317 lines
14 KiB
Python
317 lines
14 KiB
Python
##################################################
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
|
|
##################################################
|
|
import math, torch
|
|
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 as get_choices
|
|
|
|
|
|
def conv_forward(inputs, conv, choices):
|
|
iC = conv.in_channels
|
|
fill_size = list(inputs.size())
|
|
fill_size[1] = iC - fill_size[1]
|
|
filled = torch.zeros(fill_size, device=inputs.device)
|
|
xinputs = torch.cat((inputs, filled), dim=1)
|
|
outputs = conv(xinputs)
|
|
selecteds = [outputs[:,:oC] for oC in choices]
|
|
return selecteds
|
|
|
|
|
|
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_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
|
|
self.has_bn = has_bn
|
|
self.BNs = nn.ModuleList()
|
|
for i, _out in enumerate(self.choices):
|
|
self.BNs.append(nn.BatchNorm2d(_out))
|
|
if has_relu: self.relu = nn.ReLU(inplace=True)
|
|
else : self.relu = None
|
|
self.in_dim = nIn
|
|
self.out_dim = nOut
|
|
self.search_mode = 'basic'
|
|
|
|
def get_flops(self, channels, check_range=True, divide=1):
|
|
iC, oC = channels
|
|
if check_range: 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 get_range(self):
|
|
return [self.choices]
|
|
|
|
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, tuple_inputs):
|
|
assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
|
|
inputs, expected_inC, probability, index, prob = tuple_inputs
|
|
index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob)
|
|
probability = torch.squeeze(probability)
|
|
assert len(index) == 2, 'invalid length : {:}'.format(index)
|
|
# compute expected flop
|
|
#coordinates = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability)
|
|
expected_outC = (self.choices_tensor * probability).sum()
|
|
expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6)
|
|
if self.avg : out = self.avg( inputs )
|
|
else : out = inputs
|
|
# convolutional layer
|
|
out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index])
|
|
out_bns = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)]
|
|
# merge
|
|
out_channel = max([x.size(1) for x in out_bns])
|
|
outA = ChannelWiseInter(out_bns[0], out_channel)
|
|
outB = ChannelWiseInter(out_bns[1], out_channel)
|
|
out = outA * prob[0] + outB * prob[1]
|
|
#out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1])
|
|
|
|
if self.relu: out = self.relu( out )
|
|
else : out = out
|
|
return out, expected_outC, expected_flop
|
|
|
|
def basic_forward(self, inputs):
|
|
if self.avg : out = self.avg( inputs )
|
|
else : out = inputs
|
|
conv = self.conv( out )
|
|
if self.has_bn:out= self.BNs[-1]( 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 SimBlock(nn.Module):
|
|
expansion = 1
|
|
num_conv = 1
|
|
def __init__(self, inplanes, planes, stride):
|
|
super(SimBlock, self).__init__()
|
|
assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
|
|
self.conv = ConvBNReLU(inplanes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
|
|
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_range(self):
|
|
return self.conv.get_range()
|
|
|
|
def get_flops(self, channels):
|
|
assert len(channels) == 2, 'invalid channels : {:}'.format(channels)
|
|
flop_A = self.conv.get_flops([channels[0], channels[1]])
|
|
if hasattr(self.downsample, 'get_flops'):
|
|
flop_C = self.downsample.get_flops([channels[0], channels[-1]])
|
|
else:
|
|
flop_C = 0
|
|
if channels[0] != channels[-1] and self.downsample is None: # this short-cut will be added during the infer-train
|
|
flop_C = channels[0] * channels[-1] * self.conv.OutShape[0] * self.conv.OutShape[1]
|
|
return flop_A + flop_C
|
|
|
|
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, tuple_inputs):
|
|
assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
|
|
inputs, expected_inC, probability, indexes, probs = tuple_inputs
|
|
assert indexes.size(0) == 1 and probs.size(0) == 1 and probability.size(0) == 1, 'invalid size : {:}, {:}, {:}'.format(indexes.size(), probs.size(), probability.size())
|
|
out, expected_next_inC, expected_flop = self.conv( (inputs, expected_inC , probability[0], indexes[0], probs[0]) )
|
|
if self.downsample is not None:
|
|
residual, _, expected_flop_c = self.downsample( (inputs, expected_inC , probability[-1], indexes[-1], probs[-1]) )
|
|
else:
|
|
residual, expected_flop_c = inputs, 0
|
|
out = additive_func(residual, out)
|
|
return nn.functional.relu(out, inplace=True), expected_next_inC, sum([expected_flop, expected_flop_c])
|
|
|
|
def basic_forward(self, inputs):
|
|
basicblock = self.conv(inputs)
|
|
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 SearchWidthSimResNet(nn.Module):
|
|
|
|
def __init__(self, depth, num_classes):
|
|
super(SearchWidthSimResNet, self).__init__()
|
|
|
|
assert (depth - 2) % 3 == 0, 'depth should be one of 5, 8, 11, 14, ... instead of {:}'.format(depth)
|
|
layer_blocks = (depth - 2) // 3
|
|
self.message = 'SearchWidthSimResNet : 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
|
|
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 = SimBlock(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)
|
|
|
|
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)
|
|
|
|
# parameters for width
|
|
self.Ranges = []
|
|
self.layer2indexRange = []
|
|
for i, layer in enumerate(self.layers):
|
|
start_index = len(self.Ranges)
|
|
self.Ranges += layer.get_range()
|
|
self.layer2indexRange.append( (start_index, len(self.Ranges)) )
|
|
assert len(self.Ranges) + 1 == depth, 'invalid depth check {:} vs {:}'.format(len(self.Ranges) + 1, depth)
|
|
|
|
self.register_parameter('width_attentions', nn.Parameter(torch.Tensor(len(self.Ranges), get_choices(None))))
|
|
nn.init.normal_(self.width_attentions, 0, 0.01)
|
|
self.apply(initialize_resnet)
|
|
|
|
def arch_parameters(self):
|
|
return [self.width_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()
|
|
#weights = [F.softmax(x, dim=0) for x in self.width_attentions]
|
|
channels = [3]
|
|
for i, weight in enumerate(self.width_attentions):
|
|
if mode == 'genotype':
|
|
with torch.no_grad():
|
|
probe = nn.functional.softmax(weight, dim=0)
|
|
C = self.Ranges[i][ torch.argmax(probe).item() ]
|
|
elif mode == 'max':
|
|
C = self.Ranges[i][-1]
|
|
elif mode == 'fix':
|
|
C = int( math.sqrt( extra_info ) * self.Ranges[i][-1] )
|
|
elif mode == 'random':
|
|
assert isinstance(extra_info, float), 'invalid extra_info : {:}'.format(extra_info)
|
|
with torch.no_grad():
|
|
prob = nn.functional.softmax(weight, dim=0)
|
|
approximate_C = int( math.sqrt( extra_info ) * self.Ranges[i][-1] )
|
|
for j in range(prob.size(0)):
|
|
prob[j] = 1 / (abs(j - (approximate_C-self.Ranges[i][j])) + 0.2)
|
|
C = self.Ranges[i][ torch.multinomial(prob, 1, False).item() ]
|
|
else:
|
|
raise ValueError('invalid mode : {:}'.format(mode))
|
|
channels.append( C )
|
|
flop = 0
|
|
for i, layer in enumerate(self.layers):
|
|
s, e = self.layer2indexRange[i]
|
|
xchl = tuple( channels[s:e+1] )
|
|
flop+= layer.get_flops(xchl)
|
|
# the last fc layer
|
|
flop += channels[-1] * self.classifier.out_features
|
|
if config_dict is None:
|
|
return flop / 1e6
|
|
else:
|
|
config_dict['xchannels'] = channels
|
|
config_dict['super_type'] = 'infer-width'
|
|
config_dict['estimated_FLOP'] = flop / 1e6
|
|
return flop / 1e6, config_dict
|
|
|
|
def get_arch_info(self):
|
|
string = "for width, there are {:} attention probabilities.".format(len(self.width_attentions))
|
|
discrepancy = []
|
|
with torch.no_grad():
|
|
for i, att in enumerate(self.width_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.width_attentions), ' '.join(prob))
|
|
logt = ['{:.3f}'.format(x) for x in att.cpu().tolist()]
|
|
xstring += ' || {:52s}'.format(' '.join(logt))
|
|
prob = sorted( [float(x) for x in prob] )
|
|
disc = prob[-1] - prob[-2]
|
|
xstring += ' || dis={:.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_probs = nn.functional.softmax(self.width_attentions, dim=1)
|
|
selected_widths, selected_probs = select2withP(self.width_attentions, self.tau)
|
|
with torch.no_grad():
|
|
selected_widths = selected_widths.cpu()
|
|
|
|
x, last_channel_idx, expected_inC, flops = inputs, 0, 3, []
|
|
for i, layer in enumerate(self.layers):
|
|
selected_w_index = selected_widths[last_channel_idx: last_channel_idx+layer.num_conv]
|
|
selected_w_probs = selected_probs[last_channel_idx: last_channel_idx+layer.num_conv]
|
|
layer_prob = flop_probs[last_channel_idx: last_channel_idx+layer.num_conv]
|
|
x, expected_inC, expected_flop = layer( (x, expected_inC, layer_prob, selected_w_index, selected_w_probs) )
|
|
last_channel_idx += layer.num_conv
|
|
flops.append( expected_flop )
|
|
flops.append( expected_inC * (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
|