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nas_search_unet.py
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#coding:utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
from genotypes import *
from operations import *
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x
class GlobalPooling(nn.Module):
def __init__(self, in_channels, out_channels, norm_layer, up_kwargs):
super(GlobalPooling, self).__init__()
self._up_kwargs = up_kwargs
self.gap = nn.Sequential(nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, out_channels, 1, bias=False),
norm_layer(out_channels),
nn.ReLU(True))
def forward(self, x):
_, _, h, w = x.size()
pool = self.gap(x)
return F.interpolate(pool, (h,w), **self._up_kwargs)
class ConcurrentModule(nn.ModuleList):
r"""Feed to a list of modules concurrently.
The outputs of the layers are concatenated at channel dimension.
Args:
modules (iterable, optional): an iterable of modules to add
"""
def __init__(self, modules=None):
super(ConcurrentModule, self).__init__(modules)
def forward(self, x):
outputs = []
for layer in self:
outputs.append(layer(x))
return torch.cat(outputs, 1)
class FCNHead(nn.Module):
def __init__(self, in_channels, out_channels, norm_layer, up_kwargs={}, with_global=False):
super(FCNHead, self).__init__()
inter_channels = in_channels // 4
self._up_kwargs = up_kwargs
if with_global:
self.conv5 = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False),
norm_layer(inter_channels),
nn.ReLU(),
ConcurrentModule([
Identity(),
GlobalPooling(inter_channels, inter_channels,
norm_layer, self._up_kwargs),
]),
nn.Dropout2d(0.1, False),
nn.Conv2d(2*inter_channels, out_channels, 1))
else:
self.conv5 = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False),
norm_layer(inter_channels),
nn.ReLU(),
nn.Dropout2d(0.1, False),
nn.Conv2d(inter_channels, out_channels, 1))
def forward(self, x):
return self.conv5(x)
class BuildCell(nn.Module):
#c, stride, mixop_type, switch, dropout_prob
def __init__(self, genotype, c_prev_prev, c_prev, c,cell_type,dp=0):
super(BuildCell, self).__init__()
self.c_prev_prev = c_prev_prev
self.c_prev = c_prev
self.c = c
self.dropout_prob=dp
self.genotype=genotype
# the sanme feature map size (ck-2)
if c_prev_prev !=-1:
self.preprocess0=ConvOps(c_prev_prev,c,kernel_size=1, affine=True, ops_order='act_weight_norm')
else:
self.preprocess0=None
# must be exits!
self.preprocess1=ConvOps(c_prev,c,kernel_size=1, affine=True, ops_order='act_weight_norm')
# cell_type=normal_down,normal_normal,normal_up ,three types
# c, stride, mixop_type, switch, dropout_prob
if cell_type=="normal_normal":
self.op_names, self.idxs = zip(*genotype.normal_normal)
self.concat = genotype.normal_normal_concat
elif cell_type=="normal_down":
self.op_names, self.idxs = zip(*genotype.normal_down)
self.concat = genotype.normal_down_concat
else:
self.op_names, self.idxs = zip(*genotype.normal_up)
self.concat = genotype.normal_up_concat
self._compile()
def _compile(self):
assert len(self.op_names) == len(self.idxs)
self.num_meta_node = len(self.op_names) // 2
self.multiplier = len(self.concat)
self.ops = nn.ModuleList()
for name, index in zip(self.op_names, self.idxs):
op = OPS[name](self.c, None, affine=True, dp=self.dropout_prob)
self.ops += [op]
def forward(self, s0, s1):
'''
:param s0: may be is None
:param s1:
:return:
'''
if s0 is None:
assert self.preprocess0 is None,"The s0 is None but the Preprocess0 is Not None!"
s1=self.preprocess1(s1)
else:
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
# s0 may be is None
states = [s0, s1]
for i in range(self.num_meta_node):
# h1 and h2 may be is none
h1 = states[self.idxs[2*i]]
h2 = states[self.idxs[2*i+1]]
# All 8 op connections must exist
op1 = self.ops[2*i]
op2 = self.ops[2*i+1]
#print(op1.__class__.__name__,op2.__class__.__name__)
if h1 is None and h2 is None:
# h1,h2 cannot be none at the same time,
# because an intermediate node cannot have both connections pointing to the 0 node
raise ValueError("h1 is none and h2 is none ")
# the size of h1 and h2 may be different, so we need interpolate
if h1 is not None and h2 is not None:
#print("h1 is not none and h2 is not none ")
h1 = op1(h1)
h2 = op2(h2)
if h1.size() != h2.size() :
#print('h1.size{} and h2.size{}'.format(h1.size(),h2.size()))
_, _, height1, width1 = h1.size()
_, _, height2, width2 = h2.size()
if height1 > height2 or width1 > width2:
h2 = F.interpolate(h2, (height1, width1))
else:
h1 = F.interpolate(h1, (height2, width2))
s = h1+h2
elif h1 is not None:
# h2 is none
#print("h2 is none ")
h1=op1(h1)
s=h1
else:
# h1 is none
#print('h1 is none')
h2=op2(h2)
s=h2
states += [s]
# for s in states:
# if s is not None:
# print(s.size())
return torch.cat([states[i] for i in self.concat], dim=1)
class BuildNasUnet(nn.Module):
def __init__(self,genotype, input_c=3,c=16,num_classes=1, meta_node_num=4, layers=9,dp=0,
use_sharing=True,double_down_channel=True,aux=False):
super(BuildNasUnet, self).__init__()
self.dropout_prob=dp
self.input_c=input_c
self.num_class=num_classes
self.meta_node_num=meta_node_num
self.layers=layers
self.use_sharing=use_sharing
self.double_down_channel=double_down_channel
self.depth=(self.layers+1)//2
self.c_prev_prev=32
self.c_prev=64
# 3-->32
self.stem0 = ConvOps(input_c,self.c_prev_prev , kernel_size=3,stride=1,ops_order='weight_norm_act')
# 32-->64
self.stem1 = ConvOps(self.c_prev_prev,self.c_prev , kernel_size=3, stride=2, ops_order='weight_norm_act')
init_channel = c
if self.double_down_channel:
self.layers_channel = [self.meta_node_num * init_channel * pow(2, i) for i in range(0, self.depth)]
self.cell_channels = [init_channel * pow(2, i) for i in range(0, self.depth)]
else:
self.layers_channel = [self.meta_node_num * init_channel for i in range(0, self.depth)]
self.cell_channels = [init_channel for i in range(0, self.depth)]
for i in range(1, self.layers):
if i == 1:
self.cell_1_1 = BuildCell(genotype,
-1, self.c_prev, self.cell_channels[1],
cell_type="normal_down", dp=self.dropout_prob)
elif i == 2:
self.cell_2_0_0 = BuildCell(genotype,
-1, self.c_prev, self.cell_channels[0],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_2_0_1 = BuildCell(genotype,
self.c_prev, self.layers_channel[1], self.cell_channels[0],
cell_type="normal_up", dp=self.dropout_prob)
self.cell_2_2 = BuildCell(genotype,
-1, self.layers_channel[1], self.cell_channels[2],
cell_type="normal_down", dp=self.dropout_prob)
elif i == 3:
self.cell_3_1_0 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[0], self.cell_channels[1],
cell_type="normal_down", dp=self.dropout_prob)
self.cell_3_1_1 = BuildCell(genotype,
-1, self.layers_channel[1], self.cell_channels[1],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_3_1_2 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[2], self.cell_channels[1],
cell_type="normal_up", dp=self.dropout_prob)
self.cell_3_3 = BuildCell(genotype,
-1, self.layers_channel[2], self.cell_channels[3],
cell_type="normal_down", dp=self.dropout_prob)
elif i == 4:
self.cell_4_0_0 = BuildCell(genotype,
self.layers_channel[0], self.layers_channel[0], self.cell_channels[0],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_4_0_1 = BuildCell(genotype,
self.layers_channel[0], self.layers_channel[1], self.cell_channels[0],
cell_type="normal_up", dp=self.dropout_prob)
self.cell_4_2_0 = BuildCell(genotype,
self.layers_channel[2], self.layers_channel[1], self.cell_channels[2],
cell_type="normal_down", dp=self.dropout_prob)
self.cell_4_2_1 = BuildCell(genotype,
-1, self.layers_channel[2], self.cell_channels[2],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_4_2_2 = BuildCell(genotype,
self.layers_channel[2], self.layers_channel[3], self.cell_channels[2],
cell_type="normal_up", dp=self.dropout_prob)
self.cell_4_4 = BuildCell(genotype,
-1, self.layers_channel[3], self.cell_channels[4],
cell_type="normal_down", dp=self.dropout_prob)
elif i == 5:
self.cell_5_1_0 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[0], self.cell_channels[1],
cell_type="normal_down", dp=self.dropout_prob)
self.cell_5_1_1 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[1], self.cell_channels[1],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_5_1_2 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[2], self.cell_channels[1],
cell_type="normal_up", dp=self.dropout_prob)
self.cell_5_3_0 = BuildCell(genotype,
self.layers_channel[3], self.layers_channel[2], self.cell_channels[3],
cell_type="normal_down", dp=self.dropout_prob)
self.cell_5_3_1 = BuildCell(genotype,
-1, self.layers_channel[3], self.cell_channels[3],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_5_3_2 = BuildCell(genotype,
self.layers_channel[3], self.layers_channel[4], self.cell_channels[3],
cell_type="normal_up", dp=self.dropout_prob)
elif i == 6:
self.cell_6_0_0 = BuildCell(genotype,
self.layers_channel[0], self.layers_channel[0], self.cell_channels[0],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_6_0_1 = BuildCell(genotype,
self.layers_channel[0], self.layers_channel[1], self.cell_channels[0],
cell_type="normal_up", dp=self.dropout_prob)
self.cell_6_2_0 = BuildCell(genotype,
self.layers_channel[2], self.layers_channel[1], self.cell_channels[2],
cell_type="normal_down", dp=self.dropout_prob)
self.cell_6_2_1 = BuildCell(genotype,
self.layers_channel[2], self.layers_channel[2], self.cell_channels[2],
cell_type="normal_down", dp=self.dropout_prob)
self.cell_6_2_2 = BuildCell(genotype,
self.layers_channel[2], self.layers_channel[3], self.cell_channels[2],
cell_type="normal_up", dp=self.dropout_prob)
elif i == 7:
self.cell_7_1_0 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[0], self.cell_channels[1],
cell_type="normal_down", dp=self.dropout_prob)
self.cell_7_1_1 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[1], self.cell_channels[1],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_7_1_2 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[2], self.cell_channels[1],
cell_type="normal_up", dp=self.dropout_prob)
elif i == 8:
self.cell_8_0_0 = BuildCell(genotype,
self.layers_channel[0], self.layers_channel[0], self.cell_channels[0],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_8_0_1 = BuildCell(genotype,
self.layers_channel[0], self.layers_channel[1], self.cell_channels[0],
cell_type="normal_up", dp=self.dropout_prob)
self.cell_2_0_output = ConvOps(self.layers_channel[0], num_classes, kernel_size=1, dropout_rate=0.1,
ops_order='weight')
self.cell_4_0_output = ConvOps(self.layers_channel[0], num_classes, kernel_size=1, dropout_rate=0.1,
ops_order='weight')
self.cell_6_0_output = ConvOps(self.layers_channel[0], num_classes, kernel_size=1, dropout_rate=0.1,
ops_order='weight')
self.cell_8_0_output = ConvOps(self.layers_channel[0], num_classes, kernel_size=1, dropout_rate=0.1,
ops_order='weight')
self._init_weight_parameters()
def _init_weight_parameters(self):
for name, module in self.named_modules():
if isinstance(module, torch.nn.Conv2d):
nn.init.kaiming_normal_(module.weight.data, a=0, mode='fan_in')
if isinstance(module,nn.BatchNorm2d) or isinstance(module,nn.GroupNorm):
nn.init.normal_(module.weight.data, 1.0, 0.02)
nn.init.constant_(module.bias.data, 0.0)
def forward(self, network_weight,input):
'''
:param input:
:return:
'''
_, _, h, w = input.size()
# layer 0
self.stem0_f = self.stem0(input)
self.stem1_f = self.stem1(self.stem0_f)
# layer 1
self.cell_1_1_f = self.cell_1_1(None, self.stem1_f)
# layer 2
self.cell_2_0_f = self.cell_2_0_0(None, self.stem1_f) * \
network_weight[2][0][1] / (network_weight[2][0][1] + network_weight[2][0][2]) + \
self.cell_2_0_1(self.stem1_f, self.cell_1_1_f) * \
network_weight[2][0][2] / (network_weight[2][0][1] + network_weight[2][0][2])
self.cell_2_2_f = self.cell_2_2(None, self.cell_1_1_f)
# layer 3
self.cell_3_1_f = self.cell_3_1_0(self.cell_1_1_f, self.cell_2_0_f) * \
network_weight[3][1][0] + \
self.cell_3_1_1(None, self.cell_1_1_f) * \
network_weight[3][1][1] + \
self.cell_3_1_2(self.cell_1_1_f, self.cell_2_2_f) * \
network_weight[3][1][2]
self.cell_3_3_f = self.cell_3_3(None, self.cell_2_2_f)
# layer 4
self.cell_4_0_f = self.cell_4_0_0(self.stem1_f, self.cell_2_0_f) * \
network_weight[4][0][1] / (network_weight[4][0][1] + network_weight[4][0][2]) + \
self.cell_4_0_1(self.cell_2_0_f, self.cell_3_1_f * \
network_weight[4][0][2] / (network_weight[4][0][1] + network_weight[4][0][2]))
self.cell_4_2_f = self.cell_4_2_0(self.cell_2_2_f, self.cell_3_1_f) * \
network_weight[4][2][0] + \
self.cell_4_2_1(None, self.cell_2_2_f) * \
network_weight[4][2][1] + \
self.cell_4_2_2(self.cell_2_2_f, self.cell_3_3_f) * \
network_weight[4][2][2]
self.cell_4_4_f = self.cell_4_4(None, self.cell_3_3_f)
# layer 5
self.cell_5_1_f = self.cell_5_1_0(self.cell_3_1_f, self.cell_4_0_f) * \
network_weight[5][1][0] + \
self.cell_5_1_1(self.cell_1_1_f, self.cell_3_1_f) * \
network_weight[5][1][1] + \
self.cell_5_1_2(self.cell_3_1_f, self.cell_4_2_f) * \
network_weight[5][1][2]
self.cell_5_3_f = self.cell_5_3_0(self.cell_3_3_f, self.cell_4_2_f) * \
network_weight[5][3][0] + \
self.cell_5_3_1(None, self.cell_3_3_f) * \
network_weight[5][3][1] + \
self.cell_5_3_2(self.cell_3_3_f, self.cell_4_4_f) * \
network_weight[5][3][2]
# layer 6
self.cell_6_0_f = self.cell_6_0_0(self.cell_2_0_f, self.cell_4_0_f) * \
network_weight[6][0][1] / (network_weight[6][0][1] + network_weight[6][0][2]) + \
self.cell_6_0_1(self.cell_4_0_f, self.cell_5_1_f) * \
network_weight[6][0][2] / (network_weight[6][0][1] + network_weight[6][0][2])
self.cell_6_2_f = self.cell_6_2_0(self.cell_4_2_f, self.cell_5_1_f) * \
network_weight[6][2][0] + \
self.cell_6_2_1(self.cell_2_2_f, self.cell_4_2_f) * \
network_weight[6][2][1] + \
self.cell_6_2_2(self.cell_4_2_f, self.cell_5_3_f) * \
network_weight[6][2][2]
# layer 7
self.cell_7_1_f = self.cell_7_1_0(self.cell_5_1_f, self.cell_6_0_f) * \
network_weight[7][1][0] + \
self.cell_7_1_1(self.cell_3_1_f, self.cell_5_1_f) * \
network_weight[7][1][1] + \
self.cell_7_1_2(self.cell_5_1_f, self.cell_6_2_f) * \
network_weight[7][1][2]
# layer 8
self.cell_8_0_f = self.cell_8_0_0(self.cell_4_0_f, self.cell_6_0_f) * \
network_weight[8][0][1] / (network_weight[8][0][1] + network_weight[8][0][2]) + \
self.cell_8_0_1(self.cell_6_0_f, self.cell_7_1_f) * \
network_weight[8][0][2] / (network_weight[8][0][1] + network_weight[8][0][2])
self.ouput_4_0 = self.cell_4_0_output(self.cell_4_0_f)
self.ouput_6_0 = self.cell_6_0_output(self.cell_6_0_f)
self.ouput_8_0 = self.cell_8_0_output(self.cell_8_0_f)
self.ouput_4_0 = F.interpolate(self.ouput_4_0, size=(h, w), mode='bilinear', align_corners=False)
self.ouput_6_0 = F.interpolate(self.ouput_6_0, size=(h, w), mode='bilinear', align_corners=False)
self.ouput_8_0 = F.interpolate(self.ouput_8_0, size=(h, w), mode='bilinear', align_corners=False)
return [self.ouput_4_0, self.ouput_6_0, self.ouput_8_0]
class BuildNasUnetPrune(nn.Module):
def __init__(self,genotype, input_c=3,c=16,num_classes=1, meta_node_num=4, layers=9,dp=0,
use_sharing=True,double_down_channel=True,aux=False):
super(BuildNasUnetPrune, self).__init__()
self.dropout_prob=dp
self.input_c=input_c
self.num_class=num_classes
self.meta_node_num=meta_node_num
self.layers=layers
self.use_sharing=use_sharing
self.double_down_channel=double_down_channel
self.depth=(self.layers+1)//2
self.c_prev_prev=32
self.c_prev=64
# 3-->32
self.stem0 = ConvOps(input_c,self.c_prev_prev , kernel_size=3,stride=1,ops_order='weight_norm_act')
# 32-->64
self.stem1 = ConvOps(self.c_prev_prev,self.c_prev , kernel_size=3, stride=2, ops_order='weight_norm_act')
init_channel = c
if self.double_down_channel:
self.layers_channel = [self.meta_node_num * init_channel * pow(2, i) for i in range(0, self.depth)]
self.cell_channels = [init_channel * pow(2, i) for i in range(0, self.depth)]
else:
self.layers_channel = [self.meta_node_num * init_channel for i in range(0, self.depth)]
self.cell_channels = [init_channel for i in range(0, self.depth)]
for i in range(1, self.layers):
if i == 1:
self.cell_1_1 = BuildCell(genotype,
-1, self.c_prev, self.cell_channels[1],
cell_type="normal_down", dp=self.dropout_prob)
elif i == 2:
self.cell_2_0_0 = BuildCell(genotype,
-1, self.c_prev, self.cell_channels[0],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_2_0_1 = BuildCell(genotype,
self.c_prev, self.layers_channel[1], self.cell_channels[0],
cell_type="normal_up", dp=self.dropout_prob)
self.cell_2_2 = BuildCell(genotype,
-1, self.layers_channel[1], self.cell_channels[2],
cell_type="normal_down", dp=self.dropout_prob)
elif i == 3:
self.cell_3_1_0 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[0], self.cell_channels[1],
cell_type="normal_down", dp=self.dropout_prob)
self.cell_3_1_1 = BuildCell(genotype,
-1, self.layers_channel[1], self.cell_channels[1],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_3_1_2 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[2], self.cell_channels[1],
cell_type="normal_up", dp=self.dropout_prob)
self.cell_3_3 = BuildCell(genotype,
-1, self.layers_channel[2], self.cell_channels[3],
cell_type="normal_down", dp=self.dropout_prob)
elif i == 4:
self.cell_4_0_0 = BuildCell(genotype,
self.layers_channel[0], self.layers_channel[0], self.cell_channels[0],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_4_0_1 = BuildCell(genotype,
self.layers_channel[0], self.layers_channel[1], self.cell_channels[0],
cell_type="normal_up", dp=self.dropout_prob)
self.cell_4_2_0 = BuildCell(genotype,
self.layers_channel[2], self.layers_channel[1], self.cell_channels[2],
cell_type="normal_down", dp=self.dropout_prob)
self.cell_4_2_1 = BuildCell(genotype,
-1, self.layers_channel[2], self.cell_channels[2],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_4_2_2 = BuildCell(genotype,
self.layers_channel[2], self.layers_channel[3], self.cell_channels[2],
cell_type="normal_up", dp=self.dropout_prob)
self.cell_4_4 = BuildCell(genotype,
-1, self.layers_channel[3], self.cell_channels[4],
cell_type="normal_down", dp=self.dropout_prob)
elif i == 5:
self.cell_5_1_0 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[0], self.cell_channels[1],
cell_type="normal_down", dp=self.dropout_prob)
self.cell_5_1_1 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[1], self.cell_channels[1],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_5_1_2 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[2], self.cell_channels[1],
cell_type="normal_up", dp=self.dropout_prob)
self.cell_5_3_0 = BuildCell(genotype,
self.layers_channel[3], self.layers_channel[2], self.cell_channels[3],
cell_type="normal_down", dp=self.dropout_prob)
self.cell_5_3_1 = BuildCell(genotype,
-1, self.layers_channel[3], self.cell_channels[3],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_5_3_2 = BuildCell(genotype,
self.layers_channel[3], self.layers_channel[4], self.cell_channels[3],
cell_type="normal_up", dp=self.dropout_prob)
elif i == 6:
self.cell_6_0_0 = BuildCell(genotype,
self.layers_channel[0], self.layers_channel[0], self.cell_channels[0],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_6_0_1 = BuildCell(genotype,
self.layers_channel[0], self.layers_channel[1], self.cell_channels[0],
cell_type="normal_up", dp=self.dropout_prob)
self.cell_6_2_0 = BuildCell(genotype,
self.layers_channel[2], self.layers_channel[1], self.cell_channels[2],
cell_type="normal_down", dp=self.dropout_prob)
self.cell_6_2_1 = BuildCell(genotype,
self.layers_channel[2], self.layers_channel[2], self.cell_channels[2],
cell_type="normal_down", dp=self.dropout_prob)
self.cell_6_2_2 = BuildCell(genotype,
self.layers_channel[2], self.layers_channel[3], self.cell_channels[2],
cell_type="normal_up", dp=self.dropout_prob)
elif i == 7:
self.cell_7_1_0 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[0], self.cell_channels[1],
cell_type="normal_down", dp=self.dropout_prob)
self.cell_7_1_1 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[1], self.cell_channels[1],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_7_1_2 = BuildCell(genotype,
self.layers_channel[1], self.layers_channel[2], self.cell_channels[1],
cell_type="normal_up", dp=self.dropout_prob)
elif i == 8:
self.cell_8_0_0 = BuildCell(genotype,
self.layers_channel[0], self.layers_channel[0], self.cell_channels[0],
cell_type="normal_normal", dp=self.dropout_prob)
self.cell_8_0_1 = BuildCell(genotype,
self.layers_channel[0], self.layers_channel[1], self.cell_channels[0],
cell_type="normal_up", dp=self.dropout_prob)
self.cell_2_0_output = ConvOps(self.layers_channel[0], num_classes, kernel_size=1, dropout_rate=0.1,
ops_order='weight')
self.cell_4_0_output = ConvOps(self.layers_channel[0], num_classes, kernel_size=1, dropout_rate=0.1,
ops_order='weight')
self.cell_6_0_output = ConvOps(self.layers_channel[0], num_classes, kernel_size=1, dropout_rate=0.1,
ops_order='weight')
self.cell_8_0_output = ConvOps(self.layers_channel[0], num_classes, kernel_size=1, dropout_rate=0.1,
ops_order='weight')
self._init_weight_parameters()
def _init_weight_parameters(self):
for name, module in self.named_modules():
if isinstance(module, torch.nn.Conv2d):
nn.init.kaiming_normal_(module.weight.data, a=0, mode='fan_in')
if isinstance(module,nn.BatchNorm2d) or isinstance(module,nn.GroupNorm):
nn.init.normal_(module.weight.data, 1.0, 0.02)
nn.init.constant_(module.bias.data, 0.0)
def forward(self, input):
'''
:param input:
:return:
'''
_, _, h, w = input.size()
# layer 0
self.stem0_f = self.stem0(input)
self.stem1_f = self.stem1(self.stem0_f)
# layer 1
self.cell_1_1_f = self.cell_1_1(None, self.stem1_f)
# layer 2
self.cell_2_0_f = self.cell_2_0_0(None, self.stem1_f) + \
self.cell_2_0_1(self.stem1_f, self.cell_1_1_f)
self.cell_2_2_f = self.cell_2_2(None, self.cell_1_1_f)
# layer 3
self.cell_3_1_f = self.cell_3_1_0(self.cell_1_1_f, self.cell_2_0_f) + \
self.cell_3_1_1(None, self.cell_1_1_f) + \
self.cell_3_1_2(self.cell_1_1_f, self.cell_2_2_f)
self.cell_3_3_f = self.cell_3_3(None, self.cell_2_2_f)
# layer 4
self.cell_4_0_f = self.cell_4_0_0(self.stem1_f, self.cell_2_0_f) + \
self.cell_4_0_1(self.cell_2_0_f, self.cell_3_1_f)
self.cell_4_2_f = self.cell_4_2_0(self.cell_2_2_f, self.cell_3_1_f) + \
self.cell_4_2_1(None, self.cell_2_2_f) + \
self.cell_4_2_2(self.cell_2_2_f, self.cell_3_3_f)
self.cell_4_4_f = self.cell_4_4(None, self.cell_3_3_f)
# layer 5
self.cell_5_1_f = self.cell_5_1_0(self.cell_3_1_f, self.cell_4_0_f) + \
self.cell_5_1_1(self.cell_1_1_f, self.cell_3_1_f) + \
self.cell_5_1_2(self.cell_3_1_f, self.cell_4_2_f)
self.cell_5_3_f = self.cell_5_3_0(self.cell_3_3_f, self.cell_4_2_f) + \
self.cell_5_3_1(None, self.cell_3_3_f) + \
self.cell_5_3_2(self.cell_3_3_f, self.cell_4_4_f)
# layer 6
self.cell_6_0_f = self.cell_6_0_0(self.cell_2_0_f, self.cell_4_0_f)+ \
self.cell_6_0_1(self.cell_4_0_f, self.cell_5_1_f)
self.cell_6_2_f = self.cell_6_2_0(self.cell_4_2_f, self.cell_5_1_f)+ \
self.cell_6_2_1(self.cell_2_2_f, self.cell_4_2_f) + \
self.cell_6_2_2(self.cell_4_2_f, self.cell_5_3_f)
# layer 7
self.cell_7_1_f = self.cell_7_1_0(self.cell_5_1_f, self.cell_6_0_f) + \
self.cell_7_1_1(self.cell_3_1_f, self.cell_5_1_f) + \
self.cell_7_1_2(self.cell_5_1_f, self.cell_6_2_f)
# layer 8
self.cell_8_0_f = self.cell_8_0_0(self.cell_4_0_f, self.cell_6_0_f) + \
self.cell_8_0_1(self.cell_6_0_f, self.cell_7_1_f)
self.ouput_4_0 = self.cell_4_0_output(self.cell_4_0_f)
self.ouput_6_0 = self.cell_6_0_output(self.cell_6_0_f)
self.ouput_8_0 = self.cell_8_0_output(self.cell_8_0_f)
self.ouput_4_0 = F.interpolate(self.ouput_4_0, size=(h, w), mode='bilinear', align_corners=False)
self.ouput_6_0 = F.interpolate(self.ouput_6_0, size=(h, w), mode='bilinear', align_corners=False)
self.ouput_8_0 = F.interpolate(self.ouput_8_0, size=(h, w), mode='bilinear', align_corners=False)
return [self.ouput_4_0, self.ouput_6_0, self.ouput_8_0]
if __name__=="__main__":
from genotypes import *
layer7_doublechannel = Genotype(
normal_down=[('down_dil_conv', 1), ('identity', 0), ('conv', 0), ('down_dil_conv', 1), ('shuffle_conv', 0),
('down_conv', 1), ('shuffle_conv', 4), ('down_conv', 1)], normal_down_concat=range(2, 6),
normal_up=[('up_dil_conv', 1), ('identity', 0), ('up_dep_conv', 1), ('conv', 0), ('shuffle_conv', 0),
('up_dil_conv', 1), ('shuffle_conv', 4), ('up_dep_conv', 1)], normal_up_concat=range(2, 6),
normal_normal=[('identity', 1), ('identity', 0), ('conv', 0), ('identity', 1), ('shuffle_conv', 0),
('dep_conv', 2), ('shuffle_conv', 4), ('identity', 0)], normal_normal_concat=range(2, 6))
cell=BuildCell(layer7_doublechannel,c_prev_prev=-1, c_prev=256, c=32,cell_type='normal_up',dp=0)
# The final output size for normal up may not be a multiple if normal does not exist
s0=torch.FloatTensor(torch.ones(1, 128, 16, 16))
s1=torch.FloatTensor(torch.ones(1, 256, 8, 8))
output=cell(None,s1)
print(output.size())
x = torch.FloatTensor(torch.ones(1, 3, 128, 128))
network=BuildNasUnet(layer7_doublechannel,input_c=3,c=16,num_classes=1, meta_node_num=4, layers=9,dp=0,
use_sharing=True,double_down_channel=True)
print(network)
output=network(x)
for o in output:
print(o.size())
for name,parameter in network.named_parameters():
print(name,parameter.size())
#
# x = torch.FloatTensor(torch.ones(1, 3, 128, 128))
# network=BuildNasUnetPrune(layer7_doublechannel,input_c=3,c=16,num_classes=1, meta_node_num=4, layers=9,dp=0,
# use_sharing=True,double_down_channel=True)
# print(network)
# output=network(x)
# for o in output:
# print(o.size())
#
# for name,parameter in network.named_parameters():
# print(name,parameter.size())