## Coverage Report

#### Created: 2026-04-14 19:22

next uncovered line (L), next uncovered region (R), next uncovered branch (B)

    /home/liu/actions-runner/_work/ccv/ccv/lib/nnc/cmd/loss/ccv_nnc_binary_crossentropy_cpu_ref.c

    Line
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    Source
    1
    #include "ccv.h"
    2
    #include "ccv_internal.h"
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    #include "nnc/ccv_nnc.h"
    4
    #include "nnc/ccv_nnc_easy.h"
    5
    #include "nnc/ccv_nnc_internal.h"
    6
    #ifdef USE_OPENMP
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    #include <omp.h>
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    #endif
    9
    #ifdef USE_DISPATCH
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    #include <dispatch/dispatch.h>
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    #endif
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    13
    static int _ccv_nnc_binary_crossentropy_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)
    14
    14
    {
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    14
      assert(input_size == 2);
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    14
      const ccv_nnc_tensor_view_t* a = (ccv_nnc_tensor_view_t*)inputs[0];
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    14
      assert(ccv_nnc_tensor_nd(a->info.dim) <= 2);
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    14
      const ccv_nnc_tensor_view_t* b = (ccv_nnc_tensor_view_t*)inputs[1];
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    14
      assert(output_size == 1);
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    14
      ccv_nnc_tensor_view_t* c = (ccv_nnc_tensor_view_t*)outputs[0];
    21
    14
      int dim[CCV_NNC_MAX_DIM_ALLOC];
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    14
      int astride[CCV_NNC_MAX_DIM_ALLOC];
    23
    14
      int bstride[CCV_NNC_MAX_DIM_ALLOC];
    24
    14
      int cstride[CCV_NNC_MAX_DIM_ALLOC];
    25
    14
      ccv_nnc_tensor_view_get_dim(a, dim);
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    14
      assert(ccv_nnc_tensor_view_check_dim(b, dim));
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    14
      ccv_nnc_tensor_view_get_stride(a, astride);
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    14
      ccv_nnc_tensor_view_get_stride(b, bstride);
    29
    14
      ccv_nnc_tensor_view_get_stride(c, cstride);
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    14
      assert(ccv_nnc_tensor_nd(a->info.dim) <= 2);
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    14
      const int batch_size = dim[CCV_NNC_MAX_DIM];
    32
    14
      assert(ccv_nnc_tensor_count(c->info) == batch_size);
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    14
      const int count = dim[CCV_NNC_MAX_DIM + 1];
    34
    14
      const int astep = astride[CCV_NNC_MAX_DIM];
    35
    14
      const int bstep = bstride[CCV_NNC_MAX_DIM];
    36
    14
      const int cstep = ccv_nnc_tensor_nd(c->info.dim) == 1 ? 112 : cstride[2CCV_NNC_MAX_DIM2];
    37
    14
      const float pos_weight = cmd.info.binary_crossentropy.pos_weight;
    38
    14
      if (pos_weight == 1)
    39
    7
      {
    40
    62
        parallel_for7(i, batch_size) {
    41
    62
          int j;
    42
    62
          const float* const ap = a->data.f32 + i * astep;
    43
    62
          const float* const bp = b->data.f32 + i * bstep;
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    62
          float cp = 0;
    45
    6.06k
          for (j = 0; j < count; j++6.00k)
    46
    6.00k
            cp += (bp[j] - 1) * log(1 - ap[j]) - bp[j] * log(ap[j]);
    47
    62
          c->data.f32[i * cstep] = cp;
    48
    62
        } parallel_endfor
    49
    7
      } else {
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    62
        parallel_for7(i, batch_size) {
    51
    62
          int j;
    52
    62
          const float* const ap = a->data.f32 + i * astep;
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    62
          const float* const bp = b->data.f32 + i * bstep;
    54
    62
          float cp1 = 0, cp2 = 0;
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    6.06k
          for (j = 0; j < count; j++6.00k)
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    6.00k
            cp1 += (bp[j] - 1) * log(1 - ap[j]), cp2 += bp[j] * log(ap[j]);
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    62
          c->data.f32[i * cstep] = cp1 - cp2 * pos_weight;
    58
    62
        } parallel_endfor
    59
    7
      }
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    14
      return CCV_NNC_EXEC_SUCCESS;
    61
    14
    }
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    63
    static int _ccv_nnc_binary_crossentropy_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)
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    10
    {
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    10
      assert(input_size >= 3);
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    10
      assert(output_size >= 1);
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    10
      const ccv_nnc_tensor_view_t* const g = (ccv_nnc_tensor_view_t*)inputs[0];
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    10
      assert(!g || !CCV_IS_TENSOR_VIEW(g));
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    10
      const ccv_nnc_tensor_view_t* const a = (ccv_nnc_tensor_view_t*)inputs[1];
    70
    10
      const ccv_nnc_tensor_view_t* const b = (ccv_nnc_tensor_view_t*)inputs[2];
    71
    10
      ccv_nnc_tensor_view_t* const h = (ccv_nnc_tensor_view_t*)outputs[0];
    72
    10
      int dim[CCV_NNC_MAX_DIM_ALLOC];
    73
    10
      int astride[CCV_NNC_MAX_DIM_ALLOC];
    74
    10
      int bstride[CCV_NNC_MAX_DIM_ALLOC];
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    10
      int hstride[CCV_NNC_MAX_DIM_ALLOC];
    76
    10
      ccv_nnc_tensor_view_get_dim(a, dim);
    77
    10
      assert(ccv_nnc_tensor_view_check_dim(b, dim));
    78
    10
      assert(ccv_nnc_tensor_view_check_dim(h, dim));
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    10
      ccv_nnc_tensor_view_get_stride(a, astride);
    80
    10
      ccv_nnc_tensor_view_get_stride(b, bstride);
    81
    10
      ccv_nnc_tensor_view_get_stride(h, hstride);
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    10
      assert(ccv_nnc_tensor_nd(a->info.dim) <= 2);
    83
    10
      const int batch_size = dim[CCV_NNC_MAX_DIM];
    84
    10
      const int count = dim[CCV_NNC_MAX_DIM + 1];
    85
    10
      const int astep = astride[CCV_NNC_MAX_DIM];
    86
    10
      const int bstep = bstride[CCV_NNC_MAX_DIM];
    87
    10
      const int hstep = hstride[CCV_NNC_MAX_DIM];
    88
    10
      const float pos_weight = cmd.info.binary_crossentropy.pos_weight;
    89
    10
      if (pos_weight == 1)
    90
    5
      {
    91
    5
        if (g)
    92
    3
        {
    93
    3
          int gstride[CCV_NNC_MAX_DIM_ALLOC];
    94
    3
          ccv_nnc_tensor_view_get_stride(g, gstride);
    95
    3
          assert(ccv_nnc_tensor_count(g->info) == batch_size);
    96
    3
          const int gstep = ccv_nnc_tensor_nd(g->info.dim) == 1 ? 12 : gstride[1CCV_NNC_MAX_DIM1];
    97
    22
          parallel_for3(i, batch_size) {
    98
    22
            int j;
    99
    22
            const float gp = g->data.f32[i * gstep];
    100
    22
            const float* const ap = a->data.f32 + i * astep;
    101
    22
            const float* const bp = b->data.f32 + i * bstep;
    102
    22
            float* const hp = h->data.f32 + i * hstep;
    103
    2.02k
            for (j = 0; j < count; j++2.00k)
    104
    2.00k
              hp[j] = gp * (ap[j] - bp[j]) / ccv_max((1 - ap[j]) * ap[j], 1e-12);
    105
    22
          } parallel_endfor
    106
    3
        } else {
    107
    20
          parallel_for2(i, batch_size) {
    108
    20
            int j;
    109
    20
            const float* const ap = a->data.f32 + i * astep;
    110
    20
            const float* const bp = b->data.f32 + i * bstep;
    111
    20
            float* const hp = h->data.f32 + i * hstep;
    112
    2.02k
            for (j = 0; j < count; j++2.00k)
    113
    2.00k
              hp[j] = (ap[j] - bp[j]) / ccv_max((1 - ap[j]) * ap[j], 1e-12);
    114
    20
          } parallel_endfor
    115
    2
        }
    116
    5
      } else {
    117
    5
        const float pos_weight_1 = pos_weight - 1;
    118
    5
        if (g)
    119
    3
        {
    120
    3
          int gstride[CCV_NNC_MAX_DIM_ALLOC];
    121
    3
          ccv_nnc_tensor_view_get_stride(g, gstride);
    122
    3
          assert(ccv_nnc_tensor_count(g->info) == batch_size);
    123
    3
          const int gstep = ccv_nnc_tensor_nd(g->info.dim) == 1 ? 12 : gstride[1CCV_NNC_MAX_DIM1];
    124
    22
          parallel_for3(i, batch_size) {
    125
    22
            int j;
    126
    22
            const float gp = g->data.f32[i * gstep];
    127
    22
            const float* const ap = a->data.f32 + i * astep;
    128
    22
            const float* const bp = b->data.f32 + i * bstep;
    129
    22
            float* const hp = h->data.f32 + i * hstep;
    130
    2.02k
            for (j = 0; j < count; j++2.00k)
    131
    2.00k
              hp[j] = gp * (ap[j] * bp[j] * pos_weight_1 + ap[j] - pos_weight * bp[j]) / ccv_max((1 - ap[j]) * ap[j], 1e-12);
    132
    22
          } parallel_endfor
    133
    3
        } else {
    134
    20
          parallel_for2(i, batch_size) {
    135
    20
            int j;
    136
    20
            const float* const ap = a->data.f32 + i * astep;
    137
    20
            const float* const bp = b->data.f32 + i * bstep;
    138
    20
            float* const hp = h->data.f32 + i * hstep;
    139
    2.02k
            for (j = 0; j < count; j++2.00k)
    140
    2.00k
              hp[j] = (ap[j] * bp[j] * pos_weight_1 + ap[j] - pos_weight * bp[j]) / ccv_max((1 - ap[j]) * ap[j], 1e-12);
    141
    20
          } parallel_endfor
    142
    2
        }
    143
    5
      }
    144
    10
      return CCV_NNC_EXEC_SUCCESS;
    145
    10
    }
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    147
    REGISTER_COMMAND_BACKEND(CCV_NNC_BINARY_CROSSENTROPY_FORWARD, CCV_NNC_BACKEND_CPU_REF)(ccv_nnc_cmd_backend_registry_t* const registry)
    148
    1
    {
    149
    1
      registry->tensor_formats = CCV_TENSOR_FORMAT_NHWC | CCV_TENSOR_FORMAT_NCHW;
    150
    1
      registry->tensor_datatypes = CCV_32F;
    151
    1
      registry->tensor_memory = CCV_TENSOR_CPU_MEMORY;
    152
    1
      registry->algorithms = 1;
    153
    1
      registry->exec = _ccv_nnc_binary_crossentropy_forw;
    154
    1
    }
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    156
    REGISTER_COMMAND_BACKEND(CCV_NNC_BINARY_CROSSENTROPY_BACKWARD, CCV_NNC_BACKEND_CPU_REF)(ccv_nnc_cmd_backend_registry_t* const registry)
    157
    1
    {
    158
    1
      registry->tensor_formats = CCV_TENSOR_FORMAT_NHWC | CCV_TENSOR_FORMAT_NCHW;
    159
    1
      registry->tensor_datatypes = CCV_32F;
    160
    1
      registry->tensor_memory = CCV_TENSOR_CPU_MEMORY;
    161
    1
      registry->algorithms = 1;
    162
    1
      registry->exec = _ccv_nnc_binary_crossentropy_back;
    163
    1
    }