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fsanet.py
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fsanet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from capsule import CapsuleLayer
class SSRLayer(nn.Module):
def __init__(self,):
super(SSRLayer,self).__init__()
def forward(self,x):
a = x[0][:, :, 0] * 0
b = x[0][:, :, 0] * 0
c = x[0][:, :, 0] * 0
s1 = 3
s2 = 3
s3 = 3
lambda_d = 1
di = s1 // 2
dj = s2 // 2
dk = s3 // 2
V = 99
for i in range(0, s1):
a = a + (i - di + x[6]) * x[0][:, :, i]
a = a / (s1 * (1 + lambda_d * x[3]))
for j in range(0, s2):
b = b + (j - dj + x[7]) * x[1][:, :, j]
b = b / (s1 * (1 + lambda_d * x[3])) / (s2 * (1 + lambda_d * x[4]))
for k in range(0, s3):
c = c + (k - dk + x[8]) * x[2][:, :, k]
c = c / (s1 * (1 + lambda_d * x[3])) / (s2 * (1 + lambda_d * x[4])) / (
s3 * (1 + lambda_d * x[5]))
pred = (a + b + c) * V
return pred
def MatrixNorm(x): #(-1,c/3,8*8*3)
# (-1,c/3,64)
return torch.sum(x,dim = -1,keepdims = True).repeat(1,1,64)
def PrimCaps(x):
x1,x2,norm = x
return (x1@x2)/norm
def AggregatedFeatureExtraction(x,num_capsule = 3): # (-1,3,16)
s1_a = 0
s1_b = num_capsule//3
feat_s1_div = x[:,s1_a:s1_b,:] # x[:,0:1,:] (-1,1,16)
s2_a = num_capsule//3
s2_b = 2*num_capsule//3
feat_s2_div = x[:,s2_a:s2_b,:]
s3_a = 2*num_capsule//3
s3_b = num_capsule
feat_s3_div = x[:,s3_a:s3_b,:]
return feat_s1_div, feat_s2_div, feat_s3_div
class FSANet(nn.Module):
def __init__(self, S_set):
super(FSANet,self).__init__()
self.ssr_layer = SSRLayer()
self.num_capsule = S_set[0] # 3
self.dim_capsule = S_set[1] # 16
self.routings = S_set[2] # 2
self.num_primcaps = S_set[3] # 8*8*3
self.m_dim = S_set[4] # 5
# (-1,c,64) @ (-1,64,16) -> (-1,c,16)
#self.w1 = nn.Parameter(torch.randn(1,64,16))
# (-1,16,c) @ (-1,c,3) -> (-1,16,3)
#self.w2 = nn.Parameter(torch.randn(1,self.num_primcaps,3))
self.capsule_layer = CapsuleLayer(in_units=self.num_primcaps,in_channels=64,num_units=self.num_capsule,unit_size=self.dim_capsule)
self.x_layer1 = nn.Sequential(
nn.Conv2d(3,16,3,1,1),
nn.BatchNorm2d(16),
nn.ReLU(inplace = True),
nn.AvgPool2d(2)
)
self.x_layer2 = nn.Sequential(
nn.Conv2d(16,32,3,1,1),
nn.BatchNorm2d(32),
nn.ReLU(inplace = True),
nn.Conv2d(32,32,3,1,1),
nn.BatchNorm2d(32),
nn.ReLU(inplace = True),
nn.AvgPool2d(2)
)
self.x_layer3 = nn.Sequential(
nn.Conv2d(32,64,3,1,1),
nn.BatchNorm2d(64),
nn.ReLU(inplace = True),
nn.Conv2d(64,64,3,1,1),
nn.BatchNorm2d(64),
nn.ReLU(inplace = True),
nn.AvgPool2d(2)
)
self.x_layer4 = nn.Sequential(
nn.Conv2d(64,128,3,1,1),
nn.BatchNorm2d(128),
nn.ReLU(inplace = True),
nn.Conv2d(128,128,3,1,1),
nn.BatchNorm2d(128),
nn.ReLU(inplace = True),
)
self.s_layer1 = nn.Sequential(
nn.Conv2d(3,16,3,1,1),
nn.BatchNorm2d(16),
nn.Tanh(),
nn.MaxPool2d(2)
)
self.s_layer2 = nn.Sequential(
nn.Conv2d(16,32,3,1,1),
nn.BatchNorm2d(32),
nn.Tanh(),
nn.Conv2d(32,32,3,1,1),
nn.BatchNorm2d(32),
nn.Tanh(),
nn.MaxPool2d(2)
)
self.s_layer3 = nn.Sequential(
nn.Conv2d(32,64,3,1,1),
nn.BatchNorm2d(64),
nn.Tanh(),
nn.Conv2d(64,64,3,1,1),
nn.BatchNorm2d(64),
nn.Tanh(),
nn.MaxPool2d(2)
)
self.s_layer4 = nn.Sequential(
nn.Conv2d(64,128,3,1,1),
nn.BatchNorm2d(128),
nn.Tanh(),
nn.Conv2d(128,128,3,1,1),
nn.BatchNorm2d(128),
nn.Tanh(),
)
self.x_layer4_ = nn.Sequential(
nn.Conv2d(128,64,1,1,0),
nn.ReLU(inplace = True)
)
self.x_layer3_ = nn.Sequential(
nn.Conv2d(64,64,1,1,0),
nn.ReLU(inplace = True)
)
self.x_layer2_ = nn.Sequential(
nn.Conv2d(32,64,1,1,0),
nn.ReLU(inplace = True)
)
self.s_layer4_ = nn.Sequential(
nn.Conv2d(128,64,1,1,0),
nn.Tanh()
)
self.s_layer3_ = nn.Sequential(
nn.Conv2d(64,64,1,1,0),
nn.Tanh()
)
self.s_layer2_ = nn.Sequential(
nn.Conv2d(32,64,1,1,0),
nn.Tanh()
)
self.agvpool = nn.AvgPool2d(2)
self.feat_preS1 = nn.Sequential(
nn.Conv2d(64,1,1,1,0),
nn.Sigmoid()
)
self.feat_preS2 = nn.Sequential(
nn.Conv2d(64,1,1,1,0),
nn.Sigmoid()
)
self.feat_preS3 = nn.Sequential(
nn.Conv2d(64,1,1,1,0),
nn.Sigmoid()
)
self.sr_matrix1 = nn.Sequential(
nn.Linear(8*8,self.m_dim * 8*8*3),
nn.Sigmoid()
)
self.sr_matrix2 = nn.Sequential(
nn.Linear(8*8,self.m_dim * 8*8*3),
nn.Sigmoid()
)
self.sr_matrix3 = nn.Sequential(
nn.Linear(8*8,self.m_dim * 8*8*3),
nn.Sigmoid()
)
self.SL_matrix = nn.Sequential(
nn.Linear(8*8*3,int(self.num_primcaps/3)*self.m_dim),
nn.Sigmoid()
)
self.delta_s1 = nn.Sequential(
nn.Linear(4,3),
nn.Tanh()
)
self.delta_s2 = nn.Sequential(
nn.Linear(4,3),
nn.Tanh()
)
self.delta_s3 = nn.Sequential(
nn.Linear(4,3),
nn.Tanh()
)
self.local_s1 = nn.Sequential(
nn.Linear(4,3),
nn.Tanh()
)
self.local_s2 = nn.Sequential(
nn.Linear(4,3),
nn.Tanh()
)
self.local_s3 = nn.Sequential(
nn.Linear(4,3),
nn.Tanh()
)
self.pred_s1 = nn.Sequential(
nn.Linear(8,9),
nn.ReLU(inplace = True)
)
self.pred_s2 = nn.Sequential(
nn.Linear(8,9),
nn.ReLU(inplace = True)
)
self.pred_s3 = nn.Sequential(
nn.Linear(8,9),
nn.ReLU(inplace = True)
)
def forward(self,x):
x_layer1 = self.x_layer1(x) # 32
x_layer2 = self.x_layer2(x_layer1) # 16
x_layer3 = self.x_layer3(x_layer2) # 8
x_layer4 = self.x_layer4(x_layer3) # 8
s_layer1 = self.s_layer1(x)
s_layer2 = self.s_layer2(s_layer1)
s_layer3 = self.s_layer3(s_layer2)
s_layer4 = self.s_layer4(s_layer3)
s_layer4_ = self.s_layer4_(s_layer4)
x_layer4_ = self.x_layer4_(x_layer4)
s_layer3_ = self.s_layer3_(s_layer3)
x_layer3_ = self.x_layer3_(x_layer3)
s_layer2_ = self.s_layer2_(s_layer2)
x_layer2_ = self.x_layer2_(x_layer2)
feat_s1_pre = s_layer4_ * x_layer4_ # (-1,64,8,8)
feat_s2_pre = s_layer3_ * x_layer3_ # (-1,64,8,8)
feat_s3_pre = s_layer2_ * x_layer2_
feat_s3_pre = self.agvpool(feat_s3_pre) #(-1,64,8,8)
feat_preS1 = self.feat_preS1(feat_s1_pre).view(-1,8*8)
feat_preS2 = self.feat_preS2(feat_s2_pre).view(-1,8*8)
feat_preS3 = self.feat_preS3(feat_s3_pre).view(-1,8*8)
sr_matrix1 = self.sr_matrix1(feat_preS1).view(-1,5,8*8*3)
sr_matrix2 = self.sr_matrix2(feat_preS2).view(-1,5,8*8*3)
sr_matrix3 = self.sr_matrix3(feat_preS3).view(-1,5,8*8*3)
feat_pre_concat = torch.cat([feat_preS1,feat_preS2,feat_preS3],dim = 1) # (-1,8*8*3)
SL_matrix = self.SL_matrix(feat_pre_concat).view(-1,int(self.num_primcaps/3),int(self.m_dim)) # (-1,c/3,5)
S_matrix_s1 = SL_matrix @ sr_matrix1 # (-1,c/3,8*8*3)
S_matrix_s2 = SL_matrix @ sr_matrix2
S_matrix_s3 = SL_matrix @ sr_matrix3
norm_S_s1 = MatrixNorm(S_matrix_s1)
norm_S_s2 = MatrixNorm(S_matrix_s2)
norm_S_s3 = MatrixNorm(S_matrix_s3)
feat_s1_pre = feat_s1_pre.view(-1,8*8,64).transpose(1,2).contiguous()
feat_s2_pre = feat_s2_pre.view(-1,8*8,64).transpose(1,2).contiguous()
feat_s3_pre = feat_s3_pre.view(-1,8*8,64).transpose(1,2).contiguous()
feat_pre_concat = torch.cat([feat_s1_pre,feat_s2_pre,feat_s3_pre],dim = 1)
primcaps_s1 = PrimCaps([S_matrix_s1,feat_pre_concat, norm_S_s1]) # (-1,c/3,8*8*3) @ (-1,64*3,64) -> (-1,c/3,64)
primcaps_s2 = PrimCaps([S_matrix_s2,feat_pre_concat, norm_S_s2])
primcaps_s3 = PrimCaps([S_matrix_s3,feat_pre_concat, norm_S_s3])
primcaps = torch.cat([primcaps_s1,primcaps_s2,primcaps_s3],dim = 1) # (-1,c,64)
#metric_feat = primcaps @ self.w1
#metric_feat = (metric_feat.permute(0,2,1) @ self.w2).permute(0,2,1) #(-1,3,16)
capsule = self.capsule_layer(primcaps)
feat_s1_div, feat_s2_div, feat_s3_div = AggregatedFeatureExtraction(capsule) # (-1,1,16)
feat_s1_div = feat_s1_div.view(-1,16)
feat_s2_div = feat_s2_div.view(-1,16)
feat_s3_div = feat_s3_div.view(-1,16)
feat_delta_s1,feat_local_s1,feat_pred_s1 = feat_s1_div[:,0:4],feat_s1_div[:,4:8],feat_s1_div[:,8:16]
feat_delta_s2,feat_local_s2,feat_pred_s2 = feat_s2_div[:,0:4],feat_s2_div[:,4:8],feat_s2_div[:,8:16]
feat_delta_s3,feat_local_s3,feat_pred_s3 = feat_s3_div[:,0:4],feat_s3_div[:,4:8],feat_s3_div[:,8:16]
delta_s1 = self.delta_s1(feat_delta_s1)
delta_s2 = self.delta_s2(feat_delta_s2)
delta_s3 = self.delta_s3(feat_delta_s3)
local_s1 = self.local_s1(feat_local_s1)
local_s2 = self.local_s2(feat_local_s2)
local_s3 = self.local_s3(feat_local_s3)
pred_s1 = self.pred_s1(feat_pred_s1).view(-1,3,3)
pred_s2 = self.pred_s2(feat_pred_s2).view(-1,3,3)
pred_s3 = self.pred_s3(feat_pred_s3).view(-1,3,3)
return self.ssr_layer([pred_s1,pred_s2,pred_s3,delta_s1,delta_s2,delta_s3,local_s1,local_s2,local_s3])