morgan
/
chundoong-lab-ta
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
chundoong-lab-ta/APWS23/project/uNet.cu

654 lines
26 KiB
Plaintext
Raw Blame History

#include <stdlib.h>
#include <cmath>
#include <cstdint>
#include <cstdio>
#include <cstdlib>
#include "tensor.h"
#include "uNet.h"
#include "util.h"
// Parameters for U-Net
Tensor *inc_double_conv_0_weight;
Tensor *inc_double_conv_1_weight;
Tensor *inc_double_conv_1_bias;
Tensor *inc_double_conv_3_weight;
Tensor *inc_double_conv_4_weight;
Tensor *inc_double_conv_4_bias;
Tensor *down1_maxpool_conv_1_double_conv_0_weight;
Tensor *down1_maxpool_conv_1_double_conv_1_weight;
Tensor *down1_maxpool_conv_1_double_conv_1_bias;
Tensor *down1_maxpool_conv_1_double_conv_3_weight;
Tensor *down1_maxpool_conv_1_double_conv_4_weight;
Tensor *down1_maxpool_conv_1_double_conv_4_bias;
Tensor *down2_maxpool_conv_1_double_conv_0_weight;
Tensor *down2_maxpool_conv_1_double_conv_1_weight;
Tensor *down2_maxpool_conv_1_double_conv_1_bias;
Tensor *down2_maxpool_conv_1_double_conv_3_weight;
Tensor *down2_maxpool_conv_1_double_conv_4_weight;
Tensor *down2_maxpool_conv_1_double_conv_4_bias;
Tensor *up1_up_weight;
Tensor *up1_up_bias;
Tensor *up1_conv_double_conv_0_weight;
Tensor *up1_conv_double_conv_1_weight;
Tensor *up1_conv_double_conv_1_bias;
Tensor *up1_conv_double_conv_3_weight;
Tensor *up1_conv_double_conv_4_weight;
Tensor *up1_conv_double_conv_4_bias;
Tensor *up2_up_weight;
Tensor *up2_up_bias;
Tensor *up2_conv_double_conv_0_weight;
Tensor *up2_conv_double_conv_1_weight;
Tensor *up2_conv_double_conv_1_bias;
Tensor *up2_conv_double_conv_3_weight;
Tensor *up2_conv_double_conv_4_weight;
Tensor *up2_conv_double_conv_4_bias;
Tensor *outc_conv_weight;
Tensor *outc_conv_bias;
Tensor *inc_batchnorm_0_running_mean;
Tensor *inc_batchnorm_0_running_var;
Tensor *down1_batchnorm_0_running_mean;
Tensor *down1_batchnorm_0_running_var;
Tensor *down2_batchnorm_0_running_mean;
Tensor *down2_batchnorm_0_running_var;
Tensor *up1_batchnorm_0_running_mean;
Tensor *up1_batchnorm_0_running_var;
Tensor *up2_batchnorm_0_running_mean;
Tensor *up2_batchnorm_0_running_var;
Tensor *inc_batchnorm_1_running_mean;
Tensor *inc_batchnorm_1_running_var;
Tensor *down1_batchnorm_1_running_mean;
Tensor *down1_batchnorm_1_running_var;
Tensor *down2_batchnorm_1_running_mean;
Tensor *down2_batchnorm_1_running_var;
Tensor *up1_batchnorm_1_running_mean;
Tensor *up1_batchnorm_1_running_var;
Tensor *up2_batchnorm_1_running_mean;
Tensor *up2_batchnorm_1_running_var;
// intermediate features
Tensor *inc_conv_0_output;
Tensor *inc_batchnorm_0_output;
Tensor *inc_conv_1_output;
Tensor *inc_batchnorm_1_output;
Tensor *down1_maxpool2d_0_output;
Tensor *down1_conv_0_output;
Tensor *down1_batchnorm_0_output;
Tensor *down1_conv_1_output;
Tensor *down1_batchnorm_1_output;
Tensor *down2_maxpool2d_0_output;
Tensor *down2_conv_0_output;
Tensor *down2_batchnorm_0_output;
Tensor *down2_conv_1_output;
Tensor *down2_batchnorm_1_output;
Tensor *up1_convt_0_output;
Tensor *up1_concat_0_output;
Tensor *up1_conv_0_output;
Tensor *up1_batchnorm_0_output;
Tensor *up1_conv_1_output;
Tensor *up1_batchnorm_1_output;
Tensor *up2_convt_0_output;
Tensor *up2_concat_0_output;
Tensor *up2_conv_0_output;
Tensor *up2_batchnorm_0_output;
Tensor *up2_conv_1_output;
Tensor *up2_batchnorm_1_output;
Tensor *outc_conv_0_output;
// forward declaration, prototype
void Conv2d(Tensor *input, Tensor *weight, Tensor *bias, Tensor *output,
int stride, int pad, int dilation, bool has_bias);
void ReLU(Tensor *inout);
void BatchNorm2d(Tensor *input, Tensor *gamma, Tensor *beta,
Tensor *running_mean, Tensor *running_var, Tensor *output,
const float eps, const float momentum);
void ConvTranspose2d(Tensor *input, Tensor *weight, Tensor *bias,
Tensor *output, int stride, int pad);
void MaxPool2d(Tensor *input, Tensor *output);
void Concat(Tensor *input1, Tensor *input2, Tensor *output);
void uNet_initialize(int, int, char *);
void uNet(Tensor *, Tensor *);
void uNet_finalize();
/*
* uNet
* This model identifies the boundaries of the cars in an image file (input.bin)
* and removes the background.
*/
void uNet(Tensor *inputN, Tensor *outputN, int N) {
Tensor *input = new Tensor({1, 3, 128, 191});
Tensor *output = new Tensor({1, 2, 128, 191});
for (int idx = 0; idx < N; ++idx) {
memcpy(input->buf, inputN->buf + (idx * 1 * 3 * 128 * 191),
sizeof(float) * 1 * 3 * 128 * 191);
// inc(n_channels, 64)
Conv2d(input, inc_double_conv_0_weight, NULL, inc_conv_0_output, 1, 1, 1,
false);
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);
ReLU(inc_batchnorm_0_output);
Conv2d(inc_batchnorm_0_output, inc_double_conv_3_weight, NULL,
inc_conv_1_output, 1, 1, 1, false);
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);
ReLU(inc_batchnorm_1_output);
// down1(64, 128)
MaxPool2d(inc_batchnorm_1_output, down1_maxpool2d_0_output);
Conv2d(down1_maxpool2d_0_output, down1_maxpool_conv_1_double_conv_0_weight,
NULL, down1_conv_0_output, 1, 1, 1, false);
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);
ReLU(down1_batchnorm_0_output);
Conv2d(down1_batchnorm_0_output, down1_maxpool_conv_1_double_conv_3_weight,
NULL, down1_conv_1_output, 1, 1, 1, false);
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);
ReLU(down1_batchnorm_1_output);
// down2(128, 256)
MaxPool2d(down1_batchnorm_1_output, down2_maxpool2d_0_output);
Conv2d(down2_maxpool2d_0_output, down2_maxpool_conv_1_double_conv_0_weight,
NULL, down2_conv_0_output, 1, 1, 1, false);
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);
ReLU(down2_batchnorm_0_output);
Conv2d(down2_batchnorm_0_output, down2_maxpool_conv_1_double_conv_3_weight,
NULL, down2_conv_1_output, 1, 1, 1, false);
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);
ReLU(down2_batchnorm_1_output);
// up1(256, 128), (up2_concat_0_output, down1_batchnorm_1_output)
ConvTranspose2d(down2_batchnorm_1_output, up1_up_weight, up1_up_bias,
up1_convt_0_output, 2, 0);
Concat(up1_convt_0_output, down1_batchnorm_1_output, up1_concat_0_output);
Conv2d(up1_concat_0_output, up1_conv_double_conv_0_weight, NULL,
up1_conv_0_output, 1, 1, 1, false);
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);
ReLU(up1_batchnorm_0_output);
Conv2d(up1_batchnorm_0_output, up1_conv_double_conv_3_weight, NULL,
up1_conv_1_output, 1, 1, 1, false);
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);
ReLU(up1_batchnorm_1_output);
// up2(128, 64), (up1_concat_0_output, inc_batchnorm_1_output)
ConvTranspose2d(up1_batchnorm_1_output, up2_up_weight, up2_up_bias,
up2_convt_0_output, 2, 0);
Concat(up2_convt_0_output, inc_batchnorm_1_output, up2_concat_0_output);
Conv2d(up2_concat_0_output, up2_conv_double_conv_0_weight, NULL,
up2_conv_0_output, 1, 1, 1, false);
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);
ReLU(up2_batchnorm_0_output);
Conv2d(up2_batchnorm_0_output, up2_conv_double_conv_3_weight, NULL,
up2_conv_1_output, 1, 1, 1, false);
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);
ReLU(up2_batchnorm_1_output);
// outc(64, 2)
Conv2d(up2_batchnorm_1_output, outc_conv_weight, outc_conv_bias, output, 1,
0, 1, true);
memcpy(outputN->buf + (idx * 1 * 2 * 128 * 191), output->buf,
sizeof(float) * (1 * 2 * 128 * 191));
}
}
/* Operations */
/*
* 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,
"[Conv2d] Output height mismatch");
CHECK_ERROR(OW == (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1,
"[Conv2d] Output width mismatch");
CHECK_ERROR(weight->shape[1] == C && (!has_bias || bias->shape[0] == K) &&
output->shape[1] == K,
"[Conv2d] Channel size mismatch");
#ifdef TEST
#pragma omp parallel for
#endif
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,
"[BatchNorm2d] gamma, beta shape mismatch");
CHECK_ERROR(
output->shape[1] == C && output->shape[2] == H && output->shape[3] == W,
"[BatchNorm2d] Output shape mismatch");
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,
"[ConvT2d] Output height mismatch");
CHECK_ERROR(OW == (W - 1) * stride - 2 * pad + S,
"[ConvT2d] Output width mismatch");
CHECK_ERROR(
weight->shape[0] == C && bias->shape[0] == K && output->shape[1] == K,
"[ConvT2d] Channel size mismatch");
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;
}
}
}
}
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;
}
/*
* MaxPool2d
* input shape = (N, C, H, W)
* output shape = (N, OC, OH, OW)
* where OH = H / 2
* OW = W / 2
*/
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];
CHECK_ERROR(OW == W / 2, "[MaxPool2d] Output width mismatch");
CHECK_ERROR(OH == H / 2, "[MaxPool2d] Output height mismatch");
CHECK_ERROR(OC == C, "[MaxPool2d] Output channel mismatch");
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];
output->buf[oc * OH * OW + oh * OW + ow] = max4(in0, in1, in2, in3);
}
}
}
}
/*
* Concat
* input1 shape = (N, C1, H1, W1)
* input2 shape = (N, C2, H2, W2)
* output shape = (N, OC, OH, OW)
* where OH = H2, H1
* OW = W2 = W1 + 1
*/
void Concat(Tensor *input1, Tensor *input2, Tensor *output) {
int C1 = input1->shape[1], H1 = input1->shape[2], W1 = 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];
CHECK_ERROR(OC == C1 * 2 && OC == C2 * 2, "[Concat] Output channel mismatch");
CHECK_ERROR(OW == W1 + 1 && OW == W2, "[Concat] Output width mismatch");
CHECK_ERROR(OH == H1 && OH == H2, "[Concat] Output height mismatch");
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; // zero padding
else
output->buf[oc * OH * OW + oh * OW + ow] =
input1->buf[(oc - OC / 2) * H1 * W1 + oh * W1 + ow];
}
}
}
}
/*
* uNet_initialize
* Initialize the model. Do input-independent job here.
*/
void uNet_initialize(int N, char *parameter_fname) {
size_t parameter_binary_size = 0;
float *parameter =
(float *) read_binary(parameter_fname, &parameter_binary_size);
// Parameters
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);
up1_up_weight = new Tensor({256, 128, 2, 2}, parameter + OFFSET18);
up1_up_bias = new Tensor({128}, parameter + OFFSET19);
up1_conv_double_conv_0_weight =
new Tensor({128, 256, 3, 3}, parameter + OFFSET20);
up1_conv_double_conv_1_weight = new Tensor({128}, parameter + OFFSET21);
up1_conv_double_conv_1_bias = new Tensor({128}, parameter + OFFSET22);
up1_conv_double_conv_3_weight =
new Tensor({128, 128, 3, 3}, parameter + OFFSET23);
up1_conv_double_conv_4_weight = new Tensor({128}, parameter + OFFSET24);
up1_conv_double_conv_4_bias = new Tensor({128}, parameter + OFFSET25);
up2_up_weight = new Tensor({128, 64, 2, 2}, parameter + OFFSET26);
up2_up_bias = new Tensor({64}, parameter + OFFSET27);
up2_conv_double_conv_0_weight =
new Tensor({64, 128, 3, 3}, parameter + OFFSET28);
up2_conv_double_conv_1_weight = new Tensor({64}, parameter + OFFSET29);
up2_conv_double_conv_1_bias = new Tensor({64}, parameter + OFFSET30);
up2_conv_double_conv_3_weight =
new Tensor({64, 64, 3, 3}, parameter + OFFSET31);
up2_conv_double_conv_4_weight = new Tensor({64}, parameter + OFFSET32);
up2_conv_double_conv_4_bias = new Tensor({64}, parameter + OFFSET33);
outc_conv_weight = new Tensor({2, 64, 1, 1}, parameter + OFFSET34);
outc_conv_bias = new Tensor({2}, parameter + OFFSET35);
inc_batchnorm_0_running_mean = new Tensor({64}, parameter + OFFSET36);
inc_batchnorm_0_running_var = new Tensor({64}, parameter + OFFSET37);
inc_batchnorm_1_running_mean = new Tensor({64}, parameter + OFFSET38);
inc_batchnorm_1_running_var = new Tensor({64}, parameter + OFFSET39);
down1_batchnorm_0_running_mean = new Tensor({128}, parameter + OFFSET40);
down1_batchnorm_0_running_var = new Tensor({128}, parameter + OFFSET41);
down1_batchnorm_1_running_mean = new Tensor({128}, parameter + OFFSET42);
down1_batchnorm_1_running_var = new Tensor({128}, parameter + OFFSET43);
down2_batchnorm_0_running_mean = new Tensor({256}, parameter + OFFSET44);
down2_batchnorm_0_running_var = new Tensor({256}, parameter + OFFSET45);
down2_batchnorm_1_running_mean = new Tensor({256}, parameter + OFFSET46);
down2_batchnorm_1_running_var = new Tensor({256}, parameter + OFFSET47);
up1_batchnorm_0_running_mean = new Tensor({128}, parameter + OFFSET48);
up1_batchnorm_0_running_var = new Tensor({128}, parameter + OFFSET49);
up1_batchnorm_1_running_mean = new Tensor({128}, parameter + OFFSET50);
up1_batchnorm_1_running_var = new Tensor({128}, parameter + OFFSET51);
up2_batchnorm_0_running_mean = new Tensor({64}, parameter + OFFSET52);
up2_batchnorm_0_running_var = new Tensor({64}, parameter + OFFSET53);
up2_batchnorm_1_running_mean = new Tensor({64}, parameter + OFFSET54);
up2_batchnorm_1_running_var = new Tensor({64}, parameter + OFFSET55);
// Activations
inc_conv_0_output = new Tensor({1, 64, 128, 191});
inc_batchnorm_0_output = new Tensor({1, 64, 128, 191});
inc_conv_1_output = new Tensor({1, 64, 128, 191});
inc_batchnorm_1_output = new Tensor({1, 64, 128, 191});
down1_maxpool2d_0_output = new Tensor({1, 64, 64, 95});
down1_conv_0_output = new Tensor({1, 128, 64, 95});
down1_batchnorm_0_output = new Tensor({1, 128, 64, 95});
down1_conv_1_output = new Tensor({1, 128, 64, 95});
down1_batchnorm_1_output = new Tensor({1, 128, 64, 95});
down2_maxpool2d_0_output = new Tensor({1, 128, 32, 47});
down2_conv_0_output = new Tensor({1, 256, 32, 47});
down2_batchnorm_0_output = new Tensor({1, 256, 32, 47});
down2_conv_1_output = new Tensor({1, 256, 32, 47});
down2_batchnorm_1_output = new Tensor({1, 256, 32, 47});
up1_convt_0_output = new Tensor({1, 128, 64, 94});
up1_concat_0_output = new Tensor({1, 256, 64, 95});
up1_conv_0_output = new Tensor({1, 128, 64, 95});
up1_batchnorm_0_output = new Tensor({1, 128, 64, 95});
up1_conv_1_output = new Tensor({1, 128, 64, 95});
up1_batchnorm_1_output = new Tensor({1, 128, 64, 95});
up2_convt_0_output = new Tensor({1, 64, 128, 190});
up2_concat_0_output = new Tensor({1, 128, 128, 191});
up2_conv_0_output = new Tensor({1, 64, 128, 191});
up2_batchnorm_0_output = new Tensor({1, 64, 128, 191});
up2_conv_1_output = new Tensor({1, 64, 128, 191});
up2_batchnorm_1_output = new Tensor({1, 64, 128, 191});
outc_conv_0_output = new Tensor({1, 2, 128, 191});
}
/*
* uNet_finalize
* Finalize the model.
*/
void uNet_finalize() {
// delete parameters
delete inc_double_conv_0_weight;
delete inc_double_conv_1_weight;
delete inc_double_conv_1_bias;
delete inc_double_conv_3_weight;
delete inc_double_conv_4_weight;
delete inc_double_conv_4_bias;
delete down1_maxpool_conv_1_double_conv_0_weight;
delete down1_maxpool_conv_1_double_conv_1_weight;
delete down1_maxpool_conv_1_double_conv_1_bias;
delete down1_maxpool_conv_1_double_conv_3_weight;
delete down1_maxpool_conv_1_double_conv_4_weight;
delete down1_maxpool_conv_1_double_conv_4_bias;
delete down2_maxpool_conv_1_double_conv_0_weight;
delete down2_maxpool_conv_1_double_conv_1_weight;
delete down2_maxpool_conv_1_double_conv_1_bias;
delete down2_maxpool_conv_1_double_conv_3_weight;
delete down2_maxpool_conv_1_double_conv_4_weight;
delete down2_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 outc_conv_weight;
delete outc_conv_bias;
delete inc_batchnorm_0_running_mean;
delete inc_batchnorm_0_running_var;
delete down1_batchnorm_0_running_mean;
delete down1_batchnorm_0_running_var;
delete down2_batchnorm_0_running_mean;
delete down2_batchnorm_0_running_var;
delete up1_batchnorm_0_running_mean;
delete up1_batchnorm_0_running_var;
delete up2_batchnorm_0_running_mean;
delete up2_batchnorm_0_running_var;
delete inc_batchnorm_1_running_mean;
delete inc_batchnorm_1_running_var;
delete down1_batchnorm_1_running_mean;
delete down1_batchnorm_1_running_var;
delete down2_batchnorm_1_running_mean;
delete down2_batchnorm_1_running_var;
delete up1_batchnorm_1_running_mean;
delete up1_batchnorm_1_running_var;
delete up2_batchnorm_1_running_mean;
delete up2_batchnorm_1_running_var;
// delete activations
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 up1_convt_0_output;
delete up1_concat_0_output;
delete up1_conv_0_output;
delete up1_batchnorm_0_output;
delete up1_conv_1_output;
delete up1_batchnorm_1_output;
delete up2_convt_0_output;
delete up2_concat_0_output;
delete up2_conv_0_output;
delete up2_batchnorm_0_output;
delete up2_conv_1_output;
delete up2_batchnorm_1_output;
delete outc_conv_0_output;
}
Reference in New Issue View Git Blame Copy Permalink