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2019-09-28 18:24:47 +10:00
parent bfd6b648fd
commit cfb462e463
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166
lib/models/infers/InferCifarResNet.py Normal file
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import math, torch
import torch.nn as nn
import torch.nn.functional as F
from ..initialization import initialize_resnet
from ..SharedUtils import additive_func
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, iCs, stride):
super(ResNetBasicblock, self).__init__()
assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
assert isinstance(iCs, tuple) or isinstance(iCs, list), 'invalid type of iCs : {:}'.format( iCs )
assert len(iCs) == 3,'invalid lengths of iCs : {:}'.format(iCs)
self.conv_a = ConvBNReLU(iCs[0], iCs[1], 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
self.conv_b = ConvBNReLU(iCs[1], iCs[2], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False)
residual_in = iCs[0]
if stride == 2:
self.downsample = ConvBNReLU(iCs[0], iCs[2], 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
residual_in = iCs[2]
elif iCs[0] != iCs[2]:
self.downsample = ConvBNReLU(iCs[0], iCs[2], 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
else:
self.downsample = None
#self.out_dim = max(residual_in, iCs[2])
self.out_dim = iCs[2]
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, iCs, stride):
super(ResNetBottleneck, self).__init__()
assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
assert isinstance(iCs, tuple) or isinstance(iCs, list), 'invalid type of iCs : {:}'.format( iCs )
assert len(iCs) == 4,'invalid lengths of iCs : {:}'.format(iCs)
self.conv_1x1 = ConvBNReLU(iCs[0], iCs[1], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True)
self.conv_3x3 = ConvBNReLU(iCs[1], iCs[2], 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
self.conv_1x4 = ConvBNReLU(iCs[2], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False)
residual_in = iCs[0]
if stride == 2:
self.downsample = ConvBNReLU(iCs[0], iCs[3], 1, 1, 0, False, has_avg=True , has_bn=False, has_relu=False)
residual_in = iCs[3]
elif iCs[0] != iCs[3]:
self.downsample = ConvBNReLU(iCs[0], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=False, has_relu=False)
residual_in = iCs[3]
else:
self.downsample = None
#self.out_dim = max(residual_in, iCs[3])
self.out_dim = iCs[3]
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 InferCifarResNet(nn.Module):
def __init__(self, block_name, depth, xblocks, xchannels, num_classes, zero_init_residual):
super(InferCifarResNet, 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.xchannels = xchannels
self.layers = nn.ModuleList( [ ConvBNReLU(xchannels[0], xchannels[1], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
last_channel_idx = 1
for stage in range(3):
for iL in range(layer_blocks):
num_conv = block.num_conv
iCs = self.xchannels[last_channel_idx:last_channel_idx+num_conv+1]
stride = 2 if stage > 0 and iL == 0 else 1
module = block(iCs, stride)
last_channel_idx += num_conv
self.xchannels[last_channel_idx] = module.out_dim
self.layers.append ( module )
self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iCs={:}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iCs, module.out_dim, stride)
if iL + 1 == xblocks[stage]: # reach the maximum depth
out_channel = module.out_dim
for iiL in range(iL+1, layer_blocks):
last_channel_idx += num_conv
self.xchannels[last_channel_idx] = module.out_dim
break
self.avgpool = nn.AvgPool2d(8)
self.classifier = nn.Linear(self.xchannels[-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

149
lib/models/infers/InferCifarResNet_depth.py Normal file
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import math, torch
import torch.nn as nn
import torch.nn.functional as F
from ..initialization import initialize_resnet
from ..SharedUtils import additive_func
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

159
lib/models/infers/InferCifarResNet_width.py Normal file
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import math, torch
import torch.nn as nn
import torch.nn.functional as F
from ..initialization import initialize_resnet
from ..SharedUtils import additive_func
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, iCs, stride):
super(ResNetBasicblock, self).__init__()
assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
assert isinstance(iCs, tuple) or isinstance(iCs, list), 'invalid type of iCs : {:}'.format( iCs )
assert len(iCs) == 3,'invalid lengths of iCs : {:}'.format(iCs)
self.conv_a = ConvBNReLU(iCs[0], iCs[1], 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
self.conv_b = ConvBNReLU(iCs[1], iCs[2], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False)
residual_in = iCs[0]
if stride == 2:
self.downsample = ConvBNReLU(iCs[0], iCs[2], 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
residual_in = iCs[2]
elif iCs[0] != iCs[2]:
self.downsample = ConvBNReLU(iCs[0], iCs[2], 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
else:
self.downsample = None
#self.out_dim = max(residual_in, iCs[2])
self.out_dim = iCs[2]
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, iCs, stride):
super(ResNetBottleneck, self).__init__()
assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
assert isinstance(iCs, tuple) or isinstance(iCs, list), 'invalid type of iCs : {:}'.format( iCs )
assert len(iCs) == 4,'invalid lengths of iCs : {:}'.format(iCs)
self.conv_1x1 = ConvBNReLU(iCs[0], iCs[1], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True)
self.conv_3x3 = ConvBNReLU(iCs[1], iCs[2], 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
self.conv_1x4 = ConvBNReLU(iCs[2], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False)
residual_in = iCs[0]
if stride == 2:
self.downsample = ConvBNReLU(iCs[0], iCs[3], 1, 1, 0, False, has_avg=True , has_bn=False, has_relu=False)
residual_in = iCs[3]
elif iCs[0] != iCs[3]:
self.downsample = ConvBNReLU(iCs[0], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=False, has_relu=False)
residual_in = iCs[3]
else:
self.downsample = None
#self.out_dim = max(residual_in, iCs[3])
self.out_dim = iCs[3]
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 InferWidthCifarResNet(nn.Module):
def __init__(self, block_name, depth, xchannels, num_classes, zero_init_residual):
super(InferWidthCifarResNet, 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))
self.message = 'InferWidthCifarResNet : Depth : {:} , Layers for each block : {:}'.format(depth, layer_blocks)
self.num_classes = num_classes
self.xchannels = xchannels
self.layers = nn.ModuleList( [ ConvBNReLU(xchannels[0], xchannels[1], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
last_channel_idx = 1
for stage in range(3):
for iL in range(layer_blocks):
num_conv = block.num_conv
iCs = self.xchannels[last_channel_idx:last_channel_idx+num_conv+1]
stride = 2 if stage > 0 and iL == 0 else 1
module = block(iCs, stride)
last_channel_idx += num_conv
self.xchannels[last_channel_idx] = module.out_dim
self.layers.append ( module )
self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iCs={:}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iCs, module.out_dim, stride)
self.avgpool = nn.AvgPool2d(8)
self.classifier = nn.Linear(self.xchannels[-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

169
lib/models/infers/InferImagenetResNet.py Normal file
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import math, torch
import torch.nn as nn
import torch.nn.functional as F
from ..initialization import initialize_resnet
from ..SharedUtils import additive_func
class ConvBNReLU(nn.Module):
num_conv = 1
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, iCs, stride):
super(ResNetBasicblock, self).__init__()
assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
assert isinstance(iCs, tuple) or isinstance(iCs, list), 'invalid type of iCs : {:}'.format( iCs )
assert len(iCs) == 3,'invalid lengths of iCs : {:}'.format(iCs)
self.conv_a = ConvBNReLU(iCs[0], iCs[1], 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
self.conv_b = ConvBNReLU(iCs[1], iCs[2], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False)
residual_in = iCs[0]
if stride == 2:
self.downsample = ConvBNReLU(iCs[0], iCs[2], 1, 1, 0, False, has_avg=True, has_bn=True, has_relu=False)
residual_in = iCs[2]
elif iCs[0] != iCs[2]:
self.downsample = ConvBNReLU(iCs[0], iCs[2], 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
else:
self.downsample = None
#self.out_dim = max(residual_in, iCs[2])
self.out_dim = iCs[2]
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, iCs, stride):
super(ResNetBottleneck, self).__init__()
assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
assert isinstance(iCs, tuple) or isinstance(iCs, list), 'invalid type of iCs : {:}'.format( iCs )
assert len(iCs) == 4,'invalid lengths of iCs : {:}'.format(iCs)
self.conv_1x1 = ConvBNReLU(iCs[0], iCs[1], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True)
self.conv_3x3 = ConvBNReLU(iCs[1], iCs[2], 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
self.conv_1x4 = ConvBNReLU(iCs[2], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False)
residual_in = iCs[0]
if stride == 2:
self.downsample = ConvBNReLU(iCs[0], iCs[3], 1, 1, 0, False, has_avg=True , has_bn=True, has_relu=False)
residual_in = iCs[3]
elif iCs[0] != iCs[3]:
self.downsample = ConvBNReLU(iCs[0], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False)
residual_in = iCs[3]
else:
self.downsample = None
#self.out_dim = max(residual_in, iCs[3])
self.out_dim = iCs[3]
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 InferImagenetResNet(nn.Module):
def __init__(self, block_name, layers, xblocks, xchannels, deep_stem, num_classes, zero_init_residual):
super(InferImagenetResNet, self).__init__()
#Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
if block_name == 'BasicBlock':
block = ResNetBasicblock
elif block_name == 'Bottleneck':
block = ResNetBottleneck
else:
raise ValueError('invalid block : {:}'.format(block_name))
assert len(xblocks) == len(layers), 'invalid layers : {:} vs xblocks : {:}'.format(layers, xblocks)
self.message = 'InferImagenetResNet : Depth : {:} -> {:}, Layers for each block : {:}'.format(sum(layers)*block.num_conv, sum(xblocks)*block.num_conv, xblocks)
self.num_classes = num_classes
self.xchannels = xchannels
if not deep_stem:
self.layers = nn.ModuleList( [ ConvBNReLU(xchannels[0], xchannels[1], 7, 2, 3, False, has_avg=False, has_bn=True, has_relu=True) ] )
last_channel_idx = 1
else:
self.layers = nn.ModuleList( [ ConvBNReLU(xchannels[0], xchannels[1], 3, 2, 1, False, has_avg=False, has_bn=True, has_relu=True)
,ConvBNReLU(xchannels[1], xchannels[2], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
last_channel_idx = 2
self.layers.append( nn.MaxPool2d(kernel_size=3, stride=2, padding=1) )
for stage, layer_blocks in enumerate(layers):
for iL in range(layer_blocks):
num_conv = block.num_conv
iCs = self.xchannels[last_channel_idx:last_channel_idx+num_conv+1]
stride = 2 if stage > 0 and iL == 0 else 1
module = block(iCs, stride)
last_channel_idx += num_conv
self.xchannels[last_channel_idx] = module.out_dim
self.layers.append ( module )
self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iCs={:}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iCs, module.out_dim, stride)
if iL + 1 == xblocks[stage]: # reach the maximum depth
out_channel = module.out_dim
for iiL in range(iL+1, layer_blocks):
last_channel_idx += num_conv
self.xchannels[last_channel_idx] = module.out_dim
break
assert last_channel_idx + 1 == len(self.xchannels), '{:} vs {:}'.format(last_channel_idx, len(self.xchannels))
self.avgpool = nn.AdaptiveAvgPool2d((1,1))
self.classifier = nn.Linear(self.xchannels[-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

119
lib/models/infers/InferMobileNetV2.py Normal file
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# MobileNetV2: Inverted Residuals and Linear Bottlenecks, CVPR 2018
from torch import nn
from ..initialization import initialize_resnet
from ..SharedUtils import additive_func, parse_channel_info
class ConvBNReLU(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride, groups, has_bn=True, has_relu=True):
super(ConvBNReLU, self).__init__()
padding = (kernel_size - 1) // 2
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False)
if has_bn: self.bn = nn.BatchNorm2d(out_planes)
else : self.bn = None
if has_relu: self.relu = nn.ReLU6(inplace=True)
else : self.relu = None
def forward(self, x):
out = self.conv( x )
if self.bn: out = self.bn ( out )
if self.relu: out = self.relu( out )
return out
class InvertedResidual(nn.Module):
def __init__(self, channels, stride, expand_ratio, additive):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2], 'invalid stride : {:}'.format(stride)
assert len(channels) in [2, 3], 'invalid channels : {:}'.format(channels)
if len(channels) == 2:
layers = []
else:
layers = [ConvBNReLU(channels[0], channels[1], 1, 1, 1)]
layers.extend([
# dw
ConvBNReLU(channels[-2], channels[-2], 3, stride, channels[-2]),
# pw-linear
ConvBNReLU(channels[-2], channels[-1], 1, 1, 1, True, False),
])
self.conv = nn.Sequential(*layers)
self.additive = additive
if self.additive and channels[0] != channels[-1]:
self.shortcut = ConvBNReLU(channels[0], channels[-1], 1, 1, 1, True, False)
else:
self.shortcut = None
self.out_dim = channels[-1]
def forward(self, x):
out = self.conv(x)
# if self.additive: return additive_func(out, x)
if self.shortcut: return out + self.shortcut(x)
else : return out
class InferMobileNetV2(nn.Module):
def __init__(self, num_classes, xchannels, xblocks, dropout):
super(InferMobileNetV2, self).__init__()
block = InvertedResidual
inverted_residual_setting = [
# t, c, n, s
[1, 16 , 1, 1],
[6, 24 , 2, 2],
[6, 32 , 3, 2],
[6, 64 , 4, 2],
[6, 96 , 3, 1],
[6, 160, 3, 2],
[6, 320, 1, 1],
]
assert len(inverted_residual_setting) == len(xblocks), 'invalid number of layers : {:} vs {:}'.format(len(inverted_residual_setting), len(xblocks))
for block_num, ir_setting in zip(xblocks, inverted_residual_setting):
assert block_num <= ir_setting[2], '{:} vs {:}'.format(block_num, ir_setting)
xchannels = parse_channel_info(xchannels)
#for i, chs in enumerate(xchannels):
# if i > 0: assert chs[0] == xchannels[i-1][-1], 'Layer[{:}] is invalid {:} vs {:}'.format(i, xchannels[i-1], chs)
self.xchannels = xchannels
self.message = 'InferMobileNetV2 : xblocks={:}'.format(xblocks)
# building first layer
features = [ConvBNReLU(xchannels[0][0], xchannels[0][1], 3, 2, 1)]
last_channel_idx = 1
# building inverted residual blocks
for stage, (t, c, n, s) in enumerate(inverted_residual_setting):
for i in range(n):
stride = s if i == 0 else 1
additv = True if i > 0 else False
module = block(self.xchannels[last_channel_idx], stride, t, additv)
features.append(module)
self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, Cs={:}, stride={:}, expand={:}, original-C={:}".format(stage, i, n, len(features), self.xchannels[last_channel_idx], stride, t, c)
last_channel_idx += 1
if i + 1 == xblocks[stage]:
out_channel = module.out_dim
for iiL in range(i+1, n):
last_channel_idx += 1
self.xchannels[last_channel_idx][0] = module.out_dim
break
# building last several layers
features.append(ConvBNReLU(self.xchannels[last_channel_idx][0], self.xchannels[last_channel_idx][1], 1, 1, 1))
assert last_channel_idx + 2 == len(self.xchannels), '{:} vs {:}'.format(last_channel_idx, len(self.xchannels))
# make it nn.Sequential
self.features = nn.Sequential(*features)
# building classifier
self.classifier = nn.Sequential(
nn.Dropout(dropout),
nn.Linear(self.xchannels[last_channel_idx][1], num_classes),
)
# weight initialization
self.apply( initialize_resnet )
def get_message(self):
return self.message
def forward(self, inputs):
features = self.features(inputs)
vectors = features.mean([2, 3])
predicts = self.classifier(vectors)
return features, predicts

5
lib/models/infers/__init__.py Normal file
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from .InferCifarResNet_width import InferWidthCifarResNet
from .InferImagenetResNet import InferImagenetResNet
from .InferCifarResNet_depth import InferDepthCifarResNet
from .InferCifarResNet import InferCifarResNet
from .InferMobileNetV2 import InferMobileNetV2

7
lib/models/infers/shared_utils.py Normal file
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# Xuanyi Dong
def parse_channel_info(xstring):
blocks = xstring.split(' ')
blocks = [x.split('-') for x in blocks]
blocks = [[int(_) for _ in x] for x in blocks]
return blocks