|
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='') |
|
|
|
|
|
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) |
|
|
|
|
|
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') |
|
|
|
|
|
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: |
|
|
|
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): |
|
|
|
|
|
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') |
|
|
|
|
|
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() |
|
|
