721 lines
34 KiB
Plaintext
721 lines
34 KiB
Plaintext
#include "uNet.h"
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#include "util.h"
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#include "tensor.h"
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#include <stdlib.h>
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#include <cstdint>
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#include <cstdlib>
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#include <cstdio>
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#include <cmath>
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// Parameters
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Tensor *inc_double_conv_0_weight;
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Tensor *inc_double_conv_1_weight;
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Tensor *inc_double_conv_1_bias;
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Tensor *inc_double_conv_3_weight;
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Tensor *inc_double_conv_4_weight;
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Tensor *inc_double_conv_4_bias;
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Tensor *down1_maxpool_conv_1_double_conv_0_weight;
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Tensor *down1_maxpool_conv_1_double_conv_1_weight;
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Tensor *down1_maxpool_conv_1_double_conv_1_bias;
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Tensor *down1_maxpool_conv_1_double_conv_3_weight;
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Tensor *down1_maxpool_conv_1_double_conv_4_weight;
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Tensor *down1_maxpool_conv_1_double_conv_4_bias;
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Tensor *down2_maxpool_conv_1_double_conv_0_weight;
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Tensor *down2_maxpool_conv_1_double_conv_1_weight;
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Tensor *down2_maxpool_conv_1_double_conv_1_bias;
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Tensor *down2_maxpool_conv_1_double_conv_3_weight;
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Tensor *down2_maxpool_conv_1_double_conv_4_weight;
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Tensor *down2_maxpool_conv_1_double_conv_4_bias;
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Tensor *down3_maxpool_conv_1_double_conv_0_weight;
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Tensor *down3_maxpool_conv_1_double_conv_1_weight;
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Tensor *down3_maxpool_conv_1_double_conv_1_bias;
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Tensor *down3_maxpool_conv_1_double_conv_3_weight;
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Tensor *down3_maxpool_conv_1_double_conv_4_weight;
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Tensor *down3_maxpool_conv_1_double_conv_4_bias;
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Tensor *down4_maxpool_conv_1_double_conv_0_weight;
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Tensor *down4_maxpool_conv_1_double_conv_1_weight;
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Tensor *down4_maxpool_conv_1_double_conv_1_bias;
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Tensor *down4_maxpool_conv_1_double_conv_3_weight;
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Tensor *down4_maxpool_conv_1_double_conv_4_weight;
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Tensor *down4_maxpool_conv_1_double_conv_4_bias;
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Tensor *up1_up_weight;
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Tensor *up1_up_bias;
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Tensor *up1_conv_double_conv_0_weight;
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Tensor *up1_conv_double_conv_1_weight;
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Tensor *up1_conv_double_conv_1_bias ;
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Tensor *up1_conv_double_conv_3_weight;
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Tensor *up1_conv_double_conv_4_weight;
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Tensor *up1_conv_double_conv_4_bias ;
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Tensor *up2_up_weight;
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Tensor *up2_up_bias;
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Tensor *up2_conv_double_conv_0_weight;
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Tensor *up2_conv_double_conv_1_weight;
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Tensor *up2_conv_double_conv_1_bias;
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Tensor *up2_conv_double_conv_3_weight;
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Tensor *up2_conv_double_conv_4_weight;
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Tensor *up2_conv_double_conv_4_bias;
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Tensor *up3_up_weight;
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Tensor *up3_up_bias;
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Tensor *up3_conv_double_conv_0_weight;
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Tensor *up3_conv_double_conv_1_weight;
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Tensor *up3_conv_double_conv_1_bias;
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Tensor *up3_conv_double_conv_3_weight;
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Tensor *up3_conv_double_conv_4_weight;
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Tensor *up3_conv_double_conv_4_bias;
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Tensor *up4_up_weight;
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Tensor *up4_up_bias;
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Tensor *up4_conv_double_conv_0_weight;
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Tensor *up4_conv_double_conv_1_weight;
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Tensor *up4_conv_double_conv_1_bias;
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Tensor *up4_conv_double_conv_3_weight;
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Tensor *up4_conv_double_conv_4_weight;
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Tensor *up4_conv_double_conv_4_bias;
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Tensor *outc_conv_weight;
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Tensor *outc_conv_bias;
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Tensor *inc_batchnorm_0_running_mean;
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Tensor *inc_batchnorm_0_running_var;
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Tensor *down1_batchnorm_0_running_mean;
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Tensor *down1_batchnorm_0_running_var;
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Tensor *down2_batchnorm_0_running_mean;
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Tensor *down2_batchnorm_0_running_var;
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Tensor *down3_batchnorm_0_running_mean;
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Tensor *down3_batchnorm_0_running_var;
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Tensor *down4_batchnorm_0_running_mean;
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Tensor *down4_batchnorm_0_running_var;
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Tensor *up1_batchnorm_0_running_mean;
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Tensor *up1_batchnorm_0_running_var;
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Tensor *up2_batchnorm_0_running_mean;
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Tensor *up2_batchnorm_0_running_var;
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Tensor *up3_batchnorm_0_running_mean;
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Tensor *up3_batchnorm_0_running_var;
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Tensor *up4_batchnorm_0_running_mean;
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Tensor *up4_batchnorm_0_running_var;
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Tensor *inc_batchnorm_1_running_mean;
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Tensor *inc_batchnorm_1_running_var;
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Tensor *down1_batchnorm_1_running_mean;
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Tensor *down1_batchnorm_1_running_var;
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Tensor *down2_batchnorm_1_running_mean;
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Tensor *down2_batchnorm_1_running_var;
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Tensor *down3_batchnorm_1_running_mean;
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Tensor *down3_batchnorm_1_running_var;
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Tensor *down4_batchnorm_1_running_mean;
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Tensor *down4_batchnorm_1_running_var;
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Tensor *up1_batchnorm_1_running_mean;
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Tensor *up1_batchnorm_1_running_var;
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Tensor *up2_batchnorm_1_running_mean;
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Tensor *up2_batchnorm_1_running_var;
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Tensor *up3_batchnorm_1_running_mean;
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Tensor *up3_batchnorm_1_running_var;
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Tensor *up4_batchnorm_1_running_mean;
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Tensor *up4_batchnorm_1_running_var;
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// Activations
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Tensor *inc_conv_0_output = new Tensor({1, 64, 640, 959});
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Tensor *inc_batchnorm_0_output = new Tensor({1, 64, 640, 959});
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Tensor *inc_conv_1_output = new Tensor({1, 64, 640, 959});
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Tensor *inc_batchnorm_1_output = new Tensor({1, 64, 640, 959});
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Tensor *down1_maxpool2d_0_output = new Tensor({1, 64, 320, 479});
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Tensor *down1_conv_0_output = new Tensor({1, 128, 320, 479});
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Tensor *down1_batchnorm_0_output = new Tensor({1, 128, 320, 479});
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Tensor *down1_conv_1_output = new Tensor({1, 128, 320, 479});
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Tensor *down1_batchnorm_1_output = new Tensor({1, 128, 320, 479});
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Tensor *down2_maxpool2d_0_output = new Tensor({1, 128, 160, 239});
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Tensor *down2_conv_0_output = new Tensor({1, 256, 160, 239});
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Tensor *down2_batchnorm_0_output = new Tensor({1, 256, 160, 239});
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Tensor *down2_conv_1_output = new Tensor({1, 256, 160, 239});
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Tensor *down2_batchnorm_1_output = new Tensor({1, 256, 160, 239});
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Tensor *down3_maxpool2d_0_output = new Tensor({1, 256, 80, 119});
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Tensor *down3_conv_0_output = new Tensor({1, 512, 80, 119});
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Tensor *down3_batchnorm_0_output = new Tensor({1, 512, 80, 119});
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Tensor *down3_conv_1_output = new Tensor({1, 512, 80, 119});
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Tensor *down3_batchnorm_1_output = new Tensor({1, 512, 80, 119});
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Tensor *down4_maxpool2d_0_output = new Tensor({1, 512, 40, 59});
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Tensor *down4_conv_0_output = new Tensor({1, 1024, 40, 59});
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Tensor *down4_batchnorm_0_output = new Tensor({1, 1024, 40, 59});
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Tensor *down4_conv_1_output = new Tensor({1, 1024, 40, 59});
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Tensor *down4_batchnorm_1_output = new Tensor({1, 1024, 40, 59});
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Tensor *up1_convt_0_output = new Tensor({1, 512, 80, 118});
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Tensor *up1_concat_0_output = new Tensor({1, 1024, 80, 119});
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Tensor *up1_conv_0_output = new Tensor({1, 512, 80, 119});
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Tensor *up1_batchnorm_0_output = new Tensor({1, 512, 80, 119});
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Tensor *up1_conv_1_output = new Tensor({1, 512, 80, 119});
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Tensor *up1_batchnorm_1_output = new Tensor({1, 512, 80, 119});
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Tensor *up2_convt_0_output = new Tensor({1, 256, 160, 238});
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Tensor *up2_concat_0_output = new Tensor({1, 512, 160, 239});
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Tensor *up2_conv_0_output = new Tensor({1, 256, 160, 239});
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Tensor *up2_batchnorm_0_output = new Tensor({1, 256, 160, 239});
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Tensor *up2_conv_1_output = new Tensor({1, 256, 160, 239});
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Tensor *up2_batchnorm_1_output = new Tensor({1, 256, 160, 239});
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Tensor *up3_convt_0_output = new Tensor({1, 128, 320, 478});
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Tensor *up3_concat_0_output = new Tensor({1, 256, 320, 479});
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Tensor *up3_conv_0_output = new Tensor({1, 128, 320, 479});
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Tensor *up3_batchnorm_0_output = new Tensor({1, 128, 320, 479});
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Tensor *up3_conv_1_output = new Tensor({1, 128, 320, 479});
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Tensor *up3_batchnorm_1_output = new Tensor({1, 128, 320, 479});
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Tensor *up4_convt_0_output = new Tensor({1, 64, 640, 958});
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Tensor *up4_concat_0_output = new Tensor({1, 128, 640, 959});
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Tensor *up4_conv_0_output = new Tensor({1, 64, 640, 959});
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Tensor *up4_batchnorm_0_output = new Tensor({1, 64, 640, 959});
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Tensor *up4_conv_1_output = new Tensor({1, 64, 640, 959});
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Tensor *up4_batchnorm_1_output = new Tensor({1, 64, 640, 959});
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Tensor *outc_conv_0_output = new Tensor({1, 2, 640, 959});
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// forward declaration, prototype
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void Conv2d(Tensor *input, Tensor *weight, Tensor *bias, Tensor *output, int stride, int pad, int dilation, bool has_bias);
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void ReLU(Tensor *inout);
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void BatchNorm2d(Tensor *input, Tensor *gamma, Tensor *beta, Tensor *running_mean, Tensor *running_var, Tensor *output, const float eps, const float momentum);
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void ConvTranspose2d(Tensor *input, Tensor *weight, Tensor *bias, Tensor *output, int stride, int pad);
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void Softmax(Tensor *input, Tensor *output);
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void MaxPool2d(Tensor *input, Tensor *output);
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void Concat(Tensor *input1, Tensor *input2, Tensor *output); // TODO
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void uNet_initialize(int, int, char*);
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void uNet(Tensor*, Tensor*);
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void uNet_finalize();
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/*
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* uNet
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*/
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void uNet(Tensor *input, Tensor *output) {
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// inc(n_channels, 64)
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Conv2d(input, inc_double_conv_0_weight, NULL, inc_conv_0_output, 1, 1, 1, false);
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BatchNorm2d(inc_conv_0_output, inc_double_conv_1_weight, inc_double_conv_1_bias, inc_batchnorm_0_running_mean, inc_batchnorm_0_running_var, inc_batchnorm_0_output, 1e-5, 0.1);
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ReLU(inc_batchnorm_0_output);
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Conv2d(inc_batchnorm_0_output, inc_double_conv_3_weight, NULL, inc_conv_1_output, 1, 1, 1, false);
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BatchNorm2d(inc_conv_1_output, inc_double_conv_4_weight, inc_double_conv_4_bias, inc_batchnorm_1_running_mean, inc_batchnorm_1_running_var, inc_batchnorm_1_output, 1e-5, 0.1);
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ReLU(inc_batchnorm_1_output);
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// down1(64, 128)
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MaxPool2d(inc_batchnorm_1_output, down1_maxpool2d_0_output);
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Conv2d(down1_maxpool2d_0_output, down1_maxpool_conv_1_double_conv_0_weight, NULL, down1_conv_0_output, 1, 1, 1, false);
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BatchNorm2d(down1_conv_0_output, down1_maxpool_conv_1_double_conv_1_weight, down1_maxpool_conv_1_double_conv_1_bias, down1_batchnorm_0_running_mean, down1_batchnorm_0_running_var, down1_batchnorm_0_output, 1e-5, 0.1);
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ReLU(down1_batchnorm_0_output);
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Conv2d(down1_batchnorm_0_output, down1_maxpool_conv_1_double_conv_3_weight, NULL, down1_conv_1_output, 1, 1, 1, false);
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BatchNorm2d(down1_conv_1_output, down1_maxpool_conv_1_double_conv_4_weight, down1_maxpool_conv_1_double_conv_4_bias, down1_batchnorm_1_running_mean, down1_batchnorm_1_running_var, down1_batchnorm_1_output, 1e-5, 0.1);
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ReLU(down1_batchnorm_1_output);
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// down2(128, 256)
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MaxPool2d(down1_batchnorm_1_output, down2_maxpool2d_0_output);
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Conv2d(down2_maxpool2d_0_output, down2_maxpool_conv_1_double_conv_0_weight, NULL, down2_conv_0_output, 1, 1, 1, false);
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BatchNorm2d(down2_conv_0_output, down2_maxpool_conv_1_double_conv_1_weight, down2_maxpool_conv_1_double_conv_1_bias, down2_batchnorm_0_running_mean, down2_batchnorm_0_running_var, down2_batchnorm_0_output, 1e-5, 0.1);
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ReLU(down2_batchnorm_0_output);
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Conv2d(down2_batchnorm_0_output, down2_maxpool_conv_1_double_conv_3_weight, NULL, down2_conv_1_output, 1, 1, 1, false);
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BatchNorm2d(down2_conv_1_output, down2_maxpool_conv_1_double_conv_4_weight, down2_maxpool_conv_1_double_conv_4_bias, down2_batchnorm_1_running_mean, down2_batchnorm_1_running_var, down2_batchnorm_1_output, 1e-5, 0.1);
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ReLU(down2_batchnorm_1_output);
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// down3(256, 512)
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MaxPool2d(down2_batchnorm_1_output, down3_maxpool2d_0_output);
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Conv2d(down3_maxpool2d_0_output, down3_maxpool_conv_1_double_conv_0_weight, NULL, down3_conv_0_output, 1, 1, 1, false);
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BatchNorm2d(down3_conv_0_output, down3_maxpool_conv_1_double_conv_1_weight, down3_maxpool_conv_1_double_conv_1_bias, down3_batchnorm_0_running_mean, down3_batchnorm_0_running_var, down3_batchnorm_0_output, 1e-5, 0.1);
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ReLU(down3_batchnorm_0_output);
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Conv2d(down3_batchnorm_0_output, down3_maxpool_conv_1_double_conv_3_weight, NULL, down3_conv_1_output, 1, 1, 1, false);
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BatchNorm2d(down3_conv_1_output, down3_maxpool_conv_1_double_conv_4_weight, down3_maxpool_conv_1_double_conv_4_bias, down3_batchnorm_1_running_mean, down3_batchnorm_1_running_var, down3_batchnorm_1_output, 1e-5, 0.1);
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ReLU(down3_batchnorm_1_output);
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// down4(512, 1024)
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MaxPool2d(down3_batchnorm_1_output, down4_maxpool2d_0_output);
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Conv2d(down4_maxpool2d_0_output, down4_maxpool_conv_1_double_conv_0_weight, NULL, down4_conv_0_output, 1, 1, 1, false);
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BatchNorm2d(down4_conv_0_output, down4_maxpool_conv_1_double_conv_1_weight, down4_maxpool_conv_1_double_conv_1_bias, down4_batchnorm_0_running_mean, down4_batchnorm_0_running_var, down4_batchnorm_0_output, 1e-5, 0.1);
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ReLU(down4_batchnorm_0_output);
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Conv2d(down4_batchnorm_0_output, down4_maxpool_conv_1_double_conv_3_weight, NULL, down4_conv_1_output, 1, 1, 1, false);
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BatchNorm2d(down4_conv_1_output, down4_maxpool_conv_1_double_conv_4_weight, down4_maxpool_conv_1_double_conv_4_bias, down4_batchnorm_1_running_mean, down4_batchnorm_1_running_var, down4_batchnorm_1_output, 1e-5, 0.1);
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ReLU(down4_batchnorm_1_output);
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// up1(1024, 512), (down4_batchnorm_1_output, down3_batchnorm_1_output)
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ConvTranspose2d(down4_batchnorm_1_output, up1_up_weight, up1_up_bias, up1_convt_0_output, 2, 0);
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Concat(up1_convt_0_output, down3_batchnorm_1_output, up1_concat_0_output);
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Conv2d(up1_concat_0_output, up1_conv_double_conv_0_weight, NULL, up1_conv_0_output, 1, 1, 1, false);
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BatchNorm2d(up1_conv_0_output, up1_conv_double_conv_1_weight, up1_conv_double_conv_1_bias, up1_batchnorm_0_running_mean, up1_batchnorm_0_running_var, up1_batchnorm_0_output, 1e-5, 0.1);
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ReLU(up1_batchnorm_0_output);
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Conv2d(up1_batchnorm_0_output, up1_conv_double_conv_3_weight, NULL, up1_conv_1_output, 1, 1, 1, false);
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BatchNorm2d(up1_conv_1_output, up1_conv_double_conv_4_weight, up1_conv_double_conv_4_bias, up1_batchnorm_1_running_mean, up1_batchnorm_1_running_var, up1_batchnorm_1_output, 1e-5, 0.1);
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ReLU(up1_batchnorm_1_output);
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// up2(512, 256), (up1_concat_0_output, down2_batchnorm_1_output)
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ConvTranspose2d(up1_batchnorm_1_output, up2_up_weight, up2_up_bias, up2_convt_0_output, 2, 0);
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Concat(up2_convt_0_output, down2_batchnorm_1_output, up2_concat_0_output);
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Conv2d(up2_concat_0_output, up2_conv_double_conv_0_weight, NULL, up2_conv_0_output, 1, 1, 1, false);
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BatchNorm2d(up2_conv_0_output, up2_conv_double_conv_1_weight, up2_conv_double_conv_1_bias, up2_batchnorm_0_running_mean, up2_batchnorm_0_running_var, up2_batchnorm_0_output, 1e-5, 0.1);
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ReLU(up2_batchnorm_0_output);
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Conv2d(up2_batchnorm_0_output, up2_conv_double_conv_3_weight, NULL, up2_conv_1_output, 1, 1, 1, false);
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BatchNorm2d(up2_conv_1_output, up2_conv_double_conv_4_weight, up2_conv_double_conv_4_bias, up2_batchnorm_1_running_mean, up2_batchnorm_1_running_var, up2_batchnorm_1_output, 1e-5, 0.1);
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ReLU(up2_batchnorm_1_output);
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// up3(256, 128), (up2_concat_0_output, down1_batchnorm_1_output)
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ConvTranspose2d(up2_batchnorm_1_output, up3_up_weight, up3_up_bias, up3_convt_0_output, 2, 0);
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Concat(up3_convt_0_output, down1_batchnorm_1_output, up3_concat_0_output);
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Conv2d(up3_concat_0_output, up3_conv_double_conv_0_weight, NULL, up3_conv_0_output, 1, 1, 1, false);
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BatchNorm2d(up3_conv_0_output, up3_conv_double_conv_1_weight, up3_conv_double_conv_1_bias, up3_batchnorm_0_running_mean, up3_batchnorm_0_running_var, up3_batchnorm_0_output, 1e-5, 0.1);
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ReLU(up3_batchnorm_0_output);
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Conv2d(up3_batchnorm_0_output, up3_conv_double_conv_3_weight, NULL, up3_conv_1_output, 1, 1, 1, false);
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BatchNorm2d(up3_conv_1_output, up3_conv_double_conv_4_weight, up3_conv_double_conv_4_bias, up3_batchnorm_1_running_mean, up3_batchnorm_1_running_var, up3_batchnorm_1_output, 1e-5, 0.1);
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ReLU(up3_batchnorm_1_output);
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// up4(128, 64), (up3_concat_0_output, inc_batchnorm_1_output)
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ConvTranspose2d(up3_batchnorm_1_output, up4_up_weight, up4_up_bias, up4_convt_0_output, 2, 0);
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Concat(up4_convt_0_output, inc_batchnorm_1_output, up4_concat_0_output);
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Conv2d(up4_concat_0_output, up4_conv_double_conv_0_weight, NULL, up4_conv_0_output, 1, 1, 1, false);
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BatchNorm2d(up4_conv_0_output, up4_conv_double_conv_1_weight, up4_conv_double_conv_1_bias, up4_batchnorm_0_running_mean, up4_batchnorm_0_running_var, up4_batchnorm_0_output, 1e-5, 0.1);
|
|
ReLU(up4_batchnorm_0_output);
|
|
Conv2d(up4_batchnorm_0_output, up4_conv_double_conv_3_weight, NULL, up4_conv_1_output, 1, 1, 1, false);
|
|
BatchNorm2d(up4_conv_1_output, up4_conv_double_conv_4_weight, up4_conv_double_conv_4_bias, up4_batchnorm_1_running_mean, up4_batchnorm_1_running_var, up4_batchnorm_1_output, 1e-5, 0.1);
|
|
ReLU(up4_batchnorm_1_output);
|
|
|
|
/*
|
|
// outc(64, n_classes)
|
|
Conv2d(up4_concat_0_output, outc_conv_weight, outc_conv_bias, output_tensor, 1, 0, 0, true);
|
|
output = output_tensor->buf;
|
|
*/
|
|
}
|
|
|
|
|
|
|
|
/*
|
|
* Convolution
|
|
* input shape = (N, C, H, W)
|
|
* weight shape = (K, C, R, S)
|
|
* bias shape = (K)
|
|
* output shape = (N, K, OH, OW)
|
|
* where OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1,
|
|
* OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1
|
|
*/
|
|
void Conv2d(Tensor *input, Tensor *weight, Tensor *bias, Tensor *output, int stride, int pad, int dilation, bool has_bias) {
|
|
|
|
int C = input->shape[1], H = input->shape[2], W = input->shape[3];
|
|
int K = weight->shape[0], R = weight->shape[2], S = weight->shape[3];
|
|
int OH = output->shape[2], OW = output->shape[3];
|
|
CHECK_ERROR(OH == (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1, "Output height mismatch");
|
|
CHECK_ERROR(OW == (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1, "Output width mismatch");
|
|
CHECK_ERROR(weight->shape[1] == C && (!has_bias || bias->shape[0] == K) && output->shape[1] == K, "Channel size mismatch");
|
|
|
|
#pragma omp parallel for
|
|
for (int k = 0; k < K; ++k) {
|
|
for (int oh = 0; oh < OH; ++oh) {
|
|
for (int ow = 0; ow < OW; ++ow) {
|
|
float o = has_bias ? bias->buf[k] : 0;
|
|
for (int c = 0; c < C; ++c) {
|
|
for (int r = 0; r < R; ++r) {
|
|
for (int s = 0; s < S; ++s) {
|
|
int h = oh * stride - pad + r * dilation;
|
|
int w = ow * stride - pad + s * dilation;
|
|
if (h < 0 || h >= H || w < 0 || w >= W) continue;
|
|
float i = input->buf[c * H * W + h * W + w];
|
|
float f = weight->buf[k * C * R * S + c * R * S + r * S + s];
|
|
o += i * f;
|
|
}
|
|
}
|
|
}
|
|
output->buf[k * OH * OW + oh * OW + ow] = o;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/*
|
|
* ReLU
|
|
* input shape = (N, C, H, W)
|
|
* output shape = (N, C, H, W)
|
|
* Formula: y = max(x, 0)
|
|
*/
|
|
void ReLU(Tensor *inout) {
|
|
int C = inout->shape[1], H = inout->shape[2], W = inout->shape[3];
|
|
for (int c = 0; c < C; ++c) {
|
|
for (int h = 0; h < H; ++h) {
|
|
for (int w = 0; w < W; ++w) {
|
|
int idx = c * H * W + h * W + w;
|
|
inout->buf[idx] = inout->buf[idx] > 0 ? inout->buf[idx] : 0;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Batch Normaliztion
|
|
* input shape = (N, C, H, W)
|
|
* gamma shape = (C)
|
|
* beta shape = (C)
|
|
* output shape = (N, C, H, W)
|
|
*/
|
|
void BatchNorm2d(Tensor *input, Tensor *gamma, Tensor *beta, Tensor *running_mean, Tensor *running_var, Tensor *output, const float eps, const float momentum) {
|
|
int N = input->shape[0], C = input->shape[1], H = input->shape[2], W = input->shape[3];
|
|
CHECK_ERROR(gamma->shape[0] == C && beta->shape[0] == C, "gamma, beta Shape mismatch");
|
|
CHECK_ERROR(output->shape[1] == C && output->shape[2] == H && output->shape[3] == W, "output Shape mismatch");
|
|
|
|
#pragma omp parallel for
|
|
for (int c=0; c<C; ++c){
|
|
for (int n=0; n<N; ++n){
|
|
for (int h=0; h<H; ++h){
|
|
for (int w=0; w<W; ++w){
|
|
float mean = running_mean->buf[c];
|
|
float variance = running_var->buf[c];
|
|
float x = input->buf[n * C * H * W + c * H * W + h * W + w];
|
|
float x_hat = (x - mean) / sqrt(variance + eps);
|
|
output->buf[n * C * H * W + c * H * W + h * W + w] = gamma->buf[c] * x_hat + beta->buf[c];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
|
|
/*
|
|
* Transposed convolution
|
|
* input shape = (N, C, H, W)
|
|
* weight shape = (C, K, R, S)
|
|
* bias shape = (K)
|
|
* output shape = (N, K, OH, OW)
|
|
* where OH = (H - 1) * stride - 2 * pad + R
|
|
* OW = (W - 1) * stride - 2 * pad + S
|
|
*/
|
|
void ConvTranspose2d(Tensor *input, Tensor *weight, Tensor *bias, Tensor *output, int stride, int pad) {
|
|
int C = input->shape[1], H = input->shape[2], W = input->shape[3];
|
|
int K = weight->shape[1], R = weight->shape[2], S = weight->shape[3];
|
|
int OH = output->shape[2], OW = output->shape[3];
|
|
|
|
CHECK_ERROR(OH == (H - 1) * stride - 2 * pad + R, "Output height mismatch");
|
|
CHECK_ERROR(OW == (W - 1) * stride - 2 * pad + S, "Output width mismatch");
|
|
CHECK_ERROR(weight->shape[0] == C && bias->shape[0] == K && output->shape[1] == K, "Channel size mismatch");
|
|
|
|
#pragma omp parallel for
|
|
for (int k = 0; k < K; ++k) {
|
|
for (int oh = 0; oh < OH; ++oh) {
|
|
for (int ow = 0; ow < OW; ++ow) {
|
|
float o = bias->buf[k];
|
|
for (int c = 0; c < C; ++c) {
|
|
for (int r = 0; r < R; ++r) {
|
|
for (int s = 0; s < S; ++s) {
|
|
if ((oh + pad - r) % stride != 0) continue;
|
|
if ((ow + pad - s) % stride != 0) continue;
|
|
int h = (oh + pad - r) / stride;
|
|
int w = (ow + pad - s) / stride;
|
|
if (h < 0 || h >= H || w < 0 || w >= W) continue;
|
|
float i = input->buf[c * H * W + h * W + w];
|
|
float f = weight->buf[c * K * R * S + k * R * S + r * S + s];
|
|
o += i * f;
|
|
}
|
|
}
|
|
}
|
|
output->buf[k * OH * OW + oh * OW + ow] = o;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Softmax
|
|
* input shape = (N, C, H, W)
|
|
* output shape = (N, C, H, W))
|
|
* Formula: y = e^x / sum(e^x)
|
|
*/
|
|
void Softmax(Tensor *input, Tensor *output) {
|
|
int C = input->shape[1], H = input->shape[2], W = input->shape[3];
|
|
CHECK_ERROR(output->shape[1] == C && output->shape[2] == H && output->shape[3] == W, "shape mismatch");
|
|
|
|
for (int h = 0; h < H; ++h) {
|
|
for (int w = 0; w < W; ++w) {
|
|
float sum = 0;
|
|
for (int c = 0; c < C; ++c) {
|
|
sum += expf(input->buf[c * H * W + h * W + w]);
|
|
}
|
|
for (int c = 0; c < C; ++c) {
|
|
output->buf[c * H * W + h * W + w] = expf(input->buf[c * H * W + h * W + w]) / sum;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
float max4(float in0, float in1, float in2, float in3){
|
|
float max = in0;
|
|
if (in1 > max) max = in1;
|
|
if (in2 > max) max = in2;
|
|
if (in3 > max) max = in3;
|
|
return max;
|
|
}
|
|
|
|
void MaxPool2d(Tensor *input, Tensor *output){
|
|
int C = input->shape[1], H = input->shape[2], W = input->shape[3];
|
|
int OC = output->shape[1], OH = output->shape[2], OW = output->shape[3];
|
|
|
|
for (int oc=0; oc<OC; ++oc){
|
|
for (int oh=0; oh<OH; ++oh){
|
|
for (int ow=0; ow<OW; ++ow){
|
|
float in0 = input->buf[oc * H * W + 2 * oh * W + 2 * ow];
|
|
float in1 = input->buf[oc * H * W + 2 * oh * W + 2 * ow + 1];
|
|
float in2 = input->buf[oc * H * W + (2 * oh + 1) * W + 2 * ow];
|
|
float in3 = input->buf[oc * H * W + (2 * oh + 1) * W + 2 * ow + 1];
|
|
// printf("%f %f %f %f\n", in0, in1, in2, in3);
|
|
output->buf[oc * OH * OW + oh * OW + ow] = max4(in0, in1, in2, in3);
|
|
}
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
void Concat(Tensor *input1, Tensor *input2, Tensor *output){
|
|
|
|
int C = input1->shape[1], H = input1->shape[2], W = input1->shape[3];
|
|
int C2 = input2->shape[1], H2 = input2->shape[2], W2 = input2->shape[3];
|
|
int OC = output->shape[1], OH = output->shape[2], OW = output->shape[3];
|
|
|
|
//printf("C %d, H %d, W %d, C2 %d, H2 %d, W2 %d, OC %d, OH %d, OW %d\n", C, H, W, C2, H2, W2, OC, OH, OW);
|
|
for (int oc=0; oc<OC/2; ++oc){
|
|
for (int oh=0; oh<OH; ++oh){
|
|
for (int ow=0; ow<OW; ++ow){
|
|
output->buf[oc * OH * OW + oh * OW + ow] = input2->buf[oc * OH * OW + oh * OW + ow];
|
|
}
|
|
}
|
|
}
|
|
|
|
for (int oc=OC/2; oc<OC; ++oc){
|
|
for (int oh=0; oh<OH; ++oh){
|
|
for (int ow=0; ow<OW; ++ow){
|
|
if (ow == OW-1) {
|
|
output->buf[oc * OH * OW + oh * OW + ow] = 0.0;
|
|
//printf("padding!\n");
|
|
}
|
|
else {
|
|
output->buf[oc * OH * OW + oh * OW + ow] = input1->buf[(oc-OC/2) * H * W + oh * W + ow];
|
|
//printf("[%d] %f\n", (oc-OC/2) * H * W + oh * W + ow, input1->buf[(oc-OC/2) * H * W + oh * W + ow]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
|
|
/*
|
|
* uNet_initialize
|
|
* Initialize the model. Do input-independent job here.
|
|
*/
|
|
void uNet_initialize(int N, int random_seed, char *parameter_fname) {
|
|
|
|
size_t parameter_binary_size = 0;
|
|
float *parameter = (float *)read_binary(parameter_fname, ¶meter_binary_size);
|
|
|
|
inc_double_conv_0_weight = new Tensor({64,3,3,3}, parameter + OFFSET0);
|
|
inc_double_conv_1_weight = new Tensor({64}, parameter + OFFSET1);
|
|
inc_double_conv_1_bias = new Tensor({64}, parameter + OFFSET2);
|
|
inc_double_conv_3_weight = new Tensor({64,64,3,3}, parameter + OFFSET3);
|
|
inc_double_conv_4_weight = new Tensor({64}, parameter + OFFSET4);
|
|
inc_double_conv_4_bias = new Tensor({64}, parameter + OFFSET5);
|
|
|
|
down1_maxpool_conv_1_double_conv_0_weight = new Tensor({128,64,3,3}, parameter + OFFSET6);
|
|
down1_maxpool_conv_1_double_conv_1_weight = new Tensor({128}, parameter + OFFSET7);
|
|
down1_maxpool_conv_1_double_conv_1_bias = new Tensor({128}, parameter + OFFSET8);
|
|
down1_maxpool_conv_1_double_conv_3_weight = new Tensor({128,128,3,3}, parameter + OFFSET9);
|
|
down1_maxpool_conv_1_double_conv_4_weight = new Tensor({128}, parameter + OFFSET10);
|
|
down1_maxpool_conv_1_double_conv_4_bias = new Tensor({128}, parameter + OFFSET11);
|
|
down2_maxpool_conv_1_double_conv_0_weight = new Tensor({256,128,3,3}, parameter + OFFSET12);
|
|
down2_maxpool_conv_1_double_conv_1_weight = new Tensor({256}, parameter + OFFSET13);
|
|
down2_maxpool_conv_1_double_conv_1_bias = new Tensor({256}, parameter + OFFSET14);
|
|
down2_maxpool_conv_1_double_conv_3_weight = new Tensor({256,256,3,3}, parameter + OFFSET15);
|
|
down2_maxpool_conv_1_double_conv_4_weight = new Tensor({256}, parameter + OFFSET16);
|
|
down2_maxpool_conv_1_double_conv_4_bias = new Tensor({256}, parameter + OFFSET17);
|
|
down3_maxpool_conv_1_double_conv_0_weight = new Tensor({512,256,3,3}, parameter + OFFSET18);
|
|
down3_maxpool_conv_1_double_conv_1_weight = new Tensor({512}, parameter + OFFSET19);
|
|
down3_maxpool_conv_1_double_conv_1_bias = new Tensor({512}, parameter + OFFSET20);
|
|
down3_maxpool_conv_1_double_conv_3_weight = new Tensor({512,512,3,3}, parameter + OFFSET21);
|
|
down3_maxpool_conv_1_double_conv_4_weight = new Tensor({512}, parameter + OFFSET22);
|
|
down3_maxpool_conv_1_double_conv_4_bias = new Tensor({512}, parameter + OFFSET23);
|
|
down4_maxpool_conv_1_double_conv_0_weight = new Tensor({1024,512,3,3}, parameter + OFFSET24);
|
|
down4_maxpool_conv_1_double_conv_1_weight = new Tensor({1024}, parameter + OFFSET25);
|
|
down4_maxpool_conv_1_double_conv_1_bias = new Tensor({1024}, parameter + OFFSET26);
|
|
down4_maxpool_conv_1_double_conv_3_weight = new Tensor({1024,1024,3,3}, parameter + OFFSET27);
|
|
down4_maxpool_conv_1_double_conv_4_weight = new Tensor({1024}, parameter + OFFSET28);
|
|
down4_maxpool_conv_1_double_conv_4_bias = new Tensor({1024}, parameter + OFFSET29);
|
|
|
|
up1_up_weight = new Tensor({1024,512,2,2}, parameter + OFFSET30);
|
|
up1_up_bias = new Tensor({512}, parameter + OFFSET31);
|
|
up1_conv_double_conv_0_weight = new Tensor({512,1024,3,3}, parameter + OFFSET32);
|
|
up1_conv_double_conv_1_weight = new Tensor({512}, parameter + OFFSET33);
|
|
up1_conv_double_conv_1_bias = new Tensor({512}, parameter + OFFSET34);
|
|
up1_conv_double_conv_3_weight = new Tensor({512,512,3,3}, parameter + OFFSET35);
|
|
up1_conv_double_conv_4_weight = new Tensor({512}, parameter + OFFSET36);
|
|
up1_conv_double_conv_4_bias = new Tensor({512}, parameter + OFFSET37);
|
|
up2_up_weight = new Tensor({512,256,2,2}, parameter + OFFSET38);
|
|
up2_up_bias = new Tensor({256}, parameter + OFFSET39);
|
|
up2_conv_double_conv_0_weight = new Tensor({256,512,3,3}, parameter + OFFSET40);
|
|
up2_conv_double_conv_1_weight = new Tensor({256}, parameter + OFFSET41);
|
|
up2_conv_double_conv_1_bias = new Tensor({256}, parameter + OFFSET42);
|
|
up2_conv_double_conv_3_weight = new Tensor({256,256,3,3}, parameter + OFFSET43);
|
|
up2_conv_double_conv_4_weight = new Tensor({256}, parameter + OFFSET44);
|
|
up2_conv_double_conv_4_bias = new Tensor({256}, parameter + OFFSET45);
|
|
up3_up_weight = new Tensor({256,128,2,2}, parameter + OFFSET46);
|
|
up3_up_bias = new Tensor({128}, parameter + OFFSET47);
|
|
up3_conv_double_conv_0_weight = new Tensor({128,256,3,3}, parameter + OFFSET48);
|
|
up3_conv_double_conv_1_weight = new Tensor({128}, parameter + OFFSET49);
|
|
up3_conv_double_conv_1_bias = new Tensor({128}, parameter + OFFSET50);
|
|
up3_conv_double_conv_3_weight = new Tensor({128,128,3,3}, parameter + OFFSET51);
|
|
up3_conv_double_conv_4_weight = new Tensor({128}, parameter + OFFSET52);
|
|
up3_conv_double_conv_4_bias = new Tensor({128}, parameter + OFFSET53);
|
|
up4_up_weight = new Tensor({128,64,2,2}, parameter + OFFSET54);
|
|
up4_up_bias = new Tensor({64}, parameter + OFFSET55);
|
|
up4_conv_double_conv_0_weight = new Tensor({64,128,3,3}, parameter + OFFSET56);
|
|
up4_conv_double_conv_1_weight = new Tensor({64}, parameter + OFFSET57);
|
|
up4_conv_double_conv_1_bias = new Tensor({64}, parameter + OFFSET58);
|
|
up4_conv_double_conv_3_weight = new Tensor({64,64,3,3}, parameter + OFFSET59);
|
|
up4_conv_double_conv_4_weight = new Tensor({64}, parameter + OFFSET60);
|
|
up4_conv_double_conv_4_bias = new Tensor({64}, parameter + OFFSET61);
|
|
|
|
outc_conv_weight = new Tensor({2,64,1,1}, parameter + OFFSET62);
|
|
outc_conv_bias = new Tensor({2}, parameter + OFFSET63);
|
|
|
|
inc_batchnorm_0_running_mean = new Tensor({64}, parameter + OFFSET64);
|
|
inc_batchnorm_0_running_var = new Tensor({64}, parameter + OFFSET65);
|
|
inc_batchnorm_1_running_mean = new Tensor({64}, parameter + OFFSET66);
|
|
inc_batchnorm_1_running_var = new Tensor({64}, parameter + OFFSET67);
|
|
|
|
down1_batchnorm_0_running_mean = new Tensor({128}, parameter + OFFSET68);
|
|
down1_batchnorm_0_running_var = new Tensor({128}, parameter + OFFSET69);
|
|
down1_batchnorm_1_running_mean = new Tensor({128}, parameter + OFFSET70);
|
|
down1_batchnorm_1_running_var = new Tensor({128}, parameter + OFFSET71);
|
|
down2_batchnorm_0_running_mean = new Tensor({256}, parameter + OFFSET72);
|
|
down2_batchnorm_0_running_var = new Tensor({256}, parameter + OFFSET73);
|
|
down2_batchnorm_1_running_mean = new Tensor({256}, parameter + OFFSET74);
|
|
down2_batchnorm_1_running_var = new Tensor({256}, parameter + OFFSET75);
|
|
down3_batchnorm_0_running_mean = new Tensor({512}, parameter + OFFSET76);
|
|
down3_batchnorm_0_running_var = new Tensor({512}, parameter + OFFSET77);
|
|
down3_batchnorm_1_running_mean = new Tensor({512}, parameter + OFFSET78);
|
|
down3_batchnorm_1_running_var = new Tensor({512}, parameter + OFFSET79);
|
|
down4_batchnorm_0_running_mean = new Tensor({1024}, parameter + OFFSET80);
|
|
down4_batchnorm_0_running_var = new Tensor({1024}, parameter + OFFSET81);
|
|
down4_batchnorm_1_running_mean = new Tensor({1024}, parameter + OFFSET82);
|
|
down4_batchnorm_1_running_var = new Tensor({1024}, parameter + OFFSET83);
|
|
up1_batchnorm_0_running_mean = new Tensor({512}, parameter + OFFSET84);
|
|
up1_batchnorm_0_running_var = new Tensor({512}, parameter + OFFSET85);
|
|
up1_batchnorm_1_running_mean = new Tensor({512}, parameter + OFFSET86);
|
|
up1_batchnorm_1_running_var = new Tensor({512}, parameter + OFFSET87);
|
|
up2_batchnorm_0_running_mean = new Tensor({256}, parameter + OFFSET88);
|
|
up2_batchnorm_0_running_var = new Tensor({256}, parameter + OFFSET89);
|
|
up2_batchnorm_1_running_mean = new Tensor({256}, parameter + OFFSET90);
|
|
up2_batchnorm_1_running_var = new Tensor({256}, parameter + OFFSET91);
|
|
up3_batchnorm_0_running_mean = new Tensor({128}, parameter + OFFSET92);
|
|
up3_batchnorm_0_running_var = new Tensor({128}, parameter + OFFSET93);
|
|
up3_batchnorm_1_running_mean = new Tensor({128}, parameter + OFFSET94);
|
|
up3_batchnorm_1_running_var = new Tensor({128}, parameter + OFFSET95);
|
|
up4_batchnorm_0_running_mean = new Tensor({64}, parameter + OFFSET96);
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up4_batchnorm_0_running_var = new Tensor({64}, parameter + OFFSET97);
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up4_batchnorm_1_running_mean = new Tensor({64}, parameter + OFFSET98);
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up4_batchnorm_1_running_var = new Tensor({64}, parameter + OFFSET99);
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}
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/*
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* uNet_finalize
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* Finalize the model.
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*/
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void uNet_finalize() {
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delete inc_double_conv_0_weight;
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delete inc_double_conv_1_weight;
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delete inc_double_conv_1_bias;
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delete inc_double_conv_3_weight;
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delete inc_double_conv_4_weight;
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delete inc_double_conv_4_bias;
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delete down1_maxpool_conv_1_double_conv_0_weight;
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delete down1_maxpool_conv_1_double_conv_1_weight;
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delete down1_maxpool_conv_1_double_conv_1_bias;
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delete down1_maxpool_conv_1_double_conv_3_weight;
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|
delete down1_maxpool_conv_1_double_conv_4_weight;
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|
delete down1_maxpool_conv_1_double_conv_4_bias;
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|
delete down2_maxpool_conv_1_double_conv_0_weight;
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|
delete down2_maxpool_conv_1_double_conv_1_weight;
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|
delete down2_maxpool_conv_1_double_conv_1_bias;
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|
delete down2_maxpool_conv_1_double_conv_3_weight;
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|
delete down2_maxpool_conv_1_double_conv_4_weight;
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|
delete down2_maxpool_conv_1_double_conv_4_bias;
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|
delete down3_maxpool_conv_1_double_conv_0_weight;
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|
delete down3_maxpool_conv_1_double_conv_1_weight;
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|
delete down3_maxpool_conv_1_double_conv_1_bias;
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|
delete down3_maxpool_conv_1_double_conv_3_weight;
|
|
delete down3_maxpool_conv_1_double_conv_4_weight;
|
|
delete down3_maxpool_conv_1_double_conv_4_bias;
|
|
delete down4_maxpool_conv_1_double_conv_0_weight;
|
|
delete down4_maxpool_conv_1_double_conv_1_weight;
|
|
delete down4_maxpool_conv_1_double_conv_1_bias;
|
|
delete down4_maxpool_conv_1_double_conv_3_weight;
|
|
delete down4_maxpool_conv_1_double_conv_4_weight;
|
|
delete down4_maxpool_conv_1_double_conv_4_bias;
|
|
delete up1_up_weight;
|
|
delete up1_up_bias;
|
|
delete up1_conv_double_conv_0_weight;
|
|
delete up1_conv_double_conv_1_weight;
|
|
delete up1_conv_double_conv_1_bias ;
|
|
delete up1_conv_double_conv_3_weight;
|
|
delete up1_conv_double_conv_4_weight;
|
|
delete up1_conv_double_conv_4_bias ;
|
|
delete up2_up_weight;
|
|
delete up2_up_bias;
|
|
delete up2_conv_double_conv_0_weight;
|
|
delete up2_conv_double_conv_1_weight;
|
|
delete up2_conv_double_conv_1_bias;
|
|
delete up2_conv_double_conv_3_weight;
|
|
delete up2_conv_double_conv_4_weight;
|
|
delete up2_conv_double_conv_4_bias;
|
|
delete up3_up_weight;
|
|
delete up3_up_bias;
|
|
delete up3_conv_double_conv_0_weight;
|
|
delete up3_conv_double_conv_1_weight;
|
|
delete up3_conv_double_conv_1_bias;
|
|
delete up3_conv_double_conv_3_weight;
|
|
delete up3_conv_double_conv_4_weight;
|
|
delete up3_conv_double_conv_4_bias;
|
|
delete up4_up_weight;
|
|
delete up4_up_bias;
|
|
delete up4_conv_double_conv_0_weight;
|
|
delete up4_conv_double_conv_1_weight;
|
|
delete up4_conv_double_conv_1_bias;
|
|
delete up4_conv_double_conv_3_weight;
|
|
delete up4_conv_double_conv_4_weight;
|
|
delete up4_conv_double_conv_4_bias;
|
|
delete outc_conv_weight;
|
|
delete outc_conv_bias;
|
|
delete inc_conv_0_output;
|
|
delete inc_batchnorm_0_output;
|
|
delete inc_conv_1_output;
|
|
delete inc_batchnorm_1_output;
|
|
delete down1_maxpool2d_0_output;
|
|
delete down1_conv_0_output;
|
|
delete down1_batchnorm_0_output;
|
|
delete down1_conv_1_output;
|
|
delete down1_batchnorm_1_output;
|
|
delete down2_maxpool2d_0_output;
|
|
delete down2_conv_0_output;
|
|
delete down2_batchnorm_0_output;
|
|
delete down2_conv_1_output;
|
|
delete down2_batchnorm_1_output;
|
|
delete down3_maxpool2d_0_output;
|
|
delete down3_conv_0_output;
|
|
delete down3_batchnorm_0_output;
|
|
delete down3_conv_1_output;
|
|
delete down3_batchnorm_1_output;
|
|
delete down4_maxpool2d_0_output;
|
|
delete down4_conv_0_output;
|
|
delete down4_batchnorm_0_output;
|
|
delete down4_conv_1_output;
|
|
delete down4_batchnorm_1_output;
|
|
delete up1_convt_0_output;
|
|
delete up1_concat_0_output;
|
|
delete up2_convt_0_output;
|
|
delete up2_concat_0_output;
|
|
delete up3_convt_0_output;
|
|
delete up3_concat_0_output;
|
|
delete up4_convt_0_output;
|
|
delete up4_concat_0_output;
|
|
delete outc_conv_0_output;
|
|
}
|