151 lines
5.8 KiB
Python
151 lines
5.8 KiB
Python
#####################################################
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
|
|
#####################################################
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from ..initialization import initialize_resnet
|
|
|
|
|
|
class ConvBNReLU(nn.Module):
|
|
|
|
def __init__(self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu):
|
|
super(ConvBNReLU, self).__init__()
|
|
if has_avg : self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
|
|
else : self.avg = None
|
|
self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, dilation=1, groups=1, bias=bias)
|
|
if has_bn : self.bn = nn.BatchNorm2d(nOut)
|
|
else : self.bn = None
|
|
if has_relu: self.relu = nn.ReLU(inplace=True)
|
|
else : self.relu = None
|
|
|
|
def forward(self, inputs):
|
|
if self.avg : out = self.avg( inputs )
|
|
else : out = inputs
|
|
conv = self.conv( out )
|
|
if self.bn : out = self.bn( conv )
|
|
else : out = conv
|
|
if self.relu: out = self.relu( out )
|
|
else : out = out
|
|
|
|
return out
|
|
|
|
|
|
class ResNetBasicblock(nn.Module):
|
|
num_conv = 2
|
|
expansion = 1
|
|
def __init__(self, inplanes, planes, stride):
|
|
super(ResNetBasicblock, self).__init__()
|
|
assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
|
|
|
|
self.conv_a = ConvBNReLU(inplanes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
|
|
self.conv_b = ConvBNReLU( planes, planes, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False)
|
|
if stride == 2:
|
|
self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
|
|
elif inplanes != planes:
|
|
self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
|
|
else:
|
|
self.downsample = None
|
|
self.out_dim = planes
|
|
|
|
def forward(self, inputs):
|
|
basicblock = self.conv_a(inputs)
|
|
basicblock = self.conv_b(basicblock)
|
|
|
|
if self.downsample is not None:
|
|
residual = self.downsample(inputs)
|
|
else:
|
|
residual = inputs
|
|
out = residual + basicblock
|
|
return F.relu(out, inplace=True)
|
|
|
|
|
|
|
|
class ResNetBottleneck(nn.Module):
|
|
expansion = 4
|
|
num_conv = 3
|
|
def __init__(self, inplanes, planes, stride):
|
|
super(ResNetBottleneck, self).__init__()
|
|
assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
|
|
self.conv_1x1 = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True)
|
|
self.conv_3x3 = ConvBNReLU( planes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
|
|
self.conv_1x4 = ConvBNReLU(planes, planes*self.expansion, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False)
|
|
if stride == 2:
|
|
self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=True , has_bn=False, has_relu=False)
|
|
elif inplanes != planes*self.expansion:
|
|
self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=False, has_bn=False, has_relu=False)
|
|
else:
|
|
self.downsample = None
|
|
self.out_dim = planes*self.expansion
|
|
|
|
def forward(self, inputs):
|
|
|
|
bottleneck = self.conv_1x1(inputs)
|
|
bottleneck = self.conv_3x3(bottleneck)
|
|
bottleneck = self.conv_1x4(bottleneck)
|
|
|
|
if self.downsample is not None:
|
|
residual = self.downsample(inputs)
|
|
else:
|
|
residual = inputs
|
|
out = residual + bottleneck
|
|
return F.relu(out, inplace=True)
|
|
|
|
|
|
|
|
class InferDepthCifarResNet(nn.Module):
|
|
|
|
def __init__(self, block_name, depth, xblocks, num_classes, zero_init_residual):
|
|
super(InferDepthCifarResNet, self).__init__()
|
|
|
|
#Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
|
|
if block_name == 'ResNetBasicblock':
|
|
block = ResNetBasicblock
|
|
assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
|
|
layer_blocks = (depth - 2) // 6
|
|
elif block_name == 'ResNetBottleneck':
|
|
block = ResNetBottleneck
|
|
assert (depth - 2) % 9 == 0, 'depth should be one of 164'
|
|
layer_blocks = (depth - 2) // 9
|
|
else:
|
|
raise ValueError('invalid block : {:}'.format(block_name))
|
|
assert len(xblocks) == 3, 'invalid xblocks : {:}'.format(xblocks)
|
|
|
|
self.message = 'InferWidthCifarResNet : Depth : {:} , Layers for each block : {:}'.format(depth, layer_blocks)
|
|
self.num_classes = num_classes
|
|
self.layers = nn.ModuleList( [ ConvBNReLU(3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
|
|
self.channels = [16]
|
|
for stage in range(3):
|
|
for iL in range(layer_blocks):
|
|
iC = self.channels[-1]
|
|
planes = 16 * (2**stage)
|
|
stride = 2 if stage > 0 and iL == 0 else 1
|
|
module = block(iC, planes, stride)
|
|
self.channels.append( module.out_dim )
|
|
self.layers.append ( module )
|
|
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)
|
|
if iL + 1 == xblocks[stage]: # reach the maximum depth
|
|
break
|
|
|
|
self.avgpool = nn.AvgPool2d(8)
|
|
self.classifier = nn.Linear(self.channels[-1], num_classes)
|
|
|
|
self.apply(initialize_resnet)
|
|
if zero_init_residual:
|
|
for m in self.modules():
|
|
if isinstance(m, ResNetBasicblock):
|
|
nn.init.constant_(m.conv_b.bn.weight, 0)
|
|
elif isinstance(m, ResNetBottleneck):
|
|
nn.init.constant_(m.conv_1x4.bn.weight, 0)
|
|
|
|
def get_message(self):
|
|
return self.message
|
|
|
|
def forward(self, inputs):
|
|
x = inputs
|
|
for i, layer in enumerate(self.layers):
|
|
x = layer( x )
|
|
features = self.avgpool(x)
|
|
features = features.view(features.size(0), -1)
|
|
logits = self.classifier(features)
|
|
return features, logits
|