inference_rw.py

# Copyright (C) 2020 * Ltd. All rights reserved.
# author : Sanghyeon Jo <josanghyeokn@gmail.com>

import os
import sys
import copy
import shutil
import random
import argparse
import numpy as np

import imageio

import torch
import torch.nn as nn
import torch.nn.functional as F

from torchvision import transforms
from torch.utils.tensorboard import SummaryWriter

from torch.utils.data import DataLoader

from core.puzzle_utils import *
from core.networks import *
from core.datasets import *

from tools.general.io_utils import *
from tools.general.time_utils import *
from tools.general.json_utils import *

from tools.ai.log_utils import *
from tools.ai.demo_utils import *
from tools.ai.optim_utils import *
from tools.ai.torch_utils import *
from tools.ai.evaluate_utils import *

from tools.ai.augment_utils import *
from tools.ai.randaugment import *

parser = argparse.ArgumentParser()

###############################################################################
# Dataset
###############################################################################
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--num_workers', default=4, type=int)
parser.add_argument('--data_dir', default='../VOCtrainval_11-May-2012/', type=str)

###############################################################################
# Network
###############################################################################
parser.add_argument('--architecture', default='resnet50', type=str)

###############################################################################
# Inference parameters
###############################################################################
parser.add_argument('--model_name', default='', type=str)

parser.add_argument('--cam_dir', default='', type=str)
parser.add_argument('--domain', default='train', type=str)

parser.add_argument('--beta', default=10, type=int)
parser.add_argument('--exp_times', default=8, type=int)
# parser.add_argument('--threshold', default=0.25, type=float)

if __name__ == '__main__':
    ###################################################################################
    # Arguments
    ###################################################################################
    args = parser.parse_args()
    
    experiment_name = args.model_name

    if 'train' in args.domain:
        experiment_name += '@train'
    else:
        experiment_name += '@val'

    experiment_name += '@beta=%d'%args.beta
    experiment_name += '@exp_times=%d'%args.exp_times
    # experiment_name += '@threshold=%.2f'%args.threshold
    experiment_name += '@rw'
    
    cam_dir = f'./experiments/predictions/{args.cam_dir}/'
    pred_dir = create_directory(f'./experiments/predictions/{experiment_name}/')

    model_path = './experiments/models/' + f'{args.model_name}.pth'

    set_seed(args.seed)
    log_func = lambda string='': print(string)

    ###################################################################################
    # Transform, Dataset, DataLoader
    ###################################################################################
    imagenet_mean = [0.485, 0.456, 0.406]
    imagenet_std = [0.229, 0.224, 0.225]

    normalize_fn = Normalize(imagenet_mean, imagenet_std)
    
    # for mIoU
    meta_dic = read_json('./data/VOC_2012.json')
    dataset = VOC_Dataset_For_Making_CAM(args.data_dir, args.domain)
    
    ###################################################################################
    # Network
    ###################################################################################
    path_index = PathIndex(radius=10, default_size=(512 // 4, 512 // 4))
    model = AffinityNet(args.architecture, path_index)

    model = model.cuda()
    model.eval()
    
    log_func('[i] Architecture is {}'.format(args.architecture))
    log_func('[i] Total Params: %.2fM'%(calculate_parameters(model)))
    log_func()

    try:
        use_gpu = os.environ['CUDA_VISIBLE_DEVICES']
    except KeyError:
        use_gpu = '0'

    the_number_of_gpu = len(use_gpu.split(','))
    if the_number_of_gpu > 1:
        log_func('[i] the number of gpu : {}'.format(the_number_of_gpu))
        model = nn.DataParallel(model)

    load_model(model, model_path, parallel=the_number_of_gpu > 1)
    
    #################################################################################################
    # Evaluation
    #################################################################################################
    eval_timer = Timer()

    with torch.no_grad():
        length = len(dataset)
        for step, (ori_image, image_id, label, gt_mask) in enumerate(dataset):
            ori_w, ori_h = ori_image.size

            npy_path = pred_dir + image_id + '.npy'
            if os.path.isfile(npy_path):
                continue

            # preprocessing
            image = np.asarray(ori_image)
            image = normalize_fn(image)
            image = image.transpose((2, 0, 1))

            image = torch.from_numpy(image)
            flipped_image = image.flip(-1)

            images = torch.stack([image, flipped_image])
            images = images.cuda()

            # inference
            edge = model.get_edge(images)

            # postprocessing
            cam_dict = np.load(cam_dir + image_id + '.npy', allow_pickle=True).item()

            cams = cam_dict['cam']
            
            cam_downsized_values = cams.cuda()
            rw = propagate_to_edge(cam_downsized_values, edge, beta=args.beta, exp_times=args.exp_times, radius=5)
            
            rw_up = F.interpolate(rw, scale_factor=4, mode='bilinear', align_corners=False)[..., 0, :ori_h, :ori_w]
            rw_up = rw_up / torch.max(rw_up)
            
            np.save(npy_path, {"keys": cam_dict['keys'], "rw": rw_up.cpu().numpy()})
            
            sys.stdout.write('\r# Make CAM with Random Walk [{}/{}] = {:.2f}%, ({}, rw_up={}, rw={})'.format(step + 1, length, (step + 1) / length * 100, (ori_h, ori_w), rw_up.size(), rw.size()))
            sys.stdout.flush()
        print()
    
    print("python3 evaluate.py --experiment_name {} --domain {}".format(experiment_name, args.domain))