please see tensorpack/tensorpack#1259

from rl-medical.

why placement Conv3D in CPU ? and i how to placement to GPU device?

from rl-medical.

@layer_register(log_shape=True)
@convert_to_tflayer_args(
args_names=['filters', 'kernel_size'],
name_mapping={
'out_channel': 'filters',
'kernel_shape': 'kernel_size',
'stride': 'strides',
})
def Conv3D()

And

@layer_register(log_shape=True)

def Conv3D()

have what differences?

i has errrors when using first Conv3D()

File "C:\Anaconda3\envs\tf1.6\lib\site-packages\tensorpack\tfutils\argscope.py", line 45, in argscope
_check_args_exist(l.symbolic_function)
File "C:\Anaconda3\envs\tf1.6\lib\site-packages\tensorpack\tfutils\argscope.py", line 41, in _check_args_exist
assert k in args, "No argument {} in {}".format(k, l.name)
AssertionError: No argument nl in Conv3D

@amiralansary @crypdick @thanosvlo @thanosvlo
please help me!

from rl-medical.

why target/conv0/Conv3D can placement at GPU ,but conv0/Conv3D cannot placement at GPU?

{
"cat": "Tensor",
"id": 22,
"ph": "O",
"tid": 0,
"name": "conv0/Conv3D",
"args": {
"snapshot": {
"tensor_description": "dtype: DT_FLOAT\nshape {\n dim {\n size: 24\n }\n dim {\n size: 45\n }\n dim {\n size: 45\n }\n dim {\n size: 45\n }\n dim {\n size: 32\n }\n}\nallocation_description {\n requested_bytes: 279936000\n allocated_bytes: 279936000\n allocator_name: cpu\n allocation_id: 1\n has_single_reference: true\n ptr: 3082879072\n}\n"
}
},
"pid": 2,
"ts": 1562635795004270
},

{
"cat": "Tensor",
"id": 382,
"ph": "O",
"tid": 13,
"name": "target/conv0/Conv3D",
"args": {
"snapshot": {
"tensor_description": "dtype: DT_FLOAT\nshape {\n dim {\n size: 24\n }\n dim {\n size: 45\n }\n dim {\n size: 45\n }\n dim {\n size: 45\n }\n dim {\n size: 32\n }\n}\nallocation_description {\n requested_bytes: 279936000\n allocated_bytes: 279936000\n allocator_name: GPU_0_bfc\n allocation_id: 430\n has_single_reference: true\n ptr: 116364673024\n}\n"
}
},
"pid": 4,
"ts": 1562635790607019
},

from rl-medical.

@courins The slow down is obviously a result of running the code on the cpu and not the gpu. The reason of that is not clear to me, either your envs or tensorpack.
It seems that you have solved this issue using a newer version of tensorpack here tensorpack/tensorpack#1259 , by upgrading to the current tensorpack master and TF 1.13, and modify the code accodring to this

I close this issue and refer to the upgrade open issue #9

from rl-medical.