iebins/eval.py

import torch
import torch.backends.cudnn as cudnn

import os, sys
import argparse
import numpy as np
from tqdm import tqdm

from utils import post_process_depth, flip_lr, compute_errors
from networks.NewCRFDepth import NewCRFDepth


def convert_arg_line_to_args(arg_line):
    for arg in arg_line.split():
        if not arg.strip():
            continue
        yield arg


parser = argparse.ArgumentParser(description='IEBins PyTorch implementation.', fromfile_prefix_chars='@')
parser.convert_arg_line_to_args = convert_arg_line_to_args

parser.add_argument('--model_name',                type=str,   help='model name', default='iebins')
parser.add_argument('--encoder',                   type=str,   help='type of encoder, base07, large07, tiny07', default='large07')
parser.add_argument('--checkpoint_path',           type=str,   help='path to a checkpoint to load', default='')

# Dataset
parser.add_argument('--dataset',                   type=str,   help='dataset to train on, kitti or nyu', default='nyu')
parser.add_argument('--input_height',              type=int,   help='input height', default=480)
parser.add_argument('--input_width',               type=int,   help='input width',  default=640)
parser.add_argument('--max_depth',                 type=float, help='maximum depth in estimation', default=10)

# Preprocessing
parser.add_argument('--do_random_rotate',                      help='if set, will perform random rotation for augmentation', action='store_true')
parser.add_argument('--degree',                    type=float, help='random rotation maximum degree', default=2.5)
parser.add_argument('--do_kb_crop',                            help='if set, crop input images as kitti benchmark images', action='store_true')
parser.add_argument('--use_right',                             help='if set, will randomly use right images when train on KITTI', action='store_true')

# Eval
parser.add_argument('--data_path_eval',            type=str,   help='path to the data for evaluation', required=False)
parser.add_argument('--gt_path_eval',              type=str,   help='path to the groundtruth data for evaluation', required=False)
parser.add_argument('--filenames_file_eval',       type=str,   help='path to the filenames text file for evaluation', required=False)
parser.add_argument('--min_depth_eval',            type=float, help='minimum depth for evaluation', default=1e-3)
parser.add_argument('--max_depth_eval',            type=float, help='maximum depth for evaluation', default=80)
parser.add_argument('--eigen_crop',                            help='if set, crops according to Eigen NIPS14', action='store_true')
parser.add_argument('--garg_crop',                             help='if set, crops according to Garg  ECCV16', action='store_true')


if sys.argv.__len__() == 2:
    arg_filename_with_prefix = '@' + sys.argv[1]
    args = parser.parse_args([arg_filename_with_prefix])
else:
    args = parser.parse_args()

if args.dataset == 'kitti' or args.dataset == 'nyu':
    from dataloaders.dataloader import NewDataLoader


def eval(model, dataloader_eval, post_process=False):
    eval_measures = torch.zeros(10).cuda()

    for _, eval_sample_batched in enumerate(tqdm(dataloader_eval.data)):
        with torch.no_grad():
            image = torch.autograd.Variable(eval_sample_batched['image'].cuda())
            gt_depth = eval_sample_batched['depth']
            has_valid_depth = eval_sample_batched['has_valid_depth']
            if not has_valid_depth:
                # print('Invalid depth. continue.')
                continue

            pred_depths_r_list, _, _ = model(image)
            if post_process:
                image_flipped = flip_lr(image)
                pred_depths_r_list_flipped, _, _ = model(image_flipped)
                pred_depth = post_process_depth(pred_depths_r_list[-1], pred_depths_r_list_flipped[-1])

            pred_depth = pred_depth.cpu().numpy().squeeze()
            gt_depth = gt_depth.cpu().numpy().squeeze()     

        if args.do_kb_crop:
            height, width = gt_depth.shape
            top_margin = int(height - 352)
            left_margin = int((width - 1216) / 2)
            pred_depth_uncropped = np.zeros((height, width), dtype=np.float32)
            pred_depth_uncropped[top_margin:top_margin + 352, left_margin:left_margin + 1216] = pred_depth
            pred_depth = pred_depth_uncropped

        pred_depth[pred_depth < args.min_depth_eval] = args.min_depth_eval
        pred_depth[pred_depth > args.max_depth_eval] = args.max_depth_eval
        pred_depth[np.isinf(pred_depth)] = args.max_depth_eval
        pred_depth[np.isnan(pred_depth)] = args.min_depth_eval

        valid_mask = np.logical_and(gt_depth > args.min_depth_eval, gt_depth < args.max_depth_eval)

        if args.garg_crop or args.eigen_crop:
            gt_height, gt_width = gt_depth.shape
            eval_mask = np.zeros(valid_mask.shape)

            if args.garg_crop:
                eval_mask[int(0.40810811 * gt_height):int(0.99189189 * gt_height), int(0.03594771 * gt_width):int(0.96405229 * gt_width)] = 1

            elif args.eigen_crop:
                if args.dataset == 'kitti':
                    eval_mask[int(0.3324324 * gt_height):int(0.91351351 * gt_height), int(0.0359477 * gt_width):int(0.96405229 * gt_width)] = 1
                elif args.dataset == 'nyu':
                    eval_mask[45:471, 41:601] = 1

            valid_mask = np.logical_and(valid_mask, eval_mask)

        measures = compute_errors(gt_depth[valid_mask], pred_depth[valid_mask])

        eval_measures[:9] += torch.tensor(measures).cuda()
        eval_measures[9] += 1

    eval_measures_cpu = eval_measures.cpu()
    cnt = eval_measures_cpu[9].item()
    eval_measures_cpu /= cnt
    print('Computing errors for {} eval samples'.format(int(cnt)), ', post_process: ', post_process)
    print("{:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}".format('silog', 'abs_rel', 'log10', 'rms',
                                                                                    'sq_rel', 'log_rms', 'd1', 'd2',
                                                                                    'd3'))
    for i in range(8):
        print('{:7.4f}, '.format(eval_measures_cpu[i]), end='')
    print('{:7.4f}'.format(eval_measures_cpu[8]))
    return eval_measures_cpu


def main_worker(args):

    # CRF model
    model = NewCRFDepth(version=args.encoder, inv_depth=False, max_depth=args.max_depth, pretrained=None)
    model.train()

    num_params = sum([np.prod(p.size()) for p in model.parameters()])
    print("== Total number of parameters: {}".format(num_params))

    num_params_update = sum([np.prod(p.shape) for p in model.parameters() if p.requires_grad])
    print("== Total number of learning parameters: {}".format(num_params_update))

    model = torch.nn.DataParallel(model)
    model.cuda()

    print("== Model Initialized")

    if args.checkpoint_path != '':
        if os.path.isfile(args.checkpoint_path):
            print("== Loading checkpoint '{}'".format(args.checkpoint_path))
            checkpoint = torch.load(args.checkpoint_path, map_location='cpu')
            model.load_state_dict(checkpoint['model'])
            print("== Loaded checkpoint '{}'".format(args.checkpoint_path))
            del checkpoint
        else:
            print("== No checkpoint found at '{}'".format(args.checkpoint_path))

    cudnn.benchmark = True

    dataloader_eval = NewDataLoader(args, 'online_eval')

    # ===== Evaluation ======
    model.eval()
    with torch.no_grad():
        eval_measures = eval(model, dataloader_eval, post_process=True)


def main():
    torch.cuda.empty_cache()
    args.distributed = False
    ngpus_per_node = torch.cuda.device_count()
    if ngpus_per_node > 1:
        print("This machine has more than 1 gpu. Please set \'CUDA_VISIBLE_DEVICES=0\'")
        return -1
    
    main_worker(args)


if __name__ == '__main__':
    main()