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EdwardSmith1884 / Multi-View-Silhouette-and-Depth-Decomposition-for-High-Resolution-3D-Object-Representation

Repository for code to reproduce the paper Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
MIT License
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ValueError #4

Closed ancant2180 closed 4 years ago

ancant2180 commented 5 years ago

Hello, I have run into an error after installing tensorflow and tensorlayer. Data prep works just fine, but running depth.py gives me:

. . . ValueError: Cannot feed value of shape (0,) for Tensor u'images_low:0', which has shape '(64, 32, 32, 1)'

I am unsure if this is a version error. If it's not, may you point me in the right direction? I am running on Ubuntu 18.04; Using python 2.7, tensorlayer 1.8.4, tensorflow 1.13.1, and tensorflow-gpu 2.0.0b1.

EdwardSmith1884 commented 5 years ago

have you checked the shape of the tensor you are passing? it is possible it is an empty array, and the data was not made or loaded correctly. can you also paste the full error output so I can see where in the code this is happening?

xfyin1994 commented 5 years ago

Hi , I have the same problem,I have run the pre_data.py successfully. But I found that the file in "voxels"has some empty files,there are no files in the "train","test" and "valid" of "chair".

data->voxels->chair->test,train,valid are all empty files,Is this right ? Is it beacuse the code can't find any files to read?

EdwardSmith1884 commented 5 years ago

Yes that is why. There is a flag in the data_prep.py file on line 57, that currently has debug_mode set to 0 (False). If you change it to true they you will see the output messages from the binvoxer executable and the blender executable. It will show you the errors now. Can you let me know what the errors you see are?

ancant2180 commented 5 years ago

It should be noted that I am currently using a conda environment to run this program. I am trying to generate the "plane" models you have in your example

python data_prep.py -o plane -no 1000 -hi 128 -l 64 -ni 5

I too have no files in data ->voxels->plane->test,train,valid

(ten2) ancant2180@R2D2:~/Multi-View-Silhouette-and-Depth-Decomposition-for-High-Resolution-3D-Object-Representation$ python depth.py WARNING: Logging before flag parsing goes to stderr. W0808 10:14:28.324814 140104983627392 deprecation_wrapper.py:119] From /home/ancant2180/anaconda3/envs/ten2/lib/python2.7/site-packages/tensorlayer/layers/core.py:45: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.

W0808 10:14:28.340783 140104983627392 deprecation_wrapper.py:119] From /home/ancant2180/anaconda3/envs/ten2/lib/python2.7/site-packages/tensorlayer/layers/pooling.py:63: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.

W0808 10:14:28.400814 140104983627392 deprecation_wrapper.py:119] From depth.py:44: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

W0808 10:14:28.423461 140104983627392 deprecation_wrapper.py:119] From ./scripts/models.py:10: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.

W0808 10:14:28.423716 140104983627392 deprecation.py:323] From ./scripts/models.py:11: set_name_reuse (from tensorlayer.layers.core) is deprecated and will be removed after 2018-06-30. Instructions for updating: TensorLayer relies on TensorFlow to check name reusing. [TL] this method is DEPRECATED and has no effect, please remove it from your code. W0808 10:14:28.423784 140104983627392 _logging.py:16] this method is DEPRECATED and has no effect, please remove it from your code. W0808 10:14:28.423877 140104983627392 deprecation_wrapper.py:119] From /home/ancant2180/anaconda3/envs/ten2/lib/python2.7/site-packages/tensorlayer/layers/core.py:387: The name tf.get_variable_scope is deprecated. Please use tf.compat.v1.get_variable_scope instead.

[TL] InputLayer depth/input: (64, 32, 32, 2) I0808 10:14:28.424000 140104983627392 _logging.py:12] InputLayer depth/input: (64, 32, 32, 2) [TL] Conv2d depth/cnn1: shape:(3, 3, 2, 128) strides:(1, 1, 1, 1) pad:SAME act:relu I0808 10:14:28.424102 140104983627392 _logging.py:12] Conv2d depth/cnn1: shape:(3, 3, 2, 128) strides:(1, 1, 1, 1) pad:SAME act:relu W0808 10:14:28.424269 140104983627392 deprecation_wrapper.py:119] From /home/ancant2180/anaconda3/envs/ten2/lib/python2.7/site-packages/tensorlayer/layers/convolution.py:1465: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.

[TL] Conv2d depth/res1/0: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.435906 140104983627392 _logging.py:12] Conv2d depth/res1/0: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/0: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:28.444452 140104983627392 _logging.py:12] BatchNormLayer depth/res2/0: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/0: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.469222 140104983627392 _logging.py:12] Conv2d depth/res3/0: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/0: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:28.477262 140104983627392 _logging.py:12] BatchNormLayer depth/res4/0: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/0: size:(64, 32, 32, 128) fn:add I0808 10:14:28.498744 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/0: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/1: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.499356 140104983627392 _logging.py:12] Conv2d depth/res1/1: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/1: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:28.507447 140104983627392 _logging.py:12] BatchNormLayer depth/res2/1: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/1: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.528799 140104983627392 _logging.py:12] Conv2d depth/res3/1: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/1: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:28.537883 140104983627392 _logging.py:12] BatchNormLayer depth/res4/1: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/1: size:(64, 32, 32, 128) fn:add I0808 10:14:28.559768 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/1: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/2: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.560409 140104983627392 _logging.py:12] Conv2d depth/res1/2: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/2: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:28.568634 140104983627392 _logging.py:12] BatchNormLayer depth/res2/2: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/2: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.590054 140104983627392 _logging.py:12] Conv2d depth/res3/2: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/2: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:28.598541 140104983627392 _logging.py:12] BatchNormLayer depth/res4/2: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/2: size:(64, 32, 32, 128) fn:add I0808 10:14:28.620120 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/2: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/3: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.620762 140104983627392 _logging.py:12] Conv2d depth/res1/3: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/3: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:28.629204 140104983627392 _logging.py:12] BatchNormLayer depth/res2/3: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/3: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.651695 140104983627392 _logging.py:12] Conv2d depth/res3/3: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/3: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:28.659754 140104983627392 _logging.py:12] BatchNormLayer depth/res4/3: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/3: size:(64, 32, 32, 128) fn:add I0808 10:14:28.681376 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/3: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/4: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.682027 140104983627392 _logging.py:12] Conv2d depth/res1/4: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/4: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:28.690478 140104983627392 _logging.py:12] BatchNormLayer depth/res2/4: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/4: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.711932 140104983627392 _logging.py:12] Conv2d depth/res3/4: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/4: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:28.720021 140104983627392 _logging.py:12] BatchNormLayer depth/res4/4: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/4: size:(64, 32, 32, 128) fn:add I0808 10:14:28.742476 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/4: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/5: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.743125 140104983627392 _logging.py:12] Conv2d depth/res1/5: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/5: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:28.751274 140104983627392 _logging.py:12] BatchNormLayer depth/res2/5: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/5: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.773513 140104983627392 _logging.py:12] Conv2d depth/res3/5: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/5: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:28.782080 140104983627392 _logging.py:12] BatchNormLayer depth/res4/5: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/5: size:(64, 32, 32, 128) fn:add I0808 10:14:28.806241 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/5: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/6: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.806860 140104983627392 _logging.py:12] Conv2d depth/res1/6: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/6: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:28.815506 140104983627392 _logging.py:12] BatchNormLayer depth/res2/6: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/6: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.838356 140104983627392 _logging.py:12] Conv2d depth/res3/6: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/6: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:28.847183 140104983627392 _logging.py:12] BatchNormLayer depth/res4/6: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/6: size:(64, 32, 32, 128) fn:add I0808 10:14:28.869858 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/6: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/7: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.870569 140104983627392 _logging.py:12] Conv2d depth/res1/7: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/7: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:28.879465 140104983627392 _logging.py:12] BatchNormLayer depth/res2/7: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/7: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.901582 140104983627392 _logging.py:12] Conv2d depth/res3/7: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/7: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:28.909652 140104983627392 _logging.py:12] BatchNormLayer depth/res4/7: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/7: size:(64, 32, 32, 128) fn:add I0808 10:14:28.932811 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/7: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/8: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.933464 140104983627392 _logging.py:12] Conv2d depth/res1/8: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/8: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:28.941592 140104983627392 _logging.py:12] BatchNormLayer depth/res2/8: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/8: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.963202 140104983627392 _logging.py:12] Conv2d depth/res3/8: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/8: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:28.971678 140104983627392 _logging.py:12] BatchNormLayer depth/res4/8: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/8: size:(64, 32, 32, 128) fn:add I0808 10:14:28.995984 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/8: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/9: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:28.996666 140104983627392 _logging.py:12] Conv2d depth/res1/9: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/9: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.004798 140104983627392 _logging.py:12] BatchNormLayer depth/res2/9: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/9: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.026566 140104983627392 _logging.py:12] Conv2d depth/res3/9: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/9: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.034687 140104983627392 _logging.py:12] BatchNormLayer depth/res4/9: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/9: size:(64, 32, 32, 128) fn:add I0808 10:14:29.058058 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/9: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/10: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.058774 140104983627392 _logging.py:12] Conv2d depth/res1/10: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/10: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.067266 140104983627392 _logging.py:12] BatchNormLayer depth/res2/10: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/10: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.089220 140104983627392 _logging.py:12] Conv2d depth/res3/10: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/10: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.097336 140104983627392 _logging.py:12] BatchNormLayer depth/res4/10: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/10: size:(64, 32, 32, 128) fn:add I0808 10:14:29.122036 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/10: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/11: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.122725 140104983627392 _logging.py:12] Conv2d depth/res1/11: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/11: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.130918 140104983627392 _logging.py:12] BatchNormLayer depth/res2/11: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/11: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.152767 140104983627392 _logging.py:12] Conv2d depth/res3/11: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/11: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.160829 140104983627392 _logging.py:12] BatchNormLayer depth/res4/11: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/11: size:(64, 32, 32, 128) fn:add I0808 10:14:29.185481 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/11: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/12: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.186204 140104983627392 _logging.py:12] Conv2d depth/res1/12: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/12: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.194339 140104983627392 _logging.py:12] BatchNormLayer depth/res2/12: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/12: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.216140 140104983627392 _logging.py:12] Conv2d depth/res3/12: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/12: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.225064 140104983627392 _logging.py:12] BatchNormLayer depth/res4/12: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/12: size:(64, 32, 32, 128) fn:add I0808 10:14:29.305831 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/12: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/13: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.306437 140104983627392 _logging.py:12] Conv2d depth/res1/13: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/13: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.314462 140104983627392 _logging.py:12] BatchNormLayer depth/res2/13: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/13: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.336647 140104983627392 _logging.py:12] Conv2d depth/res3/13: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/13: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.345108 140104983627392 _logging.py:12] BatchNormLayer depth/res4/13: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/13: size:(64, 32, 32, 128) fn:add I0808 10:14:29.370888 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/13: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/14: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.371504 140104983627392 _logging.py:12] Conv2d depth/res1/14: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/14: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.379944 140104983627392 _logging.py:12] BatchNormLayer depth/res2/14: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/14: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.401554 140104983627392 _logging.py:12] Conv2d depth/res3/14: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/14: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.410254 140104983627392 _logging.py:12] BatchNormLayer depth/res4/14: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/14: size:(64, 32, 32, 128) fn:add I0808 10:14:29.437021 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/14: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/15: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.437644 140104983627392 _logging.py:12] Conv2d depth/res1/15: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/15: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.445843 140104983627392 _logging.py:12] BatchNormLayer depth/res2/15: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/15: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.467488 140104983627392 _logging.py:12] Conv2d depth/res3/15: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/15: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.475836 140104983627392 _logging.py:12] BatchNormLayer depth/res4/15: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/15: size:(64, 32, 32, 128) fn:add I0808 10:14:29.503990 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/15: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/cnn2: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.504600 140104983627392 _logging.py:12] Conv2d depth/cnn2: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/bn2: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.513580 140104983627392 _logging.py:12] BatchNormLayer depth/bn2: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/sum2: size:(64, 32, 32, 128) fn:add I0808 10:14:29.540002 140104983627392 _logging.py:12] ElementwiseLayer depth/sum2: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/cnn/1: shape:(3, 3, 128, 256) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.540591 140104983627392 _logging.py:12] Conv2d depth/cnn/1: shape:(3, 3, 128, 256) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] SubpixelConv2d subpixel/0: scale: 2 n_out_channel: 64 act: relu I0808 10:14:29.549416 140104983627392 _logging.py:12] SubpixelConv2d subpixel/0: scale: 2 n_out_channel: 64 act: relu [TL] Conv2d depth/cnn/2: shape:(3, 3, 64, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.550517 140104983627392 _logging.py:12] Conv2d depth/cnn/2: shape:(3, 3, 64, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] SubpixelConv2d subpixel/1: scale: 2 n_out_channel: 32 act: relu I0808 10:14:29.558818 140104983627392 _logging.py:12] SubpixelConv2d subpixel/1: scale: 2 n_out_channel: 32 act: relu [TL] Conv2d depth/cnn/3: shape:(3, 3, 32, 64) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.559844 140104983627392 _logging.py:12] Conv2d depth/cnn/3: shape:(3, 3, 32, 64) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] SubpixelConv2d subpixel/2: scale: 2 n_out_channel: 16 act: relu I0808 10:14:29.568308 140104983627392 _logging.py:12] SubpixelConv2d subpixel/2: scale: 2 n_out_channel: 16 act: relu [TL] Conv2d depth/cnnout: shape:(1, 1, 16, 1) strides:(1, 1, 1, 1) pad:SAME act:sigmoid I0808 10:14:29.569386 140104983627392 _logging.py:12] Conv2d depth/cnnout: shape:(1, 1, 16, 1) strides:(1, 1, 1, 1) pad:SAME act:sigmoid [TL] this method is DEPRECATED and has no effect, please remove it from your code. W0808 10:14:29.577799 140104983627392 _logging.py:16] this method is DEPRECATED and has no effect, please remove it from your code. [TL] InputLayer depth/input: (64, 32, 32, 2) I0808 10:14:29.577999 140104983627392 _logging.py:12] InputLayer depth/input: (64, 32, 32, 2) [TL] Conv2d depth/cnn1: shape:(3, 3, 2, 128) strides:(1, 1, 1, 1) pad:SAME act:relu I0808 10:14:29.578136 140104983627392 _logging.py:12] Conv2d depth/cnn1: shape:(3, 3, 2, 128) strides:(1, 1, 1, 1) pad:SAME act:relu [TL] Conv2d depth/res1/0: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.580144 140104983627392 _logging.py:12] Conv2d depth/res1/0: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/0: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.582206 140104983627392 _logging.py:12] BatchNormLayer depth/res2/0: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/0: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.593458 140104983627392 _logging.py:12] Conv2d depth/res3/0: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/0: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.595397 140104983627392 _logging.py:12] BatchNormLayer depth/res4/0: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/0: size:(64, 32, 32, 128) fn:add I0808 10:14:29.606547 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/0: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/1: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.607170 140104983627392 _logging.py:12] Conv2d depth/res1/1: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/1: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.609016 140104983627392 _logging.py:12] BatchNormLayer depth/res2/1: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/1: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.620486 140104983627392 _logging.py:12] Conv2d depth/res3/1: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/1: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.622411 140104983627392 _logging.py:12] BatchNormLayer depth/res4/1: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/1: size:(64, 32, 32, 128) fn:add I0808 10:14:29.633505 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/1: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/2: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.634121 140104983627392 _logging.py:12] Conv2d depth/res1/2: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/2: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.635984 140104983627392 _logging.py:12] BatchNormLayer depth/res2/2: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/2: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.647196 140104983627392 _logging.py:12] Conv2d depth/res3/2: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/2: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.649032 140104983627392 _logging.py:12] BatchNormLayer depth/res4/2: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/2: size:(64, 32, 32, 128) fn:add I0808 10:14:29.661202 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/2: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/3: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.661839 140104983627392 _logging.py:12] Conv2d depth/res1/3: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/3: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.663708 140104983627392 _logging.py:12] BatchNormLayer depth/res2/3: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/3: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.675038 140104983627392 _logging.py:12] Conv2d depth/res3/3: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/3: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.677027 140104983627392 _logging.py:12] BatchNormLayer depth/res4/3: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/3: size:(64, 32, 32, 128) fn:add I0808 10:14:29.688344 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/3: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/4: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.688936 140104983627392 _logging.py:12] Conv2d depth/res1/4: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/4: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.690907 140104983627392 _logging.py:12] BatchNormLayer depth/res2/4: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/4: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.701862 140104983627392 _logging.py:12] Conv2d depth/res3/4: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/4: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.703847 140104983627392 _logging.py:12] BatchNormLayer depth/res4/4: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/4: size:(64, 32, 32, 128) fn:add I0808 10:14:29.715656 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/4: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/5: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.716299 140104983627392 _logging.py:12] Conv2d depth/res1/5: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/5: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.718199 140104983627392 _logging.py:12] BatchNormLayer depth/res2/5: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/5: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.729269 140104983627392 _logging.py:12] Conv2d depth/res3/5: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/5: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.731219 140104983627392 _logging.py:12] BatchNormLayer depth/res4/5: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/5: size:(64, 32, 32, 128) fn:add I0808 10:14:29.743124 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/5: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/6: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.743765 140104983627392 _logging.py:12] Conv2d depth/res1/6: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/6: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.745592 140104983627392 _logging.py:12] BatchNormLayer depth/res2/6: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/6: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.757021 140104983627392 _logging.py:12] Conv2d depth/res3/6: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/6: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.758936 140104983627392 _logging.py:12] BatchNormLayer depth/res4/6: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/6: size:(64, 32, 32, 128) fn:add I0808 10:14:29.771363 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/6: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/7: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.772010 140104983627392 _logging.py:12] Conv2d depth/res1/7: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/7: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.773885 140104983627392 _logging.py:12] BatchNormLayer depth/res2/7: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/7: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.785423 140104983627392 _logging.py:12] Conv2d depth/res3/7: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/7: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.787353 140104983627392 _logging.py:12] BatchNormLayer depth/res4/7: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/7: size:(64, 32, 32, 128) fn:add I0808 10:14:29.799751 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/7: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/8: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.800388 140104983627392 _logging.py:12] Conv2d depth/res1/8: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/8: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.802289 140104983627392 _logging.py:12] BatchNormLayer depth/res2/8: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/8: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.813584 140104983627392 _logging.py:12] Conv2d depth/res3/8: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/8: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.815510 140104983627392 _logging.py:12] BatchNormLayer depth/res4/8: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/8: size:(64, 32, 32, 128) fn:add I0808 10:14:29.828242 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/8: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/9: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.828882 140104983627392 _logging.py:12] Conv2d depth/res1/9: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/9: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.830877 140104983627392 _logging.py:12] BatchNormLayer depth/res2/9: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/9: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.842176 140104983627392 _logging.py:12] Conv2d depth/res3/9: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/9: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.844055 140104983627392 _logging.py:12] BatchNormLayer depth/res4/9: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/9: size:(64, 32, 32, 128) fn:add I0808 10:14:29.857305 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/9: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/10: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.857988 140104983627392 _logging.py:12] Conv2d depth/res1/10: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/10: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.859910 140104983627392 _logging.py:12] BatchNormLayer depth/res2/10: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/10: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.871345 140104983627392 _logging.py:12] Conv2d depth/res3/10: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/10: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.873233 140104983627392 _logging.py:12] BatchNormLayer depth/res4/10: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/10: size:(64, 32, 32, 128) fn:add I0808 10:14:29.886708 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/10: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/11: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.887342 140104983627392 _logging.py:12] Conv2d depth/res1/11: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/11: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.889252 140104983627392 _logging.py:12] BatchNormLayer depth/res2/11: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/11: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.900572 140104983627392 _logging.py:12] Conv2d depth/res3/11: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/11: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.902492 140104983627392 _logging.py:12] BatchNormLayer depth/res4/11: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/11: size:(64, 32, 32, 128) fn:add I0808 10:14:29.916390 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/11: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/12: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.917006 140104983627392 _logging.py:12] Conv2d depth/res1/12: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/12: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.919012 140104983627392 _logging.py:12] BatchNormLayer depth/res2/12: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/12: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.930804 140104983627392 _logging.py:12] Conv2d depth/res3/12: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/12: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.932723 140104983627392 _logging.py:12] BatchNormLayer depth/res4/12: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/12: size:(64, 32, 32, 128) fn:add I0808 10:14:29.947494 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/12: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/13: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.948149 140104983627392 _logging.py:12] Conv2d depth/res1/13: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/13: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.950198 140104983627392 _logging.py:12] BatchNormLayer depth/res2/13: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/13: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.962272 140104983627392 _logging.py:12] Conv2d depth/res3/13: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/13: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.964210 140104983627392 _logging.py:12] BatchNormLayer depth/res4/13: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/13: size:(64, 32, 32, 128) fn:add I0808 10:14:29.979264 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/13: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/14: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.979965 140104983627392 _logging.py:12] Conv2d depth/res1/14: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/14: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:29.981923 140104983627392 _logging.py:12] BatchNormLayer depth/res2/14: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/14: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:29.993043 140104983627392 _logging.py:12] Conv2d depth/res3/14: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/14: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:29.995001 140104983627392 _logging.py:12] BatchNormLayer depth/res4/14: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/14: size:(64, 32, 32, 128) fn:add I0808 10:14:30.010754 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/14: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/res1/15: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:30.011425 140104983627392 _logging.py:12] Conv2d depth/res1/15: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res2/15: decay:0.900000 epsilon:0.000010 act:relu is_train:True I0808 10:14:30.013535 140104983627392 _logging.py:12] BatchNormLayer depth/res2/15: decay:0.900000 epsilon:0.000010 act:relu is_train:True [TL] Conv2d depth/res3/15: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:30.024666 140104983627392 _logging.py:12] Conv2d depth/res3/15: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/res4/15: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:30.026568 140104983627392 _logging.py:12] BatchNormLayer depth/res4/15: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/res5/15: size:(64, 32, 32, 128) fn:add I0808 10:14:30.042902 140104983627392 _logging.py:12] ElementwiseLayer depth/res5/15: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/cnn2: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:30.043577 140104983627392 _logging.py:12] Conv2d depth/cnn2: shape:(3, 3, 128, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] BatchNormLayer depth/bn2: decay:0.900000 epsilon:0.000010 act:identity is_train:True I0808 10:14:30.045449 140104983627392 _logging.py:12] BatchNormLayer depth/bn2: decay:0.900000 epsilon:0.000010 act:identity is_train:True [TL] ElementwiseLayer depth/sum2: size:(64, 32, 32, 128) fn:add I0808 10:14:30.059762 140104983627392 _logging.py:12] ElementwiseLayer depth/sum2: size:(64, 32, 32, 128) fn:add [TL] Conv2d depth/cnn/1: shape:(3, 3, 128, 256) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:30.060390 140104983627392 _logging.py:12] Conv2d depth/cnn/1: shape:(3, 3, 128, 256) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] SubpixelConv2d subpixel/0: scale: 2 n_out_channel: 64 act: relu I0808 10:14:30.062325 140104983627392 _logging.py:12] SubpixelConv2d subpixel/0: scale: 2 n_out_channel: 64 act: relu [TL] Conv2d depth/cnn/2: shape:(3, 3, 64, 128) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:30.063386 140104983627392 _logging.py:12] Conv2d depth/cnn/2: shape:(3, 3, 64, 128) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] SubpixelConv2d subpixel/1: scale: 2 n_out_channel: 32 act: relu I0808 10:14:30.065237 140104983627392 _logging.py:12] SubpixelConv2d subpixel/1: scale: 2 n_out_channel: 32 act: relu [TL] Conv2d depth/cnn/3: shape:(3, 3, 32, 64) strides:(1, 1, 1, 1) pad:SAME act:identity I0808 10:14:30.066335 140104983627392 _logging.py:12] Conv2d depth/cnn/3: shape:(3, 3, 32, 64) strides:(1, 1, 1, 1) pad:SAME act:identity [TL] SubpixelConv2d subpixel/2: scale: 2 n_out_channel: 16 act: relu I0808 10:14:30.068821 140104983627392 _logging.py:12] SubpixelConv2d subpixel/2: scale: 2 n_out_channel: 16 act: relu [TL] Conv2d depth/cnnout: shape:(1, 1, 16, 1) strides:(1, 1, 1, 1) pad:SAME act:sigmoid I0808 10:14:30.069854 140104983627392 _logging.py:12] Conv2d depth/cnnout: shape:(1, 1, 16, 1) strides:(1, 1, 1, 1) pad:SAME act:sigmoid [TL] param 0: depth/cnn1/W_conv2d:0 (3, 3, 2, 128) float32_ref I0808 10:14:30.098644 140104983627392 _logging.py:12] param 0: depth/cnn1/W_conv2d:0 (3, 3, 2, 128) float32_ref [TL] param 1: depth/cnn1/b_conv2d:0 (128,) float32_ref I0808 10:14:30.098846 140104983627392 _logging.py:12] param 1: depth/cnn1/b_conv2d:0 (128,) float32_ref [TL] param 2: depth/res1/0/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.098982 140104983627392 _logging.py:12] param 2: depth/res1/0/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 3: depth/res1/0/b_conv2d:0 (128,) float32_ref I0808 10:14:30.099107 140104983627392 _logging.py:12] param 3: depth/res1/0/b_conv2d:0 (128,) float32_ref [TL] param 4: depth/res2/0/beta:0 (128,) float32_ref I0808 10:14:30.099205 140104983627392 _logging.py:12] param 4: depth/res2/0/beta:0 (128,) float32_ref [TL] param 5: depth/res2/0/gamma:0 (128,) float32_ref I0808 10:14:30.099334 140104983627392 _logging.py:12] param 5: depth/res2/0/gamma:0 (128,) float32_ref [TL] param 6: depth/res2/0/moving_mean:0 (128,) float32_ref I0808 10:14:30.099461 140104983627392 _logging.py:12] param 6: depth/res2/0/moving_mean:0 (128,) float32_ref [TL] param 7: depth/res2/0/moving_variance:0 (128,) float32_ref I0808 10:14:30.099587 140104983627392 _logging.py:12] param 7: depth/res2/0/moving_variance:0 (128,) float32_ref [TL] param 8: depth/res3/0/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.099651 140104983627392 _logging.py:12] param 8: depth/res3/0/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 9: depth/res3/0/b_conv2d:0 (128,) float32_ref I0808 10:14:30.099710 140104983627392 _logging.py:12] param 9: depth/res3/0/b_conv2d:0 (128,) float32_ref [TL] param 10: depth/res4/0/beta:0 (128,) float32_ref I0808 10:14:30.099767 140104983627392 _logging.py:12] param 10: depth/res4/0/beta:0 (128,) float32_ref [TL] param 11: depth/res4/0/gamma:0 (128,) float32_ref I0808 10:14:30.099824 140104983627392 _logging.py:12] param 11: depth/res4/0/gamma:0 (128,) float32_ref [TL] param 12: depth/res4/0/moving_mean:0 (128,) float32_ref I0808 10:14:30.099881 140104983627392 _logging.py:12] param 12: depth/res4/0/moving_mean:0 (128,) float32_ref [TL] param 13: depth/res4/0/moving_variance:0 (128,) float32_ref I0808 10:14:30.099939 140104983627392 _logging.py:12] param 13: depth/res4/0/moving_variance:0 (128,) float32_ref [TL] param 14: depth/res1/1/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.100001 140104983627392 _logging.py:12] param 14: depth/res1/1/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 15: depth/res1/1/b_conv2d:0 (128,) float32_ref I0808 10:14:30.100059 140104983627392 _logging.py:12] param 15: depth/res1/1/b_conv2d:0 (128,) float32_ref [TL] param 16: depth/res2/1/beta:0 (128,) float32_ref I0808 10:14:30.100116 140104983627392 _logging.py:12] param 16: depth/res2/1/beta:0 (128,) float32_ref [TL] param 17: depth/res2/1/gamma:0 (128,) float32_ref I0808 10:14:30.100173 140104983627392 _logging.py:12] param 17: depth/res2/1/gamma:0 (128,) float32_ref [TL] param 18: depth/res2/1/moving_mean:0 (128,) float32_ref I0808 10:14:30.100230 140104983627392 _logging.py:12] param 18: depth/res2/1/moving_mean:0 (128,) float32_ref [TL] param 19: depth/res2/1/moving_variance:0 (128,) float32_ref I0808 10:14:30.100286 140104983627392 _logging.py:12] param 19: depth/res2/1/moving_variance:0 (128,) float32_ref [TL] param 20: depth/res3/1/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.100349 140104983627392 _logging.py:12] param 20: depth/res3/1/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 21: depth/res3/1/b_conv2d:0 (128,) float32_ref I0808 10:14:30.100406 140104983627392 _logging.py:12] param 21: depth/res3/1/b_conv2d:0 (128,) float32_ref [TL] param 22: depth/res4/1/beta:0 (128,) float32_ref I0808 10:14:30.100464 140104983627392 _logging.py:12] param 22: depth/res4/1/beta:0 (128,) float32_ref [TL] param 23: depth/res4/1/gamma:0 (128,) float32_ref I0808 10:14:30.100521 140104983627392 _logging.py:12] param 23: depth/res4/1/gamma:0 (128,) float32_ref [TL] param 24: depth/res4/1/moving_mean:0 (128,) float32_ref I0808 10:14:30.100578 140104983627392 _logging.py:12] param 24: depth/res4/1/moving_mean:0 (128,) float32_ref [TL] param 25: depth/res4/1/moving_variance:0 (128,) float32_ref I0808 10:14:30.100635 140104983627392 _logging.py:12] param 25: depth/res4/1/moving_variance:0 (128,) float32_ref [TL] param 26: depth/res1/2/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.100696 140104983627392 _logging.py:12] param 26: depth/res1/2/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 27: depth/res1/2/b_conv2d:0 (128,) float32_ref I0808 10:14:30.100754 140104983627392 _logging.py:12] param 27: depth/res1/2/b_conv2d:0 (128,) float32_ref [TL] param 28: depth/res2/2/beta:0 (128,) float32_ref I0808 10:14:30.100811 140104983627392 _logging.py:12] param 28: depth/res2/2/beta:0 (128,) float32_ref [TL] param 29: depth/res2/2/gamma:0 (128,) float32_ref I0808 10:14:30.100867 140104983627392 _logging.py:12] param 29: depth/res2/2/gamma:0 (128,) float32_ref [TL] param 30: depth/res2/2/moving_mean:0 (128,) float32_ref I0808 10:14:30.100924 140104983627392 _logging.py:12] param 30: depth/res2/2/moving_mean:0 (128,) float32_ref [TL] param 31: depth/res2/2/moving_variance:0 (128,) float32_ref I0808 10:14:30.100980 140104983627392 _logging.py:12] param 31: depth/res2/2/moving_variance:0 (128,) float32_ref [TL] param 32: depth/res3/2/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.101042 140104983627392 _logging.py:12] param 32: depth/res3/2/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 33: depth/res3/2/b_conv2d:0 (128,) float32_ref I0808 10:14:30.101099 140104983627392 _logging.py:12] param 33: depth/res3/2/b_conv2d:0 (128,) float32_ref [TL] param 34: depth/res4/2/beta:0 (128,) float32_ref I0808 10:14:30.101156 140104983627392 _logging.py:12] param 34: depth/res4/2/beta:0 (128,) float32_ref [TL] param 35: depth/res4/2/gamma:0 (128,) float32_ref I0808 10:14:30.101213 140104983627392 _logging.py:12] param 35: depth/res4/2/gamma:0 (128,) float32_ref [TL] param 36: depth/res4/2/moving_mean:0 (128,) float32_ref I0808 10:14:30.101269 140104983627392 _logging.py:12] param 36: depth/res4/2/moving_mean:0 (128,) float32_ref [TL] param 37: depth/res4/2/moving_variance:0 (128,) float32_ref I0808 10:14:30.101326 140104983627392 _logging.py:12] param 37: depth/res4/2/moving_variance:0 (128,) float32_ref [TL] param 38: depth/res1/3/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.101388 140104983627392 _logging.py:12] param 38: depth/res1/3/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 39: depth/res1/3/b_conv2d:0 (128,) float32_ref I0808 10:14:30.101447 140104983627392 _logging.py:12] param 39: depth/res1/3/b_conv2d:0 (128,) float32_ref [TL] param 40: depth/res2/3/beta:0 (128,) float32_ref I0808 10:14:30.101504 140104983627392 _logging.py:12] param 40: depth/res2/3/beta:0 (128,) float32_ref [TL] param 41: depth/res2/3/gamma:0 (128,) float32_ref I0808 10:14:30.101560 140104983627392 _logging.py:12] param 41: depth/res2/3/gamma:0 (128,) float32_ref [TL] param 42: depth/res2/3/moving_mean:0 (128,) float32_ref I0808 10:14:30.101615 140104983627392 _logging.py:12] param 42: depth/res2/3/moving_mean:0 (128,) float32_ref [TL] param 43: depth/res2/3/moving_variance:0 (128,) float32_ref I0808 10:14:30.101671 140104983627392 _logging.py:12] param 43: depth/res2/3/moving_variance:0 (128,) float32_ref [TL] param 44: depth/res3/3/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.101733 140104983627392 _logging.py:12] param 44: depth/res3/3/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 45: depth/res3/3/b_conv2d:0 (128,) float32_ref I0808 10:14:30.101792 140104983627392 _logging.py:12] param 45: depth/res3/3/b_conv2d:0 (128,) float32_ref [TL] param 46: depth/res4/3/beta:0 (128,) float32_ref I0808 10:14:30.101849 140104983627392 _logging.py:12] param 46: depth/res4/3/beta:0 (128,) float32_ref [TL] param 47: depth/res4/3/gamma:0 (128,) float32_ref I0808 10:14:30.101905 140104983627392 _logging.py:12] param 47: depth/res4/3/gamma:0 (128,) float32_ref [TL] param 48: depth/res4/3/moving_mean:0 (128,) float32_ref I0808 10:14:30.101960 140104983627392 _logging.py:12] param 48: depth/res4/3/moving_mean:0 (128,) float32_ref [TL] param 49: depth/res4/3/moving_variance:0 (128,) float32_ref I0808 10:14:30.102018 140104983627392 _logging.py:12] param 49: depth/res4/3/moving_variance:0 (128,) float32_ref [TL] param 50: depth/res1/4/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.102080 140104983627392 _logging.py:12] param 50: depth/res1/4/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 51: depth/res1/4/b_conv2d:0 (128,) float32_ref I0808 10:14:30.102144 140104983627392 _logging.py:12] param 51: depth/res1/4/b_conv2d:0 (128,) float32_ref [TL] param 52: depth/res2/4/beta:0 (128,) float32_ref I0808 10:14:30.102200 140104983627392 _logging.py:12] param 52: depth/res2/4/beta:0 (128,) float32_ref [TL] param 53: depth/res2/4/gamma:0 (128,) float32_ref I0808 10:14:30.102257 140104983627392 _logging.py:12] param 53: depth/res2/4/gamma:0 (128,) float32_ref [TL] param 54: depth/res2/4/moving_mean:0 (128,) float32_ref I0808 10:14:30.102313 140104983627392 _logging.py:12] param 54: depth/res2/4/moving_mean:0 (128,) float32_ref [TL] param 55: depth/res2/4/moving_variance:0 (128,) float32_ref I0808 10:14:30.102370 140104983627392 _logging.py:12] param 55: depth/res2/4/moving_variance:0 (128,) float32_ref [TL] param 56: depth/res3/4/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.102432 140104983627392 _logging.py:12] param 56: depth/res3/4/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 57: depth/res3/4/b_conv2d:0 (128,) float32_ref I0808 10:14:30.102490 140104983627392 _logging.py:12] param 57: depth/res3/4/b_conv2d:0 (128,) float32_ref [TL] param 58: depth/res4/4/beta:0 (128,) float32_ref I0808 10:14:30.102547 140104983627392 _logging.py:12] param 58: depth/res4/4/beta:0 (128,) float32_ref [TL] param 59: depth/res4/4/gamma:0 (128,) float32_ref I0808 10:14:30.102605 140104983627392 _logging.py:12] param 59: depth/res4/4/gamma:0 (128,) float32_ref [TL] param 60: depth/res4/4/moving_mean:0 (128,) float32_ref I0808 10:14:30.102662 140104983627392 _logging.py:12] param 60: depth/res4/4/moving_mean:0 (128,) float32_ref [TL] param 61: depth/res4/4/moving_variance:0 (128,) float32_ref I0808 10:14:30.102718 140104983627392 _logging.py:12] param 61: depth/res4/4/moving_variance:0 (128,) float32_ref [TL] param 62: depth/res1/5/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.102780 140104983627392 _logging.py:12] param 62: depth/res1/5/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 63: depth/res1/5/b_conv2d:0 (128,) float32_ref I0808 10:14:30.102838 140104983627392 _logging.py:12] param 63: depth/res1/5/b_conv2d:0 (128,) float32_ref [TL] param 64: depth/res2/5/beta:0 (128,) float32_ref I0808 10:14:30.102895 140104983627392 _logging.py:12] param 64: depth/res2/5/beta:0 (128,) float32_ref [TL] param 65: depth/res2/5/gamma:0 (128,) float32_ref I0808 10:14:30.102952 140104983627392 _logging.py:12] param 65: depth/res2/5/gamma:0 (128,) float32_ref [TL] param 66: depth/res2/5/moving_mean:0 (128,) float32_ref I0808 10:14:30.103008 140104983627392 _logging.py:12] param 66: depth/res2/5/moving_mean:0 (128,) float32_ref [TL] param 67: depth/res2/5/moving_variance:0 (128,) float32_ref I0808 10:14:30.103065 140104983627392 _logging.py:12] param 67: depth/res2/5/moving_variance:0 (128,) float32_ref [TL] param 68: depth/res3/5/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.103126 140104983627392 _logging.py:12] param 68: depth/res3/5/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 69: depth/res3/5/b_conv2d:0 (128,) float32_ref I0808 10:14:30.103183 140104983627392 _logging.py:12] param 69: depth/res3/5/b_conv2d:0 (128,) float32_ref [TL] param 70: depth/res4/5/beta:0 (128,) float32_ref I0808 10:14:30.103239 140104983627392 _logging.py:12] param 70: depth/res4/5/beta:0 (128,) float32_ref [TL] param 71: depth/res4/5/gamma:0 (128,) float32_ref I0808 10:14:30.103296 140104983627392 _logging.py:12] param 71: depth/res4/5/gamma:0 (128,) float32_ref [TL] param 72: depth/res4/5/moving_mean:0 (128,) float32_ref I0808 10:14:30.103353 140104983627392 _logging.py:12] param 72: depth/res4/5/moving_mean:0 (128,) float32_ref [TL] param 73: depth/res4/5/moving_variance:0 (128,) float32_ref I0808 10:14:30.103410 140104983627392 _logging.py:12] param 73: depth/res4/5/moving_variance:0 (128,) float32_ref [TL] param 74: depth/res1/6/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.103471 140104983627392 _logging.py:12] param 74: depth/res1/6/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 75: depth/res1/6/b_conv2d:0 (128,) float32_ref I0808 10:14:30.103528 140104983627392 _logging.py:12] param 75: depth/res1/6/b_conv2d:0 (128,) float32_ref [TL] param 76: depth/res2/6/beta:0 (128,) float32_ref I0808 10:14:30.103584 140104983627392 _logging.py:12] param 76: depth/res2/6/beta:0 (128,) float32_ref [TL] param 77: depth/res2/6/gamma:0 (128,) float32_ref I0808 10:14:30.103641 140104983627392 _logging.py:12] param 77: depth/res2/6/gamma:0 (128,) float32_ref [TL] param 78: depth/res2/6/moving_mean:0 (128,) float32_ref I0808 10:14:30.103696 140104983627392 _logging.py:12] param 78: depth/res2/6/moving_mean:0 (128,) float32_ref [TL] param 79: depth/res2/6/moving_variance:0 (128,) float32_ref I0808 10:14:30.103751 140104983627392 _logging.py:12] param 79: depth/res2/6/moving_variance:0 (128,) float32_ref [TL] param 80: depth/res3/6/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.103812 140104983627392 _logging.py:12] param 80: depth/res3/6/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 81: depth/res3/6/b_conv2d:0 (128,) float32_ref I0808 10:14:30.103871 140104983627392 _logging.py:12] param 81: depth/res3/6/b_conv2d:0 (128,) float32_ref [TL] param 82: depth/res4/6/beta:0 (128,) float32_ref I0808 10:14:30.103928 140104983627392 _logging.py:12] param 82: depth/res4/6/beta:0 (128,) float32_ref [TL] param 83: depth/res4/6/gamma:0 (128,) float32_ref I0808 10:14:30.103984 140104983627392 _logging.py:12] param 83: depth/res4/6/gamma:0 (128,) float32_ref [TL] param 84: depth/res4/6/moving_mean:0 (128,) float32_ref I0808 10:14:30.104039 140104983627392 _logging.py:12] param 84: depth/res4/6/moving_mean:0 (128,) float32_ref [TL] param 85: depth/res4/6/moving_variance:0 (128,) float32_ref I0808 10:14:30.104095 140104983627392 _logging.py:12] param 85: depth/res4/6/moving_variance:0 (128,) float32_ref [TL] param 86: depth/res1/7/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.104156 140104983627392 _logging.py:12] param 86: depth/res1/7/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 87: depth/res1/7/b_conv2d:0 (128,) float32_ref I0808 10:14:30.104213 140104983627392 _logging.py:12] param 87: depth/res1/7/b_conv2d:0 (128,) float32_ref [TL] param 88: depth/res2/7/beta:0 (128,) float32_ref I0808 10:14:30.104270 140104983627392 _logging.py:12] param 88: depth/res2/7/beta:0 (128,) float32_ref [TL] param 89: depth/res2/7/gamma:0 (128,) float32_ref I0808 10:14:30.104327 140104983627392 _logging.py:12] param 89: depth/res2/7/gamma:0 (128,) float32_ref [TL] param 90: depth/res2/7/moving_mean:0 (128,) float32_ref I0808 10:14:30.104384 140104983627392 _logging.py:12] param 90: depth/res2/7/moving_mean:0 (128,) float32_ref [TL] param 91: depth/res2/7/moving_variance:0 (128,) float32_ref I0808 10:14:30.104440 140104983627392 _logging.py:12] param 91: depth/res2/7/moving_variance:0 (128,) float32_ref [TL] param 92: depth/res3/7/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.104504 140104983627392 _logging.py:12] param 92: depth/res3/7/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 93: depth/res3/7/b_conv2d:0 (128,) float32_ref I0808 10:14:30.104561 140104983627392 _logging.py:12] param 93: depth/res3/7/b_conv2d:0 (128,) float32_ref [TL] param 94: depth/res4/7/beta:0 (128,) float32_ref I0808 10:14:30.104618 140104983627392 _logging.py:12] param 94: depth/res4/7/beta:0 (128,) float32_ref [TL] param 95: depth/res4/7/gamma:0 (128,) float32_ref I0808 10:14:30.104672 140104983627392 _logging.py:12] param 95: depth/res4/7/gamma:0 (128,) float32_ref [TL] param 96: depth/res4/7/moving_mean:0 (128,) float32_ref I0808 10:14:30.104728 140104983627392 _logging.py:12] param 96: depth/res4/7/moving_mean:0 (128,) float32_ref [TL] param 97: depth/res4/7/moving_variance:0 (128,) float32_ref I0808 10:14:30.104784 140104983627392 _logging.py:12] param 97: depth/res4/7/moving_variance:0 (128,) float32_ref [TL] param 98: depth/res1/8/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.104846 140104983627392 _logging.py:12] param 98: depth/res1/8/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 99: depth/res1/8/b_conv2d:0 (128,) float32_ref I0808 10:14:30.104902 140104983627392 _logging.py:12] param 99: depth/res1/8/b_conv2d:0 (128,) float32_ref [TL] param 100: depth/res2/8/beta:0 (128,) float32_ref I0808 10:14:30.104959 140104983627392 _logging.py:12] param 100: depth/res2/8/beta:0 (128,) float32_ref [TL] param 101: depth/res2/8/gamma:0 (128,) float32_ref I0808 10:14:30.105015 140104983627392 _logging.py:12] param 101: depth/res2/8/gamma:0 (128,) float32_ref [TL] param 102: depth/res2/8/moving_mean:0 (128,) float32_ref I0808 10:14:30.105072 140104983627392 _logging.py:12] param 102: depth/res2/8/moving_mean:0 (128,) float32_ref [TL] param 103: depth/res2/8/moving_variance:0 (128,) float32_ref I0808 10:14:30.105128 140104983627392 _logging.py:12] param 103: depth/res2/8/moving_variance:0 (128,) float32_ref [TL] param 104: depth/res3/8/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.105189 140104983627392 _logging.py:12] param 104: depth/res3/8/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 105: depth/res3/8/b_conv2d:0 (128,) float32_ref I0808 10:14:30.105246 140104983627392 _logging.py:12] param 105: depth/res3/8/b_conv2d:0 (128,) float32_ref [TL] param 106: depth/res4/8/beta:0 (128,) float32_ref I0808 10:14:30.105303 140104983627392 _logging.py:12] param 106: depth/res4/8/beta:0 (128,) float32_ref [TL] param 107: depth/res4/8/gamma:0 (128,) float32_ref I0808 10:14:30.105359 140104983627392 _logging.py:12] param 107: depth/res4/8/gamma:0 (128,) float32_ref [TL] param 108: depth/res4/8/moving_mean:0 (128,) float32_ref I0808 10:14:30.105413 140104983627392 _logging.py:12] param 108: depth/res4/8/moving_mean:0 (128,) float32_ref [TL] param 109: depth/res4/8/moving_variance:0 (128,) float32_ref I0808 10:14:30.105470 140104983627392 _logging.py:12] param 109: depth/res4/8/moving_variance:0 (128,) float32_ref [TL] param 110: depth/res1/9/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.105535 140104983627392 _logging.py:12] param 110: depth/res1/9/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 111: depth/res1/9/b_conv2d:0 (128,) float32_ref I0808 10:14:30.105593 140104983627392 _logging.py:12] param 111: depth/res1/9/b_conv2d:0 (128,) float32_ref [TL] param 112: depth/res2/9/beta:0 (128,) float32_ref I0808 10:14:30.105650 140104983627392 _logging.py:12] param 112: depth/res2/9/beta:0 (128,) float32_ref [TL] param 113: depth/res2/9/gamma:0 (128,) float32_ref I0808 10:14:30.105720 140104983627392 _logging.py:12] param 113: depth/res2/9/gamma:0 (128,) float32_ref [TL] param 114: depth/res2/9/moving_mean:0 (128,) float32_ref I0808 10:14:30.105791 140104983627392 _logging.py:12] param 114: depth/res2/9/moving_mean:0 (128,) float32_ref [TL] param 115: depth/res2/9/moving_variance:0 (128,) float32_ref I0808 10:14:30.105859 140104983627392 _logging.py:12] param 115: depth/res2/9/moving_variance:0 (128,) float32_ref [TL] param 116: depth/res3/9/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.105920 140104983627392 _logging.py:12] param 116: depth/res3/9/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 117: depth/res3/9/b_conv2d:0 (128,) float32_ref I0808 10:14:30.105974 140104983627392 _logging.py:12] param 117: depth/res3/9/b_conv2d:0 (128,) float32_ref [TL] param 118: depth/res4/9/beta:0 (128,) float32_ref I0808 10:14:30.106029 140104983627392 _logging.py:12] param 118: depth/res4/9/beta:0 (128,) float32_ref [TL] param 119: depth/res4/9/gamma:0 (128,) float32_ref I0808 10:14:30.106101 140104983627392 _logging.py:12] param 119: depth/res4/9/gamma:0 (128,) float32_ref [TL] param 120: depth/res4/9/moving_mean:0 (128,) float32_ref I0808 10:14:30.106173 140104983627392 _logging.py:12] param 120: depth/res4/9/moving_mean:0 (128,) float32_ref [TL] param 121: depth/res4/9/moving_variance:0 (128,) float32_ref I0808 10:14:30.106228 140104983627392 _logging.py:12] param 121: depth/res4/9/moving_variance:0 (128,) float32_ref [TL] param 122: depth/res1/10/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.106287 140104983627392 _logging.py:12] param 122: depth/res1/10/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 123: depth/res1/10/b_conv2d:0 (128,) float32_ref I0808 10:14:30.106343 140104983627392 _logging.py:12] param 123: depth/res1/10/b_conv2d:0 (128,) float32_ref [TL] param 124: depth/res2/10/beta:0 (128,) float32_ref I0808 10:14:30.106400 140104983627392 _logging.py:12] param 124: depth/res2/10/beta:0 (128,) float32_ref [TL] param 125: depth/res2/10/gamma:0 (128,) float32_ref I0808 10:14:30.106455 140104983627392 _logging.py:12] param 125: depth/res2/10/gamma:0 (128,) float32_ref [TL] param 126: depth/res2/10/moving_mean:0 (128,) float32_ref I0808 10:14:30.106509 140104983627392 _logging.py:12] param 126: depth/res2/10/moving_mean:0 (128,) float32_ref [TL] param 127: depth/res2/10/moving_variance:0 (128,) float32_ref I0808 10:14:30.106564 140104983627392 _logging.py:12] param 127: depth/res2/10/moving_variance:0 (128,) float32_ref [TL] param 128: depth/res3/10/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.106623 140104983627392 _logging.py:12] param 128: depth/res3/10/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 129: depth/res3/10/b_conv2d:0 (128,) float32_ref I0808 10:14:30.106679 140104983627392 _logging.py:12] param 129: depth/res3/10/b_conv2d:0 (128,) float32_ref [TL] param 130: depth/res4/10/beta:0 (128,) float32_ref I0808 10:14:30.106734 140104983627392 _logging.py:12] param 130: depth/res4/10/beta:0 (128,) float32_ref [TL] param 131: depth/res4/10/gamma:0 (128,) float32_ref I0808 10:14:30.106789 140104983627392 _logging.py:12] param 131: depth/res4/10/gamma:0 (128,) float32_ref [TL] param 132: depth/res4/10/moving_mean:0 (128,) float32_ref I0808 10:14:30.106843 140104983627392 _logging.py:12] param 132: depth/res4/10/moving_mean:0 (128,) float32_ref [TL] param 133: depth/res4/10/moving_variance:0 (128,) float32_ref I0808 10:14:30.106898 140104983627392 _logging.py:12] param 133: depth/res4/10/moving_variance:0 (128,) float32_ref [TL] param 134: depth/res1/11/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.106956 140104983627392 _logging.py:12] param 134: depth/res1/11/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 135: depth/res1/11/b_conv2d:0 (128,) float32_ref I0808 10:14:30.107012 140104983627392 _logging.py:12] param 135: depth/res1/11/b_conv2d:0 (128,) float32_ref [TL] param 136: depth/res2/11/beta:0 (128,) float32_ref I0808 10:14:30.107067 140104983627392 _logging.py:12] param 136: depth/res2/11/beta:0 (128,) float32_ref [TL] param 137: depth/res2/11/gamma:0 (128,) float32_ref I0808 10:14:30.107121 140104983627392 _logging.py:12] param 137: depth/res2/11/gamma:0 (128,) float32_ref [TL] param 138: depth/res2/11/moving_mean:0 (128,) float32_ref I0808 10:14:30.107176 140104983627392 _logging.py:12] param 138: depth/res2/11/moving_mean:0 (128,) float32_ref [TL] param 139: depth/res2/11/moving_variance:0 (128,) float32_ref I0808 10:14:30.107229 140104983627392 _logging.py:12] param 139: depth/res2/11/moving_variance:0 (128,) float32_ref [TL] param 140: depth/res3/11/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.107289 140104983627392 _logging.py:12] param 140: depth/res3/11/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 141: depth/res3/11/b_conv2d:0 (128,) float32_ref I0808 10:14:30.107346 140104983627392 _logging.py:12] param 141: depth/res3/11/b_conv2d:0 (128,) float32_ref [TL] param 142: depth/res4/11/beta:0 (128,) float32_ref I0808 10:14:30.107399 140104983627392 _logging.py:12] param 142: depth/res4/11/beta:0 (128,) float32_ref [TL] param 143: depth/res4/11/gamma:0 (128,) float32_ref I0808 10:14:30.107455 140104983627392 _logging.py:12] param 143: depth/res4/11/gamma:0 (128,) float32_ref [TL] param 144: depth/res4/11/moving_mean:0 (128,) float32_ref I0808 10:14:30.107508 140104983627392 _logging.py:12] param 144: depth/res4/11/moving_mean:0 (128,) float32_ref [TL] param 145: depth/res4/11/moving_variance:0 (128,) float32_ref I0808 10:14:30.107563 140104983627392 _logging.py:12] param 145: depth/res4/11/moving_variance:0 (128,) float32_ref [TL] param 146: depth/res1/12/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.107623 140104983627392 _logging.py:12] param 146: depth/res1/12/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 147: depth/res1/12/b_conv2d:0 (128,) float32_ref I0808 10:14:30.107678 140104983627392 _logging.py:12] param 147: depth/res1/12/b_conv2d:0 (128,) float32_ref [TL] param 148: depth/res2/12/beta:0 (128,) float32_ref I0808 10:14:30.107733 140104983627392 _logging.py:12] param 148: depth/res2/12/beta:0 (128,) float32_ref [TL] param 149: depth/res2/12/gamma:0 (128,) float32_ref I0808 10:14:30.107786 140104983627392 _logging.py:12] param 149: depth/res2/12/gamma:0 (128,) float32_ref [TL] param 150: depth/res2/12/moving_mean:0 (128,) float32_ref I0808 10:14:30.107841 140104983627392 _logging.py:12] param 150: depth/res2/12/moving_mean:0 (128,) float32_ref [TL] param 151: depth/res2/12/moving_variance:0 (128,) float32_ref I0808 10:14:30.107896 140104983627392 _logging.py:12] param 151: depth/res2/12/moving_variance:0 (128,) float32_ref [TL] param 152: depth/res3/12/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.107955 140104983627392 _logging.py:12] param 152: depth/res3/12/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 153: depth/res3/12/b_conv2d:0 (128,) float32_ref I0808 10:14:30.108011 140104983627392 _logging.py:12] param 153: depth/res3/12/b_conv2d:0 (128,) float32_ref [TL] param 154: depth/res4/12/beta:0 (128,) float32_ref I0808 10:14:30.108066 140104983627392 _logging.py:12] param 154: depth/res4/12/beta:0 (128,) float32_ref [TL] param 155: depth/res4/12/gamma:0 (128,) float32_ref I0808 10:14:30.108119 140104983627392 _logging.py:12] param 155: depth/res4/12/gamma:0 (128,) float32_ref [TL] param 156: depth/res4/12/moving_mean:0 (128,) float32_ref I0808 10:14:30.108175 140104983627392 _logging.py:12] param 156: depth/res4/12/moving_mean:0 (128,) float32_ref [TL] param 157: depth/res4/12/moving_variance:0 (128,) float32_ref I0808 10:14:30.108228 140104983627392 _logging.py:12] param 157: depth/res4/12/moving_variance:0 (128,) float32_ref [TL] param 158: depth/res1/13/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.108287 140104983627392 _logging.py:12] param 158: depth/res1/13/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 159: depth/res1/13/b_conv2d:0 (128,) float32_ref I0808 10:14:30.108342 140104983627392 _logging.py:12] param 159: depth/res1/13/b_conv2d:0 (128,) float32_ref [TL] param 160: depth/res2/13/beta:0 (128,) float32_ref I0808 10:14:30.108397 140104983627392 _logging.py:12] param 160: depth/res2/13/beta:0 (128,) float32_ref [TL] param 161: depth/res2/13/gamma:0 (128,) float32_ref I0808 10:14:30.108450 140104983627392 _logging.py:12] param 161: depth/res2/13/gamma:0 (128,) float32_ref [TL] param 162: depth/res2/13/moving_mean:0 (128,) float32_ref I0808 10:14:30.108505 140104983627392 _logging.py:12] param 162: depth/res2/13/moving_mean:0 (128,) float32_ref [TL] param 163: depth/res2/13/moving_variance:0 (128,) float32_ref I0808 10:14:30.108560 140104983627392 _logging.py:12] param 163: depth/res2/13/moving_variance:0 (128,) float32_ref [TL] param 164: depth/res3/13/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.108619 140104983627392 _logging.py:12] param 164: depth/res3/13/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 165: depth/res3/13/b_conv2d:0 (128,) float32_ref I0808 10:14:30.108675 140104983627392 _logging.py:12] param 165: depth/res3/13/b_conv2d:0 (128,) float32_ref [TL] param 166: depth/res4/13/beta:0 (128,) float32_ref I0808 10:14:30.108731 140104983627392 _logging.py:12] param 166: depth/res4/13/beta:0 (128,) float32_ref [TL] param 167: depth/res4/13/gamma:0 (128,) float32_ref I0808 10:14:30.108784 140104983627392 _logging.py:12] param 167: depth/res4/13/gamma:0 (128,) float32_ref [TL] param 168: depth/res4/13/moving_mean:0 (128,) float32_ref I0808 10:14:30.108839 140104983627392 _logging.py:12] param 168: depth/res4/13/moving_mean:0 (128,) float32_ref [TL] param 169: depth/res4/13/moving_variance:0 (128,) float32_ref I0808 10:14:30.108922 140104983627392 _logging.py:12] param 169: depth/res4/13/moving_variance:0 (128,) float32_ref [TL] param 170: depth/res1/14/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.108980 140104983627392 _logging.py:12] param 170: depth/res1/14/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 171: depth/res1/14/b_conv2d:0 (128,) float32_ref I0808 10:14:30.109035 140104983627392 _logging.py:12] param 171: depth/res1/14/b_conv2d:0 (128,) float32_ref [TL] param 172: depth/res2/14/beta:0 (128,) float32_ref I0808 10:14:30.109091 140104983627392 _logging.py:12] param 172: depth/res2/14/beta:0 (128,) float32_ref [TL] param 173: depth/res2/14/gamma:0 (128,) float32_ref I0808 10:14:30.109144 140104983627392 _logging.py:12] param 173: depth/res2/14/gamma:0 (128,) float32_ref [TL] param 174: depth/res2/14/moving_mean:0 (128,) float32_ref I0808 10:14:30.109199 140104983627392 _logging.py:12] param 174: depth/res2/14/moving_mean:0 (128,) float32_ref [TL] param 175: depth/res2/14/moving_variance:0 (128,) float32_ref I0808 10:14:30.109252 140104983627392 _logging.py:12] param 175: depth/res2/14/moving_variance:0 (128,) float32_ref [TL] param 176: depth/res3/14/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.109312 140104983627392 _logging.py:12] param 176: depth/res3/14/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 177: depth/res3/14/b_conv2d:0 (128,) float32_ref I0808 10:14:30.109366 140104983627392 _logging.py:12] param 177: depth/res3/14/b_conv2d:0 (128,) float32_ref [TL] param 178: depth/res4/14/beta:0 (128,) float32_ref I0808 10:14:30.109421 140104983627392 _logging.py:12] param 178: depth/res4/14/beta:0 (128,) float32_ref [TL] param 179: depth/res4/14/gamma:0 (128,) float32_ref I0808 10:14:30.109474 140104983627392 _logging.py:12] param 179: depth/res4/14/gamma:0 (128,) float32_ref [TL] param 180: depth/res4/14/moving_mean:0 (128,) float32_ref I0808 10:14:30.109529 140104983627392 _logging.py:12] param 180: depth/res4/14/moving_mean:0 (128,) float32_ref [TL] param 181: depth/res4/14/moving_variance:0 (128,) float32_ref I0808 10:14:30.109582 140104983627392 _logging.py:12] param 181: depth/res4/14/moving_variance:0 (128,) float32_ref [TL] param 182: depth/res1/15/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.109642 140104983627392 _logging.py:12] param 182: depth/res1/15/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 183: depth/res1/15/b_conv2d:0 (128,) float32_ref I0808 10:14:30.109698 140104983627392 _logging.py:12] param 183: depth/res1/15/b_conv2d:0 (128,) float32_ref [TL] param 184: depth/res2/15/beta:0 (128,) float32_ref I0808 10:14:30.109751 140104983627392 _logging.py:12] param 184: depth/res2/15/beta:0 (128,) float32_ref [TL] param 185: depth/res2/15/gamma:0 (128,) float32_ref I0808 10:14:30.109807 140104983627392 _logging.py:12] param 185: depth/res2/15/gamma:0 (128,) float32_ref [TL] param 186: depth/res2/15/moving_mean:0 (128,) float32_ref I0808 10:14:30.109860 140104983627392 _logging.py:12] param 186: depth/res2/15/moving_mean:0 (128,) float32_ref [TL] param 187: depth/res2/15/moving_variance:0 (128,) float32_ref I0808 10:14:30.109915 140104983627392 _logging.py:12] param 187: depth/res2/15/moving_variance:0 (128,) float32_ref [TL] param 188: depth/res3/15/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.109975 140104983627392 _logging.py:12] param 188: depth/res3/15/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 189: depth/res3/15/b_conv2d:0 (128,) float32_ref I0808 10:14:30.110029 140104983627392 _logging.py:12] param 189: depth/res3/15/b_conv2d:0 (128,) float32_ref [TL] param 190: depth/res4/15/beta:0 (128,) float32_ref I0808 10:14:30.110088 140104983627392 _logging.py:12] param 190: depth/res4/15/beta:0 (128,) float32_ref [TL] param 191: depth/res4/15/gamma:0 (128,) float32_ref I0808 10:14:30.110145 140104983627392 _logging.py:12] param 191: depth/res4/15/gamma:0 (128,) float32_ref [TL] param 192: depth/res4/15/moving_mean:0 (128,) float32_ref I0808 10:14:30.110199 140104983627392 _logging.py:12] param 192: depth/res4/15/moving_mean:0 (128,) float32_ref [TL] param 193: depth/res4/15/moving_variance:0 (128,) float32_ref I0808 10:14:30.110254 140104983627392 _logging.py:12] param 193: depth/res4/15/moving_variance:0 (128,) float32_ref [TL] param 194: depth/cnn2/W_conv2d:0 (3, 3, 128, 128) float32_ref I0808 10:14:30.110313 140104983627392 _logging.py:12] param 194: depth/cnn2/W_conv2d:0 (3, 3, 128, 128) float32_ref [TL] param 195: depth/cnn2/b_conv2d:0 (128,) float32_ref I0808 10:14:30.110368 140104983627392 _logging.py:12] param 195: depth/cnn2/b_conv2d:0 (128,) float32_ref [TL] param 196: depth/bn2/beta:0 (128,) float32_ref I0808 10:14:30.110445 140104983627392 _logging.py:12] param 196: depth/bn2/beta:0 (128,) float32_ref [TL] param 197: depth/bn2/gamma:0 (128,) float32_ref I0808 10:14:30.110513 140104983627392 _logging.py:12] param 197: depth/bn2/gamma:0 (128,) float32_ref [TL] param 198: depth/bn2/moving_mean:0 (128,) float32_ref I0808 10:14:30.110569 140104983627392 _logging.py:12] param 198: depth/bn2/moving_mean:0 (128,) float32_ref [TL] param 199: depth/bn2/moving_variance:0 (128,) float32_ref I0808 10:14:30.110624 140104983627392 _logging.py:12] param 199: depth/bn2/moving_variance:0 (128,) float32_ref [TL] param 200: depth/cnn/1/W_conv2d:0 (3, 3, 128, 256) float32_ref I0808 10:14:30.110682 140104983627392 _logging.py:12] param 200: depth/cnn/1/W_conv2d:0 (3, 3, 128, 256) float32_ref [TL] param 201: depth/cnn/1/b_conv2d:0 (256,) float32_ref I0808 10:14:30.110738 140104983627392 _logging.py:12] param 201: depth/cnn/1/b_conv2d:0 (256,) float32_ref [TL] param 202: depth/cnn/2/W_conv2d:0 (3, 3, 64, 128) float32_ref I0808 10:14:30.110796 140104983627392 _logging.py:12] param 202: depth/cnn/2/W_conv2d:0 (3, 3, 64, 128) float32_ref [TL] param 203: depth/cnn/2/b_conv2d:0 (128,) float32_ref I0808 10:14:30.110852 140104983627392 _logging.py:12] param 203: depth/cnn/2/b_conv2d:0 (128,) float32_ref [TL] param 204: depth/cnn/3/W_conv2d:0 (3, 3, 32, 64) float32_ref I0808 10:14:30.110912 140104983627392 _logging.py:12] param 204: depth/cnn/3/W_conv2d:0 (3, 3, 32, 64) float32_ref [TL] param 205: depth/cnn/3/b_conv2d:0 (64,) float32_ref I0808 10:14:30.110968 140104983627392 _logging.py:12] param 205: depth/cnn/3/b_conv2d:0 (64,) float32_ref [TL] param 206: depth/cnnout/W_conv2d:0 (1, 1, 16, 1) float32_ref I0808 10:14:30.111027 140104983627392 _logging.py:12] param 206: depth/cnnout/W_conv2d:0 (1, 1, 16, 1) float32_ref [TL] param 207: depth/cnnout/b_conv2d:0 (1,) float32_ref I0808 10:14:30.111082 140104983627392 _logging.py:12] param 207: depth/cnnout/b_conv2d:0 (1,) float32_ref [TL] num of params: 5277137 I0808 10:14:30.111793 140104983627392 _logging.py:12] num of params: 5277137 [TL] [] geting variables with depth I0808 10:14:30.111850 140104983627392 _logging.py:12] [] geting variables with depth [TL] got 0: depth/cnn1/W_conv2d:0 (3, 3, 2, 128) I0808 10:14:30.112032 140104983627392 _logging.py:12] got 0: depth/cnn1/W_conv2d:0 (3, 3, 2, 128) [TL] got 1: depth/cnn1/b_conv2d:0 (128,) I0808 10:14:30.112095 140104983627392 _logging.py:12] got 1: depth/cnn1/b_conv2d:0 (128,) [TL] got 2: depth/res1/0/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.112154 140104983627392 _logging.py:12] got 2: depth/res1/0/W_conv2d:0 (3, 3, 128, 128) [TL] got 3: depth/res1/0/b_conv2d:0 (128,) I0808 10:14:30.112207 140104983627392 _logging.py:12] got 3: depth/res1/0/b_conv2d:0 (128,) [TL] got 4: depth/res2/0/beta:0 (128,) I0808 10:14:30.112262 140104983627392 _logging.py:12] got 4: depth/res2/0/beta:0 (128,) [TL] got 5: depth/res2/0/gamma:0 (128,) I0808 10:14:30.112314 140104983627392 _logging.py:12] got 5: depth/res2/0/gamma:0 (128,) [TL] got 6: depth/res3/0/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.112371 140104983627392 _logging.py:12] got 6: depth/res3/0/W_conv2d:0 (3, 3, 128, 128) [TL] got 7: depth/res3/0/b_conv2d:0 (128,) I0808 10:14:30.112425 140104983627392 _logging.py:12] got 7: depth/res3/0/b_conv2d:0 (128,) [TL] got 8: depth/res4/0/beta:0 (128,) I0808 10:14:30.112478 140104983627392 _logging.py:12] got 8: depth/res4/0/beta:0 (128,) [TL] got 9: depth/res4/0/gamma:0 (128,) I0808 10:14:30.112530 140104983627392 _logging.py:12] got 9: depth/res4/0/gamma:0 (128,) [TL] got 10: depth/res1/1/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.112587 140104983627392 _logging.py:12] got 10: depth/res1/1/W_conv2d:0 (3, 3, 128, 128) [TL] got 11: depth/res1/1/b_conv2d:0 (128,) I0808 10:14:30.112642 140104983627392 _logging.py:12] got 11: depth/res1/1/b_conv2d:0 (128,) [TL] got 12: depth/res2/1/beta:0 (128,) I0808 10:14:30.112694 140104983627392 _logging.py:12] got 12: depth/res2/1/beta:0 (128,) [TL] got 13: depth/res2/1/gamma:0 (128,) I0808 10:14:30.112746 140104983627392 _logging.py:12] got 13: depth/res2/1/gamma:0 (128,) [TL] got 14: depth/res3/1/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.112803 140104983627392 _logging.py:12] got 14: depth/res3/1/W_conv2d:0 (3, 3, 128, 128) [TL] got 15: depth/res3/1/b_conv2d:0 (128,) I0808 10:14:30.112858 140104983627392 _logging.py:12] got 15: depth/res3/1/b_conv2d:0 (128,) [TL] got 16: depth/res4/1/beta:0 (128,) I0808 10:14:30.112910 140104983627392 _logging.py:12] got 16: depth/res4/1/beta:0 (128,) [TL] got 17: depth/res4/1/gamma:0 (128,) I0808 10:14:30.112962 140104983627392 _logging.py:12] got 17: depth/res4/1/gamma:0 (128,) [TL] got 18: depth/res1/2/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.113018 140104983627392 _logging.py:12] got 18: depth/res1/2/W_conv2d:0 (3, 3, 128, 128) [TL] got 19: depth/res1/2/b_conv2d:0 (128,) I0808 10:14:30.113073 140104983627392 _logging.py:12] got 19: depth/res1/2/b_conv2d:0 (128,) [TL] got 20: depth/res2/2/beta:0 (128,) I0808 10:14:30.113126 140104983627392 _logging.py:12] got 20: depth/res2/2/beta:0 (128,) [TL] got 21: depth/res2/2/gamma:0 (128,) I0808 10:14:30.113178 140104983627392 _logging.py:12] got 21: depth/res2/2/gamma:0 (128,) [TL] got 22: depth/res3/2/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.113234 140104983627392 _logging.py:12] got 22: depth/res3/2/W_conv2d:0 (3, 3, 128, 128) [TL] got 23: depth/res3/2/b_conv2d:0 (128,) I0808 10:14:30.113287 140104983627392 _logging.py:12] got 23: depth/res3/2/b_conv2d:0 (128,) [TL] got 24: depth/res4/2/beta:0 (128,) I0808 10:14:30.113341 140104983627392 _logging.py:12] got 24: depth/res4/2/beta:0 (128,) [TL] got 25: depth/res4/2/gamma:0 (128,) I0808 10:14:30.113394 140104983627392 _logging.py:12] got 25: depth/res4/2/gamma:0 (128,) [TL] got 26: depth/res1/3/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.113451 140104983627392 _logging.py:12] got 26: depth/res1/3/W_conv2d:0 (3, 3, 128, 128) [TL] got 27: depth/res1/3/b_conv2d:0 (128,) I0808 10:14:30.113504 140104983627392 _logging.py:12] got 27: depth/res1/3/b_conv2d:0 (128,) [TL] got 28: depth/res2/3/beta:0 (128,) I0808 10:14:30.113558 140104983627392 _logging.py:12] got 28: depth/res2/3/beta:0 (128,) [TL] got 29: depth/res2/3/gamma:0 (128,) I0808 10:14:30.113610 140104983627392 _logging.py:12] got 29: depth/res2/3/gamma:0 (128,) [TL] got 30: depth/res3/3/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.113667 140104983627392 _logging.py:12] got 30: depth/res3/3/W_conv2d:0 (3, 3, 128, 128) [TL] got 31: depth/res3/3/b_conv2d:0 (128,) I0808 10:14:30.113719 140104983627392 _logging.py:12] got 31: depth/res3/3/b_conv2d:0 (128,) [TL] got 32: depth/res4/3/beta:0 (128,) I0808 10:14:30.113773 140104983627392 _logging.py:12] got 32: depth/res4/3/beta:0 (128,) [TL] got 33: depth/res4/3/gamma:0 (128,) I0808 10:14:30.113825 140104983627392 _logging.py:12] got 33: depth/res4/3/gamma:0 (128,) [TL] got 34: depth/res1/4/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.113882 140104983627392 _logging.py:12] got 34: depth/res1/4/W_conv2d:0 (3, 3, 128, 128) [TL] got 35: depth/res1/4/b_conv2d:0 (128,) I0808 10:14:30.113934 140104983627392 _logging.py:12] got 35: depth/res1/4/b_conv2d:0 (128,) [TL] got 36: depth/res2/4/beta:0 (128,) I0808 10:14:30.113986 140104983627392 _logging.py:12] got 36: depth/res2/4/beta:0 (128,) [TL] got 37: depth/res2/4/gamma:0 (128,) I0808 10:14:30.114039 140104983627392 _logging.py:12] got 37: depth/res2/4/gamma:0 (128,) [TL] got 38: depth/res3/4/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.114119 140104983627392 _logging.py:12] got 38: depth/res3/4/W_conv2d:0 (3, 3, 128, 128) [TL] got 39: depth/res3/4/b_conv2d:0 (128,) I0808 10:14:30.114191 140104983627392 _logging.py:12] got 39: depth/res3/4/b_conv2d:0 (128,) [TL] got 40: depth/res4/4/beta:0 (128,) I0808 10:14:30.114243 140104983627392 _logging.py:12] got 40: depth/res4/4/beta:0 (128,) [TL] got 41: depth/res4/4/gamma:0 (128,) I0808 10:14:30.114295 140104983627392 _logging.py:12] got 41: depth/res4/4/gamma:0 (128,) [TL] got 42: depth/res1/5/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.114351 140104983627392 _logging.py:12] got 42: depth/res1/5/W_conv2d:0 (3, 3, 128, 128) [TL] got 43: depth/res1/5/b_conv2d:0 (128,) I0808 10:14:30.114404 140104983627392 _logging.py:12] got 43: depth/res1/5/b_conv2d:0 (128,) [TL] got 44: depth/res2/5/beta:0 (128,) I0808 10:14:30.114458 140104983627392 _logging.py:12] got 44: depth/res2/5/beta:0 (128,) [TL] got 45: depth/res2/5/gamma:0 (128,) I0808 10:14:30.114510 140104983627392 _logging.py:12] got 45: depth/res2/5/gamma:0 (128,) [TL] got 46: depth/res3/5/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.114566 140104983627392 _logging.py:12] got 46: depth/res3/5/W_conv2d:0 (3, 3, 128, 128) [TL] got 47: depth/res3/5/b_conv2d:0 (128,) I0808 10:14:30.114619 140104983627392 _logging.py:12] got 47: depth/res3/5/b_conv2d:0 (128,) [TL] got 48: depth/res4/5/beta:0 (128,) I0808 10:14:30.114671 140104983627392 _logging.py:12] got 48: depth/res4/5/beta:0 (128,) [TL] got 49: depth/res4/5/gamma:0 (128,) I0808 10:14:30.114723 140104983627392 _logging.py:12] got 49: depth/res4/5/gamma:0 (128,) [TL] got 50: depth/res1/6/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.114778 140104983627392 _logging.py:12] got 50: depth/res1/6/W_conv2d:0 (3, 3, 128, 128) [TL] got 51: depth/res1/6/b_conv2d:0 (128,) I0808 10:14:30.114831 140104983627392 _logging.py:12] got 51: depth/res1/6/b_conv2d:0 (128,) [TL] got 52: depth/res2/6/beta:0 (128,) I0808 10:14:30.114883 140104983627392 _logging.py:12] got 52: depth/res2/6/beta:0 (128,) [TL] got 53: depth/res2/6/gamma:0 (128,) I0808 10:14:30.114936 140104983627392 _logging.py:12] got 53: depth/res2/6/gamma:0 (128,) [TL] got 54: depth/res3/6/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.114993 140104983627392 _logging.py:12] got 54: depth/res3/6/W_conv2d:0 (3, 3, 128, 128) [TL] got 55: depth/res3/6/b_conv2d:0 (128,) I0808 10:14:30.115046 140104983627392 _logging.py:12] got 55: depth/res3/6/b_conv2d:0 (128,) [TL] got 56: depth/res4/6/beta:0 (128,) I0808 10:14:30.115097 140104983627392 _logging.py:12] got 56: depth/res4/6/beta:0 (128,) [TL] got 57: depth/res4/6/gamma:0 (128,) I0808 10:14:30.115150 140104983627392 _logging.py:12] got 57: depth/res4/6/gamma:0 (128,) [TL] got 58: depth/res1/7/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.115207 140104983627392 _logging.py:12] got 58: depth/res1/7/W_conv2d:0 (3, 3, 128, 128) [TL] got 59: depth/res1/7/b_conv2d:0 (128,) I0808 10:14:30.115262 140104983627392 _logging.py:12] got 59: depth/res1/7/b_conv2d:0 (128,) [TL] got 60: depth/res2/7/beta:0 (128,) I0808 10:14:30.115314 140104983627392 _logging.py:12] got 60: depth/res2/7/beta:0 (128,) [TL] got 61: depth/res2/7/gamma:0 (128,) I0808 10:14:30.115367 140104983627392 _logging.py:12] got 61: depth/res2/7/gamma:0 (128,) [TL] got 62: depth/res3/7/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.115423 140104983627392 _logging.py:12] got 62: depth/res3/7/W_conv2d:0 (3, 3, 128, 128) [TL] got 63: depth/res3/7/b_conv2d:0 (128,) I0808 10:14:30.115477 140104983627392 _logging.py:12] got 63: depth/res3/7/b_conv2d:0 (128,) [TL] got 64: depth/res4/7/beta:0 (128,) I0808 10:14:30.115530 140104983627392 _logging.py:12] got 64: depth/res4/7/beta:0 (128,) [TL] got 65: depth/res4/7/gamma:0 (128,) I0808 10:14:30.115581 140104983627392 _logging.py:12] got 65: depth/res4/7/gamma:0 (128,) [TL] got 66: depth/res1/8/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.115638 140104983627392 _logging.py:12] got 66: depth/res1/8/W_conv2d:0 (3, 3, 128, 128) [TL] got 67: depth/res1/8/b_conv2d:0 (128,) I0808 10:14:30.115691 140104983627392 _logging.py:12] got 67: depth/res1/8/b_conv2d:0 (128,) [TL] got 68: depth/res2/8/beta:0 (128,) I0808 10:14:30.115744 140104983627392 _logging.py:12] got 68: depth/res2/8/beta:0 (128,) [TL] got 69: depth/res2/8/gamma:0 (128,) I0808 10:14:30.115797 140104983627392 _logging.py:12] got 69: depth/res2/8/gamma:0 (128,) [TL] got 70: depth/res3/8/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.115853 140104983627392 _logging.py:12] got 70: depth/res3/8/W_conv2d:0 (3, 3, 128, 128) [TL] got 71: depth/res3/8/b_conv2d:0 (128,) I0808 10:14:30.115906 140104983627392 _logging.py:12] got 71: depth/res3/8/b_conv2d:0 (128,) [TL] got 72: depth/res4/8/beta:0 (128,) I0808 10:14:30.115958 140104983627392 _logging.py:12] got 72: depth/res4/8/beta:0 (128,) [TL] got 73: depth/res4/8/gamma:0 (128,) I0808 10:14:30.116010 140104983627392 _logging.py:12] got 73: depth/res4/8/gamma:0 (128,) [TL] got 74: depth/res1/9/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.116067 140104983627392 _logging.py:12] got 74: depth/res1/9/W_conv2d:0 (3, 3, 128, 128) [TL] got 75: depth/res1/9/b_conv2d:0 (128,) I0808 10:14:30.116122 140104983627392 _logging.py:12] got 75: depth/res1/9/b_conv2d:0 (128,) [TL] got 76: depth/res2/9/beta:0 (128,) I0808 10:14:30.116174 140104983627392 _logging.py:12] got 76: depth/res2/9/beta:0 (128,) [TL] got 77: depth/res2/9/gamma:0 (128,) I0808 10:14:30.116226 140104983627392 _logging.py:12] got 77: depth/res2/9/gamma:0 (128,) [TL] got 78: depth/res3/9/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.116282 140104983627392 _logging.py:12] got 78: depth/res3/9/W_conv2d:0 (3, 3, 128, 128) [TL] got 79: depth/res3/9/b_conv2d:0 (128,) I0808 10:14:30.116336 140104983627392 _logging.py:12] got 79: depth/res3/9/b_conv2d:0 (128,) [TL] got 80: depth/res4/9/beta:0 (128,) I0808 10:14:30.116389 140104983627392 _logging.py:12] got 80: depth/res4/9/beta:0 (128,) [TL] got 81: depth/res4/9/gamma:0 (128,) I0808 10:14:30.116441 140104983627392 _logging.py:12] got 81: depth/res4/9/gamma:0 (128,) [TL] got 82: depth/res1/10/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.116497 140104983627392 _logging.py:12] got 82: depth/res1/10/W_conv2d:0 (3, 3, 128, 128) [TL] got 83: depth/res1/10/b_conv2d:0 (128,) I0808 10:14:30.116552 140104983627392 _logging.py:12] got 83: depth/res1/10/b_conv2d:0 (128,) [TL] got 84: depth/res2/10/beta:0 (128,) I0808 10:14:30.116605 140104983627392 _logging.py:12] got 84: depth/res2/10/beta:0 (128,) [TL] got 85: depth/res2/10/gamma:0 (128,) I0808 10:14:30.116657 140104983627392 _logging.py:12] got 85: depth/res2/10/gamma:0 (128,) [TL] got 86: depth/res3/10/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.116714 140104983627392 _logging.py:12] got 86: depth/res3/10/W_conv2d:0 (3, 3, 128, 128) [TL] got 87: depth/res3/10/b_conv2d:0 (128,) I0808 10:14:30.116766 140104983627392 _logging.py:12] got 87: depth/res3/10/b_conv2d:0 (128,) [TL] got 88: depth/res4/10/beta:0 (128,) I0808 10:14:30.116820 140104983627392 _logging.py:12] got 88: depth/res4/10/beta:0 (128,) [TL] got 89: depth/res4/10/gamma:0 (128,) I0808 10:14:30.116872 140104983627392 _logging.py:12] got 89: depth/res4/10/gamma:0 (128,) [TL] got 90: depth/res1/11/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.116926 140104983627392 _logging.py:12] got 90: depth/res1/11/W_conv2d:0 (3, 3, 128, 128) [TL] got 91: depth/res1/11/b_conv2d:0 (128,) I0808 10:14:30.116981 140104983627392 _logging.py:12] got 91: depth/res1/11/b_conv2d:0 (128,) [TL] got 92: depth/res2/11/beta:0 (128,) I0808 10:14:30.117033 140104983627392 _logging.py:12] got 92: depth/res2/11/beta:0 (128,) [TL] got 93: depth/res2/11/gamma:0 (128,) I0808 10:14:30.117085 140104983627392 _logging.py:12] got 93: depth/res2/11/gamma:0 (128,) [TL] got 94: depth/res3/11/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.117141 140104983627392 _logging.py:12] got 94: depth/res3/11/W_conv2d:0 (3, 3, 128, 128) [TL] got 95: depth/res3/11/b_conv2d:0 (128,) I0808 10:14:30.117193 140104983627392 _logging.py:12] got 95: depth/res3/11/b_conv2d:0 (128,) [TL] got 96: depth/res4/11/beta:0 (128,) I0808 10:14:30.117245 140104983627392 _logging.py:12] got 96: depth/res4/11/beta:0 (128,) [TL] got 97: depth/res4/11/gamma:0 (128,) I0808 10:14:30.117297 140104983627392 _logging.py:12] got 97: depth/res4/11/gamma:0 (128,) [TL] got 98: depth/res1/12/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.117353 140104983627392 _logging.py:12] got 98: depth/res1/12/W_conv2d:0 (3, 3, 128, 128) [TL] got 99: depth/res1/12/b_conv2d:0 (128,) I0808 10:14:30.117405 140104983627392 _logging.py:12] got 99: depth/res1/12/b_conv2d:0 (128,) [TL] got 100: depth/res2/12/beta:0 (128,) I0808 10:14:30.117459 140104983627392 _logging.py:12] got 100: depth/res2/12/beta:0 (128,) [TL] got 101: depth/res2/12/gamma:0 (128,) I0808 10:14:30.117510 140104983627392 _logging.py:12] got 101: depth/res2/12/gamma:0 (128,) [TL] got 102: depth/res3/12/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.117566 140104983627392 _logging.py:12] got 102: depth/res3/12/W_conv2d:0 (3, 3, 128, 128) [TL] got 103: depth/res3/12/b_conv2d:0 (128,) I0808 10:14:30.117619 140104983627392 _logging.py:12] got 103: depth/res3/12/b_conv2d:0 (128,) [TL] got 104: depth/res4/12/beta:0 (128,) I0808 10:14:30.117671 140104983627392 _logging.py:12] got 104: depth/res4/12/beta:0 (128,) [TL] got 105: depth/res4/12/gamma:0 (128,) I0808 10:14:30.117722 140104983627392 _logging.py:12] got 105: depth/res4/12/gamma:0 (128,) [TL] got 106: depth/res1/13/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.117779 140104983627392 _logging.py:12] got 106: depth/res1/13/W_conv2d:0 (3, 3, 128, 128) [TL] got 107: depth/res1/13/b_conv2d:0 (128,) I0808 10:14:30.117831 140104983627392 _logging.py:12] got 107: depth/res1/13/b_conv2d:0 (128,) [TL] got 108: depth/res2/13/beta:0 (128,) I0808 10:14:30.117883 140104983627392 _logging.py:12] got 108: depth/res2/13/beta:0 (128,) [TL] got 109: depth/res2/13/gamma:0 (128,) I0808 10:14:30.117935 140104983627392 _logging.py:12] got 109: depth/res2/13/gamma:0 (128,) [TL] got 110: depth/res3/13/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.117991 140104983627392 _logging.py:12] got 110: depth/res3/13/W_conv2d:0 (3, 3, 128, 128) [TL] got 111: depth/res3/13/b_conv2d:0 (128,) I0808 10:14:30.118045 140104983627392 _logging.py:12] got 111: depth/res3/13/b_conv2d:0 (128,) [TL] got 112: depth/res4/13/beta:0 (128,) I0808 10:14:30.118120 140104983627392 _logging.py:12] got 112: depth/res4/13/beta:0 (128,) [TL] got 113: depth/res4/13/gamma:0 (128,) I0808 10:14:30.118191 140104983627392 _logging.py:12] got 113: depth/res4/13/gamma:0 (128,) [TL] got 114: depth/res1/14/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.118247 140104983627392 _logging.py:12] got 114: depth/res1/14/W_conv2d:0 (3, 3, 128, 128) [TL] got 115: depth/res1/14/b_conv2d:0 (128,) I0808 10:14:30.118299 140104983627392 _logging.py:12] got 115: depth/res1/14/b_conv2d:0 (128,) [TL] got 116: depth/res2/14/beta:0 (128,) I0808 10:14:30.118352 140104983627392 _logging.py:12] got 116: depth/res2/14/beta:0 (128,) [TL] got 117: depth/res2/14/gamma:0 (128,) I0808 10:14:30.118405 140104983627392 _logging.py:12] got 117: depth/res2/14/gamma:0 (128,) [TL] got 118: depth/res3/14/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.118462 140104983627392 _logging.py:12] got 118: depth/res3/14/W_conv2d:0 (3, 3, 128, 128) [TL] got 119: depth/res3/14/b_conv2d:0 (128,) I0808 10:14:30.118515 140104983627392 _logging.py:12] got 119: depth/res3/14/b_conv2d:0 (128,) [TL] got 120: depth/res4/14/beta:0 (128,) I0808 10:14:30.118566 140104983627392 _logging.py:12] got 120: depth/res4/14/beta:0 (128,) [TL] got 121: depth/res4/14/gamma:0 (128,) I0808 10:14:30.118619 140104983627392 _logging.py:12] got 121: depth/res4/14/gamma:0 (128,) [TL] got 122: depth/res1/15/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.118675 140104983627392 _logging.py:12] got 122: depth/res1/15/W_conv2d:0 (3, 3, 128, 128) [TL] got 123: depth/res1/15/b_conv2d:0 (128,) I0808 10:14:30.118729 140104983627392 _logging.py:12] got 123: depth/res1/15/b_conv2d:0 (128,) [TL] got 124: depth/res2/15/beta:0 (128,) I0808 10:14:30.118781 140104983627392 _logging.py:12] got 124: depth/res2/15/beta:0 (128,) [TL] got 125: depth/res2/15/gamma:0 (128,) I0808 10:14:30.118833 140104983627392 _logging.py:12] got 125: depth/res2/15/gamma:0 (128,) [TL] got 126: depth/res3/15/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.118891 140104983627392 _logging.py:12] got 126: depth/res3/15/W_conv2d:0 (3, 3, 128, 128) [TL] got 127: depth/res3/15/b_conv2d:0 (128,) I0808 10:14:30.118947 140104983627392 _logging.py:12] got 127: depth/res3/15/b_conv2d:0 (128,) [TL] got 128: depth/res4/15/beta:0 (128,) I0808 10:14:30.118999 140104983627392 _logging.py:12] got 128: depth/res4/15/beta:0 (128,) [TL] got 129: depth/res4/15/gamma:0 (128,) I0808 10:14:30.119051 140104983627392 _logging.py:12] got 129: depth/res4/15/gamma:0 (128,) [TL] got 130: depth/cnn2/W_conv2d:0 (3, 3, 128, 128) I0808 10:14:30.119107 140104983627392 _logging.py:12] got 130: depth/cnn2/W_conv2d:0 (3, 3, 128, 128) [TL] got 131: depth/cnn2/b_conv2d:0 (128,) I0808 10:14:30.119160 140104983627392 _logging.py:12] got 131: depth/cnn2/b_conv2d:0 (128,) [TL] got 132: depth/bn2/beta:0 (128,) I0808 10:14:30.119213 140104983627392 _logging.py:12] got 132: depth/bn2/beta:0 (128,) [TL] got 133: depth/bn2/gamma:0 (128,) I0808 10:14:30.119265 140104983627392 _logging.py:12] got 133: depth/bn2/gamma:0 (128,) [TL] got 134: depth/cnn/1/W_conv2d:0 (3, 3, 128, 256) I0808 10:14:30.119322 140104983627392 _logging.py:12] got 134: depth/cnn/1/W_conv2d:0 (3, 3, 128, 256) [TL] got 135: depth/cnn/1/b_conv2d:0 (256,) I0808 10:14:30.119375 140104983627392 _logging.py:12] got 135: depth/cnn/1/b_conv2d:0 (256,) [TL] got 136: depth/cnn/2/W_conv2d:0 (3, 3, 64, 128) I0808 10:14:30.119431 140104983627392 _logging.py:12] got 136: depth/cnn/2/W_conv2d:0 (3, 3, 64, 128) [TL] got 137: depth/cnn/2/b_conv2d:0 (128,) I0808 10:14:30.119484 140104983627392 _logging.py:12] got 137: depth/cnn/2/b_conv2d:0 (128,) [TL] got 138: depth/cnn/3/W_conv2d:0 (3, 3, 32, 64) I0808 10:14:30.119539 140104983627392 _logging.py:12] got 138: depth/cnn/3/W_conv2d:0 (3, 3, 32, 64) [TL] got 139: depth/cnn/3/b_conv2d:0 (64,) I0808 10:14:30.119594 140104983627392 _logging.py:12] got 139: depth/cnn/3/b_conv2d:0 (64,) [TL] got 140: depth/cnnout/W_conv2d:0 (1, 1, 16, 1) I0808 10:14:30.119651 140104983627392 _logging.py:12] got 140: depth/cnnout/W_conv2d:0 (1, 1, 16, 1) [TL] got 141: depth/cnnout/b_conv2d:0 (1,) I0808 10:14:30.119704 140104983627392 _logging.py:12] got 141: depth/cnnout/b_conv2d:0 (1,) W0808 10:14:30.119800 140104983627392 deprecation_wrapper.py:119] From depth.py:64: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.

2019-08-08 10:14:33.266140: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1 2019-08-08 10:14:33.293844: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-08-08 10:14:33.294426: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.62 pciBusID: 0000:01:00.0 2019-08-08 10:14:33.296971: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0 2019-08-08 10:14:33.334222: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0 2019-08-08 10:14:33.353436: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0 2019-08-08 10:14:33.360387: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0 2019-08-08 10:14:33.406543: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0 2019-08-08 10:14:33.435894: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0 2019-08-08 10:14:33.529614: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7 2019-08-08 10:14:33.529952: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-08-08 10:14:33.531450: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-08-08 10:14:33.532699: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0 2019-08-08 10:14:33.533335: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2019-08-08 10:14:33.608647: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-08-08 10:14:33.609822: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55a499803cd0 executing computations on platform CUDA. Devices: 2019-08-08 10:14:33.609836: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): GeForce GTX 1050 Ti, Compute Capability 6.1 2019-08-08 10:14:33.642470: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2808000000 Hz 2019-08-08 10:14:33.643058: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55a49901bbf0 executing computations on platform Host. Devices: 2019-08-08 10:14:33.643084: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): , 2019-08-08 10:14:33.643496: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-08-08 10:14:33.644162: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.62 pciBusID: 0000:01:00.0 2019-08-08 10:14:33.644204: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0 2019-08-08 10:14:33.644221: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0 2019-08-08 10:14:33.644236: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0 2019-08-08 10:14:33.644257: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0 2019-08-08 10:14:33.644279: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0 2019-08-08 10:14:33.644294: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0 2019-08-08 10:14:33.644308: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7 2019-08-08 10:14:33.644371: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-08-08 10:14:33.644914: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-08-08 10:14:33.645410: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0 2019-08-08 10:14:33.645932: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0 2019-08-08 10:14:33.647598: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix: 2019-08-08 10:14:33.647613: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187] 0 2019-08-08 10:14:33.647619: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0: N 2019-08-08 10:14:33.648304: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-08-08 10:14:33.648913: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-08-08 10:14:33.649475: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3435 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1) Traceback (most recent call last): File "depth.py", line 115, in temp_loss, temp_recon = sess.run([real_loss, pred], feed_dict={ images_low: valid_images_low ,side: valid_side, images_high: valid_images_high, low_up:valid_low_up})
File "/home/ancant2180/anaconda3/envs/ten2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 950, in run run_metadata_ptr) File "/home/ancant2180/anaconda3/envs/ten2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1149, in _run str(subfeed_t.get_shape()))) ValueError: Cannot feed value of shape (0,) for Tensor u'images_low:0', which has shape '(64, 32, 32, 1)'

EdwardSmith1884 commented 5 years ago

@ancant2180 it will not work if there are no files created. Can you check what happens when you remake the data, but this time with the debug_mode set to true on line 57 of the data_prep.py file. Post the error message you get here please.

xfyin1994 commented 5 years ago

Hi, I have followed what you said and set the debug_mode to true(debug_mode = 1). And there is something wrong while "converting .obj to binvoxes" in the "binvox()". These are the error messages.

------------
converting .obj to binvoxes
  0%|                                                                                                                               | 0/6778 [00:00<?, ?it/s]./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
  0%|▏                                                                                                                     | 11/6778 [00:00<05:30, 20.49it/s]./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
  0%|▎                                                                                                                     | 21/6778 [00:01<05:35, 20.13it/s]./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
  2%|██▍                                                                                                                  | 141/6778 [00:07<05:45, 19.23it/s]./binvox./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
  2%|██▌                                                                                                                  | 151/6778 [00:07<05:43, 19.29it/s]./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
  2%|██▊                                                                                                                  | 161/6778 [00:08<05:40, 19.42it/s]./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
  3%|██▉                                                                                                                  | 171/6778 [00:08<05:40, 19.42it/s]./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
  3%|███                                                                                                                  | 181/6778 [00:09<05:39, 19.44it/s]./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6778/6778 [05:51<00:00, 19.28it/s]
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
./binvox./binvox: : error while loading shared librarieserror while loading shared libraries: : lib3ds-1.so.3lib3ds-1.so.3: : cannot open shared object filecannot open shared object file: : No such file or directoryNo such file or directory

./binvox: error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory
------------
splitting data
------------
obtaining odms and models
------------
doing the training set
------------
0it [00:00, ?it/s]
------------
doing the validation set
------------
0it [00:00, ?it/s]
------------
doing the test set
------------
0it [00:00, ?it/s]
------------
rendering images
------------
doing: train
------------
  0%|                                                                                                                                                                                                                                                                                               | 0/4744 [00:00<?, ?it/s]AL lib: (WW) alc_initconfig: Failed to initialize backend "pulse"
AL lib: (WW) alc_initconfig: Failed to initialize backend "pulse"
AL lib: (WW) alc_initconfig: Failed to initialize backend "pulse"
ALSA lib confmisc.c:768:(parse_card) cannot find card '0'
ALSA lib conf.c:4292:(_snd_config_evaluate) function snd_func_card_driver returned error: No such file or directory
ALSA lib confmisc.c:392:(snd_func_concat) error evaluating strings
ALSA lib conf.c:4292:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
ALSA lib confmisc.c:768:(parse_card) cannot find card '0'ALSA lib confmisc.c:1251:(snd_func_refer) error evaluating name
ALSA lib conf.c:4292:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:4771:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM default
AL lib: (EE) ALCplaybackAlsa_open: Could not open playback device 'default': No such file or directory
AL lib: (WW) alc_initconfig: Failed to initialize backend "pulse"
ALSA lib confmisc.c:768:(parse_card) ALSA lib confmisc.c:768:(parse_card) cannot find card '0'

There are so many similar error messages in the middle, so I don't show them all. Do you know how to handle these? Thank you !

EdwardSmith1884 commented 5 years ago

Ah yes, so what is happening is I am calling the binvoxer executable from the command line over and over again on every object and it is failing every time in the same way with the error: 

error while loading shared libraries: lib3ds-1.so.3: cannot open shared object file: No such file or directory

This is a big problem because this is how we convert meshes into voxels. You need to figure out what is breaking with the binvoxer. You can do this without running everything again by just calling it directly on a .obj file until you figure out what the problem is. My first guess is lib3ds-1.so.3 is not found or installed so try: 

sudo apt-get update
sudo apt-get install lib3ds-1-3
xfyin1994 commented 5 years ago

Hi, sorry to disturb you so many times, but there are some other errors I can not solve. I have run the functions in data_prep.py one by one, and all of them can run without errors except for the last one, which is render().

print '------------'
print'downloading'
download()
print '------------'
print'downloading mlts'
process_mtl()
print '------------'
print'converting .obj to binvoxes'
binvox()
print '------------'
print'splitting data'
split()
print '------------'
print'obtaining odms and models'
convert_bin()
print '------------'
print'rendering images'
render()
print'finished eratin'

When I run the 'render()', the error messages are these. It seems that this error is something about the sound card. But I'm not sure and I can't fix it. Do you know how to do, or maybe I can ignore them? Thank you!

AL lib: (WW) alc_initconfig: Failed to initialize backend "pulse"
AL lib: (WW) alc_initconfig: Failed to initialize backend "pulse"
ALSA lib confmisc.c:768:(parse_card) ALSA lib confmisc.c:768:(parse_card) cannot find card '0'cannot find card '0'
ALSA lib conf.c:4292:(_snd_config_evaluate) function snd_func_card_driver returned error: No such file or directory
ALSA lib confmisc.c:392:(snd_func_concat) error evaluating strings

ALSA lib conf.c:4292:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
ALSA lib confmisc.c:1251:(snd_func_refer) error evaluating name
ALSA lib conf.c:4292:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:4771:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM default
AL lib: (EE) ALCplaybackAlsa_open: Could not open playback device 'default': No such file or directory

By the way, every time when I run this function(render()) and ignore the error message I mentioned above, it looks like it can work, but it will get stuck in a place every time. Here is the latest message before getting stuck. Could you please give me some guidance，thank you!


2.381065 0.000000 0.072000
Fra:1 Mem:6.20M (0.00M, Peak 19.16M) | Time:00:00.19 | Sce: Scene Ve:1076 Fa:1973 La:8
Fra:1 Mem:17.67M (0.00M, Peak 18.49M) | Time:00:00.03 | Raytree finished
Fra:1 Mem:17.67M (0.00M, Peak 18.49M) | Time:00:00.03 | Creating Environment maps
Fra:1 Mem:17.67M (0.00M, Peak 18.49M) | Time:00:00.03 | Caching Point Densities
Fra:1 Mem:17.67M (0.00M, Peak 18.49M) | Time:00:00.03 | Sce: Scene Ve:2248 Fa:3844 La:8
Fra:1 Mem:17.67M (0.00M, Peak 18.49M) | Time:00:00.03 | Loading voxel datasets
Fra:1 Mem:17.67M (0.00M, Peak 18.49M) | Time:00:00.03 | Sce: Scene Ve:2248 Fa:3844 La:8
Fra:1 Mem:17.67M (0.00M, Peak 18.49M) | Time:00:00.03 | Sce: Scene Ve:2248 Fa:3844 La:8
Fra:1 Mem:17.67M (0.00M, Peak 18.49M) | Time:00:00.03 | Volume preprocessing
Fra:1 Mem:17.67M (0.00M, Peak 18.49M) | Time:00:00.03 | Sce: Scene Ve:2248 Fa:3844 La:8
Fra:1 Mem:17.67M (0.00M, Peak 18.49M) | Time:00:00.03 | Sce: Scene Ve:2248 Fa:3844 La:8
Fra:1 Mem:6.82M (0.00M, Peak 8.10M) | Time:00:00.19 | Scene, Part 3-4
Fra:1 Mem:18.52M (0.00M, Peak 18.52M) | Time:00:00.19 | Scene, Part 1-4
Fra:1 Mem:6.04M (0.00M, Peak 8.10M) | Time:00:00.19 | Sce: Scene Ve:605 Fa:1130 La:8
Saved: data/images/chair/valid/606bd97f7337bf39b40f0ac0fb9a650d/606bd97f7337bf39b40f0ac0fb9a650d_9.png
3.511037 0.000000 0.099746
Saved: data/images/chair/valid/592cf5363737550cedee0bb2b729f22b/592cf5363737550cedee0bb2b729f22b_8.png
 Time: 00:00.20 (Saving: 00:00.00)

Fra:1 Mem:6.95M (0.00M, Peak 7.59M) | Time:00:00.13 | Scene, Part 2-4
Fra:1 Mem:6.65M (0.00M, Peak 7.59M) | Time:00:00.14 | Scene, Part 1-4
Saved: data/images/chair/valid/3d63ad34e3deca1982db9fca4b68095/3d63ad34e3deca1982db9fca4b68095_4.png
 Time: 00:00.71 (Saving: 00:00.07)

2.539361 0.000000 0.265427

Blender quit
Fra:1 Mem:9.55M (0.00M, Peak 9.57M) | Time:00:00.00 | Preparing Scene data
Fra:1 Mem:9.55M (0.00M, Peak 9.57M) | Time:00:00.00 | Creating Shadowbuffers
Fra:1 Mem:9.55M (0.00M, Peak 9.57M) | Time:00:00.00 | Raytree.. preparing
4.815856 0.000000 0.282164
Fra:1 Mem:9.92M (0.00M, Peak 10.49M) | Time:00:00.00 | Preparing Scene data
Fra:1 Mem:9.92M (0.00M, Peak 10.49M) | Time:00:00.00 | Creating Shadowbuffers
Fra:1 Mem:9.92M (0.00M, Peak 10.49M) | Time:00:00.00 | Raytree.. preparing
Fra:1 Mem:10.25M (0.00M, Peak 10.25M) | Time:00:00.00 | Raytree.. building
Fra:1 Mem:11.02M (0.00M, Peak 11.02M) | Time:00:00.00 | Raytree.. building
Fra:1 Mem:5.91M (0.00M, Peak 5.91M) | Time:00:00.00 | Preparing Scene data
Fra:1 Mem:6.10M (0.00M, Peak 6.14M) | Time:00:00.00 | Preparing Scene data
Fra:1 Mem:6.10M (0.00M, Peak 6.14M) | Time:00:00.00 | Creating Shadowbuffers
Fra:1 Mem:6.10M (0.00M, Peak 6.14M) | Time:00:00.00 | Raytree.. preparing
Fra:1 Mem:6.12M (0.00M, Peak 6.14M) | Time:00:00.00 | Raytree.. building
Fra:1 Mem:6.13M (0.00M, Peak 6.17M) | Time:00:00.00 | Raytree finished
Fra:1 Mem:6.13M (0.00M, Peak 6.17M) | Time:00:00.00 | Creating Environment maps
Fra:1 Mem:20.94M (0.00M, Peak 24.85M) | Time:00:00.12 | Raytree finished
Fra:1 Mem:20.94M (0.00M, Peak 24.85M) | Time:00:00.12 | Creating Environment maps
Fra:1 Mem:20.94M (0.00M, Peak 24.85M) | Time:00:00.12 | Caching Point Densities
Fra:1 Mem:20.94M (0.00M, Peak 24.85M) | Time:00:00.12 | Sce: Scene Ve:9473 Fa:18233 La:8
Fra:1 Mem:20.94M (0.00M, Peak 24.85M) | Time:00:00.12 | Loading voxel datasets
Fra:1 Mem:20.94M (0.00M, Peak 24.85M) | Time:00:00.12 | Sce: Scene Ve:9473 Fa:18233 La:8
Fra:1 Mem:20.94M (0.00M, Peak 24.85M) | Time:00:00.12 | Sce: Scene Ve:9473 Fa:18233 La:8
Fra:1 Mem:20.94M (0.00M, Peak 24.85M) | Time:00:00.12 | Volume preprocessing
Fra:1 Mem:20.94M (0.00M, Peak 24.85M) | Time:00:00.12 | Sce: Scene Ve:9473 Fa:18233 La:8
Fra:1 Mem:20.94M (0.00M, Peak 24.85M) | Time:00:00.12 | Sce: Scene Ve:9473 Fa:18233 La:8
Fra:1 Mem:6.13M (0.00M, Peak 6.17M) | Time:00:00.00 | Caching Point Densities
Fra:1 Mem:6.13M (0.00M, Peak 6.17M) | Time:00:00.00 | Sce: Scene Ve:58 Fa:112 La:8
Fra:1 Mem:6.13M (0.00M, Peak 6.17M) | Time:00:00.00 | Loading voxel datasets
Fra:1 Mem:6.13M (0.00M, Peak 6.17M) | Time:00:00.00 | Sce: Scene Ve:58 Fa:112 La:8
Fra:1 Mem:6.13M (0.00M, Peak 6.17M) | Time:00:00.00 | Sce: Scene Ve:58 Fa:112 La:8
Fra:1 Mem:6.13M (0.00M, Peak 6.17M) | Time:00:00.00 | Volume preprocessing
Fra:1 Mem:6.13M (0.00M, Peak 6.17M) | Time:00:00.00 | Sce: Scene Ve:58 Fa:112 La:8
Fra:1 Mem:6.13M (0.00M, Peak 6.17M) | Time:00:00.00 | Sce: Scene Ve:58 Fa:112 La:8
Fra:1 Mem:8.56M (0.00M, Peak 8.56M) | Time:00:00.00 | Preparing Scene data
 Time: 00:00.23 (Saving: 00:00.03)

Fra:1 Mem:7.47M (0.00M, Peak 7.47M) | Time:00:00.00 | Preparing Scene data
Fra:1 Mem:6.54M (0.00M, Peak 6.92M) | Time:00:00.01 | Scene, Part 4-4
Fra:1 Mem:10.22M (0.00M, Peak 11.33M) | Time:00:00.04 | Raytree finishedFra:1 Mem:10.95M (0.00M, Peak 12.67M) | Time:00:00.05 | Raytree finished
Fra:1 Mem:10.95M (0.00M, Peak 12.67M) | Time:00:00.05 | Creating Environment maps
EdwardSmith1884 commented 5 years ago

Hi, just to check, are the images being made still? if so the error doesn't really matter