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import torch
import torch.nn as nn
from torch.nn.init import kaiming_normal_, constant_
from .model_util import *
from train_util import *
# define the function includes in import *
__all__ = [
'SpixelNet1l','SpixelNet1l_bn'
]
class SpixelNet(nn.Module):
expansion = 1
def __init__(self,dataset='', batchNorm=True, train=False):
super(SpixelNet,self).__init__()
if dataset == 'ISIC_2017' or dataset=='BDS500':
input_chs = 3
class_num = 50
elif dataset == 'BraTS2017':
input_chs = 4
elif dataset == 'ACDC' or dataset == 'TCIA':
input_chs = 1
self.train = train
self.class_num = class_num
#input_chs = 4
self.batchNorm = batchNorm
self.assign_ch = 9
self.conv0a = conv(self.batchNorm, input_chs, 16, kernel_size=3)
self.conv0b = conv(self.batchNorm, 16, 16, kernel_size=3)
self.conv1a = conv(self.batchNorm, 16, 32, kernel_size=3, stride=2)
self.conv1b = conv(self.batchNorm, 32, 32, kernel_size=3)
self.conv2a = conv(self.batchNorm, 32, 64, kernel_size=3, stride=2)
self.conv2b = conv(self.batchNorm, 64, 64, kernel_size=3)
self.conv3a = conv(self.batchNorm, 64, 128, kernel_size=3, stride=2)
self.conv3b = conv(self.batchNorm, 128, 128, kernel_size=3)
self.conv4a = conv(self.batchNorm, 128, 256, kernel_size=3, stride=2)
self.conv4b = conv(self.batchNorm, 256, 256, kernel_size=3)
self.deconv3 = deconv(256, 128)
self.conv3_1 = conv(self.batchNorm, 256, 128)
self.pred_mask3 = predict_mask(128, self.assign_ch)
self.deconv2 = deconv(128, 64)
self.conv2_1 = conv(self.batchNorm, 128, 64)
self.pred_mask2 = predict_mask(64, self.assign_ch)
self.deconv1 = deconv(64, 32)
self.conv1_1 = conv(self.batchNorm, 64, 32)
self.pred_mask1 = predict_mask(32, self.assign_ch)
self.deconv0 = deconv(32, 16)
self.conv0_1 = conv(self.batchNorm, 32 , 16)
self.pred_mask0 = predict_mask(16,self.assign_ch)
self.softmax = nn.Softmax(1)
self.patch_local = nn.Sequential(
nn.Conv2d(32, self.class_num, 3, padding=1),
nn.BatchNorm2d(self.class_num),
nn.ReLU(inplace=True),
nn.Conv2d(self.class_num, self.class_num, 1),
nn.Softmax(1)
)
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
kaiming_normal_(m.weight, 0.1)
if m.bias is not None:
constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
constant_(m.weight, 1)
constant_(m.bias, 0)
def forward(self, x, patch_posi=None, patch_label=None):
out1 = self.conv0b(self.conv0a(x)) #5*5
out2 = self.conv1b(self.conv1a(out1)) #11*11
out3 = self.conv2b(self.conv2a(out2)) #23*23
out4 = self.conv3b(self.conv3a(out3)) #47*47
out5 = self.conv4b(self.conv4a(out4)) #95*95
out_deconv3 = self.deconv3(out5)
concat3 = torch.cat((out4, out_deconv3), 1)
out_conv3_1 = self.conv3_1(concat3)
out_deconv2 = self.deconv2(out_conv3_1)
concat2 = torch.cat((out3, out_deconv2), 1)
out_conv2_1 = self.conv2_1(concat2)
out_deconv1 = self.deconv1(out_conv2_1)
concat1 = torch.cat((out2, out_deconv1), 1)
out_conv1_1 = self.conv1_1(concat1)
out_deconv0 = self.deconv0(out_conv1_1)
concat0 = torch.cat((out1, out_deconv0), 1)
out_conv0_1 = self.conv0_1(concat0)
mask0 = self.pred_mask0(out_conv0_1)
prob0 = self.softmax(mask0)
if not self.train:
return prob0
else:
def self_attention(self, feat):
#conduct self attention
#feat bs x c x h x w
bs,c,h,w = feat.shape
feat_reshape = torch.reshape(feat, (bs, c, -1))
feat_TransReshape = feat_reshape.permute(0,2,1)
attn_w = torch.bmm(feat_TransReshape, feat_reshape)
attn_w = F.softmax(attn_w, dim=-1)
attn_out = torch.bmm(attn_w, feat_TransReshape)
return attn_out
def forward_patches(self, feat_map, patch_posi, patch_label):
bs, c, h, w = feat_map.shape
device = feat_map.device
patch_loss = torch.tensor([0.]).to(device)
count = 0
for i in range(bs):
label = patch_label[i].to(device)
patches = patch_posi[i]
patch_num = patches.shape[0]
for k in range(patch_num):
patch = patches[k]
patch_label = label[k]
patch_label = patch_label[None, None, :,:]
patch_label_1hot = label2one_hot_torch(patch_label, C=50)
feat_patch = torch.narrow(feat_map, dim=2, patch[0], patch[1])
feat_patch = torch.narrow(feat_patch, dim=3, patch[2], patch[3])
feat_out = self.self_attention(feat_patch)
patch_prob = self.patch_conv(feat_out)
logits = torch.log(patch_prob + 1e-8)
patch_loss += torch.sum(logits * patch_label_1hot)
count +=1
return patch_loss / count
def weight_parameters(self):
return [param for name, param in self.named_parameters() if 'weight' in name]
def bias_parameters(self):
return [param for name, param in self.named_parameters() if 'bias' in name]
def SpixelNet1l( data=None):
# Model without batch normalization
model = SpixelNet(batchNorm=False)
if data is not None:
model.load_state_dict(data['state_dict'])
return model
def SpixelNet1l_bn(dataset=None,data=None, train=False):
# model with batch normalization
model = SpixelNet(dataset=dataset,batchNorm=True, train=train)
if data is not None:
model.load_state_dict(data['state_dict'])
return model
#