761 lines
29 KiB
Python
761 lines
29 KiB
Python
##################################################
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
|
|
##################################################
|
|
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
|
|
|
|
|
|
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_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
|
|
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 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_range(self):
|
|
return self.conv_a.get_range() + self.conv_b.get_range()
|
|
|
|
def get_flops(self, channels):
|
|
assert len(channels) == 3, "invalid channels : {:}".format(channels)
|
|
flop_A = self.conv_a.get_flops([channels[0], channels[1]])
|
|
flop_B = self.conv_b.get_flops([channels[1], channels[2]])
|
|
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_b.OutShape[0]
|
|
* self.conv_b.OutShape[1]
|
|
)
|
|
return flop_A + flop_B + 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) == 2 and probs.size(0) == 2 and probability.size(0) == 2
|
|
out_a, expected_inC_a, expected_flop_a = self.conv_a(
|
|
(inputs, expected_inC, probability[0], indexes[0], probs[0])
|
|
)
|
|
out_b, expected_inC_b, expected_flop_b = self.conv_b(
|
|
(out_a, expected_inC_a, probability[1], indexes[1], probs[1])
|
|
)
|
|
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_b)
|
|
return (
|
|
nn.functional.relu(out, inplace=True),
|
|
expected_inC_b,
|
|
sum([expected_flop_a, expected_flop_b, expected_flop_c]),
|
|
)
|
|
|
|
def basic_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, channels):
|
|
assert len(channels) == 4, "invalid channels : {:}".format(channels)
|
|
flop_A = self.conv_1x1.get_flops([channels[0], channels[1]])
|
|
flop_B = self.conv_3x3.get_flops([channels[1], channels[2]])
|
|
flop_C = self.conv_1x4.get_flops([channels[2], channels[3]])
|
|
if hasattr(self.downsample, "get_flops"):
|
|
flop_D = self.downsample.get_flops([channels[0], channels[-1]])
|
|
else:
|
|
flop_D = 0
|
|
if (
|
|
channels[0] != channels[-1] and self.downsample is None
|
|
): # this short-cut will be added during the infer-train
|
|
flop_D = (
|
|
channels[0]
|
|
* channels[-1]
|
|
* self.conv_1x4.OutShape[0]
|
|
* self.conv_1x4.OutShape[1]
|
|
)
|
|
return flop_A + flop_B + flop_C + flop_D
|
|
|
|
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 basic_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)
|
|
|
|
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) == 3 and probs.size(0) == 3 and probability.size(0) == 3
|
|
out_1x1, expected_inC_1x1, expected_flop_1x1 = self.conv_1x1(
|
|
(inputs, expected_inC, probability[0], indexes[0], probs[0])
|
|
)
|
|
out_3x3, expected_inC_3x3, expected_flop_3x3 = self.conv_3x3(
|
|
(out_1x1, expected_inC_1x1, probability[1], indexes[1], probs[1])
|
|
)
|
|
out_1x4, expected_inC_1x4, expected_flop_1x4 = self.conv_1x4(
|
|
(out_3x3, expected_inC_3x3, probability[2], indexes[2], probs[2])
|
|
)
|
|
if self.downsample is not None:
|
|
residual, _, expected_flop_c = self.downsample(
|
|
(inputs, expected_inC, probability[2], indexes[2], probs[2])
|
|
)
|
|
else:
|
|
residual, expected_flop_c = inputs, 0
|
|
out = additive_func(residual, out_1x4)
|
|
return (
|
|
nn.functional.relu(out, inplace=True),
|
|
expected_inC_1x4,
|
|
sum(
|
|
[
|
|
expected_flop_1x1,
|
|
expected_flop_3x3,
|
|
expected_flop_1x4,
|
|
expected_flop_c,
|
|
]
|
|
),
|
|
)
|
|
|
|
|
|
class SearchShapeCifarResNet(nn.Module):
|
|
def __init__(self, block_name, depth, num_classes):
|
|
super(SearchShapeCifarResNet, 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)
|
|
|
|
# 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_width_choices(None))),
|
|
)
|
|
self.register_parameter(
|
|
"depth_attentions",
|
|
nn.Parameter(torch.Tensor(3, get_depth_choices(layer_blocks, True))),
|
|
)
|
|
nn.init.normal_(self.width_attentions, 0, 0.01)
|
|
nn.init.normal_(self.depth_attentions, 0, 0.01)
|
|
self.apply(initialize_resnet)
|
|
|
|
def arch_parameters(self, LR=None):
|
|
if LR is None:
|
|
return [self.width_attentions, self.depth_attentions]
|
|
else:
|
|
return [
|
|
{"params": self.width_attentions, "lr": LR},
|
|
{"params": self.depth_attentions, "lr": LR},
|
|
]
|
|
|
|
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 channels
|
|
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)
|
|
# 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" or mode == "fix":
|
|
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):
|
|
s, e = self.layer2indexRange[i]
|
|
xchl = tuple(channels[s : e + 1])
|
|
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(xchl)
|
|
else:
|
|
flop += 0 # do not use this layer
|
|
else:
|
|
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["xblocks"] = selected_layers
|
|
config_dict["super_type"] = "infer-shape"
|
|
config_dict["estimated_FLOP"] = flop / 1e6
|
|
return flop / 1e6, config_dict
|
|
|
|
def get_arch_info(self):
|
|
string = (
|
|
"for depth and width, there are {:} + {:} attention probabilities.".format(
|
|
len(self.depth_attentions), len(self.width_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)
|
|
string += "\n-----------------------------------------------"
|
|
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_width_probs = nn.functional.softmax(self.width_attentions, dim=1)
|
|
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_widths, selected_width_probs = select2withP(
|
|
self.width_attentions, self.tau
|
|
)
|
|
selected_depth_probs = select2withP(self.depth_attentions, self.tau, True)
|
|
with torch.no_grad():
|
|
selected_widths = selected_widths.cpu()
|
|
|
|
x, last_channel_idx, expected_inC, flops = inputs, 0, 3, []
|
|
feature_maps = []
|
|
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_width_probs[
|
|
last_channel_idx : last_channel_idx + layer.num_conv
|
|
]
|
|
layer_prob = flop_width_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)
|
|
)
|
|
feature_maps.append(x)
|
|
last_channel_idx += layer.num_conv
|
|
if i in self.depth_info: # aggregate the information
|
|
choices = self.depth_info[i]["choices"]
|
|
xstagei = self.depth_info[i]["stage"]
|
|
# print ('iL={:}, choices={:}, stage={:}, probs={:}'.format(i, choices, xstagei, selected_depth_probs[xstagei].cpu().tolist()))
|
|
# for A, W in zip(choices, selected_depth_probs[xstagei]):
|
|
# print('Size = {:}, W = {:}'.format(feature_maps[A].size(), W))
|
|
possible_tensors = []
|
|
max_C = max(feature_maps[A].size(1) for A in choices)
|
|
for tempi, A in enumerate(choices):
|
|
xtensor = ChannelWiseInter(feature_maps[A], max_C)
|
|
# drop_ratio = 1-(tempi+1.0)/len(choices)
|
|
# xtensor = drop_path(xtensor, drop_ratio)
|
|
possible_tensors.append(xtensor)
|
|
weighted_sum = sum(
|
|
xtensor * W
|
|
for xtensor, W in zip(
|
|
possible_tensors, selected_depth_probs[xstagei]
|
|
)
|
|
)
|
|
x = weighted_sum
|
|
|
|
if i in self.depth_at_i:
|
|
xstagei, xatti = self.depth_at_i[i]
|
|
x_expected_flop = flop_depth_probs[xstagei, xatti] * expected_flop
|
|
else:
|
|
x_expected_flop = expected_flop
|
|
flops.append(x_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
|