import torch
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
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 == 'nyu':
from dataloaders.dataloader_sun 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
_, hh, ww, _ = gt_depth.shape
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 = F.interpolate(pred_depth, [hh, ww], mode="bilinear", align_corners=False)
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
pred_depth[pred_depth > 8] = 8
gt_depth[gt_depth > 8] = 8
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()