Hi again!
I'm not sure, but it seems that the "translation" provided an error
later in the code, because I obtained a mismatching in the shapes and
size with the following message:
/$~/VoxNet/voxnet/scripts$ python train.py config/shapenet10.py
shapenet10_train.tar/
/Using cuDNN version 5105 on context None/
/Preallocating 11583/12192 Mb (0.950000) on cuda/
/Mapped name None to device cuda: TITAN X (Pascal) (0000:02:00.0)/
/2018-05-08 09:16:36,738 INFO| Metrics will be saved to metrics.jsonl/
/2018-05-08 09:16:36,738 INFO| Compiling theano functions.../
/2018-05-08 09:16:48,771 INFO| Training.../
/Traceback (most recent call last):/
/ File "train.py", line 184, in <module>/
/ main(args)/
/ File "train.py", line 152, in main/
/ lv = tfuncs['update_iter'](bi)/
/ File
"/home/anaconda2/lib/python2.7/site-packages/theano/compile/function_module.py",
line 917, in __call__/
/ storage_map=getattr(self.fn, 'storage_map', None))/
/ File
"/home/anaconda2/lib/python2.7/site-packages/theano/gof/link.py", line
325, in raise_with_op/
/ reraise(exc_type, exc_value, exc_trace)/
/ File
"/home/anaconda2/lib/python2.7/site-packages/theano/compile/function_module.py",
line 903, in __call__/
/ self.fn() if output_subset is None else\/
/ File "pygpu/blas.pyx", line 47, in pygpu.blas.pygpu_blas_rgemm/
/*pygpu.gpuarray.GpuArrayException: ('mismatched shapes', 2)*/
/*Apply node that caused the error: GpuDot22(GpuReshape{2}.0, fc1.W)*/
/Toposort index: 130/
/Inputs types: [GpuArrayType<None>(float32, matrix),
GpuArrayType<None>(float32, matrix)]/
/Inputs shapes: [(32, 32000), (6912, 128)]/
/Inputs strides: [(128000, 4), (512, 4)]/
/Inputs values: ['not shown', 'not shown']/
/Inputs type_num: [11, 11]/
/Outputs clients: [[GpuElemwise{Add}[(0, 0)]<gpuarray>(GpuDot22.0,
InplaceGpuDimShuffle{x,0}.0)]]/
/
/
/Backtrace when the node is created(use Theano flag traceback.limit=N to
make it longer):/
/ File "train.py", line 184, in <module>/
/ main(args)/
/ File "train.py", line 135, in main/
/ tfuncs, tvars = make_training_functions(cfg, model)/
/ File "train.py", line 31, in make_training_functions/
/ out = lasagne.layers.get_output(l_out, X)/
/ File
"/home/anaconda2/lib/python2.7/site-packages/lasagne/layers/helper.py",
line 197, in get_output/
/ all_outputs[layer] = layer.get_output_for(layer_inputs, **kwargs)/
/ File
"/home/anaconda2/lib/python2.7/site-packages/lasagne/layers/dense.py",
line 121, in get_output_for/
/ activation = T.dot(input, self.W)/
/ File "train.py", line 184, in <module>/
/ main(args)/
/ File "train.py", line 135, in main/
/ tfuncs, tvars = make_training_functions(cfg, model)/
/ File "train.py", line 31, in make_training_functions/
/ out = lasagne.layers.get_output(l_out, X)/
/ File
"/home/anaconda2/lib/python2.7/site-packages/lasagne/layers/helper.py",
line 197, in get_output/
/ all_outputs[layer] = layer.get_output_for(layer_inputs, **kwargs)/
/ File
"/home/anaconda2/lib/python2.7/site-packages/lasagne/layers/dense.py",
line 121, in get_output_for/
/ activation = T.dot(input, self.W)/
Do you think it is a different error?
Thank you again for any suggestion.
Il giorno giovedì 3 maggio 2018 10:12:51 UTC+2, Antonio95100 ha scritto:
Hi all,
I'm trying to train the VoxNet
<https://github.com/dimatura/voxnet>3D/Volumetric Convolutional
Neural Networks, based on Theano+Lasagne.
Since I have installed Theano 1.01 and I don't want to downgrade to
Theano 0.8.2, I modified the "max_pool_3d
<https://github.com/dimatura/voxnet/blob/master/voxnet/max_pool_3d.py>"
original code 'cause the *DownsampleFactorMax *module changed name
in the latest Theano version. So I replaced the following rows:
/op = T.signal.downsample.DownsampleFactorMax((ds[1], ds[2]),
ignore_border)/
/output = op(input_4D)/
with these:
/op = T.signal.pool.Pool(ignore_border)(input, ws=(ds[1],ds[2]))/
/output = op(input_4D)/
and I receive the following error message:
/File "/home//VoxNet/voxnet/voxnet/max_pool_3d.py", line 78, in
max_pool_3d/
/ output = op(input_4D)/
/TypeError: 'TensorVariable' object is not callable/
/
/
What is wrong in my update?
Thank you in advance for any suggestion.
Cheers.
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