mirror of
https://github.com/lordmathis/CUDANet.git
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Implement BasicConv2d test
This commit is contained in:
@@ -2,7 +2,7 @@ import torchvision
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import sys
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sys.path.append("../../tools") # Ugly hack
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from utils import export_model_weights, print_model_parameters
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from utils import export_model_weights, print_model_parameters, predict
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if __name__ == "__main__":
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@@ -11,5 +11,7 @@ if __name__ == "__main__":
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)
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inception.eval()
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print_model_parameters(inception) # print layer names and number of parameters
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# print_model_parameters(inception) # print layer names and number of parameters
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export_model_weights(inception, "inception_v3_weights.bin")
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# print(predict(inception, "bird.jpg"))
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90
examples/inception_v3/tests/basic_conv2d.py
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90
examples/inception_v3/tests/basic_conv2d.py
Normal file
@@ -0,0 +1,90 @@
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import sys
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import torch
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from torchvision.models.inception import BasicConv2d
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sys.path.append("../../../tools")
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from utils import print_cpp_vector
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basic_conv2d = BasicConv2d(
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in_channels=3,
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out_channels=6,
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kernel_size=3,
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stride=1,
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padding=1,
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)
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conv_weights = torch.tensor([
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0.18365, 0.08568, 0.08126, 0.68022, 0.41391, 0.71204, 0.66917,
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0.63586, 0.28914, 0.43624, 0.03018, 0.47986, 0.71336, 0.82706,
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0.587, 0.58516, 0.29813, 0.19312, 0.42975, 0.62522, 0.34256,
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0.28057, 0.37367, 0.54325, 0.63421, 0.46445, 0.56908, 0.95247,
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0.73934, 0.51263, 0.14464, 0.0956, 0.68846, 0.14675, 0.75427,
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0.50547, 0.37078, 0.03316, 0.42855, 0.94293, 0.73855, 0.86475,
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0.20687, 0.37793, 0.77947, 0.24402, 0.07547, 0.22212, 0.57188,
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0.5098, 0.71999, 0.63828, 0.53237, 0.42874, 0.43621, 0.87348,
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0.0073, 0.07752, 0.45232, 0.78307, 0.74813, 0.73456, 0.0378,
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0.78518, 0.6989, 0.50484, 0.74265, 0.39178, 0.91015, 0.11684,
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0.11499, 0.10394, 0.30637, 0.86116, 0.63743, 0.64142, 0.97882,
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0.30948, 0.32144, 0.76108, 0.81794, 0.50111, 0.82209, 0.49028,
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0.79417, 0.3257, 0.32221, 0.4007, 0.86371, 0.2271, 0.9414,
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0.66233, 0.60802, 0.65701, 0.41021, 0.1135, 0.21892, 0.93389,
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0.65786, 0.26068, 0.59535, 0.15048, 0.48185, 0.91072, 0.18252,
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0.64154, 0.89179, 0.54726, 0.60756, 0.31149, 0.30717, 0.79877,
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0.71727, 0.12418, 0.48471, 0.46097, 0.66898, 0.35467, 0.38027,
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0.16989, 0.88578, 0.84377, 0.26529, 0.26057, 0.30256, 0.84876,
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0.8849, 0.08982, 0.88191, 0.1944, 0.42052, 0.62898, 0.692,
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0.51155, 0.99903, 0.56947, 0.73144, 0.88091, 0.28472, 0.98895,
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0.41364, 0.1927, 0.07227, 0.421, 0.85347, 0.19329, 0.07098,
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0.19418, 0.06585, 0.49083, 0.85071, 0.96747, 0.45057, 0.54361,
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0.49552, 0.23454, 0.97412, 0.26663, 0.09274, 0.1662, 0.04784,
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0.76303
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]).reshape(6, 3, 3, 3)
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bn_weights = torch.tensor([
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0.69298, 0.27049, 0.85854, 0.52973, 0.29644, 0.68932
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])
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bn_biases = torch.tensor([
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0.74976, 0.42745, 0.22132, 0.21262, 0.03726, 0.9719
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])
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basic_conv2d.conv.weight = torch.nn.Parameter(conv_weights)
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basic_conv2d.bn.weight = torch.nn.Parameter(bn_weights)
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basic_conv2d.bn.bias = torch.nn.Parameter(bn_biases)
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input = torch.tensor([
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0.75539, 0.17641, 0.8331, 0.80627, 0.51712, 0.87756, 0.97027,
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0.21354, 0.28498, 0.05118, 0.37124, 0.40528, 0.13661, 0.08692,
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0.73809, 0.57278, 0.73534, 0.31338, 0.15362, 0.80245, 0.49524,
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0.81208, 0.24074, 0.42534, 0.62236, 0.75915, 0.06382, 0.66723,
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0.13448, 0.96896, 0.87197, 0.67366, 0.67885, 0.49345, 0.08446,
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0.94116, 0.8659, 0.22848, 0.53262, 0.51307, 0.89661, 0.72223,
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0.90541, 0.47353, 0.85476, 0.04177, 0.04039, 0.7917, 0.56188,
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0.53777, 0.91714, 0.84847, 0.16995, 0.59803, 0.05454, 0.00365,
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0.01429, 0.42586, 0.31519, 0.222, 0.9149, 0.51885, 0.82969,
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0.42778, 0.82913, 0.01303, 0.92699, 0.09225, 0.00284, 0.75769,
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0.74072, 0.59012, 0.40777, 0.0469, 0.08751, 0.23163, 0.51327,
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0.67095, 0.31971, 0.97841, 0.82292, 0.58917, 0.31565, 0.4728,
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0.41885, 0.36524, 0.28194, 0.70945, 0.36008, 0.23199, 0.71093,
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0.33364, 0.34199, 0.42114, 0.40026, 0.77819, 0.79858, 0.93793,
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0.45238, 0.97922, 0.73814, 0.11831, 0.08414, 0.56552, 0.99841,
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0.53862, 0.71138, 0.42274, 0.48724, 0.48201, 0.5361, 0.97138,
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0.27607, 0.33018, 0.07456, 0.77788, 0.58824, 0.77027, 0.3938,
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0.28081, 0.14074, 0.06907, 0.75419, 0.11888, 0.35715, 0.34481,
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0.05669, 0.21063, 0.8664, 0.00087, 0.88281, 0.55202, 0.68655,
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0.96262, 0.53907, 0.9227, 0.74055, 0.84487, 0.22792, 0.83233,
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0.42938, 0.39054, 0.59604, 0.4141, 0.25982, 0.9311, 0.35475,
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0.71432, 0.29186, 0.16604, 0.90708, 0.00171, 0.11541, 0.35719,
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0.9221, 0.18793, 0.90198, 0.29281, 0.72144, 0.54645, 0.71165,
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0.59584, 0.24041, 0.60954, 0.64945, 0.8122, 0.34145, 0.92178,
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0.99894, 0.25076, 0.45067, 0.71997, 0.09573, 0.57334, 0.63273,
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0.49469, 0.72747, 0.33449, 0.13755, 0.49458, 0.50319, 0.91328,
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0.57269, 0.21927, 0.36831, 0.88708, 0.62277, 0.08318, 0.01425,
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0.17998, 0.34614, 0.82303
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]).reshape(1, 3, 8, 8)
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with torch.no_grad():
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output = basic_conv2d(input)
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output = torch.flatten(output)
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print_cpp_vector(output)
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@@ -18,10 +18,13 @@ class BasicConv2dTest : public ::testing::Test {
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float *d_output;
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std::vector<float> input;
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std::vector<float> output;
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std::vector<float> expected;
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std::vector<float> weights;
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std::vector<float> biases;
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std::vector<float> convWeights;
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std::vector<float> convBiases;
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std::vector<float> bnWeights;
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std::vector<float> bnBiases;
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virtual void SetUp() override {
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basic_conv2d = nullptr;
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@@ -31,13 +34,202 @@ class BasicConv2dTest : public ::testing::Test {
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// Clean up
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delete basic_conv2d;
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}
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void runTest() {
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cudaError_t cudaStatus;
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basic_conv2d = new BasicConv2d(
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inputShape, inputChannels, outputChannels, kernelSize, stride,
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padding, prefix
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);
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std::pair<std::string, CUDANet::Layers::SequentialLayer *> layerPair =
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basic_conv2d->getLayers()[0];
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ASSERT_EQ(layerPair.first, prefix + ".conv");
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CUDANet::Layers::Conv2d *conv =
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dynamic_cast<CUDANet::Layers::Conv2d *>(layerPair.second);
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conv->setWeights(convWeights.data());
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conv->setBiases(convBiases.data());
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ASSERT_EQ(conv->getWeights().size(), convWeights.size());
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ASSERT_EQ(conv->getBiases().size(), convBiases.size());
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cudaStatus = cudaGetLastError();
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EXPECT_EQ(cudaStatus, cudaSuccess);
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layerPair = basic_conv2d->getLayers()[1];
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ASSERT_EQ(layerPair.first, prefix + ".bn");
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CUDANet::Layers::BatchNorm2d *bn =
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dynamic_cast<CUDANet::Layers::BatchNorm2d *>(layerPair.second);
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bn->setWeights(bnWeights.data());
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bn->setBiases(bnBiases.data());
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ASSERT_EQ(bn->getWeights().size(), bnWeights.size());
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ASSERT_EQ(bn->getBiases().size(), bnBiases.size());
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cudaStatus = cudaGetLastError();
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EXPECT_EQ(cudaStatus, cudaSuccess);
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cudaStatus =
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cudaMalloc((void **)&d_input, sizeof(float) * input.size());
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EXPECT_EQ(cudaStatus, cudaSuccess);
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cudaStatus = cudaMemcpy(
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d_input, input.data(), sizeof(float) * input.size(),
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cudaMemcpyHostToDevice
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);
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EXPECT_EQ(cudaStatus, cudaSuccess);
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d_output = basic_conv2d->forward(d_input);
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cudaStatus = cudaGetLastError();
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EXPECT_EQ(cudaStatus, cudaSuccess);
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int outputSize = basic_conv2d->getOutputSize();
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std::vector<float> output(outputSize);
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cudaStatus = cudaMemcpy(
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output.data(), d_output, sizeof(float) * output.size(),
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cudaMemcpyDeviceToHost
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);
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EXPECT_EQ(cudaStatus, cudaSuccess);
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for (int i = 0; i < output.size(); ++i) {
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EXPECT_NEAR(expected[i], output[i], 1e-5f);
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}
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}
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};
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TEST_F(BasicConv2dTest, BasicConv2dTest1) {
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inputShape = {28, 28};
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inputChannels = 1;
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outputChannels = 32;
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inputShape = {8, 8};
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inputChannels = 3;
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outputChannels = 6;
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kernelSize = {3, 3};
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stride = {1, 1};
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padding = {1, 1};
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// 3x3x3x6
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convWeights = {
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0.18365f, 0.08568f, 0.08126f, 0.68022f, 0.41391f, 0.71204f, 0.66917f,
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0.63586f, 0.28914f, 0.43624f, 0.03018f, 0.47986f, 0.71336f, 0.82706f,
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0.587f, 0.58516f, 0.29813f, 0.19312f, 0.42975f, 0.62522f, 0.34256f,
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0.28057f, 0.37367f, 0.54325f, 0.63421f, 0.46445f, 0.56908f, 0.95247f,
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0.73934f, 0.51263f, 0.14464f, 0.0956f, 0.68846f, 0.14675f, 0.75427f,
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0.50547f, 0.37078f, 0.03316f, 0.42855f, 0.94293f, 0.73855f, 0.86475f,
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0.20687f, 0.37793f, 0.77947f, 0.24402f, 0.07547f, 0.22212f, 0.57188f,
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0.5098f, 0.71999f, 0.63828f, 0.53237f, 0.42874f, 0.43621f, 0.87348f,
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0.0073f, 0.07752f, 0.45232f, 0.78307f, 0.74813f, 0.73456f, 0.0378f,
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0.78518f, 0.6989f, 0.50484f, 0.74265f, 0.39178f, 0.91015f, 0.11684f,
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0.11499f, 0.10394f, 0.30637f, 0.86116f, 0.63743f, 0.64142f, 0.97882f,
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0.30948f, 0.32144f, 0.76108f, 0.81794f, 0.50111f, 0.82209f, 0.49028f,
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0.79417f, 0.3257f, 0.32221f, 0.4007f, 0.86371f, 0.2271f, 0.9414f,
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0.66233f, 0.60802f, 0.65701f, 0.41021f, 0.1135f, 0.21892f, 0.93389f,
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0.65786f, 0.26068f, 0.59535f, 0.15048f, 0.48185f, 0.91072f, 0.18252f,
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0.64154f, 0.89179f, 0.54726f, 0.60756f, 0.31149f, 0.30717f, 0.79877f,
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0.71727f, 0.12418f, 0.48471f, 0.46097f, 0.66898f, 0.35467f, 0.38027f,
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0.16989f, 0.88578f, 0.84377f, 0.26529f, 0.26057f, 0.30256f, 0.84876f,
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0.8849f, 0.08982f, 0.88191f, 0.1944f, 0.42052f, 0.62898f, 0.692f,
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0.51155f, 0.99903f, 0.56947f, 0.73144f, 0.88091f, 0.28472f, 0.98895f,
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0.41364f, 0.1927f, 0.07227f, 0.421f, 0.85347f, 0.19329f, 0.07098f,
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0.19418f, 0.06585f, 0.49083f, 0.85071f, 0.96747f, 0.45057f, 0.54361f,
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0.49552f, 0.23454f, 0.97412f, 0.26663f, 0.09274f, 0.1662f, 0.04784f,
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0.76303f
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};
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convBiases.resize(outputChannels, 0.0f);
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bnWeights = {0.69298f, 0.27049f, 0.85854f, 0.52973f, 0.29644f, 0.68932f};
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bnBiases = {0.74976f, 0.42745f, 0.22132f, 0.21262f, 0.03726f, 0.9719f};
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input = {
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0.75539f, 0.17641f, 0.8331f, 0.80627f, 0.51712f, 0.87756f, 0.97027f,
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0.21354f, 0.28498f, 0.05118f, 0.37124f, 0.40528f, 0.13661f, 0.08692f,
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0.73809f, 0.57278f, 0.73534f, 0.31338f, 0.15362f, 0.80245f, 0.49524f,
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0.81208f, 0.24074f, 0.42534f, 0.62236f, 0.75915f, 0.06382f, 0.66723f,
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0.13448f, 0.96896f, 0.87197f, 0.67366f, 0.67885f, 0.49345f, 0.08446f,
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0.94116f, 0.8659f, 0.22848f, 0.53262f, 0.51307f, 0.89661f, 0.72223f,
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0.90541f, 0.47353f, 0.85476f, 0.04177f, 0.04039f, 0.7917f, 0.56188f,
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0.53777f, 0.91714f, 0.84847f, 0.16995f, 0.59803f, 0.05454f, 0.00365f,
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0.01429f, 0.42586f, 0.31519f, 0.222f, 0.9149f, 0.51885f, 0.82969f,
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0.42778f, 0.82913f, 0.01303f, 0.92699f, 0.09225f, 0.00284f, 0.75769f,
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0.74072f, 0.59012f, 0.40777f, 0.0469f, 0.08751f, 0.23163f, 0.51327f,
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0.67095f, 0.31971f, 0.97841f, 0.82292f, 0.58917f, 0.31565f, 0.4728f,
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0.41885f, 0.36524f, 0.28194f, 0.70945f, 0.36008f, 0.23199f, 0.71093f,
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0.33364f, 0.34199f, 0.42114f, 0.40026f, 0.77819f, 0.79858f, 0.93793f,
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0.45238f, 0.97922f, 0.73814f, 0.11831f, 0.08414f, 0.56552f, 0.99841f,
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0.53862f, 0.71138f, 0.42274f, 0.48724f, 0.48201f, 0.5361f, 0.97138f,
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0.27607f, 0.33018f, 0.07456f, 0.77788f, 0.58824f, 0.77027f, 0.3938f,
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0.28081f, 0.14074f, 0.06907f, 0.75419f, 0.11888f, 0.35715f, 0.34481f,
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0.05669f, 0.21063f, 0.8664f, 0.00087f, 0.88281f, 0.55202f, 0.68655f,
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0.96262f, 0.53907f, 0.9227f, 0.74055f, 0.84487f, 0.22792f, 0.83233f,
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0.42938f, 0.39054f, 0.59604f, 0.4141f, 0.25982f, 0.9311f, 0.35475f,
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0.71432f, 0.29186f, 0.16604f, 0.90708f, 0.00171f, 0.11541f, 0.35719f,
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0.9221f, 0.18793f, 0.90198f, 0.29281f, 0.72144f, 0.54645f, 0.71165f,
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0.59584f, 0.24041f, 0.60954f, 0.64945f, 0.8122f, 0.34145f, 0.92178f,
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0.99894f, 0.25076f, 0.45067f, 0.71997f, 0.09573f, 0.57334f, 0.63273f,
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0.49469f, 0.72747f, 0.33449f, 0.13755f, 0.49458f, 0.50319f, 0.91328f,
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0.57269f, 0.21927f, 0.36831f, 0.88708f, 0.62277f, 0.08318f, 0.01425f,
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0.17998f, 0.34614f, 0.82303f
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};
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expected = {
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0.0f, 0.49814f, 0.22097f, 0.3619f, 0.46957f, 0.69706f, 1.06759f,
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0.25578f, 0.0f, 0.91978f, 0.53499f, 0.78382f, 1.13748f, 1.27999f,
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1.39561f, 0.59403f, 0.1681f, 1.1653f, 0.9397f, 0.99945f, 1.09875f,
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1.11738f, 1.48957f, 0.39551f, 0.17473f, 1.36075f, 1.38633f, 1.10036f,
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1.66809f, 1.24004f, 1.51673f, 0.35859f, 0.50363f, 1.90002f, 1.76062f,
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1.77264f, 1.653f, 0.98297f, 0.97645f, 0.36179f, 0.65388f, 1.82326f,
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1.62819f, 1.53234f, 1.52987f, 1.1909f, 1.19085f, 0.0f, 0.0f,
|
||||
1.00418f, 0.9884f, 1.06528f, 1.10918f, 0.95965f, 1.01066f, 0.0f,
|
||||
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.06699f, 0.0f,
|
||||
0.0f, 0.0f, 0.31227f, 0.1577f, 0.24142f, 0.29244f, 0.35219f,
|
||||
0.55728f, 0.09206f, 0.18279f, 0.52608f, 0.43298f, 0.57281f, 0.64957f,
|
||||
0.67697f, 0.79076f, 0.25769f, 0.17322f, 0.45144f, 0.50649f, 0.44384f,
|
||||
0.45046f, 0.52827f, 0.65169f, 0.26233f, 0.33391f, 0.54569f, 0.61824f,
|
||||
0.71162f, 0.72201f, 0.59606f, 0.69006f, 0.17808f, 0.53409f, 0.84795f,
|
||||
0.81671f, 0.72767f, 0.70439f, 0.49824f, 0.77586f, 0.28972f, 0.41066f,
|
||||
0.78739f, 0.74518f, 0.69849f, 0.72851f, 0.58154f, 0.59843f, 0.0988f,
|
||||
0.12992f, 0.69539f, 0.58411f, 0.53047f, 0.67763f, 0.45745f, 0.42961f,
|
||||
0.02356f, 0.0f, 0.1524f, 0.17941f, 0.20621f, 0.07853f, 0.0f,
|
||||
0.01425f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
|
||||
0.0f, 0.0f, 0.0f, 0.0f, 0.53197f, 0.23141f, 0.65858f,
|
||||
0.51061f, 1.18983f, 1.88715f, 0.0f, 0.0f, 0.48249f, 0.27706f,
|
||||
0.4758f, 0.37868f, 0.19115f, 1.3417f, 0.0f, 0.0f, 0.79729f,
|
||||
0.40467f, 0.75802f, 1.25205f, 1.05397f, 0.99662f, 0.0f, 0.05866f,
|
||||
1.25683f, 1.37623f, 1.3692f, 0.8155f, 0.79031f, 0.79231f, 0.0f,
|
||||
0.66813f, 1.55738f, 0.86795f, 1.74891f, 1.46206f, 0.44267f, 0.71223f,
|
||||
0.0f, 0.01532f, 0.9517f, 0.9068f, 0.04987f, 0.68475f, 0.60834f,
|
||||
0.5695f, 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, 0.13772f, 0.0f, 0.0f, 0.54903f, 0.17714f,
|
||||
0.56106f, 0.37474f, 0.59682f, 0.80188f, 0.23357f, 0.0f, 0.3935f,
|
||||
0.10723f, 0.21271f, 0.2933f, 0.40208f, 0.98239f, 0.19075f, 0.06934f,
|
||||
0.69707f, 0.59654f, 0.72836f, 0.94042f, 0.29819f, 0.65969f, 0.15544f,
|
||||
0.21691f, 0.94429f, 0.74025f, 0.57482f, 0.85235f, 0.6364f, 0.64997f,
|
||||
0.43117f, 0.23959f, 0.86925f, 0.74496f, 1.18404f, 0.91728f, 0.66074f,
|
||||
0.14145f, 0.0f, 0.0f, 0.82383f, 0.54479f, 0.37769f, 0.37376f,
|
||||
0.18698f, 0.41482f, 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, 0.19054f, 0.0f, 0.0f, 0.13366f,
|
||||
0.02072f, 0.17679f, 0.21344f, 0.22093f, 0.39159f, 0.0f, 0.0f,
|
||||
0.21636f, 0.1152f, 0.05384f, 0.17127f, 0.31197f, 0.26403f, 0.0f,
|
||||
0.0f, 0.2079f, 0.40094f, 0.25855f, 0.2949f, 0.21378f, 0.29504f,
|
||||
0.0f, 0.0f, 0.55198f, 0.28422f, 0.44235f, 0.39818f, 0.24589f,
|
||||
0.24885f, 0.0f, 0.0f, 0.39978f, 0.49578f, 0.31662f, 0.57204f,
|
||||
0.22104f, 0.09188f, 0.0f, 0.0f, 0.30446f, 0.11957f, 0.18297f,
|
||||
0.21063f, 0.11165f, 0.1131f, 0.0f, 0.0f, 0.0f, 0.0f,
|
||||
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.04903f,
|
||||
0.0f, 0.21626f, 0.35491f, 0.86898f, 0.9025f, 0.0f, 0.36255f,
|
||||
1.46154f, 1.38429f, 1.44938f, 1.41407f, 1.45809f, 1.77706f, 0.88361f,
|
||||
0.09394f, 0.92029f, 1.01541f, 1.09078f, 1.05394f, 1.25418f, 1.40895f,
|
||||
0.78881f, 0.62721f, 1.55362f, 1.70365f, 1.83765f, 1.7833f, 1.52613f,
|
||||
1.39727f, 0.44845f, 0.80839f, 1.73151f, 1.63702f, 1.60352f, 1.63081f,
|
||||
1.5767f, 1.99697f, 0.91883f, 0.62179f, 1.8053f, 1.63263f, 1.72401f,
|
||||
2.45383f, 1.25455f, 1.07616f, 0.38183f, 0.56256f, 1.8342f, 1.49708f,
|
||||
1.54651f, 0.90693f, 0.85377f, 0.9732f, 0.0f, 0.0f, 0.42826f,
|
||||
0.47554f, 0.23275f, 0.5115f, 0.14327f, 0.23193f, 0.0f
|
||||
};
|
||||
|
||||
runTest();
|
||||
};
|
||||
Reference in New Issue
Block a user