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Test_Baseline_model.py
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# Test on Baseline (Sum for aggregation and Sign() for binarization, without logiritic regression)
import os
import torchvision
import torch
from Baseline_model import Baseline, acc_authentication
from utils_data import BalanceBatchSampler
import numpy as np
import torch.nn.functional as F
import yaml
import argparse
from vgg_face2 import VGG_Faces2
import multiprocessing
torch.manual_seed(0)
if __name__ == '__main__':
torch.multiprocessing.set_start_method('spawn', force=True)
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
# print(ROOT_DIR)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)
core_number = multiprocessing.cpu_count()
print('core number:', core_number)
# --------------------------------------------------------------------------------------
# Arguments
# --------------------------------------------------------------------------------------
with open(r'{}/args.yaml'.format(ROOT_DIR)) as file:
args_list = yaml.load(file, Loader=yaml.FullLoader)
dataroot = args_list['dataroot']
model_type = args_list['model_type']
n_classes = args_list['n_classes']
n_samples = args_list['n_samples']
m_set = args_list['m_set']
exp_name = args_list['exp_name']
num_workers = args_list['num_workers']
n_batches_valid = args_list['n_batches_valid']
upper_vgg = args_list['upper_vgg']
lossFun = args_list['loss']
parser = argparse.ArgumentParser()
parser.add_argument('--model_type', '--model', type=str, default=model_type,
help='model name (default: "resnet50_128")')
parser.add_argument('--num_workers', '--nw', type=int, default=num_workers,
help='number of workers for Dataloader (num_workers: 8)')
parser.add_argument('--m_set', '--m', type=int, default=m_set,
help='the group size')
parser.add_argument('--n_batches_valid', '--n_b_valid', type=int, default=n_batches_valid,
help='Number of batches per epoch for validation')
parser.add_argument('--upper_vgg', '--u_vgg', type=int, default=upper_vgg,
help='Number of images loaded from VGG-Face2')
parser.add_argument('--n_batch_verif', '--n_batch_verif', type=int,
help='Number of batches for verification (default: 8)')
parser.add_argument('--loss', '--loss', type=str, default=lossFun,
help='loss function (default: "loss_bc")')
parser.add_argument('--n_classes', '--n_classes', type=int, default=n_classes,
help='Number of classes in batch (default: 32)')
args = parser.parse_args()
model_type = args.model_type
num_workers = args.num_workers
m_set = args.m_set
n_batches_valid = args.n_batches_valid
upper_vgg = args.upper_vgg
n_batch_verif = args.n_batch_verif
lossFun = args.loss
n_classes = args.n_classes
if lossFun == 'loss_bc':
from loss import loss_bc as loss_fn
elif lossFun == 'loss_auc_max_v1':
from loss import loss_auc_max_v1 as loss_fn
elif lossFun == 'loss_AUCPRHingeLoss':
from loss import loss_AUCPRHingeLoss as loss_fn
if n_batches_valid == 0:
n_batches_valid = None
if upper_vgg == 0:
upper_vgg = None
# --------------------------------------------------------------------------------------
# Load datasets
# --------------------------------------------------------------------------------------
exp_name = 'lfw'
# training_dataset_root = '/nfs/nas4/marzieh/marzieh/VGG_Face2/train/'
# dataset_train = VGG_Faces2(training_dataset_root, split='train', upper=upper_vgg)
if exp_name == 'lfw':
mean_rgb = (0.485, 0.456, 0.406) # (0.5, 0.5, 0.5)
std_rgb = (0.229, 0.224, 0.225) # (0.5, 0.5, 0.5)
dataset_validation = torchvision.datasets.ImageFolder(root=dataroot,
transform=torchvision.transforms.Compose([
torchvision.transforms.Resize(256),
torchvision.transforms.CenterCrop(224),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=mean_rgb, std=std_rgb)]
))
elif exp_name == 'vgg2':
validation_dataset_root = '/nfs/nas4/marzieh/marzieh/VGG_Face2/test/'
dataset_validation = VGG_Faces2(validation_dataset_root, split='validation', upper=upper_vgg)
# --------------------------------------------------------------------------------------
# Batch Sampling: n_samples * n_samples
# --------------------------------------------------------------------------------------
batch_size = n_classes * n_samples
batch_sampler_v = BalanceBatchSampler(dataset=dataset_validation, n_classes=n_classes, n_samples=n_samples,
n_batches_epoch=n_batches_valid)
validation_loader = torch.utils.data.DataLoader(dataset_validation, batch_sampler=batch_sampler_v,
num_workers=num_workers)
batch_sampler_H0v = BalanceBatchSampler(dataset=dataset_validation, n_classes=n_classes * 2, n_samples=1,
n_batches_epoch=n_batch_verif)
H0_loader_validation = torch.utils.data.DataLoader(dataset_validation, batch_sampler=batch_sampler_H0v,
num_workers=num_workers)
H0_id_v, H0_data_v = [], []
dataloader_H0_v = iter(H0_loader_validation)
for i in range(n_batch_verif):
data = next(dataloader_H0_v)
H0_id_v.append(data[1])
H0_data_v.append(data[0])
# --------------------------------------------------------------------------------------
# Model Definitions
# --------------------------------------------------------------------------------------
model = Baseline(base_model_architecture=model_type)
model.to(device)
# --------- Test on Baseline -----------------
# model.eval()
# logisticReg.eval()
tot_loss, tot_acc = 0, 0
n_batches = len(validation_loader)
Ptp01, Ptp05, Ptp1, AUC = np.zeros(n_batches // n_batch_verif), np.zeros(n_batches // n_batch_verif), np.zeros(n_batches // n_batch_verif), np.zeros(n_batches // n_batch_verif)
vs, vf, tg = [], [], []
idx = -1
with torch.no_grad():
for batch_idx, (data, target) in enumerate(validation_loader):
data_set = data[np.arange(0, batch_size, n_samples)].to(device)
data_query = data[np.arange(1, batch_size, n_samples)].to(device)
v_set, code_set = model(data_set, m=m_set) # single vector per set
v_f, code_f = model(data_query, m=1) # single vector per query
Sim = torch.mm(F.normalize(code_set, p=2, dim=1), F.normalize(code_f, p=2, dim=1).t())
# output = logisticReg(Sim.unsqueeze(-1)).squeeze()
_, accuracy = loss_fn(Sim, len(code_f), m_set)
tot_acc += accuracy
vs.append(code_set)
vf.append(code_f)
tg.append(target)
if (batch_idx + 1) % n_batch_verif == 0:
idx += 1
vs = torch.stack(vs).flatten(start_dim=0, end_dim=1)
vf = torch.stack(vf).flatten(start_dim=0, end_dim=1)
tg = torch.stack(tg).flatten(start_dim=0, end_dim=1)
Ptp01[idx], Ptp05[idx], Ptp1[idx], AUC[idx] = acc_authentication(model, H0_id_v, H0_data_v,
tg, vf.size(0), vs, vf, m_set, n_batch_verif)
vs, vf, tg = [], [], []
avg_acc = tot_acc / n_batches
print('Evaluation --->avg_acc: %.3f' % avg_acc,
' ptp01: %.3f' % np.mean(Ptp01), 'ptp05: %.3f' % np.mean(Ptp05)
, 'ptp1: %.3f' % np.mean(Ptp1), ' auc: %.3f' % np.mean(AUC))