diff --git a/examples/inception_v3/tests/inception_a.py b/examples/inception_v3/tests/inception_a.py deleted file mode 100644 index e0f4fd6..0000000 --- a/examples/inception_v3/tests/inception_a.py +++ /dev/null @@ -1,62 +0,0 @@ -import sys - -import torch -from torchvision.models.inception import InceptionA - -sys.path.append("../../../tools") -from utils import print_cpp_vector - -torch.manual_seed(0) - -@torch.no_grad() -def init_weights(m): - if isinstance(m, torch.nn.Conv2d): - torch.nn.init.uniform_(m.weight) - elif isinstance(m, torch.nn.BatchNorm2d): - torch.nn.init.uniform_(m.weight) - torch.nn.init.uniform_(m.bias) - -with torch.no_grad(): - inception_a = InceptionA(3, 6) - inception_a.apply(init_weights) - - # branch1x1 - print_cpp_vector(torch.flatten(inception_a.branch1x1.conv.weight), "branch1x1_conv_weights") - print_cpp_vector(torch.flatten(inception_a.branch1x1.bn.weight), "branch1x1_bn_weights") - print_cpp_vector(torch.flatten(inception_a.branch1x1.bn.bias), "branch1x1_bn_bias") - - # branch5x5 - print_cpp_vector(torch.flatten(inception_a.branch5x5_1.conv.weight), "branch5x5_1_conv_weights") - print_cpp_vector(torch.flatten(inception_a.branch5x5_1.bn.weight), "branch5x5_1_bn_weights") - print_cpp_vector(torch.flatten(inception_a.branch5x5_1.bn.bias), "branch5x5_1_bn_bias") - - print_cpp_vector(torch.flatten(inception_a.branch5x5_2.conv.weight), "branch5x5_2_conv_weights") - print_cpp_vector(torch.flatten(inception_a.branch5x5_2.bn.weight), "branch5x5_2_bn_weights") - print_cpp_vector(torch.flatten(inception_a.branch5x5_2.bn.bias), "branch5x5_2_bn_bias") - - # branch3x3dbl - print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_1.conv.weight), "branch3x3dbl_1_conv_weights") - print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_1.bn.weight), "branch3x3dbl_1_bn_weights") - print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_1.bn.bias), "branch3x3dbl_1_bn_bias") - - print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_2.conv.weight), "branch3x3dbl_2_conv_weights") - print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_2.bn.weight), "branch3x3dbl_2_bn_weights") - print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_2.bn.bias), "branch3x3dbl_2_bn_bias") - - print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_3.conv.weight), "branch3x3dbl_3_conv_weights") - print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_3.bn.weight), "branch3x3dbl_3_bn_weights") - print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_3.bn.bias), "branch3x3dbl_3_bn_bias") - - # branchPool - print_cpp_vector(torch.flatten(inception_a.branch_pool.conv.weight), "branchPool_2_conv_weights") - print_cpp_vector(torch.flatten(inception_a.branch_pool.bn.weight), "branchPool_2_bn_weights") - print_cpp_vector(torch.flatten(inception_a.branch_pool.bn.bias), "branchPool_2_bn_bias") - - input_shape = (1, 3, 8, 8) - input = torch.randn(input_shape) - print_cpp_vector(torch.flatten(input), "input") - - output = inception_a(input) - output = torch.flatten(output) - print_cpp_vector(output) - diff --git a/examples/inception_v3/tests/inception_b.py b/examples/inception_v3/tests/inception_b.py deleted file mode 100644 index 2b8ccd6..0000000 --- a/examples/inception_v3/tests/inception_b.py +++ /dev/null @@ -1,47 +0,0 @@ -import sys - -import torch -from torchvision.models.inception import InceptionB - -sys.path.append("../../../tools") -from utils import print_cpp_vector - -torch.manual_seed(0) - -@torch.no_grad() -def init_weights(m): - if isinstance(m, torch.nn.Conv2d): - torch.nn.init.uniform_(m.weight) - elif isinstance(m, torch.nn.BatchNorm2d): - torch.nn.init.uniform_(m.weight) - torch.nn.init.uniform_(m.bias) - -with torch.no_grad(): - inception_b = InceptionB(3) - inception_b.apply(init_weights) - - # branch3x3 - print_cpp_vector(torch.flatten(inception_b.branch3x3.conv.weight), "branch3x3_conv_weights") - print_cpp_vector(torch.flatten(inception_b.branch3x3.bn.weight), "branch3x3_bn_weights") - print_cpp_vector(torch.flatten(inception_b.branch3x3.bn.bias), "branch3x3_bn_bias") - - # branch3x3dbl - print_cpp_vector(torch.flatten(inception_b.branch3x3dbl_1.conv.weight), "branch3x3dbl_1_conv_weights") - print_cpp_vector(torch.flatten(inception_b.branch3x3dbl_1.bn.weight), "branch3x3dbl_1_bn_weights") - print_cpp_vector(torch.flatten(inception_b.branch3x3dbl_1.bn.bias), "branch3x3dbl_1_bn_bias") - - print_cpp_vector(torch.flatten(inception_b.branch3x3dbl_2.conv.weight), "branch3x3dbl_2_conv_weights") - print_cpp_vector(torch.flatten(inception_b.branch3x3dbl_2.bn.weight), "branch3x3dbl_2_bn_weights") - print_cpp_vector(torch.flatten(inception_b.branch3x3dbl_2.bn.bias), "branch3x3dbl_2_bn_bias") - - print_cpp_vector(torch.flatten(inception_b.branch3x3dbl_3.conv.weight), "branch3x3dbl_3_conv_weights") - print_cpp_vector(torch.flatten(inception_b.branch3x3dbl_3.bn.weight), "branch3x3dbl_3_bn_weights") - print_cpp_vector(torch.flatten(inception_b.branch3x3dbl_3.bn.bias), "branch3x3dbl_3_bn_bias") - - input_shape = (1, 3, 8, 8) - input = torch.randn(input_shape) - print_cpp_vector(torch.flatten(input), "input") - - output = inception_b(input) - output = torch.flatten(output) - print_cpp_vector(output, "expected") \ No newline at end of file diff --git a/examples/inception_v3/tests/inception_blocks.py b/examples/inception_v3/tests/inception_blocks.py new file mode 100644 index 0000000..fbc5dc3 --- /dev/null +++ b/examples/inception_v3/tests/inception_blocks.py @@ -0,0 +1,83 @@ +import sys + +import torch +from torchvision.models.inception import ( + InceptionA, + InceptionB, + InceptionC, + InceptionD, + InceptionE +) + +sys.path.append("../../../tools") +from utils import print_cpp_vector, export_model_weights + +torch.manual_seed(0) + +output_size = 50 + +class InceptionBlockModel(torch.nn.Module): + def __init__(self, inception_block: torch.nn.Module, linear_in: int, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + self.inception_block = inception_block + self.fc = torch.nn.Linear(linear_in, output_size) + + def forward(self, x): + x = self.inception_block(x) + x = torch.flatten(x) + x = self.fc(x) + return x + + +@torch.no_grad() +def init_weights(m: torch.nn.Module): + if isinstance(m, torch.nn.Conv2d): + torch.nn.init.uniform_(m.weight, -1, 1) + elif isinstance(m, torch.nn.BatchNorm2d) or isinstance(m, torch.nn.Linear): + torch.nn.init.uniform_(m.weight, -1) + torch.nn.init.uniform_(m.bias, 1) + + +@torch.no_grad() +def generate_module_test_data(m: torch.nn.Module, name: str): + + print(name) + + input_shape = (1, 3, 4, 4) + input = torch.randn(input_shape) + print_cpp_vector(torch.flatten(input), "input") + + m.eval() + inception_out = m(input) + linear_in = torch.flatten(inception_out).size(0) + + inception_block = InceptionBlockModel(m, linear_in) + inception_block.apply(init_weights) + + export_model_weights(inception_block, f"resources/{name}.bin") + + inception_block.eval() + output = inception_block(input) + print_cpp_vector(torch.flatten(output), "expected") + + print() + + +if __name__ == "__main__": + m = InceptionA(3, 6) + generate_module_test_data(m, "inception_a") + + m = InceptionB(3) + generate_module_test_data(m, "inception_b") + + m = InceptionC(3, 64) + generate_module_test_data(m, "inception_c") + + m = InceptionD(3) + generate_module_test_data(m, "inception_d") + + m = InceptionE(3) + generate_module_test_data(m, "inception_e") + + + diff --git a/examples/inception_v3/tests/inception_c.py b/examples/inception_v3/tests/inception_c.py deleted file mode 100644 index ddd7cf7..0000000 --- a/examples/inception_v3/tests/inception_c.py +++ /dev/null @@ -1,73 +0,0 @@ -import sys - -import torch -from torchvision.models.inception import InceptionC - -sys.path.append("../../../tools") -from utils import print_cpp_vector - -torch.manual_seed(0) - -@torch.no_grad() -def init_weights(m): - if isinstance(m, torch.nn.Conv2d): - torch.nn.init.uniform_(m.weight) - elif isinstance(m, torch.nn.BatchNorm2d): - torch.nn.init.uniform_(m.weight) - torch.nn.init.uniform_(m.bias) - -with torch.no_grad(): - inception_c = InceptionC(3, 64) - inception_c.apply(init_weights) - - # branch1x1 - print_cpp_vector(torch.flatten(inception_c.branch1x1.conv.weight), "branch1x1_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch1x1.bn.weight), "branch1x1_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch1x1.bn.bias), "branch1x1_bn_bias") - - # branch7x7 - print_cpp_vector(torch.flatten(inception_c.branch7x7_1.conv.weight), "branch7x7_1_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7_1.bn.weight), "branch7x7_1_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7_1.bn.bias), "branch7x7_1_bn_bias") - - print_cpp_vector(torch.flatten(inception_c.branch7x7_2.conv.weight), "branch7x7_2_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7_2.bn.weight), "branch7x7_2_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7_2.bn.bias), "branch7x7_2_bn_bias") - - print_cpp_vector(torch.flatten(inception_c.branch7x7_3.conv.weight), "branch7x7_3_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7_3.bn.weight), "branch7x7_3_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7_3.bn.bias), "branch7x7_3_bn_bias") - - # branch7x7dbl - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_1.conv.weight), "branch7x7dbl_1_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_1.bn.weight), "branch7x7dbl_1_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_1.bn.bias), "branch7x7dbl_1_bn_bias") - - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_2.conv.weight), "branch7x7dbl_2_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_2.bn.weight), "branch7x7dbl_2_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_2.bn.bias), "branch7x7dbl_2_bn_bias") - - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_3.conv.weight), "branch7x7dbl_3_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_3.bn.weight), "branch7x7dbl_3_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_3.bn.bias), "branch7x7dbl_3_bn_bias") - - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_4.conv.weight), "branch7x7dbl_4_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_4.bn.weight), "branch7x7dbl_4_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_4.bn.bias), "branch7x7dbl_4_bn_bias") - - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_5.conv.weight), "branch7x7dbl_5_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_5.bn.weight), "branch7x7dbl_5_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7dbl_5.bn.bias), "branch7x7dbl_5_bn_bias") - - # branch_pool - print_cpp_vector(torch.flatten(inception_c.branch_pool.conv.weight), "branchPool_2_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch_pool.bn.weight), "branchPool_2_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch_pool.bn.bias), "branchPool_2_bn_bias") - - input_shape = (1, 3, 8, 8) - input = torch.randn(input_shape) - print_cpp_vector(torch.flatten(input), "input") - - output = inception_c(input) - output = torch.flatten(output) - print_cpp_vector(output, "expected") \ No newline at end of file diff --git a/examples/inception_v3/tests/inception_d.py b/examples/inception_v3/tests/inception_d.py deleted file mode 100644 index 3edc70a..0000000 --- a/examples/inception_v3/tests/inception_d.py +++ /dev/null @@ -1,55 +0,0 @@ -import sys - -import torch -from torchvision.models.inception import InceptionD - -sys.path.append("../../../tools") -from utils import print_cpp_vector - -torch.manual_seed(0) - -@torch.no_grad() -def init_weights(m): - if isinstance(m, torch.nn.Conv2d): - torch.nn.init.uniform_(m.weight) - elif isinstance(m, torch.nn.BatchNorm2d): - torch.nn.init.uniform_(m.weight) - torch.nn.init.uniform_(m.bias) - -with torch.no_grad(): - inception_c = InceptionD(3) - inception_c.apply(init_weights) - - # branch3x3 - print_cpp_vector(torch.flatten(inception_c.branch3x3_1.conv.weight), "branch3x3_1_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch3x3_1.bn.weight), "branch3x3_1_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch3x3_1.bn.bias), "branch3x3_1_bn_bias") - - print_cpp_vector(torch.flatten(inception_c.branch3x3_2.conv.weight), "branch3x3_2_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch3x3_2.bn.weight), "branch3x3_2_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch3x3_2.bn.bias), "branch3x3_2_bn_bias") - - # branch7x7x3 - print_cpp_vector(torch.flatten(inception_c.branch7x7x3_1.conv.weight), "branch7x7x3_1_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7x3_1.bn.weight), "branch7x7x3_1_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7x3_1.bn.bias), "branch7x7x3_1_bn_bias") - - print_cpp_vector(torch.flatten(inception_c.branch7x7x3_2.conv.weight), "branch7x7x3_2_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7x3_2.bn.weight), "branch7x7x3_2_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7x3_2.bn.bias), "branch7x7x3_2_bn_bias") - - print_cpp_vector(torch.flatten(inception_c.branch7x7x3_3.conv.weight), "branch7x7x3_3_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7x3_3.bn.weight), "branch7x7x3_3_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7x3_3.bn.bias), "branch7x7x3_3_bn_bias") - - print_cpp_vector(torch.flatten(inception_c.branch7x7x3_4.conv.weight), "branch7x7x3_4_conv_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7x3_4.bn.weight), "branch7x7x3_4_bn_weights") - print_cpp_vector(torch.flatten(inception_c.branch7x7x3_4.bn.bias), "branch7x7x3_4_bn_bias") - - input_shape = (1, 3, 8, 8) - input = torch.randn(input_shape) - print_cpp_vector(torch.flatten(input), "input") - - output = inception_c(input) - output = torch.flatten(output) - print_cpp_vector(output, "expected") \ No newline at end of file diff --git a/examples/inception_v3/tests/test_basic_conv2d.cpp b/examples/inception_v3/tests/test_basic_conv2d.cpp index 6708330..b91c6fc 100644 --- a/examples/inception_v3/tests/test_basic_conv2d.cpp +++ b/examples/inception_v3/tests/test_basic_conv2d.cpp @@ -12,7 +12,7 @@ class BasicConv2dTest : public ::testing::Test { shape2d kernelSize; shape2d stride; shape2d padding; - std::string prefix = "test"; + std::string prefix = "test."; float *d_input; float *d_output; @@ -46,7 +46,7 @@ class BasicConv2dTest : public ::testing::Test { std::pair layerPair = basic_conv2d->getLayers()[0]; - ASSERT_EQ(layerPair.first, prefix + ".conv"); + ASSERT_EQ(layerPair.first, prefix + "conv"); CUDANet::Layers::Conv2d *conv = dynamic_cast(layerPair.second); @@ -60,7 +60,7 @@ class BasicConv2dTest : public ::testing::Test { EXPECT_EQ(cudaStatus, cudaSuccess); layerPair = basic_conv2d->getLayers()[1]; - ASSERT_EQ(layerPair.first, prefix + ".bn"); + ASSERT_EQ(layerPair.first, prefix + "bn"); CUDANet::Layers::BatchNorm2d *bn = dynamic_cast(layerPair.second); diff --git a/examples/inception_v3/tests/test_fixture.hpp b/examples/inception_v3/tests/test_fixture.hpp new file mode 100644 index 0000000..13b2ec2 --- /dev/null +++ b/examples/inception_v3/tests/test_fixture.hpp @@ -0,0 +1,50 @@ +#ifndef TEST_FIXTURE_H +#define TEST_FIXTURE_H + +#include +#include + +#include "inception_v3.hpp" + +class InceptionBlockTest : public ::testing::Test { + protected: + CUDANet::Model *model; + + cudaError_t cudaStatus; + + shape2d inputShape; + int inputChannels; + + int outputSize; + + std::vector input; + std::vector expected; + + virtual void SetUp() override { + model = nullptr; + } + + virtual void TearDown() override { + // Clean up + delete model; + } + + void runTest() { + EXPECT_EQ( + input.size(), inputShape.first * inputShape.second * inputChannels + ); + + float *output = model->predict(input.data()); + + cudaStatus = cudaGetLastError(); + EXPECT_EQ(cudaStatus, cudaSuccess); + + EXPECT_EQ(outputSize, expected.size()); + + for (int i = 0; i < outputSize; ++i) { + EXPECT_NEAR(expected[i], output[i], 1e-3f); + } + } +}; + +#endif \ No newline at end of file diff --git a/examples/inception_v3/tests/test_inception_a.cpp b/examples/inception_v3/tests/test_inception_a.cpp index e7690f9..81aee51 100644 --- a/examples/inception_v3/tests/test_inception_a.cpp +++ b/examples/inception_v3/tests/test_inception_a.cpp @@ -2,33278 +2,65 @@ #include -class InceptionATest : public ::testing::Test { - protected: - InceptionA *inception_a; +#include "test_fixture.hpp" - cudaError_t cudaStatus; - - shape2d inputShape; - int inputChannels; - int poolFeatures; - std::string prefix = "test"; - - float *d_input; - float *d_output; - - std::vector input; - std::vector expected; - - std::vector branch1x1_conv_weights; - std::vector branch1x1_bn_weights; - std::vector branch1x1_bn_bias; - - std::vector branch5x5_1_conv_weights; - std::vector branch5x5_1_bn_weights; - std::vector branch5x5_1_bn_bias; - std::vector branch5x5_2_conv_weights; - std::vector branch5x5_2_bn_weights; - std::vector branch5x5_2_bn_bias; - - std::vector branch3x3dbl_1_conv_weights; - std::vector branch3x3dbl_1_bn_weights; - std::vector branch3x3dbl_1_bn_bias; - std::vector branch3x3dbl_2_conv_weights; - std::vector branch3x3dbl_2_bn_weights; - std::vector branch3x3dbl_2_bn_bias; - std::vector branch3x3dbl_3_conv_weights; - std::vector branch3x3dbl_3_bn_weights; - std::vector branch3x3dbl_3_bn_bias; - - std::vector branchPool_2_conv_weights; - std::vector branchPool_2_bn_weights; - std::vector branchPool_2_bn_bias; - - virtual void SetUp() override { - inception_a = nullptr; - } - - virtual void TearDown() override { - // Clean up - delete inception_a; - } - - void setBasicConv2dWeights( - const std::vector &conv_weights, - const std::vector &bn_weights, - const std::vector &bn_bias, - const int index - ) { - - std::pair layerPair = - inception_a->getLayers()[index]; - - CUDANet::Layers::Conv2d *conv = - dynamic_cast(layerPair.second); - EXPECT_NE(conv, nullptr); - - conv->setWeights(conv_weights.data()); - EXPECT_EQ(conv->getWeights().size(), conv_weights.size()); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - - layerPair = inception_a->getLayers()[index + 1]; - CUDANet::Layers::BatchNorm2d *bn = - dynamic_cast(layerPair.second); - EXPECT_NE(bn, nullptr); - bn->setWeights(bn_weights.data()); - EXPECT_EQ(bn->getWeights().size(), bn_weights.size()); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - - bn->setBiases(bn_bias.data()); - EXPECT_EQ(bn->getBiases().size(), bn_bias.size()); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - } - - void runTest() { +class InceptionAModel : public CUDANet::Model { + public: + InceptionAModel( + const shape2d inputShape, + const int inputChannels, + const int outputSize + ) + : CUDANet::Model(inputShape, inputChannels, outputSize) { inception_a = - new InceptionA(inputShape, inputChannels, poolFeatures, prefix); - - // Set up layer weights and bias - // Branch 1x1 - setBasicConv2dWeights(branch1x1_conv_weights, branch1x1_bn_weights, - branch1x1_bn_bias, 0); - - // Branch 5x5 - setBasicConv2dWeights(branch5x5_1_conv_weights, branch5x5_1_bn_weights, - branch5x5_1_bn_bias, 2); - setBasicConv2dWeights( - branch5x5_2_conv_weights, branch5x5_2_bn_weights, - branch5x5_2_bn_bias, 4 + new InceptionA(inputShape, inputChannels, 6, "inception_block."); + addLayer("", inception_a); + fc = new CUDANet::Layers::Dense( + inception_a->getOutputSize(), 50, + CUDANet::Layers::ActivationType::NONE ); + addLayer("fc", fc); + }; - // Branch 3x3dbl - setBasicConv2dWeights( - branch3x3dbl_1_conv_weights, - branch3x3dbl_1_bn_weights, branch3x3dbl_1_bn_bias, 6 - ); - setBasicConv2dWeights( - branch3x3dbl_2_conv_weights, - branch3x3dbl_2_bn_weights, branch3x3dbl_2_bn_bias, 8 - ); - setBasicConv2dWeights( - branch3x3dbl_3_conv_weights, - branch3x3dbl_3_bn_weights, branch3x3dbl_3_bn_bias, 10 - ); - - // Pool - setBasicConv2dWeights( - branchPool_2_conv_weights, branchPool_2_bn_weights, - branchPool_2_bn_bias, 13 // 12 is pooling layer with no weights - ); - - cudaStatus = - cudaMalloc((void **)&d_input, sizeof(float) * input.size()); - EXPECT_EQ(cudaStatus, cudaSuccess); - - cudaStatus = cudaMemcpy( - d_input, input.data(), sizeof(float) * input.size(), - cudaMemcpyHostToDevice - ); - EXPECT_EQ(cudaStatus, cudaSuccess); - - d_output = inception_a->forward(d_input); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - - int outputSize = inception_a->getOutputSize(); - std::vector output(outputSize); - cudaStatus = cudaMemcpy( - output.data(), d_output, sizeof(float) * output.size(), - cudaMemcpyDeviceToHost - ); - EXPECT_EQ(cudaStatus, cudaSuccess); - - for (int i = 0; i < output.size(); ++i) { - EXPECT_NEAR(expected[i], output[i], 1e-4f); - } + float *predict(const float *input) override { + float *d_input = inputLayer->forward(input); + d_input = inception_a->forward(d_input); + d_input = fc->forward(d_input); + return outputLayer->forward(d_input); } + + private: + InceptionA *inception_a; + CUDANet::Layers::Dense *fc; }; -TEST_F(InceptionATest, InceptionATest1) { - inputShape = {8, 8}; +TEST_F(InceptionBlockTest, InceptionATest) { + inputShape = {4, 4}; inputChannels = 3; - poolFeatures = 6; + outputSize = 50; - // Branch 1x1 - branch1x1_conv_weights = { - 0.14035f, 0.87f, 0.49507f, 0.63457f, 0.8503f, 0.42892f, 0.89685f, - 0.35726f, 0.77796f, 0.49456f, 0.44294f, 0.3545f, 0.7404f, 0.79719f, - 0.81874f, 0.56591f, 0.23804f, 0.28135f, 0.7261f, 0.72303f, 0.01476f, - 0.00422f, 0.05516f, 0.0977f, 0.817f, 0.41916f, 0.33949f, 0.50409f, - 0.04106f, 0.17697f, 0.33813f, 0.4986f, 0.3912f, 0.84137f, 0.63185f, - 0.80481f, 0.77025f, 0.74696f, 0.2595f, 0.48968f, 0.39024f, 0.26145f, - 0.08179f, 0.13974f, 0.53616f, 0.84042f, 0.70322f, 0.05655f, 0.94608f, - 0.63336f, 0.04331f, 0.77799f, 0.29832f, 0.20684f, 0.21326f, 0.86994f, - 0.45957f, 0.31301f, 0.03594f, 0.93491f, 0.48976f, 0.82067f, 0.42742f, - 0.34157f, 0.39556f, 0.6453f, 0.95871f, 0.19198f, 0.38525f, 0.26109f, - 0.57303f, 0.64278f, 0.96488f, 0.76874f, 0.80606f, 0.43369f, 0.25506f, - 0.78363f, 0.90918f, 0.24851f, 0.52155f, 0.41471f, 0.80171f, 0.73157f, - 0.29433f, 0.79109f, 0.12795f, 0.05538f, 0.74022f, 0.9928f, 0.08133f, - 0.53305f, 0.32859f, 0.63368f, 0.73449f, 0.6347f, 0.83978f, 0.63992f, - 0.40013f, 0.32182f, 0.65464f, 0.98525f, 0.63753f, 0.09577f, 0.48706f, - 0.06459f, 0.35899f, 0.14698f, 0.0443f, 0.03716f, 0.98153f, 0.60329f, - 0.36102f, 0.87736f, 0.0524f, 0.8433f, 0.61124f, 0.04173f, 0.87659f, - 0.66692f, 0.91784f, 0.45001f, 0.6712f, 0.56524f, 0.04291f, 0.86633f, - 0.36563f, 0.34724f, 0.22637f, 0.58903f, 0.50108f, 0.97894f, 0.56536f, - 0.06369f, 0.5472f, 0.58049f, 0.59144f, 0.76332f, 0.35715f, 0.22755f, - 0.69484f, 0.56777f, 0.67116f, 0.01547f, 0.35574f, 0.91945f, 0.63826f, - 0.24981f, 0.72597f, 0.12812f, 0.4399f, 0.96138f, 0.1839f, 0.96267f, - 0.73464f, 0.19284f, 0.7954f, 0.80458f, 0.35096f, 0.63366f, 0.04212f, - 0.28166f, 0.85255f, 0.28836f, 0.81535f, 0.9398f, 0.88859f, 0.71023f, - 0.65181f, 0.7191f, 0.64489f, 0.94493f, 0.04358f, 0.28434f, 0.9745f, - 0.35998f, 0.91162f, 0.42288f, 0.48354f, 0.75393f, 0.69681f, 0.5434f, - 0.43863f, 0.82944f, 0.00321f, 0.30446f, 0.26238f, 0.42421f, 0.73994f, - 0.2388f, 0.41751f, 0.32528f - }; - branch1x1_bn_weights = { - 0.47407f, 0.04623f, 0.48886f, 0.5592f, 0.22108f, 0.73679f, 0.08069f, - 0.97957f, 0.65978f, 0.28679f, 0.10456f, 0.0324f, 0.66314f, 0.56523f, - 0.39998f, 0.88273f, 0.39184f, 0.04481f, 0.44986f, 0.70338f, 0.53079f, - 0.20397f, 0.66444f, 0.90839f, 0.22067f, 0.45094f, 0.00514f, 0.84881f, - 0.30596f, 0.89577f, 0.27263f, 0.65875f, 0.76346f, 0.01824f, 0.62288f, - 0.2344f, 0.39542f, 0.79722f, 0.17107f, 0.08576f, 0.24601f, 0.45405f, - 0.30919f, 0.66244f, 0.54148f, 0.45477f, 0.88773f, 0.87448f, 0.83141f, - 0.23231f, 0.54188f, 0.16638f, 0.17186f, 0.22628f, 0.93218f, 0.09264f, - 0.42657f, 0.73366f, 0.30617f, 0.87338f, 0.85921f, 0.57763f, 0.09944f, - 0.03835f - }; - branch1x1_bn_bias = { - 0.62031f, 0.83149f, 0.48826f, 0.97693f, 0.6267f, 0.24531f, 0.05597f, - 0.67036f, 0.74984f, 0.76631f, 0.59352f, 0.82741f, 0.69363f, 0.89411f, - 0.10291f, 0.95823f, 0.88916f, 0.78917f, 0.11197f, 0.27827f, 0.12146f, - 0.2109f, 0.61785f, 0.85481f, 0.2054f, 0.46851f, 0.19641f, 0.7836f, - 0.39724f, 0.64006f, 0.82452f, 0.21275f, 0.45068f, 0.51745f, 0.17786f, - 0.7801f, 0.11937f, 0.86277f, 0.63562f, 0.21375f, 0.2226f, 0.73284f, - 0.50593f, 0.29227f, 0.39252f, 0.30781f, 0.19908f, 0.34053f, 0.63083f, - 0.00337f, 0.38632f, 0.3637f, 0.16592f, 0.42126f, 0.37936f, 0.77204f, - 0.249f, 0.2139f, 0.28461f, 0.07122f, 0.44128f, 0.72911f, 0.01348f, - 0.08469f - }; + model = new InceptionAModel(inputShape, inputChannels, outputSize); + model->loadWeights("../tests/resources/inception_a.bin"); - branch5x5_1_conv_weights = { - 0.40592f, 0.68227f, 0.89729f, 0.6025f, 0.1164f, 0.59102f, 0.76443f, - 0.11248f, 0.40529f, 0.32358f, 0.7789f, 0.82281f, 0.27459f, 0.78257f, - 0.64037f, 0.8248f, 0.72701f, 0.60117f, 0.7879f, 0.25699f, 0.50098f, - 0.42321f, 0.56732f, 0.5617f, 0.09071f, 0.87504f, 0.17652f, 0.39271f, - 0.16271f, 0.94566f, 0.61172f, 0.60632f, 0.39816f, 0.79624f, 0.60884f, - 0.87746f, 0.10607f, 0.24393f, 0.64151f, 0.54108f, 0.27384f, 0.6829f, - 0.84117f, 0.22361f, 0.19692f, 0.15355f, 0.56151f, 0.92775f, 0.71592f, - 0.53898f, 0.05741f, 0.22883f, 0.43093f, 0.37355f, 0.70848f, 0.37364f, - 0.0479f, 0.98609f, 0.0942f, 0.02199f, 0.4197f, 0.07258f, 0.19561f, - 0.53276f, 0.18248f, 0.06918f, 0.4885f, 0.20304f, 0.17599f, 0.24358f, - 0.72099f, 0.00614f, 0.41656f, 0.27854f, 0.83764f, 0.98035f, 0.28424f, - 0.87958f, 0.58171f, 0.81866f, 0.52043f, 0.2388f, 0.58878f, 0.4324f, - 0.32867f, 0.855f, 0.12668f, 0.30587f, 0.59152f, 0.84258f, 0.71723f, - 0.58072f, 0.70858f, 0.1421f, 0.71291f, 0.72656f, 0.60107f, 0.72327f, - 0.95368f, 0.61437f, 0.55123f, 0.14202f, 0.05537f, 0.6031f, 0.02767f, - 0.23824f, 0.89398f, 0.35122f, 0.78451f, 0.06465f, 0.09172f, 0.81046f, - 0.7581f, 0.89072f, 0.52056f, 0.53219f, 0.62586f, 0.15118f, 0.55665f, - 0.31869f, 0.55053f, 0.13159f, 0.04694f, 0.49177f, 0.97059f, 0.57836f, - 0.58528f, 0.31445f, 0.14219f, 0.12247f, 0.395f, 0.96587f, 0.95648f, - 0.18726f, 0.61534f, 0.96294f, 0.09031f, 0.57838f, 0.70927f, 0.93385f, - 0.62767f, 0.19104f, 0.43708f, 0.04607f - }; - branch5x5_1_bn_weights = { - 0.3362f, 0.68855f, 0.48998f, 0.22528f, 0.69107f, 0.48192f, 0.03862f, - 0.67669f, 0.67361f, 0.98495f, 0.11353f, 0.21187f, 0.75798f, 0.6694f, - 0.82612f, 0.16163f, 0.94923f, 0.97541f, 0.85442f, 0.57735f, 0.84677f, - 0.81058f, 0.40174f, 0.70013f, 0.40214f, 0.31575f, 0.57588f, 0.05591f, - 0.40089f, 0.5125f, 0.15511f, 0.23674f, 0.49023f, 0.89164f, 0.694f, - 0.46477f, 0.47043f, 0.10183f, 0.83474f, 0.36944f, 0.33459f, 0.17202f, - 0.13815f, 0.29684f, 0.66092f, 0.01933f, 0.71803f, 0.60422f - }; - branch5x5_1_bn_bias = { - 0.18202f, 0.38789f, 0.72477f, 0.33832f, 0.3964f, 0.11781f, 0.09011f, - 0.20631f, 0.13f, 0.52948f, 0.89196f, 0.9597f, 0.49102f, 0.88022f, - 0.15455f, 0.3637f, 0.69232f, 0.93616f, 0.14041f, 0.24541f, 0.04022f, - 0.68084f, 0.10773f, 0.40612f, 0.95157f, 0.02304f, 0.30338f, 0.823f, - 0.79486f, 0.22658f, 0.4587f, 0.22304f, 0.73116f, 0.51899f, 0.15443f, - 0.86934f, 0.00361f, 0.007f, 0.77655f, 0.39956f, 0.88278f, 0.19465f, - 0.74504f, 0.24557f, 0.77607f, 0.24464f, 0.9664f, 0.26574f - }; - branch5x5_2_conv_weights = { - 0.08597f, 0.24176f, 0.84453f, 0.87832f, 0.82709f, 0.26444f, 0.4171f, - 0.79713f, 0.35243f, 0.5742f, 0.0221f, 0.61738f, 0.33994f, 0.38998f, - 0.2046f, 0.31363f, 0.41697f, 0.02347f, 0.36797f, 0.66665f, 0.62142f, - 0.36588f, 0.40206f, 0.84903f, 0.3729f, 0.48862f, 0.0673f, 0.81711f, - 0.42598f, 0.28005f, 0.90843f, 0.67055f, 0.65782f, 0.88123f, 0.77584f, - 0.46451f, 0.24592f, 0.91496f, 0.71302f, 0.22351f, 0.70188f, 0.03594f, - 0.03287f, 0.06547f, 0.83612f, 0.11139f, 0.82473f, 0.78692f, 0.97122f, - 0.38209f, 0.52523f, 0.34371f, 0.52074f, 0.32248f, 0.86731f, 0.25821f, - 0.04839f, 0.71694f, 0.52662f, 0.02176f, 0.30611f, 0.48341f, 0.71358f, - 0.22553f, 0.3805f, 0.67922f, 0.48738f, 0.1602f, 0.49274f, 0.28749f, - 0.71749f, 0.61566f, 0.66702f, 0.3206f, 0.53761f, 0.00695f, 0.04465f, - 0.76769f, 0.09507f, 0.07146f, 0.61415f, 0.70109f, 0.17082f, 0.91051f, - 0.91071f, 0.71201f, 0.60028f, 0.20534f, 0.08575f, 0.26493f, 0.55652f, - 0.18892f, 0.06469f, 0.24424f, 0.98715f, 0.2798f, 0.22108f, 0.76348f, - 0.2351f, 0.93274f, 0.92879f, 0.8083f, 0.8492f, 0.94134f, 0.93174f, - 0.20563f, 0.76876f, 0.34417f, 0.55085f, 0.58072f, 0.34571f, 0.00879f, - 0.64063f, 0.88282f, 0.27091f, 0.7902f, 0.23494f, 0.16609f, 0.81611f, - 0.14587f, 0.66468f, 0.78434f, 0.12949f, 0.62471f, 0.61643f, 0.13309f, - 0.08223f, 0.72619f, 0.71343f, 0.00235f, 0.64063f, 0.92621f, 0.47893f, - 0.00618f, 0.21619f, 0.72188f, 0.02845f, 0.46632f, 0.78676f, 0.76268f, - 0.83026f, 0.37668f, 0.82293f, 0.96984f, 0.40252f, 0.41596f, 0.2356f, - 0.95507f, 0.95245f, 0.86393f, 0.20519f, 0.0053f, 0.75385f, 0.58656f, - 0.78751f, 0.56565f, 0.47936f, 0.3631f, 0.05578f, 0.35532f, 0.93253f, - 0.48095f, 0.3107f, 0.8518f, 0.34218f, 0.73089f, 0.01937f, 0.19709f, - 0.37899f, 0.67414f, 0.13661f, 0.22702f, 0.37764f, 0.86087f, 0.61483f, - 0.98966f, 0.77273f, 0.34001f, 0.69217f, 0.27933f, 0.10793f, 0.03771f, - 0.93244f, 0.30645f, 0.25326f, 0.28938f, 0.95062f, 0.31364f, 0.65215f, - 0.66449f, 0.4102f, 0.1196f, 0.84661f, 0.50647f, 0.68509f, 0.99521f, - 0.28941f, 0.13859f, 0.07602f, 0.83706f, 0.95f, 0.03613f, 0.71159f, - 0.49645f, 0.0289f, 0.40758f, 0.85911f, 0.84285f, 0.72585f, 0.76638f, - 0.9423f, 0.34418f, 0.0507f, 0.95107f, 0.66171f, 0.08132f, 0.45389f, - 0.83715f, 0.8901f, 0.08886f, 0.27082f, 0.65489f, 0.9494f, 0.56652f, - 0.37615f, 0.95584f, 0.09339f, 0.47981f, 0.60016f, 0.82728f, 0.37499f, - 0.04737f, 0.07246f, 0.5939f, 0.74206f, 0.57357f, 0.99494f, 0.80636f, - 0.50106f, 0.22847f, 0.15713f, 0.2442f, 0.3166f, 0.1269f, 0.02147f, - 0.94263f, 0.40855f, 0.65883f, 0.93235f, 0.38868f, 0.51042f, 0.15072f, - 0.33469f, 0.68231f, 0.29214f, 0.74253f, 0.17201f, 0.20467f, 0.21185f, - 0.4158f, 0.29039f, 0.82649f, 0.98767f, 0.68914f, 0.1672f, 0.42068f, - 0.2984f, 0.82432f, 0.42072f, 0.36152f, 0.01551f, 0.44445f, 0.11184f, - 0.98248f, 0.52591f, 0.9902f, 0.28343f, 0.94338f, 0.1989f, 0.15749f, - 0.7023f, 0.86295f, 0.21265f, 0.89408f, 0.23821f, 0.1507f, 0.73117f, - 0.28008f, 0.96509f, 0.31608f, 0.94386f, 0.57611f, 0.46764f, 0.57241f, - 0.02484f, 0.05f, 0.89507f, 0.85279f, 0.3068f, 0.55773f, 0.90082f, - 0.73021f, 0.34448f, 0.74274f, 0.17331f, 0.03757f, 0.16588f, 0.56178f, - 0.78734f, 0.49839f, 0.90193f, 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0.2834f, 0.31844f, 0.1473f, 0.11391f, 0.21094f, 0.24428f, - 0.12869f, 0.15755f, 0.36542f, 0.5148f, 0.39686f, 0.3609f, 0.33845f, - 0.27637f, 0.10921f, 0.13647f, 0.37798f, 0.60647f, 0.59927f, 0.54533f, - 0.40807f, 0.20128f, 0.01538f, 0.12018f, 0.39135f, 0.69127f, 0.75487f, - 0.69088f, 0.44147f, 0.1478f, 0.0f, 0.08097f, 0.33675f, 0.6245f, - 0.74847f, 0.7196f, 0.5026f, 0.17725f, 0.0f, 0.0f, 0.14755f, - 0.37501f, 0.5243f, 0.54513f, 0.37495f, 0.11294f, 0.0f, 0.0f, - 0.0f, 0.07269f, 0.19446f, 0.2473f, 0.17315f, 0.00973f, 0.0f, - 0.0f, 0.12198f, 0.12408f, 0.0f, 0.0f, 0.0f, 0.09556f, - 0.0f, 0.18167f, 0.59699f, 0.6787f, 0.23757f, 0.15048f, 0.40224f, - 0.50659f, 0.1854f, 0.25503f, 0.81488f, 1.16423f, 0.87083f, 0.77916f, - 0.74574f, 0.58314f, 0.13233f, 0.19177f, 0.84439f, 1.41889f, 1.40516f, - 1.27104f, 0.91575f, 0.37933f, 0.0f, 0.15623f, 0.8788f, 1.6435f, - 1.82084f, 1.64603f, 0.99862f, 0.23534f, 0.0f, 0.06117f, 0.75273f, - 1.48495f, 1.79793f, 1.71475f, 1.14729f, 0.31158f, 0.0f, 0.0f, - 0.26055f, 0.84751f, 1.23615f, 1.28222f, 0.8306f, 0.1487f, 0.0f, - 0.0f, 0.0f, 0.04559f, 0.36996f, 0.49885f, 0.29105f, 0.0f, - 0.0f, 0.02556f, 0.14468f, 0.15662f, 0.0f, 0.0f, 0.06465f, - 0.13032f, 0.02186f, 0.22648f, 0.47191f, 0.53544f, 0.22983f, 0.18233f, - 0.35531f, 0.40909f, 0.20977f, 0.2884f, 0.64055f, 0.88014f, 0.65868f, - 0.59858f, 0.58532f, 0.48724f, 0.19128f, 0.24292f, 0.65372f, 1.0481f, - 1.01958f, 0.92627f, 0.6985f, 0.34874f, 0.02154f, 0.21612f, 0.682f, - 1.2073f, 1.31585f, 1.1959f, 0.76387f, 0.25209f, 0.0f, 0.15434f, - 0.60718f, 1.11139f, 1.31599f, 1.2469f, 0.85954f, 0.29642f, 0.0f, - 0.0f, 0.28636f, 0.69525f, 0.95491f, 0.97253f, 0.66581f, 0.19853f, - 0.0f, 0.0f, 0.0f, 0.15715f, 0.38057f, 0.46848f, 0.3261f, - 0.02967f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.29957f, 0.42949f, 0.0f, - 0.0f, 0.11659f, 0.21812f, 0.0f, 0.0f, 0.57762f, 0.99764f, - 0.64513f, 0.56817f, 0.53126f, 0.35853f, 0.0f, 0.0f, 0.61268f, - 1.28062f, 1.23311f, 1.09356f, 0.72414f, 0.12624f, 0.0f, 0.0f, - 0.63318f, 1.52834f, 1.72288f, 1.55007f, 0.82757f, 0.0f, 0.0f, - 0.0f, 0.5221f, 1.35754f, 1.70068f, 1.62417f, 1.00136f, 0.05259f, - 0.0f, 0.0f, 0.00137f, 0.66292f, 1.08441f, 1.13203f, 0.63694f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.16351f, 0.30445f, - 0.06887f, 0.0f, 0.0f, 0.38791f, 0.49349f, 0.47736f, 0.28467f, - 0.28353f, 0.41754f, 0.47572f, 0.34433f, 0.57306f, 0.79974f, 0.83531f, - 0.55601f, 0.51027f, 0.68306f, 0.72693f, 0.52397f, 0.62246f, 0.95577f, - 1.17561f, 0.96575f, 0.8978f, 0.87879f, 0.79344f, 0.51364f, 0.58635f, - 0.96982f, 1.31856f, 1.30442f, 1.20006f, 0.97885f, 0.65928f, 0.35982f, - 0.55871f, 0.99356f, 1.46769f, 1.56476f, 1.4491f, 1.04169f, 0.56817f, - 0.24314f, 0.49341f, 0.91949f, 1.38239f, 1.57297f, 1.50322f, 1.13611f, - 0.60777f, 0.25456f, 0.315f, 0.60416f, 0.9885f, 1.23643f, 1.25042f, - 0.95408f, 0.51752f, 0.20481f, 0.16201f, 0.28491f, 0.48575f, 0.69176f, - 0.7773f, 0.64609f, 0.36739f, 0.15802f, 0.13269f, 0.27896f, 0.29481f, - 0.03472f, 0.01622f, 0.17447f, 0.25807f, 0.12534f, 0.36225f, 0.64022f, - 0.72784f, 0.37478f, 0.32179f, 0.51165f, 0.5726f, 0.33393f, 0.43299f, - 0.82297f, 1.10476f, 0.86221f, 0.80166f, 0.78285f, 0.64123f, 0.29548f, - 0.38321f, 0.84803f, 1.3014f, 1.26867f, 1.17957f, 0.91465f, 0.48541f, - 0.09988f, 0.35239f, 0.87871f, 1.4924f, 1.61055f, 1.47036f, 0.97513f, - 0.37234f, 0.0f, 0.28183f, 0.78523f, 1.36864f, 1.61001f, 1.5326f, - 1.08387f, 0.42492f, 0.0f, 0.05536f, 0.4104f, 0.88251f, 1.17864f, - 1.20271f, 0.84966f, 0.30896f, 0.0f, 0.0f, 0.00034f, 0.24751f, - 0.49891f, 0.59468f, 0.43046f, 0.0927f, 0.0f, 0.60878f, 0.64862f, - 0.65143f, 0.58858f, 0.58276f, 0.62211f, 0.6446f, 0.6122f, 0.6638f, - 0.73907f, 0.75635f, 0.67156f, 0.6573f, 0.7049f, 0.7215f, 0.66121f, - 0.67977f, 0.7837f, 0.85318f, 0.79569f, 0.77901f, 0.77264f, 0.73955f, - 0.65228f, 0.66897f, 0.78986f, 0.90019f, 0.8944f, 0.86864f, 0.80391f, - 0.70145f, 0.60629f, 0.65994f, 0.7958f, 0.94349f, 0.97541f, 0.94189f, - 0.81861f, 0.6729f, 0.57046f, 0.64203f, 0.77181f, 0.91248f, 0.97178f, - 0.95632f, 0.84826f, 0.68837f, 0.57535f, 0.5888f, 0.67866f, 0.79125f, - 0.86468f, 0.87049f, 0.78511f, 0.65364f, 0.55839f, 0.5394f, 0.57789f, - 0.63898f, 0.70005f, 0.72367f, 0.68359f, 0.60214f, 0.53826f, 0.34793f, - 0.36916f, 0.36733f, 0.33593f, 0.33484f, 0.35412f, 0.36632f, 0.34625f, - 0.37464f, 0.4167f, 0.42325f, 0.38005f, 0.37036f, 0.39533f, 0.40625f, - 0.37487f, 0.38236f, 0.43851f, 0.47275f, 0.44224f, 0.43132f, 0.42717f, - 0.41558f, 0.37064f, 0.37673f, 0.44046f, 0.49411f, 0.49344f, 0.47944f, - 0.44578f, 0.39479f, 0.34576f, 0.37256f, 0.44402f, 0.51687f, 0.53369f, - 0.51783f, 0.45447f, 0.38081f, 0.32808f, 0.36289f, 0.4306f, 0.50253f, - 0.53447f, 0.52602f, 0.46905f, 0.38668f, 0.32955f, 0.33652f, 0.38334f, - 0.44067f, 0.47905f, 0.48425f, 0.44023f, 0.37351f, 0.32225f, 0.31348f, - 0.33314f, 0.36296f, 0.39504f, 0.40958f, 0.38927f, 0.34606f, 0.31299f, - 0.46546f, 0.66697f, 0.0f, 0.0f, 0.0f, 0.19387f, 1.28227f, - 1.24841f, 0.50199f, 0.9542f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.9183f, 1.33407f, 0.40639f, 0.86297f, 0.31551f, 0.03413f, 0.0f, - 0.0f, 0.40622f, 0.66958f, 0.40998f, 0.72303f, 0.51015f, 1.51144f, - 0.84769f, 0.19471f, 0.0f, 0.0f, 0.73289f, 0.90653f, 0.6874f, - 1.10601f, 1.28863f, 0.25358f, 0.0f, 0.0f, 0.18608f, 0.41406f, - 0.97431f, 1.0734f, 0.9377f, 0.1892f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.5168f, 1.21087f, 0.71178f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.80387f, 1.35191f, 0.9865f, 0.18174f, - 0.0f, 0.89042f, 1.00741f, 0.70771f, 0.51648f, 0.4197f, 0.94808f, - 1.68834f, 1.66814f, 0.86303f, 1.11812f, 0.61751f, 0.48072f, 0.0f, - 0.57506f, 1.38451f, 1.73339f, 1.16179f, 1.63442f, 1.21271f, 0.83564f, - 0.05172f, 0.25184f, 0.94968f, 1.26097f, 1.17627f, 1.37714f, 1.04673f, - 1.56335f, 1.01828f, 0.63457f, 0.4261f, 0.77031f, 1.45029f, 1.5316f, - 1.29201f, 1.49625f, 1.60527f, 0.82104f, 0.72805f, 0.53772f, 1.0628f, - 1.02714f, 1.22906f, 1.24778f, 1.39234f, 0.93255f, 0.8089f, 0.7f, - 0.56166f, 0.3393f, 0.50049f, 0.99899f, 1.67075f, 1.36391f, 1.2315f, - 0.78211f, 0.31504f, 0.07413f, 0.34541f, 1.10185f, 1.62671f, 1.55047f, - 1.23339f, 0.94944f, 0.94458f, 0.79828f, 0.174f, 0.0f, 0.0f, - 0.00364f, 0.24836f, 0.35008f, 0.84544f, 0.74141f, 0.09779f, 0.0f, - 0.0f, 0.00392f, 0.2517f, 0.42071f, 0.01202f, 0.0f, 0.0f, - 0.0f, 0.10352f, 0.25785f, 0.37711f, 0.38906f, 0.0f, 0.0f, - 0.08534f, 0.53789f, 0.60558f, 0.51685f, 0.17048f, 0.08607f, 0.0f, - 0.0273f, 0.24019f, 0.50026f, 0.58067f, 0.28096f, 0.0f, 0.0f, - 0.0f, 0.24182f, 0.9064f, 1.19563f, 0.56218f, 0.08719f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.53558f, 0.91244f, 0.60358f, 0.09134f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.45695f, 0.91731f, 0.7053f, - 0.32695f, 0.0f, 0.0f, 1.18241f, 1.10837f, 0.64806f, 0.36425f, - 0.3675f, 0.73772f, 1.29753f, 1.36834f, 1.03903f, 1.03361f, 0.50494f, - 0.25227f, 0.0f, 0.4007f, 1.08972f, 1.4625f, 0.92244f, 0.96448f, - 0.78456f, 0.40154f, 0.05843f, 0.23353f, 0.87874f, 1.17981f, 0.78463f, - 0.88605f, 0.73064f, 1.16905f, 0.85112f, 0.65212f, 0.55161f, 0.76813f, - 0.99704f, 1.1504f, 1.10056f, 1.3203f, 1.42123f, 0.71487f, 0.56547f, - 0.31119f, 0.56771f, 0.88777f, 1.31565f, 1.49713f, 1.32642f, 0.78137f, - 0.48382f, 0.33447f, 0.25754f, 0.24697f, 0.73001f, 1.28779f, 1.59303f, - 1.07917f, 0.62578f, 0.33112f, 0.1885f, 0.05738f, 0.5032f, 1.24853f, - 1.5055f, 1.35343f, 0.81973f, 0.69389f, 0.77064f, 0.85148f, 0.12736f, - 0.0f, 0.0f, 0.46016f, 1.74609f, 1.77692f, 0.63595f, 0.9337f, - 0.0f, 0.0f, 0.0f, 0.0f, 1.2432f, 1.92382f, 0.83938f, - 1.39439f, 0.77593f, 0.07184f, 0.0f, 0.0f, 0.59092f, 1.16351f, - 0.7482f, 1.05995f, 0.5851f, 1.54265f, 0.68171f, 0.07684f, 0.0f, - 0.27053f, 1.22049f, 1.43774f, 1.13561f, 1.55213f, 1.75791f, 0.31968f, - 0.05463f, 0.0f, 0.44456f, 0.67707f, 1.26529f, 1.43365f, 1.43539f, - 0.4776f, 0.05792f, 0.0f, 0.0f, 0.0f, 0.0f, 0.96672f, - 1.94907f, 1.19428f, 0.64146f, 0.0f, 0.0f, 0.0f, 0.0f, - 1.06832f, 1.85065f, 1.62102f, 0.81106f, 0.40644f, 0.97437f, 1.15928f, - 0.14901f, 0.0f, 0.0f, 0.19325f, 1.30278f, 1.29054f, 1.05036f, - 1.52724f, 0.10438f, 0.0f, 0.0f, 0.05004f, 1.02078f, 1.38139f, - 0.31585f, 0.44747f, 0.00421f, 0.0f, 0.0f, 0.166f, 0.66148f, - 0.75682f, 0.25624f, 0.5775f, 0.6609f, 1.93669f, 1.42589f, 0.69986f, - 0.0f, 0.0f, 0.50747f, 0.76648f, 0.71581f, 1.28026f, 1.4943f, - 0.52174f, 0.0f, 0.0f, 0.0f, 0.5488f, 1.46631f, 1.68984f, - 1.11922f, 0.22356f, 0.0f, 0.0f, 0.0f, 0.0f, 0.1757f, - 0.92614f, 1.27946f, 0.63651f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.16543f, 1.31798f, 1.61128f, 0.95785f, 0.0f, 0.0f - }; + input = {-0.57666f, -1.62078f, -1.33839f, -0.84871f, 0.95678f, 0.5095f, + 0.46427f, 1.63513f, -0.86174f, 1.37982f, 2.11955f, -0.56532f, + -1.41889f, -0.24865f, 0.75658f, 1.41115f, -0.04036f, -0.13206f, + 0.54325f, -0.90184f, -0.30188f, -2.06574f, -0.12676f, 0.38189f, + 1.7959f, 0.24076f, 1.17587f, -0.21496f, 0.55819f, 0.21572f, + -1.66043f, 1.24566f, 0.837f, 0.13259f, -0.73019f, 0.87461f, + 1.38548f, -0.48258f, -0.11748f, 0.4244f, 1.14489f, 0.28394f, + -0.46594f, 1.18402f, -0.91973f, 0.63682f, -0.31897f, 0.80855f}; + + expected = {4.80097f, 47.97113f, 2.84091f, -8.17906f, -27.73839f, + -20.00487f, 25.13571f, 55.96156f, -57.32637f, 46.79503f, + 30.84768f, -2.27363f, 11.58069f, -84.01064f, -86.74448f, + -34.90844f, -31.9425f, -26.9795f, 43.22921f, 18.58556f, + 19.94732f, 9.99053f, 77.01399f, -29.40551f, 22.79751f, + -25.38616f, 78.7154f, -3.62437f, -7.37189f, -37.58518f, + 28.78344f, 46.85378f, -84.57623f, 0.10005f, 64.6466f, + -10.21144f, 51.44754f, 15.37502f, 24.96819f, -34.59124f, + 2.73933f, -32.52842f, -1.32425f, 41.48183f, -12.74939f, + -102.07105f, 5.58513f, 9.73683f, -25.97733f, -24.79673f}; runTest(); -}; +} \ No newline at end of file diff --git a/examples/inception_v3/tests/test_inception_b.cpp b/examples/inception_v3/tests/test_inception_b.cpp index ff0b3ac..583bfc7 100644 --- a/examples/inception_v3/tests/test_inception_b.cpp +++ b/examples/inception_v3/tests/test_inception_b.cpp @@ -2,22264 +2,65 @@ #include -class InceptionBTest : public ::testing::Test { - protected: - InceptionB *inception_b; +#include "test_fixture.hpp" - cudaError_t cudaStatus; +class InceptionBModel : public CUDANet::Model { + public: + InceptionBModel( + const shape2d inputShape, + const int inputChannels, + const int outputSize + ) + : CUDANet::Model(inputShape, inputChannels, outputSize) { + inception_b = + new InceptionB(inputShape, inputChannels, "inception_block."); + addLayer("", inception_b); + fc = new CUDANet::Layers::Dense( + inception_b->getOutputSize(), 50, + CUDANet::Layers::ActivationType::NONE + ); + addLayer("fc", fc); + }; - shape2d inputShape; - int inputChannels; - std::string prefix = "test"; - - float *d_input; - float *d_output; - - std::vector input; - std::vector expected; - - // Branch 3x3 - std::vector branch3x3_conv_weights; - std::vector branch3x3_bn_weights; - std::vector branch3x3_bn_bias; - - // Branch 3x3dbl - std::vector branch3x3dbl_1_conv_weights; - std::vector branch3x3dbl_1_bn_weights; - std::vector branch3x3dbl_1_bn_bias; - - std::vector branch3x3dbl_2_conv_weights; - std::vector branch3x3dbl_2_bn_weights; - std::vector branch3x3dbl_2_bn_bias; - - std::vector branch3x3dbl_3_conv_weights; - std::vector branch3x3dbl_3_bn_weights; - std::vector branch3x3dbl_3_bn_bias; - - virtual void SetUp() override { - inception_b = nullptr; + float *predict(const float *input) override { + float *d_input = inputLayer->forward(input); + d_input = inception_b->forward(d_input); + d_input = fc->forward(d_input); + return outputLayer->forward(d_input); } - virtual void TearDown() override { - // Clean up - delete inception_b; - } - - void setBasicConv2dWeights( - const std::vector &conv_weights, - const std::vector &bn_weights, - const std::vector &bn_bias, - const int index - ) { - std::pair layerPair = - inception_b->getLayers()[index]; - CUDANet::Layers::Conv2d *conv = - dynamic_cast(layerPair.second); - EXPECT_NE(conv, nullptr); - - EXPECT_EQ(conv->getWeights().size(), conv_weights.size()); - conv->setWeights(conv_weights.data()); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - - layerPair = inception_b->getLayers()[index + 1]; - CUDANet::Layers::BatchNorm2d *bn = - dynamic_cast(layerPair.second); - EXPECT_NE(bn, nullptr); - bn->setWeights(bn_weights.data()); - EXPECT_EQ(bn->getWeights().size(), bn_weights.size()); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - - bn->setBiases(bn_bias.data()); - EXPECT_EQ(bn->getBiases().size(), bn_bias.size()); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - } - - void runTest() { - inception_b = new InceptionB(inputShape, inputChannels, prefix); - - // Branch 3x3 - setBasicConv2dWeights( - branch3x3_conv_weights, branch3x3_bn_weights, branch3x3_bn_bias, 0 - ); - - // Branch 3x3dbl - setBasicConv2dWeights( - branch3x3dbl_1_conv_weights, branch3x3dbl_1_bn_weights, - branch3x3dbl_1_bn_bias, 2 - ); - setBasicConv2dWeights( - branch3x3dbl_2_conv_weights, branch3x3dbl_2_bn_weights, - branch3x3dbl_2_bn_bias, 4 - ); - setBasicConv2dWeights( - branch3x3dbl_3_conv_weights, branch3x3dbl_3_bn_weights, - branch3x3dbl_3_bn_bias, 6 - ); - - cudaStatus = - cudaMalloc((void **)&d_input, sizeof(float) * input.size()); - EXPECT_EQ(cudaStatus, cudaSuccess); - - cudaStatus = cudaMemcpy( - d_input, input.data(), sizeof(float) * input.size(), - cudaMemcpyHostToDevice - ); - EXPECT_EQ(cudaStatus, cudaSuccess); - - d_output = inception_b->forward(d_input); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - - int outputSize = inception_b->getOutputSize(); - std::vector output(outputSize); - cudaStatus = cudaMemcpy( - output.data(), d_output, sizeof(float) * output.size(), - cudaMemcpyDeviceToHost - ); - EXPECT_EQ(cudaStatus, cudaSuccess); - - for (int i = 0; i < output.size(); ++i) { - EXPECT_NEAR(expected[i], output[i], 1e-4f); - } - } + private: + InceptionB *inception_b; + CUDANet::Layers::Dense *fc; }; -TEST_F(InceptionBTest, InceptionBTest) { - inputShape = {8, 8}; +TEST_F(InceptionBlockTest, InceptionBTest) { + inputShape = {4, 4}; inputChannels = 3; + outputSize = 50; - branch3x3_conv_weights = { - 0.89877f, 0.90844f, 0.60508f, 0.57276f, 0.97136f, 0.17469f, 0.70112f, - 0.29836f, 0.69324f, 0.19741f, 0.92454f, 0.81947f, 0.38747f, 0.87168f, - 0.3149f, 0.6762f, 0.99598f, 0.59192f, 0.56356f, 0.516f, 0.13606f, - 0.82533f, 0.09276f, 0.01238f, 0.91433f, 0.98897f, 0.50562f, 0.66751f, - 0.06835f, 0.31861f, 0.63859f, 0.2346f, 0.44812f, 0.39634f, 0.48205f, - 0.30536f, 0.48277f, 0.56022f, 0.24611f, 0.42914f, 0.38819f, 0.34172f, - 0.3477f, 0.7151f, 0.08586f, 0.20871f, 0.68848f, 0.11114f, 0.80465f, - 0.03489f, 0.31407f, 0.83018f, 0.00206f, 0.49544f, 0.02654f, 0.67775f, - 0.15567f, 0.00693f, 0.23026f, 0.99947f, 0.81985f, 0.24822f, 0.39351f, - 0.51794f, 0.80247f, 0.39553f, 0.70717f, 0.53646f, 0.14289f, 0.71125f, - 0.24717f, 0.23756f, 0.17854f, 0.55379f, 0.83471f, 0.15484f, 0.44584f, - 0.34271f, 0.48088f, 0.95703f, 0.83406f, 0.86443f, 0.71561f, 0.61269f, - 0.68396f, 0.87557f, 0.70743f, 0.06807f, 0.90207f, 0.21423f, 0.81734f, - 0.42532f, 0.11541f, 0.22261f, 0.83728f, 0.78345f, 0.95193f, 0.63486f, - 0.20764f, 0.64194f, 0.87922f, 0.22128f, 0.41965f, 0.03098f, 0.71169f, - 0.30741f, 0.77469f, 0.96139f, 0.35047f, 0.77735f, 0.35213f, 0.15507f, - 0.51588f, 0.45559f, 0.0207f, 0.35421f, 0.02208f, 0.88934f, 0.53629f, - 0.78938f, 0.44639f, 0.03503f, 0.84631f, 0.86135f, 0.80786f, 0.50165f, - 0.96f, 0.0146f, 0.55269f, 0.7567f, 0.1099f, 0.98483f, 0.5552f, - 0.00798f, 0.84775f, 0.76901f, 0.91591f, 0.93669f, 0.83261f, 0.73944f, - 0.21115f, 0.86265f, 0.94424f, 0.06299f, 0.39085f, 0.95429f, 0.14856f, - 0.25347f, 0.94564f, 0.02192f, 0.98531f, 0.75161f, 0.55074f, 0.7982f, - 0.80061f, 0.85451f, 0.50409f, 0.40509f, 0.73391f, 0.03606f, 0.85276f, - 0.22752f, 0.80622f, 0.31371f, 0.17014f, 0.53106f, 0.34761f, 0.96826f, - 0.09296f, 0.05953f, 0.54845f, 0.73247f, 0.53397f, 0.03138f, 0.83974f, - 0.39606f, 0.73184f, 0.93775f, 0.83385f, 0.65844f, 0.44162f, 0.80241f, - 0.35885f, 0.99808f, 0.13415f, 0.37032f, 0.0239f, 0.91363f, 0.42734f, - 0.08028f, 0.85057f, 0.51628f, 0.48872f, 0.06414f, 0.75502f, 0.26352f, - 0.89407f, 0.02937f, 0.11401f, 0.04777f, 0.603f, 0.17498f, 0.33476f, - 0.11418f, 0.73163f, 0.51815f, 0.12167f, 0.7555f, 0.38461f, 0.07168f, - 0.05878f, 0.95325f, 0.10078f, 0.44229f, 0.90572f, 0.80078f, 0.19839f, - 0.71807f, 0.85415f, 0.33075f, 0.97426f, 0.64524f, 0.51455f, 0.37456f, - 0.68945f, 0.51818f, 0.79313f, 0.14129f, 0.79028f, 0.99675f, 0.52871f, - 0.48636f, 0.85131f, 0.40156f, 0.88417f, 0.77805f, 0.01072f, 0.90577f, - 0.36065f, 0.2373f, 0.7062f, 0.84285f, 0.85547f, 0.89825f, 0.31274f, - 0.6668f, 0.70976f, 0.85477f, 0.79489f, 0.94526f, 0.09467f, 0.29359f, - 0.99535f, 0.79173f, 0.39617f, 0.84164f, 0.13564f, 0.73702f, 0.22798f, - 0.72293f, 0.48729f, 0.67091f, 0.02912f, 0.27401f, 0.86057f, 0.73085f, - 0.73053f, 0.8589f, 0.33079f, 0.61949f, 0.49383f, 0.16221f, 0.62589f, - 0.16119f, 0.34051f, 0.59901f, 0.70291f, 0.91649f, 0.9203f, 0.36237f, - 0.92354f, 0.72486f, 0.35305f, 0.39519f, 0.9683f, 0.91354f, 0.29639f, - 0.79898f, 0.41425f, 0.04028f, 0.0804f, 0.61777f, 0.40566f, 0.62451f, - 0.46861f, 0.39264f, 0.72336f, 0.79918f, 0.17559f, 0.2429f, 0.13319f, - 0.41645f, 0.26875f, 0.28533f, 0.01808f, 0.58673f, 0.5906f, 0.76073f, - 0.26227f, 0.64192f, 0.80223f, 0.43142f, 0.63072f, 0.53215f, 0.42888f, - 0.21985f, 0.01275f, 0.32793f, 0.5827f, 0.24424f, 0.3267f, 0.35949f, - 0.43991f, 0.26543f, 0.54165f, 0.82644f, 0.84785f, 0.17131f, 0.86792f, - 0.28304f, 0.14779f, 0.74832f, 0.45206f, 0.49646f, 0.08967f, 0.86352f, - 0.86149f, 0.24733f, 0.65971f, 0.30472f, 0.20891f, 0.71494f, 0.58137f, - 0.82759f, 0.93868f, 0.87961f, 0.54174f, 0.7482f, 0.5936f, 0.00088f, - 0.2326f, 0.01699f, 0.22431f, 0.13572f, 0.1047f, 0.03183f, 0.44748f, - 0.39752f, 0.94392f, 0.02247f, 0.41904f, 0.08061f, 0.76781f, 0.09272f, - 0.64664f, 0.50264f, 0.51818f, 0.03397f, 0.25969f, 0.09798f, 0.95008f, - 0.25434f, 0.25559f, 0.18211f, 0.60708f, 0.96948f, 0.49327f, 0.91095f, - 0.85884f, 0.41032f, 0.78528f, 0.52836f, 0.86046f, 0.85486f, 0.18935f, - 0.39454f, 0.24499f, 0.93327f, 0.30625f, 0.20394f, 0.58328f, 0.25221f, - 0.865f, 0.83266f, 0.4336f, 0.1037f, 0.87487f, 0.54506f, 0.84028f, - 0.67728f, 0.27049f, 0.50556f, 0.33149f, 0.2333f, 0.40051f, 0.7861f, - 0.01087f, 0.92607f, 0.09026f, 0.68213f, 0.54829f, 0.36419f, 0.58589f, - 0.40289f, 0.39434f, 0.94578f, 0.15731f, 0.72019f, 0.18919f, 0.6022f, - 0.20158f, 0.95279f, 0.65165f, 0.68703f, 0.24653f, 0.49528f, 0.72191f, - 0.68588f, 0.719f, 0.911f, 0.69511f, 0.60123f, 0.2674f, 0.65839f, - 0.15926f, 0.57702f, 0.72295f, 0.70705f, 0.64985f, 0.93819f, 0.91294f, - 0.62204f, 0.98937f, 0.18267f, 0.9087f, 0.86309f, 0.61201f, 0.3635f, - 0.92079f, 0.63867f, 0.88684f, 0.81251f, 0.22203f, 0.3878f, 0.62979f, - 0.68078f, 0.79106f, 0.65809f, 0.55754f, 0.39186f, 0.94472f, 0.70151f, - 0.93778f, 0.00473f, 0.20754f, 0.93491f, 0.80112f, 0.07404f, 0.18972f, - 0.77194f, 0.47955f, 0.43986f, 0.20243f, 0.62233f, 0.3058f, 0.521f, - 0.91143f, 0.75242f, 0.38689f, 0.68945f, 0.22057f, 0.7395f, 0.77515f, - 0.71273f, 0.55928f, 0.92598f, 0.46183f, 0.91208f, 0.6041f, 0.09676f, - 0.72408f, 0.99136f, 0.99398f, 0.05727f, 0.82008f, 0.51027f, 0.97169f, - 0.02265f, 0.71009f, 0.97265f, 0.53167f, 0.84958f, 0.85914f, 0.87761f, - 0.74759f, 0.59341f, 0.23531f, 0.11055f, 0.05729f, 0.65956f, 0.31218f, - 0.12072f, 0.19204f, 0.01808f, 0.32727f, 0.60978f, 0.26051f, 0.69353f, - 0.75483f, 0.33912f, 0.90354f, 0.06126f, 0.44379f, 0.36672f, 0.9885f, - 0.75956f, 0.31636f, 0.58724f, 0.28266f, 0.73473f, 0.46286f, 0.81425f, - 0.8865f, 0.24944f, 0.94103f, 0.29749f, 0.88167f, 0.48625f, 0.76614f, - 0.94828f, 0.71733f, 0.91301f, 0.07725f, 0.23022f, 0.14175f, 0.6703f, - 0.14541f, 0.01488f, 0.94386f, 0.03886f, 0.21992f, 0.13286f, 0.3509f, - 0.07494f, 0.35456f, 0.03471f, 0.44573f, 0.24472f, 0.75132f, 0.37734f, - 0.35331f, 0.96796f, 0.94663f, 0.10375f, 0.99949f, 0.75293f, 0.5055f, - 0.33808f, 0.23572f, 0.47359f, 0.02988f, 0.06528f, 0.75925f, 0.47501f, - 0.03568f, 0.22974f, 0.12231f, 0.1519f, 0.08786f, 0.6495f, 0.31089f, - 0.30126f, 0.56843f, 0.80709f, 0.43712f, 0.22717f, 0.12139f, 0.94571f, - 0.88491f, 0.14778f, 0.9452f, 0.70841f, 0.65153f, 0.52934f, 0.41154f, - 0.78889f, 0.79327f, 0.95114f, 0.6556f, 0.48095f, 0.07354f, 0.22031f, - 0.54018f, 0.27711f, 0.80532f, 0.36065f, 0.79728f, 0.32806f, 0.0465f, - 0.64779f, 0.10014f, 0.58689f, 0.76158f, 0.05312f, 0.60206f, 0.67593f, - 0.04299f, 0.28003f, 0.24285f, 0.25548f, 0.26632f, 0.94735f, 0.2756f, - 0.58554f, 0.4503f, 0.35791f, 0.87677f, 0.90138f, 0.41751f, 0.25254f, - 0.90586f, 0.60354f, 0.21376f, 0.42891f, 0.89857f, 0.16817f, 0.18393f, - 0.78719f, 0.06157f, 0.33609f, 0.50888f, 0.97108f, 0.65355f, 0.99732f, - 0.00131f, 0.27203f, 0.20301f, 0.17533f, 0.13738f, 0.60138f, 0.51586f, - 0.06844f, 0.80557f, 0.2398f, 0.14626f, 0.39306f, 0.54313f, 0.2986f, - 0.19098f, 0.75638f, 0.60558f, 0.61301f, 0.81839f, 0.12008f, 0.02738f, - 0.22381f, 0.98124f, 0.24196f, 0.65629f, 0.70815f, 0.42353f, 0.82913f, - 0.40313f, 0.01403f, 0.36959f, 0.23797f, 0.69531f, 0.15879f, 0.36141f, - 0.5848f, 0.88773f, 0.16676f, 0.5851f, 0.75844f, 0.9943f, 0.02149f, - 0.22921f, 0.27035f, 0.89132f, 0.91661f, 0.5707f, 0.13096f, 0.47012f, - 0.00085f, 0.75398f, 0.79965f, 0.71072f, 0.09057f, 0.96321f, 0.86364f, - 0.57262f, 0.91205f, 0.77726f, 0.46892f, 0.96985f, 0.48168f, 0.74605f, - 0.07564f, 0.18152f, 0.15796f, 0.68624f, 0.14774f, 0.95289f, 0.8921f, - 0.35679f, 0.04472f, 0.69684f, 0.07066f, 0.39866f, 0.44206f, 0.60867f, - 0.87097f, 0.87883f, 0.91516f, 0.36715f, 0.60651f, 0.50725f, 0.05099f, - 0.05486f, 0.81169f, 0.4467f, 0.38f, 0.72106f, 0.80755f, 0.52035f, - 0.68097f, 0.39032f, 0.77882f, 0.19688f, 0.37792f, 0.14522f, 0.51224f, - 0.10635f, 0.17889f, 0.75738f, 0.96854f, 0.71958f, 0.96089f, 0.97972f, - 0.48279f, 0.3051f, 0.17027f, 0.14693f, 0.51232f, 0.62416f, 0.29322f, - 0.04067f, 0.28645f, 0.26075f, 0.87743f, 0.52539f, 0.56659f, 0.84756f, - 0.67476f, 0.25487f, 0.2909f, 0.77892f, 0.59675f, 0.91417f, 0.17993f, - 0.23579f, 0.18961f, 0.09158f, 0.99909f, 0.85205f, 0.88895f, 0.33631f, - 0.46569f, 0.04472f, 0.3986f, 0.37579f, 0.31494f, 0.5914f, 0.10613f, - 0.05109f, 0.98897f, 0.00741f, 0.10066f, 0.25414f, 0.19448f, 0.06229f, - 0.88502f, 0.2729f, 0.5856f, 0.48612f, 0.48485f, 0.1199f, 0.2535f, - 0.45172f, 0.08542f, 0.03043f, 0.9333f, 0.97607f, 0.8007f, 0.10148f, - 0.56831f, 0.03805f, 0.85698f, 0.17982f, 0.9039f, 0.19573f, 0.83206f, - 0.78464f, 0.04824f, 0.04006f, 0.54717f, 0.57581f, 0.33232f, 0.19555f, - 0.662f, 0.06601f, 0.4879f, 0.42737f, 0.9968f, 0.34766f, 0.20461f, - 0.33539f, 0.96033f, 0.35122f, 0.33851f, 0.37097f, 0.22535f, 0.05834f, - 0.60731f, 0.65717f, 0.01126f, 0.43652f, 0.30951f, 0.77804f, 0.58056f, - 0.36442f, 0.14761f, 0.22589f, 0.63425f, 0.93038f, 0.02736f, 0.7404f, - 0.4295f, 0.39142f, 0.31623f, 0.41113f, 0.87525f, 0.72443f, 0.04434f, - 0.88357f, 0.27344f, 0.49819f, 0.68133f, 0.64122f, 0.89762f, 0.73102f, - 0.59702f, 0.48899f, 0.6269f, 0.00888f, 0.86613f, 0.34259f, 0.80682f, - 0.25217f, 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0.37936f, 0.25316f, 0.86856f, 0.52468f, 0.45634f, 0.56453f, - 0.15245f, 0.0f, 1.34978f, 0.33897f, 0.02597f, 1.70071f, 0.72924f, - 0.54225f, 0.50812f, 0.0f, 0.0f, 1.7489f, 0.14307f, 0.0f, - 2.29976f, 0.74714f, 0.45275f, 0.93365f, 0.51879f, 0.14065f, 1.69521f, - 0.69667f, 0.39325f, 2.0429f, 1.08879f, 0.90452f, 0.78335f, 0.33343f, - 0.0f, 1.64674f, 0.52382f, 0.1562f, 2.04914f, 0.94202f, 0.7252f, - 0.23645f, 0.18748f, 0.14564f, 0.32296f, 0.20908f, 0.17403f, 0.36238f, - 0.25597f, 0.23715f, 0.93332f, 0.34674f, 0.0f, 1.976f, 0.5898f, - 0.14189f, 2.42569f, 1.0851f, 0.84671f, 0.83433f, 0.77955f, 0.73038f, - 0.93513f, 0.8025f, 0.76341f, 0.98205f, 0.85341f, 0.82778f, 0.34536f, - 0.0f, 0.0f, 1.56757f, 0.00841f, 0.0f, 2.13035f, 0.59856f, - 0.32043f, 0.78674f, 0.67645f, 0.57639f, 0.99007f, 0.72328f, 0.64136f, - 1.0841f, 0.82899f, 0.78071f, 0.69014f, 0.0f, 0.0f, 1.95406f, - 0.28992f, 0.0f, 2.53116f, 0.90754f, 0.67964f, 0.01489f, 0.0f, - 0.0f, 0.43336f, 0.0f, 0.0f, 0.62968f, 0.11784f, 0.0f, - 0.40224f, 0.0f, 0.0f, 1.28265f, 0.09926f, 0.0f, 1.69637f, - 0.56385f, 0.40266f, 0.18704f, 0.0f, 0.0f, 0.73451f, 0.0f, - 0.0f, 0.98418f, 0.23377f, 0.12214f, 0.94312f, 0.39997f, 0.0f, - 2.06114f, 0.67761f, 0.17027f, 2.57144f, 1.2209f, 0.93121f, 0.37736f, - 0.0f, 0.0f, 1.58854f, 0.1038f, 0.0f, 2.1319f, 0.67538f, - 0.38485f, 0.91714f, 0.43973f, 0.0528f, 1.74483f, 0.65024f, 0.32658f, - 2.12522f, 1.07195f, 0.90766f, 0.75576f, 0.51959f, 0.32021f, 1.16043f, - 0.60439f, 0.45331f, 1.35427f, 0.83096f, 0.7329f, 0.09011f, 0.0f, - 0.0f, 0.85064f, 0.0f, 0.0f, 1.19066f, 0.27488f, 0.09577f, - 0.47699f, 0.0f, 0.0f, 1.67106f, 0.12582f, 0.0f, 2.20539f, - 0.68207f, 0.36019f, 0.46809f, 0.35004f, 0.25152f, 0.67264f, 0.39684f, - 0.32196f, 0.77248f, 0.51067f, 0.46885f, 0.94935f, 0.26787f, 0.0f, - 2.16125f, 0.57698f, 0.05852f, 2.69671f, 1.15122f, 0.88965f, 0.68983f, - 0.40379f, 0.18213f, 1.19074f, 0.52645f, 0.31333f, 1.41787f, 0.76427f, - 0.65997f, 0.38996f, 0.11495f, 0.0f, 0.92367f, 0.25947f, 0.05289f, - 1.15984f, 0.50654f, 0.38215f, 0.79729f, 0.37944f, 0.0f, 1.66987f, - 0.57919f, 0.19531f, 2.06806f, 1.03087f, 0.7616f, 0.30665f, 0.0f, - 0.0f, 0.93767f, 0.10385f, 0.0f, 1.21713f, 0.39789f, 0.25855f, - 0.16054f, 0.0f, 0.0f, 1.17715f, 0.0f, 0.0f, 1.62534f, - 0.38257f, 0.17563f, 0.43881f, 0.0f, 0.0f, 1.55833f, 0.15078f, - 0.0f, 2.03589f, 0.66092f, 0.42531f, 0.10455f, 0.0f, 0.0f, - 0.34878f, 0.0436f, 0.0f, 0.45424f, 0.15995f, 0.10153f, 0.43859f, - 0.0f, 0.0f, 1.66929f, 0.05623f, 0.0f, 2.24416f, 0.73448f, - 0.36805f, 0.34043f, 0.0f, 0.0f, 1.47668f, 0.0f, 0.0f, - 1.98806f, 0.52526f, 0.26843f, 0.8057f, 0.26997f, 0.0f, 1.76221f, - 0.48837f, 0.13027f, 2.24129f, 1.00775f, 0.81785f, 0.45161f, 0.36936f, - 0.30194f, 0.59027f, 0.39861f, 0.34687f, 0.65546f, 0.47444f, 0.44f, - 0.0f, 0.0f, 0.0f, 1.1275f, 0.0f, 0.0f, 1.61668f, - 0.26936f, 0.0f, 0.04203f, 0.0f, 0.0f, 1.26688f, 0.0f, - 0.0f, 1.82371f, 0.37113f, 0.0225f, 0.59211f, 0.3915f, 0.19997f, - 1.00462f, 0.49961f, 0.31838f, 1.18184f, 0.67795f, 0.57707f, 0.16571f, - 0.0f, 0.0f, 1.36924f, 0.0f, 0.0f, 1.89968f, 0.40705f, - 0.12127f, 0.17538f, 0.0f, 0.0f, 0.67098f, 0.01846f, 0.0f, - 0.89863f, 0.2579f, 0.16655f, 0.56758f, 0.31853f, 0.0892f, 1.04422f, - 0.43893f, 0.2354f, 1.25386f, 0.68044f, 0.5489f, 0.15819f, 0.0f, - 0.0f, 0.50249f, 0.04915f, 0.0f, 0.66587f, 0.2307f, 0.1598f, - 0.43099f, 0.09068f, 0.0f, 1.05466f, 0.25278f, 0.0f, 1.35f, - 0.57395f, 0.43022f, 0.66847f, 0.4187f, 0.17894f, 1.17057f, 0.54508f, - 0.31212f, 1.39508f, 0.77512f, 0.64277f, 0.44432f, 0.08353f, 0.0f, - 1.04819f, 0.233f, 0.01689f, 1.33252f, 0.55263f, 0.42027f, 0.02823f, - 0.0f, 0.0f, 0.77068f, 0.0f, 0.0f, 1.11363f, 0.15886f, - 0.0f, 0.1005f, 0.0f, 0.0f, 1.39785f, 0.0f, 0.0f, - 1.99555f, 0.39543f, 0.09762f, 0.28096f, 0.10737f, 0.0f, 0.60876f, - 0.18169f, 0.04666f, 0.76343f, 0.35221f, 0.26118f, 0.81804f, 0.57149f, - 0.37363f, 1.24615f, 0.67201f, 0.49119f, 1.43964f, 0.88416f, 0.78482f, - 0.62287f, 0.17873f, 0.0f, 1.44165f, 0.38895f, 0.05572f, 1.82223f, - 0.79748f, 0.60749f, 0.39216f, 0.17167f, 0.0f, 0.80146f, 0.25653f, - 0.08719f, 0.98687f, 0.47036f, 0.34542f, 0.34072f, 0.0f, 0.0f, - 1.39923f, 0.05405f, 0.0f, 1.87229f, 0.57369f, 0.32361f, 0.96913f, - 0.50579f, 0.06748f, 1.88279f, 0.73231f, 0.3169f, 2.29043f, 1.15323f, - 0.9398f, 0.20497f, 0.0f, 0.0f, 0.90906f, 0.0f, 0.0f, - 1.23133f, 0.31534f, 0.13855f, 0.61841f, 0.21002f, 0.0f, 1.33515f, - 0.39688f, 0.09823f, 1.67581f, 0.7522f, 0.60422f, 0.81733f, 0.51601f, - 0.24663f, 1.36064f, 0.64324f, 0.43752f, 1.61977f, 0.9294f, 0.78347f, - 0.12828f, 0.0f, 0.0f, 0.81194f, 0.0f, 0.0f, 1.11355f, - 0.25887f, 0.08155f, 0.2041f, 0.0f, 0.0f, 1.10478f, 0.0f, - 0.0f, 1.52576f, 0.36868f, 0.14214f, 0.95266f, 0.4843f, 0.08688f, - 1.81495f, 0.69715f, 0.35733f, 2.20376f, 1.10618f, 0.90765f, 0.318f, - 0.0897f, 0.0f, 0.67916f, 0.17744f, 0.03181f, 0.84916f, 0.34972f, - 0.28589f, 0.96265f, 1.10661f, 1.10661f, 1.9366f, 1.9366f, 1.10661f, - 1.97012f, 1.97012f, 0.61619f, 1.5378f, 1.5378f, 1.15463f, 2.17354f, - 1.14293f, 1.14293f, 2.17354f, 0.82042f, 1.44057f, 1.52133f, 1.46144f, - 0.68397f, 1.84748f, 0.67179f, 1.49215f, 1.84748f, 1.60111f, 1.49215f - }; + model = new InceptionBModel(inputShape, inputChannels, outputSize); + model->loadWeights("../tests/resources/inception_b.bin"); + + input = {-0.74714f, 0.70954f, 1.21276f, 0.55136f, -2.02672f, 0.42895f, + -0.61635f, -0.37651f, -2.0056f, 2.06836f, -0.62239f, -1.18187f, + 1.73166f, -0.44721f, 1.42673f, -0.75294f, 2.85223f, 1.33567f, + -1.28691f, -0.59681f, 0.49173f, -0.7806f, -0.28427f, 0.01525f, + -1.70295f, -0.0927f, -0.04545f, -1.0465f, 0.22519f, 1.69716f, + -0.33748f, 0.15713f, -0.83215f, -0.54749f, -0.66072f, -0.18568f, + 0.98517f, 0.32883f, -0.28338f, 0.81102f, 0.70454f, 0.84246f, + 0.93766f, -0.83322f, 0.58987f, 1.23888f, -0.6962f, 0.68079f}; + + expected = {-19.94974f, 93.30141f, 85.76035f, 214.1964f, -4.30855f, + -92.65581f, -37.0993f, -96.92029f, -145.99411f, 177.49068f, + 185.37115f, 263.2403f, -158.78972f, -435.17844f, 208.36617f, + -481.91907f, 179.2296f, 42.68469f, 661.90039f, 192.62759f, + 146.78622f, 59.37774f, -107.44885f, 578.51874f, 745.61536f, + 528.30847f, -54.60599f, 237.63603f, 198.97778f, 9.95003f, + 61.68781f, -156.87708f, -166.12646f, 294.20853f, 382.63782f, + 53.15688f, -74.18913f, 7.70657f, 202.17197f, -121.06818f, + 32.45838f, -401.04041f, 94.91491f, 240.54332f, 171.52502f, + -121.58651f, 419.89447f, 161.91119f, -13.53201f, -74.76675f}; runTest(); } \ No newline at end of file diff --git a/examples/inception_v3/tests/test_inception_c.cpp b/examples/inception_v3/tests/test_inception_c.cpp index 071f585..ef0a098 100644 --- a/examples/inception_v3/tests/test_inception_c.cpp +++ b/examples/inception_v3/tests/test_inception_c.cpp @@ -2,48831 +2,68 @@ #include -class InceptionCTest : public ::testing::Test { - protected: - InceptionC *inception_c; +#include "test_fixture.hpp" - cudaError_t cudaStatus; - - shape2d inputShape; - int inputChannels; - int nChannels_7x7; - std::string prefix = "test"; - - float *d_input; - float *d_output; - - std::vector input; - std::vector expected; - - // Branch 1x1 - std::vector branch1x1_conv_weights; - std::vector branch1x1_bn_weights; - std::vector branch1x1_bn_bias; - - // Branch 7x7 - std::vector branch7x7_1_conv_weights; - std::vector branch7x7_1_bn_weights; - std::vector branch7x7_1_bn_bias; - std::vector branch7x7_2_conv_weights; - std::vector branch7x7_2_bn_weights; - std::vector branch7x7_2_bn_bias; - std::vector branch7x7_3_conv_weights; - std::vector branch7x7_3_bn_weights; - std::vector branch7x7_3_bn_bias; - - // Branch 7x7dbl - std::vector branch7x7dbl_1_conv_weights; - std::vector branch7x7dbl_1_bn_weights; - std::vector branch7x7dbl_1_bn_bias; - std::vector branch7x7dbl_2_conv_weights; - std::vector branch7x7dbl_2_bn_weights; - std::vector branch7x7dbl_2_bn_bias; - std::vector branch7x7dbl_3_conv_weights; - std::vector branch7x7dbl_3_bn_weights; - std::vector branch7x7dbl_3_bn_bias; - std::vector branch7x7dbl_4_conv_weights; - std::vector branch7x7dbl_4_bn_weights; - std::vector branch7x7dbl_4_bn_bias; - std::vector branch7x7dbl_5_conv_weights; - std::vector branch7x7dbl_5_bn_weights; - std::vector branch7x7dbl_5_bn_bias; - - // Branch Pool - std::vector branchPool_2_conv_weights; - std::vector branchPool_2_bn_weights; - std::vector branchPool_2_bn_bias; - - virtual void SetUp() override { - inception_c = nullptr; - } - - virtual void TearDown() override { - delete inception_c; - } - - void setBasicConv2dWeights( - const std::vector &conv_weights, - const std::vector &bn_weights, - const std::vector &bn_bias, - const int index - ) { - std::pair layerPair = - inception_c->getLayers()[index]; - - CUDANet::Layers::Conv2d *conv = - dynamic_cast(layerPair.second); - EXPECT_NE(conv, nullptr); - - conv->setWeights(conv_weights.data()); - EXPECT_EQ(conv->getWeights().size(), conv_weights.size()); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - - layerPair = inception_c->getLayers()[index + 1]; - CUDANet::Layers::BatchNorm2d *bn = - dynamic_cast(layerPair.second); - EXPECT_NE(bn, nullptr); - bn->setWeights(bn_weights.data()); - EXPECT_EQ(bn->getWeights().size(), bn_weights.size()); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - - bn->setBiases(bn_bias.data()); - EXPECT_EQ(bn->getBiases().size(), bn_bias.size()); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - } - - void runTest() { +class InceptionCModel : public CUDANet::Model { + public: + InceptionCModel( + const shape2d inputShape, + const int inputChannels, + const int outputSize + ) + : CUDANet::Model(inputShape, inputChannels, outputSize) { inception_c = - new InceptionC(inputShape, inputChannels, nChannels_7x7, prefix); + new InceptionC(inputShape, inputChannels, 64, "inception_block."); + addLayer("", inception_c); + fc = new CUDANet::Layers::Dense( + inception_c->getOutputSize(), 50, + CUDANet::Layers::ActivationType::NONE + ); + addLayer("fc", fc); + }; - // Branch 1x1 - setBasicConv2dWeights( - branch1x1_conv_weights, branch1x1_bn_weights, branch1x1_bn_bias, 0 - ); - - // Branch 7x7 - setBasicConv2dWeights( - branch7x7_1_conv_weights, branch7x7_1_bn_weights, - branch7x7_1_bn_bias, 2 - ); - setBasicConv2dWeights( - branch7x7_2_conv_weights, branch7x7_2_bn_weights, - branch7x7_2_bn_bias, 4 - ); - setBasicConv2dWeights( - branch7x7_3_conv_weights, branch7x7_3_bn_weights, - branch7x7_3_bn_bias, 6 - ); - - // Branch 7x7dbl - setBasicConv2dWeights( - branch7x7dbl_1_conv_weights, branch7x7dbl_1_bn_weights, - branch7x7dbl_1_bn_bias, 8 - ); - setBasicConv2dWeights( - branch7x7dbl_2_conv_weights, branch7x7dbl_2_bn_weights, - branch7x7dbl_2_bn_bias, 10 - ); - setBasicConv2dWeights( - branch7x7dbl_3_conv_weights, branch7x7dbl_3_bn_weights, - branch7x7dbl_3_bn_bias, 12 - ); - setBasicConv2dWeights( - branch7x7dbl_4_conv_weights, branch7x7dbl_4_bn_weights, - branch7x7dbl_4_bn_bias, 14 - ); - setBasicConv2dWeights( - branch7x7dbl_5_conv_weights, branch7x7dbl_5_bn_weights, - branch7x7dbl_5_bn_bias, 16 - ); - - // Branch Pool - setBasicConv2dWeights( - branchPool_2_conv_weights, branchPool_2_bn_weights, - branchPool_2_bn_bias, 19 - ); - - cudaStatus = - cudaMalloc((void **)&d_input, sizeof(float) * input.size()); - EXPECT_EQ(cudaStatus, cudaSuccess); - - cudaStatus = cudaMemcpy( - d_input, input.data(), sizeof(float) * input.size(), - cudaMemcpyHostToDevice - ); - EXPECT_EQ(cudaStatus, cudaSuccess); - - d_output = inception_c->forward(d_input); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - - int outputSize = inception_c->getOutputSize(); - std::vector output(outputSize); - cudaStatus = cudaMemcpy( - output.data(), d_output, sizeof(float) * output.size(), - cudaMemcpyDeviceToHost - ); - EXPECT_EQ(cudaStatus, cudaSuccess); - - for (int i = 0; i < output.size(); ++i) { - EXPECT_NEAR(expected[i], output[i], 1e-4f); - } + float *predict(const float *input) override { + float *d_input = inputLayer->forward(input); + d_input = inception_c->forward(d_input); + d_input = fc->forward(d_input); + return outputLayer->forward(d_input); } + + private: + InceptionC *inception_c; + CUDANet::Layers::Dense *fc; }; -TEST_F(InceptionCTest, InceptionCTest) { - inputShape = {8, 8}; - inputChannels = 3; - nChannels_7x7 = 64; - branch1x1_conv_weights = { - 0.12007f, 0.88884f, 0.16127f, 0.35578f, 0.19766f, 0.75753f, 0.9034f, - 0.92307f, 0.49189f, 0.1098f, 0.42835f, 0.74927f, 0.17301f, 0.62718f, - 0.48846f, 0.64851f, 0.88455f, 0.15915f, 0.48627f, 0.13632f, 0.40633f, - 0.18239f, 0.14446f, 0.55419f, 0.76708f, 0.92782f, 0.62154f, 0.37415f, - 0.75452f, 0.11262f, 0.20865f, 0.58603f, 0.28842f, 0.90232f, 0.40749f, - 0.30953f, 0.84942f, 0.06163f, 0.3735f, 0.57911f, 0.68648f, 0.99568f, - 0.53437f, 0.77007f, 0.59613f, 0.60682f, 0.28401f, 0.98812f, 0.92939f, - 0.57444f, 0.52438f, 0.46225f, 0.281f, 0.89128f, 0.60684f, 0.63859f, - 0.80427f, 0.36481f, 0.07544f, 0.86734f, 0.37191f, 0.39769f, 0.4306f, - 0.43373f, 0.48473f, 0.54824f, 0.81132f, 0.62534f, 0.9026f, 0.74182f, - 0.73631f, 0.27562f, 0.9844f, 0.94794f, 0.54867f, 0.61572f, 0.88843f, - 0.51195f, 0.01922f, 0.72201f, 0.81995f, 0.71104f, 0.59368f, 0.36052f, - 0.34633f, 0.86858f, 0.80141f, 0.83442f, 0.96445f, 0.27295f, 0.6729f, - 0.2498f, 0.08091f, 0.58932f, 0.76274f, 0.37979f, 0.42165f, 0.28618f, - 0.32582f, 0.12677f, 0.06601f, 0.44033f, 0.53636f, 0.8995f, 0.01f, - 0.09966f, 0.69264f, 0.77221f, 0.9964f, 0.00599f, 0.40629f, 0.33725f, - 0.82788f, 0.02123f, 0.18133f, 0.30186f, 0.94069f, 0.61147f, 0.97531f, - 0.86767f, 0.48148f, 0.28518f, 0.80679f, 0.90401f, 0.27938f, 0.63661f, - 0.96167f, 0.96963f, 0.07918f, 0.92579f, 0.16599f, 0.44359f, 0.98031f, - 0.42873f, 0.38078f, 0.00497f, 0.74063f, 0.5853f, 0.67204f, 0.70573f, - 0.54319f, 0.03546f, 0.07655f, 0.61637f, 0.87156f, 0.04287f, 0.5928f, - 0.30629f, 0.05028f, 0.99762f, 0.4542f, 0.50875f, 0.27107f, 0.20027f, - 0.11978f, 0.47357f, 0.73132f, 0.24454f, 0.03852f, 0.3598f, 0.84572f, - 0.55119f, 0.71503f, 0.49223f, 0.93463f, 0.22629f, 0.90249f, 0.12543f, - 0.91335f, 0.64652f, 0.39107f, 0.49482f, 0.61466f, 0.34679f, 0.4112f, - 0.60346f, 0.15523f, 0.01544f, 0.6478f, 0.03307f, 0.39953f, 0.48517f, - 0.09073f, 0.36605f, 0.87323f, 0.58379f, 0.41365f, 0.44249f, 0.41969f, - 0.2406f, 0.22718f, 0.89297f, 0.58128f, 0.06857f, 0.28738f, 0.66086f, - 0.87153f, 0.88137f, 0.90485f, 0.75694f, 0.77991f, 0.84878f, 0.28917f, - 0.21031f, 0.56565f, 0.93652f, 0.02402f, 0.75211f, 0.17181f, 0.54177f, - 0.04773f, 0.7209f, 0.12816f, 0.59153f, 0.35391f, 0.13738f, 0.28112f, - 0.54251f, 0.37678f, 0.14035f, 0.23404f, 0.18587f, 0.55455f, 0.54918f, - 0.15604f, 0.25332f, 0.0683f, 0.34779f, 0.0001f, 0.76167f, 0.96824f, - 0.75856f, 0.18982f, 0.83902f, 0.16593f, 0.88106f, 0.94732f, 0.8535f, - 0.99699f, 0.54299f, 0.18359f, 0.05031f, 0.22247f, 0.74809f, 0.27785f, - 0.95566f, 0.02538f, 0.70952f, 0.65472f, 0.31223f, 0.83745f, 0.8893f, - 0.20908f, 0.57582f, 0.19771f, 0.8975f, 0.07732f, 0.12265f, 0.24247f, - 0.08853f, 0.46625f, 0.36784f, 0.77099f, 0.07904f, 0.59006f, 0.65049f, - 0.62297f, 0.23683f, 0.51593f, 0.83576f, 0.82031f, 0.56603f, 0.52223f, - 0.39416f, 0.08404f, 0.38923f, 0.18099f, 0.89494f, 0.03392f, 0.62074f, - 0.02973f, 0.8629f, 0.17312f, 0.09898f, 0.36541f, 0.60285f, 0.73306f, - 0.9819f, 0.84673f, 0.5577f, 0.95153f, 0.46828f, 0.64506f, 0.84513f, - 0.92734f, 0.59663f, 0.12054f, 0.06315f, 0.48379f, 0.36919f, 0.04665f, - 0.7994f, 0.96901f, 0.0209f, 0.35639f, 0.03772f, 0.3955f, 0.62766f, - 0.58753f, 0.79789f, 0.39716f, 0.01902f, 0.48038f, 0.53003f, 0.81932f, - 0.31123f, 0.36918f, 0.57473f, 0.34405f, 0.86589f, 0.37809f, 0.62036f, - 0.8006f, 0.37525f, 0.13661f, 0.45393f, 0.24193f, 0.65669f, 0.77928f, - 0.41561f, 0.13657f, 0.25297f, 0.49036f, 0.61681f, 0.50583f, 0.61012f, - 0.98089f, 0.89171f, 0.38401f, 0.58017f, 0.38297f, 0.49693f, 0.10952f, - 0.95152f, 0.6584f, 0.39814f, 0.51293f, 0.38315f, 0.36678f, 0.14002f, - 0.89189f, 0.70552f, 0.48165f, 0.14001f, 0.54771f, 0.89519f, 0.36662f, - 0.33823f, 0.70897f, 0.82337f, 0.24633f, 0.83904f, 0.51409f, 0.33022f, - 0.38713f, 0.46741f, 0.54312f, 0.5655f, 0.51425f, 0.31695f, 0.1682f, - 0.33539f, 0.75157f, 0.49032f, 0.6367f, 0.19942f, 0.34138f, 0.0255f, - 0.73071f, 0.21698f, 0.24919f, 0.37342f, 0.99837f, 0.83426f, 0.51891f, - 0.67124f, 0.47161f, 0.85651f, 0.53633f, 0.41846f, 0.12299f, 0.60286f, - 0.13433f, 0.67695f, 0.55006f, 0.96872f, 0.27537f, 0.98429f, 0.71643f, - 0.55378f, 0.04061f, 0.65642f, 0.68393f, 0.39254f, 0.32839f, 0.31617f, - 0.80719f, 0.68656f, 0.83743f, 0.92743f, 0.73186f, 0.95904f, 0.39744f, - 0.68925f, 0.31835f, 0.23127f, 0.74562f, 0.41251f, 0.65146f, 0.64949f, - 0.76434f, 0.09152f, 0.17684f, 0.90619f, 0.21256f, 0.06708f, 0.24297f, - 0.06879f, 0.43735f, 0.51356f, 0.44677f, 0.9001f, 0.49578f, 0.14914f, - 0.88617f, 0.29814f, 0.02808f, 0.2234f, 0.74633f, 0.33895f, 0.17821f, - 0.46732f, 0.9459f, 0.28334f, 0.54914f, 0.20203f, 0.56062f, 0.40301f, - 0.33988f, 0.83907f, 0.8311f, 0.0184f, 0.29255f, 0.87139f, 0.00139f, - 0.37163f, 0.85388f, 0.62787f, 0.77813f, 0.18419f, 0.05526f, 0.04112f, - 0.5548f, 0.05673f, 0.23685f, 0.85038f, 0.15786f, 0.07784f, 0.45601f, - 0.31546f, 0.52621f, 0.27254f, 0.27382f, 0.34216f, 0.41889f, 0.19776f, - 0.64677f, 0.32853f, 0.07016f, 0.2246f, 0.28733f, 0.86153f, 0.08266f, - 0.89759f, 0.3584f, 0.5571f, 0.48252f, 0.89011f, 0.31564f, 0.22413f, - 0.20587f, 0.88846f, 0.50867f, 0.2199f, 0.40773f, 0.44038f, 0.61884f, - 0.21655f, 0.95121f, 0.95179f, 0.12011f, 0.39645f, 0.55856f, 0.44058f, - 0.03147f, 0.66454f, 0.15697f, 0.33277f, 0.1945f, 0.92129f, 0.42806f, - 0.32412f, 0.68403f, 0.98058f, 0.75483f, 0.98549f, 0.89169f, 0.88581f, - 0.53969f, 0.19126f, 0.83488f, 0.46591f, 0.67737f, 0.91493f, 0.21109f, - 0.24882f, 0.59715f, 0.33349f, 0.3166f, 0.63455f, 0.08456f, 0.23693f, - 0.14153f, 0.23991f, 0.37132f, 0.79662f, 0.3038f, 0.84422f, 0.77358f, - 0.19849f, 0.76641f, 0.66427f, 0.01015f, 0.17185f, 0.96281f, 0.8034f, - 0.88591f, 0.92692f, 0.59886f, 0.29105f, 0.23611f, 0.40868f, 0.60006f, - 0.86721f, 0.08793f, 0.36164f, 0.90415f, 0.85043f, 0.18978f, 0.80895f, - 0.30351f, 0.44588f, 0.99119f, 0.631f, 0.71073f, 0.44436f, 0.76188f, - 0.11029f, 0.25636f, 0.1058f, 0.20452f, 0.36064f, 0.70633f, 0.26141f, - 0.8148f, 0.562f - }; - branch1x1_bn_weights = { - 0.15308f, 0.06895f, 0.31661f, 0.12344f, 0.39979f, 0.12112f, 0.63328f, - 0.7312f, 0.12881f, 0.11836f, 0.69003f, 0.42622f, 0.24349f, 0.67882f, - 0.61108f, 0.7296f, 0.83295f, 0.65878f, 0.48321f, 0.09019f, 0.56587f, - 0.09666f, 0.68351f, 0.92617f, 0.45851f, 0.76845f, 0.87519f, 0.79224f, - 0.91477f, 0.49415f, 0.00768f, 0.40906f, 0.53205f, 0.64999f, 0.87018f, - 0.6335f, 0.51166f, 0.94985f, 0.18463f, 0.03787f, 0.07493f, 0.44754f, - 0.60983f, 0.67477f, 0.63739f, 0.77599f, 0.77548f, 0.73687f, 0.41179f, - 0.25339f, 0.007f, 0.5007f, 0.97304f, 0.99633f, 0.95116f, 0.39029f, - 0.73937f, 0.77625f, 0.01564f, 0.76227f, 0.3828f, 0.6256f, 0.96263f, - 0.71529f, 0.49333f, 0.56438f, 0.54747f, 0.00622f, 0.43485f, 0.28346f, - 0.59125f, 0.7792f, 0.46047f, 0.65611f, 0.26147f, 0.26049f, 0.76877f, - 0.92122f, 0.98243f, 0.53441f, 0.41222f, 0.32917f, 0.72788f, 0.22573f, - 0.93312f, 0.4827f, 0.32557f, 0.87673f, 0.48831f, 0.68914f, 0.01622f, - 0.69178f, 0.74918f, 0.40008f, 0.45852f, 0.89053f, 0.5308f, 0.07272f, - 0.28872f, 0.26416f, 0.00527f, 0.11573f, 0.18359f, 0.17276f, 0.9169f, - 0.57067f, 0.26713f, 0.49157f, 0.60626f, 0.72175f, 0.79042f, 0.18218f, - 0.72638f, 0.22555f, 0.68014f, 0.38556f, 0.50093f, 0.72069f, 0.48917f, - 0.61065f, 0.21022f, 0.03307f, 0.35856f, 0.8262f, 0.41089f, 0.32728f, - 0.27895f, 0.00507f, 0.17991f, 0.65297f, 0.50981f, 0.79522f, 0.26236f, - 0.41639f, 0.2543f, 0.28931f, 0.99372f, 0.66152f, 0.59756f, 0.67484f, - 0.55538f, 0.34215f, 0.51431f, 0.50641f, 0.16702f, 0.78852f, 0.76634f, - 0.80988f, 0.83128f, 0.37379f, 0.28412f, 0.55415f, 0.8064f, 0.26948f, - 0.22378f, 0.54763f, 0.49631f, 0.77659f, 0.11712f, 0.17817f, 0.97859f, - 0.40639f, 0.05387f, 0.87503f, 0.66947f, 0.85772f, 0.55338f, 0.08184f, - 0.39216f, 0.10511f, 0.69828f, 0.60541f, 0.54712f, 0.66291f, 0.73615f, - 0.71163f, 0.36516f, 0.39569f, 0.89574f, 0.4112f, 0.40041f, 0.08737f, - 0.56506f, 0.4546f, 0.87626f, 0.32428f, 0.18748f, 0.22576f, 0.71528f, - 0.44057f, 0.68605f, 0.36046f - }; - branch1x1_bn_bias = { - 0.43269f, 0.16216f, 0.80636f, 0.37216f, 0.84575f, 0.31582f, 0.02729f, - 0.81258f, 0.80039f, 0.37127f, 0.29622f, 0.93315f, 0.90207f, 0.51266f, - 0.6961f, 0.35111f, 0.82503f, 0.78798f, 0.89209f, 0.85087f, 0.32326f, - 0.22775f, 0.80719f, 0.85097f, 0.14354f, 0.86681f, 0.69949f, 0.30094f, - 0.62642f, 0.27508f, 0.1458f, 0.29887f, 0.91717f, 0.29631f, 0.02848f, - 0.87435f, 0.68089f, 0.35364f, 0.42352f, 0.97611f, 0.68506f, 0.934f, - 0.45583f, 0.38097f, 0.20364f, 0.50734f, 0.19733f, 0.94702f, 0.25424f, - 0.28819f, 0.05904f, 0.44984f, 0.8157f, 0.10521f, 0.50805f, 0.31394f, - 0.36521f, 0.53426f, 0.54642f, 0.64644f, 0.14942f, 0.10798f, 0.31374f, - 0.62893f, 0.16985f, 0.94127f, 0.59073f, 0.44048f, 0.53656f, 0.39109f, - 0.98991f, 0.66683f, 0.71863f, 0.91137f, 0.69594f, 0.60847f, 0.37518f, - 0.80779f, 0.47571f, 0.39684f, 0.12581f, 0.64508f, 0.1826f, 0.39182f, - 0.75615f, 0.14524f, 0.19832f, 0.15491f, 0.42984f, 0.4714f, 0.11958f, - 0.54067f, 0.30748f, 0.86598f, 0.65795f, 0.50121f, 0.76687f, 0.216f, - 0.98438f, 0.70124f, 0.78234f, 0.03103f, 0.7427f, 0.74134f, 0.50847f, - 0.05284f, 0.136f, 0.23542f, 0.14034f, 0.48833f, 0.45358f, 0.37315f, - 0.34363f, 0.32645f, 0.04158f, 0.15466f, 0.95826f, 0.87894f, 0.27823f, - 0.35036f, 0.469f, 0.78779f, 0.65726f, 0.49713f, 0.55312f, 0.61459f, - 0.12504f, 0.63453f, 0.51884f, 0.44272f, 0.54421f, 0.00893f, 0.99812f, - 0.7724f, 0.85346f, 0.07114f, 0.05366f, 0.91442f, 0.41102f, 0.66437f, - 0.0375f, 0.29632f, 0.67119f, 0.95597f, 0.36537f, 0.26459f, 0.00918f, - 0.43908f, 0.15501f, 0.65754f, 0.56877f, 0.53597f, 0.30631f, 0.3995f, - 0.41379f, 0.22745f, 0.27015f, 0.04374f, 0.73718f, 0.9814f, 0.04164f, - 0.79071f, 0.84333f, 0.12031f, 0.1267f, 0.64101f, 0.04222f, 0.63481f, - 0.6413f, 0.65166f, 0.51084f, 0.69028f, 0.10982f, 0.7088f, 0.58161f, - 0.24679f, 0.74557f, 0.73333f, 0.84353f, 0.59622f, 0.57133f, 0.60134f, - 0.27689f, 0.62568f, 0.75479f, 0.2565f, 0.4802f, 0.33828f, 0.23708f, - 0.95119f, 0.46434f, 0.49899f - }; - branch7x7_1_conv_weights = { - 0.53301f, 0.25751f, 0.33141f, 0.81402f, 0.71238f, 0.60193f, 0.53411f, - 0.47679f, 0.94001f, 0.23897f, 0.61307f, 0.66747f, 0.65784f, 0.38442f, - 0.8929f, 0.68271f, 0.49614f, 0.68671f, 0.60713f, 0.88259f, 0.22766f, - 0.91183f, 0.35168f, 0.25796f, 0.57243f, 0.84846f, 0.64829f, 0.41946f, - 0.59633f, 0.91969f, 0.05914f, 0.57362f, 0.51734f, 0.50428f, 0.62342f, - 0.90503f, 0.51366f, 0.16315f, 0.24625f, 0.0878f, 0.18107f, 0.31856f, - 0.8616f, 0.43831f, 0.95748f, 0.88888f, 0.99868f, 0.42006f, 0.36842f, - 0.69117f, 0.35373f, 0.34027f, 0.58986f, 0.76291f, 0.48437f, 0.04311f, - 0.14637f, 0.82533f, 0.28737f, 0.01977f, 0.74329f, 0.46631f, 0.33277f, - 0.58814f, 0.92255f, 0.79349f, 0.43918f, 0.86766f, 0.52611f, 0.82706f, - 0.95125f, 0.42583f, 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- 0.74575f, 0.0f, 0.38459f, 0.0f, 0.0f, 0.0f, 0.89071f, - 2.34151f, 1.91784f, 0.5773f, 0.19389f, 0.43351f, 1.0113f, 1.55793f, - 1.74199f, 1.20208f, 1.27011f, 1.52037f, 1.60028f, 1.64962f, 1.33878f, - 2.19721f, 2.07984f, 0.512f, 0.0f, 0.0f, 0.0f, 0.03459f, - 0.51433f, 1.1636f, 1.42544f, 0.29593f, 0.0f, 0.40506f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.31297f, 1.16665f, 0.75702f, 0.60842f, - 0.0f, 0.20744f, 0.02355f, 0.62542f, 0.77382f, 0.89911f, 0.40282f, - 0.05154f, 0.0f, 0.1161f, 0.41084f, 0.64351f, 0.68878f, 0.56157f, - 0.55628f, 0.35131f, 0.69576f, 0.0801f, 0.46131f, 0.0f, 0.28537f, - 1.21755f, 1.15415f, 0.29794f, 0.61752f, 0.45633f, 1.02753f, 0.32076f, - 0.54535f, 1.40688f, 1.23458f, 0.47095f, 0.86938f, 0.9406f, 1.31191f, - 0.88996f, 0.99105f, 0.73613f, 0.7437f, 1.15153f, 1.44586f, 1.63159f, - 1.61099f, 1.65644f, 1.46314f, 0.0f, 0.0f, 0.39353f, 0.7126f, - 1.21903f, 1.14003f, 1.03215f, 1.07884f, 0.02572f, 0.0f, 0.7774f, - 0.74187f, 0.8261f, 0.46836f, 0.44486f, 0.71818f, 0.33383f, 0.29642f, - 0.27899f, 0.15502f, 0.01993f, 0.02537f, 0.0f, 0.15482f, 0.31606f, - 0.19285f, 0.23455f, 0.07337f, 0.0f, 0.0f, 0.0f, 0.12631f, - 0.17932f, 0.06251f, 0.19242f, 0.22293f, 0.07963f, 0.13704f, 0.0f, - 0.18128f, 0.46613f, 0.43176f, 0.46022f, 0.43106f, 0.34132f, 0.37045f, - 0.05266f, 0.17677f, 0.27987f, 0.38422f, 0.24282f, 0.53103f, 0.27048f, - 0.52248f, 0.20227f, 0.35846f, 0.35404f, 0.41f, 0.44595f, 0.3969f, - 0.25049f, 0.31686f, 0.28862f, 0.37906f, 0.21451f, 0.20155f, 0.21111f, - 0.11974f, 0.00677f, 0.09559f, 0.31076f, 0.43669f, 0.41854f, 0.35266f, - 0.47294f, 0.10141f, 0.10248f, 0.01279f, 0.21868f, 0.2835f, 1.25715f, - 0.98724f, 1.12769f, 0.40953f, 0.23665f, 0.12024f, 0.0f, 0.37509f, - 0.92939f, 0.23072f, 0.48725f, 0.04324f, 0.0f, 0.0f, 0.0f, - 0.40128f, 0.83393f, 0.07567f, 0.45356f, 0.3835f, 0.0f, 0.0f, - 0.0f, 0.5147f, 1.99559f, 1.57682f, 1.35523f, 1.46021f, 0.974f, - 0.98936f, 0.0f, 0.84524f, 1.74883f, 1.92332f, 0.93322f, 1.68772f, - 0.92459f, 1.97983f, 1.19244f, 1.78107f, 1.55181f, 1.76592f, 1.96111f, - 1.81015f, 1.33255f, 1.41841f, 1.69501f, 1.9585f, 0.85301f, 0.4741f, - 0.53241f, 0.00432f, 0.0f, 0.36537f, 1.36825f, 1.89499f, 1.42754f, - 0.88411f, 1.59486f, 0.14311f, 0.18545f, 0.0f, 0.43188f, 0.93296f, - 0.40685f, 0.31119f, 0.26589f, 0.12224f, 0.19486f, 0.15221f, 0.31633f, - 0.33937f, 0.35121f, 0.24995f, 0.14805f, 0.0633f, 0.18283f, 0.26358f, - 0.33367f, 0.31541f, 0.26438f, 0.29381f, 0.22668f, 0.32021f, 0.18491f, - 0.27534f, 0.10069f, 0.20655f, 0.39053f, 0.39171f, 0.17907f, 0.25703f, - 0.23446f, 0.38326f, 0.2382f, 0.2596f, 0.44652f, 0.3963f, 0.23783f, - 0.31488f, 0.3612f, 0.41709f, 0.33464f, 0.33854f, 0.28215f, 0.27594f, - 0.37439f, 0.45662f, 0.52277f, 0.51512f, 0.51411f, 0.45417f, 0.10436f, - 0.0f, 0.23463f, 0.33898f, 0.47488f, 0.43386f, 0.36639f, 0.35816f, - 0.10401f, 0.03334f, 0.2937f, 0.34007f, 0.36017f, 0.28866f, 0.25261f, - 0.30318f, 0.249f, 0.05567f, 0.03876f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.08773f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.39148f, 0.28709f, 0.0f, - 0.07213f, 0.0f, 0.131f, 0.0f, 0.0f, 0.45656f, 0.40462f, - 0.0f, 0.18292f, 0.1141f, 0.4056f, 0.1739f, 0.29466f, 0.15566f, - 0.1895f, 0.36626f, 0.45098f, 0.45314f, 0.44605f, 0.53053f, 0.49454f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.05269f, 0.11122f, 0.23223f, - 0.3287f, 0.0f, 0.0f, 0.14636f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.03369f, 0.50198f, 0.39499f, 0.27249f, 0.12756f, 0.14552f, - 0.11905f, 0.36952f, 0.45855f, 0.48966f, 0.40311f, 0.22512f, 0.05633f, - 0.17776f, 0.28774f, 0.42212f, 0.38724f, 0.24874f, 0.37843f, 0.30829f, - 0.49653f, 0.32417f, 0.45406f, 0.20811f, 0.25078f, 0.41268f, 0.49234f, - 0.2653f, 0.32329f, 0.33004f, 0.57589f, 0.32245f, 0.23603f, 0.37424f, - 0.35808f, 0.2467f, 0.44869f, 0.45982f, 0.51309f, 0.27968f, 0.28472f, - 0.25826f, 0.24793f, 0.37767f, 0.49072f, 0.56957f, 0.60205f, 0.49775f, - 0.42923f, 0.04182f, 0.0f, 0.31771f, 0.52227f, 0.64962f, 0.54155f, - 0.38886f, 0.36205f, 0.09257f, 0.06744f, 0.38368f, 0.4681f, 0.4876f, - 0.43566f, 0.37903f, 0.38467f, 0.79087f, 0.36868f, 0.21852f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.1284f, 0.52008f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.01014f, 0.00451f, 0.0f, - 0.0f, 0.17292f, 0.0f, 0.0f, 0.0f, 0.0f, 1.07886f, - 0.95225f, 0.25698f, 0.48714f, 0.18393f, 0.72102f, 0.0f, 0.0f, - 0.92773f, 0.95711f, 0.0343f, 0.87459f, 0.48113f, 1.25088f, 0.33702f, - 0.7011f, 0.46671f, 0.57276f, 0.9881f, 1.15425f, 1.05601f, 1.1499f, - 1.15355f, 1.14575f, 0.0f, 0.0f, 0.0f, 0.00437f, 0.24803f, - 0.345f, 0.64244f, 0.9082f, 0.0f, 0.0f, 0.69753f, 0.00496f, - 0.08046f, 0.0f, 0.0f, 0.31226f, 1.34492f, 1.09492f, 0.8665f, - 0.42217f, 0.29015f, 0.2417f, 0.62615f, 1.00816f, 1.2817f, 0.94327f, - 0.6906f, 0.17949f, 0.30125f, 0.44565f, 0.63417f, 0.85085f, 0.69256f, - 0.74196f, 0.78863f, 1.15327f, 0.6432f, 0.95398f, 0.21707f, 0.67774f, - 1.38355f, 1.45921f, 1.07341f, 1.15032f, 1.02938f, 1.50039f, 0.62253f, - 0.66782f, 1.08861f, 1.18917f, 0.76717f, 1.50105f, 1.17679f, 1.62478f, - 0.79672f, 1.02042f, 0.95353f, 1.01232f, 1.29626f, 1.43202f, 1.3757f, - 1.51408f, 1.3146f, 1.31212f, 0.36572f, 0.14783f, 0.83162f, 1.0578f, - 1.14686f, 1.08812f, 1.1249f, 1.25373f, 0.72387f, 0.56951f, 1.31377f, - 0.94974f, 0.98835f, 0.75978f, 0.94269f, 1.05845f - }; +TEST_F(InceptionBlockTest, InceptionCTest) { + inputShape = {4, 4}; + inputChannels = 3; + outputSize = 50; + + model = new InceptionCModel(inputShape, inputChannels, outputSize); + model->loadWeights("../tests/resources/inception_c.bin"); + + input = {-0.50514f, 1.61672f, -0.53398f, -0.00431f, 0.30868f, -1.17392f, + -2.15633f, -1.34838f, 0.02578f, 1.33409f, 0.25805f, -0.93777f, + 0.5875f, -1.2065f, 0.15659f, -1.8198f, 0.22255f, 0.52929f, + -0.83366f, -0.38078f, 0.02898f, 0.48225f, 0.14351f, -1.08989f, + -2.06598f, -0.25796f, -0.79819f, 0.38487f, -1.02079f, 0.41253f, + 0.53986f, -0.65411f, -0.32057f, 2.15608f, -0.99935f, 0.10825f, + -1.60163f, -0.27932f, -0.20508f, 1.31193f, -0.7601f, -0.0586f, + -0.21923f, -0.85385f, -1.10512f, -0.22181f, 0.94507f, -0.09808f}; + + expected = {-9231.45508f, -11854.50684f, -2690.15942f, -6366.60303f, + -6953.01855f, -2204.80371f, 1670.89551f, 18207.81641f, + -8896.50977f, 10661.94434f, 3338.31055f, -3853.95947f, + 1445.87354f, -9627.54297f, 4166.00635f, -22477.38477f, + 11400.2207f, 8139.3877f, 8114.41602f, -2006.37793f, + -9130.33398f, 10554.69824f, 5194.41016f, -7031.67969f, + -10880.09277f, -4093.95068f, 6500.65967f, -459.13672f, + -10640.70215f, 6096.37842f, 12178.46094f, 5894.95117f, + -3034.80225f, -5177.80518f, -6112.60449f, -7296.75879f, + -1134.77344f, -13472.27637f, 8982.56543f, -3773.67334f, + -4207.74609f, -4001.82129f, -6682.51953f, -12314.57617f, + -6180.21875f, -886.62231f, 5490.0752f, 4868.64893f, + -12725.73633f, -3121.33716f}; runTest(); -} \ No newline at end of file +} diff --git a/examples/inception_v3/tests/test_inception_d.cpp b/examples/inception_v3/tests/test_inception_d.cpp index 4c1f657..7cb02fd 100644 --- a/examples/inception_v3/tests/test_inception_d.cpp +++ b/examples/inception_v3/tests/test_inception_d.cpp @@ -2,179 +2,67 @@ #include -class InceptionDTest : public ::testing::Test { - protected: - InceptionD *inception_d; +#include "test_fixture.hpp" - cudaError_t cudaStatus; +class InceptionDModel : public CUDANet::Model { + public: + InceptionDModel( + const shape2d inputShape, + const int inputChannels, + const int outputSize + ) + : CUDANet::Model(inputShape, inputChannels, outputSize) { + inception_d = + new InceptionD(inputShape, inputChannels, "inception_block."); + addLayer("", inception_d); + fc = new CUDANet::Layers::Dense( + inception_d->getOutputSize(), 50, + CUDANet::Layers::ActivationType::NONE + ); + addLayer("fc", fc); + }; - shape2d inputShape; - int inputChannels; - int nChannels_7x7; - std::string prefix = "test"; - - float *d_input; - float *d_output; - - std::vector input; - std::vector expected; - - // Branch 3x3 - std::vector branch3x3_1_conv_weights; - std::vector branch3x3_1_bn_weights; - std::vector branch3x3_1_bn_bias; - std::vector branch3x3_2_conv_weights; - std::vector branch3x3_2_bn_weights; - std::vector branch3x3_2_bn_bias; - - // Branch 7x7x3 - std::vector branch7x7x3_1_conv_weights; - std::vector branch7x7x3_1_bn_weights; - std::vector branch7x7x3_1_bn_bias; - std::vector branch7x7x3_2_conv_weights; - std::vector branch7x7x3_2_bn_weights; - std::vector branch7x7x3_2_bn_bias; - std::vector branch7x7x3_3_conv_weights; - std::vector branch7x7x3_3_bn_weights; - std::vector branch7x7x3_3_bn_bias; - std::vector branch7x7x3_4_conv_weights; - std::vector branch7x7x3_4_bn_weights; - std::vector branch7x7x3_4_bn_bias; - - virtual void SetUp() override { - inception_d = nullptr; + float *predict(const float *input) override { + float *d_input = inputLayer->forward(input); + d_input = inception_d->forward(d_input); + d_input = fc->forward(d_input); + return outputLayer->forward(d_input); } - virtual void TearDown() override { - // Clean up - delete inception_d; - } - - void setBasicConv2dWeights( - const std::vector &conv_weights, - const std::vector &bn_weights, - const std::vector &bn_bias, - const int index - ) { - std::pair layerPair = - inception_d->getLayers()[index]; - - CUDANet::Layers::Conv2d *conv = - dynamic_cast(layerPair.second); - EXPECT_NE(conv, nullptr); - - EXPECT_EQ(conv->getWeights().size(), conv_weights.size()); - conv->setWeights(conv_weights.data()); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - - layerPair = inception_d->getLayers()[index + 1]; - CUDANet::Layers::BatchNorm2d *bn = - dynamic_cast(layerPair.second); - EXPECT_NE(bn, nullptr); - bn->setWeights(bn_weights.data()); - EXPECT_EQ(bn->getWeights().size(), bn_weights.size()); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - - bn->setBiases(bn_bias.data()); - EXPECT_EQ(bn->getBiases().size(), bn_bias.size()); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - } - - void runTest() { - inception_d = new InceptionD(inputShape, inputChannels, prefix); - - // Branch 3x3 - setBasicConv2dWeights( - branch3x3_1_conv_weights, branch3x3_1_bn_weights, - branch3x3_1_bn_bias, 0 - ); - - setBasicConv2dWeights( - branch3x3_2_conv_weights, branch3x3_2_bn_weights, - branch3x3_2_bn_bias, 2 - ); - - // Branch 7x7x3 - setBasicConv2dWeights( - branch7x7x3_1_conv_weights, branch7x7x3_1_bn_weights, - branch7x7x3_1_bn_bias, 4 - ); - setBasicConv2dWeights( - branch7x7x3_2_conv_weights, branch7x7x3_2_bn_weights, - branch7x7x3_2_bn_bias, 6 - ); - setBasicConv2dWeights( - branch7x7x3_3_conv_weights, branch7x7x3_3_bn_weights, - branch7x7x3_3_bn_bias, 8 - ); - setBasicConv2dWeights( - branch7x7x3_4_conv_weights, branch7x7x3_4_bn_weights, - branch7x7x3_4_bn_bias, 10 - ); - - cudaStatus = - cudaMalloc((void **)&d_input, sizeof(float) * input.size()); - EXPECT_EQ(cudaStatus, cudaSuccess); - - cudaStatus = cudaMemcpy( - d_input, input.data(), sizeof(float) * input.size(), - cudaMemcpyHostToDevice - ); - EXPECT_EQ(cudaStatus, cudaSuccess); - - d_output = inception_d->forward(d_input); - - cudaStatus = cudaGetLastError(); - EXPECT_EQ(cudaStatus, cudaSuccess); - - int outputSize = inception_d->getOutputSize(); - EXPECT_EQ(outputSize, expected.size()); - - std::vector output(outputSize); - cudaStatus = cudaMemcpy( - output.data(), d_output, sizeof(float) * output.size(), - cudaMemcpyDeviceToHost - ); - EXPECT_EQ(cudaStatus, cudaSuccess); - - for (int i = 0; i < output.size(); ++i) { - EXPECT_NEAR(expected[i], output[i], 1e-4f); - } - } + private: + InceptionD *inception_d; + CUDANet::Layers::Dense *fc; }; -TEST_F(InceptionDTest, InceptionDTest) { - inputShape = {8, 8}; +TEST_F(InceptionBlockTest, InceptionDTest) { + inputShape = {4, 4}; inputChannels = 3; + outputSize = 50; - -branch3x3_1_conv_weights = {0.8163f, 0.26935f, 0.55867f, 0.99657f, 0.31553f, 0.64898f, 0.26511f, 0.93341f, 0.2397f, 0.83351f, 0.68847f, 0.11506f, 0.59343f, 0.70322f, 0.95058f, 0.83292f, 0.28994f, 0.12742f, 0.59496f, 0.16717f, 0.18488f, 0.23951f, 0.22714f, 0.74889f, 0.6178f, 0.50815f, 0.97838f, 0.21824f, 0.07231f, 0.80874f, 0.63601f, 0.77502f, 0.94698f, 0.18786f, 0.1652f, 0.67263f, 0.34826f, 0.13332f, 0.68433f, 0.96556f, 0.65599f, 0.86771f, 0.95552f, 0.02309f, 0.54105f, 0.73964f, 0.52716f, 0.32551f, 0.88524f, 0.01867f, 0.64067f, 0.11296f, 0.58167f, 0.56283f, 0.48583f, 0.69592f, 0.94626f, 0.17289f, 0.90153f, 0.99684f, 0.40049f, 0.71327f, 0.56061f, 0.30168f, 0.79742f, 0.36275f, 0.2357f, 0.0693f, 0.84774f, 0.23713f, 0.36939f, 0.51385f, 0.74198f, 0.94886f, 0.37834f, 0.20026f, 0.31483f, 0.32187f, 0.40544f, 0.24939f, 0.98874f, 0.68208f, 0.70431f, 0.49229f, 0.79489f, 0.00646f, 0.27566f, 0.47802f, 0.28182f, 0.56474f, 0.37574f, 0.55745f, 0.72823f, 0.47882f, 0.49969f, 0.35125f, 0.29825f, 0.82286f, 0.50398f, 0.64756f, 0.82529f, 0.61475f, 0.49286f, 0.78597f, 0.32016f, 0.55402f, 0.42686f, 0.49368f, 0.12006f, 0.22453f, 0.80683f, 0.93353f, 0.31474f, 0.24103f, 0.89892f, 0.87166f, 0.73707f, 0.83487f, 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0.0f, 0.32305f, 0.0f, 0.0f, 1.36816f, 0.72507f, 0.0f, 1.10505f, 0.86974f, 0.0f, 0.86552f, 0.19312f, 0.0f, 1.88827f, 1.29908f, 0.0f, 1.61583f, 1.42903f, 2.1356f, 1.27161f, 1.01686f, 0.60194f, 1.56806f, 1.99195f, 0.87522f, 0.58058f, 1.35147f, 1.3234f, 1.3234f, 0.83733f, 1.30572f, 1.59796f, 0.83733f, 1.30572f, 0.36337f, 1.28305f, 0.95409f, 1.50656f, 3.19676f, 1.86822f, 0.84547f, 3.19676f, 1.04282f, 1.03696f, 0.23844f}; + model = new InceptionDModel(inputShape, inputChannels, outputSize); + model->loadWeights("../tests/resources/inception_d.bin"); -runTest(); + input = {0.11909f, 0.39688f, -0.48224f, 2.5266f, -0.72496f, -0.4192f, + 0.74736f, 1.15859f, 1.83703f, -0.68566f, -1.18465f, 2.22915f, + -0.48233f, -1.38496f, -0.23985f, -2.01891f, -0.61103f, -0.52143f, + 1.33207f, 0.11988f, 0.57453f, 0.26697f, -0.4713f, 0.26025f, + -0.55807f, -0.02672f, -0.18203f, 0.59285f, 0.28061f, -0.69015f, + -0.54148f, -1.64196f, -1.34975f, 0.4348f, 0.57761f, 1.47873f, + -0.89471f, -0.0348f, -1.49654f, -1.18578f, -2.013f, -0.47656f, + -0.16578f, 0.21603f, -0.23605f, -0.53382f, -0.25789f, 2.30887f}; + expected = { + -778.66046f, 2780.01416f, -908.03717f, 720.61572f, 975.1803f, + -2017.04016f, 2678.03955f, -2089.99609f, -1231.16272f, 4078.28247f, + -765.89209f, -2531.9021f, -1590.11182f, 6677.42822f, 174.45618f, + -1065.43262f, 4505.68066f, 3798.1748f, 1419.7229f, 2433.96948f, + 355.61597f, 1356.61279f, -2179.37061f, -973.08789f, 2414.1543f, + -2190.11792f, -157.86133f, 1810.07166f, 2140.48706f, 8073.00488f, + 2629.58789f, 4686.91992f, -3285.09985f, 5723.23584f, 1181.26648f, + -5476.90723f, 4895.85547f, -1787.32935f, 2138.9646f, 1336.84277f, + -3492.97656f, 3706.74121f, 703.98871f, -2263.92188f, 4441.91016f, + -3471.9314f, 3354.59106f, 5038.75928f, -3676.13037f, 563.34637f + }; + + runTest(); } \ No newline at end of file diff --git a/examples/inception_v3/tests/test_inception_e.cpp b/examples/inception_v3/tests/test_inception_e.cpp new file mode 100644 index 0000000..8585147 --- /dev/null +++ b/examples/inception_v3/tests/test_inception_e.cpp @@ -0,0 +1,69 @@ +#include + +#include + +#include "test_fixture.hpp" + +class InceptionEModel : public CUDANet::Model { + public: + InceptionEModel( + const shape2d inputShape, + const int inputChannels, + const int outputSize + ) + : CUDANet::Model(inputShape, inputChannels, outputSize) { + inception_e = + new InceptionE(inputShape, inputChannels, "inception_block."); + addLayer("", inception_e); + fc = new CUDANet::Layers::Dense( + inception_e->getOutputSize(), 50, + CUDANet::Layers::ActivationType::NONE + ); + addLayer("fc", fc); + }; + + float *predict(const float *input) override { + float *d_input = inputLayer->forward(input); + d_input = inception_e->forward(d_input); + d_input = fc->forward(d_input); + return outputLayer->forward(d_input); + } + + private: + InceptionE *inception_e; + CUDANet::Layers::Dense *fc; +}; + +TEST_F(InceptionBlockTest, InceptionETest) { + inputShape = {4, 4}; + inputChannels = 3; + outputSize = 50; + + model = new InceptionEModel(inputShape, inputChannels, outputSize); + model->loadWeights("../tests/resources/inception_e.bin"); + + input = {1.85083f, 0.11234f, 0.05994f, -1.02453f, 0.21205f, -0.67387f, + 0.66981f, -0.40378f, 0.34194f, 0.92048f, 0.87556f, 0.81094f, + -1.55728f, -0.70326f, -0.25078f, -0.10276f, 1.10463f, -2.40992f, + -1.7226f, -0.18546f, 0.14397f, -1.24784f, -0.35248f, -1.28729f, + 0.44803f, 1.68539f, -1.05037f, 0.32115f, -0.12896f, 1.02391f, + 0.95329f, -0.81876f, -1.05828f, 0.09282f, -0.38344f, 2.05074f, + 2.1034f, 1.65832f, 1.63788f, -1.32596f, -1.43412f, -1.28353f, + 0.70226f, 0.9459f, 0.8579f, 0.15361f, 0.34449f, -1.70587f}; + + expected = {1614.15283f, -11319.01855f, 614.40479f, 5280.0293f, + 1914.45007f, -2937.50317f, -11177.16113f, 3215.01245f, + 6249.16992f, 5654.91357f, -11702.27148f, 13057.32422f, + 8665.35742f, 3911.11743f, 5239.45947f, -11552.88477f, + -8056.7666f, -16426.19922f, -1383.04346f, 6573.53125f, + -12226.16992f, -6641.0957f, -9614.80078f, -9313.30273f, + 7023.68848f, 2089.5752f, 1095.53369f, -1387.65698f, + -7928.21729f, -9489.18848f, 4159.78613f, -690.03442f, + -8356.81738f, 12364.08203f, 8226.95703f, 8822.66602f, + -5462.90381f, -1037.42773f, 12958.68555f, -666.58423f, + 2032.38574f, -9534.14062f, -947.41333f, 689.37158f, + 4585.76465f, -23245.36719f, 975.83398f, -1253.45703f, + -14745.35059f, -2588.05493f}; + + runTest(); +} \ No newline at end of file