module): def __init__(self, input_channels, internal_neurons): &...
总结几个比较好的CNN模块。
代码：
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class SEBlock(nn.Module):
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def __init__(self, input_channels, internal_neurons):
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super(SEBlock, self).__init__()
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self.down = nn.Conv2d(in_channels=input_channels, out_channels=internal_neurons, kernel_size=1, stride=1,
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bias=True, padding_mode='same')
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self.up = nn.Conv2d(in_channels=internal_neurons, out_channels=input_channels, kernel_size=1, stride=1,
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bias=True, padding_mode='same')
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def forward(self, inputs):
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x = F.avg_pool2d(inputs, kernel_size=inputs.size(3))
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x = self.down(x)
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x = F.leaky_relu(x)
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x = self.up(x)
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x = F.sigmoid(x)
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x = x.repeat(1, 1, inputs.size(2), inputs.size(3))
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return inputs * x
代码
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class CropLayer(nn.Module):
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# E.g., (-1, 0) means this layer should crop the first and last rows of the feature map. And (0, -1) crops the first and last columns
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def __init__(self, crop_set):
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super(CropLayer, self).__init__()
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self.rows_to_crop = - crop_set[0]
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self.cols_to_crop = - crop_set[1]
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assert self.rows_to_crop >= 0
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assert self.cols_to_crop >= 0
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def forward(self, input):
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if self.rows_to_crop == 0 and self.cols_to_crop == 0:
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return input
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elif self.rows_to_crop > 0 and self.cols_to_crop == 0:
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return input[:, :, self.rows_to_crop:-self.rows_to_crop, :]
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elif self.rows_to_crop == 0 and self.cols_to_crop > 0:
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return input[:, :, :, self.cols_to_crop:-self.cols_to_crop]
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else:
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return input[:, :, self.rows_to_crop:-self.rows_to_crop, self.cols_to_crop:-self.cols_to_crop]
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class ACBlock(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=1, dilation=1, groups=1,
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padding_mode='same', deploy=False,
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use_affine=True, reduce_gamma=False, use_last_bn=False, gamma_init=None):
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super(ACBlock, self).__init__()
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self.deploy = deploy
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if deploy:
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self.fused_conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
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kernel_size=(kernel_size, kernel_size), stride=stride,
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padding=padding, dilation=dilation, groups=groups, bias=True,
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padding_mode=padding_mode)
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else:
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self.square_conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
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kernel_size=(kernel_size, kernel_size), stride=stride,
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padding=padding, dilation=dilation, groups=groups, bias=False,
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padding_mode=padding_mode)
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self.square_bn = nn.BatchNorm2d(num_features=out_channels, affine=use_affine)
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center_offset_from_origin_border = padding - kernel_size // 2
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ver_pad_or_crop = (padding, center_offset_from_origin_border)
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hor_pad_or_crop = (center_offset_from_origin_border, padding)
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if center_offset_from_origin_border >= 0:
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self.ver_conv_crop_layer = nn.Identity()
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ver_conv_padding = ver_pad_or_crop
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self.hor_conv_crop_layer = nn.Identity()
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hor_conv_padding = hor_pad_or_crop
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else:
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self.ver_conv_crop_layer = CropLayer(crop_set=ver_pad_or_crop)
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ver_conv_padding = (0, 0)
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self.hor_conv_crop_layer = CropLayer(crop_set=hor_pad_or_crop)
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hor_conv_padding = (0, 0)
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self.ver_conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(kernel_size, 1),
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stride=stride,
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padding=ver_conv_padding, dilation=dilation, groups=groups, bias=False,
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padding_mode=padding_mode)
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self.hor_conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(1, kernel_size),
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stride=stride,
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padding=hor_conv_padding, dilation=dilation, groups=groups, bias=False,
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padding_mode=padding_mode)
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self.ver_bn = nn.BatchNorm2d(num_features=out_channels, affine=use_affine)
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self.hor_bn = nn.BatchNorm2d(num_features=out_channels, affine=use_affine)
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if reduce_gamma:
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assert not use_last_bn
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self.init_gamma(1.0 / 3)
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if use_last_bn:
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assert not reduce_gamma
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self.last_bn = nn.BatchNorm2d(num_features=out_channels, affine=True)
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if gamma_init is not None:
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assert not reduce_gamma
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self.init_gamma(gamma_init)
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def init_gamma(self, gamma_value):
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init.constant_(self.square_bn.weight, gamma_value)
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init.constant_(self.ver_bn.weight, gamma_value)
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init.constant_(self.hor_bn.weight, gamma_value)
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print('init gamma of square, ver and hor as ', gamma_value)
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def single_init(self):
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init.constant_(self.square_bn.weight, 1.0)
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init.constant_(self.ver_bn.weight, 0.0)
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init.constant_(self.hor_bn.weight, 0.0)
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print('init gamma of square as 1, ver and hor as 0')
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def forward(self, input):
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if self.deploy:
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return self.fused_conv(input)
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else:
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square_outputs = self.square_conv(input)
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square_outputs = self.square_bn(square_outputs)
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vertical_outputs = self.ver_conv_crop_layer(input)
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vertical_outputs = self.ver_conv(vertical_outputs)
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vertical_outputs = self.ver_bn(vertical_outputs)
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horizontal_outputs = self.hor_conv_crop_layer(input)
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horizontal_outputs = self.hor_conv(horizontal_outputs)
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horizontal_outputs = self.hor_bn(horizontal_outputs)
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result = square_outputs + vertical_outputs + horizontal_outputs
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if hasattr(self, 'last_bn'):
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return self.last_bn(result)
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return result
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class eca_layer(nn.Module):
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"""Constructs a ECA module.
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Args:
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channel: Number of channels of the input feature map
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k_size: Adaptive selection of kernel size
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"""
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def __init__(self, channel, k_size=3):
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super(eca_layer, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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# x: input features with shape [b, c, h, w]
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b, c, h, w = x.size()
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# feature descriptor on the global spatial information
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y = self.avg_pool(x)
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# Two different branches of ECA module
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y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
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# Multi-scale information fusion
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y = self.sigmoid(y)
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return x * y.expand_as(x)
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class ChannelAttention(nn.Module):
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def __init__(self, in_planes, ratio=16):
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super(ChannelAttention, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.max_pool = nn.AdaptiveMaxPool2d(1)
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self.fc1 = nn.Conv2d(in_planes, in_planes // 16, 1, bias=False)
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self.relu1 = nn.LeakyReLU(negative_slope=0.01, inplace=False)
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self.fc2 = nn.Conv2d(in_planes // 16, in_planes, 1, bias=False)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
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max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
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out = avg_out + max_out
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return self.sigmoid(out)
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class ConvBN(nn.Sequential):
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def __init__(self, in_planes, out_planes, kernel_size, stride=1, groups=1):
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if not isinstance(kernel_size, int):
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padding = [(i - 1) // 2 for i in kernel_size]
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else:
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padding = (kernel_size - 1) // 2
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super(ConvBN, self).__init__(OrderedDict([
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('conv', nn.Conv2d(in_planes, out_planes, kernel_size, stride,
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padding=padding, groups=groups, bias=False)),
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('bn', nn.BatchNorm2d(out_planes)),
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#('Mish', Mish())
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('Mish', nn.LeakyReLU(negative_slope=0.3, inplace=False))
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]))
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ResBlock
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class ResBlock(nn.Module):
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"""
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Sequential residual blocks each of which consists of \
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two convolution layers.
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Args:
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ch (int): number of input and output channels.
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nblocks (int): number of residual blocks.
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shortcut (bool): if True, residual tensor addition is enabled.
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"""
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def __init__(self, ch, nblocks=1, shortcut=True):
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super().__init__()
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self.shortcut = shortcut
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self.module_list = nn.ModuleList()
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for i in range(nblocks):
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resblock_one = nn.ModuleList()
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resblock_one.append(ConvBN(ch, ch, 1))
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resblock_one.append(Mish())
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resblock_one.append(ConvBN(ch, ch, 3))
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resblock_one.append(Mish())
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self.module_list.append(resblock_one)
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def forward(self, x):
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for module in self.module_list:
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h = x
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for res in module:
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h = res(h)
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x = x + h if self.shortcut else h
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return x
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文章来源: wanghao.blog.csdn.net，作者：AI浩，版权归原作者所有，如需转载，请联系作者。
原文链接：wanghao.blog.csdn.net/article/details/113103089
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