150 lines
5.7 KiB
Python
150 lines
5.7 KiB
Python
import math, torch
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import torch.nn as nn
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import torch.nn.functional as F
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from ..initialization import initialize_resnet
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from ..SharedUtils import additive_func
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class ConvBNReLU(nn.Module):
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def __init__(self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu):
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super(ConvBNReLU, self).__init__()
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if has_avg : self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
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else : self.avg = None
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self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, dilation=1, groups=1, bias=bias)
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if has_bn : self.bn = nn.BatchNorm2d(nOut)
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else : self.bn = None
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if has_relu: self.relu = nn.ReLU(inplace=True)
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else : self.relu = None
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def forward(self, inputs):
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if self.avg : out = self.avg( inputs )
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else : out = inputs
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conv = self.conv( out )
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if self.bn : out = self.bn( conv )
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else : out = conv
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if self.relu: out = self.relu( out )
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else : out = out
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return out
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class ResNetBasicblock(nn.Module):
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num_conv = 2
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expansion = 1
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def __init__(self, inplanes, planes, stride):
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super(ResNetBasicblock, self).__init__()
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assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
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self.conv_a = ConvBNReLU(inplanes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
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self.conv_b = ConvBNReLU( planes, planes, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False)
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if stride == 2:
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self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
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elif inplanes != planes:
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self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
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else:
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self.downsample = None
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self.out_dim = planes
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def forward(self, inputs):
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basicblock = self.conv_a(inputs)
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basicblock = self.conv_b(basicblock)
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if self.downsample is not None:
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residual = self.downsample(inputs)
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else:
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residual = inputs
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out = residual + basicblock
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return F.relu(out, inplace=True)
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class ResNetBottleneck(nn.Module):
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expansion = 4
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num_conv = 3
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def __init__(self, inplanes, planes, stride):
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super(ResNetBottleneck, self).__init__()
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assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
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self.conv_1x1 = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True)
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self.conv_3x3 = ConvBNReLU( planes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
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self.conv_1x4 = ConvBNReLU(planes, planes*self.expansion, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False)
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if stride == 2:
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self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=True , has_bn=False, has_relu=False)
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elif inplanes != planes*self.expansion:
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self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=False, has_bn=False, has_relu=False)
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else:
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self.downsample = None
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self.out_dim = planes*self.expansion
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def forward(self, inputs):
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bottleneck = self.conv_1x1(inputs)
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bottleneck = self.conv_3x3(bottleneck)
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bottleneck = self.conv_1x4(bottleneck)
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if self.downsample is not None:
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residual = self.downsample(inputs)
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else:
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residual = inputs
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out = residual + bottleneck
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return F.relu(out, inplace=True)
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class InferDepthCifarResNet(nn.Module):
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def __init__(self, block_name, depth, xblocks, num_classes, zero_init_residual):
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super(InferDepthCifarResNet, self).__init__()
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#Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
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if block_name == 'ResNetBasicblock':
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block = ResNetBasicblock
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assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
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layer_blocks = (depth - 2) // 6
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elif block_name == 'ResNetBottleneck':
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block = ResNetBottleneck
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assert (depth - 2) % 9 == 0, 'depth should be one of 164'
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layer_blocks = (depth - 2) // 9
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else:
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raise ValueError('invalid block : {:}'.format(block_name))
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assert len(xblocks) == 3, 'invalid xblocks : {:}'.format(xblocks)
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self.message = 'InferWidthCifarResNet : Depth : {:} , Layers for each block : {:}'.format(depth, layer_blocks)
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self.num_classes = num_classes
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self.layers = nn.ModuleList( [ ConvBNReLU(3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
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self.channels = [16]
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for stage in range(3):
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for iL in range(layer_blocks):
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iC = self.channels[-1]
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planes = 16 * (2**stage)
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stride = 2 if stage > 0 and iL == 0 else 1
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module = block(iC, planes, stride)
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self.channels.append( module.out_dim )
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self.layers.append ( module )
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self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, planes, module.out_dim, stride)
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if iL + 1 == xblocks[stage]: # reach the maximum depth
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break
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self.avgpool = nn.AvgPool2d(8)
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self.classifier = nn.Linear(self.channels[-1], num_classes)
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self.apply(initialize_resnet)
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if zero_init_residual:
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for m in self.modules():
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if isinstance(m, ResNetBasicblock):
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nn.init.constant_(m.conv_b.bn.weight, 0)
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elif isinstance(m, ResNetBottleneck):
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nn.init.constant_(m.conv_1x4.bn.weight, 0)
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def get_message(self):
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return self.message
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def forward(self, inputs):
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x = inputs
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for i, layer in enumerate(self.layers):
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x = layer( x )
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features = self.avgpool(x)
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features = features.view(features.size(0), -1)
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logits = self.classifier(features)
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return features, logits
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