#include "ccv.h"

#include "ccv_internal.h"

#include "nnc/ccv_nnc.h"

#include "nnc/ccv_nnc_easy.h"

#include "nnc/ccv_nnc_internal.h"

#ifdef USE_OPENMP

#include <omp.h>

#endif

#ifdef USE_DISPATCH

#include <dispatch/dispatch.h>

#endif

static int _ccv_nnc_min_forw(const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint, const int flags, ccv_nnc_tensor_t* const* const inputs, const int input_size, ccv_nnc_tensor_t* const* const outputs, const int output_size, ccv_nnc_stream_context_t* const stream_context)

{

  ccv_nnc_tensor_view_t* const a = (ccv_nnc_tensor_view_t*)inputs[0];

  ccv_nnc_tensor_view_t* const b = (ccv_nnc_tensor_view_t*)inputs[1];

  ccv_nnc_tensor_view_t* const c = (ccv_nnc_tensor_view_t*)outputs[0];

  // Assuming this is float 32.

  int dim[CCV_NNC_MAX_DIM_ALLOC];

  int astride[CCV_NNC_MAX_DIM_ALLOC];

  int bstride[CCV_NNC_MAX_DIM_ALLOC];

  int cstride[CCV_NNC_MAX_DIM_ALLOC];

  assert(ccv_nnc_tensor_nd(a->info.dim) <= CCV_NNC_MAX_DIM + 2);

  assert(ccv_nnc_tensor_nd(b->info.dim) <= CCV_NNC_MAX_DIM + 2);

  assert(ccv_nnc_tensor_nd(c->info.dim) <= CCV_NNC_MAX_DIM + 2);

  ccv_nnc_tensor_view_get_dim(a, dim);

  assert(ccv_nnc_tensor_view_check_dim(b, dim));

  assert(ccv_nnc_tensor_view_check_dim(c, dim));

  int x;

  if (!CCV_IS_TENSOR_VIEW(a) && 
!3
CCV_IS_TENSOR_VIEW3
(b) && 
!3
CCV_IS_TENSOR_VIEW3
(c))

  {

    // Super optimal case, just do one for-loop for sum.

    const int tensor_count = ccv_nnc_tensor_count(a->info);

    for (x = 0; x < tensor_count; 
x++1.02k
)

      c->data.f32[x] = ccv_min(a->data.f32[x], b->data.f32[x]);

    return CCV_NNC_EXEC_SUCCESS;

  }

  assert(CCV_NNC_MAX_DIM == 2); // Need to change this logic for CCV_NNC_MAX_DIM == other number.

  ccv_nnc_tensor_view_get_stride(a, astride);

  ccv_nnc_tensor_view_get_stride(b, bstride);

  ccv_nnc_tensor_view_get_stride(c, cstride);

  int i[CCV_NNC_MAX_DIM + 2];

  float* const ap = a->data.f32;

  float* const bp = b->data.f32;

  float* const cp = c->data.f32;

  const int count = dim[2] * dim[3];

  if (astride[2] == dim[3] && bstride[2] == dim[3] && cstride[2] == dim[3])

  {

    // Special casing if the ainc[3] is the same as dim[3]

    for (i[0] = 0; i[0] < dim[0]; 
i[0]++1
)

    {

      float* ap0 = ap + i[0] * astride[0];

      float* bp0 = bp + i[0] * bstride[0];

      float* cp0 = cp + i[0] * cstride[0];

      for (i[1] = 0; i[1] < dim[1]; 
i[1]++2
)

      {

        for (x = 0; x < count; 
x++12
)

          cp0[x] = ccv_min(ap0[x], bp0[x]);

        ap0 += astride[1];

        bp0 += bstride[1];

        cp0 += cstride[1];

      }

    }

    return CCV_NNC_EXEC_SUCCESS;

  }

  // Non-optimal case, need to do skip copy.

  for (i[0] = 0; i[0] < dim[0]; i[0]++)

  {

    float* const ap0 = ap + i[0] * astride[0];

    float* const bp0 = bp + i[0] * bstride[0];

    float* const cp0 = cp + i[0] * cstride[0];

    for (i[1] = 0; i[1] < dim[1]; i[1]++)

    {

      float* ap1 = ap0 + i[1] * astride[1];

      float* bp1 = bp0 + i[1] * bstride[1];

      float* cp1 = cp0 + i[1] * cstride[1];

      for (i[2] = 0; i[2] < dim[2]; i[2]++)

      {

        for (x = 0; x < dim[3]; x++)

          cp1[x] = ccv_min(ap1[x], bp1[x]);

        ap1 += astride[2];

        bp1 += bstride[2];

        cp1 += cstride[2];

      }

    }

  }

  return CCV_NNC_EXEC_SUCCESS;

}

static int _ccv_nnc_min_back(const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint, const int flags, ccv_nnc_tensor_t* const* const inputs, const int input_size, ccv_nnc_tensor_t* const* const outputs, const int output_size, ccv_nnc_stream_context_t* const stream_context)

{

  ccv_nnc_tensor_view_t* const g = (ccv_nnc_tensor_view_t*)inputs[0];

  ccv_nnc_tensor_view_t* const a = (ccv_nnc_tensor_view_t*)inputs[1];

  ccv_nnc_tensor_view_t* const b = (ccv_nnc_tensor_view_t*)inputs[2];

  ccv_nnc_tensor_view_t* const ha = (ccv_nnc_tensor_view_t*)outputs[0];

  ccv_nnc_tensor_view_t* const hb = (ccv_nnc_tensor_view_t*)outputs[1];

  // Assuming this is float 32.

  int dim[CCV_NNC_MAX_DIM_ALLOC];

  int gstride[CCV_NNC_MAX_DIM_ALLOC];

  int astride[CCV_NNC_MAX_DIM_ALLOC];

  int bstride[CCV_NNC_MAX_DIM_ALLOC];

  int hastride[CCV_NNC_MAX_DIM_ALLOC];

  int hbstride[CCV_NNC_MAX_DIM_ALLOC];

  assert(ccv_nnc_tensor_nd(a->info.dim) <= CCV_NNC_MAX_DIM + 2);

  assert(ccv_nnc_tensor_nd(b->info.dim) <= CCV_NNC_MAX_DIM + 2);

  assert(ccv_nnc_tensor_nd(ha->info.dim) <= CCV_NNC_MAX_DIM + 2);

  assert(ccv_nnc_tensor_nd(hb->info.dim) <= CCV_NNC_MAX_DIM + 2);

  ccv_nnc_tensor_view_get_dim(a, dim);

  assert(ccv_nnc_tensor_view_check_dim(b, dim));

  assert(ccv_nnc_tensor_view_check_dim(ha, dim));

  assert(ccv_nnc_tensor_view_check_dim(hb, dim));

  if (g)

  {

    assert(ccv_nnc_tensor_nd(g->info.dim) <= CCV_NNC_MAX_DIM + 2);

    assert(ccv_nnc_tensor_view_check_dim(g, dim));

    int x;

    if (!CCV_IS_TENSOR_VIEW(g) && !CCV_IS_TENSOR_VIEW(a) && 
!2
CCV_IS_TENSOR_VIEW2
(b) && 
!2
CCV_IS_TENSOR_VIEW2
(ha) && 
!2
CCV_IS_TENSOR_VIEW2
(hb))

    {

      // Super optimal case, just do one for-loop for sum.

      const int tensor_count = ccv_nnc_tensor_count(a->info);

      for (x = 0; x < tensor_count; 
x++1.01k
)

        if (a->data.f32[x] < b->data.f32[x])

        {

          ha->data.f32[x] = g->data.f32[x];

          hb->data.f32[x] = 0;

        } else if (a->data.f32[x] > b->data.f32[x]) {

          hb->data.f32[x] = g->data.f32[x];

          ha->data.f32[x] = 0;

        } else

          ha->data.f32[x] = hb->data.f32[x] = g->data.f32[x];

      return CCV_NNC_EXEC_SUCCESS;

    }

    assert(CCV_NNC_MAX_DIM == 2); // Need to change this logic for CCV_NNC_MAX_DIM == other number.

    ccv_nnc_tensor_view_get_stride(g, gstride);

    ccv_nnc_tensor_view_get_stride(a, astride);

    ccv_nnc_tensor_view_get_stride(b, bstride);

    ccv_nnc_tensor_view_get_stride(ha, hastride);

    ccv_nnc_tensor_view_get_stride(hb, hbstride);

    int i[CCV_NNC_MAX_DIM + 2];

    float* const gp = g->data.f32;

    float* const ap = a->data.f32;

    float* const bp = b->data.f32;

    float* const hap = ha->data.f32;

    float* const hbp = hb->data.f32;

    const int count = dim[2] * dim[3];

    if (astride[2] == dim[3] && bstride[2] == dim[3] && hastride[2] == dim[3] && hbstride[2] == dim[3])

    {

      // Special casing if the ainc[3] is the same as dim[3]

      for (i[0] = 0; i[0] < dim[0]; 
i[0]++1
)

      {

        float* gp0 = gp + i[0] * gstride[0];

        float* ap0 = ap + i[0] * astride[0];

        float* bp0 = bp + i[0] * bstride[0];

        float* hap0 = hap + i[0] * hastride[0];

        float* hbp0 = hbp + i[0] * hbstride[0];

        for (i[1] = 0; i[1] < dim[1]; 
i[1]++2
)

        {

          for (x = 0; x < count; 
x++12
)

            if (ap0[x] < bp0[x]) {

              hap0[x] = gp0[x];

              hbp0[x] = 0;

            } else if (ap0[x] > bp0[x]) {

              hbp0[x] = gp0[x];

              hap0[x] = 0;

            } else

              hap0[x] = hbp0[x] = gp0[x];

          gp0 += gstride[1];

          ap0 += astride[1];

          bp0 += bstride[1];

          hap0 += hastride[1];

          hbp0 += hbstride[1];

        }

      }

      return CCV_NNC_EXEC_SUCCESS;

    }

    // Non-optimal case, need to do skip copy.

    for (i[0] = 0; i[0] < dim[0]; i[0]++)

    {

      float* const gp0 = gp + i[0] * gstride[0];

      float* const ap0 = ap + i[0] * astride[0];

      float* const bp0 = bp + i[0] * bstride[0];

      float* const hap0 = hap + i[0] * hastride[0];

      float* const hbp0 = hbp + i[0] * hbstride[0];

      for (i[1] = 0; i[1] < dim[1]; i[1]++)

      {

        float* gp1 = gp0 + i[1] * gstride[1];

        float* ap1 = ap0 + i[1] * astride[1];

        float* bp1 = bp0 + i[1] * bstride[1];

        float* hap1 = hap0 + i[1] * hastride[1];

        float* hbp1 = hbp0 + i[1] * hbstride[1];

        for (i[2] = 0; i[2] < dim[2]; i[2]++)

        {

          for (x = 0; x < dim[3]; x++)

            if (ap1[x] < bp1[x]) {

              hap1[x] = gp1[x];

              hbp1[x] = 0;

            } else if (ap1[x] > bp1[x]) {

              hbp1[x] = gp1[x];

              hap1[x] = 0;

            } else

              hap1[x] = hbp1[x] = gp1[x];

          gp1 += gstride[2];

          ap1 += astride[2];

          bp1 += bstride[2];

          hap1 += hastride[2];

          hbp1 += hbstride[2];

        }

      }

    }

  } else {

    int x;

    if (!CCV_IS_TENSOR_VIEW(a) && 
!1
CCV_IS_TENSOR_VIEW1
(b) && 
!1
CCV_IS_TENSOR_VIEW1
(ha) && 
!1
CCV_IS_TENSOR_VIEW1
(hb))

    {

      // Super optimal case, just do one for-loop for sum.

      const int tensor_count = ccv_nnc_tensor_count(a->info);

      for (x = 0; x < tensor_count; 
x++12
)

        if (a->data.f32[x] < b->data.f32[x]) {

          ha->data.f32[x] = 1;

          hb->data.f32[x] = 0;

        } else if (a->data.f32[x] > b->data.f32[x]) {

          ha->data.f32[x] = 0;

          hb->data.f32[x] = 1;

        } else

          ha->data.f32[x] = hb->data.f32[x] = 1;

      return CCV_NNC_EXEC_SUCCESS;

    }

    assert(CCV_NNC_MAX_DIM == 2); // Need to change this logic for CCV_NNC_MAX_DIM == other number.

    ccv_nnc_tensor_view_get_stride(a, astride);

    ccv_nnc_tensor_view_get_stride(b, bstride);

    ccv_nnc_tensor_view_get_stride(ha, hastride);

    ccv_nnc_tensor_view_get_stride(hb, hbstride);

    int i[CCV_NNC_MAX_DIM + 2];

    float* const ap = a->data.f32;

    float* const bp = b->data.f32;

    float* const hap = ha->data.f32;

    float* const hbp = hb->data.f32;

    const int count = dim[2] * dim[3];

    if (astride[2] == dim[3] && bstride[2] == dim[3] && hastride[2] == dim[3] && hbstride[2] == dim[3])

    {

      // Special casing if the ainc[3] is the same as dim[3]

      for (i[0] = 0; i[0] < dim[0]; 
i[0]++1
)

      {

        float* ap0 = ap + i[0] * astride[0];

        float* bp0 = bp + i[0] * bstride[0];

        float* hap0 = hap + i[0] * hastride[0];

        float* hbp0 = hbp + i[0] * hbstride[0];

        for (i[1] = 0; i[1] < dim[1]; 
i[1]++2
)

        {

          for (x = 0; x < count; 
x++12
)

            if (ap0[x] < bp0[x]) {

              hap0[x] = 1;

              hbp0[x] = 0;

            } else if (ap0[x] > bp0[x]) {

              hap0[x] = 0;

              hbp0[x] = 1;

            } else

              hap0[x] = hbp0[x] = 1;

          ap0 += astride[1];

          bp0 += bstride[1];

          hap0 += hastride[1];

          hbp0 += hbstride[1];

        }

      }

      return CCV_NNC_EXEC_SUCCESS;

    }

    // Non-optimal case, need to do skip copy.

    for (i[0] = 0; i[0] < dim[0]; i[0]++)

    {

      float* const ap0 = ap + i[0] * astride[0];

      float* const bp0 = bp + i[0] * bstride[0];

      float* const hap0 = hap + i[0] * hastride[0];

      float* const hbp0 = hbp + i[0] * hbstride[0];

      for (i[1] = 0; i[1] < dim[1]; i[1]++)

      {

        float* ap1 = ap0 + i[1] * astride[1];

        float* bp1 = bp0 + i[1] * bstride[1];

        float* hap1 = hap0 + i[1] * hastride[1];

        float* hbp1 = hbp0 + i[1] * hbstride[1];

        for (i[2] = 0; i[2] < dim[2]; i[2]++)

        {

          for (x = 0; x < dim[3]; x++)

            if (ap1[x] < bp1[x]) {

              hap1[x] = 1;

              hbp1[x] = 0;

            } else if (ap1[x] > bp1[x]) {

              hap1[x] = 0;

              hbp1[x] = 1;

            } else

              hap1[x] = hbp1[x] = 1;

          ap1 += astride[2];

          bp1 += bstride[2];

          hap1 += hastride[2];

          hbp1 += hbstride[2];

        }

      }

    }

  }

  return CCV_NNC_EXEC_SUCCESS;

}

REGISTER_COMMAND_BACKEND(CCV_NNC_MIN_FORWARD, CCV_NNC_BACKEND_CPU_REF)(ccv_nnc_cmd_backend_registry_t* const registry)

{

  registry->tensor_formats = CCV_TENSOR_FORMAT_NHWC | CCV_TENSOR_FORMAT_NCHW | CCV_TENSOR_FORMAT_CHWN;

  registry->tensor_datatypes = CCV_32F;

  registry->tensor_memory = CCV_TENSOR_CPU_MEMORY;

  registry->algorithms = 1;

  registry->exec = _ccv_nnc_min_forw;

}

REGISTER_COMMAND_BACKEND(CCV_NNC_MIN_BACKWARD, CCV_NNC_BACKEND_CPU_REF)(ccv_nnc_cmd_backend_registry_t* const registry)

{

  registry->tensor_formats = CCV_TENSOR_FORMAT_NHWC | CCV_TENSOR_FORMAT_NCHW | CCV_TENSOR_FORMAT_CHWN;

  registry->tensor_datatypes = CCV_32F;

  registry->tensor_memory = CCV_TENSOR_CPU_MEMORY;

  registry->algorithms = 1;

  registry->exec = _ccv_nnc_min_back;

}