/*---------------------------------------------------------------------------*/
static resolv_status_t
set_connection_address(uip_ipaddr_t *ipaddr, int *port)
{
#ifndef UDP_CONNECTION_ADDR
#if RESOLV_CONF_SUPPORTS_MDNS && RESOLV_CONF_SUPPORTS_DNS_SD
#define UDP_CONNECTION_ADDR _server._udp.local
#elif RESOLV_CONF_SUPPORTS_MDNS
#define UDP_CONNECTION_ADDR cus.local
#elif UIP_CONF_ROUTER
#define UDP_CONNECTION_ADDR fd00:0:0:0:0212:7404:0004:0404
#else
#define UDP_CONNECTION_ADDR fe80:0:0:0:6466:6666:6666:6666
#endif
#endif /* !UDP_CONNECTION_ADDR */
#define _QUOTEME(x) #x
#define QUOTEME(x) _QUOTEME(x)
resolv_status_t status = RESOLV_STATUS_ERROR;
if(uiplib_ipaddrconv(QUOTEME(UDP_CONNECTION_ADDR), ipaddr) == 0) {
/*
* We are looking for a hostname and not an IP address.
*/
uip_ipaddr_t *resolved_addr = NULL;
#if RESOLV_CONF_SUPPORTS_MDNS && RESOLV_CONF_SUPPORTS_DNS_SD
status = resolv_service_lookup(QUOTEME(UDP_CONNECTION_ADDR),&resolved_addr,port);
#else
status = resolv_lookup(QUOTEME(UDP_CONNECTION_ADDR),&resolved_addr);
#endif /* RESOLV_CONF_SUPPORTS_MDNS && RESOLV_CONF_SUPPORTS_DNS_SD */
if(status == RESOLV_STATUS_UNCACHED || status == RESOLV_STATUS_EXPIRED) {
PRINTF("Attempting to look up %s\n",QUOTEME(UDP_CONNECTION_ADDR));
#if RESOLV_CONF_SUPPORTS_MDNS && RESOLV_CONF_SUPPORTS_DNS_SD
resolv_query_service(QUOTEME(UDP_CONNECTION_ADDR));
#else
resolv_query(QUOTEME(UDP_CONNECTION_ADDR));
#endif /* RESOLV_CONF_SUPPORTS_MDNS && RESOLV_CONF_SUPPORTS_DNS_SD */
status = RESOLV_STATUS_RESOLVING;
} else if(status == RESOLV_STATUS_CACHED && resolved_addr != NULL
#if RESOLV_CONF_SUPPORTS_MDNS && RESOLV_CONF_SUPPORTS_DNS_SD
&& port != NULL
#endif /* RESOLV_CONF_SUPPORTS_MDNS && RESOLV_CONF_SUPPORTS_DNS_SD */
) {
PRINTF("Lookup of \"%s\" succeded!\n",QUOTEME(UDP_CONNECTION_ADDR));
} else if(status == RESOLV_STATUS_RESOLVING) {
PRINTF("Still looking up \"%s\"...\n",QUOTEME(UDP_CONNECTION_ADDR));
} else {
PRINTF("Lookup of \"%s\" failed. status = %d\n",QUOTEME(UDP_CONNECTION_ADDR),status);
}
if(resolved_addr)
uip_ipaddr_copy(ipaddr, resolved_addr);
} else {
status = RESOLV_STATUS_CACHED;
}
return status;
}
/*---------------------------------------------------------------------------*/
static void
set_connection_address(uip_ipaddr_t *ipaddr)
{
#define _QUOTEME(x) #x
#define QUOTEME(x) _QUOTEME(x)
#ifdef UDP_CONNECTION_ADDR
if(uiplib_ipaddrconv(QUOTEME(UDP_CONNECTION_ADDR), ipaddr) == 0) {
PRINTF("UDP client failed to parse address '%s'\n", QUOTEME(UDP_CONNECTION_ADDR));
}
#else
uip_ip6addr(ipaddr, 0xfe80, 0x0000, 0x0000, 0x0000, 0x6500, 0x0012, 0x91c3, 0x2501);
#endif /* UDP_CONNECTION_ADDR */
}
/*---------------------------------------------------------------------------*/
static void
set_connection_address(uip_ipaddr_t *ipaddr)
{
#define _QUOTEME(x) #x
#define QUOTEME(x) _QUOTEME(x)
#ifdef UDP_CONNECTION_ADDR
if(uiplib_ipaddrconv(QUOTEME(UDP_CONNECTION_ADDR), ipaddr) == 0) {
PRINTF("UDP client failed to parse address '%s'\n", QUOTEME(UDP_CONNECTION_ADDR));
}
#elif UIP_CONF_ROUTER
uip_ip6addr(ipaddr,0xaaaa,0,0,0,0x0212,0x7404,0x0004,0x0404);
#else
uip_ip6addr(ipaddr,0xfe80,0,0,0,0x6466,0x6666,0x6666,0x6666);
#endif /* UDP_CONNECTION_ADDR */
}
void ahnentafel_generate_c(const struct ahnentafel_t* bs, FILE* stream)
{
fputs(
GENERATED_FILE_PROLOGUE
"#define item_t " QUOTEME(item_t) "\n"
"#define ARRLEN(a) (sizeof(a)/sizeof(*a))\n"
"#define N 0\n"
"static const item_t array[] =" S_EOL "{\n", stream);
CALL_PRINT_ARRAY(item_t, stream, bs->bst, bs->bst_size);
fputs(S_EOL "};\n"
FUNCTION_DEFINITION S_EOL
"{" S_EOL
"int i = 0;" S_EOL
"if (item == N)" S_EOL
"return 0;" S_EOL
"do" S_EOL
"{" S_EOL
"if (array[i] == item)" S_EOL
"return &array[i];" S_EOL
"if (array[i] > item)" S_EOL
"i = 2 * i + 1;" S_EOL
"else" S_EOL
"i = 2 * i + 2;" S_EOL
"}" S_EOL
"while (i < (int)ARRLEN(array));" S_EOL
"return 0;" S_EOL
"}" S_EOL, stream);
}
char* pacparser_version(void) {
#ifndef VERSION
print_error("WARNING: VERSION not defined.");
return "";
#endif
return QUOTEME(VERSION);
}
void hopscotch_generate_c(const struct hopscotch_t* h, FILE* stream)
{
int num_buckets = 1 << h->bits_used;
fputs(
GENERATED_FILE_PROLOGUE
"#define item_t " QUOTEME(item_t) "\n"
"static const item_t array[] =" S_EOL "{\n", stream);
CALL_PRINT_ARRAY(item_t, stream, h->buckets, num_buckets + h->neighborhood_size - 1);
fputs(S_EOL "};\n"
FUNCTION_DEFINITION S_EOL
"{" S_EOL
"item_t key =" S_EOL, stream);
output_mask(stream, h->mask, h->bits_used);
fputs(
";\n"
"const item_t *p = &array[key];\n"
"#define CHECK() if (*p == item) return p; else ++p;\n"
, stream);
for (int i = 0; i < h->neighborhood_size; i++)
fputs("CHECK()" S_EOL, stream);
fputs(
"return 0;" S_EOL
"}" S_EOL
, stream);
}
const std::string& GetCompiler()
{
static const std::string compiler = ""
#ifdef __GNUC__
//"gcc-" QUOTEME(__GNUC__) "." QUOTEME(__GNUC_MINOR__) "." QUOTEME(__GNUC_PATCHLEVEL__);
"gcc-" __VERSION__;
#elif defined(_MSC_VER)
#ifdef _MSC_FULL_VER
"msvc-" QUOTEME(_MSC_FULL_VER);
#else
"msvc-" QUOTEME(_MSC_VER);
#endif
#elif defined(__VERSION__)
"unknown-" __VERSION__;
#else
"unknown";
#endif
return compiler;
}
/*---------------------------------------------------------------------------*/
static void
set_connection_address(uip_ipaddr_t *ipaddr)
{
#define _QUOTEME(x) #x
#define QUOTEME(x) _QUOTEME(x)
#ifdef UDP_CONNECTION_ADDR
if(uiplib_ipaddrconv(QUOTEME(UDP_CONNECTION_ADDR), ipaddr) == 0) {
PRINTF("UDP client failed to parse address '%s'\n", QUOTEME(UDP_CONNECTION_ADDR));
}
#elif UIP_CONF_ROUTER
// uip_ip6addr(ipaddr,0xaaaa,0,0,0,0x0212,0x7404,0x0004,0x0404);
// IP_CLIENT
// define IP_SERVER fe80::2a01:22ff:fe33:4455
uip_ip6addr(ipaddr,0xaaaa,0,0,0,0x0212,0x7404,0x0004,0x0404);
#else
// uip_ip6addr(ipaddr,0xfe80,0,0,0,0x6466,0x6666,0x6666,0x6666);
uip_ip6addr(ipaddr,0xfe80,0,0,0,0x2a01,0x22ff,0xfe33,0x4455);
#endif /* UDP_CONNECTION_ADDR */
}
const std::string& GetBuildEnvironment() {
static const std::string environment = "boost-"
#ifdef BOOST_VERSION
QUOTEME(BOOST_VERSION)
#else
"unknown"
#endif
", "
#ifdef BOOST_STDLIB
BOOST_STDLIB;
#else
"unknown stdlib";
#endif
return environment;
}
/*---------------------------------------------------------------------------*/
static resolv_status_t
set_connection_address(uip_ipaddr_t *ipaddr)
{
#ifndef UDP_CONNECTION_ADDR
//#if RESOLV_CONF_SUPPORTS_MDNS
#if 0
#define UDP_CONNECTION_ADDR contiki-udp-server.local
#elif UIP_CONF_ROUTER
#define UDP_CONNECTION_ADDR aaaa:0:0:0:0201:2dcf:4629:04b4
#else
#define UDP_CONNECTION_ADDR fe80:0:0:0:6466:6666:6666:6666
#endif
#endif /* !UDP_CONNECTION_ADDR */
#define _QUOTEME(x) #x
#define QUOTEME(x) _QUOTEME(x)
resolv_status_t status = RESOLV_STATUS_ERROR;
if(uiplib_ipaddrconv(QUOTEME(UDP_CONNECTION_ADDR), ipaddr) == 0) {
uip_ipaddr_t *resolved_addr = NULL;
status = resolv_lookup(QUOTEME(UDP_CONNECTION_ADDR),&resolved_addr);
if(status == RESOLV_STATUS_UNCACHED || status == RESOLV_STATUS_EXPIRED) {
PRINTF("Attempting to look up %s\n",QUOTEME(UDP_CONNECTION_ADDR));
resolv_query(QUOTEME(UDP_CONNECTION_ADDR));
status = RESOLV_STATUS_RESOLVING;
} else if(status == RESOLV_STATUS_CACHED && resolved_addr != NULL) {
PRINTF("Lookup of \"%s\" succeded!\n",QUOTEME(UDP_CONNECTION_ADDR));
} else if(status == RESOLV_STATUS_RESOLVING) {
PRINTF("Still looking up \"%s\"...\n",QUOTEME(UDP_CONNECTION_ADDR));
} else {
PRINTF("Lookup of \"%s\" failed. status = %d\n",QUOTEME(UDP_CONNECTION_ADDR),status);
}
if(resolved_addr)
uip_ipaddr_copy(ipaddr, resolved_addr);
} else {
status = RESOLV_STATUS_CACHED;
}
return status;
}
////////////////////////////////////////////////////////////////////
//// OUTPUT //////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////
ffmpegOutput::ffmpegOutput(QWidget *parent) :
QDialog(parent),
ui(new Ui::ffmpegOutput)
{
ui->setupUi(this);
logging=false;
QDir path;
#ifdef DEBUGGING
logLocation.setPath(QString(QUOTEME(PWD_PRO))); // for debug mode read preset in path
#else
#ifdef Q_WS_X11
logLocation.setPath(QDir::homePath()+"/.ffmpeg-gui");
#endif
// directory not exist, create
if (!logLocation.exists()) logLocation.mkdir(logLocation.absolutePath());
// dont copy files to home, only put here the modified/created by users
/* #ifdef Q_WS_X11
QDir systemPath;
QFileInfoList entries;
QFile file;
systemPath.setPath("/usr/local/share/ffmpeg/");
entries= systemPath.entryInfoList(QStringList("*.ffpreset"),QDir::Files,QDir::Name);
foreach (QFileInfo f, entries) {
file.setFileName(f.absoluteFilePath());
file.copy(QDir::homePath()+"/.ffmpeg-gui/"+f.fileName());
}
#endif
*/
#endif
html_log = new QFile(logLocation.absolutePath()+"/ffmpegLog.html");
ui->PTE_ffmpeg_error->hide();
}
void linear_sse_generate_c(const struct linear_sse_t* bs, FILE* stream)
{
fputs(
GENERATED_FILE_PROLOGUE
"#include <emmintrin.h>\n"
, stream);
fprintf(stream, "#define CACHE_LINE_BYTES %d\n", CACHE_LINE_BYTES);
fputs(
"#define item_t " QUOTEME(item_t) "\n"
"#ifdef WIN32\n"
"#define ALIGN(n) __declspec(align(n))\n"
"#else\n"
"#define ALIGN(n) __attribute__ ((aligned(n)))\n"
"#endif\n"
"ALIGN(CACHE_LINE_BYTES) static const item_t array[] =" S_EOL "{\n"
, stream);
CALL_PRINT_ARRAY(item_t, stream, bs->array, bs->size);
fputs(
S_EOL "};\n"
"#define ARRLEN(a) (sizeof(a)/sizeof(*a))\n"
"#define VEC_SIZE 16\n"
"#define VEC_ITEMS (VEC_SIZE/sizeof(item_t))\n"
FUNCTION_DEFINITION S_EOL
"{" S_EOL
#if item_bits == 64
"__m128i s = _mm_set_epi32(item >> 32, item & UINT32_MAX, item >> 32, item & UINT32_MAX);" S_EOL
#else
"__m128i s = _mm_set1_epi" QUOTEME(item_bits) "(item);" S_EOL
#endif
"int n = ARRLEN(array);" S_EOL
"n -= n % VEC_ITEMS;" S_EOL
"const __m128i *p = (__m128i*)array;" S_EOL
"for (; p < (__m128i*)&array[n]; p++)" S_EOL
"{" S_EOL
"__m128i g = *p;" S_EOL
#if item_bits == 16
"__m128i temp = _mm_cmpeq_epi" QUOTEME(item_bits) "(s, g);" S_EOL
"unsigned ans = _mm_movemask_epi8(temp);" S_EOL
"if (ans)" S_EOL
"for (unsigned i = 0; i < VEC_ITEMS; i++)" S_EOL
"if (ans & (1 << (i*2)))" S_EOL
"return (item_t*)p + i;" S_EOL
#elif item_bits == 32
"__m128i temp = _mm_cmpeq_epi" QUOTEME(item_bits) "(s, g);" S_EOL
"unsigned ans = _mm_movemask_ps(*(__m128*)&temp);" S_EOL
"if (ans)" S_EOL
"for (unsigned i = 0; i < VEC_ITEMS; i++)" S_EOL
"if (ans & (1 << i))" S_EOL
"return (item_t*)p + i;" S_EOL
#elif item_bits == 64
"__m128i temp = _mm_cmpeq_epi32(s, g);" S_EOL
"unsigned ans = _mm_movemask_ps(*(__m128*)&temp);" S_EOL
"if ((ans & 3) == 3) return (item_t*)p;" S_EOL
"if ((ans & 12) == 12) return (item_t*)p + 1;" S_EOL
#else
#error item_bits must be wither 16,32 or 64 !
#endif
"}" S_EOL
"for (item_t *pp = (item_t*)p; pp <= &array[ARRLEN(array) - 1]; pp++)" S_EOL
"if (*pp == item)" S_EOL
"return pp;" S_EOL
"return 0;" S_EOL
"}" S_EOL, stream);
}
cl_kernel get_kernel(char *kern_name, cl_context context, cl_device_id device)
{
cl_program program;
cl_kernel kernel;
cl_int err = 0;
char *fin_program_src;
const char *program_source =
"void sum_reduce_and_store(__local float *sdata,\n"
"__global float *store_arr,\n"
"float value,\n"
"int store_off)\n"
"{\n"
//Note that this draws from NVIDIA's reduction example:
//- Doesn't use % operator.
//- Uses contiguous threads.
//- Uses sequential addressing -- no divergence or bank conflicts.
//- Is completely unrolled.
// local size must be a power of 2 and (>= 64 or == 1)
"unsigned int lsz = get_local_size(0);\n"
"unsigned int lid = get_local_id(0);\n"
"sdata[lid] = value;\n"
"barrier(CLK_LOCAL_MEM_FENCE);\n"
// do reduction in shared mem
"if (lsz != 1) {\n"
"if (lsz >= 512) { if (lid < 256) { sdata[lid] += sdata[lid + 256]; } barrier(CLK_LOCAL_MEM_FENCE); }\n"
"if (lsz >= 256) { if (lid < 128) { sdata[lid] += sdata[lid + 128]; } barrier(CLK_LOCAL_MEM_FENCE); }\n"
"if (lsz >= 128) { if (lid < 64) { sdata[lid] += sdata[lid + 64]; } barrier(CLK_LOCAL_MEM_FENCE); }\n"
//Avoid extra if statements by only using local size >= 64
"if (lid < 32) { sdata[lid] += sdata[lid + 32]; } barrier(CLK_LOCAL_MEM_FENCE);\n"
"if (lid < 16) { sdata[lid] += sdata[lid + 16]; } barrier(CLK_LOCAL_MEM_FENCE);\n"
"if (lid < 8) { sdata[lid] += sdata[lid + 8]; } barrier(CLK_LOCAL_MEM_FENCE);\n"
"if (lid < 4) { sdata[lid] += sdata[lid + 4]; } barrier(CLK_LOCAL_MEM_FENCE);\n"
"if (lid < 2) { sdata[lid] += sdata[lid + 2]; } barrier(CLK_LOCAL_MEM_FENCE);\n"
"if (lid < 1) { sdata[lid] += sdata[lid + 1]; } barrier(CLK_LOCAL_MEM_FENCE);\n"
"}\n"
// write result for this block to global mem
"if (lid == 0) store_arr[store_off] = sdata[0];\n"
"barrier(CLK_LOCAL_MEM_FENCE);\n"
"}\n"
"void global_sum_and_reduce(__local float *reduce_s,\n"
"__global float *reduce_arr,\n"
"int beg_off,\n"
"int arr_len)\n"
"{\n"
"unsigned int lsz = get_local_size(0);\n"
"unsigned int i;"
"float value = 0.0f;\n"
"if (get_group_id(0) != 0)\n"
"return;\n"
//Reduce the entire array using one work group
"for(i = get_local_id(0); i < arr_len; i += lsz)\n"
"if (i < arr_len)\n"
"value += reduce_arr[beg_off+i];\n"
"sum_reduce_and_store(reduce_s, reduce_arr, value, beg_off);\n"
"}\n"
"__kernel void consolidate_train(__constant unsigned int *sizes,\n"
"__global unsigned int *num_layers,\n"
"__global unsigned int *num_neurons, \n"
"__global unsigned int *num_inputs,\n"
"__global unsigned int *num_outputs,\n"
"__global float *MSE_values,\n"
"__global float *num_bit_fail,\n"
"__global float *train_errors,\n"
"__global float *weights_deltas,\n"
"__local float *reduce_s)\n"
"{\n"
"unsigned int input_sz = get_global_size(0);\n"
"unsigned int gnum;\n"
"unsigned int l;\n"
//Calculate the number of groups used in the training run
"if (sizes[5] %% sizes[7])\n"
"gnum = 1 + (sizes[5] / sizes[7]);\n"
"else\n"
"gnum = sizes[5] / sizes[7];\n"
//Calculate for all layers
"for(l = 0; l < num_layers[get_global_id(1)]; ++l) {\n"
"unsigned int part_layer_off = get_global_id(1)*sizes[1]+l;\n"
"unsigned int num_neurons_l = num_neurons[part_layer_off];\n"
"unsigned int n_layer_off = sizes[2]*part_layer_off;\n"
"unsigned int o_layer_off = sizes[4]*part_layer_off;\n"
"unsigned int n;\n"
//Calcalate for all neurons
"for(n = 0; n < num_neurons[part_layer_off]; ++n) {\n"
"unsigned int num_outputs_l = num_outputs[n_layer_off+n];\n"
"unsigned int num_inputs_l = num_inputs[n_layer_off+n];\n"
"unsigned int o;\n"
//Calculate for all outputs
"for(o = 0; o < num_outputs_l; ++o) {\n"
"unsigned int i;\n"
//Sum delta data
"for(i = 0; i < num_inputs_l; ++i) {\n"
"global_sum_and_reduce(reduce_s, weights_deltas,\n"
"((o_layer_off+o)*sizes[3]+i)*gnum, gnum);\n"
// "if(get_global_id(0) == 0)\n"
// "printf(\"d (l, n, o, i) (delta): (%%5d %%5d %%5d %%5d) (%%5d %%10f)\\n\", l, n, o, i, ((o_layer_off+o)*sizes[3]+i)*gsz, weights_deltas[((o_layer_off+o)*sizes[3]+i)*gsz]);\n"
"}\n"
"global_sum_and_reduce(reduce_s, train_errors,\n"
"(o_layer_off+o)*sizes[5], sizes[5]);\n"
// "printf(\"e (l, n, o) (errs): (%%5d %%5d %%5d) (%%5d %%10f)\\n\", l, n, o, (o_layer_off+o)*input_sz, train_errors[(o_layer_off+o)*input_sz]);\n"
"}\n"
"o_layer_off += num_outputs_l;\n"
"}\n"
"}\n"
"global_sum_and_reduce(reduce_s, MSE_values, get_global_id(1)*sizes[5], sizes[5]);\n"
"global_sum_and_reduce(reduce_s, num_bit_fail, get_global_id(1)*sizes[5], sizes[5]);\n"
// "printf(\"m (msev): (%%10f)\\n\", MSE_values[get_global_id(1)*input_sz]);\n"
// "printf(\"n (fail): (%%10f)\\n\", num_bit_fail[get_global_id(1)*input_sz]);\n"
"}\n"
"float activation_derived(float steepness, int act_func,\n"
"__global float *outputs,\n"
"__global float *sums, int o_i)\n"
"{\n"
"switch (act_func)\n"
"{\n"
"case %d:\n"
"case %d:\n"
"case %d:\n"
"return " QUOTEME(fann_linear_derive(steepness, outputs[o_i])) ";\n"
"case %d:\n"
"case %d:\n"
"return " QUOTEME(fann_sigmoid_derive(steepness, fann_clip(outputs[o_i], 0.01f, 0.99f))) ";\n"
"case %d:\n"
"case %d:\n"
"return " QUOTEME(fann_sigmoid_symmetric_derive(steepness, fann_clip(outputs[o_i], -0.98f, 0.98f))) ";\n"
"case %d:\n"
"return " QUOTEME(fann_gaussian_derive(steepness, outputs[o_i], sums[o_i])) ";\n"
"case %d:\n"
"return " QUOTEME(fann_gaussian_symmetric_derive(steepness, outputs[o_i], sums[o_i])) ";\n"
"case %d:\n"
"return " QUOTEME(fann_elliot_derive(steepness, fann_clip(outputs[o_i], 0.01f, 0.99f), sums[o_i])) ";\n"
"case %d:\n"
"return " QUOTEME(fann_elliot_symmetric_derive(steepness, fann_clip(outputs[o_i], -0.98f, 0.98f), sums[o_i])) ";\n"
"case %d:\n"
"return " QUOTEME(fann_sin_symmetric_derive(steepness, sums[o_i])) ";\n"
"case %d:\n"
"return " QUOTEME(fann_cos_symmetric_derive(steepness, sums[o_i])) ";\n"
"case %d:\n"
"return " QUOTEME(fann_sin_derive(steepness, sums[o_i])) ";\n"
"case %d:\n"
"return " QUOTEME(fann_cos_derive(steepness, sums[o_i])) ";\n"
"case %d: //This should be an error\n"
"case %d: //This should be an error\n"
"case %d: //FIXME\n"
"return -99.0;\n"
"}\n"
"}\n"
"void backpropagate_MSE(__constant unsigned int *sizes,\n"
"__global unsigned int *num_layers,\n"
"__global unsigned int *num_neurons, \n"
"__global unsigned int *num_inputs,\n"
"__global unsigned int *num_outputs,\n"
"__global float *steepness,\n"
"__global int *activation,\n"
"__global float *weights,\n"
"__global float *inputs,\n"
"__global float *sums,\n"
"__global float *outputs,\n"
"__global float *train_errors,\n"
"__global float *weights_deltas,\n"
//Shared areas for caching
"__local float *steep_s,\n"
"__local int *act_s,\n"
"__local float *weights_s,\n"
"__local float *reduce_s )\n"
"{\n"
"unsigned int input_id = get_global_id(0);\n"
"unsigned int lid = get_local_id(0);\n"
"unsigned int lsz = get_local_size(0);\n"
"unsigned int gnum;\n"
"unsigned int gid = get_group_id(0);\n"
"int l;\n"
//Calculate the number of groups used in the training run
"if (sizes[5] %% sizes[7])\n"
"gnum = 1 + (sizes[5] / sizes[7]);\n"
"else\n"
"gnum = sizes[5] / sizes[7];\n"
//Calculate for all layers
"for(l = num_layers[get_global_id(1)]-1; l >= 0; --l) {\n"
"unsigned int part_layer_off = get_global_id(1)*sizes[1]+l;\n"
"unsigned int num_neurons_l = num_neurons[part_layer_off];\n"
"unsigned int n_layer_off = sizes[2]*part_layer_off;\n"
"unsigned int o_layer_off = sizes[4]*part_layer_off;\n"
"unsigned int output_off = o_layer_off-sizes[4];\n"
"unsigned int n;\n"
//Copy steepness & activation to shared mem
"barrier(CLK_LOCAL_MEM_FENCE);\n"
"for(n = 0; n < num_neurons_l; n += lsz) {\n"
"unsigned int neuron_num = n+lid;\n"
"if (neuron_num < num_neurons[part_layer_off]){\n"
"steep_s[neuron_num] = steepness[n_layer_off+neuron_num];\n"
"act_s[neuron_num] = activation[n_layer_off+neuron_num];\n"
"}\n"
"}\n"
"barrier(CLK_LOCAL_MEM_FENCE);\n"
//Clear all the previous layer's train_errors
"for(n = 0; n < num_neurons_l && l != 0; ++n) {\n"
"unsigned int num_outputs_l = num_outputs[n_layer_off+n];\n"
"unsigned int o;\n"
//Zero all outputs
"for(o = 0; o < num_outputs_l; ++o) {\n"
//Don't overrun data
"if (sizes[5] > input_id)\n"
"train_errors[output_off*sizes[5]+input_id] = 0.0f;\n"
"++output_off;\n"
"}\n"
"}\n"
//Reset
"output_off = o_layer_off;\n"
//Calcalate for all neurons
"for(n = 0; n < num_neurons[part_layer_off]; ++n) {\n"
"unsigned int num_outputs_l = num_outputs[n_layer_off+n];\n"
"unsigned int num_inputs_l = num_inputs[n_layer_off+n];\n"
"unsigned int o;\n"
//Calculate for all outputs
"for(o = 0; o < num_outputs_l; ++o) {\n"
"unsigned int i;\n"
"unsigned int o_i = output_off*sizes[5]+input_id;\n"
"float error;\n"
//Multiply errors with the activation function derivative.
"if (sizes[5] > input_id)\n"
"train_errors[o_i] = error =\n"
"train_errors[o_i]*activation_derived(steep_s[n], act_s[n], outputs, sums, o_i);\n"
//Weight & sum data from the inputs & bias
"for(i = 0; i < num_inputs_l; ++i) {\n"
"unsigned int weights_i = 0;\n"
"unsigned int prev_output_i = 0;\n"
"float delta = 0.0f;\n"
//Weights aren't used for first layer
"if (l != 0) {\n"
"weights_i = (sizes[3]*o+i) %% lsz;\n"
//Load shared memory as appropriate
"if (weights_i == 0) {\n"
"barrier(CLK_LOCAL_MEM_FENCE);\n"
"if (sizes[3]*o+i+lid < sizes[3]*num_outputs_l)\n"
"weights_s[lid] = weights[output_off*sizes[3]+i+lid];\n"
"barrier(CLK_LOCAL_MEM_FENCE);\n"
"}\n"
"}\n"
//Don't overrun data
"if (sizes[5] > input_id) {\n"
//Figure out what input was used
"if(i == num_inputs_l-1){\n"
"prev_output_i = (o_layer_off-sizes[4]+i)*sizes[5]+input_id;\n"
"delta = error;\n"
"} else if(l == 0) {\n"
"delta = inputs[i*sizes[5]+input_id] * error;\n"
"} else {\n"
"prev_output_i = (o_layer_off-sizes[4]+i)*sizes[5]+input_id;\n"
"delta = outputs[prev_output_i] * error;\n"
"}\n"
"}\n"
// "printf(\"(id, l, n, o, i) (delta): (%%5d %%5d %%5d %%5d %%5d) (%%10f)\\n\", input_id, l, n, o, i, error*input);\n"
// Calculate the weight deltas
// Due to memory requirements we're reducing the deltas here
"sum_reduce_and_store(reduce_s, weights_deltas, delta,\n"
"(output_off*sizes[3]+i)*gnum+gid);\n"
// "weights_deltas[(output_off*sizes[3]+i)*gnum+gid] = gnum;\n"
// "printf(\"(id, l, n, o, i) (fin delta): (%%5d %%5d %%5d %%5d %%5d) (%%10f)\\n\", input_id, l, n, o, i, weights_deltas[(output_off*sizes[3]+i)*gnum+gid]);\n"
//Calculate the error for previous layer
"if(l != 0 && sizes[5] > input_id)\n"
"train_errors[prev_output_i] += error * weights_s[weights_i];\n"
"}\n"
"++output_off;\n"
"}\n"
"}\n"
"}\n"
"}\n"
"void compute_MSE(__constant unsigned int *sizes,\n"
"__global float *f_params,\n"
"__global unsigned int *num_layers,\n"
"__global unsigned int *num_neurons, \n"
"__global unsigned int *num_outputs,\n"
"__global int *activation,\n"
"__global float *outputs,\n"
"__global float *train_errors,\n"
"__global float *actual_outputs,\n"
"__global float *MSE_values,\n"
"__global float *num_bit_fail,\n"
"__local int *act_s)\n"
"{\n"
"unsigned int ann_id = get_global_id(1);\n"
"unsigned int input_id = get_global_id(0);\n"
"unsigned int out_neuron_index = ann_id*sizes[1]+num_layers[ann_id]-1;\n"
"unsigned int out_off = out_neuron_index*sizes[4]*sizes[5]+input_id;\n"
"unsigned int num_neurons_l = num_neurons[out_neuron_index];\n"
"unsigned int layer_off = sizes[2]*out_neuron_index;\n"
"unsigned int n;\n"
"unsigned int layer_o = 0;\n"
"unsigned int bit_fail = 0;\n"
"float MSE_value = 0.0f;\n"
//Copy steepness & activation to shared mem
"for(n = 0; n < num_neurons_l; n += get_local_size(0)) {\n"
"unsigned int neuron_off = n+get_local_id(0);\n"
"if (neuron_off < num_neurons_l)\n"
"act_s[neuron_off] = activation[layer_off+neuron_off];\n"
"}\n"
"barrier(CLK_LOCAL_MEM_FENCE);\n"
//Don't use the extra threads
"if (input_id >= sizes[5])\n"
"return;\n"
//Calcalate for all neurons
"for(n = 0; n < num_neurons_l; ++n) {\n"
"unsigned int num_outputs_l = num_outputs[layer_off+n];\n"
"unsigned int act_out_off = (ann_id*sizes[4]+layer_o)*sizes[5]+input_id;\n"
"unsigned int o;\n"
//Calculate for all outputs
"for(o = 0; o < num_outputs_l; ++o) {\n"
"unsigned int out_index = out_off+(layer_o+o)*sizes[5];\n"
"float neuron_diff = actual_outputs[act_out_off+o*sizes[5]] - outputs[out_index];\n"
//Update MSE macro follows
"switch (act_s[n]) {\n"
"case %d:\n"
"case %d:\n"
"case %d:\n"
"case %d:\n"
"case %d:\n"
"case %d:\n"
"case %d:\n"
"case %d:\n"
"neuron_diff *= 0.5f;\n"
"}\n"
"MSE_value += neuron_diff * neuron_diff;\n"
"if(fabs(neuron_diff) >= f_params[0])\n"
"++bit_fail;\n"
// "printf(\"neuron_diff MSE_value: %%10f %%10f\\n\", neuron_diff, MSE_value);\n"
//Update error
"if (sizes[9]) {\n"
"if(neuron_diff < -.9999999f)\n"
"neuron_diff = -17.0f;\n"
"else if(neuron_diff > .9999999f)\n"
"neuron_diff = 17.0f;\n"
"else\n"
"neuron_diff = log((1.0f + neuron_diff) / (1.0f - neuron_diff));\n"
"}\n"
// "printf(\"train_error out_index: %%10f %%5d\\n\", neuron_diff, out_index);\n"
"train_errors[out_index] = neuron_diff;\n"
//Don't update ann->training_params->num_MSE because it can be calculated later
// "printf(\"(%%5d %%5d %%5d) train_errors actual_output neuron_value: %%10f %%10f %%10f\\n\", input_id, n, o, train_errors[out_index], actual_outputs[act_out_off+o*sizes[5]], outputs[out_index]);\n"
"}\n"
"layer_o += num_outputs_l;\n"
"}\n"
"unsigned int net_index = ann_id*sizes[5]+input_id;\n"
"num_bit_fail[net_index] = bit_fail;\n"
"MSE_values[net_index] = MSE_value;\n"
"}\n"
"float calc_act(float sum, float steepness, int act)\n"
"{\n"
"float max_sum;\n"
"sum *= steepness;\n"
"max_sum = 150.0f/steepness;\n"
"if(sum > max_sum)\n"
"sum = max_sum;\n"
"else if(sum < -max_sum)\n"
"sum = -max_sum;\n"
"switch(act)\n"
"{\n"
"case %d:\n"
"return sum;\n"
"case %d:\n"
"return ((sum < 0.0f) ? 0.0f : (sum > 1.0f) ? 1.0f : sum);\n"
"case %d:\n"
"return ((sum < -1.0f) ? -1.0f : (sum > 1.0f) ? 1.0f : sum);\n"
"case %d:\n"
"return " QUOTEME(fann_sigmoid_real(sum)) ";\n"
"case %d:\n"
"return " QUOTEME(fann_sigmoid_symmetric_real(sum)) ";\n"
"case %d:\n"
"return " QUOTEME(fann_stepwise(-2.64665293693542480469e+00f, -1.47221934795379638672e+00f, -5.49306154251098632812e-01f, 5.49306154251098632812e-01f, 1.47221934795379638672e+00f, 2.64665293693542480469e+00f, -9.90000009536743164062e-01f, -8.99999976158142089844e-01f, -5.00000000000000000000e-01f, 5.00000000000000000000e-01f, 8.99999976158142089844e-01f, 9.90000009536743164062e-01f, -1.0f, 1.0f, sum)) ";\n"
"case %d:\n"
"return " QUOTEME(fann_stepwise(-2.64665246009826660156e+00f, -1.47221946716308593750e+00f, -5.49306154251098632812e-01f, 5.49306154251098632812e-01f, 1.47221934795379638672e+00f, 2.64665293693542480469e+00f, 4.99999988824129104614e-03f, 5.00000007450580596924e-02f, 2.50000000000000000000e-01f, 7.50000000000000000000e-01f, 9.49999988079071044922e-01f, 9.95000004768371582031e-01f, 0.0f, 1.0f, sum)) ";\n"
"case %d:\n"
"return ((sum < 0.0f) ? 0.0f : 1.0f);\n"
"case %d:\n"
"return ((sum < 0.0f) ? -1.0f : 1.0f);\n"
"case %d:\n"
"return " QUOTEME(fann_gaussian_real(sum)) ";\n"
"case %d:\n"
"return " QUOTEME(fann_gaussian_symmetric_real(sum)) ";\n"
"case %d:\n"
"return " QUOTEME(fann_elliot_real(sum)) ";\n"
"case %d:\n"
"return " QUOTEME(fann_elliot_symmetric_real(sum)) ";\n"
"case %d:\n"
"return " QUOTEME(fann_sin_symmetric_real(sum)) ";\n"
"case %d:\n"
"return " QUOTEME(fann_cos_symmetric_real(sum)) ";\n"
"case %d:\n"
"return " QUOTEME(fann_sin_real(sum)) ";\n"
"case %d:\n"
"return " QUOTEME(fann_cos_real(sum)) ";\n"
"case %d:\n"
"return 0;\n"
"}\n"
"}\n"
"__kernel void run(__constant unsigned int *sizes,\n"
"__global float *f_params,\n"
"__global unsigned int *num_layers,\n"
"__global unsigned int *num_neurons,\n"
"__global unsigned int *num_inputs,\n"
"__global unsigned int *num_outputs,\n"
"__global float *steepness,\n"
"__global int *activation,\n"
"__global float *weights,\n"
"__global float *inputs,\n"
"__global float *sums,\n"
"__global float *outputs,\n"
"__local float *steep_s,\n"
"__local int *act_s,\n"
"__local float *weights_s )\n"
"{\n"
"unsigned int input_id = get_global_id(0);\n"
"unsigned int ann_id = get_global_id(1);\n"
"unsigned int lid = get_local_id(0);\n"
"unsigned int lsz = get_local_size(0);\n"
"unsigned int l;\n"
//Calculate for all layers
"for(l = 0; l < num_layers[ann_id]; ++l) {\n"
"unsigned int part_layer_off = ann_id*sizes[1]+l;\n"
"unsigned int n;\n"
"unsigned int num_neurons_l = num_neurons[part_layer_off];\n"
"unsigned int n_layer_off = sizes[2]*part_layer_off;\n"
"unsigned int o_layer_off = sizes[4]*part_layer_off;\n"
"unsigned int output_off = o_layer_off;\n"
//Copy steepness & activation to shared mem
"barrier(CLK_LOCAL_MEM_FENCE);\n"
"for(n = 0; n < num_neurons[part_layer_off]; n += lsz) {\n"
"unsigned int neuron_num = n+lid;\n"
"if (neuron_num < num_neurons[part_layer_off]){\n"
"steep_s[neuron_num] = steepness[n_layer_off+neuron_num];\n"
"act_s[neuron_num] = activation[n_layer_off+neuron_num];\n"
"}\n"
"}\n"
"barrier(CLK_LOCAL_MEM_FENCE);\n"
//Calcalate for all neurons
"for(n = 0; n < num_neurons[part_layer_off]; ++n) {\n"
"unsigned int num_outputs_l = num_outputs[n_layer_off+n];\n"
"unsigned int num_inputs_l = num_inputs[n_layer_off+n];\n"
"unsigned int o;\n"
//Calculate for all outputs
"for(o = 0; o < num_outputs_l; ++o) {\n"
"unsigned int i;\n"
"float sum = 0.0f;\n"
//Weight & sum data from the inputs & bias
"for(i = 0; i < num_inputs_l; ++i) {\n"
"float in_val;\n"
"unsigned int weights_i = (sizes[3]*o+i) %% lsz;\n"
//Load shared memory as appropriate
"if (weights_i == 0) {\n"
"barrier(CLK_LOCAL_MEM_FENCE);\n"
"if (sizes[3]*o+i+lid < sizes[3]*num_outputs_l)\n"
"weights_s[lid] = weights[output_off*sizes[3]+i+lid];\n"
"barrier(CLK_LOCAL_MEM_FENCE);\n"
"}\n"
//Don't overrun data
"if (sizes[5] <= input_id)\n"
"continue;\n"
"if (i == num_inputs_l-1) {\n"
//Bias
"in_val = 1.0f;\n"
// "printf(\"%%5d %%2d %%2d %%2d %%2d: %%15fCL %%15fCL\\n\", input_id, l, n, o ,i, weights_s[weights_i], in_val);\n"
"} else if (l == 0) {\n"
//Handle input from user
"in_val = inputs[i*sizes[5]+input_id];\n"
// "printf(\"%%5d %%2d %%2d %%2d %%2d: %%15fCL %%15fCL\\n\", input_id, l, n, o ,i, weights_s[weights_i], in_val);\n"
"} else {\n"
//Handle input from neurons
"in_val = outputs[(o_layer_off-sizes[4]+i)*sizes[5]+input_id];\n"
// "printf(\"%%5d %%2d %%2d %%2d %%2d: %%15fCL %%15fCL N:%%5d\\n\", input_id, l, n, o ,i, weights_s[weights_i], in_val, (o_layer_off-sizes[4]+i)*sizes[5]+input_id);\n"
"}\n"
//Weight & sum
"sum += weights_s[weights_i]*in_val;\n"
"}\n"
//Don't overrun data
"if (sizes[5] > input_id){\n"
//Save into output data array
"sums[output_off*sizes[5]+input_id] = sum;\n"
"outputs[output_off*sizes[5]+input_id] = calc_act(sum, steep_s[n], act_s[n]);\n"
// "printf(\"%%5d %%2d %%2d %%2d %%2d: %%15f %%15f N:%%5d\\n\", input_id, l, n, o ,i, sums[output_off*sizes[5]+input_id], outputs[output_off*sizes[5]+input_id], output_off*sizes[5]+input_id);\n"
"}\n"
"++output_off;\n"
"}\n"
"}\n"
"}\n"
"}"
/*
+ remember to set ann->training_params->num_MSE on return
+ remember to init train errors like in fann_compute_MSE()
+ remember that the group size must be a power of two >= 64 or == 1 for reduce to work
remember to set neuron->num_backprop_done on return
*/
"__kernel void train_batch(\n"//ANN structure
"__constant unsigned int *sizes,\n"
"__global float *f_params,\n"
"__global unsigned int *num_layers,\n"
"__global unsigned int *num_neurons,\n"
"__global unsigned int *num_inputs,\n"
"__global unsigned int *num_outputs,\n"
//Network values
"__global float *steepness,\n"
"__global int *activation,\n"
"__global float *weights,\n"
//Per-run data
"__global float *inputs,\n"
"__global float *sums,\n"
"__global float *outputs,\n"
//Per-run training memory
"__global float *train_errors,\n"
"__global float *actual_outputs,\n"
"__global float *MSE_values,\n"
"__global float *num_bit_fail,\n"
"__global float *weights_deltas,\n"
//Shared areas
"__local float *steep_s,\n"
"__local int *act_s,\n"
"__local float *weights_s,\n"
"__local float *reduce_s)\n"
"{\n"
//Do the normal procedure of an epoch
"run(sizes, f_params, num_layers, num_neurons, num_inputs, num_outputs,\n"
"steepness, activation, weights, inputs, sums, outputs,\n"
"steep_s, act_s, weights_s);\n"
"compute_MSE(sizes, f_params, num_layers, num_neurons, num_outputs,\n"
"activation, outputs, train_errors, actual_outputs,\n"
"MSE_values, num_bit_fail, act_s);\n"
"backpropagate_MSE(sizes, num_layers, num_neurons, num_inputs, num_outputs,\n"
"steepness, activation, weights, inputs, sums,\n"
"outputs, train_errors, weights_deltas,\n"
"steep_s, act_s, weights_s, reduce_s);\n"
"}\n";
//Insert enum values here because I can't seem to do it at compile time
fin_program_src = calloc(128000, sizeof(char));
sprintf(fin_program_src, program_source,
FANN_LINEAR, FANN_LINEAR_PIECE,
FANN_LINEAR_PIECE_SYMMETRIC, FANN_SIGMOID, FANN_SIGMOID_STEPWISE,
FANN_SIGMOID_SYMMETRIC, FANN_SIGMOID_SYMMETRIC_STEPWISE, FANN_GAUSSIAN,
FANN_GAUSSIAN_SYMMETRIC, FANN_ELLIOT, FANN_ELLIOT_SYMMETRIC,
FANN_SIN_SYMMETRIC, FANN_COS_SYMMETRIC, FANN_SIN, FANN_COS,
FANN_THRESHOLD_SYMMETRIC, FANN_THRESHOLD, FANN_GAUSSIAN_STEPWISE,
FANN_LINEAR_PIECE_SYMMETRIC, FANN_THRESHOLD_SYMMETRIC,
FANN_SIGMOID_SYMMETRIC, FANN_SIGMOID_SYMMETRIC_STEPWISE,
FANN_ELLIOT_SYMMETRIC, FANN_GAUSSIAN_SYMMETRIC, FANN_SIN_SYMMETRIC,
FANN_COS_SYMMETRIC,
FANN_LINEAR, FANN_LINEAR_PIECE,
FANN_LINEAR_PIECE_SYMMETRIC, FANN_SIGMOID, FANN_SIGMOID_SYMMETRIC,
FANN_SIGMOID_SYMMETRIC_STEPWISE, FANN_SIGMOID_STEPWISE,
FANN_THRESHOLD, FANN_THRESHOLD_SYMMETRIC, FANN_GAUSSIAN,
FANN_GAUSSIAN_SYMMETRIC, FANN_ELLIOT, FANN_ELLIOT_SYMMETRIC,
FANN_SIN_SYMMETRIC, FANN_COS_SYMMETRIC, FANN_SIN, FANN_COS,
FANN_GAUSSIAN_STEPWISE);
program = clCreateProgramWithSource(context, 1, (const char**)&fin_program_src,
NULL, &err);
assert(err == CL_SUCCESS);
err = clBuildProgram(program, 1, &device, "-cl-fast-relaxed-math -Werror", NULL, NULL);
//Detailed debugging info
if (err != CL_SUCCESS)
{
size_t len;
char *buffer = (char *)calloc(128000, sizeof(char));
printf("Error: Failed to build program executable!\n");
printf("clBuildProgram return:\n");
if(err == CL_INVALID_PROGRAM)
printf("CL_INVALID_PROGRAM\n");
else if(err == CL_INVALID_VALUE)
printf("CL_INVALID_VALUE\n");
else if(err == CL_INVALID_BINARY)
printf("CL_INVALID_BINARY\n");
else if(err == CL_INVALID_BUILD_OPTIONS)
printf("CL_INVALID_BUILD_OPTIONS\n");
else if(err == CL_INVALID_OPERATION)
printf("CL_INVALID_OPERATION\n");
else if(err == CL_COMPILER_NOT_AVAILABLE)
printf("CL_COMPILER_NOT_AVAILABLE\n");
else if(err == CL_BUILD_PROGRAM_FAILURE)
printf("CL_BUILD_PROGRAM_FAILURE\n");
else if(err == CL_OUT_OF_HOST_MEMORY)
printf("CL_OUT_OF_HOST_MEMORY\n");
err = clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_STATUS, 128000*sizeof(char), buffer, &len);
assert(err == CL_SUCCESS);
printf("Build Status:\n");
if(buffer[0] == CL_BUILD_NONE)
printf("CL_BUILD_NONE\n");
else if(buffer[0] == CL_BUILD_ERROR)
printf("CL_BUILD_ERROR\n");
else if(buffer[0] == CL_BUILD_SUCCESS)
printf("CL_BUILD_SUCCESS\n");
else if(buffer[0] == CL_BUILD_IN_PROGRESS)
printf("CL_BUILD_IN_PROGRESS\n");
err = clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG, 128000*sizeof(char), buffer, &len);
printf("Get Build Info:\n");
switch (err) {
case CL_INVALID_DEVICE:
printf("CL_INVALID_DEVICE\n"); break;
case CL_INVALID_VALUE:
printf("CL_INVALID_VALUE\n"); break;
case CL_INVALID_PROGRAM:
printf("CL_INVALID_PROGRAM\n"); break;
}
printf("Build Info:\n%s\nProgram Source:\n%s\n", buffer, fin_program_src);
free(buffer);
exit(1);
}
kernel = clCreateKernel(program, kern_name, &err);
clReleaseProgram(program);
free(fin_program_src);
return kernel;
}
void parse(const char *buf) {
uint16_t count = 0;
char *loop;
char ch;
while ((ch = *buf++)) {
switch (ch) {
case '0':
case '1':
case '2':
case '3':
case '4':
case '5':
case '6':
case '7':
case '8':
case '9':
x = ch - '0';
while (*buf >= '0' && *buf <= '9') {
x = x*10 + (*buf++ - '0');
}
break;
case 'p':
send_num(x);
send_str(PSTR("\r\n"));
break;
case 'a':
case 'b':
case 'c':
case 'd':
case 'e':
case 'f':
port = ch - 'a';
pin = x % 8;
break;
case 'i':
*(uint8_t *)(0x21 + port * 3) &= ~(1 << pin); // direction = input
x = *(uint8_t *)(0x20 + port * 3) & (1 << pin) ? 1 : 0; // x = pin
break;
case 'o':
if (x % 2) {
*(uint8_t *)(0x22 + port * 3) |= (1 << pin); // pin = hi
} else {
*(uint8_t *)(0x22 + port * 3) &= ~(1 << pin); // pin = low
}
*(uint8_t *)(0x21 + port * 3) |= (1 << pin); // direction = output
break;
case 'm':
_delay_ms(x);
break;
case 'u':
_delay_loop_2(x*(F_CPU/4000000UL));
break;
case '{':
count = x;
loop = buf;
while ((ch = *buf++) && ch != '}') {
}
case '}':
if (count) {
count--;
buf = loop;
}
break;
case 'k':
x = count;
break;
case '_':
while ((ch = *buf++) && ch != '_') {
usb_serial_putchar(ch);
}
send_str(PSTR("\r\n"));
break;
case 's':
x = analogRead(x);
break;
case 'v':
#define QUOTEME_(x) #x
#define QUOTEME(x) QUOTEME_(x)
send_str(PSTR(QUOTEME(MCU)));
send_str(PSTR("\r\n"));
break;
case 'h':
send_str(PSTR("0-9<num>\tenter number\r\n<num>p\t\tprint number\r\n<num>a-f<pin>\tselect pin\r\n<pin>i<num>\tinput\r\n<pin><num>o\toutput\r\n<num>m\t\tmsec delay\r\n<num>u\t\tusec delay\r\n<num>{}\t\trepeat\r\nk<num>\t\tloop count\r\n_<words>_\tprint words\r\n<num>s<num>\tanalog sample\r\nv\t\tprint version\r\nh\t\tprint help\r\n"));
break;
}
}
}
