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op_wisdom_t
(str_vals=(type=Convolution),nda_vals=(biases=(dims=(out_chan=16)),filts=(dims=(out_chan=16,in_chan=192,y=1,x=1)),in=(dims=(img=1,chan=192,y=28,x=28)),in_pad=(tn=none,dims=(y=0,x=0)),kern_sz=(tn=none,dims=(y=1,x=1)),out=(dims=(img=1,chan=16,y=28,x=28)),out_chans=(tn=uint32_t,v=16),stride=(tn=none,dims=(y=1,x=1))))
kg
out
0105000000666C6F61740101DADA000000000000000004000000010000000031000003000000696D671000000010030000040000006368616E1C0000001C00000001000000791C00000001000000010000007805000000666C6F61740031000000000000018D905F1F69F48806000000002DCDD54337000000F0DC11495004914814704048169B4B48FEC3E7473CBFE947C702A247131BAA47FF504F4715C249478EF269475D593147390A404737EC25470B73FD46217C054783DD174766330147E9550147103F0047361F0047F673C046327CAE464E53CC465A5BCB463276A0463131B54691FBA146DD189F46DB549C46F42D95462A6EA546190A9D463C605443BADD6C44EABECC43BA60614491603644EB9D8444B32C1544582E1C44DCBF844400000000E249B241B4A5C74200000000000000009CBC13410000000079BAF442B8372F4288789B4200000000B7DB4443DBC4CC40
op_tun

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# command lines run:
# boda-native case (generates out-boda-native.py; error check results are in test_compute.txt)
boda test_compute --model-name=googlenet_conv --wins-per-image=1 --imgs='(pil_fn=%(boda_test_dir)/pascal/head_1/%%s.txt)' --run-cnet='(in_dims=(img=1,y=1024,x=2048))' --cf2='(mode=rtc,per_call_fn=out-boda-native.py,op_tune=(use_culibs=0,k1conv=1,tconv=1),enable_write_xpose=1)' --max-err=1
# cudnn-v5 (via boda) case (generates out-cudnn-v5.py; only convs are cudnn; error check results are in test_compute.txt)
boda test_compute --model-name=googlenet_conv --wins-per-image=1 --imgs='(pil_fn=%(boda_test_dir)/pascal/head_1/%%s.txt)' --run-cnet='(in_dims=(img=1,y=1024,x=2048))' --cf2='(mode=rtc,per_call_fn=out-cudnn-v5.py,op_tune=(use_culibs=1))' --max-err=1
# to generate profiles
python ../../pysrc/flops.py --per-layer=1 --ai-mnk=1 --per-layer-in-info=1 --profile=1 --net-fn out-boda-native.py 
python ../../pysrc/flops.py --per-layer=1 --ai-mnk=1 --per-layer-in-info=1 --profile=1 --net-fn out-cudnn-v5

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//typedef unsigned uint32_t;
typedef int int32_t;
//typedef long long int64_t;
#define CUCL_GLOBAL_KERNEL kernel
#define GASQ global
#define GLOB_ID_1D get_global_id(0)
#define LOC_ID_1D get_local_id(0)
#define GRP_ID_1D get_group_id(0)
#define LOC_SZ_1D get_local_size(0)

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import sys
import xml.etree.ElementTree as xee

def main( args ):
    assert len(args)
    if len(args) != 3: raise ValueError( "usage: %s save_in.xml save_out.xml" % (args[0],) )
    doc=xee.fromstring(open(args[1]).read())

    for node in doc.iter():
        #if node.text is None: node.text = " "; continue # work around TT's inability to parse <foo /> style tags

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// private

struct pyra_t {
  // impl. note: can be c++
  uint32_t get_num_levels( void ) { ... }
  img_t get_level( uint32_t l ) { return levs[l]; }
};

pyra_t * new_pyra_t( uint32_t const max_w, uint32_t const max_h, ... other params ) { return new pyra_t(...); }
void del_pyra_t( pyra_t *p ) { delete p; }

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lists of operations for forward pass for alexnet and googlenet

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python fmtr.py < foo.prototxt.template > foo.prototxt.template.prototxt name=bar; cat foo.prototxt.template.prototxt