Arthur151
commented
2 years ago The bug is reported at warning
WARNING:root:model trained_models/ROMP_HRNet32_V1.pkl not exist!
results_dict['mesh_rendering_orgimgs']['figs'] is not the bug.
Please download the trained model from here
mahsaep
Thanks so much for sharing the code!
I am trying to test ROMP on demo images and save the output predicted mesh on the image with " CUDA_VISIBLE_DEVICES=0 python -u -m romp.predict.image --configs_yml='configs/image.yml'"
However, it terminates does not process any of the images and terminates. One bug that I found was that in romp/predict/image.py line:49 results_dict['mesh_rendering_orgimgs']['figs'] should be changed to results_dict['org_img']['figs'], but I'm still facing the problem:
CUDA_VISIBLE_DEVICES=0 python -u -m romp.predict.image --configs_yml='configs/image.yml' yaml_timestamp /home/mahsa/Downloads/ROMP-master/active_configs/active_context_2021-12-13_13_25_29.yaml Loading the configurations from configs/image.yml INFO:root:{'tab': 'hrnet_cm64_process_images', 'configs_yml': 'configs/image.yml', 'inputs': 'demo/images', 'output_dir': 'demo/image_results', 'interactive_vis': False, 'show_largest_person_only': False, 'show_mesh_stand_on_image': False, 'soi_camera': 'far', 'make_tracking': False, 'temporal_optimization': False, 'save_dict_results': True, 'save_visualization_on_img': True, 'fps_save': 24, 'character': 'smpl', 'renderer': 'pyrender', 'f': None, 'model_return_loss': False, 'model_version': 1, 'multi_person': True, 'new_training': False, 'perspective_proj': False, 'FOV': 60, 'focal_length': 443.4, 'lr': 0.0003, 'adjust_lr_factor': 0.1, 'weight_decay': 1e-06, 'epoch': 120, 'fine_tune': True, 'GPUS': 0, 'batch_size': 64, 'input_size': 512, 'master_batch_size': -1, 'nw': 4, 'optimizer_type': 'Adam', 'pretrain': 'simplebaseline', 'fix_backbone_training_scratch': False, 'backbone': 'hrnet', 'model_precision': 'fp32', 'deconv_num': 0, 'head_block_num': 2, 'merge_smpl_camera_head': False, 'use_coordmaps': True, 'hrnet_pretrain': '/home/mahsa/Downloads/ROMP-master/trained_models/pretrain_hrnet.pkl', 'resnet_pretrain': '/home/mahsa/Downloads/ROMP-master/trained_models/pretrain_resnet.pkl', 'loss_thresh': 1000, 'max_supervise_num': -1, 'supervise_cam_params': False, 'match_preds_to_gts_for_supervision': False, 'matching_mode': 'all', 'supervise_global_rot': False, 'HMloss_type': 'MSE', 'eval': False, 'eval_datasets': 'pw3d', 'val_batch_size': 4, 'test_interval': 2000, 'fast_eval_iter': -1, 'top_n_error_vis': 6, 'eval_2dpose': False, 'calc_pck': False, 'PCK_thresh': 150, 'calc_PVE_error': False, 'centermap_size': 64, 'centermap_conf_thresh': 0.25, 'collision_aware_centermap': False, 'collision_factor': 0.2, 'center_def_kp': True, 'local_rank': 0, 'distributed_training': False, 'distillation_learning': False, 'teacher_model_path': '/export/home/suny/CenterMesh/trained_models/3dpw_88_57.8.pkl', 'print_freq': 50, 'model_path': 'trained_models/ROMP_HRNet32_V1.pkl', 'log_path': '/home/mahsa/Downloads/log/', 'learn_2dpose': False, 'learn_AE': False, 'learn_kp2doffset': False, 'shuffle_crop_mode': False, 'shuffle_crop_ratio_3d': 0.9, 'shuffle_crop_ratio_2d': 0.1, 'Synthetic_occlusion_ratio': 0, 'color_jittering_ratio': 0.2, 'rotate_prob': 0.2, 'dataset_rootdir': '/home/mahsa/Downloads/dataset/', 'dataset': 'h36m,mpii,coco,aich,up,ochuman,lsp,movi', 'voc_dir': '/home/mahsa/Downloads/dataset/VOCdevkit/VOC2012/', 'max_person': 64, 'homogenize_pose_space': False, 'use_eft': True, 'smpl_mesh_root_align': False, 'Rot_type': '6D', 'rot_dim': 6, 'cam_dim': 3, 'beta_dim': 10, 'smpl_joint_num': 22, 'smpl_model_path': '/home/mahsa/Downloads/ROMP-master/model_data/parameters', 'smpl_J_reg_h37m_path': '/home/mahsa/Downloads/ROMP-master/model_data/parameters/J_regressor_h36m.npy', 'smpl_J_reg_extra_path': '/home/mahsa/Downloads/ROMP-master/model_data/parameters/J_regressor_extra.npy', 'smpl_uvmap': '/home/mahsa/Downloads/ROMP-master/model_data/parameters/smpl_vt_ft.npz', 'wardrobe': '/home/mahsa/Downloads/ROMP-master/model_data/wardrobe', 'mesh_cloth': 'ghostwhite', 'nvxia_model_path': '/home/mahsa/Downloads/ROMP-master/model_data/characters/nvxia', 'track_memory_usage': False, 'adjust_lr_epoch': [], 'kernel_sizes': [5], 'collect_subdirs': False, 'save_mesh': True, 'save_centermap': False} INFO:root:------------------------------------------------------------------ INFO:root:start building model. Using ROMP v1 Confidence: 0.25 INFO:root:using fine_tune model: trained_models/ROMP_HRNet32_V1.pkl WARNING:root:model trained_models/ROMP_HRNet32_V1.pkl not exist! INFO:root:Train all layers, except: ['_result_parser.params_map_parser.smpl_model.betas'] Initialization finished! Processing demo/images, saving to demo/image_results INFO:root:gathering datasets Loading 3 images to process /home/mahsa/anaconda3/envs/rmp/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /opt/conda/conda-bld/pytorch_1623448234945/work/c10/core/TensorImpl.h:1156.) return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) /home/mahsa/anaconda3/envs/rmp/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at /opt/conda/conda-bld/pytorch_1623448234945/work/aten/src/ATen/native/BinaryOps.cpp:467.) return torch.floor_divide(self, other) Processed 0 / 3 images
I would appreciate your help!