341 lines
14 KiB
Python
341 lines
14 KiB
Python
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import math, torch
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from collections import OrderedDict
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from bisect import bisect_right
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import torch.nn as nn
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from ..initialization import initialize_resnet
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from ..SharedUtils import additive_func
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from .SoftSelect import select2withP, ChannelWiseInter
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from .SoftSelect import linear_forward
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from .SoftSelect import get_width_choices
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def get_depth_choices(nDepth, return_num):
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if nDepth == 2:
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choices = (1, 2)
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elif nDepth == 3:
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choices = (1, 2, 3)
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elif nDepth > 3:
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choices = list(range(1, nDepth+1, 2))
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if choices[-1] < nDepth: choices.append(nDepth)
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else:
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raise ValueError('invalid nDepth : {:}'.format(nDepth))
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if return_num: return len(choices)
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else : return choices
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class ConvBNReLU(nn.Module):
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num_conv = 1
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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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self.InShape = None
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self.OutShape = None
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self.choices = get_width_choices(nOut)
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self.register_buffer('choices_tensor', torch.Tensor( self.choices ))
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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=False)
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else : self.relu = None
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self.in_dim = nIn
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self.out_dim = nOut
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def get_flops(self, divide=1):
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iC, oC = self.in_dim, self.out_dim
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assert iC <= self.conv.in_channels and oC <= self.conv.out_channels, '{:} vs {:} | {:} vs {:}'.format(iC, self.conv.in_channels, oC, self.conv.out_channels)
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assert isinstance(self.InShape, tuple) and len(self.InShape) == 2, 'invalid in-shape : {:}'.format(self.InShape)
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assert isinstance(self.OutShape, tuple) and len(self.OutShape) == 2, 'invalid out-shape : {:}'.format(self.OutShape)
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#conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups
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conv_per_position_flops = (self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups)
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all_positions = self.OutShape[0] * self.OutShape[1]
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flops = (conv_per_position_flops * all_positions / divide) * iC * oC
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if self.conv.bias is not None: flops += all_positions / divide
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return flops
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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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if self.InShape is None:
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self.InShape = (inputs.size(-2), inputs.size(-1))
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self.OutShape = (out.size(-2) , out.size(-1))
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return out
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class ResNetBasicblock(nn.Module):
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expansion = 1
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num_conv = 2
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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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self.search_mode = 'basic'
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def get_flops(self, divide=1):
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flop_A = self.conv_a.get_flops(divide)
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flop_B = self.conv_b.get_flops(divide)
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if hasattr(self.downsample, 'get_flops'):
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flop_C = self.downsample.get_flops(divide)
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else:
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flop_C = 0
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return flop_A + flop_B + flop_C
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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: residual = self.downsample(inputs)
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else : residual = inputs
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out = additive_func(residual, basicblock)
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return nn.functional.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=True , 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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self.search_mode = 'basic'
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def get_range(self):
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return self.conv_1x1.get_range() + self.conv_3x3.get_range() + self.conv_1x4.get_range()
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def get_flops(self, divide):
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flop_A = self.conv_1x1.get_flops(divide)
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flop_B = self.conv_3x3.get_flops(divide)
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flop_C = self.conv_1x4.get_flops(divide)
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if hasattr(self.downsample, 'get_flops'):
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flop_D = self.downsample.get_flops(divide)
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else:
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flop_D = 0
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return flop_A + flop_B + flop_C + flop_D
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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: residual = self.downsample(inputs)
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else : residual = inputs
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out = additive_func(residual, bottleneck)
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return nn.functional.relu(out, inplace=True)
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class SearchDepthCifarResNet(nn.Module):
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def __init__(self, block_name, depth, num_classes):
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super(SearchDepthCifarResNet, 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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self.message = 'SearchShapeCifarResNet : Depth : {:} , Layers for each block : {:}'.format(depth, layer_blocks)
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self.num_classes = num_classes
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self.channels = [16]
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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.InShape = None
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self.depth_info = OrderedDict()
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self.depth_at_i = OrderedDict()
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for stage in range(3):
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cur_block_choices = get_depth_choices(layer_blocks, False)
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assert cur_block_choices[-1] == layer_blocks, 'stage={:}, {:} vs {:}'.format(stage, cur_block_choices, layer_blocks)
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self.message += "\nstage={:} ::: depth-block-choices={:} for {:} blocks.".format(stage, cur_block_choices, layer_blocks)
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block_choices, xstart = [], len(self.layers)
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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={:3d}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iC, module.out_dim, stride)
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# added for depth
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layer_index = len(self.layers) - 1
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if iL + 1 in cur_block_choices: block_choices.append( layer_index )
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if iL + 1 == layer_blocks:
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self.depth_info[layer_index] = {'choices': block_choices,
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'stage' : stage,
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'xstart' : xstart}
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self.depth_info_list = []
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for xend, info in self.depth_info.items():
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self.depth_info_list.append( (xend, info) )
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xstart, xstage = info['xstart'], info['stage']
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for ilayer in range(xstart, xend+1):
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idx = bisect_right(info['choices'], ilayer-1)
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self.depth_at_i[ilayer] = (xstage, idx)
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self.avgpool = nn.AvgPool2d(8)
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self.classifier = nn.Linear(module.out_dim, num_classes)
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self.InShape = None
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self.tau = -1
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self.search_mode = 'basic'
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#assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth)
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self.register_parameter('depth_attentions', nn.Parameter(torch.Tensor(3, get_depth_choices(layer_blocks, True))))
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nn.init.normal_(self.depth_attentions, 0, 0.01)
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self.apply(initialize_resnet)
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def arch_parameters(self):
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return [self.depth_attentions]
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def base_parameters(self):
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return list(self.layers.parameters()) + list(self.avgpool.parameters()) + list(self.classifier.parameters())
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def get_flop(self, mode, config_dict, extra_info):
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if config_dict is not None: config_dict = config_dict.copy()
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# select depth
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if mode == 'genotype':
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with torch.no_grad():
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depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
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choices = torch.argmax(depth_probs, dim=1).cpu().tolist()
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elif mode == 'max':
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choices = [depth_probs.size(1)-1 for _ in range(depth_probs.size(0))]
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elif mode == 'random':
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with torch.no_grad():
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depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
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choices = torch.multinomial(depth_probs, 1, False).cpu().tolist()
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else:
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raise ValueError('invalid mode : {:}'.format(mode))
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selected_layers = []
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for choice, xvalue in zip(choices, self.depth_info_list):
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xtemp = xvalue[1]['choices'][choice] - xvalue[1]['xstart'] + 1
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selected_layers.append(xtemp)
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flop = 0
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for i, layer in enumerate(self.layers):
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if i in self.depth_at_i:
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xstagei, xatti = self.depth_at_i[i]
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if xatti <= choices[xstagei]: # leave this depth
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flop+= layer.get_flops()
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else:
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flop+= 0 # do not use this layer
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else:
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flop+= layer.get_flops()
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# the last fc layer
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flop += self.classifier.in_features * self.classifier.out_features
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if config_dict is None:
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return flop / 1e6
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else:
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config_dict['xblocks'] = selected_layers
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config_dict['super_type'] = 'infer-depth'
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config_dict['estimated_FLOP'] = flop / 1e6
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return flop / 1e6, config_dict
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def get_arch_info(self):
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string = "for depth, there are {:} attention probabilities.".format(len(self.depth_attentions))
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string+= '\n{:}'.format(self.depth_info)
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discrepancy = []
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with torch.no_grad():
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for i, att in enumerate(self.depth_attentions):
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prob = nn.functional.softmax(att, dim=0)
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prob = prob.cpu() ; selc = prob.argmax().item() ; prob = prob.tolist()
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prob = ['{:.3f}'.format(x) for x in prob]
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xstring = '{:03d}/{:03d}-th : {:}'.format(i, len(self.depth_attentions), ' '.join(prob))
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logt = ['{:.4f}'.format(x) for x in att.cpu().tolist()]
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xstring += ' || {:17s}'.format(' '.join(logt))
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prob = sorted( [float(x) for x in prob] )
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disc = prob[-1] - prob[-2]
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xstring += ' || discrepancy={:.2f} || select={:}/{:}'.format(disc, selc, len(prob))
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discrepancy.append( disc )
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string += '\n{:}'.format(xstring)
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return string, discrepancy
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def set_tau(self, tau_max, tau_min, epoch_ratio):
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assert epoch_ratio >= 0 and epoch_ratio <= 1, 'invalid epoch-ratio : {:}'.format(epoch_ratio)
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tau = tau_min + (tau_max-tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2
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self.tau = tau
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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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if self.search_mode == 'basic':
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return self.basic_forward(inputs)
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elif self.search_mode == 'search':
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return self.search_forward(inputs)
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else:
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raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
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def search_forward(self, inputs):
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flop_depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
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flop_depth_probs = torch.flip( torch.cumsum( torch.flip(flop_depth_probs, [1]), 1 ), [1] )
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selected_depth_probs = select2withP(self.depth_attentions, self.tau, True)
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x, flops = inputs, []
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feature_maps = []
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for i, layer in enumerate(self.layers):
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layer_i = layer( x )
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feature_maps.append( layer_i )
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if i in self.depth_info: # aggregate the information
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choices = self.depth_info[i]['choices']
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xstagei = self.depth_info[i]['stage']
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possible_tensors = []
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for tempi, A in enumerate(choices):
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xtensor = feature_maps[A]
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possible_tensors.append( xtensor )
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weighted_sum = sum( xtensor * W for xtensor, W in zip(possible_tensors, selected_depth_probs[xstagei]) )
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x = weighted_sum
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else:
|
||
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x = layer_i
|
||
|
|
|
||
|
|
if i in self.depth_at_i:
|
||
|
|
xstagei, xatti = self.depth_at_i[i]
|
||
|
|
#print ('layer-{:03d}, stage={:}, att={:}, prob={:}, flop={:}'.format(i, xstagei, xatti, flop_depth_probs[xstagei, xatti].item(), layer.get_flops(1e6)))
|
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|
|
x_expected_flop = flop_depth_probs[xstagei, xatti] * layer.get_flops(1e6)
|
||
|
|
else:
|
||
|
|
x_expected_flop = layer.get_flops(1e6)
|
||
|
|
flops.append( x_expected_flop )
|
||
|
|
flops.append( (self.classifier.in_features * self.classifier.out_features*1.0/1e6) )
|
||
|
|
|
||
|
|
features = self.avgpool(x)
|
||
|
|
features = features.view(features.size(0), -1)
|
||
|
|
logits = linear_forward(features, self.classifier)
|
||
|
|
return logits, torch.stack( [sum(flops)] )
|
||
|
|
|
||
|
|
def basic_forward(self, inputs):
|
||
|
|
if self.InShape is None: self.InShape = (inputs.size(-2), inputs.size(-1))
|
||
|
|
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
|