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test.py
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test.py
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#-*- coding:utf-8 -*-
import argparse
import logging
from pathlib import Path
import time
import os
import shutil
import numpy as np
import torch
import cv2 as cv
from torch.utils import data
from torch.utils.data import DataLoader
import torchvision
from torchvision import datasets, transforms
import torchvision.utils as vutils
from tensorboardX import SummaryWriter
from torch.autograd import Variable
from dataset.datasets import MPIIDatasets, GazeCaptureDatasets
from models.gaze_mobilenetv3 import MobileNetV3
from models.efficientnet import EfficientNet, AuxiliaryNet
from loss.loss import GazeLoss, L2Loss, L1Loss
from utils.utils import AverageMeter, ProgressMeter, mean_angle_error
def print_args(args):
for arg in vars(args):
s = arg + ': ' + str(getattr(args, arg))
logging.info(s)
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
if is_best:
torch.save(state, filename)
logging.info('Save checkpoint to {0:}'.format(filename))
shutil.copyfile(filename, 'model_best.pth.tar')
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected')
def validate(args, val_dataloader, model, auxiliarynet, epoch):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
error = AverageMeter('error', ':6.2f')
progress = ProgressMeter(len(val_dataloader), batch_time, data_time, losses, error,
prefix="Val Epoch: [{}]".format(epoch))
model.eval()
# auxiliarynet.eval()
end = time.time()
with torch.no_grad():
end = time.time()
for i, (patch, gaze_norm_g, head_norm, rot_vec_norm) in enumerate(val_dataloader):
# measure data loading time
data_time.update(time.time() - end)
patch = patch.to(args.device)
gaze_norm_g = gaze_norm_g.to(args.device)
head_norm = head_norm.to(args.device)
rot_vec_norm = rot_vec_norm.to(args.device)
# model = model.to(args.device)
gaze_pred, _ = model(patch)
# hp_pred = auxiliarynet(features)
head_norm = 10 * head_norm
gaze_norm_g = 10 * gaze_norm_g
# loss = criterion(gaze_norm_g, head_norm, gaze_pred[:,0:2], gaze_pred[:,2:4])
angle_error = mean_angle_error(gaze_pred.cpu().detach().numpy()/10,
gaze_norm_g.cpu().detach().numpy()/10,
rot_vec_norm.cpu().detach().numpy())
# losses.update(loss.item())
error.update(angle_error)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if (i+1) % args.print_freq == 0:
progress.print(i+1)
# img = patch.cpu().detach().numpy()[0].deepcopy()
# to_visualize = draw_gaze(img[0], (0.25 * img.shape[1], 0.25 * img.shape[1]), gaze_pred,
# gaze_norm_g, length=80.0, thickness=1)
# cv2.imshow('vis', to_visualize)
# cv2.waitKey(1)
return losses.get_avg(), error.get_avg()
def main(args):
# Step 1: parse args config
logging.basicConfig(
format=
'[%(asctime)s] [p%(process)s] [%(pathname)s:%(lineno)d] [%(levelname)s] %(message)s',
level=logging.INFO,
handlers=[
logging.FileHandler(args.log_file, mode='w'),
logging.StreamHandler()
])
print_args(args)
# Step 2: model, criterion, optimizer, scheduler
# model = MobileNetV3(mode='large').to(args.device)
model = EfficientNet.from_name(args.arch).to(args.device)
# auxiliarynet = AuxiliaryNet().to(args.device)
auxiliarynet = None
checkpoint = torch.load(args.model_path)
model.load_state_dict(checkpoint['model'])
# step 3: data
# argumetion
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])])
# mpiidataset = MPIIDatasets(args.dataroot, train=True, transforms=transform)
# train_dataset = GazeCaptureDatasets(args.dataroot, train=True, transforms=transform)
# mpii_val_dataset = MPIIDatasets(args.val_dataroot, train=False, transforms=transform)
val_dataset = GazeCaptureDatasets(args.val_dataroot, train=True, transforms=transform)
val_dataloader = DataLoader(
val_dataset,
batch_size=args.val_batchsize,
shuffle=False,
num_workers=args.workers)
# step 4: run
val_loss, val_error = validate(args, val_dataloader, model,
auxiliarynet, 1)
print("val_loss: '{}' val_error: '{}'".format(val_loss, val_error))
def parse_args():
parser = argparse.ArgumentParser(description='Trainging Template')
# general
parser.add_argument('-j', '--workers', default=0, type=int)
parser.add_argument('--devices_id', default='0', type=str) #TBD
parser.add_argument('--device', default=torch.device("cuda" if torch.cuda.is_available() else "cpu"), type=str)
parser.add_argument('--test_initial', default='false', type=str2bool) #TBD
parser.add_argument('-p', '--print-freq', default=100, type=int,
metavar='N', help='print frequency (default: 10)')
# training
## -- optimizer
parser.add_argument('--base_lr', default=0.001, type=float)
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float)
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
# -- net arch
parser.add_argument('-a', '--arch', metavar='ARCH', default='efficientnet-b0',
help='model architecture (default: efficientnet-b0)')
parser.add_argument(
'--log_file', default="./checkpoint/train.logs", type=str)
parser.add_argument(
'--tensorboard', default="./checkpoint/tensorboard", type=str)
parser.add_argument('--model_path', default='model_best.pth.tar', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
# --dataset
# parser.add_argument(
# '--dataroot',
# default='./data/MPIIFaceGaze/norm_list.txt',
# type=str,
# metavar='PATH')
parser.add_argument(
'--val_dataroot',
default= './data/MPIIFaceGaze/norm_list.txt',
type=str,
metavar='PATH')
# parser.add_argument(
# '--dataroot',
# default='./data/GazeCapture/norm_list.txt',
# type=str,
# metavar='PATH')
# parser.add_argument(
# '--val_dataroot',
# default= './data/GazeCapture/norm_list.txt',
# type=str,
# metavar='PATH')
parser.add_argument('--val_batchsize', default=16, type=int)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
main(args)