Coverage Report

Created: 2021-04-12 03:25

/home/liu/buildslave/linux-x64-runtests/build/test/unit/nnc/dropout.tests.c
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Source (jump to first uncovered line)
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#include "case.h"
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#include "ccv_case.h"
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#include "ccv_nnc_case.h"
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#include <ccv.h>
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#include <nnc/ccv_nnc.h>
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#include <nnc/ccv_nnc_easy.h>
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TEST_SETUP()
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{
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  ccv_nnc_init();
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}
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TEST_CASE("dropout 40% of a 20x50 matrix")
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1
{
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1
  ccv_nnc_tensor_t* const a = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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1
  ccv_nnc_tensor_t* const b = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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1
  int i;
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1.00k
  for (i = 0; i < 20 * 50; 
i++1.00k
)
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1.00k
    a->data.f32[i] = (i + 1) * 0.01;
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1
  ccv_nnc_tensor_param_t output_info[2];
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1
  ccv_nnc_hint_tensor_auto(CMD_DROPOUT_FORWARD(0.4), &a->info, 1, ccv_nnc_no_hint, output_info, 2);
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1
  ccv_nnc_tensor_t* const c = ccv_nnc_tensor_new(0, output_info[1], 0);
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  ccv_nnc_cmd_exec(CMD_DROPOUT_FORWARD(0.4), ccv_nnc_no_hint, 0, TENSOR_LIST(a), TENSOR_LIST(b, c), 0);
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1
  int zero_count = 0;
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1.00k
  for (i = 0; i < 20 * 50; 
i++1.00k
)
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1.00k
    if (b->data.f32[i] == 0)
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413
      ++zero_count;
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587
    else {
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      REQUIRE_EQ_WITH_TOLERANCE(a->data.f32[i] / 0.6, b->data.f32[i], 1e-5, "should be scaled up by 1 / 0.6");
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587
    }
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1
  REQUIRE_EQ_WITH_TOLERANCE((float)zero_count / (20 * 50), 0.4, 2 * 1e-2, "should be within 2%% of error");
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1
  ccv_nnc_tensor_free(a);
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1
  ccv_nnc_tensor_free(b);
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1
  ccv_nnc_tensor_free(c);
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1
}
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TEST_CASE("dropout gradient for 40% of a 20x30 matrix")
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1
{
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1
  ccv_nnc_tensor_t* const a = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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1
  ccv_nnc_tensor_t* const b = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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1
  int i;
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1.00k
  for (i = 0; i < 20 * 50; 
i++1.00k
)
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1.00k
    a->data.f32[i] = (i + 1) * 0.01;
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1
  ccv_nnc_tensor_param_t output_info[2];
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1
  ccv_nnc_hint_tensor_auto(CMD_DROPOUT_FORWARD(0.4), &a->info, 1, ccv_nnc_no_hint, output_info, 2);
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1
  ccv_nnc_tensor_t* const c = ccv_nnc_tensor_new(0, output_info[1], 0);
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  ccv_nnc_cmd_exec(CMD_DROPOUT_FORWARD(0.4), ccv_nnc_no_hint, 0, TENSOR_LIST(a), TENSOR_LIST(b, c), 0);
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1
  ccv_nnc_tensor_t* const g = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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1.00k
  for (i = 0; i < 20 * 50; 
i++1.00k
)
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1.00k
    g->data.f32[i] = i + 1;
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1
  ccv_nnc_tensor_t* const h = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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  ccv_nnc_cmd_exec(CMD_DROPOUT_BACKWARD(0.4), ccv_nnc_no_hint, 0, TENSOR_LIST(g, 0, 0, 0, c), TENSOR_LIST(h), 0);
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1
  int zero_count = 0;
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1.00k
  for (i = 0; i < 20 * 50; 
i++1.00k
)
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1.00k
    if (h->data.f32[i] == 0)
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413
      ++zero_count;
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1
  REQUIRE_EQ_WITH_TOLERANCE((float)zero_count / (20 * 50), 0.4, 2 * 1e-2, "should be within 2%% of error");
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1
  ccv_nnc_tensor_t* const ht = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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1
  ccv_nnc_tensor_zero(ht);
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1.00k
  for (i = 0; i < 20 * 50; 
i++1.00k
)
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1.00k
    if (b->data.f32[i] != 0)
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      ht->data.f32[i] = (i + 1) / 0.6;
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1
  REQUIRE_TENSOR_EQ(h, ht, "propagated gradient should simply match the dropout");
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1
  ccv_nnc_tensor_free(a);
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1
  ccv_nnc_tensor_free(b);
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1
  ccv_nnc_tensor_free(c);
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  ccv_nnc_tensor_free(g);
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1
  ccv_nnc_tensor_free(h);
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  ccv_nnc_tensor_free(ht);
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1
}
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TEST_CASE("dropout entire matrix with 20% chance")
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{
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1
  ccv_nnc_tensor_t* const a = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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  ccv_nnc_tensor_t* const b = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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1
  int i;
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1.00k
  for (i = 0; i < 20 * 50; 
i++1.00k
)
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1.00k
    a->data.f32[i] = (i + 1) * 0.01;
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1
  ccv_nnc_tensor_param_t output_info[2];
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  ccv_nnc_hint_tensor_auto(CMD_DROPOUT_FORWARD(0.4), &a->info, 1, ccv_nnc_no_hint, output_info, 2);
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  ccv_nnc_tensor_t* const c = ccv_nnc_tensor_new(0, output_info[1], 0);
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  ccv_nnc_cmd_exec(CMD_DROPOUT_FORWARD(0.2, 1), ccv_nnc_no_hint, 0, TENSOR_LIST(a), TENSOR_LIST(b, c), 0);
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1
  ccv_nnc_tensor_t* const d = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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1
  if (b->data.f32[0] == 0)
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1.00k
    
for (i = 0; 1
i < 20 * 50;
i++1.00k
)
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1.00k
      d->data.f32[i] = 0;
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0
  else
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0
    for (i = 0; i < 20 * 50; i++)
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0
      d->data.f32[i] = a->data.f32[i] / 0.8;
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1
  REQUIRE_TENSOR_EQ(b, d, "dropout chance should be equal");
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1
  ccv_nnc_tensor_free(a);
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1
  ccv_nnc_tensor_free(b);
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1
  ccv_nnc_tensor_free(c);
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  ccv_nnc_tensor_free(d);
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1
}
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TEST_CASE("dropout gradient entire matrix with 20% chance")
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{
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1
  ccv_nnc_tensor_t* const a = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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1
  ccv_nnc_tensor_t* const b = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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1
  int i;
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1.00k
  for (i = 0; i < 20 * 50; 
i++1.00k
)
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1.00k
    a->data.f32[i] = (i + 1) * 0.01;
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1
  ccv_nnc_tensor_param_t output_info[2];
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1
  ccv_nnc_hint_tensor_auto(CMD_DROPOUT_FORWARD(0.4), &a->info, 1, ccv_nnc_no_hint, output_info, 2);
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  ccv_nnc_tensor_t* const c = ccv_nnc_tensor_new(0, output_info[1], 0);
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  ccv_nnc_cmd_exec(CMD_DROPOUT_FORWARD(0.2, 1), ccv_nnc_no_hint, 0, TENSOR_LIST(a), TENSOR_LIST(b, c), 0);
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1
  ccv_nnc_tensor_t* const g = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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1.00k
  for (i = 0; i < 20 * 50; 
i++1.00k
)
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1.00k
    g->data.f32[i] = i + 1;
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1
  ccv_nnc_tensor_t* const h = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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1
  ccv_nnc_cmd_exec(CMD_DROPOUT_BACKWARD(0.2, 1), ccv_nnc_no_hint, 0, TENSOR_LIST(g, 0, 0, 0, c), TENSOR_LIST(h), 0);
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1
  ccv_nnc_tensor_t* const d = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 20, 50), 0);
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1
  if (b->data.f32[0] == 0)
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1.00k
    
for (i = 0; 1
i < 20 * 50;
i++1.00k
)
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1.00k
      d->data.f32[i] = 0;
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0
  else
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0
    for (i = 0; i < 20 * 50; i++)
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0
      d->data.f32[i] = g->data.f32[i] / 0.8;
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1
  REQUIRE_TENSOR_EQ(h, d, "dropout chance should be equal");
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1
  ccv_nnc_tensor_free(a);
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1
  ccv_nnc_tensor_free(b);
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1
  ccv_nnc_tensor_free(c);
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1
  ccv_nnc_tensor_free(g);
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  ccv_nnc_tensor_free(h);
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  ccv_nnc_tensor_free(d);
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1
}
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#include "case_main.h"