Coverage Report

Created: 2019-07-03 22:50

/home/liu/buildslave/linux-x64-runtests/build/test/unit/nnc/winograd.tests.c
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Count
Source
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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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#include <3rdparty/dsfmt/dSFMT.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("convolutional network of 3x3 on 56x56 with non-uniform weights")
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1
{
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1
  ccv_nnc_tensor_t* a = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 56, 56, 128), 0);
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1
  ccv_nnc_tensor_t* b = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 56, 56, 128), 0);
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1
  ccv_nnc_cmd_t cmd = CMD_CONVOLUTION_FORWARD(1, 128, 3, 3, 128);
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1
  ccv_nnc_hint_t hint = ccv_nnc_hint_auto(cmd.info, a->info, b->info);
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1
  ccv_nnc_tensor_t* w = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 128, 3, 3, 128), 0);
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1
  ccv_nnc_tensor_t* bias = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 128), 0);
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1
  // configure the inlets.
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1
  dsfmt_t dsfmt;
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1
  dsfmt_init_gen_rand(&dsfmt, 0);
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1
  int i;
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147k
  for (i = 0; i < 128 * 3 * 3 * 128; 
i++147k
)
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147k
    w->data.f32[i] = dsfmt_genrand_open_close(&dsfmt) / (3 * 3 * 128);
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401k
  for (i = 0; i < 56 * 56 * 128; 
i++401k
)
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401k
    a->data.f32[i] = dsfmt_genrand_open_close(&dsfmt);
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129
  for (i = 0; i < 128; 
i++128
)
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128
    bias->data.f32[i] = (float)i / 128;
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1
  ccv_nnc_cmd_exec(cmd, hint, 0, TENSOR_LIST(a, w, bias), TENSOR_LIST(b), 0);
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1
  ccv_nnc_tensor_t* c = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 56, 56, 128), 0);
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1
  cmd.backend = CCV_NNC_BACKEND_CPU_OPT;
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1
  cmd.algorithm = 2; // CCV_NNC_CMD_OPT_CONV_ALGO_WINOGRAD
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1
  ccv_nnc_cmd_exec(cmd, hint, 0, TENSOR_LIST(a, w, bias), TENSOR_LIST(c), 0);
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1
  REQUIRE_TENSOR_EQ(b, c, "56x56 matrix should be exactly the same from reference implementation and winograd.");
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1
  ccv_nnc_tensor_free(c);
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1
  ccv_nnc_tensor_free(bias);
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1
  ccv_nnc_tensor_free(w);
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1
  ccv_nnc_tensor_free(b);
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1
  ccv_nnc_tensor_free(a);
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1
}
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TEST_CASE("convolutional network of 3x3 on 55x55 with non-uniform weights")
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1
{
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1
  ccv_nnc_tensor_t* a = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 55, 55, 128), 0);
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1
  ccv_nnc_tensor_t* b = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 55, 55, 128), 0);
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1
  ccv_nnc_cmd_t cmd = CMD_CONVOLUTION_FORWARD(1, 128, 3, 3, 128);
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1
  ccv_nnc_hint_t hint = ccv_nnc_hint_auto(cmd.info, a->info, b->info);
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1
  ccv_nnc_tensor_t* w = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 128, 3, 3, 128), 0);
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1
  ccv_nnc_tensor_t* bias = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 128), 0);
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1
  // configure the inlets.
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1
  dsfmt_t dsfmt;
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1
  dsfmt_init_gen_rand(&dsfmt, 0);
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1
  int i;
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147k
  for (i = 0; i < 128 * 3 * 3 * 128; 
i++147k
)
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147k
    w->data.f32[i] = dsfmt_genrand_open_close(&dsfmt) / (3 * 3 * 128);
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387k
  for (i = 0; i < 55 * 55 * 128; 
i++387k
)
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387k
    a->data.f32[i] = dsfmt_genrand_open_close(&dsfmt);
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129
  for (i = 0; i < 128; 
i++128
)
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128
    bias->data.f32[i] = (float)i / 128;
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1
  ccv_nnc_cmd_exec(cmd, hint, 0, TENSOR_LIST(a, w, bias), TENSOR_LIST(b), 0);
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1
  ccv_nnc_tensor_t* c = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 55, 55, 128), 0);
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1
  cmd.backend = CCV_NNC_BACKEND_CPU_OPT;
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1
  cmd.algorithm = 2; // CCV_NNC_CMD_OPT_CONV_ALGO_WINOGRAD
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1
  ccv_nnc_cmd_exec(cmd, hint, 0, TENSOR_LIST(a, w, bias), TENSOR_LIST(c), 0);
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1
  REQUIRE_TENSOR_EQ(b, c, "55x55 matrix should be exactly the same from reference implementation and winograd.");
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1
  ccv_nnc_tensor_free(c);
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1
  ccv_nnc_tensor_free(bias);
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1
  ccv_nnc_tensor_free(w);
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1
  ccv_nnc_tensor_free(b);
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1
  ccv_nnc_tensor_free(a);
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1
}
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TEST_CASE("convolutional network of 3x3 on 224x224 with non-uniform weights and RGB channels")
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1
{
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1
  ccv_nnc_tensor_t* a = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 224, 224, 3), 0);
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1
  ccv_nnc_tensor_t* b = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 224, 224, 128), 0);
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  ccv_nnc_cmd_t cmd = CMD_CONVOLUTION_FORWARD(1, 128, 3, 3, 3);
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1
  ccv_nnc_hint_t hint = ccv_nnc_hint_auto(cmd.info, a->info, b->info);
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  ccv_nnc_tensor_t* w = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 128, 3, 3, 3), 0);
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1
  ccv_nnc_tensor_t* bias = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 128), 0);
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1
  // configure the inlets.
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1
  dsfmt_t dsfmt;
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1
  dsfmt_init_gen_rand(&dsfmt, 0);
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1
  int i;
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3.45k
  for (i = 0; i < 3 * 3 * 3 * 128; 
i++3.45k
)
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3.45k
    w->data.f32[i] = dsfmt_genrand_open_close(&dsfmt) / (3 * 3 * 3);
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150k
  for (i = 0; i < 224 * 224 * 3; 
i++150k
)
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150k
    a->data.f32[i] = dsfmt_genrand_open_close(&dsfmt);
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129
  for (i = 0; i < 128; 
i++128
)
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128
    bias->data.f32[i] = (float)i / 128;
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1
  ccv_nnc_cmd_exec(cmd, hint, 0, TENSOR_LIST(a, w, bias), TENSOR_LIST(b), 0);
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1
  ccv_nnc_tensor_t* c = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 224, 224, 128), 0);
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1
  cmd.backend = CCV_NNC_BACKEND_CPU_OPT;
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1
  cmd.algorithm = 2; // CCV_NNC_CMD_OPT_CONV_ALGO_WINOGRAD
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1
  ccv_nnc_cmd_exec(cmd, hint, 0, TENSOR_LIST(a, w, bias), TENSOR_LIST(c), 0);
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1
  REQUIRE_TENSOR_EQ(b, c, "224x224 matrix should be exactly the same from reference implementation and winograd.");
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1
  ccv_nnc_tensor_free(c);
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1
  ccv_nnc_tensor_free(bias);
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1
  ccv_nnc_tensor_free(w);
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1
  ccv_nnc_tensor_free(b);
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1
  ccv_nnc_tensor_free(a);
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1
}
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TEST_CASE("convolutional network of 3x3 on 56x56 with no bias")
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1
{
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1
  ccv_nnc_tensor_t* a = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 56, 56, 128), 0);
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1
  ccv_nnc_tensor_t* b = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 56, 56, 128), 0);
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1
  ccv_nnc_cmd_t cmd = CMD_CONVOLUTION_FORWARD(1, 128, 3, 3, 128);
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1
  ccv_nnc_hint_t hint = ccv_nnc_hint_auto(cmd.info, a->info, b->info);
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1
  ccv_nnc_tensor_t* w = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 128, 3, 3, 128), 0);
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1
  ccv_nnc_tensor_t* bias = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 128), 0);
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1
  // configure the inlets.
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1
  dsfmt_t dsfmt;
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1
  dsfmt_init_gen_rand(&dsfmt, 0);
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1
  int i;
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147k
  for (i = 0; i < 128 * 3 * 3 * 128; 
i++147k
)
120
147k
    w->data.f32[i] = dsfmt_genrand_open_close(&dsfmt) / (3 * 3 * 128);
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401k
  for (i = 0; i < 56 * 56 * 128; 
i++401k
)
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401k
    a->data.f32[i] = dsfmt_genrand_open_close(&dsfmt);
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129
  for (i = 0; i < 128; 
i++128
)
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128
    bias->data.f32[i] = 0;
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1
  ccv_nnc_cmd_exec(cmd, hint, 0, TENSOR_LIST(a, w, bias), TENSOR_LIST(b), 0);
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1
  ccv_nnc_tensor_t* c = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 56, 56, 128), 0);
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1
  cmd.backend = CCV_NNC_BACKEND_CPU_OPT;
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1
  cmd.algorithm = 2; // CCV_NNC_CMD_OPT_CONV_ALGO_WINOGRAD
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1
  ccv_nnc_cmd_exec(cmd, hint, 0, TENSOR_LIST(a, w), TENSOR_LIST(c), 0);
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1
  REQUIRE_TENSOR_EQ(b, c, "56x56 matrix should be exactly the same from reference implementation and winograd.");
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1
  ccv_nnc_tensor_free(c);
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1
  ccv_nnc_tensor_free(bias);
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1
  ccv_nnc_tensor_free(w);
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1
  ccv_nnc_tensor_free(b);
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1
  ccv_nnc_tensor_free(a);
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1
}
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#include "case_main.h"