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""" Full assembly of the parts to form the complete network """
import os, sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
# import ../db.py
from tag.tag import Stage
from .unet_parts import *
from torch import nn
import torch
from .res_net import resnet34, resnet18, resnet50, resnet101, resnet152, BasicBlock, Bottleneck, ResNet
import torch.nn.functional as F
from torch.autograd import Variable
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
import math
class SaveFeatures():
features = None
# def __init__(self, m): self.hook = m.register_forward_hook(self.hook_fn)
# def hook_fn(self, module, input, output): self.features = output
# def remove(self): self.hook.remove()
def __init__(self,m):
self._outputs_lists = {}
self.mymodule = m
m.register_forward_hook(hook=self.save_output_hook)
def save_output_hook(self, _, input, output):
self._outputs_lists[input[0].device.index] = output
self.features = self._outputs_lists
def forward(self, x) -> list:
self._outputs_lists[x.device.index] = []
self.mymodule(x)
return self._outputs_lists[x.device.index]
class UnetStageBlock(nn.Module):
def __init__(self, stage, up_in, x_in, n_out, ratio):
super().__init__()
# super(UnetBlock, self).__init__()
up_out = x_out = n_out // 2
self.x_conv = nn.Conv2d(x_in, x_out, 1)
self.g_fc = nn.Linear(7,x_out * 2)
self.tr_conv = nn.ConvTranspose2d(up_in, up_out, 2, stride=2)
self.stage = stage
self.bn = nn.BatchNorm2d(n_out)
self.pointwise = nn.Conv2d(14, n_out, kernel_size=1)
self.depthwise = nn.Conv2d(n_out, n_out, kernel_size=3, stride=ratio , padding=1, groups=up_out)
def forward(self, up_p, x_p, give):
up_p = self.tr_conv(up_p)
x_p = self.x_conv(x_p)
cat_p = torch.cat([up_p, x_p], dim=1)
res = self.bn(F.relu(cat_p))
# g_p = self.g_fc(give).unsqueeze(-1).unsqueeze(-1) * res
g_p = self.depthwise(self.pointwise(give))
res = self.stage(g_p,res)
return res
class UnetBlock(nn.Module):
def __init__(self, up_in, x_in, n_out):
super().__init__()
# super(UnetBlock, self).__init__()
up_out = x_out = n_out // 2
self.x_conv = nn.Conv2d(x_in, x_out, 1)
self.tr_conv = nn.ConvTranspose2d(up_in, up_out, 2, stride=2)
self.bn = nn.BatchNorm2d(n_out)
def forward(self, up_p, x_p):
up_p = self.tr_conv(up_p)
x_p = self.x_conv(x_p)
cat_p = torch.cat([up_p, x_p], dim=1)
res = self.bn(F.relu(cat_p))
return res
class TransUNet(nn.Module):
def __init__(self, args, resnet='resnet34', num_classes=2, pretrained=False,
in_chans=3,
inplanes=64,
num_layers=(3, 4, 6, 3),
num_chs=(256, 512, 1024, 2048),
num_strides=(1, 2, 2, 2),
num_heads=(1, 1, 1, 1),
num_parts=(1, 1, 1, 1),
patch_sizes=(1, 1, 1, 1),
drop_path=0.1,
num_enc_heads=(1, 1, 1, 1),
act=nn.GELU,
ffn_exp=3,
has_last_encoder=False
):
super().__init__()
# super(ResUnet, self).__init__()
''' ~~~~~ For the embedding transformer~~~~~'''
cut, lr_cut = [8, 6]
dim = args.dim #dim of transformer sequence, D of E
img_size = 8 #emebeding 8*8*512
channels = 512
patch_size = args.patch_size
depth = args.depth
heads = args.heads
mlp_dim = args.mlp_dim
dim_head = 64
assert img_size % patch_size == 0 , 'Image dimensions must be divisible by the patch size.'
num_patches = (img_size // patch_size) ** 2
patch_dim = channels * patch_size * patch_size
self.to_patch_embedding = nn.Sequential(
Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
nn.Linear(patch_dim, dim),
)
self.mlp_head = nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, dim)
)
'''~~~~~~End of embedding transformer~~~~~'''
'unet and goinnet parameters'
if resnet == 'resnet34':
base_model = resnet34
elif resnet == 'resnet18':
base_model = resnet18
elif resnet == 'resnet50':
base_model = resnet50
elif resnet == 'resnet101':
base_model = resnet101
elif resnet == 'resnet152':
base_model = resnet152
else:
raise Exception('The Resnet Model only accept resnet18, resnet34, resnet50,'
'resnet101 and resnet152')
'''define the stage for goinnet giving'''
last_chs = (256,256,256,256)
num_chs = (256, 256, 256, 256)
down_samples = (2,4,8,16)
n_l = 1
stage_list = []
for i in range(4):
stage_list.append(
Stage(last_chs[i],
num_chs[i],
n_l,
num_heads=num_heads[i], #1,2,4,8
num_parts = (patch_sizes[i]**2 * (args.image_size // down_samples[i] // patch_sizes[i])**2),
patch_size=patch_sizes[i], #8,8,8,8
drop_path=drop_path, #0.05
ffn_exp=ffn_exp, #mlp hidden fea
last_enc=has_last_encoder and i == len(num_layers) - 1)
)
self.stages = nn.ModuleList(stage_list)
patch_s = 8
separator_list = []
for i in range(7):
separator_list.append(
Stage(256,
2,
n_l,
num_heads=1, #1,2,4,8
num_parts = (patch_s**2 * (args.image_size // 2 // patch_s)**2),
patch_size=patch_s, #8,8,8,8
drop_path=drop_path, #0.05
ffn_exp=ffn_exp #mlp hidden fea
))
self.s_list = nn.ModuleList(separator_list)
# self.stage_encoding = Stage(512,
# 512,
# 1,
# num_heads=16, #1,2,4,8
# num_parts = (8**2),
# patch_size=8, #8,8,8,8
# drop_path=drop_path, #0.05
# ffn_exp=ffn_exp, #mlp hidden fea
# last_enc=has_last_encoder and i == len(num_layers) - 1)
'''end'''
layers = list(base_model(pretrained=pretrained).children())[:cut]
self.check_layer = layers
base_layers = nn.Sequential(*layers)
self.rn = base_layers
self.num_classes = num_classes
self.sfs = [SaveFeatures(base_layers[i]) for i in [2, 4, 5, 6]]
self.up1 = UnetStageBlock(self.stages[3], 512, 256, 256,16)
self.up2 = UnetStageBlock(self.stages[2], 256, 128, 256,8)
self.up3 = UnetStageBlock(self.stages[1], 256, 64, 256,4)
self.up4 = UnetStageBlock(self.stages[0], 256, 64, 256,2)
self.up5 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred1 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred2 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred3 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred4 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred5 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred6 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred7 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.munet = MUNet(args)
'''~~~ self definition ~~~'''
self.fc = nn.Linear(7,dim)
self.tr_conv = nn.ConvTranspose2d(512, 512, 2, stride=2)
def forward(self, x, cond, mod = 'train'):
aux = {}
mergf = []
img = x
# x = torch.cat((x,heatmap),1)
x = F.relu(self.rn(x)) # x = [b_size, 2048, 8, 8]
emb = x
if mod == 'shuffle':
self.up1.eval()
self.up2.eval()
self.up3.eval()
self.up4.eval()
self.up5.eval()
'''~~~ 0: agg ~~~'''
x = self.up1(x, self.sfs[3].features[x.device.index], cond)
mergf.append(x)
x = self.up2(x, self.sfs[2].features[x.device.index], cond)
mergf.append(x)
x = self.up3(x, self.sfs[1].features[x.device.index], cond)
mergf.append(x)
x = self.up4(x, self.sfs[0].features[x.device.index], cond)
fea = x
output = self.up5(x)
'''end'''
if mod == 'shuffle':
aux['mergfs'] = mergf
return output, aux
ave, maps = self.munet(img, output.detach())
'''~~~ 0: ENDs ~~~'''
pred_stack = torch.stack(maps) #7,b,c,w,w
pred_stack_t = F.sigmoid(pred_stack)
self_pred = pred_stack_t * torch.div(pred_stack_t, torch.sum(pred_stack_t, dim = 0, keepdim=True)) #7,b,c,w,w
self_pred = rearrange(self_pred, "a b c h w -> b (a c) h w").contiguous() #b,7c,w,w
cond = self_pred
# maps = [nn.Upsample(scale_factor=2, mode='bilinear')(a) for a in maps]
aux['maps'] = maps
aux['cond'] = cond
aux['mergfs'] = mergf
aux['emb'] = emb
return output, aux
def close(self):
for sf in self.sfs: sf.remove()
class MUNet(nn.Module):
def __init__(self, args, resnet='resnet34', num_classes=2, pretrained=False):
super().__init__()
# super(ResUnet, self).__init__()
''' ~~~~~ For the embedding transformer~~~~~'''
cut, lr_cut = [8, 6]
'unet and goinnet parameters'
if resnet == 'resnet34':
base_model = resnet34
elif resnet == 'resnet18':
base_model = resnet18
elif resnet == 'resnet50':
base_model = resnet50
elif resnet == 'resnet101':
base_model = resnet101
elif resnet == 'resnet152':
base_model = resnet152
else:
raise Exception('The Resnet Model only accept resnet18, resnet34, resnet50,'
'resnet101 and resnet152')
layers = list(base_model(pretrained=pretrained,inplanes = 5).children())[:cut]
self.check_layer = layers
base_layers = nn.Sequential(*layers)
self.rn = base_layers
self.num_classes = num_classes
self.sfs = [SaveFeatures(base_layers[i]) for i in [2, 4, 5, 6]]
self.up1 = UnetBlock(512, 256, 256)
self.up2 = UnetBlock(256, 128, 256)
self.up3 = UnetBlock(256, 64, 256)
self.up4 = UnetBlock(256, 64, 256)
self.up5 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred1 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred2 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred3 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred4 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred5 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred6 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred7 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
def forward(self, x, heatmap):
x = torch.cat((x,heatmap),1)
x = F.relu(self.rn(x)) # x = [b_size, 2048, 8, 8]
'''~~~ 0: Decoder ~~~'''
x = self.up1(x, self.sfs[3].features[x.device.index])
cond3 = x
x = self.up2(x, self.sfs[2].features[x.device.index])
cond2 = x
x = self.up3(x, self.sfs[1].features[x.device.index])
cond1 = x
x = self.up4(x, self.sfs[0].features[x.device.index])
cond0 = x
fea = x
output = self.up5(x)
'''~~~ 0: ENDs ~~~'''
out1 = self.pred1(fea)
out2 = self.pred2(fea)
out3 = self.pred3(fea)
out4 = self.pred4(fea)
out5 = self.pred5(fea)
out6 = self.pred6(fea)
out7 = self.pred7(fea)
'''
if self.num_classes==1:
output = x_out[:, 0]
else:
output = x_out[:, :self.num_classes]
'''
return (out1+out2+out3+out4+out5+out6+out7) / 7, [out1,out2,out3,out4,out5,out6,out7]
def close(self):
for sf in self.sfs: sf.remove()
class UNet(nn.Module):
def __init__(self, args, resnet='resnet34', num_classes=2, pretrained=False):
super().__init__()
# super(ResUnet, self).__init__()
''' ~~~~~ For the embedding transformer~~~~~'''
cut, lr_cut = [8, 6]
'unet and goinnet parameters'
if resnet == 'resnet34':
base_model = resnet34
elif resnet == 'resnet18':
base_model = resnet18
elif resnet == 'resnet50':
base_model = resnet50
elif resnet == 'resnet101':
base_model = resnet101
elif resnet == 'resnet152':
base_model = resnet152
else:
raise Exception('The Resnet Model only accept resnet18, resnet34, resnet50,'
'resnet101 and resnet152')
layers = list(base_model(pretrained=pretrained,inplanes = 3).children())[:cut]
self.check_layer = layers
base_layers = nn.Sequential(*layers)
self.rn = base_layers
self.num_classes = num_classes
self.sfs = [SaveFeatures(base_layers[i]) for i in [2, 4, 5, 6]]
self.up1 = UnetBlock(512, 256, 256)
self.up2 = UnetBlock(256, 128, 256)
self.up3 = UnetBlock(256, 64, 256)
self.up4 = UnetBlock(256, 64, 256)
self.up5 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred1 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred2 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred3 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred4 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred5 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred6 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
self.pred7 = nn.ConvTranspose2d(256, self.num_classes, 2, stride=2)
def forward(self, x):
x = F.relu(self.rn(x)) # x = [b_size, 2048, 8, 8]
'''~~~ 0: Decoder ~~~'''
x = self.up1(x, self.sfs[3].features[x.device.index])
x = self.up2(x, self.sfs[2].features[x.device.index])
x = self.up3(x, self.sfs[1].features[x.device.index])
x = self.up4(x, self.sfs[0].features[x.device.index])
fea = x
output = self.up5(x)
'''~~~ 0: ENDs ~~~'''
'''
if self.num_classes==1:
output = x_out[:, 0]
else:
output = x_out[:, :self.num_classes]
'''
return output
def close(self):
for sf in self.sfs: sf.remove()
class GoinNet(nn.Module):
def __init__(self,
args,
resnet,
in_chans=3,
inplanes=64,
num_layers=(3, 4, 6, 3),
num_chs=(256, 512, 1024, 2048),
num_strides=(1, 2, 2, 2),
num_heads=(1, 1, 1, 1),
num_parts=(1, 1, 1, 1),
patch_sizes=(1, 1, 1, 1),
drop_path=0.1,
num_enc_heads=(1, 1, 1, 1),
act=nn.GELU,
ffn_exp=3,
has_last_encoder=False,
pretrained=False
):
self.inplanes = 64
super(GoinNet, self).__init__()
cut, lr_cut = [8, 6]
self.conv1 = nn.Conv2d(2, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
last_chs = (64,64,128,256)
num_chs = (64, 64, 128, 256)
down_samples = (2,4,8,16)
n_l = 1
self.stage = Stage(last_chs[i],
num_chs[i],
n_l,
num_heads=num_heads[i], #1,2,4,8
num_parts = (patch_sizes[i]**2 * (args.image_size // down_samples[i] // patch_sizes[i])**2) ,
patch_size=patch_sizes[i], #8,8,8,8
drop_path=drop_path, #0.05
ffn_exp=ffn_exp, #mlp hidden fea
last_enc=has_last_encoder and i == len(num_layers) - 1)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, img, x, p):
x = torch.cat((img,x),1)
x = F.relu(self.rn(x))
x = self.stages[0](p[0].features[x.device.index], self.sfs[0].features[x.device.index])
turn0 = x
x = self.stages[1](p[1].features[x.device.index], self.sfs[1].features[x.device.index])
turn1 = x
x = self.stages[2](p[2].features[x.device.index], self.sfs[2].features[x.device.index])
turn2 = x
x = self.stages[3](p[3].features[x.device.index], self.sfs[3].features[x.device.index])
turn3 = x
return x, [turn0, turn1, turn2, turn3]
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