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eval_survface_1n.py
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eval_survface_1n.py
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# Evaluation on QMUL-SurvFace 1:N identification with a single model
import os
import torch
import torch.nn.functional as F
import torchvision.transforms as transforms
from PIL import Image
from scipy.io import loadmat
from torch.utils.data import Dataset, DataLoader
from backbones import get_model
device = torch.device("cuda:0")
#Training dataset
class QUML_trainset(Dataset):
def __init__(self, transform, img_size=112):
self.transform = transform
self.img_size = img_size
self.img_files = []
self.labels = []
self.class_dict = {}
train_dir = "training_set"
name_list = sorted(os.listdir(train_dir))
ID = 0
for name in name_list:
name_dir = os.path.join(train_dir,name)
img_list = os.listdir(name_dir)
for img_name in img_list:
img_dir = os.path.join(name_dir,img_name)
self.img_files.append(img_dir)
self.labels.append(ID)
ID+=1
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
label = self.labels[idx]
img = Image.open(self.img_files[idx]).resize((self.img_size,self.img_size))
img = self.transform(img)
return img,label
#Evaluation dataset
class QUML_evalset(Dataset):
def __init__(self, set_arg, transform, meta_path, img_size=112):
self.set_arg = set_arg
self.transform = transform
self.img_size = img_size
self.img_files = []
self.labels = []
self.class_dict = {}
if set_arg == "U":
base = "Face_Identification_Test_Set/unmated_probe"
base = os.path.join(meta_path, base)
imgs = os.listdir(base)
self.labels = [-1]*len(imgs)
for img in imgs:
self.img_files.append(os.path.join(base,img))
else:
if set_arg == "G":
base = "Face_Identification_Test_Set/gallery"
base = os.path.join(meta_path, base)
meta = loadmat(meta_path+"Face_Identification_Test_Set/gallery_img_ID_pairs.mat")
imgs = meta["gallery_set"].reshape(-1)
labels = (meta["gallery_ids"]-1).reshape(-1).tolist()
elif set_arg == "K":
base = "Face_Identification_Test_Set/mated_probe"
base = os.path.join(meta_path, base)
meta = loadmat(meta_path+"Face_Identification_Test_Set/mated_probe_img_ID_pairs.mat")
imgs = meta["mated_probe_set"].reshape(-1)
labels = (meta["mated_probe_ids"]-1).reshape(-1).tolist()
ID = -1
for img,lab in zip(imgs,labels):
if lab not in self.class_dict.keys():
ID += 1
self.class_dict[lab] = ID
self.img_files.append(os.path.join(base,img[0]))
self.labels.append(ID)
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
label = self.labels[idx]
img = Image.open(self.img_files[idx]).resize((self.img_size,self.img_size))
img = self.transform(img)
return img,label
def get_lr(optimizer):
return optimizer.param_groups[0]['lr']
def dir_at_far(dir_tensor, far):
idx = torch.argmin(torch.abs(dir_tensor[:,2]-far)) #find the dir which is closest to far
return dir_tensor[idx, 1].item() #known the far, find the corresponding dir
if __name__ =="__main__":
meta_path = '../../fr/SurvFace/'
#prepare evaluation set
trf = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(torch.FloatTensor([0.5,0.5,0.5]),torch.FloatTensor([0.5,0.5,0.5]))
])
Gset = QUML_evalset("G",trf,meta_path=meta_path)
Kset = QUML_evalset("K",trf,meta_path=meta_path)
Uset = QUML_evalset("U",trf,meta_path=meta_path)
num_cls = len(set(Gset.labels))
print("num_cls:", num_cls)
Gloader = DataLoader(Gset, batch_size=200, num_workers=4)
Kloader = DataLoader(Kset, batch_size=200, num_workers=4)
Uloader = DataLoader(Uset, batch_size=200, num_workers=4)
#load model
model = 'r50'
weight = 'output/surv_r50_reso112/model.pt'
net = get_model(model, resolution=112, fp16=False)
net.to(device)
net.load_state_dict(torch.load(weight))
# extract features
net.eval()
with torch.no_grad():
# make gallery prototypes: averaged features
G_feat = torch.zeros(num_cls, 512).to(device)
cardinality = torch.zeros(num_cls, dtype=torch.int64).to(device)
for batch,(img,label) in enumerate(Gloader):
img,label=img.to(device),label.to(device)
feat = net(img)[0]
for i in range(label.size(0)):
G_feat[label[i]] += feat[i]
cardinality[label[i]] += 1
G_feat = torch.div(G_feat.T, cardinality).T
print("G_feat:",G_feat.shape)
# extract features of known probe set K
for batch,(img,label) in enumerate(Kloader):
img,label=img.to(device),label.to(device)
if batch==0:
K_feat = net(img)[0]
K_label = label
else:
K_feat = torch.cat((K_feat, net(img)[0]), dim=0)
K_label = torch.cat((K_label, label),dim=0)
print("K_feat:",K_feat.shape)
# extract features of unknown probe set U
for batch,(img,label) in enumerate(Uloader):
img = img.to(device)
if batch==0:
U_feat = net(img)[0]
else:
U_feat = torch.cat((U_feat, net(img)[0]), dim=0)
print("U_feat:",U_feat.shape)
#compute cosine similarity
G_feat = F.normalize(G_feat, dim=1)
K_feat = F.normalize(K_feat, dim=1)
U_feat = F.normalize(U_feat, dim=1)
K_sim = torch.mm(K_feat, G_feat.T)
U_sim = torch.mm(U_feat, G_feat.T)
K_val, pred = torch.topk(K_sim, k=20, dim=1) #top-20
U_val, _ = torch.max(U_sim, dim=1)
# compute DIR & FAR w.r.t. different thresholds
corr_mask = pred.eq(K_label.view(-1,1))
DIR_ = torch.zeros(1000,3)
for i,th in enumerate(torch.linspace(min(K_val.min(),U_val.min()), U_val.max(), 1000)):
mask = corr_mask & (K_val > th)
dir_ = mask.sum().item()/K_feat.size(0)
far_ = (U_val>th).sum().item()/U_feat.size(0)
DIR_[i] = torch.FloatTensor([th, dir_, far_])
for far in [0.01,0.1,0.2,0.3]:
print("TPIR20 @ FAR={}: {:.2f}%".format(far,dir_at_far(DIR_, far)*100))