19 lines
2.0 KiB
Plaintext
19 lines
2.0 KiB
Plaintext
|
<03>
|
|||
|
|
<EFBFBD><[c @ s? d d l m Z m Z d d l m Z d d l Z d <00> Z d S( i<><69><EFBFBD><EFBFBD>( t Lambdat concatenate( t ModelNc s t | t t f <00> r* t | <00> <00> | } n | <00> t <00> <00> } <00> f d <00> } g } x* t t | j <00> <00> D] } | j g <00> qg Wxt | <00> D]\ } } t j d | <16> <00><> t j
|
|||
|
|
d | <16> <00><> g } xf | j D][ } t | j <00> j
<00> <00> d } t | d | d i | d 6<> d 6<>| <00> }
|
|||
|
|
| j |
|
|||
|
|
<00> q<> W| | <00> } t | t <00> sU| g } n x/ t t | <00> <00> D] } | | j | | <19> qhWWd QXWd QXq<58> Wt j d <00> <00>] g }
x? t | j | <00> D]+ \ } } |
j t | d
|
|||
|
|
d d | <00><02> q<>Wt | j |
<00> SWd QXd S(
Nc
|
|||
|
|
s<> t j | <00> } | d } | d } | | } | <00> d k rN | | | } n | } t j | | g d d <00>} t j | | d g d d <00>} | | } t j | | | <00> S( Ni t axisi ( t tft shapet concatt slice(
|
|||
|
|
t datat it partsR t
|
|||
|
|
batch_sizet input_shapet stept sizet stridet start( t num_gpus( sR /home/dlsaavedra/Desktop/Tesis/8.-YOLO/keras-yolo3-master/utils/multi_gpu_model.pyt get_slice
s
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
s /gpu:%ds
|
|||
|
|
replica_%di t output_shapet argumentsR R
|
|||
|
|
s /cpu:0R i t name( t
|
|||
|
|
isinstancet listt tuplet lent ranget outputst appendt enumerateR t devicet
|
|||
|
|
name_scopet inputst get_shapet as_listR t zipt output_namesR R ( t modelt gpust target_gpu_idsR t all_outputsR t gpu_idR t xR t slice_iR t ot mergedR ( ( R sR /home/dlsaavedra/Desktop/Tesis/8.-YOLO/keras-yolo3-master/utils/multi_gpu_model.pyt multi_gpu_model s>
|
|||
|
|
) ( t keras.layersR R t keras.modelsR t
|
|||
|
|
tensorflowR R. ( ( ( sR /home/dlsaavedra/Desktop/Tesis/8.-YOLO/keras-yolo3-master/utils/multi_gpu_model.pyt <module> s
|