diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv1.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv1.bin
deleted file mode 100644
index 89ab6ad37cac94360f7f87c93676f353829f1deb..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv1.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv1_bias.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv1_bias.bin
deleted file mode 100644
index 0a2a381337e13fe52959c838b4a2bedab3c3f8ab..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv1_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
-h4Q;¤ ù;34¼j0_½G½–h;ìz/½ðÇÊ:àk¥¼{l½t+O;u¼8™¨¼d »”½®¼}8›<íO’¼äÕ¿»¤#½„ö¼”u<¼¿l…¼f¢;Ð4½ŠO ½>Øž¼7K¼04½ ÎG:à'½ÔOF½M=;
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv2.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv2.bin
deleted file mode 100644
index 6cd00b88c5be6e212f2d3a37c8ea2a8edb1ceca7..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv2.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv2_bias.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv2_bias.bin
deleted file mode 100644
index c0adf3e885ce855a0cc9d1b4b12f73665187159e..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv2_bias.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc1.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc1.bin
deleted file mode 100644
index 152c5bb0baae480f6b8d317889fc68f8d77247b6..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc1.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc1_bias.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc1_bias.bin
deleted file mode 100644
index 58221f45cdc56049b2edc29c244ea9d797a87fb5..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc1_bias.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc2.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc2.bin
deleted file mode 100644
index 97d78a9610b15be285661c1d762026c9fa4100cb..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc2.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc2_bias.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc2_bias.bin
deleted file mode 100644
index cbda59beef150dfbca756621286f042ec8e247bf..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc2_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
-Ê%”½ù b½Ó„g½W•½$VĽéum½'Ƶ½J§’½·¾¶½›¢½
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/input.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/input.bin
deleted file mode 100644
index 4d2423f74188cfe0364185ccb66837785ccf4c4e..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/input.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/labels.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/labels.bin
deleted file mode 100644
index 5e1f3881897f4729d6d90ff208a08ccdabb8fe7c..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/labels.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/labels32.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/labels32.bin
deleted file mode 100644
index 6f1d7576cd18621a2cf646d0dd835846623589e5..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/labels32.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/quant_ranges.txt b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/quant_ranges.txt
deleted file mode 100644
index af4d13d6f8e6b5902ff743b07ef6875d644df91a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/quant_ranges.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-0 1 -1 1 -1 1 -1 1
--1 1 -1 1 -1 1 -1 1
--1 1 -1 1 -1 1 -1 1
--1 1 -1 1 -1 1 -1 1
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layer_composition.txt# b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layer_composition.txt#
deleted file mode 100644
index 10692997a90e4490a91ad3d0e6e04285754144fd..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layer_composition.txt#
+++ /dev/null
@@ -1,83 +0,0 @@
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-pool
-dense add
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layers.txt# b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layers.txt#
deleted file mode 100644
index 0bd2b554374c10d748a652f52e5427c716be0084..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layers.txt#
+++ /dev/null
@@ -1,83 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-#tensorBatchNorm1
-#tensorRelu1
-#tensorDepthwiseConv1
-#tensorBatchNorm2
-#tensorRelu2
-Conv2,10000,32,32,32,64,32,1,1
-#tensorBatchNorm3
-#tensorRelu3
-#tensorDepthwiseConv2
-#tensorBatchNorm4
-#tensorRelu4
-Conv3,10000,64,16,16,128,64,1,1
-#tensorBatchNorm5
-#tensorRelu5
-#tensorDepthwiseConv3
-#tensorBatchNorm6
-#tensorRelu6
-Conv4,10000,128,16,16,128,128,1,1
-#tensorBatchNorm7
-#tensorRelu7
-#tensorDepthwiseConv4
-#tensorBatchNorm8
-#tensorRelu8
-Conv5,10000,128,8,8,256,128,1,1
-#tensorBatchNorm9
-#tensorRelu9
-#tensorDepthwiseConv5
-#tensorBatchNorm10
-#tensorRelu10
-Conv6,10000,256,8,8,256,256,1,1
-#tensorBatchNorm11
-#tensorRelu11
-#tensorDepthwiseConv6
-#tensorBatchNorm12
-#tensorRelu12
-Conv7,10000,256,4,4,512,256,1,1
-#tensorBatchNorm13
-#tensorRelu13
-#tensorDepthwiseConv7
-#tensorBatchNorm14
-#tensorRelu14
-Conv8,10000,512,4,4,512,512,1,1
-#tensorBatchNorm15
-#tensorRelu15
-#tensorDepthwiseConv8
-#tensorBatchNorm16
-#tensorRelu16
-Conv9,10000,512,4,4,512,512,1,1
-#tensorBatchNorm17
-#tensorRelu17
-#tensorDepthwiseConv9
-#tensorBatchNorm18
-#tensorRelu18
-Conv10,10000,512,4,4,512,512,1,1
-#tensorBatchNorm19
-#tensorRelu19
-#tensorDepthwiseConv10
-#tensorBatchNorm20
-#tensorRelu20
-Conv11,10000,512,4,4,512,512,1,1
-#tensorBatchNorm21
-#tensorRelu21
-#tensorDepthwiseConv11
-#tensorBatchNorm22
-#tensorRelu22
-Conv12,10000,512,4,4,512,512,1,1
-#tensorBatchNorm23
-#tensorRelu23
-#tensorDepthwiseConv12
-#tensorBatchNorm24
-#tensorRelu24
-Conv13,10000,512,2,2,1024,512,1,1
-#tensorBatchNorm25
-#tensorRelu25
-#tensorDepthwiseConv13
-#tensorBatchNorm26
-#tensorRelu26
-Conv14,10000,1024,2,2,1024,1024,1,1
-#tensorBatchNorm27
-#tensorRelu27
-#tensorPooling1
-FC1,10000,1024,1024,10
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/approxhpvm_src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/approxhpvm_src.cc
deleted file mode 100644
index 5089eb912bcb5335c96c04f6d98f5d17ab761c72..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/approxhpvm_src.cc
+++ /dev/null
@@ -1,2400 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/stat.h>
-#include <cstring>
-#include <visc.h>
-#include <tensorTypes.h>
-#include <tensorUtils.h>
-
-void var_0_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_1_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_2_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_3_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 32);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_5_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_6_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_8_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_9_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 64);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_11_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_12_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_13_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_14_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_15_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 128);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_16_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_17_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_18_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_19_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_20_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_21_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 128);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_22_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_23_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_24_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_25_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_26_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_27_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 256);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_28_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_29_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_30_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_31_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_32_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_33_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 256);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_34_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_35_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_36_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_37_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_38_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_39_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_40_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_41_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_42_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_43_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_44_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_45_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_46_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_47_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_48_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_49_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_50_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_51_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_52_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_53_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_54_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_55_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_56_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_57_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_58_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_59_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_60_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_61_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_62_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_63_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_64_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_65_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_66_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_67_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_68_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_69_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_70_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_71_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_72_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_73_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_74_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_75_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 1024);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_76_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_77_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_78_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_79_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_80_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_81_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_avg(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_82_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_83_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_84_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_softmax(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void root(void* input, size_t input_bytes,
- void* conv2d_1_w, size_t conv2d_1_w_bytes,
- void* batch_normalization_1_gamma, size_t batch_normalization_1_gamma_bytes,
- void* batch_normalization_1_beta, size_t batch_normalization_1_beta_bytes,
- void* batch_normalization_1_mean, size_t batch_normalization_1_mean_bytes,
- void* batch_normalization_1_variance, size_t batch_normalization_1_variance_bytes,
- void* depthwise_conv2d_1_w, size_t depthwise_conv2d_1_w_bytes,
- void* batch_normalization_2_gamma, size_t batch_normalization_2_gamma_bytes,
- void* batch_normalization_2_beta, size_t batch_normalization_2_beta_bytes,
- void* batch_normalization_2_mean, size_t batch_normalization_2_mean_bytes,
- void* batch_normalization_2_variance, size_t batch_normalization_2_variance_bytes,
- void* conv2d_2_w, size_t conv2d_2_w_bytes,
- void* batch_normalization_3_gamma, size_t batch_normalization_3_gamma_bytes,
- void* batch_normalization_3_beta, size_t batch_normalization_3_beta_bytes,
- void* batch_normalization_3_mean, size_t batch_normalization_3_mean_bytes,
- void* batch_normalization_3_variance, size_t batch_normalization_3_variance_bytes,
- void* depthwise_conv2d_2_w, size_t depthwise_conv2d_2_w_bytes,
- void* batch_normalization_4_gamma, size_t batch_normalization_4_gamma_bytes,
- void* batch_normalization_4_beta, size_t batch_normalization_4_beta_bytes,
- void* batch_normalization_4_mean, size_t batch_normalization_4_mean_bytes,
- void* batch_normalization_4_variance, size_t batch_normalization_4_variance_bytes,
- void* conv2d_3_w, size_t conv2d_3_w_bytes,
- void* batch_normalization_5_gamma, size_t batch_normalization_5_gamma_bytes,
- void* batch_normalization_5_beta, size_t batch_normalization_5_beta_bytes,
- void* batch_normalization_5_mean, size_t batch_normalization_5_mean_bytes,
- void* batch_normalization_5_variance, size_t batch_normalization_5_variance_bytes,
- void* depthwise_conv2d_3_w, size_t depthwise_conv2d_3_w_bytes,
- void* batch_normalization_6_gamma, size_t batch_normalization_6_gamma_bytes,
- void* batch_normalization_6_beta, size_t batch_normalization_6_beta_bytes,
- void* batch_normalization_6_mean, size_t batch_normalization_6_mean_bytes,
- void* batch_normalization_6_variance, size_t batch_normalization_6_variance_bytes,
- void* conv2d_4_w, size_t conv2d_4_w_bytes,
- void* batch_normalization_7_gamma, size_t batch_normalization_7_gamma_bytes,
- void* batch_normalization_7_beta, size_t batch_normalization_7_beta_bytes,
- void* batch_normalization_7_mean, size_t batch_normalization_7_mean_bytes,
- void* batch_normalization_7_variance, size_t batch_normalization_7_variance_bytes,
- void* depthwise_conv2d_4_w, size_t depthwise_conv2d_4_w_bytes,
- void* batch_normalization_8_gamma, size_t batch_normalization_8_gamma_bytes,
- void* batch_normalization_8_beta, size_t batch_normalization_8_beta_bytes,
- void* batch_normalization_8_mean, size_t batch_normalization_8_mean_bytes,
- void* batch_normalization_8_variance, size_t batch_normalization_8_variance_bytes,
- void* conv2d_5_w, size_t conv2d_5_w_bytes,
- void* batch_normalization_9_gamma, size_t batch_normalization_9_gamma_bytes,
- void* batch_normalization_9_beta, size_t batch_normalization_9_beta_bytes,
- void* batch_normalization_9_mean, size_t batch_normalization_9_mean_bytes,
- void* batch_normalization_9_variance, size_t batch_normalization_9_variance_bytes,
- void* depthwise_conv2d_5_w, size_t depthwise_conv2d_5_w_bytes,
- void* batch_normalization_10_gamma, size_t batch_normalization_10_gamma_bytes,
- void* batch_normalization_10_beta, size_t batch_normalization_10_beta_bytes,
- void* batch_normalization_10_mean, size_t batch_normalization_10_mean_bytes,
- void* batch_normalization_10_variance, size_t batch_normalization_10_variance_bytes,
- void* conv2d_6_w, size_t conv2d_6_w_bytes,
- void* batch_normalization_11_gamma, size_t batch_normalization_11_gamma_bytes,
- void* batch_normalization_11_beta, size_t batch_normalization_11_beta_bytes,
- void* batch_normalization_11_mean, size_t batch_normalization_11_mean_bytes,
- void* batch_normalization_11_variance, size_t batch_normalization_11_variance_bytes,
- void* depthwise_conv2d_6_w, size_t depthwise_conv2d_6_w_bytes,
- void* batch_normalization_12_gamma, size_t batch_normalization_12_gamma_bytes,
- void* batch_normalization_12_beta, size_t batch_normalization_12_beta_bytes,
- void* batch_normalization_12_mean, size_t batch_normalization_12_mean_bytes,
- void* batch_normalization_12_variance, size_t batch_normalization_12_variance_bytes,
- void* conv2d_7_w, size_t conv2d_7_w_bytes,
- void* batch_normalization_13_gamma, size_t batch_normalization_13_gamma_bytes,
- void* batch_normalization_13_beta, size_t batch_normalization_13_beta_bytes,
- void* batch_normalization_13_mean, size_t batch_normalization_13_mean_bytes,
- void* batch_normalization_13_variance, size_t batch_normalization_13_variance_bytes,
- void* depthwise_conv2d_7_w, size_t depthwise_conv2d_7_w_bytes,
- void* batch_normalization_14_gamma, size_t batch_normalization_14_gamma_bytes,
- void* batch_normalization_14_beta, size_t batch_normalization_14_beta_bytes,
- void* batch_normalization_14_mean, size_t batch_normalization_14_mean_bytes,
- void* batch_normalization_14_variance, size_t batch_normalization_14_variance_bytes,
- void* conv2d_8_w, size_t conv2d_8_w_bytes,
- void* batch_normalization_15_gamma, size_t batch_normalization_15_gamma_bytes,
- void* batch_normalization_15_beta, size_t batch_normalization_15_beta_bytes,
- void* batch_normalization_15_mean, size_t batch_normalization_15_mean_bytes,
- void* batch_normalization_15_variance, size_t batch_normalization_15_variance_bytes,
- void* depthwise_conv2d_8_w, size_t depthwise_conv2d_8_w_bytes,
- void* batch_normalization_16_gamma, size_t batch_normalization_16_gamma_bytes,
- void* batch_normalization_16_beta, size_t batch_normalization_16_beta_bytes,
- void* batch_normalization_16_mean, size_t batch_normalization_16_mean_bytes,
- void* batch_normalization_16_variance, size_t batch_normalization_16_variance_bytes,
- void* conv2d_9_w, size_t conv2d_9_w_bytes,
- void* batch_normalization_17_gamma, size_t batch_normalization_17_gamma_bytes,
- void* batch_normalization_17_beta, size_t batch_normalization_17_beta_bytes,
- void* batch_normalization_17_mean, size_t batch_normalization_17_mean_bytes,
- void* batch_normalization_17_variance, size_t batch_normalization_17_variance_bytes,
- void* depthwise_conv2d_9_w, size_t depthwise_conv2d_9_w_bytes,
- void* batch_normalization_18_gamma, size_t batch_normalization_18_gamma_bytes,
- void* batch_normalization_18_beta, size_t batch_normalization_18_beta_bytes,
- void* batch_normalization_18_mean, size_t batch_normalization_18_mean_bytes,
- void* batch_normalization_18_variance, size_t batch_normalization_18_variance_bytes,
- void* conv2d_10_w, size_t conv2d_10_w_bytes,
- void* batch_normalization_19_gamma, size_t batch_normalization_19_gamma_bytes,
- void* batch_normalization_19_beta, size_t batch_normalization_19_beta_bytes,
- void* batch_normalization_19_mean, size_t batch_normalization_19_mean_bytes,
- void* batch_normalization_19_variance, size_t batch_normalization_19_variance_bytes,
- void* depthwise_conv2d_10_w, size_t depthwise_conv2d_10_w_bytes,
- void* batch_normalization_20_gamma, size_t batch_normalization_20_gamma_bytes,
- void* batch_normalization_20_beta, size_t batch_normalization_20_beta_bytes,
- void* batch_normalization_20_mean, size_t batch_normalization_20_mean_bytes,
- void* batch_normalization_20_variance, size_t batch_normalization_20_variance_bytes,
- void* conv2d_11_w, size_t conv2d_11_w_bytes,
- void* batch_normalization_21_gamma, size_t batch_normalization_21_gamma_bytes,
- void* batch_normalization_21_beta, size_t batch_normalization_21_beta_bytes,
- void* batch_normalization_21_mean, size_t batch_normalization_21_mean_bytes,
- void* batch_normalization_21_variance, size_t batch_normalization_21_variance_bytes,
- void* depthwise_conv2d_11_w, size_t depthwise_conv2d_11_w_bytes,
- void* batch_normalization_22_gamma, size_t batch_normalization_22_gamma_bytes,
- void* batch_normalization_22_beta, size_t batch_normalization_22_beta_bytes,
- void* batch_normalization_22_mean, size_t batch_normalization_22_mean_bytes,
- void* batch_normalization_22_variance, size_t batch_normalization_22_variance_bytes,
- void* conv2d_12_w, size_t conv2d_12_w_bytes,
- void* batch_normalization_23_gamma, size_t batch_normalization_23_gamma_bytes,
- void* batch_normalization_23_beta, size_t batch_normalization_23_beta_bytes,
- void* batch_normalization_23_mean, size_t batch_normalization_23_mean_bytes,
- void* batch_normalization_23_variance, size_t batch_normalization_23_variance_bytes,
- void* depthwise_conv2d_12_w, size_t depthwise_conv2d_12_w_bytes,
- void* batch_normalization_24_gamma, size_t batch_normalization_24_gamma_bytes,
- void* batch_normalization_24_beta, size_t batch_normalization_24_beta_bytes,
- void* batch_normalization_24_mean, size_t batch_normalization_24_mean_bytes,
- void* batch_normalization_24_variance, size_t batch_normalization_24_variance_bytes,
- void* conv2d_13_w, size_t conv2d_13_w_bytes,
- void* batch_normalization_25_gamma, size_t batch_normalization_25_gamma_bytes,
- void* batch_normalization_25_beta, size_t batch_normalization_25_beta_bytes,
- void* batch_normalization_25_mean, size_t batch_normalization_25_mean_bytes,
- void* batch_normalization_25_variance, size_t batch_normalization_25_variance_bytes,
- void* depthwise_conv2d_13_w, size_t depthwise_conv2d_13_w_bytes,
- void* batch_normalization_26_gamma, size_t batch_normalization_26_gamma_bytes,
- void* batch_normalization_26_beta, size_t batch_normalization_26_beta_bytes,
- void* batch_normalization_26_mean, size_t batch_normalization_26_mean_bytes,
- void* batch_normalization_26_variance, size_t batch_normalization_26_variance_bytes,
- void* conv2d_14_w, size_t conv2d_14_w_bytes,
- void* batch_normalization_27_gamma, size_t batch_normalization_27_gamma_bytes,
- void* batch_normalization_27_beta, size_t batch_normalization_27_beta_bytes,
- void* batch_normalization_27_mean, size_t batch_normalization_27_mean_bytes,
- void* batch_normalization_27_variance, size_t batch_normalization_27_variance_bytes,
- void* dense_1_w, size_t dense_1_w_bytes,
- void* dense_1_b, size_t dense_1_b_bytes){
-
-
- __visc__hint(visc::CPU_TARGET);
- __visc__attributes(138, input, conv2d_1_w, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, depthwise_conv2d_1_w, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, conv2d_2_w, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, depthwise_conv2d_2_w, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, conv2d_3_w, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, depthwise_conv2d_3_w, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, conv2d_4_w, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, depthwise_conv2d_4_w, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, conv2d_5_w, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, depthwise_conv2d_5_w, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, conv2d_6_w, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, depthwise_conv2d_6_w, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, conv2d_7_w, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, depthwise_conv2d_7_w, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, conv2d_8_w, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, depthwise_conv2d_8_w, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, conv2d_9_w, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, depthwise_conv2d_9_w, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, conv2d_10_w, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, depthwise_conv2d_10_w, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, conv2d_11_w, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, depthwise_conv2d_11_w, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, conv2d_12_w, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, depthwise_conv2d_12_w, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, conv2d_13_w, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, depthwise_conv2d_13_w, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, conv2d_14_w, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, dense_1_w, dense_1_b, 0);
-
-
- void* var_0 = __visc__createNodeND(0, var_0_node);
-
- __visc__bindIn(var_0, 0, 0, 0);
- __visc__bindIn(var_0, 1, 1, 0);
- __visc__bindIn(var_0, 2, 2, 0);
- __visc__bindIn(var_0, 3, 3, 0);
-
- void* var_1 = __visc__createNodeND(0, var_1_node);
-
- __visc__edge(var_0, var_1, 1, 0, 0, 0);
- __visc__edge(var_0, var_1, 1, 1, 1, 0);
- __visc__bindIn(var_1, 4, 2, 0);
- __visc__bindIn(var_1, 5, 3, 0);
- __visc__bindIn(var_1, 6, 4, 0);
- __visc__bindIn(var_1, 7, 5, 0);
- __visc__bindIn(var_1, 8, 6, 0);
- __visc__bindIn(var_1, 9, 7, 0);
- __visc__bindIn(var_1, 10, 8, 0);
- __visc__bindIn(var_1, 11, 9, 0);
-
- void* var_2 = __visc__createNodeND(0, var_2_node);
-
- __visc__edge(var_1, var_2, 1, 0, 0, 0);
- __visc__edge(var_1, var_2, 1, 1, 1, 0);
-
- void* var_3 = __visc__createNodeND(0, var_3_node);
-
- __visc__edge(var_2, var_3, 1, 0, 0, 0);
- __visc__edge(var_2, var_3, 1, 1, 1, 0);
- __visc__bindIn(var_3, 12, 2, 0);
- __visc__bindIn(var_3, 13, 3, 0);
-
- void* var_4 = __visc__createNodeND(0, var_4_node);
-
- __visc__edge(var_3, var_4, 1, 0, 0, 0);
- __visc__edge(var_3, var_4, 1, 1, 1, 0);
- __visc__bindIn(var_4, 14, 2, 0);
- __visc__bindIn(var_4, 15, 3, 0);
- __visc__bindIn(var_4, 16, 4, 0);
- __visc__bindIn(var_4, 17, 5, 0);
- __visc__bindIn(var_4, 18, 6, 0);
- __visc__bindIn(var_4, 19, 7, 0);
- __visc__bindIn(var_4, 20, 8, 0);
- __visc__bindIn(var_4, 21, 9, 0);
-
- void* var_5 = __visc__createNodeND(0, var_5_node);
-
- __visc__edge(var_4, var_5, 1, 0, 0, 0);
- __visc__edge(var_4, var_5, 1, 1, 1, 0);
-
- void* var_6 = __visc__createNodeND(0, var_6_node);
-
- __visc__edge(var_5, var_6, 1, 0, 0, 0);
- __visc__edge(var_5, var_6, 1, 1, 1, 0);
- __visc__bindIn(var_6, 22, 2, 0);
- __visc__bindIn(var_6, 23, 3, 0);
-
- void* var_7 = __visc__createNodeND(0, var_7_node);
-
- __visc__edge(var_6, var_7, 1, 0, 0, 0);
- __visc__edge(var_6, var_7, 1, 1, 1, 0);
- __visc__bindIn(var_7, 24, 2, 0);
- __visc__bindIn(var_7, 25, 3, 0);
- __visc__bindIn(var_7, 26, 4, 0);
- __visc__bindIn(var_7, 27, 5, 0);
- __visc__bindIn(var_7, 28, 6, 0);
- __visc__bindIn(var_7, 29, 7, 0);
- __visc__bindIn(var_7, 30, 8, 0);
- __visc__bindIn(var_7, 31, 9, 0);
-
- void* var_8 = __visc__createNodeND(0, var_8_node);
-
- __visc__edge(var_7, var_8, 1, 0, 0, 0);
- __visc__edge(var_7, var_8, 1, 1, 1, 0);
-
- void* var_9 = __visc__createNodeND(0, var_9_node);
-
- __visc__edge(var_8, var_9, 1, 0, 0, 0);
- __visc__edge(var_8, var_9, 1, 1, 1, 0);
- __visc__bindIn(var_9, 32, 2, 0);
- __visc__bindIn(var_9, 33, 3, 0);
-
- void* var_10 = __visc__createNodeND(0, var_10_node);
-
- __visc__edge(var_9, var_10, 1, 0, 0, 0);
- __visc__edge(var_9, var_10, 1, 1, 1, 0);
- __visc__bindIn(var_10, 34, 2, 0);
- __visc__bindIn(var_10, 35, 3, 0);
- __visc__bindIn(var_10, 36, 4, 0);
- __visc__bindIn(var_10, 37, 5, 0);
- __visc__bindIn(var_10, 38, 6, 0);
- __visc__bindIn(var_10, 39, 7, 0);
- __visc__bindIn(var_10, 40, 8, 0);
- __visc__bindIn(var_10, 41, 9, 0);
-
- void* var_11 = __visc__createNodeND(0, var_11_node);
-
- __visc__edge(var_10, var_11, 1, 0, 0, 0);
- __visc__edge(var_10, var_11, 1, 1, 1, 0);
-
- void* var_12 = __visc__createNodeND(0, var_12_node);
-
- __visc__edge(var_11, var_12, 1, 0, 0, 0);
- __visc__edge(var_11, var_12, 1, 1, 1, 0);
- __visc__bindIn(var_12, 42, 2, 0);
- __visc__bindIn(var_12, 43, 3, 0);
-
- void* var_13 = __visc__createNodeND(0, var_13_node);
-
- __visc__edge(var_12, var_13, 1, 0, 0, 0);
- __visc__edge(var_12, var_13, 1, 1, 1, 0);
- __visc__bindIn(var_13, 44, 2, 0);
- __visc__bindIn(var_13, 45, 3, 0);
- __visc__bindIn(var_13, 46, 4, 0);
- __visc__bindIn(var_13, 47, 5, 0);
- __visc__bindIn(var_13, 48, 6, 0);
- __visc__bindIn(var_13, 49, 7, 0);
- __visc__bindIn(var_13, 50, 8, 0);
- __visc__bindIn(var_13, 51, 9, 0);
-
- void* var_14 = __visc__createNodeND(0, var_14_node);
-
- __visc__edge(var_13, var_14, 1, 0, 0, 0);
- __visc__edge(var_13, var_14, 1, 1, 1, 0);
-
- void* var_15 = __visc__createNodeND(0, var_15_node);
-
- __visc__edge(var_14, var_15, 1, 0, 0, 0);
- __visc__edge(var_14, var_15, 1, 1, 1, 0);
- __visc__bindIn(var_15, 52, 2, 0);
- __visc__bindIn(var_15, 53, 3, 0);
-
- void* var_16 = __visc__createNodeND(0, var_16_node);
-
- __visc__edge(var_15, var_16, 1, 0, 0, 0);
- __visc__edge(var_15, var_16, 1, 1, 1, 0);
- __visc__bindIn(var_16, 54, 2, 0);
- __visc__bindIn(var_16, 55, 3, 0);
- __visc__bindIn(var_16, 56, 4, 0);
- __visc__bindIn(var_16, 57, 5, 0);
- __visc__bindIn(var_16, 58, 6, 0);
- __visc__bindIn(var_16, 59, 7, 0);
- __visc__bindIn(var_16, 60, 8, 0);
- __visc__bindIn(var_16, 61, 9, 0);
-
- void* var_17 = __visc__createNodeND(0, var_17_node);
-
- __visc__edge(var_16, var_17, 1, 0, 0, 0);
- __visc__edge(var_16, var_17, 1, 1, 1, 0);
-
- void* var_18 = __visc__createNodeND(0, var_18_node);
-
- __visc__edge(var_17, var_18, 1, 0, 0, 0);
- __visc__edge(var_17, var_18, 1, 1, 1, 0);
- __visc__bindIn(var_18, 62, 2, 0);
- __visc__bindIn(var_18, 63, 3, 0);
-
- void* var_19 = __visc__createNodeND(0, var_19_node);
-
- __visc__edge(var_18, var_19, 1, 0, 0, 0);
- __visc__edge(var_18, var_19, 1, 1, 1, 0);
- __visc__bindIn(var_19, 64, 2, 0);
- __visc__bindIn(var_19, 65, 3, 0);
- __visc__bindIn(var_19, 66, 4, 0);
- __visc__bindIn(var_19, 67, 5, 0);
- __visc__bindIn(var_19, 68, 6, 0);
- __visc__bindIn(var_19, 69, 7, 0);
- __visc__bindIn(var_19, 70, 8, 0);
- __visc__bindIn(var_19, 71, 9, 0);
-
- void* var_20 = __visc__createNodeND(0, var_20_node);
-
- __visc__edge(var_19, var_20, 1, 0, 0, 0);
- __visc__edge(var_19, var_20, 1, 1, 1, 0);
-
- void* var_21 = __visc__createNodeND(0, var_21_node);
-
- __visc__edge(var_20, var_21, 1, 0, 0, 0);
- __visc__edge(var_20, var_21, 1, 1, 1, 0);
- __visc__bindIn(var_21, 72, 2, 0);
- __visc__bindIn(var_21, 73, 3, 0);
-
- void* var_22 = __visc__createNodeND(0, var_22_node);
-
- __visc__edge(var_21, var_22, 1, 0, 0, 0);
- __visc__edge(var_21, var_22, 1, 1, 1, 0);
- __visc__bindIn(var_22, 74, 2, 0);
- __visc__bindIn(var_22, 75, 3, 0);
- __visc__bindIn(var_22, 76, 4, 0);
- __visc__bindIn(var_22, 77, 5, 0);
- __visc__bindIn(var_22, 78, 6, 0);
- __visc__bindIn(var_22, 79, 7, 0);
- __visc__bindIn(var_22, 80, 8, 0);
- __visc__bindIn(var_22, 81, 9, 0);
-
- void* var_23 = __visc__createNodeND(0, var_23_node);
-
- __visc__edge(var_22, var_23, 1, 0, 0, 0);
- __visc__edge(var_22, var_23, 1, 1, 1, 0);
-
- void* var_24 = __visc__createNodeND(0, var_24_node);
-
- __visc__edge(var_23, var_24, 1, 0, 0, 0);
- __visc__edge(var_23, var_24, 1, 1, 1, 0);
- __visc__bindIn(var_24, 82, 2, 0);
- __visc__bindIn(var_24, 83, 3, 0);
-
- void* var_25 = __visc__createNodeND(0, var_25_node);
-
- __visc__edge(var_24, var_25, 1, 0, 0, 0);
- __visc__edge(var_24, var_25, 1, 1, 1, 0);
- __visc__bindIn(var_25, 84, 2, 0);
- __visc__bindIn(var_25, 85, 3, 0);
- __visc__bindIn(var_25, 86, 4, 0);
- __visc__bindIn(var_25, 87, 5, 0);
- __visc__bindIn(var_25, 88, 6, 0);
- __visc__bindIn(var_25, 89, 7, 0);
- __visc__bindIn(var_25, 90, 8, 0);
- __visc__bindIn(var_25, 91, 9, 0);
-
- void* var_26 = __visc__createNodeND(0, var_26_node);
-
- __visc__edge(var_25, var_26, 1, 0, 0, 0);
- __visc__edge(var_25, var_26, 1, 1, 1, 0);
-
- void* var_27 = __visc__createNodeND(0, var_27_node);
-
- __visc__edge(var_26, var_27, 1, 0, 0, 0);
- __visc__edge(var_26, var_27, 1, 1, 1, 0);
- __visc__bindIn(var_27, 92, 2, 0);
- __visc__bindIn(var_27, 93, 3, 0);
-
- void* var_28 = __visc__createNodeND(0, var_28_node);
-
- __visc__edge(var_27, var_28, 1, 0, 0, 0);
- __visc__edge(var_27, var_28, 1, 1, 1, 0);
- __visc__bindIn(var_28, 94, 2, 0);
- __visc__bindIn(var_28, 95, 3, 0);
- __visc__bindIn(var_28, 96, 4, 0);
- __visc__bindIn(var_28, 97, 5, 0);
- __visc__bindIn(var_28, 98, 6, 0);
- __visc__bindIn(var_28, 99, 7, 0);
- __visc__bindIn(var_28, 100, 8, 0);
- __visc__bindIn(var_28, 101, 9, 0);
-
- void* var_29 = __visc__createNodeND(0, var_29_node);
-
- __visc__edge(var_28, var_29, 1, 0, 0, 0);
- __visc__edge(var_28, var_29, 1, 1, 1, 0);
-
- void* var_30 = __visc__createNodeND(0, var_30_node);
-
- __visc__edge(var_29, var_30, 1, 0, 0, 0);
- __visc__edge(var_29, var_30, 1, 1, 1, 0);
- __visc__bindIn(var_30, 102, 2, 0);
- __visc__bindIn(var_30, 103, 3, 0);
-
- void* var_31 = __visc__createNodeND(0, var_31_node);
-
- __visc__edge(var_30, var_31, 1, 0, 0, 0);
- __visc__edge(var_30, var_31, 1, 1, 1, 0);
- __visc__bindIn(var_31, 104, 2, 0);
- __visc__bindIn(var_31, 105, 3, 0);
- __visc__bindIn(var_31, 106, 4, 0);
- __visc__bindIn(var_31, 107, 5, 0);
- __visc__bindIn(var_31, 108, 6, 0);
- __visc__bindIn(var_31, 109, 7, 0);
- __visc__bindIn(var_31, 110, 8, 0);
- __visc__bindIn(var_31, 111, 9, 0);
-
- void* var_32 = __visc__createNodeND(0, var_32_node);
-
- __visc__edge(var_31, var_32, 1, 0, 0, 0);
- __visc__edge(var_31, var_32, 1, 1, 1, 0);
-
- void* var_33 = __visc__createNodeND(0, var_33_node);
-
- __visc__edge(var_32, var_33, 1, 0, 0, 0);
- __visc__edge(var_32, var_33, 1, 1, 1, 0);
- __visc__bindIn(var_33, 112, 2, 0);
- __visc__bindIn(var_33, 113, 3, 0);
-
- void* var_34 = __visc__createNodeND(0, var_34_node);
-
- __visc__edge(var_33, var_34, 1, 0, 0, 0);
- __visc__edge(var_33, var_34, 1, 1, 1, 0);
- __visc__bindIn(var_34, 114, 2, 0);
- __visc__bindIn(var_34, 115, 3, 0);
- __visc__bindIn(var_34, 116, 4, 0);
- __visc__bindIn(var_34, 117, 5, 0);
- __visc__bindIn(var_34, 118, 6, 0);
- __visc__bindIn(var_34, 119, 7, 0);
- __visc__bindIn(var_34, 120, 8, 0);
- __visc__bindIn(var_34, 121, 9, 0);
-
- void* var_35 = __visc__createNodeND(0, var_35_node);
-
- __visc__edge(var_34, var_35, 1, 0, 0, 0);
- __visc__edge(var_34, var_35, 1, 1, 1, 0);
-
- void* var_36 = __visc__createNodeND(0, var_36_node);
-
- __visc__edge(var_35, var_36, 1, 0, 0, 0);
- __visc__edge(var_35, var_36, 1, 1, 1, 0);
- __visc__bindIn(var_36, 122, 2, 0);
- __visc__bindIn(var_36, 123, 3, 0);
-
- void* var_37 = __visc__createNodeND(0, var_37_node);
-
- __visc__edge(var_36, var_37, 1, 0, 0, 0);
- __visc__edge(var_36, var_37, 1, 1, 1, 0);
- __visc__bindIn(var_37, 124, 2, 0);
- __visc__bindIn(var_37, 125, 3, 0);
- __visc__bindIn(var_37, 126, 4, 0);
- __visc__bindIn(var_37, 127, 5, 0);
- __visc__bindIn(var_37, 128, 6, 0);
- __visc__bindIn(var_37, 129, 7, 0);
- __visc__bindIn(var_37, 130, 8, 0);
- __visc__bindIn(var_37, 131, 9, 0);
-
- void* var_38 = __visc__createNodeND(0, var_38_node);
-
- __visc__edge(var_37, var_38, 1, 0, 0, 0);
- __visc__edge(var_37, var_38, 1, 1, 1, 0);
-
- void* var_39 = __visc__createNodeND(0, var_39_node);
-
- __visc__edge(var_38, var_39, 1, 0, 0, 0);
- __visc__edge(var_38, var_39, 1, 1, 1, 0);
- __visc__bindIn(var_39, 132, 2, 0);
- __visc__bindIn(var_39, 133, 3, 0);
-
- void* var_40 = __visc__createNodeND(0, var_40_node);
-
- __visc__edge(var_39, var_40, 1, 0, 0, 0);
- __visc__edge(var_39, var_40, 1, 1, 1, 0);
- __visc__bindIn(var_40, 134, 2, 0);
- __visc__bindIn(var_40, 135, 3, 0);
- __visc__bindIn(var_40, 136, 4, 0);
- __visc__bindIn(var_40, 137, 5, 0);
- __visc__bindIn(var_40, 138, 6, 0);
- __visc__bindIn(var_40, 139, 7, 0);
- __visc__bindIn(var_40, 140, 8, 0);
- __visc__bindIn(var_40, 141, 9, 0);
-
- void* var_41 = __visc__createNodeND(0, var_41_node);
-
- __visc__edge(var_40, var_41, 1, 0, 0, 0);
- __visc__edge(var_40, var_41, 1, 1, 1, 0);
-
- void* var_42 = __visc__createNodeND(0, var_42_node);
-
- __visc__edge(var_41, var_42, 1, 0, 0, 0);
- __visc__edge(var_41, var_42, 1, 1, 1, 0);
- __visc__bindIn(var_42, 142, 2, 0);
- __visc__bindIn(var_42, 143, 3, 0);
-
- void* var_43 = __visc__createNodeND(0, var_43_node);
-
- __visc__edge(var_42, var_43, 1, 0, 0, 0);
- __visc__edge(var_42, var_43, 1, 1, 1, 0);
- __visc__bindIn(var_43, 144, 2, 0);
- __visc__bindIn(var_43, 145, 3, 0);
- __visc__bindIn(var_43, 146, 4, 0);
- __visc__bindIn(var_43, 147, 5, 0);
- __visc__bindIn(var_43, 148, 6, 0);
- __visc__bindIn(var_43, 149, 7, 0);
- __visc__bindIn(var_43, 150, 8, 0);
- __visc__bindIn(var_43, 151, 9, 0);
-
- void* var_44 = __visc__createNodeND(0, var_44_node);
-
- __visc__edge(var_43, var_44, 1, 0, 0, 0);
- __visc__edge(var_43, var_44, 1, 1, 1, 0);
-
- void* var_45 = __visc__createNodeND(0, var_45_node);
-
- __visc__edge(var_44, var_45, 1, 0, 0, 0);
- __visc__edge(var_44, var_45, 1, 1, 1, 0);
- __visc__bindIn(var_45, 152, 2, 0);
- __visc__bindIn(var_45, 153, 3, 0);
-
- void* var_46 = __visc__createNodeND(0, var_46_node);
-
- __visc__edge(var_45, var_46, 1, 0, 0, 0);
- __visc__edge(var_45, var_46, 1, 1, 1, 0);
- __visc__bindIn(var_46, 154, 2, 0);
- __visc__bindIn(var_46, 155, 3, 0);
- __visc__bindIn(var_46, 156, 4, 0);
- __visc__bindIn(var_46, 157, 5, 0);
- __visc__bindIn(var_46, 158, 6, 0);
- __visc__bindIn(var_46, 159, 7, 0);
- __visc__bindIn(var_46, 160, 8, 0);
- __visc__bindIn(var_46, 161, 9, 0);
-
- void* var_47 = __visc__createNodeND(0, var_47_node);
-
- __visc__edge(var_46, var_47, 1, 0, 0, 0);
- __visc__edge(var_46, var_47, 1, 1, 1, 0);
-
- void* var_48 = __visc__createNodeND(0, var_48_node);
-
- __visc__edge(var_47, var_48, 1, 0, 0, 0);
- __visc__edge(var_47, var_48, 1, 1, 1, 0);
- __visc__bindIn(var_48, 162, 2, 0);
- __visc__bindIn(var_48, 163, 3, 0);
-
- void* var_49 = __visc__createNodeND(0, var_49_node);
-
- __visc__edge(var_48, var_49, 1, 0, 0, 0);
- __visc__edge(var_48, var_49, 1, 1, 1, 0);
- __visc__bindIn(var_49, 164, 2, 0);
- __visc__bindIn(var_49, 165, 3, 0);
- __visc__bindIn(var_49, 166, 4, 0);
- __visc__bindIn(var_49, 167, 5, 0);
- __visc__bindIn(var_49, 168, 6, 0);
- __visc__bindIn(var_49, 169, 7, 0);
- __visc__bindIn(var_49, 170, 8, 0);
- __visc__bindIn(var_49, 171, 9, 0);
-
- void* var_50 = __visc__createNodeND(0, var_50_node);
-
- __visc__edge(var_49, var_50, 1, 0, 0, 0);
- __visc__edge(var_49, var_50, 1, 1, 1, 0);
-
- void* var_51 = __visc__createNodeND(0, var_51_node);
-
- __visc__edge(var_50, var_51, 1, 0, 0, 0);
- __visc__edge(var_50, var_51, 1, 1, 1, 0);
- __visc__bindIn(var_51, 172, 2, 0);
- __visc__bindIn(var_51, 173, 3, 0);
-
- void* var_52 = __visc__createNodeND(0, var_52_node);
-
- __visc__edge(var_51, var_52, 1, 0, 0, 0);
- __visc__edge(var_51, var_52, 1, 1, 1, 0);
- __visc__bindIn(var_52, 174, 2, 0);
- __visc__bindIn(var_52, 175, 3, 0);
- __visc__bindIn(var_52, 176, 4, 0);
- __visc__bindIn(var_52, 177, 5, 0);
- __visc__bindIn(var_52, 178, 6, 0);
- __visc__bindIn(var_52, 179, 7, 0);
- __visc__bindIn(var_52, 180, 8, 0);
- __visc__bindIn(var_52, 181, 9, 0);
-
- void* var_53 = __visc__createNodeND(0, var_53_node);
-
- __visc__edge(var_52, var_53, 1, 0, 0, 0);
- __visc__edge(var_52, var_53, 1, 1, 1, 0);
-
- void* var_54 = __visc__createNodeND(0, var_54_node);
-
- __visc__edge(var_53, var_54, 1, 0, 0, 0);
- __visc__edge(var_53, var_54, 1, 1, 1, 0);
- __visc__bindIn(var_54, 182, 2, 0);
- __visc__bindIn(var_54, 183, 3, 0);
-
- void* var_55 = __visc__createNodeND(0, var_55_node);
-
- __visc__edge(var_54, var_55, 1, 0, 0, 0);
- __visc__edge(var_54, var_55, 1, 1, 1, 0);
- __visc__bindIn(var_55, 184, 2, 0);
- __visc__bindIn(var_55, 185, 3, 0);
- __visc__bindIn(var_55, 186, 4, 0);
- __visc__bindIn(var_55, 187, 5, 0);
- __visc__bindIn(var_55, 188, 6, 0);
- __visc__bindIn(var_55, 189, 7, 0);
- __visc__bindIn(var_55, 190, 8, 0);
- __visc__bindIn(var_55, 191, 9, 0);
-
- void* var_56 = __visc__createNodeND(0, var_56_node);
-
- __visc__edge(var_55, var_56, 1, 0, 0, 0);
- __visc__edge(var_55, var_56, 1, 1, 1, 0);
-
- void* var_57 = __visc__createNodeND(0, var_57_node);
-
- __visc__edge(var_56, var_57, 1, 0, 0, 0);
- __visc__edge(var_56, var_57, 1, 1, 1, 0);
- __visc__bindIn(var_57, 192, 2, 0);
- __visc__bindIn(var_57, 193, 3, 0);
-
- void* var_58 = __visc__createNodeND(0, var_58_node);
-
- __visc__edge(var_57, var_58, 1, 0, 0, 0);
- __visc__edge(var_57, var_58, 1, 1, 1, 0);
- __visc__bindIn(var_58, 194, 2, 0);
- __visc__bindIn(var_58, 195, 3, 0);
- __visc__bindIn(var_58, 196, 4, 0);
- __visc__bindIn(var_58, 197, 5, 0);
- __visc__bindIn(var_58, 198, 6, 0);
- __visc__bindIn(var_58, 199, 7, 0);
- __visc__bindIn(var_58, 200, 8, 0);
- __visc__bindIn(var_58, 201, 9, 0);
-
- void* var_59 = __visc__createNodeND(0, var_59_node);
-
- __visc__edge(var_58, var_59, 1, 0, 0, 0);
- __visc__edge(var_58, var_59, 1, 1, 1, 0);
-
- void* var_60 = __visc__createNodeND(0, var_60_node);
-
- __visc__edge(var_59, var_60, 1, 0, 0, 0);
- __visc__edge(var_59, var_60, 1, 1, 1, 0);
- __visc__bindIn(var_60, 202, 2, 0);
- __visc__bindIn(var_60, 203, 3, 0);
-
- void* var_61 = __visc__createNodeND(0, var_61_node);
-
- __visc__edge(var_60, var_61, 1, 0, 0, 0);
- __visc__edge(var_60, var_61, 1, 1, 1, 0);
- __visc__bindIn(var_61, 204, 2, 0);
- __visc__bindIn(var_61, 205, 3, 0);
- __visc__bindIn(var_61, 206, 4, 0);
- __visc__bindIn(var_61, 207, 5, 0);
- __visc__bindIn(var_61, 208, 6, 0);
- __visc__bindIn(var_61, 209, 7, 0);
- __visc__bindIn(var_61, 210, 8, 0);
- __visc__bindIn(var_61, 211, 9, 0);
-
- void* var_62 = __visc__createNodeND(0, var_62_node);
-
- __visc__edge(var_61, var_62, 1, 0, 0, 0);
- __visc__edge(var_61, var_62, 1, 1, 1, 0);
-
- void* var_63 = __visc__createNodeND(0, var_63_node);
-
- __visc__edge(var_62, var_63, 1, 0, 0, 0);
- __visc__edge(var_62, var_63, 1, 1, 1, 0);
- __visc__bindIn(var_63, 212, 2, 0);
- __visc__bindIn(var_63, 213, 3, 0);
-
- void* var_64 = __visc__createNodeND(0, var_64_node);
-
- __visc__edge(var_63, var_64, 1, 0, 0, 0);
- __visc__edge(var_63, var_64, 1, 1, 1, 0);
- __visc__bindIn(var_64, 214, 2, 0);
- __visc__bindIn(var_64, 215, 3, 0);
- __visc__bindIn(var_64, 216, 4, 0);
- __visc__bindIn(var_64, 217, 5, 0);
- __visc__bindIn(var_64, 218, 6, 0);
- __visc__bindIn(var_64, 219, 7, 0);
- __visc__bindIn(var_64, 220, 8, 0);
- __visc__bindIn(var_64, 221, 9, 0);
-
- void* var_65 = __visc__createNodeND(0, var_65_node);
-
- __visc__edge(var_64, var_65, 1, 0, 0, 0);
- __visc__edge(var_64, var_65, 1, 1, 1, 0);
-
- void* var_66 = __visc__createNodeND(0, var_66_node);
-
- __visc__edge(var_65, var_66, 1, 0, 0, 0);
- __visc__edge(var_65, var_66, 1, 1, 1, 0);
- __visc__bindIn(var_66, 222, 2, 0);
- __visc__bindIn(var_66, 223, 3, 0);
-
- void* var_67 = __visc__createNodeND(0, var_67_node);
-
- __visc__edge(var_66, var_67, 1, 0, 0, 0);
- __visc__edge(var_66, var_67, 1, 1, 1, 0);
- __visc__bindIn(var_67, 224, 2, 0);
- __visc__bindIn(var_67, 225, 3, 0);
- __visc__bindIn(var_67, 226, 4, 0);
- __visc__bindIn(var_67, 227, 5, 0);
- __visc__bindIn(var_67, 228, 6, 0);
- __visc__bindIn(var_67, 229, 7, 0);
- __visc__bindIn(var_67, 230, 8, 0);
- __visc__bindIn(var_67, 231, 9, 0);
-
- void* var_68 = __visc__createNodeND(0, var_68_node);
-
- __visc__edge(var_67, var_68, 1, 0, 0, 0);
- __visc__edge(var_67, var_68, 1, 1, 1, 0);
-
- void* var_69 = __visc__createNodeND(0, var_69_node);
-
- __visc__edge(var_68, var_69, 1, 0, 0, 0);
- __visc__edge(var_68, var_69, 1, 1, 1, 0);
- __visc__bindIn(var_69, 232, 2, 0);
- __visc__bindIn(var_69, 233, 3, 0);
-
- void* var_70 = __visc__createNodeND(0, var_70_node);
-
- __visc__edge(var_69, var_70, 1, 0, 0, 0);
- __visc__edge(var_69, var_70, 1, 1, 1, 0);
- __visc__bindIn(var_70, 234, 2, 0);
- __visc__bindIn(var_70, 235, 3, 0);
- __visc__bindIn(var_70, 236, 4, 0);
- __visc__bindIn(var_70, 237, 5, 0);
- __visc__bindIn(var_70, 238, 6, 0);
- __visc__bindIn(var_70, 239, 7, 0);
- __visc__bindIn(var_70, 240, 8, 0);
- __visc__bindIn(var_70, 241, 9, 0);
-
- void* var_71 = __visc__createNodeND(0, var_71_node);
-
- __visc__edge(var_70, var_71, 1, 0, 0, 0);
- __visc__edge(var_70, var_71, 1, 1, 1, 0);
-
- void* var_72 = __visc__createNodeND(0, var_72_node);
-
- __visc__edge(var_71, var_72, 1, 0, 0, 0);
- __visc__edge(var_71, var_72, 1, 1, 1, 0);
- __visc__bindIn(var_72, 242, 2, 0);
- __visc__bindIn(var_72, 243, 3, 0);
-
- void* var_73 = __visc__createNodeND(0, var_73_node);
-
- __visc__edge(var_72, var_73, 1, 0, 0, 0);
- __visc__edge(var_72, var_73, 1, 1, 1, 0);
- __visc__bindIn(var_73, 244, 2, 0);
- __visc__bindIn(var_73, 245, 3, 0);
- __visc__bindIn(var_73, 246, 4, 0);
- __visc__bindIn(var_73, 247, 5, 0);
- __visc__bindIn(var_73, 248, 6, 0);
- __visc__bindIn(var_73, 249, 7, 0);
- __visc__bindIn(var_73, 250, 8, 0);
- __visc__bindIn(var_73, 251, 9, 0);
-
- void* var_74 = __visc__createNodeND(0, var_74_node);
-
- __visc__edge(var_73, var_74, 1, 0, 0, 0);
- __visc__edge(var_73, var_74, 1, 1, 1, 0);
-
- void* var_75 = __visc__createNodeND(0, var_75_node);
-
- __visc__edge(var_74, var_75, 1, 0, 0, 0);
- __visc__edge(var_74, var_75, 1, 1, 1, 0);
- __visc__bindIn(var_75, 252, 2, 0);
- __visc__bindIn(var_75, 253, 3, 0);
-
- void* var_76 = __visc__createNodeND(0, var_76_node);
-
- __visc__edge(var_75, var_76, 1, 0, 0, 0);
- __visc__edge(var_75, var_76, 1, 1, 1, 0);
- __visc__bindIn(var_76, 254, 2, 0);
- __visc__bindIn(var_76, 255, 3, 0);
- __visc__bindIn(var_76, 256, 4, 0);
- __visc__bindIn(var_76, 257, 5, 0);
- __visc__bindIn(var_76, 258, 6, 0);
- __visc__bindIn(var_76, 259, 7, 0);
- __visc__bindIn(var_76, 260, 8, 0);
- __visc__bindIn(var_76, 261, 9, 0);
-
- void* var_77 = __visc__createNodeND(0, var_77_node);
-
- __visc__edge(var_76, var_77, 1, 0, 0, 0);
- __visc__edge(var_76, var_77, 1, 1, 1, 0);
-
- void* var_78 = __visc__createNodeND(0, var_78_node);
-
- __visc__edge(var_77, var_78, 1, 0, 0, 0);
- __visc__edge(var_77, var_78, 1, 1, 1, 0);
- __visc__bindIn(var_78, 262, 2, 0);
- __visc__bindIn(var_78, 263, 3, 0);
-
- void* var_79 = __visc__createNodeND(0, var_79_node);
-
- __visc__edge(var_78, var_79, 1, 0, 0, 0);
- __visc__edge(var_78, var_79, 1, 1, 1, 0);
- __visc__bindIn(var_79, 264, 2, 0);
- __visc__bindIn(var_79, 265, 3, 0);
- __visc__bindIn(var_79, 266, 4, 0);
- __visc__bindIn(var_79, 267, 5, 0);
- __visc__bindIn(var_79, 268, 6, 0);
- __visc__bindIn(var_79, 269, 7, 0);
- __visc__bindIn(var_79, 270, 8, 0);
- __visc__bindIn(var_79, 271, 9, 0);
-
- void* var_80 = __visc__createNodeND(0, var_80_node);
-
- __visc__edge(var_79, var_80, 1, 0, 0, 0);
- __visc__edge(var_79, var_80, 1, 1, 1, 0);
-
- void* var_81 = __visc__createNodeND(0, var_81_node);
-
- __visc__edge(var_80, var_81, 1, 0, 0, 0);
- __visc__edge(var_80, var_81, 1, 1, 1, 0);
-
- void* var_82 = __visc__createNodeND(0, var_82_node);
-
- __visc__edge(var_81, var_82, 1, 0, 0, 0);
- __visc__edge(var_81, var_82, 1, 1, 1, 0);
- __visc__bindIn(var_82, 272, 2, 0);
- __visc__bindIn(var_82, 273, 3, 0);
-
- void* var_83 = __visc__createNodeND(0, var_83_node);
-
- __visc__edge(var_82, var_83, 1, 0, 0, 0);
- __visc__edge(var_82, var_83, 1, 1, 1, 0);
- __visc__bindIn(var_83, 274, 2, 0);
- __visc__bindIn(var_83, 275, 3, 0);
-
- void* var_84 = __visc__createNodeND(0, var_84_node);
-
- __visc__edge(var_83, var_84, 1, 0, 0, 0);
- __visc__edge(var_83, var_84, 1, 1, 1, 0);
-
- __visc__bindOut(var_84, 0, 0, 0);
- __visc__bindOut(var_84, 1, 1, 0);
-
-}
-
-struct ret_t {
- void* tensor;
- size_t bytes;
-};
-
-typedef struct __attribute__((__packed__)) {
- void* input;
- size_t input_bytes;
- void* conv2d_1_w;
- size_t conv2d_1_w_bytes;
- void* batch_normalization_1_gamma;
- size_t batch_normalization_1_gamma_bytes;
- void* batch_normalization_1_beta;
- size_t batch_normalization_1_beta_bytes;
- void* batch_normalization_1_mean;
- size_t batch_normalization_1_mean_bytes;
- void* batch_normalization_1_variance;
- size_t batch_normalization_1_variance_bytes;
- void* depthwise_conv2d_1_w;
- size_t depthwise_conv2d_1_w_bytes;
- void* batch_normalization_2_gamma;
- size_t batch_normalization_2_gamma_bytes;
- void* batch_normalization_2_beta;
- size_t batch_normalization_2_beta_bytes;
- void* batch_normalization_2_mean;
- size_t batch_normalization_2_mean_bytes;
- void* batch_normalization_2_variance;
- size_t batch_normalization_2_variance_bytes;
- void* conv2d_2_w;
- size_t conv2d_2_w_bytes;
- void* batch_normalization_3_gamma;
- size_t batch_normalization_3_gamma_bytes;
- void* batch_normalization_3_beta;
- size_t batch_normalization_3_beta_bytes;
- void* batch_normalization_3_mean;
- size_t batch_normalization_3_mean_bytes;
- void* batch_normalization_3_variance;
- size_t batch_normalization_3_variance_bytes;
- void* depthwise_conv2d_2_w;
- size_t depthwise_conv2d_2_w_bytes;
- void* batch_normalization_4_gamma;
- size_t batch_normalization_4_gamma_bytes;
- void* batch_normalization_4_beta;
- size_t batch_normalization_4_beta_bytes;
- void* batch_normalization_4_mean;
- size_t batch_normalization_4_mean_bytes;
- void* batch_normalization_4_variance;
- size_t batch_normalization_4_variance_bytes;
- void* conv2d_3_w;
- size_t conv2d_3_w_bytes;
- void* batch_normalization_5_gamma;
- size_t batch_normalization_5_gamma_bytes;
- void* batch_normalization_5_beta;
- size_t batch_normalization_5_beta_bytes;
- void* batch_normalization_5_mean;
- size_t batch_normalization_5_mean_bytes;
- void* batch_normalization_5_variance;
- size_t batch_normalization_5_variance_bytes;
- void* depthwise_conv2d_3_w;
- size_t depthwise_conv2d_3_w_bytes;
- void* batch_normalization_6_gamma;
- size_t batch_normalization_6_gamma_bytes;
- void* batch_normalization_6_beta;
- size_t batch_normalization_6_beta_bytes;
- void* batch_normalization_6_mean;
- size_t batch_normalization_6_mean_bytes;
- void* batch_normalization_6_variance;
- size_t batch_normalization_6_variance_bytes;
- void* conv2d_4_w;
- size_t conv2d_4_w_bytes;
- void* batch_normalization_7_gamma;
- size_t batch_normalization_7_gamma_bytes;
- void* batch_normalization_7_beta;
- size_t batch_normalization_7_beta_bytes;
- void* batch_normalization_7_mean;
- size_t batch_normalization_7_mean_bytes;
- void* batch_normalization_7_variance;
- size_t batch_normalization_7_variance_bytes;
- void* depthwise_conv2d_4_w;
- size_t depthwise_conv2d_4_w_bytes;
- void* batch_normalization_8_gamma;
- size_t batch_normalization_8_gamma_bytes;
- void* batch_normalization_8_beta;
- size_t batch_normalization_8_beta_bytes;
- void* batch_normalization_8_mean;
- size_t batch_normalization_8_mean_bytes;
- void* batch_normalization_8_variance;
- size_t batch_normalization_8_variance_bytes;
- void* conv2d_5_w;
- size_t conv2d_5_w_bytes;
- void* batch_normalization_9_gamma;
- size_t batch_normalization_9_gamma_bytes;
- void* batch_normalization_9_beta;
- size_t batch_normalization_9_beta_bytes;
- void* batch_normalization_9_mean;
- size_t batch_normalization_9_mean_bytes;
- void* batch_normalization_9_variance;
- size_t batch_normalization_9_variance_bytes;
- void* depthwise_conv2d_5_w;
- size_t depthwise_conv2d_5_w_bytes;
- void* batch_normalization_10_gamma;
- size_t batch_normalization_10_gamma_bytes;
- void* batch_normalization_10_beta;
- size_t batch_normalization_10_beta_bytes;
- void* batch_normalization_10_mean;
- size_t batch_normalization_10_mean_bytes;
- void* batch_normalization_10_variance;
- size_t batch_normalization_10_variance_bytes;
- void* conv2d_6_w;
- size_t conv2d_6_w_bytes;
- void* batch_normalization_11_gamma;
- size_t batch_normalization_11_gamma_bytes;
- void* batch_normalization_11_beta;
- size_t batch_normalization_11_beta_bytes;
- void* batch_normalization_11_mean;
- size_t batch_normalization_11_mean_bytes;
- void* batch_normalization_11_variance;
- size_t batch_normalization_11_variance_bytes;
- void* depthwise_conv2d_6_w;
- size_t depthwise_conv2d_6_w_bytes;
- void* batch_normalization_12_gamma;
- size_t batch_normalization_12_gamma_bytes;
- void* batch_normalization_12_beta;
- size_t batch_normalization_12_beta_bytes;
- void* batch_normalization_12_mean;
- size_t batch_normalization_12_mean_bytes;
- void* batch_normalization_12_variance;
- size_t batch_normalization_12_variance_bytes;
- void* conv2d_7_w;
- size_t conv2d_7_w_bytes;
- void* batch_normalization_13_gamma;
- size_t batch_normalization_13_gamma_bytes;
- void* batch_normalization_13_beta;
- size_t batch_normalization_13_beta_bytes;
- void* batch_normalization_13_mean;
- size_t batch_normalization_13_mean_bytes;
- void* batch_normalization_13_variance;
- size_t batch_normalization_13_variance_bytes;
- void* depthwise_conv2d_7_w;
- size_t depthwise_conv2d_7_w_bytes;
- void* batch_normalization_14_gamma;
- size_t batch_normalization_14_gamma_bytes;
- void* batch_normalization_14_beta;
- size_t batch_normalization_14_beta_bytes;
- void* batch_normalization_14_mean;
- size_t batch_normalization_14_mean_bytes;
- void* batch_normalization_14_variance;
- size_t batch_normalization_14_variance_bytes;
- void* conv2d_8_w;
- size_t conv2d_8_w_bytes;
- void* batch_normalization_15_gamma;
- size_t batch_normalization_15_gamma_bytes;
- void* batch_normalization_15_beta;
- size_t batch_normalization_15_beta_bytes;
- void* batch_normalization_15_mean;
- size_t batch_normalization_15_mean_bytes;
- void* batch_normalization_15_variance;
- size_t batch_normalization_15_variance_bytes;
- void* depthwise_conv2d_8_w;
- size_t depthwise_conv2d_8_w_bytes;
- void* batch_normalization_16_gamma;
- size_t batch_normalization_16_gamma_bytes;
- void* batch_normalization_16_beta;
- size_t batch_normalization_16_beta_bytes;
- void* batch_normalization_16_mean;
- size_t batch_normalization_16_mean_bytes;
- void* batch_normalization_16_variance;
- size_t batch_normalization_16_variance_bytes;
- void* conv2d_9_w;
- size_t conv2d_9_w_bytes;
- void* batch_normalization_17_gamma;
- size_t batch_normalization_17_gamma_bytes;
- void* batch_normalization_17_beta;
- size_t batch_normalization_17_beta_bytes;
- void* batch_normalization_17_mean;
- size_t batch_normalization_17_mean_bytes;
- void* batch_normalization_17_variance;
- size_t batch_normalization_17_variance_bytes;
- void* depthwise_conv2d_9_w;
- size_t depthwise_conv2d_9_w_bytes;
- void* batch_normalization_18_gamma;
- size_t batch_normalization_18_gamma_bytes;
- void* batch_normalization_18_beta;
- size_t batch_normalization_18_beta_bytes;
- void* batch_normalization_18_mean;
- size_t batch_normalization_18_mean_bytes;
- void* batch_normalization_18_variance;
- size_t batch_normalization_18_variance_bytes;
- void* conv2d_10_w;
- size_t conv2d_10_w_bytes;
- void* batch_normalization_19_gamma;
- size_t batch_normalization_19_gamma_bytes;
- void* batch_normalization_19_beta;
- size_t batch_normalization_19_beta_bytes;
- void* batch_normalization_19_mean;
- size_t batch_normalization_19_mean_bytes;
- void* batch_normalization_19_variance;
- size_t batch_normalization_19_variance_bytes;
- void* depthwise_conv2d_10_w;
- size_t depthwise_conv2d_10_w_bytes;
- void* batch_normalization_20_gamma;
- size_t batch_normalization_20_gamma_bytes;
- void* batch_normalization_20_beta;
- size_t batch_normalization_20_beta_bytes;
- void* batch_normalization_20_mean;
- size_t batch_normalization_20_mean_bytes;
- void* batch_normalization_20_variance;
- size_t batch_normalization_20_variance_bytes;
- void* conv2d_11_w;
- size_t conv2d_11_w_bytes;
- void* batch_normalization_21_gamma;
- size_t batch_normalization_21_gamma_bytes;
- void* batch_normalization_21_beta;
- size_t batch_normalization_21_beta_bytes;
- void* batch_normalization_21_mean;
- size_t batch_normalization_21_mean_bytes;
- void* batch_normalization_21_variance;
- size_t batch_normalization_21_variance_bytes;
- void* depthwise_conv2d_11_w;
- size_t depthwise_conv2d_11_w_bytes;
- void* batch_normalization_22_gamma;
- size_t batch_normalization_22_gamma_bytes;
- void* batch_normalization_22_beta;
- size_t batch_normalization_22_beta_bytes;
- void* batch_normalization_22_mean;
- size_t batch_normalization_22_mean_bytes;
- void* batch_normalization_22_variance;
- size_t batch_normalization_22_variance_bytes;
- void* conv2d_12_w;
- size_t conv2d_12_w_bytes;
- void* batch_normalization_23_gamma;
- size_t batch_normalization_23_gamma_bytes;
- void* batch_normalization_23_beta;
- size_t batch_normalization_23_beta_bytes;
- void* batch_normalization_23_mean;
- size_t batch_normalization_23_mean_bytes;
- void* batch_normalization_23_variance;
- size_t batch_normalization_23_variance_bytes;
- void* depthwise_conv2d_12_w;
- size_t depthwise_conv2d_12_w_bytes;
- void* batch_normalization_24_gamma;
- size_t batch_normalization_24_gamma_bytes;
- void* batch_normalization_24_beta;
- size_t batch_normalization_24_beta_bytes;
- void* batch_normalization_24_mean;
- size_t batch_normalization_24_mean_bytes;
- void* batch_normalization_24_variance;
- size_t batch_normalization_24_variance_bytes;
- void* conv2d_13_w;
- size_t conv2d_13_w_bytes;
- void* batch_normalization_25_gamma;
- size_t batch_normalization_25_gamma_bytes;
- void* batch_normalization_25_beta;
- size_t batch_normalization_25_beta_bytes;
- void* batch_normalization_25_mean;
- size_t batch_normalization_25_mean_bytes;
- void* batch_normalization_25_variance;
- size_t batch_normalization_25_variance_bytes;
- void* depthwise_conv2d_13_w;
- size_t depthwise_conv2d_13_w_bytes;
- void* batch_normalization_26_gamma;
- size_t batch_normalization_26_gamma_bytes;
- void* batch_normalization_26_beta;
- size_t batch_normalization_26_beta_bytes;
- void* batch_normalization_26_mean;
- size_t batch_normalization_26_mean_bytes;
- void* batch_normalization_26_variance;
- size_t batch_normalization_26_variance_bytes;
- void* conv2d_14_w;
- size_t conv2d_14_w_bytes;
- void* batch_normalization_27_gamma;
- size_t batch_normalization_27_gamma_bytes;
- void* batch_normalization_27_beta;
- size_t batch_normalization_27_beta_bytes;
- void* batch_normalization_27_mean;
- size_t batch_normalization_27_mean_bytes;
- void* batch_normalization_27_variance;
- size_t batch_normalization_27_variance_bytes;
- void* dense_1_w;
- size_t dense_1_w_bytes;
- void* dense_1_b;
- size_t dense_1_b_bytes;
-
- struct ret_t r;
-}
-RootIn;
-
-int main(){
-
-std::string dir_prefix = std::string("data/mobilenet_quant/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
-void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
-void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
-void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
-void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
-void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
-void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
-void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
-void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
-void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
-void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
-void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
-void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
-void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
-void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
-void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
-void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
-void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
-void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
-void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
-void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
-void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
-void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
-void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
-void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
-void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
-void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
-void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
-void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
-void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
-void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
-void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
-void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
-void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
-void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
-void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
-void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
-void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
-void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
-void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
-void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
-void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
-void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
-void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
-void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
-void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
-void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
-void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
-void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
-void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
-void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
-std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
-void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
-void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
-void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
-void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
-std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
-void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
-std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
-void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
-void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
-void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
-void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
-std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
-void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
-void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
-void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
-void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-
-__visc__init();
-RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn)));
-
-args->input = input;
-args->input_bytes = 0;
-args->conv2d_1_w = conv2d_1_w;
-args->conv2d_1_w_bytes = 0;
-args->batch_normalization_1_gamma = batch_normalization_1_gamma;
-args->batch_normalization_1_gamma_bytes = 0;
-args->batch_normalization_1_beta = batch_normalization_1_beta;
-args->batch_normalization_1_beta_bytes = 0;
-args->batch_normalization_1_mean = batch_normalization_1_mean;
-args->batch_normalization_1_mean_bytes = 0;
-args->batch_normalization_1_variance = batch_normalization_1_variance;
-args->batch_normalization_1_variance_bytes = 0;
-args->depthwise_conv2d_1_w = depthwise_conv2d_1_w;
-args->depthwise_conv2d_1_w_bytes = 0;
-args->batch_normalization_2_gamma = batch_normalization_2_gamma;
-args->batch_normalization_2_gamma_bytes = 0;
-args->batch_normalization_2_beta = batch_normalization_2_beta;
-args->batch_normalization_2_beta_bytes = 0;
-args->batch_normalization_2_mean = batch_normalization_2_mean;
-args->batch_normalization_2_mean_bytes = 0;
-args->batch_normalization_2_variance = batch_normalization_2_variance;
-args->batch_normalization_2_variance_bytes = 0;
-args->conv2d_2_w = conv2d_2_w;
-args->conv2d_2_w_bytes = 0;
-args->batch_normalization_3_gamma = batch_normalization_3_gamma;
-args->batch_normalization_3_gamma_bytes = 0;
-args->batch_normalization_3_beta = batch_normalization_3_beta;
-args->batch_normalization_3_beta_bytes = 0;
-args->batch_normalization_3_mean = batch_normalization_3_mean;
-args->batch_normalization_3_mean_bytes = 0;
-args->batch_normalization_3_variance = batch_normalization_3_variance;
-args->batch_normalization_3_variance_bytes = 0;
-args->depthwise_conv2d_2_w = depthwise_conv2d_2_w;
-args->depthwise_conv2d_2_w_bytes = 0;
-args->batch_normalization_4_gamma = batch_normalization_4_gamma;
-args->batch_normalization_4_gamma_bytes = 0;
-args->batch_normalization_4_beta = batch_normalization_4_beta;
-args->batch_normalization_4_beta_bytes = 0;
-args->batch_normalization_4_mean = batch_normalization_4_mean;
-args->batch_normalization_4_mean_bytes = 0;
-args->batch_normalization_4_variance = batch_normalization_4_variance;
-args->batch_normalization_4_variance_bytes = 0;
-args->conv2d_3_w = conv2d_3_w;
-args->conv2d_3_w_bytes = 0;
-args->batch_normalization_5_gamma = batch_normalization_5_gamma;
-args->batch_normalization_5_gamma_bytes = 0;
-args->batch_normalization_5_beta = batch_normalization_5_beta;
-args->batch_normalization_5_beta_bytes = 0;
-args->batch_normalization_5_mean = batch_normalization_5_mean;
-args->batch_normalization_5_mean_bytes = 0;
-args->batch_normalization_5_variance = batch_normalization_5_variance;
-args->batch_normalization_5_variance_bytes = 0;
-args->depthwise_conv2d_3_w = depthwise_conv2d_3_w;
-args->depthwise_conv2d_3_w_bytes = 0;
-args->batch_normalization_6_gamma = batch_normalization_6_gamma;
-args->batch_normalization_6_gamma_bytes = 0;
-args->batch_normalization_6_beta = batch_normalization_6_beta;
-args->batch_normalization_6_beta_bytes = 0;
-args->batch_normalization_6_mean = batch_normalization_6_mean;
-args->batch_normalization_6_mean_bytes = 0;
-args->batch_normalization_6_variance = batch_normalization_6_variance;
-args->batch_normalization_6_variance_bytes = 0;
-args->conv2d_4_w = conv2d_4_w;
-args->conv2d_4_w_bytes = 0;
-args->batch_normalization_7_gamma = batch_normalization_7_gamma;
-args->batch_normalization_7_gamma_bytes = 0;
-args->batch_normalization_7_beta = batch_normalization_7_beta;
-args->batch_normalization_7_beta_bytes = 0;
-args->batch_normalization_7_mean = batch_normalization_7_mean;
-args->batch_normalization_7_mean_bytes = 0;
-args->batch_normalization_7_variance = batch_normalization_7_variance;
-args->batch_normalization_7_variance_bytes = 0;
-args->depthwise_conv2d_4_w = depthwise_conv2d_4_w;
-args->depthwise_conv2d_4_w_bytes = 0;
-args->batch_normalization_8_gamma = batch_normalization_8_gamma;
-args->batch_normalization_8_gamma_bytes = 0;
-args->batch_normalization_8_beta = batch_normalization_8_beta;
-args->batch_normalization_8_beta_bytes = 0;
-args->batch_normalization_8_mean = batch_normalization_8_mean;
-args->batch_normalization_8_mean_bytes = 0;
-args->batch_normalization_8_variance = batch_normalization_8_variance;
-args->batch_normalization_8_variance_bytes = 0;
-args->conv2d_5_w = conv2d_5_w;
-args->conv2d_5_w_bytes = 0;
-args->batch_normalization_9_gamma = batch_normalization_9_gamma;
-args->batch_normalization_9_gamma_bytes = 0;
-args->batch_normalization_9_beta = batch_normalization_9_beta;
-args->batch_normalization_9_beta_bytes = 0;
-args->batch_normalization_9_mean = batch_normalization_9_mean;
-args->batch_normalization_9_mean_bytes = 0;
-args->batch_normalization_9_variance = batch_normalization_9_variance;
-args->batch_normalization_9_variance_bytes = 0;
-args->depthwise_conv2d_5_w = depthwise_conv2d_5_w;
-args->depthwise_conv2d_5_w_bytes = 0;
-args->batch_normalization_10_gamma = batch_normalization_10_gamma;
-args->batch_normalization_10_gamma_bytes = 0;
-args->batch_normalization_10_beta = batch_normalization_10_beta;
-args->batch_normalization_10_beta_bytes = 0;
-args->batch_normalization_10_mean = batch_normalization_10_mean;
-args->batch_normalization_10_mean_bytes = 0;
-args->batch_normalization_10_variance = batch_normalization_10_variance;
-args->batch_normalization_10_variance_bytes = 0;
-args->conv2d_6_w = conv2d_6_w;
-args->conv2d_6_w_bytes = 0;
-args->batch_normalization_11_gamma = batch_normalization_11_gamma;
-args->batch_normalization_11_gamma_bytes = 0;
-args->batch_normalization_11_beta = batch_normalization_11_beta;
-args->batch_normalization_11_beta_bytes = 0;
-args->batch_normalization_11_mean = batch_normalization_11_mean;
-args->batch_normalization_11_mean_bytes = 0;
-args->batch_normalization_11_variance = batch_normalization_11_variance;
-args->batch_normalization_11_variance_bytes = 0;
-args->depthwise_conv2d_6_w = depthwise_conv2d_6_w;
-args->depthwise_conv2d_6_w_bytes = 0;
-args->batch_normalization_12_gamma = batch_normalization_12_gamma;
-args->batch_normalization_12_gamma_bytes = 0;
-args->batch_normalization_12_beta = batch_normalization_12_beta;
-args->batch_normalization_12_beta_bytes = 0;
-args->batch_normalization_12_mean = batch_normalization_12_mean;
-args->batch_normalization_12_mean_bytes = 0;
-args->batch_normalization_12_variance = batch_normalization_12_variance;
-args->batch_normalization_12_variance_bytes = 0;
-args->conv2d_7_w = conv2d_7_w;
-args->conv2d_7_w_bytes = 0;
-args->batch_normalization_13_gamma = batch_normalization_13_gamma;
-args->batch_normalization_13_gamma_bytes = 0;
-args->batch_normalization_13_beta = batch_normalization_13_beta;
-args->batch_normalization_13_beta_bytes = 0;
-args->batch_normalization_13_mean = batch_normalization_13_mean;
-args->batch_normalization_13_mean_bytes = 0;
-args->batch_normalization_13_variance = batch_normalization_13_variance;
-args->batch_normalization_13_variance_bytes = 0;
-args->depthwise_conv2d_7_w = depthwise_conv2d_7_w;
-args->depthwise_conv2d_7_w_bytes = 0;
-args->batch_normalization_14_gamma = batch_normalization_14_gamma;
-args->batch_normalization_14_gamma_bytes = 0;
-args->batch_normalization_14_beta = batch_normalization_14_beta;
-args->batch_normalization_14_beta_bytes = 0;
-args->batch_normalization_14_mean = batch_normalization_14_mean;
-args->batch_normalization_14_mean_bytes = 0;
-args->batch_normalization_14_variance = batch_normalization_14_variance;
-args->batch_normalization_14_variance_bytes = 0;
-args->conv2d_8_w = conv2d_8_w;
-args->conv2d_8_w_bytes = 0;
-args->batch_normalization_15_gamma = batch_normalization_15_gamma;
-args->batch_normalization_15_gamma_bytes = 0;
-args->batch_normalization_15_beta = batch_normalization_15_beta;
-args->batch_normalization_15_beta_bytes = 0;
-args->batch_normalization_15_mean = batch_normalization_15_mean;
-args->batch_normalization_15_mean_bytes = 0;
-args->batch_normalization_15_variance = batch_normalization_15_variance;
-args->batch_normalization_15_variance_bytes = 0;
-args->depthwise_conv2d_8_w = depthwise_conv2d_8_w;
-args->depthwise_conv2d_8_w_bytes = 0;
-args->batch_normalization_16_gamma = batch_normalization_16_gamma;
-args->batch_normalization_16_gamma_bytes = 0;
-args->batch_normalization_16_beta = batch_normalization_16_beta;
-args->batch_normalization_16_beta_bytes = 0;
-args->batch_normalization_16_mean = batch_normalization_16_mean;
-args->batch_normalization_16_mean_bytes = 0;
-args->batch_normalization_16_variance = batch_normalization_16_variance;
-args->batch_normalization_16_variance_bytes = 0;
-args->conv2d_9_w = conv2d_9_w;
-args->conv2d_9_w_bytes = 0;
-args->batch_normalization_17_gamma = batch_normalization_17_gamma;
-args->batch_normalization_17_gamma_bytes = 0;
-args->batch_normalization_17_beta = batch_normalization_17_beta;
-args->batch_normalization_17_beta_bytes = 0;
-args->batch_normalization_17_mean = batch_normalization_17_mean;
-args->batch_normalization_17_mean_bytes = 0;
-args->batch_normalization_17_variance = batch_normalization_17_variance;
-args->batch_normalization_17_variance_bytes = 0;
-args->depthwise_conv2d_9_w = depthwise_conv2d_9_w;
-args->depthwise_conv2d_9_w_bytes = 0;
-args->batch_normalization_18_gamma = batch_normalization_18_gamma;
-args->batch_normalization_18_gamma_bytes = 0;
-args->batch_normalization_18_beta = batch_normalization_18_beta;
-args->batch_normalization_18_beta_bytes = 0;
-args->batch_normalization_18_mean = batch_normalization_18_mean;
-args->batch_normalization_18_mean_bytes = 0;
-args->batch_normalization_18_variance = batch_normalization_18_variance;
-args->batch_normalization_18_variance_bytes = 0;
-args->conv2d_10_w = conv2d_10_w;
-args->conv2d_10_w_bytes = 0;
-args->batch_normalization_19_gamma = batch_normalization_19_gamma;
-args->batch_normalization_19_gamma_bytes = 0;
-args->batch_normalization_19_beta = batch_normalization_19_beta;
-args->batch_normalization_19_beta_bytes = 0;
-args->batch_normalization_19_mean = batch_normalization_19_mean;
-args->batch_normalization_19_mean_bytes = 0;
-args->batch_normalization_19_variance = batch_normalization_19_variance;
-args->batch_normalization_19_variance_bytes = 0;
-args->depthwise_conv2d_10_w = depthwise_conv2d_10_w;
-args->depthwise_conv2d_10_w_bytes = 0;
-args->batch_normalization_20_gamma = batch_normalization_20_gamma;
-args->batch_normalization_20_gamma_bytes = 0;
-args->batch_normalization_20_beta = batch_normalization_20_beta;
-args->batch_normalization_20_beta_bytes = 0;
-args->batch_normalization_20_mean = batch_normalization_20_mean;
-args->batch_normalization_20_mean_bytes = 0;
-args->batch_normalization_20_variance = batch_normalization_20_variance;
-args->batch_normalization_20_variance_bytes = 0;
-args->conv2d_11_w = conv2d_11_w;
-args->conv2d_11_w_bytes = 0;
-args->batch_normalization_21_gamma = batch_normalization_21_gamma;
-args->batch_normalization_21_gamma_bytes = 0;
-args->batch_normalization_21_beta = batch_normalization_21_beta;
-args->batch_normalization_21_beta_bytes = 0;
-args->batch_normalization_21_mean = batch_normalization_21_mean;
-args->batch_normalization_21_mean_bytes = 0;
-args->batch_normalization_21_variance = batch_normalization_21_variance;
-args->batch_normalization_21_variance_bytes = 0;
-args->depthwise_conv2d_11_w = depthwise_conv2d_11_w;
-args->depthwise_conv2d_11_w_bytes = 0;
-args->batch_normalization_22_gamma = batch_normalization_22_gamma;
-args->batch_normalization_22_gamma_bytes = 0;
-args->batch_normalization_22_beta = batch_normalization_22_beta;
-args->batch_normalization_22_beta_bytes = 0;
-args->batch_normalization_22_mean = batch_normalization_22_mean;
-args->batch_normalization_22_mean_bytes = 0;
-args->batch_normalization_22_variance = batch_normalization_22_variance;
-args->batch_normalization_22_variance_bytes = 0;
-args->conv2d_12_w = conv2d_12_w;
-args->conv2d_12_w_bytes = 0;
-args->batch_normalization_23_gamma = batch_normalization_23_gamma;
-args->batch_normalization_23_gamma_bytes = 0;
-args->batch_normalization_23_beta = batch_normalization_23_beta;
-args->batch_normalization_23_beta_bytes = 0;
-args->batch_normalization_23_mean = batch_normalization_23_mean;
-args->batch_normalization_23_mean_bytes = 0;
-args->batch_normalization_23_variance = batch_normalization_23_variance;
-args->batch_normalization_23_variance_bytes = 0;
-args->depthwise_conv2d_12_w = depthwise_conv2d_12_w;
-args->depthwise_conv2d_12_w_bytes = 0;
-args->batch_normalization_24_gamma = batch_normalization_24_gamma;
-args->batch_normalization_24_gamma_bytes = 0;
-args->batch_normalization_24_beta = batch_normalization_24_beta;
-args->batch_normalization_24_beta_bytes = 0;
-args->batch_normalization_24_mean = batch_normalization_24_mean;
-args->batch_normalization_24_mean_bytes = 0;
-args->batch_normalization_24_variance = batch_normalization_24_variance;
-args->batch_normalization_24_variance_bytes = 0;
-args->conv2d_13_w = conv2d_13_w;
-args->conv2d_13_w_bytes = 0;
-args->batch_normalization_25_gamma = batch_normalization_25_gamma;
-args->batch_normalization_25_gamma_bytes = 0;
-args->batch_normalization_25_beta = batch_normalization_25_beta;
-args->batch_normalization_25_beta_bytes = 0;
-args->batch_normalization_25_mean = batch_normalization_25_mean;
-args->batch_normalization_25_mean_bytes = 0;
-args->batch_normalization_25_variance = batch_normalization_25_variance;
-args->batch_normalization_25_variance_bytes = 0;
-args->depthwise_conv2d_13_w = depthwise_conv2d_13_w;
-args->depthwise_conv2d_13_w_bytes = 0;
-args->batch_normalization_26_gamma = batch_normalization_26_gamma;
-args->batch_normalization_26_gamma_bytes = 0;
-args->batch_normalization_26_beta = batch_normalization_26_beta;
-args->batch_normalization_26_beta_bytes = 0;
-args->batch_normalization_26_mean = batch_normalization_26_mean;
-args->batch_normalization_26_mean_bytes = 0;
-args->batch_normalization_26_variance = batch_normalization_26_variance;
-args->batch_normalization_26_variance_bytes = 0;
-args->conv2d_14_w = conv2d_14_w;
-args->conv2d_14_w_bytes = 0;
-args->batch_normalization_27_gamma = batch_normalization_27_gamma;
-args->batch_normalization_27_gamma_bytes = 0;
-args->batch_normalization_27_beta = batch_normalization_27_beta;
-args->batch_normalization_27_beta_bytes = 0;
-args->batch_normalization_27_mean = batch_normalization_27_mean;
-args->batch_normalization_27_mean_bytes = 0;
-args->batch_normalization_27_variance = batch_normalization_27_variance;
-args->batch_normalization_27_variance_bytes = 0;
-args->dense_1_w = dense_1_w;
-args->dense_1_w_bytes = 0;
-args->dense_1_b = dense_1_b;
-args->dense_1_b_bytes = 0;
-
-void* dfg = __visc__launch(0, root, (void*) args);
-
-__visc__wait(dfg);
-
-void *result = static_cast<RootIn*>(args)->input;
-hpvm_request_tensor(result, 0);
-
-__visc__cleanup();
- computeAccuracy2(labels, 10000, result);
-return 0;
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_beta.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_beta.bin
deleted file mode 100644
index bb1eb07a8e262d2f4d941578fd4c19d6a90c7562..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_beta.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_gamma.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_gamma.bin
deleted file mode 100644
index 931c8925b89f363a41d3cf81483bde60abafba61..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_gamma.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_mean.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_mean.bin
deleted file mode 100644
index 633bdc9fd4a9ef052ca8b6ab488a156002e3d4b5..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_mean.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_variance.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_variance.bin
deleted file mode 100644
index f92c73f59eb5eb35ca94e3ce006e5f3c4f60ecef..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_variance.bin
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_beta.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_beta.bin
deleted file mode 100644
index 5918477d3638e851c3fdfc47dc550cea3afa7d50..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_beta.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_gamma.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_gamma.bin
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/labels32.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/labels32.bin
deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/layer_composition.txt b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/layer_composition.txt
deleted file mode 100644
index 10692997a90e4490a91ad3d0e6e04285754144fd..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/layer_composition.txt
+++ /dev/null
@@ -1,83 +0,0 @@
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-pool
-dense add
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/layers.txt b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/layers.txt
deleted file mode 100644
index 0bd2b554374c10d748a652f52e5427c716be0084..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/layers.txt
+++ /dev/null
@@ -1,83 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-#tensorBatchNorm1
-#tensorRelu1
-#tensorDepthwiseConv1
-#tensorBatchNorm2
-#tensorRelu2
-Conv2,10000,32,32,32,64,32,1,1
-#tensorBatchNorm3
-#tensorRelu3
-#tensorDepthwiseConv2
-#tensorBatchNorm4
-#tensorRelu4
-Conv3,10000,64,16,16,128,64,1,1
-#tensorBatchNorm5
-#tensorRelu5
-#tensorDepthwiseConv3
-#tensorBatchNorm6
-#tensorRelu6
-Conv4,10000,128,16,16,128,128,1,1
-#tensorBatchNorm7
-#tensorRelu7
-#tensorDepthwiseConv4
-#tensorBatchNorm8
-#tensorRelu8
-Conv5,10000,128,8,8,256,128,1,1
-#tensorBatchNorm9
-#tensorRelu9
-#tensorDepthwiseConv5
-#tensorBatchNorm10
-#tensorRelu10
-Conv6,10000,256,8,8,256,256,1,1
-#tensorBatchNorm11
-#tensorRelu11
-#tensorDepthwiseConv6
-#tensorBatchNorm12
-#tensorRelu12
-Conv7,10000,256,4,4,512,256,1,1
-#tensorBatchNorm13
-#tensorRelu13
-#tensorDepthwiseConv7
-#tensorBatchNorm14
-#tensorRelu14
-Conv8,10000,512,4,4,512,512,1,1
-#tensorBatchNorm15
-#tensorRelu15
-#tensorDepthwiseConv8
-#tensorBatchNorm16
-#tensorRelu16
-Conv9,10000,512,4,4,512,512,1,1
-#tensorBatchNorm17
-#tensorRelu17
-#tensorDepthwiseConv9
-#tensorBatchNorm18
-#tensorRelu18
-Conv10,10000,512,4,4,512,512,1,1
-#tensorBatchNorm19
-#tensorRelu19
-#tensorDepthwiseConv10
-#tensorBatchNorm20
-#tensorRelu20
-Conv11,10000,512,4,4,512,512,1,1
-#tensorBatchNorm21
-#tensorRelu21
-#tensorDepthwiseConv11
-#tensorBatchNorm22
-#tensorRelu22
-Conv12,10000,512,4,4,512,512,1,1
-#tensorBatchNorm23
-#tensorRelu23
-#tensorDepthwiseConv12
-#tensorBatchNorm24
-#tensorRelu24
-Conv13,10000,512,2,2,1024,512,1,1
-#tensorBatchNorm25
-#tensorRelu25
-#tensorDepthwiseConv13
-#tensorBatchNorm26
-#tensorRelu26
-Conv14,10000,1024,2,2,1024,1024,1,1
-#tensorBatchNorm27
-#tensorRelu27
-#tensorPooling1
-FC1,10000,1024,1024,10
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_layers.txt b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_layers.txt
deleted file mode 100644
index c2a4a29509ad89724905c869ff900f8ecaa5bf8c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_layers.txt
+++ /dev/null
@@ -1,83 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-NML1
-NML2
-NML3
-NML4
-NML5
-Conv3,10000,32,32,32,64,32,1,1
-NML6
-NML7
-NML8
-NML9
-NML10
-Conv5,10000,64,16,16,128,64,1,1
-NML11
-NML12
-NML13
-NML14
-NML15
-Conv7,10000,128,16,16,128,128,1,1
-NML16
-NML17
-NML18
-NML19
-NML20
-Conv9,10000,128,8,8,256,128,1,1
-NML21
-NML22
-NML23
-NML24
-NML25
-Conv11,10000,256,8,8,256,256,1,1
-NML26
-NML27
-NML28
-NML29
-NML30
-Conv13,10000,256,4,4,512,256,1,1
-NML31
-NML32
-NML33
-NML34
-NML35
-Conv15,10000,512,4,4,512,512,1,1
-NML36
-NML37
-NML38
-NML39
-NML40
-Conv17,10000,512,4,4,512,512,1,1
-NML41
-NML42
-NML43
-NML44
-NML45
-Conv19,10000,512,4,4,512,512,1,1
-NML46
-NML47
-NML48
-NML49
-NML50
-Conv21,10000,512,4,4,512,512,1,1
-NML51
-NML52
-NML53
-NML54
-NML55
-Conv23,10000,512,4,4,512,512,1,1
-NML56
-NML57
-NML58
-NML59
-NML60
-Conv25,10000,512,2,2,1024,512,1,1
-NML61
-NML62
-NML63
-NML64
-NML65
-Conv27,10000,1024,2,2,1024,1024,1,1
-NML66
-NML67
-NML68
-FC1,10000,1024,1024,10
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_ops.txt b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_ops.txt
deleted file mode 100644
index 8e18f2ec58cddb9ab0251229b1e908b23b71d6bc..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_ops.txt
+++ /dev/null
@@ -1,165 +0,0 @@
-#Conv1,1
-Conv1
-#NML1,1
-BatchNorm1
-#NML2,1
-Relu1
-#NML3,1
-Conv2
-#NML4,1
-BatchNorm2
-#NML5,1
-Relu2
-#Conv3,1
-Conv3
-#NML6,1
-BatchNorm3
-#NML7,1
-Relu3
-#NML8,1
-Conv4
-#NML9,1
-BatchNorm4
-#NML10,1
-Relu4
-#Conv5,1
-Conv5
-#NML11,1
-BatchNorm5
-#NML12,1
-Relu5
-#NML13,1
-Conv6
-#NML14,1
-BatchNorm6
-#NML15,1
-Relu6
-#Conv7,1
-Conv7
-#NML16,1
-BatchNorm7
-#NML17,1
-Relu7
-#NML18,1
-Conv8
-#NML19,1
-BatchNorm8
-#NML20,1
-Relu8
-#Conv9,1
-Conv9
-#NML21,1
-BatchNorm9
-#NML22,1
-Relu9
-#NML23,1
-Conv10
-#NML24,1
-BatchNorm10
-#NML25,1
-Relu10
-#Conv11,1
-Conv11
-#NML26,1
-BatchNorm11
-#NML27,1
-Relu11
-#NML28,1
-Conv12
-#NML29,1
-BatchNorm12
-#NML30,1
-Relu12
-#Conv13,1
-Conv13
-#NML31,1
-BatchNorm13
-#NML32,1
-Relu13
-#NML33,1
-Conv14
-#NML34,1
-BatchNorm14
-#NML35,1
-Relu14
-#Conv15,1
-Conv15
-#NML36,1
-BatchNorm15
-#NML37,1
-Relu15
-#NML38,1
-Conv16
-#NML39,1
-BatchNorm16
-#NML40,1
-Relu16
-#Conv17,1
-Conv17
-#NML41,1
-BatchNorm17
-#NML42,1
-Relu17
-#NML43,1
-Conv18
-#NML44,1
-BatchNorm18
-#NML45,1
-Relu18
-#Conv19,1
-Conv19
-#NML46,1
-BatchNorm19
-#NML47,1
-Relu19
-#NML48,1
-Conv20
-#NML49,1
-BatchNorm20
-#NML50,1
-Relu20
-#Conv21,1
-conv21
-#NML51,1
-BatchNorm21
-#NML52,1
-Relu21
-#NML53,1
-Conv22
-#NML54,1
-BatchNorm22
-#NML55,1
-Relu22
-#Conv23,1
-Conv23
-#NML56,1
-BatchNorm23
-#NML57,1
-Relu23
-#NML58,1
-Conv24
-#NML59,1
-BatchNorm24
-#NML60,1
-Relu24
-#Conv25,1
-Conv25
-#NML61,1
-BatchNorm25
-#NML62,1
-Relu25
-#NML63,1
-Conv26
-#NML64,1
-BatchNorm26
-#NML65,1
-Relu26
-#Conv27,1
-Conv27
-#NML66,1
-BatchNorm27
-#NML67,1
-Relu27
-#NML68,1
-Pool1
-FC1,10000,1024,1024,10
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/promise_src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/promise_src.cc
deleted file mode 100644
index 146bc640cc4b1e8da65e3e7bb6cb5c7f2a007399..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/promise_src.cc
+++ /dev/null
@@ -1,420 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-int total_runs = 100;
-for (int i = 0 ; i < total_runs; i++){
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-
-
-std::string dir_prefix = std::string("data/mobilenet_quant/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
-void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
-void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
-void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
-void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
-void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
-void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
-void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
-void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
-void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
-void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
-void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
-void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
-void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
-void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
-void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
-void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
-void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
-void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
-void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
-void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
-void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
-void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
-void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
-void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
-void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
-void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
-void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
-void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
-void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
-void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
-void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
-void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
-void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
-void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
-void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
-void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
-void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
-void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
-void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
-void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
-void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
-void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
-void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
-void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
-void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
-void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
-void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
-void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
-void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
-void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
-std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
-void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
-void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
-void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
-void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
-std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
-void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
-std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
-void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
-void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
-void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
-void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
-std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
-void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
-void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
-void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
-void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -2.196306920051575, 1.347581704139706, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -60.89275047302246, 51.99256916046146, 9);
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_2 = tensorRelu(var_1);
-void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_5 = tensorRelu(var_4);
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 5.713541553974245, conv2d_2_w, -0.9317721160650253, 1.0774258937835774, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.518589503288269, 6.810842518806449, 9);
-void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_8 = tensorRelu(var_7);
-void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
-void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_11 = tensorRelu(var_10);
-void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.932139402866376, conv2d_3_w, -0.5316544661521911, 0.5753790403604531, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.482631235122681, 3.96730119752885, 9);
-void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_14 = tensorRelu(var_13);
-void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
-void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_17 = tensorRelu(var_16);
-void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.103263397693674, conv2d_4_w, -0.36234098821878435, 0.4076913900375366, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.04261828327179, 3.88677932929993, 9);
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_20 = tensorRelu(var_19);
-void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_23 = tensorRelu(var_22);
-void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 5.383221302509475, conv2d_5_w, -0.3131200549006462, 0.29357679939270065, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.921469215393066, 4.338679324150087, 9);
-void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_26 = tensorRelu(var_25);
-void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
-void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_29 = tensorRelu(var_28);
-void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 4.316738154411368, conv2d_6_w, -0.23299247801303866, 0.2580290257930756, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.207789947509766, 3.932436970710759, 9);
-void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_32 = tensorRelu(var_31);
-void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_35 = tensorRelu(var_34);
-void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 5.830408106803901, conv2d_7_w, -0.20233777219057084, 0.18998308175802117, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.298286915779113, 4.848135117530843, 9);
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorConvolution(var_38, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
-void* var_40 = tensorBatchNorm(var_39, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
-void* var_41 = tensorRelu(var_40);
-void* var_42 = ConvLayer_PROMISE(var_41, 0.0, 4.446417809963227, conv2d_8_w, -0.17442735651135444, 0.17695830866694454, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.347910885810852, 3.6144364695549145, 9);
-void* var_43 = tensorBatchNorm(var_42, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
-void* var_44 = tensorRelu(var_43);
-void* var_45 = tensorConvolution(var_44, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
-void* var_46 = tensorBatchNorm(var_45, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
-void* var_47 = tensorRelu(var_46);
-void* var_48 = ConvLayer_PROMISE(var_47, 0.0, 4.518095604896667, conv2d_9_w, -0.14546796187758446, 0.15256431668996823, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.0287702755928043, 2.9487365779876953, 9);
-void* var_49 = tensorBatchNorm(var_48, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
-void* var_50 = tensorRelu(var_49);
-void* var_51 = tensorConvolution(var_50, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
-void* var_52 = tensorBatchNorm(var_51, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
-void* var_53 = tensorRelu(var_52);
-void* var_54 = ConvLayer_PROMISE(var_53, 0.0, 6.348575634956407, conv2d_10_w, -0.13025874522328376, 0.13558243343234128, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.2293100805282595, 3.5315046372413645, 9);
-void* var_55 = tensorBatchNorm(var_54, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
-void* var_56 = tensorRelu(var_55);
-void* var_57 = tensorConvolution(var_56, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
-void* var_58 = tensorBatchNorm(var_57, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
-void* var_59 = tensorRelu(var_58);
-void* var_60 = ConvLayer_PROMISE(var_59, 0.0, 5.221003110408843, conv2d_11_w, -0.11900172759592534, 0.12536374783515936, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.038203780174255, 4.004009407043483, 9);
-void* var_61 = tensorBatchNorm(var_60, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
-void* var_62 = tensorRelu(var_61);
-void* var_63 = tensorConvolution(var_62, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
-void* var_64 = tensorBatchNorm(var_63, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
-void* var_65 = tensorRelu(var_64);
-void* var_66 = ConvLayer_PROMISE(var_65, 0.0, 5.732498347759442, conv2d_12_w, -0.10839721685647964, 0.11625668607652187, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.3111015114784244, 4.462933233261136, 9);
-void* var_67 = tensorBatchNorm(var_66, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
-void* var_68 = tensorRelu(var_67);
-void* var_69 = tensorConvolution(var_68, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
-void* var_70 = tensorBatchNorm(var_69, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
-void* var_71 = tensorRelu(var_70);
-void* var_72 = ConvLayer_PROMISE(var_71, 0.0, 7.240498211860681, conv2d_13_w, -0.08623744961619377, 0.08859449951350662, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.175431394577027, 6.2043294754027345, 9);
-void* var_73 = tensorBatchNorm(var_72, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
-void* var_74 = tensorRelu(var_73);
-void* var_75 = tensorConvolution(var_74, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
-void* var_76 = tensorBatchNorm(var_75, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
-void* var_77 = tensorRelu(var_76);
-void* var_78 = ConvLayer_PROMISE(var_77, 0.0, 7.813958834648251, conv2d_14_w, -0.06813025139272214, 0.07002027779817581, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -10.920566423416137, 2.6442912578582534, 9);
-void* var_79 = tensorBatchNorm(var_78, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
-void* var_80 = tensorRelu(var_79);
-void* var_81 = tensorPooling(var_80,1,2,2,0,0,2,2);
-void* var_82 = FCLayer_PROMISE(var_81, 0.0, 2.8692066650391013, dense_1_w, -0.22301019695401192, 0.1442659378200768, dense_1_b, -0.1654396, 0.23336112, -1, -12.245949958801269, 23.80532513427739, 9);
-void* var_83 = tensorSoftmax(var_82);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_83);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-}
-
-dumpExecutionAccuracies();
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/quant_ranges.txt b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/quant_ranges.txt
deleted file mode 100644
index 9ea66b8485dc19a8f2f9abfc5981e023f22ce521..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/quant_ranges.txt
+++ /dev/null
@@ -1,15 +0,0 @@
--1.9892114 2.126797 -2.19630692005 1.34758170414 0.0 0.0 -60.892750473 51.9925691605
-0.0 5.71354155397 -0.931772116065 1.07742589378 0.0 0.0 -6.51858950329 6.81084251881
-0.0 4.93213940287 -0.531654466152 0.57537904036 0.0 0.0 -4.48263123512 3.96730119753
-0.0 4.10326339769 -0.362340988219 0.407691390038 0.0 0.0 -4.04261828327 3.8867793293
-0.0 5.38322130251 -0.313120054901 0.293576799393 0.0 0.0 -5.92146921539 4.33867932415
-0.0 4.31673815441 -0.232992478013 0.258029025793 0.0 0.0 -4.20778994751 3.93243697071
-0.0 5.8304081068 -0.202337772191 0.189983081758 0.0 0.0 -6.29828691578 4.84813511753
-0.0 4.44641780996 -0.174427356511 0.176958308667 0.0 0.0 -4.34791088581 3.61443646955
-0.0 4.5180956049 -0.145467961878 0.15256431669 0.0 0.0 -3.02877027559 2.94873657799
-0.0 6.34857563496 -0.130258745223 0.135582433432 0.0 0.0 -4.22931008053 3.53150463724
-0.0 5.22100311041 -0.119001727596 0.125363747835 0.0 0.0 -4.03820378017 4.00400940704
-0.0 5.73249834776 -0.108397216856 0.116256686077 0.0 0.0 -3.31110151148 4.46293323326
-0.0 7.24049821186 -0.0862374496162 0.0885944995135 0.0 0.0 -4.17543139458 6.2043294754
-0.0 7.81395883465 -0.0681302513927 0.0700202777982 0.0 0.0 -10.9205664234 2.64429125786
-0.0 2.86920666504 -0.223010196954 0.14426593782 -0.1654396 0.23336112 -12.2459499588 23.8053251343
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/src.cc
deleted file mode 100644
index 25aec9bde3bc1aac157e2acc368dcddf866e455d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/src.cc
+++ /dev/null
@@ -1,413 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("data/mobilenet_quant/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
-void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
-void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
-void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
-void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
-void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
-void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
-void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
-void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
-void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
-void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
-void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
-void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
-void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
-void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
-void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
-void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
-void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
-void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
-void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
-void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
-void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
-void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
-void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
-void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
-void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
-void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
-void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
-void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
-void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
-void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
-void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
-void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
-void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
-void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
-void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
-void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
-void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
-void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
-void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
-void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
-void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
-void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
-void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
-void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
-void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
-void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
-void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
-void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
-void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
-void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
-std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
-void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
-void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
-void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
-void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
-std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
-void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
-std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
-void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
-void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
-void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
-void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
-std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
-void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
-void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
-void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
-void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_2 = tensorRelu(var_1);
-void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_6 = tensorRelu(var_5);
-void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
-void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_9 = tensorRelu(var_8);
-void* var_11 = tensorConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
-void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_13 = tensorRelu(var_12);
-void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
-void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_16 = tensorRelu(var_15);
-void* var_18 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_20 = tensorRelu(var_19);
-void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_23 = tensorRelu(var_22);
-void* var_26 = tensorConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
-void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_28 = tensorRelu(var_27);
-void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
-void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_31 = tensorRelu(var_30);
-void* var_33 = tensorConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_35 = tensorRelu(var_34);
-void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_38 = tensorRelu(var_37);
-void* var_41 = tensorConvolution(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
-void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_43 = tensorRelu(var_42);
-void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
-void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_46 = tensorRelu(var_45);
-void* var_48 = tensorConvolution(var_46, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
-void* var_49 = tensorBatchNorm(var_48, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
-void* var_50 = tensorRelu(var_49);
-void* var_51 = tensorConvolution(var_50, conv2d_8_w, 0, 0, 1, 1, 1, 1);
-void* var_52 = tensorBatchNorm(var_51, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
-void* var_53 = tensorRelu(var_52);
-void* var_55 = tensorConvolution(var_53, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
-void* var_56 = tensorBatchNorm(var_55, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
-void* var_57 = tensorRelu(var_56);
-void* var_58 = tensorConvolution(var_57, conv2d_9_w, 0, 0, 1, 1, 1, 1);
-void* var_59 = tensorBatchNorm(var_58, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
-void* var_60 = tensorRelu(var_59);
-void* var_63 = tensorConvolution(var_60, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
-void* var_64 = tensorBatchNorm(var_63, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
-void* var_65 = tensorRelu(var_64);
-void* var_66 = tensorConvolution(var_65, conv2d_10_w, 0, 0, 1, 1, 1, 1);
-void* var_67 = tensorBatchNorm(var_66, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
-void* var_68 = tensorRelu(var_67);
-void* var_70 = tensorConvolution(var_68, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
-void* var_71 = tensorBatchNorm(var_70, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
-void* var_72 = tensorRelu(var_71);
-void* var_73 = tensorConvolution(var_72, conv2d_11_w, 0, 0, 1, 1, 1, 1);
-void* var_74 = tensorBatchNorm(var_73, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
-void* var_75 = tensorRelu(var_74);
-void* var_77 = tensorConvolution(var_75, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
-void* var_78 = tensorBatchNorm(var_77, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
-void* var_79 = tensorRelu(var_78);
-void* var_80 = tensorConvolution(var_79, conv2d_12_w, 0, 0, 1, 1, 1, 1);
-void* var_81 = tensorBatchNorm(var_80, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
-void* var_82 = tensorRelu(var_81);
-void* var_85 = tensorConvolution(var_82, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
-void* var_86 = tensorBatchNorm(var_85, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
-void* var_87 = tensorRelu(var_86);
-void* var_88 = tensorConvolution(var_87, conv2d_13_w, 0, 0, 1, 1, 1, 1);
-void* var_89 = tensorBatchNorm(var_88, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
-void* var_90 = tensorRelu(var_89);
-void* var_92 = tensorConvolution(var_90, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
-void* var_93 = tensorBatchNorm(var_92, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
-void* var_94 = tensorRelu(var_93);
-void* var_95 = tensorConvolution(var_94, conv2d_14_w, 0, 0, 1, 1, 1, 1);
-void* var_96 = tensorBatchNorm(var_95, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
-void* var_97 = tensorRelu(var_96);
-void* var_99 = tensorPooling(var_97,1,2,2,0,0,2,2);
-void* var_101 = tensorGemmGPU(var_99, dense_1_w);
-void* var_102 = tensorAdd(var_101, dense_1_b);
-void* var_103 = tensorSoftmax(var_102);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_103);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/approxhpvm_src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/approxhpvm_src.cc
deleted file mode 100644
index dc0c873c63333299981591cb5654cb38be9d4092..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/approxhpvm_src.cc
+++ /dev/null
@@ -1,1224 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/stat.h>
-#include <cstring>
-#include <visc.h>
-#include <tensorTypes.h>
-#include <tensorUtils.h>
-
-void var_0_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_1_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_2_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_3_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 32);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_5_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_6_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_8_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_9_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 64);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_11_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_12_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_13_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_14_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_15_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 128);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_16_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_17_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_18_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_19_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_20_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_21_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 128);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_22_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_23_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_24_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_25_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_26_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_27_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 256);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_28_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_29_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_30_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_31_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_32_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_33_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 256);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_34_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_35_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_36_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_37_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_38_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_39_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_avg(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_40_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_41_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_42_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_softmax(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void root(void* input, size_t input_bytes,
- void* conv2d_1_w, size_t conv2d_1_w_bytes,
- void* batch_normalization_1_gamma, size_t batch_normalization_1_gamma_bytes,
- void* batch_normalization_1_beta, size_t batch_normalization_1_beta_bytes,
- void* batch_normalization_1_mean, size_t batch_normalization_1_mean_bytes,
- void* batch_normalization_1_variance, size_t batch_normalization_1_variance_bytes,
- void* depthwise_conv2d_1_w, size_t depthwise_conv2d_1_w_bytes,
- void* batch_normalization_2_gamma, size_t batch_normalization_2_gamma_bytes,
- void* batch_normalization_2_beta, size_t batch_normalization_2_beta_bytes,
- void* batch_normalization_2_mean, size_t batch_normalization_2_mean_bytes,
- void* batch_normalization_2_variance, size_t batch_normalization_2_variance_bytes,
- void* conv2d_2_w, size_t conv2d_2_w_bytes,
- void* batch_normalization_3_gamma, size_t batch_normalization_3_gamma_bytes,
- void* batch_normalization_3_beta, size_t batch_normalization_3_beta_bytes,
- void* batch_normalization_3_mean, size_t batch_normalization_3_mean_bytes,
- void* batch_normalization_3_variance, size_t batch_normalization_3_variance_bytes,
- void* depthwise_conv2d_2_w, size_t depthwise_conv2d_2_w_bytes,
- void* batch_normalization_4_gamma, size_t batch_normalization_4_gamma_bytes,
- void* batch_normalization_4_beta, size_t batch_normalization_4_beta_bytes,
- void* batch_normalization_4_mean, size_t batch_normalization_4_mean_bytes,
- void* batch_normalization_4_variance, size_t batch_normalization_4_variance_bytes,
- void* conv2d_3_w, size_t conv2d_3_w_bytes,
- void* batch_normalization_5_gamma, size_t batch_normalization_5_gamma_bytes,
- void* batch_normalization_5_beta, size_t batch_normalization_5_beta_bytes,
- void* batch_normalization_5_mean, size_t batch_normalization_5_mean_bytes,
- void* batch_normalization_5_variance, size_t batch_normalization_5_variance_bytes,
- void* depthwise_conv2d_3_w, size_t depthwise_conv2d_3_w_bytes,
- void* batch_normalization_6_gamma, size_t batch_normalization_6_gamma_bytes,
- void* batch_normalization_6_beta, size_t batch_normalization_6_beta_bytes,
- void* batch_normalization_6_mean, size_t batch_normalization_6_mean_bytes,
- void* batch_normalization_6_variance, size_t batch_normalization_6_variance_bytes,
- void* conv2d_4_w, size_t conv2d_4_w_bytes,
- void* batch_normalization_7_gamma, size_t batch_normalization_7_gamma_bytes,
- void* batch_normalization_7_beta, size_t batch_normalization_7_beta_bytes,
- void* batch_normalization_7_mean, size_t batch_normalization_7_mean_bytes,
- void* batch_normalization_7_variance, size_t batch_normalization_7_variance_bytes,
- void* depthwise_conv2d_4_w, size_t depthwise_conv2d_4_w_bytes,
- void* batch_normalization_8_gamma, size_t batch_normalization_8_gamma_bytes,
- void* batch_normalization_8_beta, size_t batch_normalization_8_beta_bytes,
- void* batch_normalization_8_mean, size_t batch_normalization_8_mean_bytes,
- void* batch_normalization_8_variance, size_t batch_normalization_8_variance_bytes,
- void* conv2d_5_w, size_t conv2d_5_w_bytes,
- void* batch_normalization_9_gamma, size_t batch_normalization_9_gamma_bytes,
- void* batch_normalization_9_beta, size_t batch_normalization_9_beta_bytes,
- void* batch_normalization_9_mean, size_t batch_normalization_9_mean_bytes,
- void* batch_normalization_9_variance, size_t batch_normalization_9_variance_bytes,
- void* depthwise_conv2d_5_w, size_t depthwise_conv2d_5_w_bytes,
- void* batch_normalization_10_gamma, size_t batch_normalization_10_gamma_bytes,
- void* batch_normalization_10_beta, size_t batch_normalization_10_beta_bytes,
- void* batch_normalization_10_mean, size_t batch_normalization_10_mean_bytes,
- void* batch_normalization_10_variance, size_t batch_normalization_10_variance_bytes,
- void* conv2d_6_w, size_t conv2d_6_w_bytes,
- void* batch_normalization_11_gamma, size_t batch_normalization_11_gamma_bytes,
- void* batch_normalization_11_beta, size_t batch_normalization_11_beta_bytes,
- void* batch_normalization_11_mean, size_t batch_normalization_11_mean_bytes,
- void* batch_normalization_11_variance, size_t batch_normalization_11_variance_bytes,
- void* depthwise_conv2d_6_w, size_t depthwise_conv2d_6_w_bytes,
- void* batch_normalization_12_gamma, size_t batch_normalization_12_gamma_bytes,
- void* batch_normalization_12_beta, size_t batch_normalization_12_beta_bytes,
- void* batch_normalization_12_mean, size_t batch_normalization_12_mean_bytes,
- void* batch_normalization_12_variance, size_t batch_normalization_12_variance_bytes,
- void* conv2d_7_w, size_t conv2d_7_w_bytes,
- void* batch_normalization_13_gamma, size_t batch_normalization_13_gamma_bytes,
- void* batch_normalization_13_beta, size_t batch_normalization_13_beta_bytes,
- void* batch_normalization_13_mean, size_t batch_normalization_13_mean_bytes,
- void* batch_normalization_13_variance, size_t batch_normalization_13_variance_bytes,
- void* dense_1_w, size_t dense_1_w_bytes,
- void* dense_1_b, size_t dense_1_b_bytes){
-
-
- __visc__hint(visc::CPU_TARGET);
- __visc__attributes(68, input, conv2d_1_w, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, depthwise_conv2d_1_w, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, conv2d_2_w, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, depthwise_conv2d_2_w, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, conv2d_3_w, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, depthwise_conv2d_3_w, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, conv2d_4_w, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, depthwise_conv2d_4_w, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, conv2d_5_w, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, depthwise_conv2d_5_w, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, conv2d_6_w, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, depthwise_conv2d_6_w, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, conv2d_7_w, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, dense_1_w, dense_1_b, 0);
-
-
- void* var_0 = __visc__createNodeND(0, var_0_node);
-
- __visc__bindIn(var_0, 0, 0, 0);
- __visc__bindIn(var_0, 1, 1, 0);
- __visc__bindIn(var_0, 2, 2, 0);
- __visc__bindIn(var_0, 3, 3, 0);
-
- void* var_1 = __visc__createNodeND(0, var_1_node);
-
- __visc__edge(var_0, var_1, 1, 0, 0, 0);
- __visc__edge(var_0, var_1, 1, 1, 1, 0);
- __visc__bindIn(var_1, 4, 2, 0);
- __visc__bindIn(var_1, 5, 3, 0);
- __visc__bindIn(var_1, 6, 4, 0);
- __visc__bindIn(var_1, 7, 5, 0);
- __visc__bindIn(var_1, 8, 6, 0);
- __visc__bindIn(var_1, 9, 7, 0);
- __visc__bindIn(var_1, 10, 8, 0);
- __visc__bindIn(var_1, 11, 9, 0);
-
- void* var_2 = __visc__createNodeND(0, var_2_node);
-
- __visc__edge(var_1, var_2, 1, 0, 0, 0);
- __visc__edge(var_1, var_2, 1, 1, 1, 0);
-
- void* var_3 = __visc__createNodeND(0, var_3_node);
-
- __visc__edge(var_2, var_3, 1, 0, 0, 0);
- __visc__edge(var_2, var_3, 1, 1, 1, 0);
- __visc__bindIn(var_3, 12, 2, 0);
- __visc__bindIn(var_3, 13, 3, 0);
-
- void* var_4 = __visc__createNodeND(0, var_4_node);
-
- __visc__edge(var_3, var_4, 1, 0, 0, 0);
- __visc__edge(var_3, var_4, 1, 1, 1, 0);
- __visc__bindIn(var_4, 14, 2, 0);
- __visc__bindIn(var_4, 15, 3, 0);
- __visc__bindIn(var_4, 16, 4, 0);
- __visc__bindIn(var_4, 17, 5, 0);
- __visc__bindIn(var_4, 18, 6, 0);
- __visc__bindIn(var_4, 19, 7, 0);
- __visc__bindIn(var_4, 20, 8, 0);
- __visc__bindIn(var_4, 21, 9, 0);
-
- void* var_5 = __visc__createNodeND(0, var_5_node);
-
- __visc__edge(var_4, var_5, 1, 0, 0, 0);
- __visc__edge(var_4, var_5, 1, 1, 1, 0);
-
- void* var_6 = __visc__createNodeND(0, var_6_node);
-
- __visc__edge(var_5, var_6, 1, 0, 0, 0);
- __visc__edge(var_5, var_6, 1, 1, 1, 0);
- __visc__bindIn(var_6, 22, 2, 0);
- __visc__bindIn(var_6, 23, 3, 0);
-
- void* var_7 = __visc__createNodeND(0, var_7_node);
-
- __visc__edge(var_6, var_7, 1, 0, 0, 0);
- __visc__edge(var_6, var_7, 1, 1, 1, 0);
- __visc__bindIn(var_7, 24, 2, 0);
- __visc__bindIn(var_7, 25, 3, 0);
- __visc__bindIn(var_7, 26, 4, 0);
- __visc__bindIn(var_7, 27, 5, 0);
- __visc__bindIn(var_7, 28, 6, 0);
- __visc__bindIn(var_7, 29, 7, 0);
- __visc__bindIn(var_7, 30, 8, 0);
- __visc__bindIn(var_7, 31, 9, 0);
-
- void* var_8 = __visc__createNodeND(0, var_8_node);
-
- __visc__edge(var_7, var_8, 1, 0, 0, 0);
- __visc__edge(var_7, var_8, 1, 1, 1, 0);
-
- void* var_9 = __visc__createNodeND(0, var_9_node);
-
- __visc__edge(var_8, var_9, 1, 0, 0, 0);
- __visc__edge(var_8, var_9, 1, 1, 1, 0);
- __visc__bindIn(var_9, 32, 2, 0);
- __visc__bindIn(var_9, 33, 3, 0);
-
- void* var_10 = __visc__createNodeND(0, var_10_node);
-
- __visc__edge(var_9, var_10, 1, 0, 0, 0);
- __visc__edge(var_9, var_10, 1, 1, 1, 0);
- __visc__bindIn(var_10, 34, 2, 0);
- __visc__bindIn(var_10, 35, 3, 0);
- __visc__bindIn(var_10, 36, 4, 0);
- __visc__bindIn(var_10, 37, 5, 0);
- __visc__bindIn(var_10, 38, 6, 0);
- __visc__bindIn(var_10, 39, 7, 0);
- __visc__bindIn(var_10, 40, 8, 0);
- __visc__bindIn(var_10, 41, 9, 0);
-
- void* var_11 = __visc__createNodeND(0, var_11_node);
-
- __visc__edge(var_10, var_11, 1, 0, 0, 0);
- __visc__edge(var_10, var_11, 1, 1, 1, 0);
-
- void* var_12 = __visc__createNodeND(0, var_12_node);
-
- __visc__edge(var_11, var_12, 1, 0, 0, 0);
- __visc__edge(var_11, var_12, 1, 1, 1, 0);
- __visc__bindIn(var_12, 42, 2, 0);
- __visc__bindIn(var_12, 43, 3, 0);
-
- void* var_13 = __visc__createNodeND(0, var_13_node);
-
- __visc__edge(var_12, var_13, 1, 0, 0, 0);
- __visc__edge(var_12, var_13, 1, 1, 1, 0);
- __visc__bindIn(var_13, 44, 2, 0);
- __visc__bindIn(var_13, 45, 3, 0);
- __visc__bindIn(var_13, 46, 4, 0);
- __visc__bindIn(var_13, 47, 5, 0);
- __visc__bindIn(var_13, 48, 6, 0);
- __visc__bindIn(var_13, 49, 7, 0);
- __visc__bindIn(var_13, 50, 8, 0);
- __visc__bindIn(var_13, 51, 9, 0);
-
- void* var_14 = __visc__createNodeND(0, var_14_node);
-
- __visc__edge(var_13, var_14, 1, 0, 0, 0);
- __visc__edge(var_13, var_14, 1, 1, 1, 0);
-
- void* var_15 = __visc__createNodeND(0, var_15_node);
-
- __visc__edge(var_14, var_15, 1, 0, 0, 0);
- __visc__edge(var_14, var_15, 1, 1, 1, 0);
- __visc__bindIn(var_15, 52, 2, 0);
- __visc__bindIn(var_15, 53, 3, 0);
-
- void* var_16 = __visc__createNodeND(0, var_16_node);
-
- __visc__edge(var_15, var_16, 1, 0, 0, 0);
- __visc__edge(var_15, var_16, 1, 1, 1, 0);
- __visc__bindIn(var_16, 54, 2, 0);
- __visc__bindIn(var_16, 55, 3, 0);
- __visc__bindIn(var_16, 56, 4, 0);
- __visc__bindIn(var_16, 57, 5, 0);
- __visc__bindIn(var_16, 58, 6, 0);
- __visc__bindIn(var_16, 59, 7, 0);
- __visc__bindIn(var_16, 60, 8, 0);
- __visc__bindIn(var_16, 61, 9, 0);
-
- void* var_17 = __visc__createNodeND(0, var_17_node);
-
- __visc__edge(var_16, var_17, 1, 0, 0, 0);
- __visc__edge(var_16, var_17, 1, 1, 1, 0);
-
- void* var_18 = __visc__createNodeND(0, var_18_node);
-
- __visc__edge(var_17, var_18, 1, 0, 0, 0);
- __visc__edge(var_17, var_18, 1, 1, 1, 0);
- __visc__bindIn(var_18, 62, 2, 0);
- __visc__bindIn(var_18, 63, 3, 0);
-
- void* var_19 = __visc__createNodeND(0, var_19_node);
-
- __visc__edge(var_18, var_19, 1, 0, 0, 0);
- __visc__edge(var_18, var_19, 1, 1, 1, 0);
- __visc__bindIn(var_19, 64, 2, 0);
- __visc__bindIn(var_19, 65, 3, 0);
- __visc__bindIn(var_19, 66, 4, 0);
- __visc__bindIn(var_19, 67, 5, 0);
- __visc__bindIn(var_19, 68, 6, 0);
- __visc__bindIn(var_19, 69, 7, 0);
- __visc__bindIn(var_19, 70, 8, 0);
- __visc__bindIn(var_19, 71, 9, 0);
-
- void* var_20 = __visc__createNodeND(0, var_20_node);
-
- __visc__edge(var_19, var_20, 1, 0, 0, 0);
- __visc__edge(var_19, var_20, 1, 1, 1, 0);
-
- void* var_21 = __visc__createNodeND(0, var_21_node);
-
- __visc__edge(var_20, var_21, 1, 0, 0, 0);
- __visc__edge(var_20, var_21, 1, 1, 1, 0);
- __visc__bindIn(var_21, 72, 2, 0);
- __visc__bindIn(var_21, 73, 3, 0);
-
- void* var_22 = __visc__createNodeND(0, var_22_node);
-
- __visc__edge(var_21, var_22, 1, 0, 0, 0);
- __visc__edge(var_21, var_22, 1, 1, 1, 0);
- __visc__bindIn(var_22, 74, 2, 0);
- __visc__bindIn(var_22, 75, 3, 0);
- __visc__bindIn(var_22, 76, 4, 0);
- __visc__bindIn(var_22, 77, 5, 0);
- __visc__bindIn(var_22, 78, 6, 0);
- __visc__bindIn(var_22, 79, 7, 0);
- __visc__bindIn(var_22, 80, 8, 0);
- __visc__bindIn(var_22, 81, 9, 0);
-
- void* var_23 = __visc__createNodeND(0, var_23_node);
-
- __visc__edge(var_22, var_23, 1, 0, 0, 0);
- __visc__edge(var_22, var_23, 1, 1, 1, 0);
-
- void* var_24 = __visc__createNodeND(0, var_24_node);
-
- __visc__edge(var_23, var_24, 1, 0, 0, 0);
- __visc__edge(var_23, var_24, 1, 1, 1, 0);
- __visc__bindIn(var_24, 82, 2, 0);
- __visc__bindIn(var_24, 83, 3, 0);
-
- void* var_25 = __visc__createNodeND(0, var_25_node);
-
- __visc__edge(var_24, var_25, 1, 0, 0, 0);
- __visc__edge(var_24, var_25, 1, 1, 1, 0);
- __visc__bindIn(var_25, 84, 2, 0);
- __visc__bindIn(var_25, 85, 3, 0);
- __visc__bindIn(var_25, 86, 4, 0);
- __visc__bindIn(var_25, 87, 5, 0);
- __visc__bindIn(var_25, 88, 6, 0);
- __visc__bindIn(var_25, 89, 7, 0);
- __visc__bindIn(var_25, 90, 8, 0);
- __visc__bindIn(var_25, 91, 9, 0);
-
- void* var_26 = __visc__createNodeND(0, var_26_node);
-
- __visc__edge(var_25, var_26, 1, 0, 0, 0);
- __visc__edge(var_25, var_26, 1, 1, 1, 0);
-
- void* var_27 = __visc__createNodeND(0, var_27_node);
-
- __visc__edge(var_26, var_27, 1, 0, 0, 0);
- __visc__edge(var_26, var_27, 1, 1, 1, 0);
- __visc__bindIn(var_27, 92, 2, 0);
- __visc__bindIn(var_27, 93, 3, 0);
-
- void* var_28 = __visc__createNodeND(0, var_28_node);
-
- __visc__edge(var_27, var_28, 1, 0, 0, 0);
- __visc__edge(var_27, var_28, 1, 1, 1, 0);
- __visc__bindIn(var_28, 94, 2, 0);
- __visc__bindIn(var_28, 95, 3, 0);
- __visc__bindIn(var_28, 96, 4, 0);
- __visc__bindIn(var_28, 97, 5, 0);
- __visc__bindIn(var_28, 98, 6, 0);
- __visc__bindIn(var_28, 99, 7, 0);
- __visc__bindIn(var_28, 100, 8, 0);
- __visc__bindIn(var_28, 101, 9, 0);
-
- void* var_29 = __visc__createNodeND(0, var_29_node);
-
- __visc__edge(var_28, var_29, 1, 0, 0, 0);
- __visc__edge(var_28, var_29, 1, 1, 1, 0);
-
- void* var_30 = __visc__createNodeND(0, var_30_node);
-
- __visc__edge(var_29, var_30, 1, 0, 0, 0);
- __visc__edge(var_29, var_30, 1, 1, 1, 0);
- __visc__bindIn(var_30, 102, 2, 0);
- __visc__bindIn(var_30, 103, 3, 0);
-
- void* var_31 = __visc__createNodeND(0, var_31_node);
-
- __visc__edge(var_30, var_31, 1, 0, 0, 0);
- __visc__edge(var_30, var_31, 1, 1, 1, 0);
- __visc__bindIn(var_31, 104, 2, 0);
- __visc__bindIn(var_31, 105, 3, 0);
- __visc__bindIn(var_31, 106, 4, 0);
- __visc__bindIn(var_31, 107, 5, 0);
- __visc__bindIn(var_31, 108, 6, 0);
- __visc__bindIn(var_31, 109, 7, 0);
- __visc__bindIn(var_31, 110, 8, 0);
- __visc__bindIn(var_31, 111, 9, 0);
-
- void* var_32 = __visc__createNodeND(0, var_32_node);
-
- __visc__edge(var_31, var_32, 1, 0, 0, 0);
- __visc__edge(var_31, var_32, 1, 1, 1, 0);
-
- void* var_33 = __visc__createNodeND(0, var_33_node);
-
- __visc__edge(var_32, var_33, 1, 0, 0, 0);
- __visc__edge(var_32, var_33, 1, 1, 1, 0);
- __visc__bindIn(var_33, 112, 2, 0);
- __visc__bindIn(var_33, 113, 3, 0);
-
- void* var_34 = __visc__createNodeND(0, var_34_node);
-
- __visc__edge(var_33, var_34, 1, 0, 0, 0);
- __visc__edge(var_33, var_34, 1, 1, 1, 0);
- __visc__bindIn(var_34, 114, 2, 0);
- __visc__bindIn(var_34, 115, 3, 0);
- __visc__bindIn(var_34, 116, 4, 0);
- __visc__bindIn(var_34, 117, 5, 0);
- __visc__bindIn(var_34, 118, 6, 0);
- __visc__bindIn(var_34, 119, 7, 0);
- __visc__bindIn(var_34, 120, 8, 0);
- __visc__bindIn(var_34, 121, 9, 0);
-
- void* var_35 = __visc__createNodeND(0, var_35_node);
-
- __visc__edge(var_34, var_35, 1, 0, 0, 0);
- __visc__edge(var_34, var_35, 1, 1, 1, 0);
-
- void* var_36 = __visc__createNodeND(0, var_36_node);
-
- __visc__edge(var_35, var_36, 1, 0, 0, 0);
- __visc__edge(var_35, var_36, 1, 1, 1, 0);
- __visc__bindIn(var_36, 122, 2, 0);
- __visc__bindIn(var_36, 123, 3, 0);
-
- void* var_37 = __visc__createNodeND(0, var_37_node);
-
- __visc__edge(var_36, var_37, 1, 0, 0, 0);
- __visc__edge(var_36, var_37, 1, 1, 1, 0);
- __visc__bindIn(var_37, 124, 2, 0);
- __visc__bindIn(var_37, 125, 3, 0);
- __visc__bindIn(var_37, 126, 4, 0);
- __visc__bindIn(var_37, 127, 5, 0);
- __visc__bindIn(var_37, 128, 6, 0);
- __visc__bindIn(var_37, 129, 7, 0);
- __visc__bindIn(var_37, 130, 8, 0);
- __visc__bindIn(var_37, 131, 9, 0);
-
- void* var_38 = __visc__createNodeND(0, var_38_node);
-
- __visc__edge(var_37, var_38, 1, 0, 0, 0);
- __visc__edge(var_37, var_38, 1, 1, 1, 0);
-
- void* var_39 = __visc__createNodeND(0, var_39_node);
-
- __visc__edge(var_38, var_39, 1, 0, 0, 0);
- __visc__edge(var_38, var_39, 1, 1, 1, 0);
-
- void* var_40 = __visc__createNodeND(0, var_40_node);
-
- __visc__edge(var_39, var_40, 1, 0, 0, 0);
- __visc__edge(var_39, var_40, 1, 1, 1, 0);
- __visc__bindIn(var_40, 132, 2, 0);
- __visc__bindIn(var_40, 133, 3, 0);
-
- void* var_41 = __visc__createNodeND(0, var_41_node);
-
- __visc__edge(var_40, var_41, 1, 0, 0, 0);
- __visc__edge(var_40, var_41, 1, 1, 1, 0);
- __visc__bindIn(var_41, 134, 2, 0);
- __visc__bindIn(var_41, 135, 3, 0);
-
- void* var_42 = __visc__createNodeND(0, var_42_node);
-
- __visc__edge(var_41, var_42, 1, 0, 0, 0);
- __visc__edge(var_41, var_42, 1, 1, 1, 0);
-
- __visc__bindOut(var_42, 0, 0, 0);
- __visc__bindOut(var_42, 1, 1, 0);
-
-}
-
-struct ret_t {
- void* tensor;
- size_t bytes;
-};
-
-typedef struct __attribute__((__packed__)) {
- void* input;
- size_t input_bytes;
- void* conv2d_1_w;
- size_t conv2d_1_w_bytes;
- void* batch_normalization_1_gamma;
- size_t batch_normalization_1_gamma_bytes;
- void* batch_normalization_1_beta;
- size_t batch_normalization_1_beta_bytes;
- void* batch_normalization_1_mean;
- size_t batch_normalization_1_mean_bytes;
- void* batch_normalization_1_variance;
- size_t batch_normalization_1_variance_bytes;
- void* depthwise_conv2d_1_w;
- size_t depthwise_conv2d_1_w_bytes;
- void* batch_normalization_2_gamma;
- size_t batch_normalization_2_gamma_bytes;
- void* batch_normalization_2_beta;
- size_t batch_normalization_2_beta_bytes;
- void* batch_normalization_2_mean;
- size_t batch_normalization_2_mean_bytes;
- void* batch_normalization_2_variance;
- size_t batch_normalization_2_variance_bytes;
- void* conv2d_2_w;
- size_t conv2d_2_w_bytes;
- void* batch_normalization_3_gamma;
- size_t batch_normalization_3_gamma_bytes;
- void* batch_normalization_3_beta;
- size_t batch_normalization_3_beta_bytes;
- void* batch_normalization_3_mean;
- size_t batch_normalization_3_mean_bytes;
- void* batch_normalization_3_variance;
- size_t batch_normalization_3_variance_bytes;
- void* depthwise_conv2d_2_w;
- size_t depthwise_conv2d_2_w_bytes;
- void* batch_normalization_4_gamma;
- size_t batch_normalization_4_gamma_bytes;
- void* batch_normalization_4_beta;
- size_t batch_normalization_4_beta_bytes;
- void* batch_normalization_4_mean;
- size_t batch_normalization_4_mean_bytes;
- void* batch_normalization_4_variance;
- size_t batch_normalization_4_variance_bytes;
- void* conv2d_3_w;
- size_t conv2d_3_w_bytes;
- void* batch_normalization_5_gamma;
- size_t batch_normalization_5_gamma_bytes;
- void* batch_normalization_5_beta;
- size_t batch_normalization_5_beta_bytes;
- void* batch_normalization_5_mean;
- size_t batch_normalization_5_mean_bytes;
- void* batch_normalization_5_variance;
- size_t batch_normalization_5_variance_bytes;
- void* depthwise_conv2d_3_w;
- size_t depthwise_conv2d_3_w_bytes;
- void* batch_normalization_6_gamma;
- size_t batch_normalization_6_gamma_bytes;
- void* batch_normalization_6_beta;
- size_t batch_normalization_6_beta_bytes;
- void* batch_normalization_6_mean;
- size_t batch_normalization_6_mean_bytes;
- void* batch_normalization_6_variance;
- size_t batch_normalization_6_variance_bytes;
- void* conv2d_4_w;
- size_t conv2d_4_w_bytes;
- void* batch_normalization_7_gamma;
- size_t batch_normalization_7_gamma_bytes;
- void* batch_normalization_7_beta;
- size_t batch_normalization_7_beta_bytes;
- void* batch_normalization_7_mean;
- size_t batch_normalization_7_mean_bytes;
- void* batch_normalization_7_variance;
- size_t batch_normalization_7_variance_bytes;
- void* depthwise_conv2d_4_w;
- size_t depthwise_conv2d_4_w_bytes;
- void* batch_normalization_8_gamma;
- size_t batch_normalization_8_gamma_bytes;
- void* batch_normalization_8_beta;
- size_t batch_normalization_8_beta_bytes;
- void* batch_normalization_8_mean;
- size_t batch_normalization_8_mean_bytes;
- void* batch_normalization_8_variance;
- size_t batch_normalization_8_variance_bytes;
- void* conv2d_5_w;
- size_t conv2d_5_w_bytes;
- void* batch_normalization_9_gamma;
- size_t batch_normalization_9_gamma_bytes;
- void* batch_normalization_9_beta;
- size_t batch_normalization_9_beta_bytes;
- void* batch_normalization_9_mean;
- size_t batch_normalization_9_mean_bytes;
- void* batch_normalization_9_variance;
- size_t batch_normalization_9_variance_bytes;
- void* depthwise_conv2d_5_w;
- size_t depthwise_conv2d_5_w_bytes;
- void* batch_normalization_10_gamma;
- size_t batch_normalization_10_gamma_bytes;
- void* batch_normalization_10_beta;
- size_t batch_normalization_10_beta_bytes;
- void* batch_normalization_10_mean;
- size_t batch_normalization_10_mean_bytes;
- void* batch_normalization_10_variance;
- size_t batch_normalization_10_variance_bytes;
- void* conv2d_6_w;
- size_t conv2d_6_w_bytes;
- void* batch_normalization_11_gamma;
- size_t batch_normalization_11_gamma_bytes;
- void* batch_normalization_11_beta;
- size_t batch_normalization_11_beta_bytes;
- void* batch_normalization_11_mean;
- size_t batch_normalization_11_mean_bytes;
- void* batch_normalization_11_variance;
- size_t batch_normalization_11_variance_bytes;
- void* depthwise_conv2d_6_w;
- size_t depthwise_conv2d_6_w_bytes;
- void* batch_normalization_12_gamma;
- size_t batch_normalization_12_gamma_bytes;
- void* batch_normalization_12_beta;
- size_t batch_normalization_12_beta_bytes;
- void* batch_normalization_12_mean;
- size_t batch_normalization_12_mean_bytes;
- void* batch_normalization_12_variance;
- size_t batch_normalization_12_variance_bytes;
- void* conv2d_7_w;
- size_t conv2d_7_w_bytes;
- void* batch_normalization_13_gamma;
- size_t batch_normalization_13_gamma_bytes;
- void* batch_normalization_13_beta;
- size_t batch_normalization_13_beta_bytes;
- void* batch_normalization_13_mean;
- size_t batch_normalization_13_mean_bytes;
- void* batch_normalization_13_variance;
- size_t batch_normalization_13_variance_bytes;
- void* dense_1_w;
- size_t dense_1_w_bytes;
- void* dense_1_b;
- size_t dense_1_b_bytes;
-
- struct ret_t r;
-}
-RootIn;
-
-int main(){
-
-std::string dir_prefix = std::string("data/mobilenet_shallow_nathan/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-
-__visc__init();
-RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn)));
-
-args->input = input;
-args->input_bytes = 0;
-args->conv2d_1_w = conv2d_1_w;
-args->conv2d_1_w_bytes = 0;
-args->batch_normalization_1_gamma = batch_normalization_1_gamma;
-args->batch_normalization_1_gamma_bytes = 0;
-args->batch_normalization_1_beta = batch_normalization_1_beta;
-args->batch_normalization_1_beta_bytes = 0;
-args->batch_normalization_1_mean = batch_normalization_1_mean;
-args->batch_normalization_1_mean_bytes = 0;
-args->batch_normalization_1_variance = batch_normalization_1_variance;
-args->batch_normalization_1_variance_bytes = 0;
-args->depthwise_conv2d_1_w = depthwise_conv2d_1_w;
-args->depthwise_conv2d_1_w_bytes = 0;
-args->batch_normalization_2_gamma = batch_normalization_2_gamma;
-args->batch_normalization_2_gamma_bytes = 0;
-args->batch_normalization_2_beta = batch_normalization_2_beta;
-args->batch_normalization_2_beta_bytes = 0;
-args->batch_normalization_2_mean = batch_normalization_2_mean;
-args->batch_normalization_2_mean_bytes = 0;
-args->batch_normalization_2_variance = batch_normalization_2_variance;
-args->batch_normalization_2_variance_bytes = 0;
-args->conv2d_2_w = conv2d_2_w;
-args->conv2d_2_w_bytes = 0;
-args->batch_normalization_3_gamma = batch_normalization_3_gamma;
-args->batch_normalization_3_gamma_bytes = 0;
-args->batch_normalization_3_beta = batch_normalization_3_beta;
-args->batch_normalization_3_beta_bytes = 0;
-args->batch_normalization_3_mean = batch_normalization_3_mean;
-args->batch_normalization_3_mean_bytes = 0;
-args->batch_normalization_3_variance = batch_normalization_3_variance;
-args->batch_normalization_3_variance_bytes = 0;
-args->depthwise_conv2d_2_w = depthwise_conv2d_2_w;
-args->depthwise_conv2d_2_w_bytes = 0;
-args->batch_normalization_4_gamma = batch_normalization_4_gamma;
-args->batch_normalization_4_gamma_bytes = 0;
-args->batch_normalization_4_beta = batch_normalization_4_beta;
-args->batch_normalization_4_beta_bytes = 0;
-args->batch_normalization_4_mean = batch_normalization_4_mean;
-args->batch_normalization_4_mean_bytes = 0;
-args->batch_normalization_4_variance = batch_normalization_4_variance;
-args->batch_normalization_4_variance_bytes = 0;
-args->conv2d_3_w = conv2d_3_w;
-args->conv2d_3_w_bytes = 0;
-args->batch_normalization_5_gamma = batch_normalization_5_gamma;
-args->batch_normalization_5_gamma_bytes = 0;
-args->batch_normalization_5_beta = batch_normalization_5_beta;
-args->batch_normalization_5_beta_bytes = 0;
-args->batch_normalization_5_mean = batch_normalization_5_mean;
-args->batch_normalization_5_mean_bytes = 0;
-args->batch_normalization_5_variance = batch_normalization_5_variance;
-args->batch_normalization_5_variance_bytes = 0;
-args->depthwise_conv2d_3_w = depthwise_conv2d_3_w;
-args->depthwise_conv2d_3_w_bytes = 0;
-args->batch_normalization_6_gamma = batch_normalization_6_gamma;
-args->batch_normalization_6_gamma_bytes = 0;
-args->batch_normalization_6_beta = batch_normalization_6_beta;
-args->batch_normalization_6_beta_bytes = 0;
-args->batch_normalization_6_mean = batch_normalization_6_mean;
-args->batch_normalization_6_mean_bytes = 0;
-args->batch_normalization_6_variance = batch_normalization_6_variance;
-args->batch_normalization_6_variance_bytes = 0;
-args->conv2d_4_w = conv2d_4_w;
-args->conv2d_4_w_bytes = 0;
-args->batch_normalization_7_gamma = batch_normalization_7_gamma;
-args->batch_normalization_7_gamma_bytes = 0;
-args->batch_normalization_7_beta = batch_normalization_7_beta;
-args->batch_normalization_7_beta_bytes = 0;
-args->batch_normalization_7_mean = batch_normalization_7_mean;
-args->batch_normalization_7_mean_bytes = 0;
-args->batch_normalization_7_variance = batch_normalization_7_variance;
-args->batch_normalization_7_variance_bytes = 0;
-args->depthwise_conv2d_4_w = depthwise_conv2d_4_w;
-args->depthwise_conv2d_4_w_bytes = 0;
-args->batch_normalization_8_gamma = batch_normalization_8_gamma;
-args->batch_normalization_8_gamma_bytes = 0;
-args->batch_normalization_8_beta = batch_normalization_8_beta;
-args->batch_normalization_8_beta_bytes = 0;
-args->batch_normalization_8_mean = batch_normalization_8_mean;
-args->batch_normalization_8_mean_bytes = 0;
-args->batch_normalization_8_variance = batch_normalization_8_variance;
-args->batch_normalization_8_variance_bytes = 0;
-args->conv2d_5_w = conv2d_5_w;
-args->conv2d_5_w_bytes = 0;
-args->batch_normalization_9_gamma = batch_normalization_9_gamma;
-args->batch_normalization_9_gamma_bytes = 0;
-args->batch_normalization_9_beta = batch_normalization_9_beta;
-args->batch_normalization_9_beta_bytes = 0;
-args->batch_normalization_9_mean = batch_normalization_9_mean;
-args->batch_normalization_9_mean_bytes = 0;
-args->batch_normalization_9_variance = batch_normalization_9_variance;
-args->batch_normalization_9_variance_bytes = 0;
-args->depthwise_conv2d_5_w = depthwise_conv2d_5_w;
-args->depthwise_conv2d_5_w_bytes = 0;
-args->batch_normalization_10_gamma = batch_normalization_10_gamma;
-args->batch_normalization_10_gamma_bytes = 0;
-args->batch_normalization_10_beta = batch_normalization_10_beta;
-args->batch_normalization_10_beta_bytes = 0;
-args->batch_normalization_10_mean = batch_normalization_10_mean;
-args->batch_normalization_10_mean_bytes = 0;
-args->batch_normalization_10_variance = batch_normalization_10_variance;
-args->batch_normalization_10_variance_bytes = 0;
-args->conv2d_6_w = conv2d_6_w;
-args->conv2d_6_w_bytes = 0;
-args->batch_normalization_11_gamma = batch_normalization_11_gamma;
-args->batch_normalization_11_gamma_bytes = 0;
-args->batch_normalization_11_beta = batch_normalization_11_beta;
-args->batch_normalization_11_beta_bytes = 0;
-args->batch_normalization_11_mean = batch_normalization_11_mean;
-args->batch_normalization_11_mean_bytes = 0;
-args->batch_normalization_11_variance = batch_normalization_11_variance;
-args->batch_normalization_11_variance_bytes = 0;
-args->depthwise_conv2d_6_w = depthwise_conv2d_6_w;
-args->depthwise_conv2d_6_w_bytes = 0;
-args->batch_normalization_12_gamma = batch_normalization_12_gamma;
-args->batch_normalization_12_gamma_bytes = 0;
-args->batch_normalization_12_beta = batch_normalization_12_beta;
-args->batch_normalization_12_beta_bytes = 0;
-args->batch_normalization_12_mean = batch_normalization_12_mean;
-args->batch_normalization_12_mean_bytes = 0;
-args->batch_normalization_12_variance = batch_normalization_12_variance;
-args->batch_normalization_12_variance_bytes = 0;
-args->conv2d_7_w = conv2d_7_w;
-args->conv2d_7_w_bytes = 0;
-args->batch_normalization_13_gamma = batch_normalization_13_gamma;
-args->batch_normalization_13_gamma_bytes = 0;
-args->batch_normalization_13_beta = batch_normalization_13_beta;
-args->batch_normalization_13_beta_bytes = 0;
-args->batch_normalization_13_mean = batch_normalization_13_mean;
-args->batch_normalization_13_mean_bytes = 0;
-args->batch_normalization_13_variance = batch_normalization_13_variance;
-args->batch_normalization_13_variance_bytes = 0;
-args->dense_1_w = dense_1_w;
-args->dense_1_w_bytes = 0;
-args->dense_1_b = dense_1_b;
-args->dense_1_b_bytes = 0;
-
-void* dfg = __visc__launch(0, root, (void*) args);
-
-__visc__wait(dfg);
-
-void *result = static_cast<RootIn*>(args)->input;
-hpvm_request_tensor(result, 0);
-
-__visc__cleanup();
- computeAccuracy2(labels, 10000, result);
-return 0;
-
-}
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/dense_1_b.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/dense_1_b.bin
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--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/dense_1_b.bin
+++ /dev/null
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-êÞ^>ÂX`¾q =Ï·‡>Hp‚>°¾¾B—b>6ÁU¾$ƒt¾½M¾
\ No newline at end of file
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layer_composition.txt b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layer_composition.txt
deleted file mode 100644
index 9b8b3f7e11a428a28fecbde2c204bf39b7e02703..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layer_composition.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-pool
-dense add
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layers.txt b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layers.txt
deleted file mode 100644
index a9415755180a7ebdceb89b7e3e6d6cee258b18c4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layers.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-#tensorBatchNorm1
-#tensorRelu1
-#tensorDepthwiseConv1
-#tensorBatchNorm2
-#tensorRelu2
-Conv2,10000,32,32,32,64,32,1,1
-#tensorBatchNorm3
-#tensorRelu3
-#tensorDepthwiseConv2
-#tensorBatchNorm4
-#tensorRelu4
-Conv3,10000,64,16,16,128,64,1,1
-#tensorBatchNorm5
-#tensorRelu5
-#tensorDepthwiseConv3
-#tensorBatchNorm6
-#tensorRelu6
-Conv4,10000,128,16,16,128,128,1,1
-#tensorBatchNorm7
-#tensorRelu7
-#tensorDepthwiseConv4
-#tensorBatchNorm8
-#tensorRelu8
-Conv5,10000,128,8,8,256,128,1,1
-#tensorBatchNorm9
-#tensorRelu9
-#tensorDepthwiseConv5
-#tensorBatchNorm10
-#tensorRelu10
-Conv6,10000,256,8,8,256,256,1,1
-#tensorBatchNorm11
-#tensorRelu11
-#tensorDepthwiseConv6
-#tensorBatchNorm12
-#tensorRelu12
-Conv7,10000,256,4,4,512,256,1,1
-#tensorBatchNorm13
-#tensorRelu13
-#tensorPooling1
-FC1,10000,2048,2048,10
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/promise_src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/promise_src.cc
deleted file mode 100644
index c5fd3606da51281bc2c583e98f024bd2f54f837b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/promise_src.cc
+++ /dev/null
@@ -1,238 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-int total_runs = 100;
-for (int i = 0 ; i < total_runs; i++){
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-
-
-std::string dir_prefix = std::string("data/mobilenet_shallow_nathan/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -1.5164621164798737, 1.6472081774473288, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -9.868980642318725, 10.560956018447879, 9);
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_2 = tensorRelu(var_1);
-void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_5 = tensorRelu(var_4);
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 6.821381127357554, conv2d_2_w, -1.1834390873908995, 1.2731596627235617, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -9.875998497009277, 7.51305247974393, 9);
-void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_8 = tensorRelu(var_7);
-void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
-void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_11 = tensorRelu(var_10);
-void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.826067455768602, conv2d_3_w, -0.599876856982708, 0.6812073457241064, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.633289833068848, 5.177892235755925, 9);
-void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_14 = tensorRelu(var_13);
-void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
-void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_17 = tensorRelu(var_16);
-void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.02646304416659, conv2d_4_w, -0.4555967862010002, 0.4942613914608956, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.316803941726685, 4.605850250244146, 9);
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_20 = tensorRelu(var_19);
-void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_23 = tensorRelu(var_22);
-void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 4.532649063110355, conv2d_5_w, -0.35657615590095515, 0.3382165088057521, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.1012511816024775, 4.3630500688553, 9);
-void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_26 = tensorRelu(var_25);
-void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
-void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_29 = tensorRelu(var_28);
-void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 3.9874704387188977, conv2d_6_w, -0.28502783328294756, 0.28604640334844594, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.243851703643799, 3.486250406742097, 9);
-void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_32 = tensorRelu(var_31);
-void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_35 = tensorRelu(var_34);
-void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 6.563065901756522, conv2d_7_w, -0.18946402323246003, 0.19012390717864017, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.938115713119507, 3.538363476753238, 9);
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorPooling(var_38,1,2,2,0,0,2,2);
-void* var_40 = FCLayer_PROMISE(var_39, 0.0, 1.8908388000727185, dense_1_w, -0.35140394401550296, 0.422872786462307, dense_1_b, -0.23878151, 0.26507422, -1, -14.630816223144532, 27.27252123260504, 9);
-void* var_41 = tensorSoftmax(var_40);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_41);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-}
-
-dumpExecutionAccuracies();
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/src.cc
deleted file mode 100644
index 6599f7d0ea0be6a76c4154d25b3a7be2c6724115..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/src.cc
+++ /dev/null
@@ -1,231 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("data/mobilenet_shallow_nathan/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_2 = tensorRelu(var_1);
-void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_6 = tensorRelu(var_5);
-void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
-void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_9 = tensorRelu(var_8);
-void* var_11 = tensorConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
-void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_13 = tensorRelu(var_12);
-void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
-void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_16 = tensorRelu(var_15);
-void* var_18 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_20 = tensorRelu(var_19);
-void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_23 = tensorRelu(var_22);
-void* var_26 = tensorConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
-void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_28 = tensorRelu(var_27);
-void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
-void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_31 = tensorRelu(var_30);
-void* var_33 = tensorConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_35 = tensorRelu(var_34);
-void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_38 = tensorRelu(var_37);
-void* var_41 = tensorConvolution(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
-void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_43 = tensorRelu(var_42);
-void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
-void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_46 = tensorRelu(var_45);
-void* var_47 = tensorPooling(var_46,1,2,2,0,0,2,2);
-void* var_49 = tensorGemmGPU(var_47, dense_1_w);
-void* var_50 = tensorAdd(var_49, dense_1_b);
-void* var_51 = tensorSoftmax(var_50);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_51);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
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-)¯¼YÅG¾^þ™½-‚ä¾R¾Ý¾‰c¾l2&?D¾
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p¾À’`¾M·4½Õø=š× ¾õ: ½Ž“½_ºN½µf¼Û]K¾à·õ½>°ù¼“(½®èྮb<
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-Dz\¾—ý(¾n¬½ãå¾0
-¾h.¾á"Œ¾â%ˆ>†Ÿ¾BÁ½B¦¿_ V¾e“ѽD„¾EàϽUIf¾Ç3N¾zì7¾7¨a¾–Ž‚¾3í¼ Ã?¾Ðl¶½üß…¾Ss¡½µ
Š¾ïÞ¾WŸ¾ ô¾í÷³¾³Õ½
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-î<ÂQ¾ˆlO½»®9È“¾´$>³Ð ½q( ?S)¾ T¾Œ'>€ù…>¡^¾U2 ?µ”œ>Ï ˜¾…]¿‹úZ½ƒNá½U½ÔY>qÅ#>›bä>B"?J˜ ?m+ý¾¹zɾ“½½Œ*z¼mc?Ç-x=
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+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/conv2d_1_w.bin b/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/conv2d_1_w.bin
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+++ /dev/null
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-oD??êÙ€?àkü½žHÝ>Ô61¾½< =¡¶(>Ž—¿œˆ>"W«¿¨^B½F[$¿:’ì>¾ýë¼Í
-'¿]3¡>Üx!¿¿N?}Þ=oCÀ”=¾é½*¢Ÿ>í#œ¿Gk?ŠxÞ>YÌ¢¾$l¢¼)Ó¿² ¿6¸q¿ä —¿,à>? ÿѽ£ïξFlü¾%»¾ò—X¿³¶:>IZ
À±@q½bõ‰½‚yŸ¾Ü&,¿÷‰µ¿‹8 ¿qÞD?*ƒ;¿û뽚ۗ½‰>ô'*¿pÑ¡¾—Á>Âý\>*Y5¾åsP½Êè’>E¥¿—1Q¿¢*)¾ç©–¿GSµ¿›JÔ¾
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/conv2d_21_w.bin b/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/conv2d_21_w.bin
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/conv2d_2_b.bin b/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/conv2d_2_b.bin
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+++ /dev/null
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-ç‹=߇˜>Ä@Y>žÁ9>âË»Sé>“4W>Ó€"=.ÊU½l‘¬>¬ÅÕ>!ì=\¿V>µ¤> 2ô¼
eŽ¾
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-¹¥¶=MÖt¾À,J>Ê;\¿éX‹½4û;=–šF>«+¾i¬¦½Õ¹ß=G¹=Ø)3½–c&½>bμž±>}¬°½
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ž¾
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>aý >é?}#’>Ñ8Ç>Y«¬<UÀ>A ¾ªð—¾ìª=FR¸½ãI> D>
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/promise_src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/promise_src.cc
deleted file mode 100644
index f6e7e32153a5e89a68798f809cf4166285b408ea..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/promise_src.cc
+++ /dev/null
@@ -1,162 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-
-std::string dir_prefix = std::string("resnet18_cifar10_promise/");
-std::string input_path = dir_prefix + std::string("input.bin");
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-std::string labels_path = dir_prefix + std::string("labels.bin");
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
-void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
-void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
-std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
-void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
-void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
-std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
-void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
-void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
-void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
-void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
-void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
-void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
-void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
-void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
-void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
-void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
-void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-void* var_0 = ConvLayer_PROMISE(input, -0.5500815, 0.60786617, conv2d_1_w, -1.0248864, 1.2929907, conv2d_1_b, -0.36291853, 0.2533059, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.9356618, 9);
-void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 2.9356618, conv2d_2_w, -0.69884616, 0.71849966, conv2d_2_b, -0.2781147, 0.45571187, 1, 1, 1, 1, -1, 0, 1, 0.0, 4.0425158, 9);
-void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 4.0425158, conv2d_3_w, -0.59568167, 0.7714691, conv2d_3_b, -0.8602873, 0.19743633, 1, 1, 1, 1, -1, 0, -1, -10.203314, 9.055045, 9);
-void* var_3 = tensorAdd(var_0, var_2);
-void* var_4 = tensorRelu(var_3);
-void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 9.734258, conv2d_4_w, -0.41976976, 0.43748936, conv2d_4_b, -0.7021962, 0.3033103, 1, 1, 1, 1, -1, 0, 1, 0.0, 7.0053105, 9);
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 7.0053105, conv2d_5_w, -0.46757826, 0.4635873, conv2d_5_b, -0.20662616, 0.1778044, 1, 1, 1, 1, -1, 0, -1, -4.8778534, 6.7311873, 9);
-void* var_7 = tensorAdd(var_4, var_6);
-void* var_8 = tensorRelu(var_7);
-void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 10.858562, conv2d_6_w, -0.64404047, 0.45383143, conv2d_6_b, -0.819547, 0.38550296, 1, 1, 1, 1, -1, 0, 1, 0.0, 8.843336, 9);
-void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 8.843336, conv2d_7_w, -0.41986948, 0.33654243, conv2d_7_b, -0.3563013, 0.22371122, 1, 1, 1, 1, -1, 0, -1, -10.204111, 5.4952374, 9);
-void* var_11 = tensorAdd(var_8, var_10);
-void* var_12 = tensorRelu(var_11);
-void* var_13 = ConvLayer_PROMISE(var_12, 0.0, 11.359337, conv2d_8_w, -0.4805263, 0.50655717, conv2d_8_b, -0.296758, 0.7742441, 1, 1, 2, 2, -1, 0, 1, 0.0, 19.303282, 9);
-void* var_14 = ConvLayer_PROMISE(var_13, 0.0, 19.303282, conv2d_9_w, -0.52083415, 0.45517674, conv2d_9_b, -0.20242067, 0.8236838, 1, 1, 1, 1, -1, 0, -1, -24.404322, 24.37361, 9);
-void* var_15 = ConvLayer_PROMISE(var_12, 0.0, 11.359337, conv2d_10_w, -0.5338656, 1.3395424, conv2d_10_b, -0.20242067, 0.8236838, 0, 0, 2, 2, -1, 0, -1, -6.497986, 12.8968935, 9);
-void* var_16 = tensorAdd(var_15, var_14);
-void* var_17 = tensorRelu(var_16);
-void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 29.462997, conv2d_11_w, -0.34429058, 0.43629733, conv2d_11_b, -1.0744808, 0.056708273, 1, 1, 1, 1, -1, 0, 1, 0.0, 24.329395, 9);
-void* var_19 = ConvLayer_PROMISE(var_18, 0.0, 24.329395, conv2d_12_w, -0.30342352, 0.39493486, conv2d_12_b, -0.44630566, 0.6492069, 1, 1, 1, 1, -1, 0, -1, -9.780206, 20.320444, 9);
-void* var_20 = tensorAdd(var_17, var_19);
-void* var_21 = tensorRelu(var_20);
-void* var_22 = ConvLayer_PROMISE(var_21, 0.0, 29.647404, conv2d_13_w, -0.38351893, 0.45775774, conv2d_13_b, -1.4733055, -0.014426912, 1, 1, 1, 1, -1, 0, 1, 0.0, 25.600573, 9);
-void* var_23 = ConvLayer_PROMISE(var_22, 0.0, 25.600573, conv2d_14_w, -0.25695276, 0.45372736, conv2d_14_b, -0.5259744, 0.26591402, 1, 1, 1, 1, -1, 0, -1, -10.179335, 27.598986, 9);
-void* var_24 = tensorAdd(var_21, var_23);
-void* var_25 = tensorRelu(var_24);
-void* var_26 = ConvLayer_PROMISE(var_25, 0.0, 42.450073, conv2d_15_w, -0.55299705, 0.5443531, conv2d_15_b, -0.71790683, 1.2730768, 1, 1, 2, 2, -1, 0, 1, 0.0, 48.943645, 9);
-void* var_27 = ConvLayer_PROMISE(var_26, 0.0, 48.943645, conv2d_16_w, -0.4203967, 0.48641303, conv2d_16_b, -0.90653443, 1.3546854, 1, 1, 1, 1, -1, 0, -1, -75.016396, 112.3873, 9);
-void* var_28 = ConvLayer_PROMISE(var_25, 0.0, 42.450073, conv2d_17_w, -0.4365755, 0.84913826, conv2d_17_b, -0.90653443, 1.3546851, 0, 0, 2, 2, -1, 0, -1, -13.93596, 24.614315, 9);
-void* var_29 = tensorAdd(var_28, var_27);
-void* var_30 = tensorRelu(var_29);
-void* var_31 = ConvLayer_PROMISE(var_30, 0.0, 126.758545, conv2d_18_w, -0.38657624, 0.5228989, conv2d_18_b, -1.2083547, 0.76361173, 1, 1, 1, 1, -1, 0, 1, 0.0, 130.02768, 9);
-void* var_32 = ConvLayer_PROMISE(var_31, 0.0, 130.02768, conv2d_19_w, -0.40857902, 0.575035, conv2d_19_b, -1.8731614, 1.0960501, 1, 1, 1, 1, -1, 0, -1, -97.91927, 130.89008, 9);
-void* var_33 = tensorAdd(var_30, var_32);
-void* var_34 = tensorRelu(var_33);
-void* var_35 = ConvLayer_PROMISE(var_34, 0.0, 220.4094, conv2d_20_w, -0.33079496, 0.5893278, conv2d_20_b, -1.0234511, 1.0016295, 1, 1, 1, 1, -1, 0, 1, 0.0, 268.19254, 9);
-void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 268.19254, conv2d_21_w, -0.27897888, 0.38280907, conv2d_21_b, -2.2086356, 1.0066502, 1, 1, 1, 1, -1, 0, -1, -235.08034, 264.33008, 9);
-void* var_37 = tensorAdd(var_34, var_36);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorPooling(var_38,1,8,8,0,0,8,8);
-void* var_40 = FCLayer_PROMISE(var_39, 0.0, 39.821262, dense_1_w, -1.5092047, 1.0279838, dense_1_b, -0.49379802, 0.61032647, -1, -84.49565, 60.312202, 9);
-void* var_41 = tensorSoftmax(var_40);
-
-computeAccuracy2(labels,10000,var_41);
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/quant_ranges.txt b/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/quant_ranges.txt
deleted file mode 100644
index af0279b1d2980d8c8d71f20f3ef8c3f3da585699..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/quant_ranges.txt
+++ /dev/null
@@ -1,22 +0,0 @@
--0.5500815 0.60786617 -1.0248864 1.2929907 -0.36291853 0.2533059 0.0 0.753551840782
-0.0 0.753551840782 -0.69884616 0.71849966 -0.2781147 0.45571187 0.0 1.01057458043
-0.0 1.01057458043 -0.59568167 0.7714691 -0.8602873 0.19743633 -1.84771883726 1.87930787086
-0.0 2.33981014252 -0.41976976 0.43748936 -0.7021962 0.3033103 0.0 1.04317724705
-0.0 1.04317724705 -0.46757826 0.4635873 -0.20662616 0.1778044 -0.829483509064 0.786805033684
-0.0 2.49733686686 -0.64404047 0.45383143 -0.819547 0.38550296 0.0 0.897360802293
-0.0 0.897360802293 -0.41986948 0.33654243 -0.3563013 0.22371122 -0.957150224447 0.54919362247
-0.0 2.37362146616 -0.4805263 0.50655717 -0.296758 0.7742441 0.0 3.01592136621
-0.0 3.01592136621 -0.52083415 0.45517674 -0.20242067 0.8236838 -5.2759475708 5.79733039856
-0.0 2.37362146616 -0.5338656 1.3395424 -0.20242067 0.8236838 -0.738995380998 2.33600783587
-0.0 7.07933432579 -0.34429058 0.43629733 -1.0744808 0.056708273 0.0 1.58645607233
-0.0 1.58645607233 -0.30342352 0.39493486 -0.44630566 0.6492069 -1.49672914267 1.29970229745
-0.0 7.11914063454 -0.38351893 0.45775774 -1.4733055 -0.014426912 0.0 1.52876508832
-0.0 1.52876508832 -0.25695276 0.45372736 -0.5259744 0.26591402 -1.59576894164 1.08074297309
-0.0 6.94405080318 -0.55299705 0.5443531 -0.71790683 1.2730768 0.0 10.3651468277
-0.0 10.3651468277 -0.4203967 0.48641303 -0.90653443 1.3546854 -22.372925148 17.2033731079
-0.0 6.94405080318 -0.4365755 0.84913826 -0.90653443 1.3546851 -3.66810325861 4.87814051151
-0.0 18.8401451111 -0.38657624 0.5228989 -1.2083547 0.76361173 0.0 19.1229192352
-0.0 19.1229192352 -0.40857902 0.575035 -1.8731614 1.0960501 -31.3229312897 14.8234729958
-0.0 23.7382488823 -0.33079496 0.5893278 -1.0234511 1.0016295 0.0 19.5892774963
-0.0 19.5892774963 -0.27897888 0.38280907 -2.2086356 1.0066502 -34.4416886902 20.9890329933
-0.0 10.8541981602 -1.5092047 1.0279838 -0.49379802 0.61032647 -40.9121678543 25.7082381058
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/src.cc
deleted file mode 100644
index e82c54651aaa8b8df2ab34b65490aa79b730c327..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/src.cc
+++ /dev/null
@@ -1,193 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("resnet18_cifar10_promise/");
-std::string input_path = dir_prefix + std::string("input.bin");
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-std::string labels_path = dir_prefix + std::string("labels.bin");
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
-void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
-void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
-std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
-void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
-void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
-std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
-void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
-void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
-void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
-void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
-void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
-void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
-void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
-void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
-void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
-void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
-void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-void* var_2 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
-void* var_3 = tensorAdd(var_2, conv2d_1_b);
-void* var_4 = tensorRelu(var_3);
-void* var_6 = tensorConvolution(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0);
-void* var_7 = tensorAdd(var_6, conv2d_2_b);
-void* var_8 = tensorRelu(var_7);
-void* var_10 = tensorConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
-void* var_11 = tensorAdd(var_10, conv2d_3_b);
-void* var_12 = tensorAdd(var_4, var_11);
-void* var_13 = tensorRelu(var_12);
-void* var_15 = tensorConvolution(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0);
-void* var_16 = tensorAdd(var_15, conv2d_4_b);
-void* var_17 = tensorRelu(var_16);
-void* var_19 = tensorConvolution(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0);
-void* var_20 = tensorAdd(var_19, conv2d_5_b);
-void* var_21 = tensorAdd(var_13, var_20);
-void* var_22 = tensorRelu(var_21);
-void* var_24 = tensorConvolution(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0);
-void* var_25 = tensorAdd(var_24, conv2d_6_b);
-void* var_26 = tensorRelu(var_25);
-void* var_28 = tensorConvolution(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0);
-void* var_29 = tensorAdd(var_28, conv2d_7_b);
-void* var_30 = tensorAdd(var_22, var_29);
-void* var_31 = tensorRelu(var_30);
-void* var_33 = tensorConvolution(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0);
-void* var_34 = tensorAdd(var_33, conv2d_8_b);
-void* var_35 = tensorRelu(var_34);
-void* var_37 = tensorConvolution(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0);
-void* var_38 = tensorAdd(var_37, conv2d_9_b);
-void* var_40 = tensorConvolution(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0);
-void* var_41 = tensorAdd(var_40, conv2d_10_b);
-void* var_42 = tensorAdd(var_41, var_38);
-void* var_43 = tensorRelu(var_42);
-void* var_45 = tensorConvolution(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0);
-void* var_46 = tensorAdd(var_45, conv2d_11_b);
-void* var_47 = tensorRelu(var_46);
-void* var_49 = tensorConvolution(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0);
-void* var_50 = tensorAdd(var_49, conv2d_12_b);
-void* var_51 = tensorAdd(var_43, var_50);
-void* var_52 = tensorRelu(var_51);
-void* var_54 = tensorConvolution(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0);
-void* var_55 = tensorAdd(var_54, conv2d_13_b);
-void* var_56 = tensorRelu(var_55);
-void* var_58 = tensorConvolution(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0);
-void* var_59 = tensorAdd(var_58, conv2d_14_b);
-void* var_60 = tensorAdd(var_52, var_59);
-void* var_61 = tensorRelu(var_60);
-void* var_63 = tensorConvolution(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0);
-void* var_64 = tensorAdd(var_63, conv2d_15_b);
-void* var_65 = tensorRelu(var_64);
-void* var_67 = tensorConvolution(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0);
-void* var_68 = tensorAdd(var_67, conv2d_16_b);
-void* var_70 = tensorConvolution(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0);
-void* var_71 = tensorAdd(var_70, conv2d_17_b);
-void* var_72 = tensorAdd(var_71, var_68);
-void* var_73 = tensorRelu(var_72);
-void* var_75 = tensorConvolution(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0);
-void* var_76 = tensorAdd(var_75, conv2d_18_b);
-void* var_77 = tensorRelu(var_76);
-void* var_79 = tensorConvolution(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0);
-void* var_80 = tensorAdd(var_79, conv2d_19_b);
-void* var_81 = tensorAdd(var_73, var_80);
-void* var_82 = tensorRelu(var_81);
-void* var_84 = tensorConvolution(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0);
-void* var_85 = tensorAdd(var_84, conv2d_20_b);
-void* var_86 = tensorRelu(var_85);
-void* var_88 = tensorConvolution(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0);
-void* var_89 = tensorAdd(var_88, conv2d_21_b);
-void* var_90 = tensorAdd(var_82, var_89);
-void* var_91 = tensorRelu(var_90);
-void* var_92 = tensorPooling(var_91,1,8,8,0,0,8,8);
-void* var_94 = tensorGemmGPU(var_92, dense_1_w);
-void* var_95 = tensorAdd(var_94, dense_1_b);
-void* var_96 = tensorSoftmax(var_95);
-
-computeAccuracy2(labels,10000,var_96);
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
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+++ /dev/null
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-!Ü=!J¹<«¾j>ÓOä=ŸÐî:²_¾Dr=~>Y«=Ö>³ ½iÛ½ñ3½,3¾ŠÛ>Ç Û=¸ ¾uŒ>ž\<Z7=Z§¬=''6¾o ¨;Ñ»õ¾à¡t¾©¾ òG½àÓ¨¾¥å¼ôÿ]¾,O¨=¶’<»lÏ=¥Ò">¡'Œ½ÃŠ=®¤T¾Z==2éÏ=üjë½ûHF¿õ¾ “œ¼£}=³¬¾ßä¼Ó°”>ÛïQ?VÍ<; =8ñ‰½‹xr¾¢°}<ÿz>îšG¾{“ ½ax6¾¶Î‚»$Ôë¾DpÎ=Š|5¾Á2>¤tY>ä©=
\ No newline at end of file
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-
Sî>:Ä}¾õ…>ºpž?ñ‡¿’ß²>Vâ–¿Q0å<qö>Ò/“<
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/quant_ranges.txt b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/quant_ranges.txt
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--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/quant_ranges.txt
+++ /dev/null
@@ -1,15 +0,0 @@
--1.8816367 2.0934217 -0.53275156 0.49437004 -0.6403629 0.2490165 0.0 1.35908746719
-0.0 1.35908746719 -0.2688396 0.20639156 -0.7745511 0.82006615 0.0 2.52123117924
-0.0 2.52123117924 -0.16776876 0.14878987 -0.35283303 0.5154362 0.0 1.20119857848
-0.0 1.20119857848 -0.088948585 0.114222586 -0.30250227 0.36856708 0.0 1.03598809302
-0.0 1.03598809302 -0.07739562 0.10973293 -0.15568458 0.17634983 0.0 0.300495595038
-0.0 0.300495595038 -0.051649556 0.05435231 -0.07395447 0.07996062 0.0 0.11490475405
-0.0 0.11490475405 -0.043513633 0.07577866 -0.06921874 0.02660573 0.0 0.16232508488
-0.0 0.16232508488 -0.033842053 0.045218028 -0.022827804 0.023845317 0.0 0.124249965735
-0.0 0.124249965735 -0.02211613 0.032084666 -0.02699063 0.03773564 0.0 0.174634486511
-0.0 0.174634486511 -0.01979376 0.034854397 -0.036107242 0.07056531 0.0 0.575175762177
-0.0 0.575175762177 -0.03452098 0.046055835 -0.051925894 0.07039055 0.0 0.771875114441
-0.0 0.771875114441 -0.025946895 0.040090334 -0.06049362 0.12658806 0.0 1.17285169065
-0.0 1.17285169065 -0.021766115 0.03315237 -0.20705001 0.117947325 0.0 2.00157693863
-0.0 2.00157693863 -0.042597745 0.046707444 -0.21937433 0.2545502 0.0 2.00236111879
-0.0 2.00236111879 -0.32550547 0.30829763 -1.1787822 1.2378151 -18.2514705467 24.1736344528
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/src.cc
deleted file mode 100644
index 44179ee9f39c9547270b45fc84249835350bee5f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/src.cc
+++ /dev/null
@@ -1,141 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("vgg16_cifar10/");
-std::string input_path = dir_prefix + std::string("input.bin");
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-std::string labels_path = dir_prefix + std::string("labels.bin");
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,10);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
-void* var_1 = tensorAdd(var_0, conv2d_1_b);
-void* var_2 = tensorRelu(var_1);
-void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
-void* var_5 = tensorAdd(var_4, conv2d_2_b);
-void* var_6 = tensorRelu(var_5);
-void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
-void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
-void* var_9 = tensorAdd(var_8, conv2d_3_b);
-void* var_10 = tensorRelu(var_9);
-void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
-void* var_13 = tensorAdd(var_12, conv2d_4_b);
-void* var_14 = tensorRelu(var_13);
-void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
-void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
-void* var_17 = tensorAdd(var_16, conv2d_5_b);
-void* var_18 = tensorRelu(var_17);
-void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
-void* var_21 = tensorAdd(var_20, conv2d_6_b);
-void* var_22 = tensorRelu(var_21);
-void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
-void* var_25 = tensorAdd(var_24, conv2d_7_b);
-void* var_26 = tensorRelu(var_25);
-void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
-void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
-void* var_29 = tensorAdd(var_28, conv2d_8_b);
-void* var_30 = tensorRelu(var_29);
-void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
-void* var_33 = tensorAdd(var_32, conv2d_9_b);
-void* var_34 = tensorRelu(var_33);
-void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
-void* var_37 = tensorAdd(var_36, conv2d_10_b);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
-void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
-void* var_41 = tensorAdd(var_40, conv2d_11_b);
-void* var_42 = tensorRelu(var_41);
-void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
-void* var_45 = tensorAdd(var_44, conv2d_12_b);
-void* var_46 = tensorRelu(var_45);
-void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
-void* var_49 = tensorAdd(var_48, conv2d_13_b);
-void* var_50 = tensorRelu(var_49);
-void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
-void* var_54 = tensorGemmGPU(var_51, dense_1_w);
-void* var_55 = tensorAdd(var_54, dense_1_b);
-void* var_56 = tensorRelu(var_55);
-void* var_58 = tensorGemmGPU(var_56, dense_2_w);
-void* var_59 = tensorAdd(var_58, dense_2_b);
-void* var_60 = tensorSoftmax(var_59);
-
-computeAccuracy2(labels,10000,var_60);
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/vgg16_cifar_calib.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/vgg16_cifar_calib.bin
deleted file mode 100644
index 43bc1e5b985604c5a17fe67d2db4fec82e12042d..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/vgg16_cifar_calib.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/vgg16_train_labels.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/vgg16_train_labels.bin
deleted file mode 100644
index 9be730fd6f397987a6948a8d9196c7e156675d1b..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/vgg16_train_labels.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/approxhpvm_src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/approxhpvm_src.cc
deleted file mode 100644
index 8084e3723a6141ac0e99729b8455bcc529ac7a0f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/approxhpvm_src.cc
+++ /dev/null
@@ -1,982 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/stat.h>
-#include <cstring>
-#include <visc.h>
-#include <tensorTypes.h>
-#include <tensorUtils.h>
-
-void var_0_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_1_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_2_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_3_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_5_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_6_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_8_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_9_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_11_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_12_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_13_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_14_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_15_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_16_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_17_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_18_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_19_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_20_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_21_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_22_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_23_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_24_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_25_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_26_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_27_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_28_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_29_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_30_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_31_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_32_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_33_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_34_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_35_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_36_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_37_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_38_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_39_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_40_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_41_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_42_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_43_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_44_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_45_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_46_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_47_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_48_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_49_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_softmax(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void root(void* input, size_t input_bytes,
- void* conv2d_1_w, size_t conv2d_1_w_bytes,
- void* conv2d_1_b, size_t conv2d_1_b_bytes,
- void* conv2d_2_w, size_t conv2d_2_w_bytes,
- void* conv2d_2_b, size_t conv2d_2_b_bytes,
- void* conv2d_3_w, size_t conv2d_3_w_bytes,
- void* conv2d_3_b, size_t conv2d_3_b_bytes,
- void* conv2d_4_w, size_t conv2d_4_w_bytes,
- void* conv2d_4_b, size_t conv2d_4_b_bytes,
- void* conv2d_5_w, size_t conv2d_5_w_bytes,
- void* conv2d_5_b, size_t conv2d_5_b_bytes,
- void* conv2d_6_w, size_t conv2d_6_w_bytes,
- void* conv2d_6_b, size_t conv2d_6_b_bytes,
- void* conv2d_7_w, size_t conv2d_7_w_bytes,
- void* conv2d_7_b, size_t conv2d_7_b_bytes,
- void* conv2d_8_w, size_t conv2d_8_w_bytes,
- void* conv2d_8_b, size_t conv2d_8_b_bytes,
- void* conv2d_9_w, size_t conv2d_9_w_bytes,
- void* conv2d_9_b, size_t conv2d_9_b_bytes,
- void* conv2d_10_w, size_t conv2d_10_w_bytes,
- void* conv2d_10_b, size_t conv2d_10_b_bytes,
- void* conv2d_11_w, size_t conv2d_11_w_bytes,
- void* conv2d_11_b, size_t conv2d_11_b_bytes,
- void* conv2d_12_w, size_t conv2d_12_w_bytes,
- void* conv2d_12_b, size_t conv2d_12_b_bytes,
- void* conv2d_13_w, size_t conv2d_13_w_bytes,
- void* conv2d_13_b, size_t conv2d_13_b_bytes,
- void* dense_1_w, size_t dense_1_w_bytes,
- void* dense_1_b, size_t dense_1_b_bytes,
- void* dense_2_w, size_t dense_2_w_bytes,
- void* dense_2_b, size_t dense_2_b_bytes){
-
-
- __visc__hint(visc::CPU_TARGET);
- __visc__attributes(31, input, conv2d_1_w, conv2d_1_b, conv2d_2_w, conv2d_2_b, conv2d_3_w, conv2d_3_b, conv2d_4_w, conv2d_4_b, conv2d_5_w, conv2d_5_b, conv2d_6_w, conv2d_6_b, conv2d_7_w, conv2d_7_b, conv2d_8_w, conv2d_8_b, conv2d_9_w, conv2d_9_b, conv2d_10_w, conv2d_10_b, conv2d_11_w, conv2d_11_b, conv2d_12_w, conv2d_12_b, conv2d_13_w, conv2d_13_b, dense_1_w, dense_1_b, dense_2_w, dense_2_b, 0);
-
-
- void* var_0 = __visc__createNodeND(0, var_0_node);
-
- __visc__bindIn(var_0, 0, 0, 0);
- __visc__bindIn(var_0, 1, 1, 0);
- __visc__bindIn(var_0, 2, 2, 0);
- __visc__bindIn(var_0, 3, 3, 0);
-
- void* var_1 = __visc__createNodeND(0, var_1_node);
-
- __visc__edge(var_0, var_1, 1, 0, 0, 0);
- __visc__edge(var_0, var_1, 1, 1, 1, 0);
- __visc__bindIn(var_1, 4, 2, 0);
- __visc__bindIn(var_1, 5, 3, 0);
-
- void* var_2 = __visc__createNodeND(0, var_2_node);
-
- __visc__edge(var_1, var_2, 1, 0, 0, 0);
- __visc__edge(var_1, var_2, 1, 1, 1, 0);
-
- void* var_3 = __visc__createNodeND(0, var_3_node);
-
- __visc__edge(var_2, var_3, 1, 0, 0, 0);
- __visc__edge(var_2, var_3, 1, 1, 1, 0);
- __visc__bindIn(var_3, 6, 2, 0);
- __visc__bindIn(var_3, 7, 3, 0);
-
- void* var_4 = __visc__createNodeND(0, var_4_node);
-
- __visc__edge(var_3, var_4, 1, 0, 0, 0);
- __visc__edge(var_3, var_4, 1, 1, 1, 0);
- __visc__bindIn(var_4, 8, 2, 0);
- __visc__bindIn(var_4, 9, 3, 0);
-
- void* var_5 = __visc__createNodeND(0, var_5_node);
-
- __visc__edge(var_4, var_5, 1, 0, 0, 0);
- __visc__edge(var_4, var_5, 1, 1, 1, 0);
-
- void* var_6 = __visc__createNodeND(0, var_6_node);
-
- __visc__edge(var_5, var_6, 1, 0, 0, 0);
- __visc__edge(var_5, var_6, 1, 1, 1, 0);
-
- void* var_7 = __visc__createNodeND(0, var_7_node);
-
- __visc__edge(var_6, var_7, 1, 0, 0, 0);
- __visc__edge(var_6, var_7, 1, 1, 1, 0);
- __visc__bindIn(var_7, 10, 2, 0);
- __visc__bindIn(var_7, 11, 3, 0);
-
- void* var_8 = __visc__createNodeND(0, var_8_node);
-
- __visc__edge(var_7, var_8, 1, 0, 0, 0);
- __visc__edge(var_7, var_8, 1, 1, 1, 0);
- __visc__bindIn(var_8, 12, 2, 0);
- __visc__bindIn(var_8, 13, 3, 0);
-
- void* var_9 = __visc__createNodeND(0, var_9_node);
-
- __visc__edge(var_8, var_9, 1, 0, 0, 0);
- __visc__edge(var_8, var_9, 1, 1, 1, 0);
-
- void* var_10 = __visc__createNodeND(0, var_10_node);
-
- __visc__edge(var_9, var_10, 1, 0, 0, 0);
- __visc__edge(var_9, var_10, 1, 1, 1, 0);
- __visc__bindIn(var_10, 14, 2, 0);
- __visc__bindIn(var_10, 15, 3, 0);
-
- void* var_11 = __visc__createNodeND(0, var_11_node);
-
- __visc__edge(var_10, var_11, 1, 0, 0, 0);
- __visc__edge(var_10, var_11, 1, 1, 1, 0);
- __visc__bindIn(var_11, 16, 2, 0);
- __visc__bindIn(var_11, 17, 3, 0);
-
- void* var_12 = __visc__createNodeND(0, var_12_node);
-
- __visc__edge(var_11, var_12, 1, 0, 0, 0);
- __visc__edge(var_11, var_12, 1, 1, 1, 0);
-
- void* var_13 = __visc__createNodeND(0, var_13_node);
-
- __visc__edge(var_12, var_13, 1, 0, 0, 0);
- __visc__edge(var_12, var_13, 1, 1, 1, 0);
-
- void* var_14 = __visc__createNodeND(0, var_14_node);
-
- __visc__edge(var_13, var_14, 1, 0, 0, 0);
- __visc__edge(var_13, var_14, 1, 1, 1, 0);
- __visc__bindIn(var_14, 18, 2, 0);
- __visc__bindIn(var_14, 19, 3, 0);
-
- void* var_15 = __visc__createNodeND(0, var_15_node);
-
- __visc__edge(var_14, var_15, 1, 0, 0, 0);
- __visc__edge(var_14, var_15, 1, 1, 1, 0);
- __visc__bindIn(var_15, 20, 2, 0);
- __visc__bindIn(var_15, 21, 3, 0);
-
- void* var_16 = __visc__createNodeND(0, var_16_node);
-
- __visc__edge(var_15, var_16, 1, 0, 0, 0);
- __visc__edge(var_15, var_16, 1, 1, 1, 0);
-
- void* var_17 = __visc__createNodeND(0, var_17_node);
-
- __visc__edge(var_16, var_17, 1, 0, 0, 0);
- __visc__edge(var_16, var_17, 1, 1, 1, 0);
- __visc__bindIn(var_17, 22, 2, 0);
- __visc__bindIn(var_17, 23, 3, 0);
-
- void* var_18 = __visc__createNodeND(0, var_18_node);
-
- __visc__edge(var_17, var_18, 1, 0, 0, 0);
- __visc__edge(var_17, var_18, 1, 1, 1, 0);
- __visc__bindIn(var_18, 24, 2, 0);
- __visc__bindIn(var_18, 25, 3, 0);
-
- void* var_19 = __visc__createNodeND(0, var_19_node);
-
- __visc__edge(var_18, var_19, 1, 0, 0, 0);
- __visc__edge(var_18, var_19, 1, 1, 1, 0);
-
- void* var_20 = __visc__createNodeND(0, var_20_node);
-
- __visc__edge(var_19, var_20, 1, 0, 0, 0);
- __visc__edge(var_19, var_20, 1, 1, 1, 0);
- __visc__bindIn(var_20, 26, 2, 0);
- __visc__bindIn(var_20, 27, 3, 0);
-
- void* var_21 = __visc__createNodeND(0, var_21_node);
-
- __visc__edge(var_20, var_21, 1, 0, 0, 0);
- __visc__edge(var_20, var_21, 1, 1, 1, 0);
- __visc__bindIn(var_21, 28, 2, 0);
- __visc__bindIn(var_21, 29, 3, 0);
-
- void* var_22 = __visc__createNodeND(0, var_22_node);
-
- __visc__edge(var_21, var_22, 1, 0, 0, 0);
- __visc__edge(var_21, var_22, 1, 1, 1, 0);
-
- void* var_23 = __visc__createNodeND(0, var_23_node);
-
- __visc__edge(var_22, var_23, 1, 0, 0, 0);
- __visc__edge(var_22, var_23, 1, 1, 1, 0);
-
- void* var_24 = __visc__createNodeND(0, var_24_node);
-
- __visc__edge(var_23, var_24, 1, 0, 0, 0);
- __visc__edge(var_23, var_24, 1, 1, 1, 0);
- __visc__bindIn(var_24, 30, 2, 0);
- __visc__bindIn(var_24, 31, 3, 0);
-
- void* var_25 = __visc__createNodeND(0, var_25_node);
-
- __visc__edge(var_24, var_25, 1, 0, 0, 0);
- __visc__edge(var_24, var_25, 1, 1, 1, 0);
- __visc__bindIn(var_25, 32, 2, 0);
- __visc__bindIn(var_25, 33, 3, 0);
-
- void* var_26 = __visc__createNodeND(0, var_26_node);
-
- __visc__edge(var_25, var_26, 1, 0, 0, 0);
- __visc__edge(var_25, var_26, 1, 1, 1, 0);
-
- void* var_27 = __visc__createNodeND(0, var_27_node);
-
- __visc__edge(var_26, var_27, 1, 0, 0, 0);
- __visc__edge(var_26, var_27, 1, 1, 1, 0);
- __visc__bindIn(var_27, 34, 2, 0);
- __visc__bindIn(var_27, 35, 3, 0);
-
- void* var_28 = __visc__createNodeND(0, var_28_node);
-
- __visc__edge(var_27, var_28, 1, 0, 0, 0);
- __visc__edge(var_27, var_28, 1, 1, 1, 0);
- __visc__bindIn(var_28, 36, 2, 0);
- __visc__bindIn(var_28, 37, 3, 0);
-
- void* var_29 = __visc__createNodeND(0, var_29_node);
-
- __visc__edge(var_28, var_29, 1, 0, 0, 0);
- __visc__edge(var_28, var_29, 1, 1, 1, 0);
-
- void* var_30 = __visc__createNodeND(0, var_30_node);
-
- __visc__edge(var_29, var_30, 1, 0, 0, 0);
- __visc__edge(var_29, var_30, 1, 1, 1, 0);
- __visc__bindIn(var_30, 38, 2, 0);
- __visc__bindIn(var_30, 39, 3, 0);
-
- void* var_31 = __visc__createNodeND(0, var_31_node);
-
- __visc__edge(var_30, var_31, 1, 0, 0, 0);
- __visc__edge(var_30, var_31, 1, 1, 1, 0);
- __visc__bindIn(var_31, 40, 2, 0);
- __visc__bindIn(var_31, 41, 3, 0);
-
- void* var_32 = __visc__createNodeND(0, var_32_node);
-
- __visc__edge(var_31, var_32, 1, 0, 0, 0);
- __visc__edge(var_31, var_32, 1, 1, 1, 0);
-
- void* var_33 = __visc__createNodeND(0, var_33_node);
-
- __visc__edge(var_32, var_33, 1, 0, 0, 0);
- __visc__edge(var_32, var_33, 1, 1, 1, 0);
-
- void* var_34 = __visc__createNodeND(0, var_34_node);
-
- __visc__edge(var_33, var_34, 1, 0, 0, 0);
- __visc__edge(var_33, var_34, 1, 1, 1, 0);
- __visc__bindIn(var_34, 42, 2, 0);
- __visc__bindIn(var_34, 43, 3, 0);
-
- void* var_35 = __visc__createNodeND(0, var_35_node);
-
- __visc__edge(var_34, var_35, 1, 0, 0, 0);
- __visc__edge(var_34, var_35, 1, 1, 1, 0);
- __visc__bindIn(var_35, 44, 2, 0);
- __visc__bindIn(var_35, 45, 3, 0);
-
- void* var_36 = __visc__createNodeND(0, var_36_node);
-
- __visc__edge(var_35, var_36, 1, 0, 0, 0);
- __visc__edge(var_35, var_36, 1, 1, 1, 0);
-
- void* var_37 = __visc__createNodeND(0, var_37_node);
-
- __visc__edge(var_36, var_37, 1, 0, 0, 0);
- __visc__edge(var_36, var_37, 1, 1, 1, 0);
- __visc__bindIn(var_37, 46, 2, 0);
- __visc__bindIn(var_37, 47, 3, 0);
-
- void* var_38 = __visc__createNodeND(0, var_38_node);
-
- __visc__edge(var_37, var_38, 1, 0, 0, 0);
- __visc__edge(var_37, var_38, 1, 1, 1, 0);
- __visc__bindIn(var_38, 48, 2, 0);
- __visc__bindIn(var_38, 49, 3, 0);
-
- void* var_39 = __visc__createNodeND(0, var_39_node);
-
- __visc__edge(var_38, var_39, 1, 0, 0, 0);
- __visc__edge(var_38, var_39, 1, 1, 1, 0);
-
- void* var_40 = __visc__createNodeND(0, var_40_node);
-
- __visc__edge(var_39, var_40, 1, 0, 0, 0);
- __visc__edge(var_39, var_40, 1, 1, 1, 0);
- __visc__bindIn(var_40, 50, 2, 0);
- __visc__bindIn(var_40, 51, 3, 0);
-
- void* var_41 = __visc__createNodeND(0, var_41_node);
-
- __visc__edge(var_40, var_41, 1, 0, 0, 0);
- __visc__edge(var_40, var_41, 1, 1, 1, 0);
- __visc__bindIn(var_41, 52, 2, 0);
- __visc__bindIn(var_41, 53, 3, 0);
-
- void* var_42 = __visc__createNodeND(0, var_42_node);
-
- __visc__edge(var_41, var_42, 1, 0, 0, 0);
- __visc__edge(var_41, var_42, 1, 1, 1, 0);
-
- void* var_43 = __visc__createNodeND(0, var_43_node);
-
- __visc__edge(var_42, var_43, 1, 0, 0, 0);
- __visc__edge(var_42, var_43, 1, 1, 1, 0);
-
- void* var_44 = __visc__createNodeND(0, var_44_node);
-
- __visc__edge(var_43, var_44, 1, 0, 0, 0);
- __visc__edge(var_43, var_44, 1, 1, 1, 0);
- __visc__bindIn(var_44, 54, 2, 0);
- __visc__bindIn(var_44, 55, 3, 0);
-
- void* var_45 = __visc__createNodeND(0, var_45_node);
-
- __visc__edge(var_44, var_45, 1, 0, 0, 0);
- __visc__edge(var_44, var_45, 1, 1, 1, 0);
- __visc__bindIn(var_45, 56, 2, 0);
- __visc__bindIn(var_45, 57, 3, 0);
-
- void* var_46 = __visc__createNodeND(0, var_46_node);
-
- __visc__edge(var_45, var_46, 1, 0, 0, 0);
- __visc__edge(var_45, var_46, 1, 1, 1, 0);
-
- void* var_47 = __visc__createNodeND(0, var_47_node);
-
- __visc__edge(var_46, var_47, 1, 0, 0, 0);
- __visc__edge(var_46, var_47, 1, 1, 1, 0);
- __visc__bindIn(var_47, 58, 2, 0);
- __visc__bindIn(var_47, 59, 3, 0);
-
- void* var_48 = __visc__createNodeND(0, var_48_node);
-
- __visc__edge(var_47, var_48, 1, 0, 0, 0);
- __visc__edge(var_47, var_48, 1, 1, 1, 0);
- __visc__bindIn(var_48, 60, 2, 0);
- __visc__bindIn(var_48, 61, 3, 0);
-
- void* var_49 = __visc__createNodeND(0, var_49_node);
-
- __visc__edge(var_48, var_49, 1, 0, 0, 0);
- __visc__edge(var_48, var_49, 1, 1, 1, 0);
-
- __visc__bindOut(var_49, 0, 0, 0);
- __visc__bindOut(var_49, 1, 1, 0);
-
-}
-
-struct ret_t {
- void* tensor;
- size_t bytes;
-};
-
-typedef struct __attribute__((__packed__)) {
- void* input;
- size_t input_bytes;
- void* conv2d_1_w;
- size_t conv2d_1_w_bytes;
- void* conv2d_1_b;
- size_t conv2d_1_b_bytes;
- void* conv2d_2_w;
- size_t conv2d_2_w_bytes;
- void* conv2d_2_b;
- size_t conv2d_2_b_bytes;
- void* conv2d_3_w;
- size_t conv2d_3_w_bytes;
- void* conv2d_3_b;
- size_t conv2d_3_b_bytes;
- void* conv2d_4_w;
- size_t conv2d_4_w_bytes;
- void* conv2d_4_b;
- size_t conv2d_4_b_bytes;
- void* conv2d_5_w;
- size_t conv2d_5_w_bytes;
- void* conv2d_5_b;
- size_t conv2d_5_b_bytes;
- void* conv2d_6_w;
- size_t conv2d_6_w_bytes;
- void* conv2d_6_b;
- size_t conv2d_6_b_bytes;
- void* conv2d_7_w;
- size_t conv2d_7_w_bytes;
- void* conv2d_7_b;
- size_t conv2d_7_b_bytes;
- void* conv2d_8_w;
- size_t conv2d_8_w_bytes;
- void* conv2d_8_b;
- size_t conv2d_8_b_bytes;
- void* conv2d_9_w;
- size_t conv2d_9_w_bytes;
- void* conv2d_9_b;
- size_t conv2d_9_b_bytes;
- void* conv2d_10_w;
- size_t conv2d_10_w_bytes;
- void* conv2d_10_b;
- size_t conv2d_10_b_bytes;
- void* conv2d_11_w;
- size_t conv2d_11_w_bytes;
- void* conv2d_11_b;
- size_t conv2d_11_b_bytes;
- void* conv2d_12_w;
- size_t conv2d_12_w_bytes;
- void* conv2d_12_b;
- size_t conv2d_12_b_bytes;
- void* conv2d_13_w;
- size_t conv2d_13_w_bytes;
- void* conv2d_13_b;
- size_t conv2d_13_b_bytes;
- void* dense_1_w;
- size_t dense_1_w_bytes;
- void* dense_1_b;
- size_t dense_1_b_bytes;
- void* dense_2_w;
- size_t dense_2_w_bytes;
- void* dense_2_b;
- size_t dense_2_b_bytes;
-
- struct ret_t r;
-}
-RootIn;
-
-int main(){
-
-std::string dir_prefix = std::string("vgg16_cifar100_test/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-
-__visc__init();
-RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn)));
-
-args->input = input;
-args->input_bytes = 0;
-args->conv2d_1_w = conv2d_1_w;
-args->conv2d_1_w_bytes = 0;
-args->conv2d_1_b = conv2d_1_b;
-args->conv2d_1_b_bytes = 0;
-args->conv2d_2_w = conv2d_2_w;
-args->conv2d_2_w_bytes = 0;
-args->conv2d_2_b = conv2d_2_b;
-args->conv2d_2_b_bytes = 0;
-args->conv2d_3_w = conv2d_3_w;
-args->conv2d_3_w_bytes = 0;
-args->conv2d_3_b = conv2d_3_b;
-args->conv2d_3_b_bytes = 0;
-args->conv2d_4_w = conv2d_4_w;
-args->conv2d_4_w_bytes = 0;
-args->conv2d_4_b = conv2d_4_b;
-args->conv2d_4_b_bytes = 0;
-args->conv2d_5_w = conv2d_5_w;
-args->conv2d_5_w_bytes = 0;
-args->conv2d_5_b = conv2d_5_b;
-args->conv2d_5_b_bytes = 0;
-args->conv2d_6_w = conv2d_6_w;
-args->conv2d_6_w_bytes = 0;
-args->conv2d_6_b = conv2d_6_b;
-args->conv2d_6_b_bytes = 0;
-args->conv2d_7_w = conv2d_7_w;
-args->conv2d_7_w_bytes = 0;
-args->conv2d_7_b = conv2d_7_b;
-args->conv2d_7_b_bytes = 0;
-args->conv2d_8_w = conv2d_8_w;
-args->conv2d_8_w_bytes = 0;
-args->conv2d_8_b = conv2d_8_b;
-args->conv2d_8_b_bytes = 0;
-args->conv2d_9_w = conv2d_9_w;
-args->conv2d_9_w_bytes = 0;
-args->conv2d_9_b = conv2d_9_b;
-args->conv2d_9_b_bytes = 0;
-args->conv2d_10_w = conv2d_10_w;
-args->conv2d_10_w_bytes = 0;
-args->conv2d_10_b = conv2d_10_b;
-args->conv2d_10_b_bytes = 0;
-args->conv2d_11_w = conv2d_11_w;
-args->conv2d_11_w_bytes = 0;
-args->conv2d_11_b = conv2d_11_b;
-args->conv2d_11_b_bytes = 0;
-args->conv2d_12_w = conv2d_12_w;
-args->conv2d_12_w_bytes = 0;
-args->conv2d_12_b = conv2d_12_b;
-args->conv2d_12_b_bytes = 0;
-args->conv2d_13_w = conv2d_13_w;
-args->conv2d_13_w_bytes = 0;
-args->conv2d_13_b = conv2d_13_b;
-args->conv2d_13_b_bytes = 0;
-args->dense_1_w = dense_1_w;
-args->dense_1_w_bytes = 0;
-args->dense_1_b = dense_1_b;
-args->dense_1_b_bytes = 0;
-args->dense_2_w = dense_2_w;
-args->dense_2_w_bytes = 0;
-args->dense_2_b = dense_2_b;
-args->dense_2_b_bytes = 0;
-
-void* dfg = __visc__launch(0, root, (void*) args);
-
-__visc__wait(dfg);
-
-void *result = static_cast<RootIn*>(args)->input;
-hpvm_request_tensor(result, 0);
-
-__visc__cleanup();
- computeAccuracy2(labels, 10000, result);
-return 0;
-
-}
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_2_b.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_2_b.bin
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--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_2_b.bin
+++ /dev/null
@@ -1,2 +0,0 @@
-ãèÏ;ÿCú=×_Q>-î½Z5Ì=&$¿í÷‡>È,…>sv¾è2 ¼.ãÏ<Ã4$>׆0>Û)¿vg§¾Çô¬¿‡·K=Žõ¼=9ó¾Ÿî²¼ée=¬P¡?•¿Ì¾º}Œ=½ ~>–·"¼bKƒ>«î—¾Hu >v$™¾Æj—?cß8>OÏñ=kÇ<>ÙYM>(¿jît>щ>Ä…&¾gH¿·g&=«¿
-k>`M¶¿ ?³0T>ò£>u‹>]ˆÁ½®‚¿m‚¿M—a>ù䑽2> ë>¯ñ
¿W[…>² >#z·>Õ®Ú;›J¸=ž r=€Ã=£>
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_2_w.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_2_w.bin
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_6_b.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_6_b.bin
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index e2999812df22d44cf3e658d32986e6ccbabc9d4c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_6_b.bin
+++ /dev/null
@@ -1,5 +0,0 @@
-Qwü¾ ¶–>ó<
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->.Íô=”í¾
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/labels32.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/labels32.bin
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index 0e29aa05a086316e36429e20e1a13580e1b0c36a..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/layer_composition.txt b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/layer_composition.txt
deleted file mode 100644
index 79818d6f010035c6e19f12881749f4d5b3d3c253..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/layer_composition.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-conv add activation
-conv add activation pool
-conv add activation
-conv add activation pool
-conv add activation
-conv add activation
-conv add activation pool
-conv add activation
-conv add activation
-conv add activation pool
-conv add activation
-conv add activation
-conv add activation pool
-dense add activation
-dense add
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/layers.txt b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/layers.txt
deleted file mode 100644
index 7eaa520e4a2e451d5ccec5c8737dec8be8458369..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/layers.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-Conv1,10000,3,32,32,64,3,3,3
-Conv2,10000,64,32,32,64,64,3,3
-Conv3,10000,64,16,16,128,64,3,3
-Conv4,10000,128,16,16,128,128,3,3
-Conv5,10000,128,8,8,256,128,3,3
-Conv6,10000,256,8,8,256,256,3,3
-Conv7,10000,256,8,8,256,256,3,3
-Conv8,10000,256,4,4,512,256,3,3
-Conv9,10000,512,4,4,512,512,3,3
-Conv10,10000,512,4,4,512,512,3,3
-Conv11,10000,512,2,2,512,512,3,3
-Conv12,10000,512,2,2,512,512,3,3
-Conv13,10000,512,2,2,512,512,3,3
-FC1,10000,512,512,512
-FC2,10000,512,512,100
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/promise_src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/promise_src.cc
deleted file mode 100644
index 0f28f2bfd69d9e8c4895e782bd02173eefcd0993..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/promise_src.cc
+++ /dev/null
@@ -1,138 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-int total_runs = 100;
-for (int i = 0 ; i < total_runs; i++){
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-
-
-std::string dir_prefix = std::string("vgg16_cifar100_test/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = ConvLayer_PROMISE(input, -1.7829767, 1.9456929, conv2d_1_w, -0.7450515, 0.71249133, conv2d_1_b, -1.5885142, 0.275554, 1, 1, 1, 1, -1, 0, 1, 0.0, 8.190712, 9);
-void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 8.190712, conv2d_2_w, -0.30790088, 0.43504623, conv2d_2_b, -1.4242363, 1.2602744, 1, 1, 1, 1, 0, 2, 1, 0.0, 19.023172, 9);
-void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 19.023172, conv2d_3_w, -0.29189092, 0.26958522, conv2d_3_b, -1.0527138, 0.9075671, 1, 1, 1, 1, -1, 0, 1, 0.0, 14.428051, 9);
-void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 14.428051, conv2d_4_w, -0.15521508, 0.1829038, conv2d_4_b, -0.845419, 1.9358484, 1, 1, 1, 1, 0, 2, 1, 0.0, 23.065294, 9);
-void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 23.065294, conv2d_5_w, -0.13149762, 0.14811686, conv2d_5_b, -0.7162557, 1.0370971, 1, 1, 1, 1, -1, 0, 1, 0.0, 15.165984, 9);
-void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 15.165984, conv2d_6_w, -0.06236292, 0.08321518, conv2d_6_b, -0.9067523, 0.9922458, 1, 1, 1, 1, -1, 0, 1, 0.0, 13.664733, 9);
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 13.664733, conv2d_7_w, -0.06471479, 0.1024472, conv2d_7_b, -0.15943134, 0.7988499, 1, 1, 1, 1, 0, 2, 1, 0.0, 19.025272, 9);
-void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 19.025272, conv2d_8_w, -0.06320205, 0.08291938, conv2d_8_b, -0.32540628, 0.5203079, 1, 1, 1, 1, -1, 0, 1, 0.0, 6.727217, 9);
-void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 6.727217, conv2d_9_w, -0.037707984, 0.051601283, conv2d_9_b, -0.25622904, 0.11251946, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.2003012, 9);
-void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 3.2003012, conv2d_10_w, -0.056007143, 0.09549151, conv2d_10_b, -0.11591503, 0.06267536, 1, 1, 1, 1, 0, 2, 1, 0.0, 4.321189, 9);
-void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 4.321189, conv2d_11_w, -0.060094673, 0.10868926, conv2d_11_b, -0.105962686, 0.09584572, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.936297, 9);
-void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 2.936297, conv2d_12_w, -0.034618977, 0.05792674, conv2d_12_b, -0.4237576, 0.11035452, 1, 1, 1, 1, -1, 0, 1, 0.0, 4.87262, 9);
-void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.87262, conv2d_13_w, -0.035480656, 0.058295887, conv2d_13_b, -0.21477045, 0.14263579, 1, 1, 1, 1, 0, 2, 1, 0.0, 10.32133, 9);
-void* var_13 = FCLayer_PROMISE(var_12, 0.0, 10.32133, dense_1_w, -0.08929961, 0.11301676, dense_1_b, -0.20798548, 0.47405547, 1, 0.0, 13.91, 9);
-void* var_14 = FCLayer_PROMISE(var_13, 0.0, 13.91, dense_2_w, -0.6627122, 0.35539475, dense_2_b, -1.0631907, 0.9830786, -1, -70.45701, 87.34367, 9);
-void* var_15 = tensorSoftmax(var_14);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_15);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-}
-
-dumpExecutionAccuracies();
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/quant_ranges.txt b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/quant_ranges.txt
deleted file mode 100644
index 4e614e1664822d2ecf6fa426a7eb2fd7c362a2e7..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/quant_ranges.txt
+++ /dev/null
@@ -1,15 +0,0 @@
--1.7829767 1.9456929 -0.7450515 0.71249133 -1.5885142 0.275554 0.0 8.190712
-0.0 8.190712 -0.30790088 0.43504623 -1.4242363 1.2602744 0.0 19.023172
-0.0 19.023172 -0.29189092 0.26958522 -1.0527138 0.9075671 0.0 14.428051
-0.0 14.428051 -0.15521508 0.1829038 -0.845419 1.9358484 0.0 23.065294
-0.0 23.065294 -0.13149762 0.14811686 -0.7162557 1.0370971 0.0 15.165984
-0.0 15.165984 -0.06236292 0.08321518 -0.9067523 0.9922458 0.0 13.664733
-0.0 13.664733 -0.06471479 0.1024472 -0.15943134 0.7988499 0.0 19.025272
-0.0 19.025272 -0.06320205 0.08291938 -0.32540628 0.5203079 0.0 6.727217
-0.0 6.727217 -0.037707984 0.051601283 -0.25622904 0.11251946 0.0 3.2003012
-0.0 3.2003012 -0.056007143 0.09549151 -0.11591503 0.06267536 0.0 4.321189
-0.0 4.321189 -0.060094673 0.10868926 -0.105962686 0.09584572 0.0 2.936297
-0.0 2.936297 -0.034618977 0.05792674 -0.4237576 0.11035452 0.0 4.87262
-0.0 4.87262 -0.035480656 0.058295887 -0.21477045 0.14263579 0.0 10.32133
-0.0 10.32133 -0.08929961 0.11301676 -0.20798548 0.47405547 0.0 13.91
-0.0 13.91 -0.6627122 0.35539475 -1.0631907 0.9830786 -70.45701 87.34367
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/src.cc
deleted file mode 100644
index bb792eaf71e851a5bf9791362aa09991dbc8ef68..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/src.cc
+++ /dev/null
@@ -1,164 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("vgg16_cifar100_test/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
-void* var_1 = tensorAdd(var_0, conv2d_1_b);
-void* var_2 = tensorRelu(var_1);
-void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
-void* var_5 = tensorAdd(var_4, conv2d_2_b);
-void* var_6 = tensorRelu(var_5);
-void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
-void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
-void* var_9 = tensorAdd(var_8, conv2d_3_b);
-void* var_10 = tensorRelu(var_9);
-void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
-void* var_13 = tensorAdd(var_12, conv2d_4_b);
-void* var_14 = tensorRelu(var_13);
-void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
-void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
-void* var_17 = tensorAdd(var_16, conv2d_5_b);
-void* var_18 = tensorRelu(var_17);
-void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
-void* var_21 = tensorAdd(var_20, conv2d_6_b);
-void* var_22 = tensorRelu(var_21);
-void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
-void* var_25 = tensorAdd(var_24, conv2d_7_b);
-void* var_26 = tensorRelu(var_25);
-void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
-void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
-void* var_29 = tensorAdd(var_28, conv2d_8_b);
-void* var_30 = tensorRelu(var_29);
-void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
-void* var_33 = tensorAdd(var_32, conv2d_9_b);
-void* var_34 = tensorRelu(var_33);
-void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
-void* var_37 = tensorAdd(var_36, conv2d_10_b);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
-void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
-void* var_41 = tensorAdd(var_40, conv2d_11_b);
-void* var_42 = tensorRelu(var_41);
-void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
-void* var_45 = tensorAdd(var_44, conv2d_12_b);
-void* var_46 = tensorRelu(var_45);
-void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
-void* var_49 = tensorAdd(var_48, conv2d_13_b);
-void* var_50 = tensorRelu(var_49);
-void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
-void* var_54 = tensorGemmGPU(var_51, dense_1_w);
-void* var_55 = tensorAdd(var_54, dense_1_b);
-void* var_56 = tensorRelu(var_55);
-void* var_58 = tensorGemmGPU(var_56, dense_2_w);
-void* var_59 = tensorAdd(var_58, dense_2_b);
-void* var_60 = tensorSoftmax(var_59);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_60);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}