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model.py
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import random
import torch
import torchvision
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
from PIL import Image
import PIL.ImageOps
import pytorch_lightning as pl
import numpy as np
from pytorch_lightning.callbacks import ModelCheckpoint
import argparse
class ContrastiveLoss(torch.nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin):
super().__init__()
self.margin = margin
def forward(self, output1, output2, label):
euclidean_distance = F.pairwise_distance(output1, output2, keepdim = True)
loss_contrastive = torch.mean((1-label) * torch.pow(euclidean_distance, 2) + (label) * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))
return loss_contrastive
class SiameseNetworkDataset(Dataset):
def __init__(self,imageFolderDataset,transform=None,should_invert=None):
self.imageFolderDataset = imageFolderDataset
self.transform = transform
self.should_invert = should_invert
def __getitem__(self,index):
img0_tuple = random.choice(self.imageFolderDataset.imgs)
#we need to make sure approx 50% of images are in the same class
should_get_same_class = random.randint(0,1)
if should_get_same_class:
while True:
#keep looping till the same class image is found
img1_tuple = random.choice(self.imageFolderDataset.imgs)
if img0_tuple[1]==img1_tuple[1]:
break
else:
while True:
#keep looping till a different class image is found
img1_tuple = random.choice(self.imageFolderDataset.imgs)
if img0_tuple[1] !=img1_tuple[1]:
break
img0 = Image.open(img0_tuple[0])
img1 = Image.open(img1_tuple[0])
img0 = img0.convert("L")
img1 = img1.convert("L")
if self.should_invert:
img0 = PIL.ImageOps.invert(img0)
img1 = PIL.ImageOps.invert(img1)
if self.transform is not None:
img0 = self.transform(img0)
img1 = self.transform(img1)
return img0, img1 , torch.from_numpy(np.array([int(img1_tuple[1]!=img0_tuple[1])],dtype=np.float32))
def __len__(self):
return len(self.imageFolderDataset.imgs)
class SiameseNetwork(pl.LightningModule):
def __init__(self, margin, learning_rate, resize, imageFolderTrain, imageFolderTest, batch_size, should_invert):
super().__init__()
self.imageFolderTrain = imageFolderTrain
self.imageFolderTest= imageFolderTest
self.learning_rate = learning_rate
self.criterion = ContrastiveLoss(margin=margin)
self.batch_size = batch_size
self.should_invert = should_invert
self.transform = transforms.Compose([transforms.Resize((resize,resize)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()])
self.cnn1 = nn.Sequential(
nn.ReflectionPad2d(1),
nn.Conv2d(1, 4, kernel_size=3),
nn.ReLU(inplace=True),
nn.BatchNorm2d(4),
nn.ReflectionPad2d(1),
nn.Conv2d(4, 8, kernel_size=3),
nn.ReLU(inplace=True),
nn.BatchNorm2d(8),
nn.ReflectionPad2d(1),
nn.Conv2d(8, 8, kernel_size=3),
nn.ReLU(inplace=True),
nn.BatchNorm2d(8),
)
self.fc1 = nn.Sequential(
nn.Linear(8*100*100, 500),
nn.ReLU(inplace=True),
nn.Linear(500, 500),
nn.ReLU(inplace=True),
nn.Linear(500, 5))
def forward_once(self, x):
output = self.cnn1(x)
output = output.view(output.size()[0], -1)
output = self.fc1(output)
return output
def forward(self, input1, input2):
output1 = self.forward_once(input1)
output2 = self.forward_once(input2)
return output1, output2
def training_step(self, batch, batch_idx):
x0, x1 , y = batch
output1,output2 = self(x0, x1)
loss = self.criterion(output1,output2, y)
return loss
def validation_step(self, batch, batch_idx):
x0, x1 , y = batch
output1,output2 = self(x0, x1)
loss = self.criterion(output1,output2,y)
self.log('val_loss', loss, prog_bar=True)
return loss
def test_step(self, batch, batch_idx):
return self.validation_step(batch, batch_idx)
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)
return optimizer
def prepare_data(self):
self.DatasetFolder = dset.ImageFolder(self.imageFolderTrain)
self.DatasetFolder_testing = dset.ImageFolder(self.imageFolderTest)
def setup(self, stage=None):
self.siamese_dataset_train = SiameseNetworkDataset(imageFolderDataset=self.DatasetFolder,
transform=self.transform
,should_invert=self.should_invert)
self.siamese_dataset_test = SiameseNetworkDataset(imageFolderDataset=self.DatasetFolder_testing,
transform=self.transform
,should_invert=self.should_invert)
def train_dataloader(self):
return DataLoader(self.siamese_dataset_train, batch_size=self.batch_size)
def test_dataloader(self):
return DataLoader(self.siamese_dataset_test, batch_size=self.batch_size)
if __name__=='__main__':
parser = argparse.ArgumentParser(
description='Siamese Network - Face Recognition',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--gpus', default=1, type=int)
parser.add_argument('--batch_size', default=64, type=int)
parser.add_argument('--pretrain_epochs', default=5000, type=int)
parser.add_argument('--margin', default=1.0, type=float)
parser.add_argument('--should_invert', default=False)
parser.add_argument('--imageFolderTrain', default=None)
parser.add_argument('--imageFolderTest', default=None)
parser.add_argument('--learning_rate', default=2e-2, type=float)
parser.add_argument('--resize', default=100, type=int)
args = parser.parse_args()
print(args)
model = SiameseNetwork(margin= args.margin, learning_rate=args.learning_rate, resize=args.resize, imageFolderTrain=args.imageFolderTrain,
imageFolderTest=args.imageFolderTest, batch_size=args.batch_size, should_invert=args.should_invert)
trainer = pl.Trainer(gpus=args.gpus, max_epochs=args.pretrain_epochs, progress_bar_refresh_rate=20)
trainer.fit(model)
trainer.save_checkpoint("siamese_face_recognition.ckpt")
trainer.test()