Coverage Report

Created: 2021-04-14 19:30

/home/liu/buildslave/linux-x64-runtests/build/test/unit/nnc/concat.tests.c
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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("concatenate several tensors together")
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{
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  ccv_cnnp_model_t* const concat = ccv_cnnp_concat(0, "concat");
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  ccv_cnnp_model_t* const dense = ccv_cnnp_dense(1, 1, "linear");
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  ccv_cnnp_model_t* const full = ccv_cnnp_sequential_new(MODEL_LIST(concat, dense), "full");
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  ccv_nnc_tensor_param_t a_params = CPU_TENSOR_NCHW(32F, 1);
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  ccv_nnc_tensor_param_t b_params = CPU_TENSOR_NCHW(32F, 2);
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  ccv_cnnp_model_compile(full, TENSOR_PARAM_LIST(a_params, b_params), CMD_NOOP(), CMD_NOOP());
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  CNNP_MODEL_GEN(full, CCV_NNC_LONG_DOT_GRAPH);
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  ccv_nnc_tensor_t* const a = ccv_nnc_tensor_new(0, a_params, 0);
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  a->data.f32[0] = -0.5;
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  ccv_nnc_tensor_t* const b = ccv_nnc_tensor_new(0, b_params, 0);
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  b->data.f32[0] = 0.3;
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  b->data.f32[1] = 2;
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  ccv_nnc_tensor_t* const c = ccv_nnc_tensor_new(0, CPU_TENSOR_NCHW(32F, 1), 0);
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  ccv_cnnp_model_evaluate(full, (ccv_cnnp_evaluate_param_t){}, TENSOR_LIST(a, b), TENSOR_LIST(c), 0, 0);
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  ccv_cnnp_model_parameters_map(full, ccv_cnnp_model_parameters(full, ALL_PARAMETERS, ALL_PARAMETERS), CMD_SET_FORWARD(1), ccv_nnc_no_hint, 0, 0);
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  ccv_cnnp_model_evaluate(full, (ccv_cnnp_evaluate_param_t){}, TENSOR_LIST(a, b), TENSOR_LIST(c), 0, 0);
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  REQUIRE_EQ_WITH_TOLERANCE(c->data.f32[0], -0.5 + 0.3 + 2, 1e-5, "should be equal");
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  ccv_nnc_tensor_free(a);
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  ccv_nnc_tensor_free(b);
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  ccv_nnc_tensor_free(c);
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  ccv_cnnp_model_free(full);
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}
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TEST_CASE("concatenate several tensors together and make sure they are simplified away")
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{
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  ccv_cnnp_model_t* const x_dense = ccv_cnnp_dense(1, 1, "linear");
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  ccv_cnnp_model_t* const y_dense = ccv_cnnp_dense(2, 1, "linear");
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  ccv_cnnp_model_t* const concat = ccv_cnnp_concat(0, "concat");
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  ccv_cnnp_model_t* const dense = ccv_cnnp_dense(1, 1, "linear");
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  ccv_cnnp_model_io_t const x = ccv_cnnp_input();
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  ccv_cnnp_model_io_t const y = ccv_cnnp_input();
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  ccv_cnnp_model_io_t xz = ccv_cnnp_model_apply(x_dense, MODEL_IO_LIST(x));
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  ccv_cnnp_model_io_t yz = ccv_cnnp_model_apply(y_dense, MODEL_IO_LIST(y));
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  ccv_cnnp_model_io_t z = ccv_cnnp_model_apply(concat, MODEL_IO_LIST(xz, yz));
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  z = ccv_cnnp_model_apply(dense, MODEL_IO_LIST(z));
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  ccv_cnnp_model_t* const full = ccv_cnnp_model_new(MODEL_IO_LIST(x, y), MODEL_IO_LIST(z), "full");
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  ccv_nnc_tensor_param_t a_params = CPU_TENSOR_NCHW(32F, 1);
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  ccv_nnc_tensor_param_t b_params = CPU_TENSOR_NCHW(32F, 2);
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  ccv_cnnp_model_compile(full, TENSOR_PARAM_LIST(a_params, b_params), CMD_NOOP(), CMD_NOOP());
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  CNNP_MODEL_GEN(full, CCV_NNC_LONG_DOT_GRAPH);
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  ccv_nnc_tensor_t* const a = ccv_nnc_tensor_new(0, a_params, 0);
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  a->data.f32[0] = -0.5;
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  ccv_nnc_tensor_t* const b = ccv_nnc_tensor_new(0, b_params, 0);
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  b->data.f32[0] = 0.3;
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  b->data.f32[1] = 2;
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  ccv_nnc_tensor_t* const c = ccv_nnc_tensor_new(0, CPU_TENSOR_NCHW(32F, 1), 0);
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  ccv_cnnp_model_evaluate(full, (ccv_cnnp_evaluate_param_t){}, TENSOR_LIST(a, b), TENSOR_LIST(c), 0, 0);
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  ccv_cnnp_model_parameters_map(full, ccv_cnnp_model_parameters(x_dense, ALL_PARAMETERS, ALL_PARAMETERS), CMD_SET_FORWARD(0.5), ccv_nnc_no_hint, 0, 0);
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  ccv_cnnp_model_parameters_map(full, ccv_cnnp_model_parameters(y_dense, ALL_PARAMETERS, ALL_PARAMETERS), CMD_SET_FORWARD(-0.5), ccv_nnc_no_hint, 0, 0);
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  ccv_cnnp_model_parameters_map(full, ccv_cnnp_model_parameters(dense, ALL_PARAMETERS, ALL_PARAMETERS), CMD_SET_FORWARD(1), ccv_nnc_no_hint, 0, 0);
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  ccv_cnnp_model_evaluate(full, (ccv_cnnp_evaluate_param_t){}, TENSOR_LIST(a, b), TENSOR_LIST(c), 0, 0);
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  REQUIRE_EQ_WITH_TOLERANCE(c->data.f32[0], -0.5 * 0.5 + (0.3 + 2) * -0.5 * 2, 1e-5, "should be equal");
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  ccv_nnc_tensor_free(a);
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  ccv_nnc_tensor_free(b);
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  ccv_nnc_tensor_free(c);
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  ccv_cnnp_model_free(full);
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}
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#include "case_main.h"