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test script for process a list of images

See original GitHub issue

Hi @hangzhaomit , i have write a script (based on your test .py) to read a list of images for batch convert. Hope it helps~

import os
import datetime
import argparse
from distutils.version import LooseVersion
# Numerical libs
import numpy as np
import torch
import torch.nn as nn
from import loadmat
# Our libs
from dataset import TestDataset
from models import ModelBuilder, SegmentationModule
from utils import colorEncode
from lib.nn import user_scattered_collate, async_copy_to
from lib.utils import as_numpy, mark_volatile
import as torchdata
import cv2

def visualize_result(data, preds, args):
    colors = loadmat('data/color150.mat')['colors']
    (img, info) = data
    pred_color = colorEncode(preds, colors)

    im_vis = np.concatenate((img, pred_color),

    img_name = info.split('/')[-1]
                img_name.replace('.jpg', '.png')), im_vis)

def test(segmentation_module, loader, args):

    for i, batch_data in enumerate(loader):
        # process data
        batch_data = batch_data[0]
        segSize = (batch_data['img_ori'].shape[0],

        img_resized_list = batch_data['img_data']

        with torch.no_grad():
            pred = torch.zeros(1, args.num_class, segSize[0], segSize[1])

            for img in img_resized_list:
                feed_dict = batch_data.copy()
                feed_dict['img_data'] = img
                del feed_dict['img_ori']
                del feed_dict['info']
                feed_dict = async_copy_to(feed_dict, args.gpu_id)

                # forward pass
                pred_tmp = segmentation_module(feed_dict, segSize=segSize)
                pred = pred + pred_tmp.cpu() / len(args.imgSize)

            _, preds = torch.max(pred, dim=1)
            preds = as_numpy(preds.squeeze(0))

            (batch_data['img_ori'], batch_data['info']),
            preds, args)

        print('[{}] iter {}'
              .format("%Y-%m-%d %H:%M:%S"), i))

def main(args):
    builder = ModelBuilder()
    net_encoder = builder.build_encoder(
    net_decoder = builder.build_decoder(

    crit = nn.NLLLoss(ignore_index=-1)

    segmentation_module = SegmentationModule(net_encoder, net_decoder, crit)

 ################# modified #################
   with open(args.test_list, 'r') as f:
         files =
         for file_ in files:
             test_img =[{'fpath_img':file_}]
             dataset_val = TestDataset(test_img, args, max_sample=args.num_val)
             loader_val = torchdata.DataLoader(

         test(segmentation_module, loader_val, args)

         print('Inference done!')
 ################# modified #################

if __name__ == '__main__':
    assert LooseVersion(torch.__version__) >= LooseVersion('0.4.0'), \
        'PyTorch>=0.4.0 is required'

    parser = argparse.ArgumentParser()
    # Path related arguments
    parser.add_argument('--test_img', required=True)
    parser.add_argument('--model_path', required=True,
                        help='folder to model path')
    parser.add_argument('--suffix', default='_epoch_20.pth',
                        help="which snapshot to load")

    # Model related arguments
    parser.add_argument('--arch_encoder', default='resnet50_dilated8',
                        help="architecture of net_encoder")
    parser.add_argument('--arch_decoder', default='ppm_bilinear_deepsup',
                        help="architecture of net_decoder")
    parser.add_argument('--fc_dim', default=2048, type=int,
                        help='number of features between encoder and decoder')

    # Data related arguments
    parser.add_argument('--num_val', default=-1, type=int,
                        help='number of images to evalutate')
    parser.add_argument('--num_class', default=150, type=int,
                        help='number of classes')
    parser.add_argument('--batch_size', default=1, type=int,
                        help='batchsize. current only supports 1')
    parser.add_argument('--imgSize', default=[300, 400, 500, 600],
                        nargs='+', type=int,
                        help='list of input image sizes.'
                             'for multiscale testing, e.g. 300 400 500')
    parser.add_argument('--imgMaxSize', default=1000, type=int,
                        help='maximum input image size of long edge')
    parser.add_argument('--padding_constant', default=8, type=int,
                        help='maxmimum downsampling rate of the network')
    parser.add_argument('--segm_downsampling_rate', default=8, type=int,
                        help='downsampling rate of the segmentation label')

    parser.add_argument('--result', default='.',
                        help='folder to output visualization results')
    parser.add_argument('--gpu_id', default=0, type=int,
                        help='gpu_id for evaluation')

    args = parser.parse_args()

    args.weights_encoder = os.path.join(args.model_path,
                                        'encoder' + args.suffix)
    args.weights_decoder = os.path.join(args.model_path,
                                        'decoder' + args.suffix)

    assert os.path.exists(args.weights_encoder) and \
        os.path.exists(args.weights_encoder), 'checkpoint does not exitst!'

    if not os.path.isdir(args.result):


Issue Analytics

  • State:closed
  • Created 5 years ago
  • Reactions:1
  • Comments:7 (1 by maintainers)

github_iconTop GitHub Comments

hangzhaomitcommented, May 18, 2018

Thanks for this. Actually there is no need to write a loop over the Dataset loader and test function. TestDataset() supports a list of images as input, so there is no need to change the code.

jeewa985commented, Nov 20, 2019

@Jason-xin thanks for your suggestion. When I tried your suggestion with as follows. test_edit

I am getting following error. test_edit_error

Highly appreciate if you have any suggestion or if you can share the code you have used to test lits of images in a folder.

Read more comments on GitHub >

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