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