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pre_train_select.py
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pre_train_select.py
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import torch
import torchvision
from torchvision import datasets,transforms
from torchvision.transforms import functional as TF
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.functional as fun
import torch.optim as optim
from PIL import Image
import numpy as np
from torch.autograd import Variable
import sys
import os
from skimage import io
import matplotlib.pyplot as plt
import pylab
import time,datetime
from config import batch_size,pre_output_path,epoch_num,loss_image_path,OnServer,UseF1
from model.model import EndtoEndModel,ETE_stage1,ETE_select,ETE_stage2,label_channel,label_list,make_inverse,calc_centroid
from data.loaddata import data_loader_Aug
import warnings
warnings.filterwarnings("ignore")
train_data=data_loader_Aug("train",batch_size,"stage1");
test_data=data_loader_Aug("test",batch_size,"stage1");
val_data=data_loader_Aug("val",batch_size,"stage1");
use_gpu = torch.cuda.is_available()
if OnServer:
device = torch.device("cuda:5" if torch.cuda.is_available() else "cpu")
else:
import matplotlib;matplotlib.use('TkAgg');
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_stage1=ETE_stage1(device);
model_select=ETE_select(device);
bestloss=1000000
def calc_centroid_old(tensor):#input [batch_size,9,128,128]->output [batch_size,9,2]
input=tensor.float()+1e-10;
n, l, h, w = input.shape
index_y=torch.from_numpy(np.arange(h)).float().to(tensor.device);
index_x=torch.from_numpy(np.arange(w)).float().to(tensor.device);
center_x=input.sum(2)*index_x.view(1,1,-1);
center_x=center_x.sum(2,keepdim=True)/input.sum([2, 3]).view(n, l, 1);
center_y=input.sum(3)*index_y.view(1,1,-1);
center_y=center_y.sum(2,keepdim=True)/input.sum([2, 3]).view(n, l, 1);
output = torch.cat([center_x, center_y], 2)
return output;
def train(epoch):
model_stage1.train();
model_select.train();
'''
part1_time=0;
part2_time=0;
part3_time=0;
prev_time=time.time();
'''
k=0;
for batch_idx,sample in enumerate(train_data.get_loader()):
'''
now_time=time.time();
part3_time+=now_time-prev_time;
prev_time=now_time;
'''
if (use_gpu):
sample['image']=sample['image'].to(device)
sample['label']=sample['label'].to(device)
sample['size'][0]=sample['size'][0].to(device);
sample['size'][1]=sample['size'][1].to(device);
optimizer_stage1.zero_grad();
optimizer_select.zero_grad();
stage1_label=model_stage1(sample['image'])
theta=model_select(stage1_label,sample['size'])
theta_label = torch.zeros((sample['image'].size()[0],6,2,3),device=device,requires_grad=False); #[batch_size,6,2,3]
W=1024.0;
H=1024.0;
'''
cens = torch.floor(calc_centroid_old(sample['label'])) #[batch_size,9,2]
for i in range(sample['image'].size()[0]):
for j in range(9):
cens[i,j,0]=cens[i,j,0]*(sample['size'][0][i]-1.0)/(128.0-1.0)
cens[i,j,1]=cens[i,j,1]*(sample['size'][1][i]-1.0)/(128.0-1.0)
points = torch.floor(torch.cat([cens[:, 1:6],cens[:, 6:9].mean(dim=1, keepdim=True)],dim=1)) #[batch_size,6,2]
'''
'''
points2 = torch.floor(calc_centroid(sample['label_org'])) #[batch_size,9,2]
print("cens resize:");
print(points);
print("cens org:");
print(points2);
print("delta");
print(points.cpu()-points2);
input("wait");
'''
points=torch.floor(calc_centroid(sample['label_org']))
for i in range(6):
theta_label[:,i,0,0]=(81.0-1.0)/(W-1.0);
theta_label[:,i,1,1]=(81.0-1.0)/(H-1.0);
theta_label[:,i,0,2]=-1+2*points[:,i,0]/(W-1.0);
theta_label[:,i,1,2]=-1+2*points[:,i,1]/(H-1.0);
if (torch.min(theta_label)<-1 or torch.max(theta_label)>1):
print("FUCK");
print(k);
'''
for i in range(sample['image'].shape[0]):
if (not os.path.exists("./data/select_pre/"+train_data.get_namelist()[(k+i)%2000])):
os.mkdir("./data/select_pre/"+train_data.get_namelist()[(k+i)%2000]);
image=sample['image_org'][i].cpu().clone();
image=transforms.ToPILImage()(image).convert('RGB')
plt.imshow(image);
plt.show(block=True);
image.save('./data/select_pre/'+train_data.get_namelist()[(k+i)%2000]+'/'+str((k+i)//2000)+'_img'+'.jpg',quality=100);
for j in range(6):
affine_stage2=F.affine_grid(theta_label[i][j].unsqueeze(0),(1,1,81,81),align_corners=True);
image=F.grid_sample(sample['label_org'][i][label_list[j][1]].unsqueeze(0).unsqueeze(0).to(device),affine_stage2,align_corners=True);
image=image.squeeze(0).cpu();
image=transforms.ToPILImage()(image);
image.save('./data/select_pre/'+train_data.get_namelist()[(k+i)%2000]+'/'+str((k+i)//2000)+'_'+str(j)+'_thetalabel'+'.jpg',quality=100);
image=sample['label_org'][i][label_list[j][1]]
image=transforms.ToPILImage()(image);
image.save('./data/select_pre/'+train_data.get_namelist()[(k+i)%2000]+'/'+str((k+i)//2000)+'_'+str(j)+'_orglabel'+'.jpg',quality=100);
#plt.imshow(image);
#plt.show(block=True);
'''
k+=sample['image'].shape[0];
loss=fun.smooth_l1_loss(theta, theta_label);
'''
now_time=time.time();
part1_time+=now_time-prev_time;
prev_time=now_time;
'''
loss.backward()
optimizer_select.step();
optimizer_stage1.step();
'''
now_time=time.time();
part2_time+=now_time-prev_time;
prev_time=now_time;
'''
if (batch_idx%250==0):
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(sample['image']), len(train_data.get_loader().dataset),
100. * batch_idx / len(train_data.get_loader()),loss))
'''
print("batch_idx=",batch_idx);
print("part1_time=",part1_time);
print("part2_time=",part2_time);
print("part3_time=",part3_time);
'''
def test():
model_stage1.eval();
model_select.eval();
global bestloss
test_loss=0
for sample in val_data.get_loader():
if (use_gpu):
sample['image']=sample['image'].to(device)
sample['label']=sample['label'].to(device)
sample['size'][0]=sample['size'][0].to(device);
sample['size'][1]=sample['size'][1].to(device);
stage1_label=model_stage1(sample['image'])
theta=model_select(stage1_label,sample['size'])
theta_label = torch.zeros((sample['image'].size()[0],6,2,3),device=device,requires_grad=False); #[batch_size,6,2,3]
W=1024.0;
H=1024.0;
'''
cens = torch.floor(calc_centroid_old(sample['label'])) #[batch_size,9,2]
for i in range(sample['image'].size()[0]):
for j in range(9):
cens[i,j,0]=cens[i,j,0]*(sample['size'][0][i]-1.0)/(128.0-1.0)
cens[i,j,1]=cens[i,j,1]*(sample['size'][1][i]-1.0)/(128.0-1.0)
points = torch.floor(torch.cat([cens[:, 1:6],cens[:, 6:9].mean(dim=1, keepdim=True)],dim=1)) #[batch_size,6,2]
'''
points=torch.floor(calc_centroid(sample['label_org']))
for i in range(6):
theta_label[:,i,0,0]=(81.0-1.0)/(W-1.0);
theta_label[:,i,1,1]=(81.0-1.0)/(H-1.0);
theta_label[:,i,0,2]=-1+2*points[:,i,0]/(W-1.0);
theta_label[:,i,1,2]=-1+2*points[:,i,1]/(H-1.0);
loss=fun.smooth_l1_loss(theta, theta_label);
test_loss+=fun.smooth_l1_loss(theta, theta_label).data;
test_loss/=len(val_data.get_loader().dataset)
print('\nTest set: {} Cases，Average loss: {:.8f}\n'.format(
len(test_data.get_loader().dataset),test_loss))
loss_list.append(test_loss.data.cpu().numpy());
if (test_loss<bestloss):
bestloss=test_loss
print("Best data Stage1 Updata\n");
torch.save(model_stage1,"./preBestNet_stage1")
torch.save(model_select,"./preBestNet_select")
def printoutput():
CheckBest=True;
if (CheckBest):
model_stage1=torch.load("./preBestnet_stage1",map_location="cpu")
model_select=torch.load("./preBestnet_select",map_location="cpu")
else:
model_stage1=torch.load("./preNetdata_stage1",map_location="cpu")
model_select=torch.load("./preNetdata_select",map_location="cpu")
model_select.select.change_device(device);
if (use_gpu):
model_stage1=model_stage1.to(device)
model_select=model_select.to(device)
unloader = transforms.ToPILImage()
k=0;
loss=0;
for sample in test_data.get_loader():
if (use_gpu):
sample['image']=sample['image'].to(device)
sample['label']=sample['label'].to(device)
sample['size'][0]=sample['size'][0].to(device);
sample['size'][1]=sample['size'][1].to(device);
sample['label_org']=sample['label_org'].to(device)
stage1_label=model_stage1(sample['image'])
theta=model_select(stage1_label,sample['size'])
theta_label = torch.zeros((sample['image'].size()[0],6,2,3),device=device,requires_grad=False); #[batch_size,6,2,3]
W=1024.0;
H=1024.0;
'''
cens = torch.floor(calc_centroid_old(sample['label'])) #[batch_size,9,2]
for i in range(sample['image'].size()[0]):
for j in range(9):
cens[i,j,0]=cens[i,j,0]*(sample['size'][0][i]-1.0)/(128.0-1.0)
cens[i,j,1]=cens[i,j,1]*(sample['size'][1][i]-1.0)/(128.0-1.0)
points = torch.floor(torch.cat([cens[:, 1:6],cens[:, 6:9].mean(dim=1, keepdim=True)],dim=1)) #[batch_size,6,2]
points2 = torch.floor(calc_centroid(sample['label_org'])) #[batch_size,9,2]
theta_label2 = torch.zeros((sample['image'].size()[0],6,2,3),device=device,requires_grad=False); #[batch_size,6,2,3]
for i in range(6):
theta_label2[:,i,0,0]=(81.0-1.0)/(W-1.0);
theta_label2[:,i,1,1]=(81.0-1.0)/(H-1.0);
theta_label2[:,i,0,2]=-1+2*points2[:,i,0]/(W-1.0);
theta_label2[:,i,1,2]=-1+2*points2[:,i,1]/(H-1.0);
if (abs(torch.max(points.cpu()-points2.cpu()))>20 or abs(torch.min(points.cpu()-points2.cpu()))>20):
print("points resize:");
print(points);
print("points org:");
print(points2);
print("delta");
print(points.cpu()-points2.cpu());
for i in range(sample['image'].size()[0]):
print(test_data.get_namelist()[k+i]);
input("wait");
'''
points=torch.floor(calc_centroid(sample['label_org']))
for i in range(6):
theta_label[:,i,0,0]=(81.0-1.0)/(W-1.0);
theta_label[:,i,1,1]=(81.0-1.0)/(H-1.0);
theta_label[:,i,0,2]=-1+2*points[:,i,0]/(W-1.0);
theta_label[:,i,1,2]=-1+2*points[:,i,1]/(H-1.0);
loss+=fun.smooth_l1_loss(theta, theta_label).detach().data;
'''
f=open("1.txt",mode='w');
print(sample['label'].size())
for k in range(9):
for i in range(sample['label'][0][k].size()[0]):
for j in range(sample['label'][0][k].size()[1]):
print(float(sample['label'][0][k][i][j].data),end=' ',file=f);
print(file=f);
f.close();
for i in range(batch_size):
for j in range(9):
image=sample['label'][i][j].cpu().clone();
image=transforms.ToPILImage()(image).convert('L')
plt.imshow(image);
plt.show(block=True);
input("check")
'''
'''
print(theta);
print(theta_label);
input("check")
'''
output=[];
for i in range(sample['image'].size()[0]):
'''
if (test_data.get_namelist()[k]=="13601661_1"):
print(cens[i]);
print(points[i]);
print(theta_label[i]);
input("wait")
'''
path=pre_output_path+'/'+test_data.get_namelist()[k];
if not os.path.exists(path):
os.makedirs(path);
image=sample['image'][i].cpu().clone();
image =unloader(image)
image.save(path+'/'+test_data.get_namelist()[k]+'_img.jpg',quality=100);
image=sample['image_org'][i].cpu().clone();
image2 =unloader(image)
image2.save(path+'/'+test_data.get_namelist()[k]+'_img_org.jpg',quality=100);
output2=stage1_label[i].cpu().clone();
output2=torch.softmax(output2, dim=0).argmax(dim=0, keepdim=False)
output2=output2.unsqueeze(0);
output2=torch.zeros(9,128,128).scatter_(0, output2, 255);
output.append(output2);
for j in range(9):
image3=unloader(np.uint8(output[i][j].numpy()))
#image3=transforms.ToPILImage()(output[i][j].cpu().detach()).convert('L')
image3.save(path+'/'+'stage1_'+test_data.get_namelist()[k]+'lbl0'+str(j)+'.jpg',quality=100);
image3=transforms.ToPILImage()(sample['label_org'][i][j].cpu().detach()).convert('L')
image3.save(path+'/'+test_data.get_namelist()[k]+'lbl0'+str(j)+'_label'+'.jpg',quality=100);
image=image.to(device).unsqueeze(0);
for j in range(6):
affine_stage2=F.affine_grid(theta[i,j].unsqueeze(0),(1,3,81,81),align_corners=True);
image3=F.grid_sample(sample['image_org'][i].to(device).unsqueeze(0),affine_stage2,align_corners=True);
image3=unloader(image3[0].cpu());
image3.save(path+'/'+'select_'+test_data.get_namelist()[k]+'lbl0'+str(j)+'.jpg',quality=100);
affine_stage2=F.affine_grid(theta_label[i,j].unsqueeze(0),(1,3,81,81),align_corners=True);
image3=F.grid_sample(sample['image_org'][i].to(device).unsqueeze(0),affine_stage2,align_corners=True);
image3=unloader(image3[0].cpu());
image3.save(path+'/'+'select_'+test_data.get_namelist()[k]+'lbl0'+str(j)+'_thetalabel'+'.jpg',quality=100);
'''
affine_stage2=F.affine_grid(theta_label2[i,j].unsqueeze(0),(1,3,81,81),align_corners=True);
image3=F.grid_sample(sample['image_org'][i].to(device).unsqueeze(0),affine_stage2,align_corners=True);
image3=unloader(image3[0].cpu());
image3.save(path+'/'+'select_'+test_data.get_namelist()[k]+'lbl0'+str(j)+'_thetalabel2'+'.jpg',quality=100);
'''
affine_stage2=F.affine_grid(theta[i,j].unsqueeze(0),(1,1,81,81),align_corners=True);
image3=F.grid_sample(sample['label_org'][i][label_list[j][1]].unsqueeze(0).unsqueeze(0),affine_stage2,align_corners=True);
image3=transforms.ToPILImage()(image3[0].squeeze(0).cpu().detach()).convert('L');
image3.save(path+'/'+'select_'+test_data.get_namelist()[k]+'lbl0'+str(j)+'_label'+'.jpg',quality=100);
affine_stage2=F.affine_grid(theta_label[i,j].unsqueeze(0),(1,1,81,81),align_corners=True);
image3=F.grid_sample(sample['label_org'][i][label_list[j][1]].unsqueeze(0).unsqueeze(0),affine_stage2,align_corners=True);
image3=transforms.ToPILImage()(image3[0].squeeze(0).cpu().detach()).convert('L');
image3.save(path+'/'+'select_'+test_data.get_namelist()[k]+'lbl0'+str(j)+'_label'+'_thetalabel'+'.jpg',quality=100);
k+=1
if (k>=test_data.get_len()):break
if (k>=test_data.get_len()):break
loss/=len(val_data.get_loader().dataset);
print('\nTest set: {} Cases，Average loss: {:.8f}\n'.format(len(test_data.get_loader().dataset),loss))
print("printoutput Finish");
def makeplt(title):
loss_list=np.load(loss_image_path+'\\loss_list_'+plttitle+'.npy')
loss_list=loss_list.tolist();
x_list=np.load(loss_image_path+'\\x_list_'+plttitle+'.npy')
x_list=x_list.tolist();
fig = plt.figure()
plt.title(title);
ax1 = fig.add_subplot(111)
ax1.plot(x_list, loss_list,'r',label="loss")
ax1.set_xlabel("epoch")
ax1.set_ylabel("Loss")
ax1.legend(loc=2);
plt.savefig(loss_image_path+'\\loss_'+plttitle+'.jpg');
plt.close(1)
loss_list=[];
x_list=[];
print("use_gpu=",use_gpu)
if (use_gpu):
model_stage1=model_stage1.to(device)
model_select=model_select.to(device)
optimizer_stage1=optim.Adam(model_stage1.parameters(),lr=0.01)
optimizer_select=optim.Adam(model_select.parameters(),lr=0.01)
scheduler_stage1=optim.lr_scheduler.StepLR(optimizer_stage1, step_size=5, gamma=0.5)
scheduler_select=optim.lr_scheduler.StepLR(optimizer_select, step_size=5, gamma=0.5)
plttitle="PreTrain_stage1select"
Training=OnServer;
#Training=True;
if Training:
for epoch in range(epoch_num):
x_list.append(epoch);
train(epoch)
scheduler_stage1.step()
scheduler_select.step()
test()
torch.save(model_stage1,"./preNetdata_stage1")
torch.save(model_select,"./preNetdata_select")
x_list_stage1=np.array(x_list)
np.save(loss_image_path+'\\x_list_'+plttitle+'.npy',x_list_stage1)
loss_list_stage1=np.array(loss_list)
np.save(loss_image_path+'\\loss_list_'+plttitle+'.npy',loss_list_stage1)
makeplt(plttitle);
if (not OnServer):
printoutput()
print("FINISH!");