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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,119 @@ | ||
| #pragma once | ||
| #include "Tensor.h" | ||
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| class Layer | ||
| { | ||
| public: | ||
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| Layer() | ||
| {} | ||
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| Layer(Layer &layer) | ||
| { | ||
| *this = layer; | ||
| } | ||
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| virtual Tensor calculate(Tensor output, Tensor input); | ||
| virtual void initWeight(); | ||
| ~Layer() {} | ||
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| public: | ||
| Tensor weight; | ||
| string name; | ||
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| }; | ||
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| class ConvolutionLayer : public Layer { | ||
| public: | ||
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| ConvolutionLayer(ConvolutionLayer &layer) | ||
| { | ||
| *this = layer; | ||
| } | ||
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| ConvolutionLayer(int C, int K, int KH, int KW, int Stride, bool bias = false) | ||
| : C(C), K(K), KH(KH), KW(KW), stride(Stride) | ||
| { | ||
| name = "Convolution Layer"; | ||
| initWeight(); | ||
| } | ||
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| Tensor calculate(Tensor output, Tensor input) | ||
| { | ||
| // 1. weight tensor | ||
| float* weight_data = weight.getData(); | ||
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| // 2. input tensor | ||
| float* input_data = input.getData(); | ||
| int IN = input.getShape()[0]; | ||
| int IC = input.getShape()[1]; | ||
| int IH = input.getShape()[2]; | ||
| int IW = input.getShape()[3]; | ||
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| // 3. output tenosr 재설정 | ||
| int OH = ((IH - KH) / stride) + 1; | ||
| int OW = ((IW - KW) / stride) + 1; | ||
| output.reConstruct(IN, K, OH, OW); | ||
| float* output_data = output.getData(); | ||
|
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| // 4. 연산 수행 | ||
| std::cout << "===== Convolution ===== \n" << std::endl; | ||
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| int C_offset_i, C_offset_o, C_offset_k, H_offset_i, H_offset_o, H_offset_k, W_offset_i, W_offset_o, W_offset_k, g_idx_i, g_idx_o, g_idx_k; | ||
| int N_offset_i = IC * IH * IW; | ||
| int N_offset_o = K * OH * OW; | ||
| int N_offset_k = IC * KH * KW; | ||
| for (int n_idx = 0; n_idx < IN; n_idx++) { | ||
| C_offset_i = n_idx * N_offset_i; | ||
| C_offset_o = n_idx * N_offset_o; | ||
| for (int k_idx = 0; k_idx < K; k_idx++) { | ||
| C_offset_k = k_idx * N_offset_k; | ||
| H_offset_o = k_idx * OH * OW + C_offset_o; | ||
| for (int c_idx = 0; c_idx < IC; c_idx++) { | ||
| H_offset_i = c_idx * IH * IW + C_offset_i; | ||
| H_offset_k = c_idx * KH * KW + C_offset_k; | ||
| for (int rowStride = 0; rowStride < OH; rowStride++) { | ||
| W_offset_o = rowStride * OW + H_offset_o; | ||
| for (int colStride = 0; colStride < OW; colStride++) { | ||
| float sum = 0; | ||
| g_idx_o = colStride + W_offset_o; | ||
| for (int y = rowStride * stride; y < rowStride * stride + KH; y++) { | ||
| W_offset_i = y * IW + H_offset_i; | ||
| W_offset_k = (y - rowStride * stride) * KH + H_offset_k; | ||
| for (int x = colStride * stride; x < colStride * stride + KW; x++) { | ||
| g_idx_i = x + W_offset_i; | ||
| g_idx_k = (x - colStride * stride) + W_offset_k; | ||
| sum += input_data[g_idx_i] * weight_data[g_idx_k]; | ||
| } | ||
| } | ||
| output_data[g_idx_o] += sum; | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| // kaiming HE Uniform initialization | ||
| void initWeight() | ||
| { | ||
| vector<int> weightShape{K, C, KH, KW}; | ||
| weight.setShape(weightShape); | ||
| float min = -sqrt(5); | ||
| float max = sqrt(5); | ||
| mt19937 random((random_device()())); | ||
| uniform_real_distribution<float> dist(min, max); | ||
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| std::cout << "===== Init Weight (kaiming HE Uniform) =====" << std::endl; | ||
| for (int i = 0; i < weight.getTotSize(); i++) { | ||
| weight.getData()[i] = dist(random); | ||
| } | ||
| } | ||
|
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| public: | ||
| int KH; | ||
| int KW; | ||
| int stride; | ||
| int K; | ||
| int C; | ||
| }; |
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