Comments (2)
Seems like the dataloader meets problem. Can you check if your formatted data is correct, and the path is assigned correctly? Or which tensorflow version are you using?
from geonet.
Seems like the dataloader meets problem. Can you check if your formatted data is correct, and the path is assigned correctly? Or which tensorflow version are you using?
{'add_dispnet': True,
'add_flownet': False,
'add_posenet': True,
'alpha_recon_image': 0.85,
'batch_size': 4,
'checkpoint_dir': 'checkpoint/checkpoint_depth/',
'dataset_dir': 'data_preprocessing/dump_data_depth/',
'depth_test_split': 'eigen',
'disp_smooth_weight': 0.5,
'dispnet_encoder': 'resnet50',
'flow_consistency_alpha': 3.0,
'flow_consistency_beta': 0.05,
'flow_consistency_weight': 0.2,
'flow_smooth_weight': 0.2,
'flow_warp_weight': 1.0,
'flownet_type': 'residual',
'img_height': 128,
'img_width': 416,
'init_ckpt_file': None,
'learning_rate': 0.0002,
'max_steps': 350000,
'max_to_keep': 20,
'mode': 'train_rigid',
'num_scales': 4,
'num_source': 2,
'num_threads': 32,
'output_dir': None,
'pose_test_seq': 9,
'rigid_warp_weight': 1.0,
'save_ckpt_freq': 5000,
'scale_normalize': False,
'seq_length': 3}
[<tf.Tensor 'gradients/image_sampling_3/split_grad/concat:0' shape=(8, 16, 52, 2) dtype=float32>, None, None]
[<tf.Tensor 'gradients/image_sampling_7/split_grad/concat:0' shape=(8, 16, 52, 2) dtype=float32>, None, None]
[<tf.Tensor 'gradients/image_sampling_2/split_grad/concat:0' shape=(8, 32, 104, 2) dtype=float32>, None, None]
[<tf.Tensor 'gradients/image_sampling_6/split_grad/concat:0' shape=(8, 32, 104, 2) dtype=float32>, None, None]
[<tf.Tensor 'gradients/image_sampling_1/split_grad/concat:0' shape=(8, 64, 208, 2) dtype=float32>, None, None]
[<tf.Tensor 'gradients/image_sampling_5/split_grad/concat:0' shape=(8, 64, 208, 2) dtype=float32>, None, None]
[<tf.Tensor 'gradients/image_sampling/split_grad/concat:0' shape=(8, 128, 416, 2) dtype=float32>, None, None]
[<tf.Tensor 'gradients/image_sampling_4/split_grad/concat:0' shape=(8, 128, 416, 2) dtype=float32>, None, None]
2019-06-09 03:40:26.142938: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2019-06-09 03:40:26.142960: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2019-06-09 03:40:26.142982: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2019-06-09 03:40:26.142989: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2019-06-09 03:40:26.143012: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
Trainable variables:
depth_net/Conv/weights:0
depth_net/Conv/BatchNorm/beta:0
depth_net/Conv_1/weights:0
depth_net/Conv_1/BatchNorm/beta:0
depth_net/Conv_2/weights:0
depth_net/Conv_2/BatchNorm/beta:0
depth_net/Conv_3/weights:0
depth_net/Conv_3/BatchNorm/beta:0
depth_net/Conv_4/weights:0
depth_net/Conv_4/BatchNorm/beta:0
depth_net/Conv_5/weights:0
depth_net/Conv_5/BatchNorm/beta:0
depth_net/Conv_6/weights:0
depth_net/Conv_6/BatchNorm/beta:0
depth_net/Conv_7/weights:0
depth_net/Conv_7/BatchNorm/beta:0
depth_net/Conv_8/weights:0
depth_net/Conv_8/BatchNorm/beta:0
depth_net/Conv_9/weights:0
depth_net/Conv_9/BatchNorm/beta:0
depth_net/Conv_10/weights:0
depth_net/Conv_10/BatchNorm/beta:0
depth_net/Conv_11/weights:0
depth_net/Conv_11/BatchNorm/beta:0
depth_net/Conv_12/weights:0
depth_net/Conv_12/BatchNorm/beta:0
depth_net/Conv_13/weights:0
depth_net/Conv_13/BatchNorm/beta:0
depth_net/Conv_14/weights:0
depth_net/Conv_14/BatchNorm/beta:0
depth_net/Conv_15/weights:0
depth_net/Conv_15/BatchNorm/beta:0
depth_net/Conv_16/weights:0
depth_net/Conv_16/BatchNorm/beta:0
depth_net/Conv_17/weights:0
depth_net/Conv_17/BatchNorm/beta:0
depth_net/Conv_18/weights:0
depth_net/Conv_18/BatchNorm/beta:0
depth_net/Conv_19/weights:0
depth_net/Conv_19/BatchNorm/beta:0
depth_net/Conv_20/weights:0
depth_net/Conv_20/BatchNorm/beta:0
depth_net/Conv_21/weights:0
depth_net/Conv_21/BatchNorm/beta:0
depth_net/Conv_22/weights:0
depth_net/Conv_22/BatchNorm/beta:0
depth_net/Conv_23/weights:0
depth_net/Conv_23/BatchNorm/beta:0
depth_net/Conv_24/weights:0
depth_net/Conv_24/BatchNorm/beta:0
depth_net/Conv_25/weights:0
depth_net/Conv_25/BatchNorm/beta:0
depth_net/Conv_26/weights:0
depth_net/Conv_26/BatchNorm/beta:0
depth_net/Conv_27/weights:0
depth_net/Conv_27/BatchNorm/beta:0
depth_net/Conv_28/weights:0
depth_net/Conv_28/BatchNorm/beta:0
depth_net/Conv_29/weights:0
depth_net/Conv_29/BatchNorm/beta:0
depth_net/Conv_30/weights:0
depth_net/Conv_30/BatchNorm/beta:0
depth_net/Conv_31/weights:0
depth_net/Conv_31/BatchNorm/beta:0
depth_net/Conv_32/weights:0
depth_net/Conv_32/BatchNorm/beta:0
depth_net/Conv_33/weights:0
depth_net/Conv_33/BatchNorm/beta:0
depth_net/Conv_34/weights:0
depth_net/Conv_34/BatchNorm/beta:0
depth_net/Conv_35/weights:0
depth_net/Conv_35/BatchNorm/beta:0
depth_net/Conv_36/weights:0
depth_net/Conv_36/BatchNorm/beta:0
depth_net/Conv_37/weights:0
depth_net/Conv_37/BatchNorm/beta:0
depth_net/Conv_38/weights:0
depth_net/Conv_38/BatchNorm/beta:0
depth_net/Conv_39/weights:0
depth_net/Conv_39/BatchNorm/beta:0
depth_net/Conv_40/weights:0
depth_net/Conv_40/BatchNorm/beta:0
depth_net/Conv_41/weights:0
depth_net/Conv_41/BatchNorm/beta:0
depth_net/Conv_42/weights:0
depth_net/Conv_42/BatchNorm/beta:0
depth_net/Conv_43/weights:0
depth_net/Conv_43/BatchNorm/beta:0
depth_net/Conv_44/weights:0
depth_net/Conv_44/BatchNorm/beta:0
depth_net/Conv_45/weights:0
depth_net/Conv_45/BatchNorm/beta:0
depth_net/Conv_46/weights:0
depth_net/Conv_46/BatchNorm/beta:0
depth_net/Conv_47/weights:0
depth_net/Conv_47/BatchNorm/beta:0
depth_net/Conv_48/weights:0
depth_net/Conv_48/BatchNorm/beta:0
depth_net/Conv_49/weights:0
depth_net/Conv_49/BatchNorm/beta:0
depth_net/Conv_50/weights:0
depth_net/Conv_50/BatchNorm/beta:0
depth_net/Conv_51/weights:0
depth_net/Conv_51/BatchNorm/beta:0
depth_net/Conv_52/weights:0
depth_net/Conv_52/BatchNorm/beta:0
depth_net/Conv_53/weights:0
depth_net/Conv_53/BatchNorm/beta:0
depth_net/Conv_54/weights:0
depth_net/Conv_54/BatchNorm/beta:0
depth_net/Conv_55/weights:0
depth_net/Conv_55/BatchNorm/beta:0
depth_net/Conv_56/weights:0
depth_net/Conv_56/BatchNorm/beta:0
depth_net/Conv_57/weights:0
depth_net/Conv_57/BatchNorm/beta:0
depth_net/Conv_58/weights:0
depth_net/Conv_58/BatchNorm/beta:0
depth_net/Conv_59/weights:0
depth_net/Conv_59/BatchNorm/beta:0
depth_net/Conv_60/weights:0
depth_net/Conv_60/BatchNorm/beta:0
depth_net/Conv_61/weights:0
depth_net/Conv_61/BatchNorm/beta:0
depth_net/Conv_62/weights:0
depth_net/Conv_62/BatchNorm/beta:0
depth_net/Conv_63/weights:0
depth_net/Conv_63/BatchNorm/beta:0
depth_net/Conv_64/weights:0
depth_net/Conv_64/BatchNorm/beta:0
depth_net/Conv_65/weights:0
depth_net/Conv_65/BatchNorm/beta:0
depth_net/Conv_66/weights:0
depth_net/Conv_66/BatchNorm/beta:0
depth_net/Conv_67/weights:0
depth_net/Conv_67/BatchNorm/beta:0
depth_net/Conv_68/weights:0
depth_net/Conv_68/BatchNorm/beta:0
depth_net/Conv_69/weights:0
depth_net/Conv_69/BatchNorm/beta:0
depth_net/Conv_70/weights:0
depth_net/Conv_70/BatchNorm/beta:0
depth_net/Conv_71/weights:0
depth_net/Conv_71/biases:0
depth_net/Conv_72/weights:0
depth_net/Conv_72/BatchNorm/beta:0
depth_net/Conv_73/weights:0
depth_net/Conv_73/BatchNorm/beta:0
depth_net/Conv_74/weights:0
depth_net/Conv_74/biases:0
depth_net/Conv_75/weights:0
depth_net/Conv_75/BatchNorm/beta:0
depth_net/Conv_76/weights:0
depth_net/Conv_76/BatchNorm/beta:0
depth_net/Conv_77/weights:0
depth_net/Conv_77/biases:0
depth_net/Conv_78/weights:0
depth_net/Conv_78/BatchNorm/beta:0
depth_net/Conv_79/weights:0
depth_net/Conv_79/BatchNorm/beta:0
depth_net/Conv_80/weights:0
depth_net/Conv_80/biases:0
pose_net/Conv/weights:0
pose_net/Conv/BatchNorm/beta:0
pose_net/Conv_1/weights:0
pose_net/Conv_1/BatchNorm/beta:0
pose_net/Conv_2/weights:0
pose_net/Conv_2/BatchNorm/beta:0
pose_net/Conv_3/weights:0
pose_net/Conv_3/BatchNorm/beta:0
pose_net/Conv_4/weights:0
pose_net/Conv_4/BatchNorm/beta:0
pose_net/Conv_5/weights:0
pose_net/Conv_5/BatchNorm/beta:0
pose_net/Conv_6/weights:0
pose_net/Conv_6/BatchNorm/beta:0
pose_net/Conv_7/weights:0
pose_net/Conv_7/biases:0
('parameter_count =', 60039504)
It is stopped here.It can not continue training.here is my training code.
Train DepthNet
python geonet_main.py --mode=train_rigid --dataset_dir=data_preprocessing/dump_data_depth/ --checkpoint_dir=checkpoint/checkpoint_depth/ --learning_rate=0.0002 --seq_length=3 --batch_size=4 --max_steps=350000
from geonet.
Related Issues (20)
- AttributError while preparing training data HOT 1
- Flag add_flownet not recognized by Tensorflow HOT 1
- Output problem HOT 5
- training problem HOT 1
- How to use optical flow model to test my videos? HOT 1
- Error while camera pose testing HOT 11
- Version of GeoNet HOT 2
- pretrained model problems HOT 1
- about flow_test. HOT 1
- Creating ORB-SLAM (full) snippets HOT 4
- Hello, author. Some problems about generating training model.
- Upgrading tf.contrib.slim to tf 2.0 HOT 2
- How to get depth ground-truth HOT 3
- Loss and training of the Rigid Structure Reconstructor HOT 3
- About FLOPs and parameters HOT 1
- No response in training process HOT 4
- ckpt file problem HOT 2
- How are target and source frames selected? HOT 1
- Draw trajectory HOT 1
- Could you provide the fps of your model?
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from geonet.