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BCE.py
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BCE.py
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import torch
import torch.nn as nn
import matplotlib.pyplot as plt
from torch.utils.data import Dataset,DataLoader
import torchvision.transforms as transforms
from torch.autograd import Variable
import cv2
import numpy as np
import os
import random
import sys
if sys.argv[1]== '--load':
weightname=sys.argv[2]
tempna='./'
name=tempna+weightname
test_only=1
else:
weightname=sys.argv[2]
tempna='./'
name=tempna+weightname
test_only=0
N=16
class custom_dset(Dataset):
def __init__(self,
img_path,
txt_path,
img_transform1,
img_transform2,
):
with open(txt_path, 'r') as f:
lines = f.readlines()
self.img1_list = [
os.path.join(img_path, i.split()[0]) for i in lines
]
self.img2_list = [
os.path.join(img_path, i.split()[1]) for i in lines
]
self.label_list = [i.split()[2] for i in lines]
self.img_transform1 = img_transform1
self.img_transform2 = img_transform2
def __getitem__(self, index):
img1_path = self.img1_list[index]
img2_path = self.img2_list[index]
label = self.label_list[index]
label=int(label)
img1 = cv2.imread(img1_path)
img2 = cv2.imread(img2_path)
img1 = img1.astype(np.float)/255
img2 = img2.astype(np.float)/255
img1 = cv2.resize(img1,(128,128), interpolation = cv2.INTER_AREA)
img2 = cv2.resize(img2,(128,128), interpolation = cv2.INTER_AREA)
img1 = self.img_transform1(img1)
img2 = self.img_transform2(img2)
return img1,img2,label
def __len__(self):
return len(self.label_list)
class Rescale(object):
def __call__(self, img):
if random.random()<0.7:
f = round(0.1*random.randint(7, 13),2)
if f>1:
img = cv2.resize(img,None,fx=f, fy=f, interpolation = cv2.INTER_CUBIC)
a = int(round((f*128-128)/2))
img = img[a:a+128,a:a+128]
else:
img = cv2.resize(img,None,fx=f, fy=f, interpolation = cv2.INTER_AREA)
a= int(round((128-f*128)/2))
temp=np.zeros([128,128,3],dtype=np.uint8)
temp.fill(0)
for i in range(img.shape[0]):
for j in range(img.shape[1]):
temp[i+a,j+a]=img[i,j]
img=temp
return img
class Flip(object):
def __call__(self,img):
if random.random()<0.7:
return cv2.flip(img,1)
return img
class Rotate(object):
def __call__(self,img):
if random.random()<0.7:
angle=random.random()*60-30
rows,cols,cn = img.shape
M = cv2.getRotationMatrix2D((cols/2,rows/2),angle,1)
img = cv2.warpAffine(img,M,(cols,rows))
return img
return img
class Translate(object):
def __call__(self,img):
if random.random()<0.7:
x=random.random()*20-10
y=random.random()*20-10
rows,cols,cn = img.shape
M= np.float32([[1,0,x],[0,1,y]])
img = cv2.warpAffine(img,M,(cols,rows))
return img
transform1 = transforms.Compose([Rescale(),Flip(),Translate(),Rotate(),transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
transform2 = transforms.Compose([Rescale(),Flip(),Translate(),Rotate(),transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_set = custom_dset('./lfw', './train.txt',transform1,transform2)
train_loader = DataLoader(train_set, batch_size=N, shuffle=True, num_workers=2)
lr = 1e-5
num_epoches = 30
class Cnn(nn.Module):
def __init__(self):
super(Cnn, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, 5, 1, 2),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(2, 2),
)
self.conv2 = nn.Sequential(
nn.Conv2d(64, 128, 5, 1, 2),
nn.ReLU(),
nn.BatchNorm2d(128),
nn.MaxPool2d(2, 2),
)
self.conv3 = nn.Sequential(
nn.Conv2d(128, 256, 3, 1, 1),
nn.ReLU(),
nn.BatchNorm2d(256),
nn.MaxPool2d(2, 2),
)
self.conv4 =nn.Sequential(
nn.Conv2d(256, 512, 3, 1, 1),
nn.ReLU(),
nn.BatchNorm2d(512),
)
self.fc1 = nn.Sequential(
nn.Linear(131072, 1024),
nn.ReLU(),
nn.BatchNorm2d(1024),
)
self.fc2 = nn.Sequential(
nn.Linear(2048, 1),
nn.Sigmoid(),
)
def forward(self, x,y):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = x.view(x.size(0), -1)
x = self.fc1(x)
y = self.conv1(y)
y = self.conv2(y)
y = self.conv3(y)
y = self.conv4(y)
y = y.view(y.size(0), -1)
y = self.fc1(y)
f = torch.cat((x,y),1)
f = self.fc2(f)
return f
net=Cnn()
if torch.cuda.is_available() :
net = net.cuda()
optimizer = torch.optim.Adam(net.parameters(), lr)
loss_func = nn.BCELoss()
l_his=[]
if test_only==0:
for epoch in range(num_epoches):
print('Epoch:', epoch + 1, 'Training...')
running_loss = 0.0
for i,data in enumerate(train_loader, 0):
image1s,image2s,labels=data
if torch.cuda.is_available():
image1s = image1s.cuda()
image2s = image2s.cuda()
labels = labels.cuda()
image1s, image2s, labels = Variable(image1s), Variable(image2s), Variable(labels.float())
optimizer.zero_grad()
f=net(image1s,image2s)
loss = loss_func(f,labels)
loss.backward()
optimizer.step()
if i % 20 == 19:
l_his.append(loss.data[0])
# print statistics
running_loss += loss.data[0]
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
print('Finished Training')
torch.save(net.state_dict(), name)
fig = plt.figure()
ax = plt.subplot(111)
ax.plot(l_his)
plt.xlabel('Steps')
plt.ylabel('Loss')
fig.savefig('plotad.png')
else:
net.load_state_dict(torch.load(name))
#test data
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
test_set = custom_dset('./lfw', './train.txt',transform,transform)
test_loader = DataLoader(test_set, batch_size=N, shuffle=False, num_workers=2)
correct = 0
total = 0
for i,data in enumerate(test_loader,0):
image1s,image2s,labels = data
if torch.cuda.is_available():
image1s = image1s.cuda()
image2s = image2s.cuda()
labels = labels.cuda()
image1s, image2s, labels = Variable(image1s), Variable(image2s), Variable(labels.float())
# print(labels)
outputs = net(image1s,image2s)
outputs = outputs.cpu()
for j in range(outputs.size()[0]):
if ((outputs.data.numpy()[j]<0.5)):
if labels.data.cpu().numpy()[j]==0:
correct +=1
total+=1
else:
total+=1
else:
if labels.data.cpu().numpy()[j]==1:
correct +=1
total+=1
else:
total+=1
print('Accuracy of the network on the train images: %d %%' % (
100 * correct / total))
test_set = custom_dset('./lfw', './test.txt',transform,transform)
test_loader = DataLoader(test_set, batch_size=N, shuffle=False, num_workers=2)
correct = 0
total = 0
for i,data in enumerate(test_loader,0):
image1s,image2s,labels = data
if torch.cuda.is_available():
image1s = image1s.cuda()
image2s = image2s.cuda()
labels = labels.cuda()
image1s, image2s, labels = Variable(image1s), Variable(image2s), Variable(labels.float())
# print(labels)
outputs = net(image1s,image2s)
outputs = outputs.cpu()
for j in range(outputs.size()[0]):
if ((outputs.data.numpy()[j]<0.5)):
if labels.data.cpu().numpy()[j]==0:
correct +=1
total+=1
else:
total+=1
else:
if labels.data.cpu().numpy()[j]==1:
correct +=1
total+=1
else:
total+=1
print('Accuracy of the network on the test images: %d %%' % (
100 * correct / total))