diff --git a/CMakeLists.txt b/CMakeLists.txt index a8439bc..b3a27c5 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -4,7 +4,6 @@ project(facedetection) cmake_minimum_required(VERSION 2.8) -option(ENABLE_INT8 "use int8" OFF) option(ENABLE_AVX2 "use avx2" OFF) option(ENABLE_NEON "whether use neon, if use arm please set it on" OFF) option(DEMO "build the demo" OFF) @@ -20,11 +19,6 @@ SET(fdt_lib_shared ${fdt_lib_name}_shared) FILE(GLOB_RECURSE fdt_source_files ${fdt_src_dir}/*.cpp) LIST(SORT fdt_source_files) -if(ENABLE_INT8) - message("using int8") - add_definitions(-D_ENABLE_INT8) -endif() - if(ENABLE_AVX2) message("using avx2") add_definitions(-D_ENABLE_AVX2) diff --git a/ChangeLog b/ChangeLog index 905fcd0..5fa8454 100644 --- a/ChangeLog +++ b/ChangeLog @@ -1,3 +1,8 @@ +2019-09-14 +--------------------- +* Update the model to v2. The computational cost is doubled. But the speed is almost the same with the previous one because int8 convolutional operation is carried out by AVX2. +* NEON support is not finished. + 2019-03-13 --------------------- * Release the source code and the model files. Removed the binary libary. diff --git a/README.md b/README.md index 70ab4c7..b999614 100644 --- a/README.md +++ b/README.md @@ -60,23 +60,25 @@ make ## CNN-based Face Detection on Windows + | Method |Time | FPS |Time | FPS | |--------------------|--------------|-------------|--------------|-------------| | | X64 |X64 | X64 |X64 | | |Single-thread |Single-thread|Multi-thread |Multi-thread | |OpenCV Haar+AdaBoost (640x480)| -- | -- | 12.33ms | 81.1 | -|cnn (CPU, 640x480) | 64.21ms | 15.57 | 15.59ms | 64.16 | -|cnn (CPU, 320x240) | 15.23ms | 65.68 | 3.99ms | 250.40 | -|cnn (CPU, 160x120) | 3.47ms | 288.08 | 0.95ms | 1052.20 | -|cnn (CPU, 128x96) | 2.35ms | 425.95 | 0.64ms | 1562.10 | +|cnn (CPU, 640x480) | 64.55ms | 15.49 | 15.78ms | 63.36 | +|cnn (CPU, 320x240) | 15.48ms | 64.60 | 3.92ms | 255.01 | +|cnn (CPU, 160x120) | 3.86ms | 259.01 | 1.07ms | 938.71 | +|cnn (CPU, 128x96) | 2.46ms | 406.33 | 0.68ms | 1479.79 | * OpenCV Haar+AdaBoost runs with minimal face size 48x48 * Face detection only, and no landmark detection included -* Minimal face size ~12x12 +* Minimal face size ~10x10 * Intel(R) Core(TM) i7-7700 CPU @ 3.6GHz ## CNN-based Face Detection on ARM Linux (Raspberry Pi 3 B+) +(* to be updated) | Method |Time | FPS |Time | FPS | |--------------------|--------------|-------------|--------------|-------------| | |Single-thread |Single-thread|Multi-thread |Multi-thread | @@ -86,7 +88,7 @@ make |cnn (CPU, 128x96) | 17.78ms | 56.24 | 6.12ms | 163.50 | * Face detection only, and no landmark detection included. -* Minimal face size ~12x12 +* Minimal face size ~10x10 * Raspberry Pi 3 B+, Broadcom BCM2837B0, Cortex-A53 (ARMv8) 64-bit SoC @ 1.4GHz diff --git a/models/caffe/yufacedetectnet-open-v2.caffemodel b/models/caffe/yufacedetectnet-open-v2.caffemodel new file mode 100644 index 0000000..5da179c Binary files /dev/null and b/models/caffe/yufacedetectnet-open-v2.caffemodel differ diff --git a/models/caffe/yufacedetectnet-open-v2.prototxt b/models/caffe/yufacedetectnet-open-v2.prototxt new file mode 100644 index 0000000..d6ccb8d --- /dev/null +++ b/models/caffe/yufacedetectnet-open-v2.prototxt @@ -0,0 +1,1080 @@ +name: "YuFaceDetectNet" + +input: "data" + +input_shape { + dim: 1 + dim: 3 + dim: 240 + dim: 320 +} + +#CONV1########################################################### + +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + pad: 1 + stride: 2 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + +layer { + name: "relu1_1" + type: "ReLU" + bottom: "conv1_1" + top: "conv1_1" +} +layer { + name: "conv1_2" + type: "Convolution" + bottom: "conv1_1" + top: "conv1_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 16 + pad: 0 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + + +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +#CONV2########################################################## +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1" + top: "conv2_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + + +layer { + name: "relu2_1" + type: "ReLU" + bottom: "conv2_1" + top: "conv2_1" +} +layer { + name: "conv2_2" + type: "Convolution" + bottom: "conv2_1" + top: "conv2_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 16 + pad: 0 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +#CONV3########################################################## + +layer { + name: "pool2" + type: "Pooling" + bottom: "conv2_2" + top: "pool2" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2" + top: "conv3_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + +layer { + name: "relu3_1" + type: "ReLU" + bottom: "conv3_1" + top: "conv3_1" +} +layer { + name: "conv3_2" + type: "Convolution" + bottom: "conv3_1" + top: "conv3_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + pad: 0 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + + +layer { + name: "relu3_2" + type: "ReLU" + bottom: "conv3_2" + top: "conv3_2" +} +layer { + name: "conv3_3" + type: "Convolution" + bottom: "conv3_2" + top: "conv3_3" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} + + +#CONV4########################################################## + +layer { + name: "pool3" + type: "Pooling" + bottom: "conv3_3" + top: "pool3" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3" + top: "conv4_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + +layer { + name: "relu4_1" + type: "ReLU" + bottom: "conv4_1" + top: "conv4_1" +} +layer { + name: "conv4_2" + type: "Convolution" + bottom: "conv4_1" + top: "conv4_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + pad: 0 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + +layer { + name: "relu4_3" + type: "ReLU" + bottom: "conv4_3" + top: "conv4_3" +} + +#CONV5########################################################## + +layer { + name: "pool4" + type: "Pooling" + bottom: "conv4_3" + top: "pool4" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv5_1" + type: "Convolution" + bottom: "pool4" + top: "conv5_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + dilation: 1 + } +} + +layer { + name: "relu5_1" + type: "ReLU" + bottom: "conv5_1" + top: "conv5_1" +} +layer { + name: "conv5_2" + type: "Convolution" + bottom: "conv5_1" + top: "conv5_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_term: false + dilation: 1 + } +} + +layer { + name: "relu5_2" + type: "ReLU" + bottom: "conv5_2" + top: "conv5_2" +} +layer { + name: "conv5_3" + type: "Convolution" + bottom: "conv5_2" + top: "conv5_3" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + dilation: 1 + } +} + +layer { + name: "relu5_3" + type: "ReLU" + bottom: "conv5_3" + top: "conv5_3" +} + +#CONV6########################################################## + +layer { + name: "pool5" + type: "Pooling" + bottom: "conv5_3" + top: "pool5" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} + +layer { + name: "conv6_1" + type: "Convolution" + bottom: "pool5" + top: "conv6_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + dilation: 1 + } +} + +layer { + name: "relu6_1" + type: "ReLU" + bottom: "conv6_1" + top: "conv6_1" +} +layer { + name: "conv6_2" + type: "Convolution" + bottom: "conv6_1" + top: "conv6_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_term: false + dilation: 1 + } +} + +layer { + name: "relu6_2" + type: "ReLU" + bottom: "conv6_2" + top: "conv6_2" +} +layer { + name: "conv6_3" + type: "Convolution" + bottom: "conv6_2" + top: "conv6_3" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + dilation: 1 + } +} + +layer { + name: "relu6_3" + type: "ReLU" + bottom: "conv6_3" + top: "conv6_3" +} + +#PRIORBOX3########################################## +layer { + name: "conv3_3_norm" + type: "Normalize" + bottom: "conv3_3" + top: "conv3_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 10.0 + } + channel_shared: false + } +} +layer { + name: "conv3_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv3_3_norm" + top: "conv3_3_norm_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 12 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv3_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv3_3_norm_mbox_loc" + top: "conv3_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv3_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv3_3_norm_mbox_loc_perm" + top: "conv3_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv3_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv3_3_norm" + top: "conv3_3_norm_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 6 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv3_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv3_3_norm_mbox_conf" + top: "conv3_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv3_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv3_3_norm_mbox_conf_perm" + top: "conv3_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + + name: "conv3_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv3_3_norm" + bottom: "data" + top: "conv3_3_norm_mbox_priorbox" + prior_box_param { + min_size: 10.0 + min_size: 16.0 + min_size: 24.0 + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + step: 8.0 + offset: 0.5 + } +} +#PRIORBOX4########################################## +layer { + name: "conv4_3_norm" + type: "Normalize" + bottom: "conv4_3" + top: "conv4_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 8.0 + } + channel_shared: false + } +} +layer { + name: "conv4_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 8 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv4_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_loc" + top: "conv4_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_loc_perm" + top: "conv4_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 4 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv4_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_conf" + top: "conv4_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_conf_perm" + top: "conv4_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + + name: "conv4_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv4_3_norm" + bottom: "data" + top: "conv4_3_norm_mbox_priorbox" + prior_box_param { + min_size: 32.0 + min_size: 48.0 + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + step: 16.0 + offset: 0.5 + } +} +#PRIORBOX5########################################## +layer { + name: "conv5_3_norm" + type: "Normalize" + bottom: "conv5_3" + top: "conv5_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 5.0 + } + channel_shared: false + } +} +layer { + name: "conv5_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv5_3_norm" + top: "conv5_3_norm_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 8 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv5_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv5_3_norm_mbox_loc" + top: "conv5_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv5_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv5_3_norm_mbox_loc_perm" + top: "conv5_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv5_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv5_3_norm" + top: "conv5_3_norm_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 4 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv5_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv5_3_norm_mbox_conf" + top: "conv5_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv5_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv5_3_norm_mbox_conf_perm" + top: "conv5_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + + name: "conv5_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv5_3_norm" + bottom: "data" + top: "conv5_3_norm_mbox_priorbox" + prior_box_param { + min_size: 64.0 + min_size: 96.0 + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + step: 32.0 + offset: 0.5 + } +} + +#PRIORBOX6########################################## +layer { + name: "conv6_3_norm" + type: "Normalize" + bottom: "conv6_3" + top: "conv6_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 5.0 + } + channel_shared: false + } +} +layer { + name: "conv6_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv6_3_norm" + top: "conv6_3_norm_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 12 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv6_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv6_3_norm_mbox_loc" + top: "conv6_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv6_3_norm_mbox_loc_perm" + top: "conv6_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv6_3_norm" + top: "conv6_3_norm_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 6 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv6_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv6_3_norm_mbox_conf" + top: "conv6_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv6_3_norm_mbox_conf_perm" + top: "conv6_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + + name: "conv6_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv6_3_norm" + bottom: "data" + top: "conv6_3_norm_mbox_priorbox" + prior_box_param { + min_size: 128.0 + min_size: 192.0 + min_size: 256.0 + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + step: 64.0 + offset: 0.5 + } +} +######################################################## +layer { + name: "mbox_loc" + type: "Concat" + bottom: "conv3_3_norm_mbox_loc_flat" + bottom: "conv4_3_norm_mbox_loc_flat" + bottom: "conv5_3_norm_mbox_loc_flat" + bottom: "conv6_3_norm_mbox_loc_flat" + top: "mbox_loc" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_conf" + type: "Concat" + bottom: "conv3_3_norm_mbox_conf_flat" + bottom: "conv4_3_norm_mbox_conf_flat" + bottom: "conv5_3_norm_mbox_conf_flat" + bottom: "conv6_3_norm_mbox_conf_flat" + top: "mbox_conf" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_priorbox" + type: "Concat" + bottom: "conv3_3_norm_mbox_priorbox" + bottom: "conv4_3_norm_mbox_priorbox" + bottom: "conv5_3_norm_mbox_priorbox" + bottom: "conv6_3_norm_mbox_priorbox" + top: "mbox_priorbox" + concat_param { + axis: 2 + } +} + +##################################################### +layer { + name: "mbox_conf_reshape" + type: "Reshape" + bottom: "mbox_conf" + top: "mbox_conf_reshape" + reshape_param { + shape { + dim: 0 + dim: -1 + dim: 2 + } + } +} +layer { + name: "mbox_conf_softmax" + type: "Softmax" + bottom: "mbox_conf_reshape" + top: "mbox_conf_softmax" + softmax_param { + axis: 2 + } +} +layer { + name: "mbox_conf_flatten" + type: "Flatten" + bottom: "mbox_conf_softmax" + top: "mbox_conf_flatten" + flatten_param { + axis: 1 + } +} + +layer { + name: "detection_out" + type: "DetectionOutput" + bottom: "mbox_loc" + bottom: "mbox_conf_flatten" + bottom: "mbox_priorbox" +# bottom: "data" + top: "detection_out" + include { + phase: TEST + } + transform_param { + mean_value: 103.94 + mean_value: 116.78 + mean_value: 123.68 + } + detection_output_param { + num_classes: 2 + share_location: true + background_label_id: 0 + nms_param { + nms_threshold: 0.15 + top_k: 100 + } + code_type: CENTER_SIZE + keep_top_k: 50 + confidence_threshold: 0.01 + visualize: false + visualize_threshold: 0.3 + } +} + + diff --git a/models/caffe/yufacedetectnet-open-v2.solver.prototxt b/models/caffe/yufacedetectnet-open-v2.solver.prototxt new file mode 100644 index 0000000..b685957 --- /dev/null +++ b/models/caffe/yufacedetectnet-open-v2.solver.prototxt @@ -0,0 +1,18 @@ +train_net: "./yufacedetectnet.train.prototxt" + +base_lr: 0.001 +lr_policy: "poly" +power: 2 + +display: 10 +max_iter: 200000 +momentum: 0.9 +weight_decay: 0.0005 +snapshot: 5000 +snapshot_prefix: "./models/yufacedetectnet-open-v2" +solver_mode: GPU +debug_info: false +snapshot_after_train: true +average_loss: 790 +type: "SGD" +store_blobs_in_old_format: true diff --git a/models/caffe/yufacedetectnet-open-v2.train.prototxt b/models/caffe/yufacedetectnet-open-v2.train.prototxt new file mode 100644 index 0000000..96b52be --- /dev/null +++ b/models/caffe/yufacedetectnet-open-v2.train.prototxt @@ -0,0 +1,1159 @@ +name: "YuFaceDetectNet" + +layer { + name: "data" + type: "AnnotatedData" + top: "data" + top: "label" + include { + phase: TRAIN + } + transform_param { + mirror: true +# mean_value: 104.0 +# mean_value: 117.0 +# mean_value: 123.0 + resize_param { + prob: 1.0 + resize_mode: WARP + height: 240 + width: 320 + interp_mode: LINEAR + interp_mode: AREA + interp_mode: NEAREST + interp_mode: CUBIC + interp_mode: LANCZOS4 + } + emit_constraint { + emit_type: CENTER + } + distort_param { + brightness_prob: 0.5 + brightness_delta: 32.0 + contrast_prob: 0.5 + contrast_lower: 0.5 + contrast_upper: 1.5 + hue_prob: 0.5 + hue_delta: 18.0 + saturation_prob: 0.5 + saturation_lower: 0.5 + saturation_upper: 1.5 + random_order_prob: 0.0 + } + } + data_param { + source: "../FACE/lmdb/FACE_trainval_lmdb/" + batch_size: 16 + backend: LMDB + } + annotated_data_param { + batch_sampler { + sampler { + min_scale: 1.0 + max_scale: 1.0 + min_aspect_ratio: 1.0 + max_aspect_ratio: 1.0 + } + sample_constraint { + min_object_coverage: 1.0 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.300000011921 + max_scale: 1.0 + min_aspect_ratio: 1.0 + max_aspect_ratio: 1.0 + } + sample_constraint { + min_object_coverage: 1.0 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.300000011921 + max_scale: 1.0 + min_aspect_ratio: 1.0 + max_aspect_ratio: 1.0 + } + sample_constraint { + min_object_coverage: 1.0 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.300000011921 + max_scale: 1.0 + min_aspect_ratio: 1.0 + max_aspect_ratio: 1.0 + } + sample_constraint { + min_object_coverage: 1.0 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.300000011921 + max_scale: 1.0 + min_aspect_ratio: 1.0 + max_aspect_ratio: 1.0 + } + sample_constraint { + min_object_coverage: 1.0 + } + max_sample: 1 + max_trials: 50 + } + label_map_file: "../labelmap_face.prototxt" + } +} +#CONV1########################################################### + +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + pad: 1 + stride: 2 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + +layer { + name: "relu1_1" + type: "ReLU" + bottom: "conv1_1" + top: "conv1_1" +} +layer { + name: "conv1_2" + type: "Convolution" + bottom: "conv1_1" + top: "conv1_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 16 + pad: 0 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + + +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +#CONV2########################################################## +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1" + top: "conv2_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + + +layer { + name: "relu2_1" + type: "ReLU" + bottom: "conv2_1" + top: "conv2_1" +} +layer { + name: "conv2_2" + type: "Convolution" + bottom: "conv2_1" + top: "conv2_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 16 + pad: 0 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +#CONV3########################################################## + +layer { + name: "pool2" + type: "Pooling" + bottom: "conv2_2" + top: "pool2" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2" + top: "conv3_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + +layer { + name: "relu3_1" + type: "ReLU" + bottom: "conv3_1" + top: "conv3_1" +} +layer { + name: "conv3_2" + type: "Convolution" + bottom: "conv3_1" + top: "conv3_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + pad: 0 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + + +layer { + name: "relu3_2" + type: "ReLU" + bottom: "conv3_2" + top: "conv3_2" +} +layer { + name: "conv3_3" + type: "Convolution" + bottom: "conv3_2" + top: "conv3_3" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} + + +#CONV4########################################################## + +layer { + name: "pool3" + type: "Pooling" + bottom: "conv3_3" + top: "pool3" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3" + top: "conv4_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + +layer { + name: "relu4_1" + type: "ReLU" + bottom: "conv4_1" + top: "conv4_1" +} +layer { + name: "conv4_2" + type: "Convolution" + bottom: "conv4_1" + top: "conv4_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + pad: 0 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + } +} + +layer { + name: "relu4_3" + type: "ReLU" + bottom: "conv4_3" + top: "conv4_3" +} + +#CONV5########################################################## + +layer { + name: "pool4" + type: "Pooling" + bottom: "conv4_3" + top: "pool4" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv5_1" + type: "Convolution" + bottom: "pool4" + top: "conv5_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + dilation: 1 + } +} + +layer { + name: "relu5_1" + type: "ReLU" + bottom: "conv5_1" + top: "conv5_1" +} +layer { + name: "conv5_2" + type: "Convolution" + bottom: "conv5_1" + top: "conv5_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_term: false + dilation: 1 + } +} + +layer { + name: "relu5_2" + type: "ReLU" + bottom: "conv5_2" + top: "conv5_2" +} +layer { + name: "conv5_3" + type: "Convolution" + bottom: "conv5_2" + top: "conv5_3" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + dilation: 1 + } +} + +layer { + name: "relu5_3" + type: "ReLU" + bottom: "conv5_3" + top: "conv5_3" +} + +#CONV6########################################################## + +layer { + name: "pool5" + type: "Pooling" + bottom: "conv5_3" + top: "pool5" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} + +layer { + name: "conv6_1" + type: "Convolution" + bottom: "pool5" + top: "conv6_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + dilation: 1 + } +} + +layer { + name: "relu6_1" + type: "ReLU" + bottom: "conv6_1" + top: "conv6_1" +} +layer { + name: "conv6_2" + type: "Convolution" + bottom: "conv6_1" + top: "conv6_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_term: false + dilation: 1 + } +} + +layer { + name: "relu6_2" + type: "ReLU" + bottom: "conv6_2" + top: "conv6_2" +} +layer { + name: "conv6_3" + type: "Convolution" + bottom: "conv6_2" + top: "conv6_3" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_term: false + dilation: 1 + } +} + +layer { + name: "relu6_3" + type: "ReLU" + bottom: "conv6_3" + top: "conv6_3" +} + +#PRIORBOX3########################################## +layer { + name: "conv3_3_norm" + type: "Normalize" + bottom: "conv3_3" + top: "conv3_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 10.0 + } + channel_shared: false + } +} +layer { + name: "conv3_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv3_3_norm" + top: "conv3_3_norm_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 12 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv3_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv3_3_norm_mbox_loc" + top: "conv3_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv3_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv3_3_norm_mbox_loc_perm" + top: "conv3_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv3_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv3_3_norm" + top: "conv3_3_norm_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 6 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv3_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv3_3_norm_mbox_conf" + top: "conv3_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv3_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv3_3_norm_mbox_conf_perm" + top: "conv3_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + + name: "conv3_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv3_3_norm" + bottom: "data" + top: "conv3_3_norm_mbox_priorbox" + prior_box_param { + min_size: 10.0 + min_size: 16.0 + min_size: 24.0 + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + step: 8.0 + offset: 0.5 + } +} +#PRIORBOX4########################################## +layer { + name: "conv4_3_norm" + type: "Normalize" + bottom: "conv4_3" + top: "conv4_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 8.0 + } + channel_shared: false + } +} +layer { + name: "conv4_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 8 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv4_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_loc" + top: "conv4_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_loc_perm" + top: "conv4_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 4 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv4_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_conf" + top: "conv4_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_conf_perm" + top: "conv4_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + + name: "conv4_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv4_3_norm" + bottom: "data" + top: "conv4_3_norm_mbox_priorbox" + prior_box_param { + min_size: 32.0 + min_size: 48.0 + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + step: 16.0 + offset: 0.5 + } +} +#PRIORBOX5########################################## +layer { + name: "conv5_3_norm" + type: "Normalize" + bottom: "conv5_3" + top: "conv5_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 5.0 + } + channel_shared: false + } +} +layer { + name: "conv5_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv5_3_norm" + top: "conv5_3_norm_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 8 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv5_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv5_3_norm_mbox_loc" + top: "conv5_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv5_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv5_3_norm_mbox_loc_perm" + top: "conv5_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv5_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv5_3_norm" + top: "conv5_3_norm_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 4 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv5_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv5_3_norm_mbox_conf" + top: "conv5_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv5_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv5_3_norm_mbox_conf_perm" + top: "conv5_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + + name: "conv5_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv5_3_norm" + bottom: "data" + top: "conv5_3_norm_mbox_priorbox" + prior_box_param { + min_size: 64.0 + min_size: 96.0 + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + step: 32.0 + offset: 0.5 + } +} + +#PRIORBOX6########################################## +layer { + name: "conv6_3_norm" + type: "Normalize" + bottom: "conv6_3" + top: "conv6_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 5.0 + } + channel_shared: false + } +} +layer { + name: "conv6_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv6_3_norm" + top: "conv6_3_norm_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 12 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv6_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv6_3_norm_mbox_loc" + top: "conv6_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv6_3_norm_mbox_loc_perm" + top: "conv6_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv6_3_norm" + top: "conv6_3_norm_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 6 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_term: false + } +} +layer { + name: "conv6_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv6_3_norm_mbox_conf" + top: "conv6_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv6_3_norm_mbox_conf_perm" + top: "conv6_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + + name: "conv6_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv6_3_norm" + bottom: "data" + top: "conv6_3_norm_mbox_priorbox" + prior_box_param { + min_size: 128.0 + min_size: 192.0 + min_size: 256.0 + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + step: 64.0 + offset: 0.5 + } +} +######################################################## +layer { + name: "mbox_loc" + type: "Concat" + bottom: "conv3_3_norm_mbox_loc_flat" + bottom: "conv4_3_norm_mbox_loc_flat" + bottom: "conv5_3_norm_mbox_loc_flat" + bottom: "conv6_3_norm_mbox_loc_flat" + top: "mbox_loc" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_conf" + type: "Concat" + bottom: "conv3_3_norm_mbox_conf_flat" + bottom: "conv4_3_norm_mbox_conf_flat" + bottom: "conv5_3_norm_mbox_conf_flat" + bottom: "conv6_3_norm_mbox_conf_flat" + top: "mbox_conf" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_priorbox" + type: "Concat" + bottom: "conv3_3_norm_mbox_priorbox" + bottom: "conv4_3_norm_mbox_priorbox" + bottom: "conv5_3_norm_mbox_priorbox" + bottom: "conv6_3_norm_mbox_priorbox" + top: "mbox_priorbox" + concat_param { + axis: 2 + } +} + +##################################################### +layer { + + name: "mbox_loss" + type: "MultiBoxLoss" + bottom: "mbox_loc" + bottom: "mbox_conf" + bottom: "mbox_priorbox" + bottom: "label" + top: "mbox_loss" + include { + phase: TRAIN + } + propagate_down: true + propagate_down: true + propagate_down: false + propagate_down: false + loss_param { + normalization: VALID + } + multibox_loss_param { + loc_loss_type: L2 + conf_loss_type: SOFTMAX + loc_weight: 1.0 + num_classes: 2 + share_location: true + match_type: PER_PREDICTION + overlap_threshold: 0.34999999404 + use_prior_for_matching: true + background_label_id: 0 + use_difficult_gt: true + neg_pos_ratio: 3.0 + neg_overlap: 0.34999999404 + code_type: CENTER_SIZE + ignore_cross_boundary_bbox: false + mining_type: MAX_NEGATIVE + } +} + diff --git a/src/facedetectcnn-floatdata.cpp b/src/facedetectcnn-floatdata.cpp deleted file mode 100644 index 02c1adf..0000000 --- a/src/facedetectcnn-floatdata.cpp +++ /dev/null @@ -1,69 +0,0 @@ -/* -By downloading, copying, installing or using the software you agree to this license. -If you do not agree to this license, do not download, install, -copy or use the software. - - - License Agreement For libfacedetection - (3-clause BSD License) - -Copyright (c) 2018-2019, Shiqi Yu, all rights reserved. -shiqi.yu@gmail.com - -Redistribution and use in source and binary forms, with or without modification, -are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - - * Neither the names of the copyright holders nor the names of the contributors - may be used to endorse or promote products derived from this software - without specific prior written permission. - -This software is provided by the copyright holders and contributors "as is" and -any express or implied warranties, including, but not limited to, the implied -warranties of merchantability and fitness for a particular purpose are disclaimed. -In no event shall copyright holders or contributors be liable for any direct, -indirect, incidental, special, exemplary, or consequential damages -(including, but not limited to, procurement of substitute goods or services; -loss of use, data, or profits; or business interruption) however caused -and on any theory of liability, whether in contract, strict liability, -or tort (including negligence or otherwise) arising in any way out of -the use of this software, even if advised of the possibility of such damage. -*/ - - -#include "facedetectcnn.h" - -#if !defined(_ENABLE_INT8_CONV) -float f11[3 * 3 * 3 * 16] = { -0.10323552787303925f, 0.091436468064785f, -0.06634827703237534f, -0.16194047033786774f, -0.05926813557744026f, -0.10717106610536575f, 0.08953118324279785f, -0.04259021580219269f, 0.1855120211839676f, 0.014029358513653278f, -0.08977503329515457f, 0.008712727576494217f, 0.2110186070203781f, 0.029065759852528572f, 0.09566275030374527f, 0.11044635623693466f, -0.04305144026875496f, 0.09614435583353043f, 0.12196748703718185f, 0.3463044762611389f, 0.09864009916782379f, 0.01652682200074196f, 0.013193036429584026f, 0.050391629338264465f, -0.18506930768489838f, 0.11151964962482452f, -0.02679646946489811f, -0.12301726639270782f, 0.3212146461009979f, -0.11927391588687897f, -0.0839323103427887f, 0.1850830614566803f, 0.17904537916183472f, 0.014323921874165535f, 0.18595696985721588f, 0.2185066044330597f, -0.022203296422958374f, 0.2329387217760086f, -0.10303963720798492f, -0.18416886031627655f, 0.4616665840148926f, 0.32115089893341064f, -0.022068357095122337f, 0.09006277471780777f, 0.3977658450603485f, 0.032956961542367935f, -0.06099429354071617f, -0.365568071603775f, -0.537703812122345f, -0.325501024723053f, -0.02745884656906128f, -0.1757955551147461f, -0.3229336142539978f, -0.20610292255878448f, -0.10355434566736221f, -0.040982943028211594f, -0.13145026564598083f, 0.11036408692598343f, -0.11235859990119934f, 0.009411397390067577f, 0.2219371199607849f, -0.19792765378952026f, 0.09546755999326706f, -0.09798788279294968f, -0.05694679170846939f, -0.1786590814590454f, -0.14965659379959106f, 0.10242363065481186f, -0.37445494532585144f, -0.1658264845609665f, -0.2666710913181305f, -0.13896358013153076f, -0.203233540058136f, 0.04900119826197624f, 0.16945983469486237f, 0.05869029462337494f, 0.08644654601812363f, 0.3413703143596649f, 0.13871242105960846f, 0.11653023958206177f, 0.08883991837501526f, -0.19602760672569275f, -0.5570704340934753f, -0.017433615401387215f, 0.004288394935429096f, -0.16344524919986725f, 0.06708696484565735f, 0.17196619510650635f, 0.36204636096954346f, 0.19274592399597168f, -0.4119720458984375f, -0.33902737498283386f, 0.36163580417633057f, -0.05770261585712433f, -0.005231833551079035f, 0.2001112699508667f, -0.004859193228185177f, -0.051858577877283096f, 0.10230810195207596f, -0.5778149962425232f, -0.3627490997314453f, 0.40850144624710083f, -0.13901734352111816f, -0.27833428978919983f, 0.4087519347667694f, 0.25477346777915955f, 0.1940428465604782f, 0.4015578627586365f, 0.12479879707098007f, -0.17621539533138275f, -0.05332517251372337f, 0.0034066594671458006f, 0.024840692058205605f, -0.2807779610157013f, 0.0132907098159194f, 0.22638428211212158f, 0.08330383151769638f, -0.30256834626197815f, -0.3516656160354614f, -0.08681325614452362f, -0.07008201628923416f, -0.2627027928829193f, -0.18545754253864288f, 0.07098354399204254f, 0.2060180902481079f, -0.18349342048168182f, -0.5359899401664734f, -0.27279627323150635f, -0.31917279958724976f, 0.07884801179170609f, 0.371257483959198f, 0.18018709123134613f, 0.26155614852905273f, 0.7880668640136719f, 0.4403829574584961f, 0.041618578135967255f, 0.036111120134592056f, 0.15389417111873627f, -0.20206712186336517f, -0.06161710247397423f, 0.25166982412338257f, 0.0690750777721405f, -0.3119058907032013f, 0.015053428709506989f, -0.13125260174274445f, -0.16078726947307587f, -0.0005250186659395695f, -0.4871733784675598f, -0.11767728626728058f, 0.05522938817739487f, -0.14854876697063446f, -0.2500261068344116f, 0.23821589350700378f, 0.06494273245334625f, 0.5853068232536316f, 0.35957807302474976f, -0.3143596053123474f, 0.14675690233707428f, 0.49957534670829773f, -0.41780146956443787f, -0.15417058765888214f, 0.12114553153514862f, 0.23252186179161072f, 0.5525776147842407f, 0.1509275585412979f, 0.029641488566994667f, -0.059286292642354965f, -0.025803687050938606f, -0.6448644995689392f, -0.27350836992263794f, -0.0059609743766486645f, 0.23938366770744324f, 0.3941429853439331f, 0.22730059921741486f, -0.10967317223548889f, 0.1741182506084442f, 0.2908245027065277f, -0.5893892049789429f, -0.26820215582847595f, -0.08447045087814331f, -0.003638113848865032f, -0.2341543585062027f, -0.2966718375682831f, 0.38649696111679077f, 0.2813425064086914f, -0.25772303342819214f, -0.009714342653751373f, 0.12603949010372162f, 0.07957343012094498f, 0.03248312324285507f, -0.04982234165072441f, 0.09013952314853668f, 0.41064804792404175f, 0.24905720353126526f, -0.11306051909923553f, -0.08904588222503662f, -0.2720736265182495f, 0.1358858048915863f, -0.047043804079294205f, 0.15848571062088013f, -0.009490013122558594f, 0.7002939581871033f, -0.033164091408252716f, -0.31674376130104065f, 0.3378877639770508f, 0.02246052771806717f, 0.011424998752772808f, -0.2598447799682617f, -0.33196577429771423f, -0.2249375581741333f, 0.23526565730571747f, -0.3146249055862427f, -0.25894489884376526f, 0.009864198975265026f, -0.11417868733406067f, 0.07356985658407211f, -0.010862724855542183f, -0.23375500738620758f, 0.06112244352698326f, -0.029337987303733826f, -0.1278124749660492f, 0.2212931513786316f, 0.10317697376012802f, -0.11258269101381302f, 0.31544098258018494f, -0.0888175517320633f, -0.263501912355423f, 0.13698270916938782f, 0.04778124764561653f, -0.2842898666858673f, -0.2210678607225418f, 0.04132501408457756f, -0.38963446021080017f, -0.04872019588947296f, 0.15062087774276733f, -0.04905424267053604f, -0.5310825705528259f, 0.8348880410194397f, 0.12456674128770828f, -0.25136706233024597f, 0.4320843815803528f, 0.028088603168725967f, 0.007738249842077494f, 0.005294680595397949f, -0.2561836242675781f, -0.10391563922166824f, 0.02825651317834854f, -0.0034108576364815235f, -0.36603692173957825f, 0.3780118525028229f, 0.2153821438550949f, -0.040916379541158676f, -0.05752607807517052f, -0.26705676317214966f, -0.16176648437976837f, 0.18874984979629517f, -0.026302192360162735f, -0.15965092182159424f, 0.026687616482377052f, 0.3608611822128296f, -0.04497913271188736f, 0.08867018669843674f, -0.12136513739824295f, -0.6738488674163818f, 0.08818763494491577f, 0.0666867196559906f, -0.22492027282714844f, 0.4389871656894684f, 0.6051971316337585f, -0.10256391018629074f, -0.010816727764904499f, -0.40958887338638306f, -0.6025528311729431f, 0.2878674864768982f, -0.21967607736587524f, -0.17255717515945435f, 0.4299536943435669f, 0.487377405166626f, 0.26689672470092773f, 0.01203760877251625f, -0.22002805769443512f, -0.28940632939338684f, 0.30577653646469116f, 0.02863515540957451f, -0.42432016134262085f, 0.49232807755470276f, 0.23174072802066803f, -0.0968833789229393f, -0.11551958322525024f, 0.6712652444839478f, 0.9565862417221069f, -0.5908235311508179f, -0.00013162786490283906f, 0.6316040754318237f, -0.6929608583450317f, -0.6177705526351929f, -0.3415260314941406f, 0.23024576902389526f, -0.08337239921092987f, -0.10679541528224945f, 0.27423378825187683f, 0.0798078402876854f, -0.1405021846294403f, -0.04097948968410492f, -0.08808817714452744f, -0.18255729973316193f, -0.13721691071987152f, 0.012006506323814392f, -0.09976433962583542f, 0.026637764647603035f, -0.15411624312400818f, -0.23368918895721436f, -0.3574374318122864f, -0.2947174608707428f, -0.1276031881570816f, 0.3465866148471832f, 0.3849790394306183f, 0.2959367036819458f, 0.3210279047489166f, 0.35571855306625366f, -0.30703800916671753f, 0.17177122831344604f, 0.1080264002084732f, -0.43148061633110046f, -0.14955352246761322f, 0.17638060450553894f, 0.18522746860980988f, -0.04481720179319382f, 0.138199120759964f, -0.14120453596115112f, 0.2020857036113739f, -0.1934312880039215f, -0.01670672930777073f, -0.09568257629871368f, 0.4523850679397583f, -0.12567771971225739f, -0.09664637595415115f, 0.19928506016731262f, -0.12047473341226578f, 0.18293313682079315f, -0.4365415871143341f, -0.45017072558403015f, 0.3360207974910736f, 0.9315518140792847f, 0.49294283986091614f, -0.21704716980457306f, 0.2681174874305725f, 0.036119285970926285f, -0.42267200350761414f, -0.7720140218734741f, -0.4455108940601349f, -0.5904025435447693f, 0.11383157968521118f, -0.2595859467983246f, -0.5150834918022156f, 0.31199073791503906f, 0.07007858902215958f, 0.08517242223024368f, 0.4588204026222229f, 0.45638173818588257f, -0.30750352144241333f, 0.7134356498718262f, 0.08795424550771713f, -0.31774893403053284f, 0.39302217960357666f, -0.1299743354320526f, -0.35901010036468506f, -0.18293966352939606f, -0.10135392844676971f, -0.1374647170305252f, 0.5267758965492249f, -0.37590596079826355f, 0.021651171147823334f, 0.40258750319480896f, -0.34374651312828064f, 0.29655125737190247f, -0.05295853316783905f, -0.26972225308418274f, -0.19272872805595398f, 0.15636484324932098f, 0.11886804550886154f, 0.13368842005729675f, 0.3399967551231384f, 0.08204824477434158f, -0.0957726389169693f, 0.19317302107810974f, -0.004725121892988682f, 0.2204739898443222f, 0.34730401635169983f, 0.3182560205459595f, -0.007988831959664822f, 0.26781824231147766f, 0.18611927330493927f, 0.08902383595705032f, 0.0946035161614418f, 0.021605610847473145f, 0.2200011909008026f, -0.1838226020336151f, -0.1571388691663742f, -0.26750579476356506f, -0.29753202199935913f, -0.4041495621204376f, -0.18920592963695526f, -0.5360573530197144f, -0.39123350381851196f, -0.04095260798931122f, 0.0331394337117672f, 0.10965199023485184f, 0.038695938885211945f, -0.2588050961494446f, 0.15311121940612793f, 0.10944230854511261f, -0.44261789321899414f, 0.059360381215810776f, 0.09118583053350449f, 0.35752224922180176f, 0.21364891529083252f, 0.07502326369285583f, -0.36977052688598633f, 0.29865095019340515f, 0.02719065546989441f, -0.6298578977584839f, -0.03524460643529892f, 0.17367950081825256f, 0.2071729600429535f, 0.025192266330122948f, 0.3867643177509308f, -0.39635637402534485f, -0.046798501163721085f, 0.2173483967781067f, -0.6184401512145996f, 0.13464544713497162f }; -float f12[1 * 1 * 16 * 16] = { 0.008034661412239075f, 0.1153125911951065f, 0.12193906307220459f, -0.48518651723861694f, -0.37717998027801514f, -0.03733878582715988f, 0.018039081245660782f, -0.11929033696651459f, -0.026088399812579155f, -0.1533343493938446f, -0.08638916909694672f, 0.1568709909915924f, 0.23992685973644257f, -0.39002299308776855f, -0.05596257001161575f, 0.08053334057331085f, 0.3720945715904236f, -0.39930468797683716f, 0.1765986979007721f, -0.20461033284664154f, 0.264289528131485f, 0.24024735391139984f, -0.1689462959766388f, -0.37662652134895325f, 0.16669012606143951f, -0.248611718416214f, 0.06941534578800201f, 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-float f6conf[3 * 3 * 128 * 6] = { -0.004640610422939062f, 0.003279842669144273f, 0.024647027254104614f, 0.02107776142656803f, 0.09078972786664963f, -0.020398877561092377f, -0.033685971051454544f, 0.0230269692838192f, -0.0195436030626297f, -0.011692643165588379f, 0.027098286896944046f, -0.018365411087870598f, -0.027182383462786674f, -0.015767822042107582f, 0.038268379867076874f, -0.024007851257920265f, -0.033790964633226395f, 0.01737089455127716f, -0.026181047782301903f, -0.002183688571676612f, -0.018449749797582626f, -0.03296241536736488f, 0.052722662687301636f, 0.04461846873164177f, 0.0011588676134124398f, 0.01087749470025301f, 0.013277909718453884f, -0.016557440161705017f, -0.01169276051223278f, 0.021780725568532944f, 0.026668472215533257f, 0.024537518620491028f, 0.06915964931249619f, -0.005345185287296772f, -0.006650357972830534f, 0.022892070934176445f, -0.005765882320702076f, 0.01943250745534897f, 0.03997732326388359f, 0.014219975098967552f, -0.011866552755236626f, 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f11,f12,f21,f22,f31,f32,f33,f41,f42,f43,f51,f52,f53,f61,f62,f63,f3loc,f3conf,f4loc,f4conf,f5loc,f5conf,f6loc,f6conf }; -float param_pConvCoefScales[24] = { 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f }; -#endif \ No newline at end of file diff --git a/src/facedetectcnn-int8data.cpp b/src/facedetectcnn-int8data.cpp index d891a59..d9fc372 100644 --- a/src/facedetectcnn-int8data.cpp +++ b/src/facedetectcnn-int8data.cpp @@ -38,31 +38,29 @@ the use of this software, even if advised of the possibility of such damage. #include "facedetectcnn.h" -#if defined(_ENABLE_INT8_CONV) -signed char f11[432] = { -14, 12, -9, -21, -8, -14, 12, -6, 25, 2, -12, 1, 28, 4, 13, 15, -6, 13, 16, 46, 13, 2, 2, 7, -25, 15, -4, -16, 43, -16, -11, 25, 24, 2, 25, 29, -3, 31, -14, -24, 61, 43, -3, 12, 53, 4, -8, -49, -71, -43, -4, -23, -43, -27, -14, -5, -17, 15, -15, 1, 29, -26, 13, -13, -8, -24, -20, 14, -50, -22, -35, -18, -27, 7, 22, 8, 11, 45, 18, 15, 12, -26, -74, -2, 1, -22, 9, 23, 48, 26, -55, -45, 48, -8, -1, 27, -1, -7, 14, -77, -48, 54, -18, -37, 54, 34, 26, 53, 17, -23, -7, 0, 3, -37, 2, 30, 11, -40, -47, -12, -9, -35, -25, 9, 27, -24, -71, -36, -42, 10, 49, 24, 35, 105, 58, 6, 5, 20, -27, -8, 33, 9, -41, 2, -17, -21, 0, -65, -16, 7, -20, -33, 32, 9, 78, 48, -42, 19, 66, -55, -20, 16, 31, 73, 20, 4, -8, -3, -86, -36, -1, 32, 52, 30, -15, 23, 39, -78, -36, -11, 0, -31, -39, 51, 37, -34, -1, 17, 11, 4, -7, 12, 55, 33, -15, -12, -36, 18, -6, 21, -1, 93, -4, -42, 45, 3, 2, -34, -44, -30, 31, -42, -34, 1, -15, 10, -1, -31, 8, -4, -17, 29, 14, -15, 42, -12, -35, 18, 6, -38, -29, 5, -52, -6, 20, -7, -71, 111, 17, -33, 57, 4, 1, 1, -34, -14, 4, 0, -49, 50, 29, -5, -8, -35, -21, 25, -3, -21, 4, 48, -6, 12, -16, -89, 12, 9, -30, 58, 80, -14, -1, -54, -80, 38, -29, -23, 57, 65, 35, 2, -29, -38, 41, 4, -56, 65, 31, -13, -15, 89, 127, -78, 0, 84, -92, -82, -45, 31, -11, -14, 36, 11, -19, -5, -12, -24, -18, 2, -13, 4, -20, -31, -47, -39, -17, 46, 51, 39, 43, 47, -41, 23, 14, -57, -20, 23, 25, -6, 18, -19, 27, -26, -2, -13, 60, -17, -13, 26, -16, 24, -58, -60, 45, 124, 65, -29, 36, 5, -56, -102, -59, -78, 15, -34, -68, 41, 9, 11, 61, 61, -41, 95, 12, -42, 52, -17, -48, -24, -13, -18, 70, -50, 3, 53, -46, 39, -7, -36, -26, 21, 16, 18, 45, 11, -13, 26, -1, 29, 46, 42, -1, 36, 25, 12, 13, 3, 29, -24, -21, -36, -40, -54, -25, -71, -52, -5, 4, 15, 5, -34, 20, 15, -59, 8, 12, 47, 28, 10, -49, 40, 4, -84, -5, 23, 28, 3, 51, -53, -6, 29, -82, 18 }; -signed char f12[256] = { 1, 13, 14, -55, -43, -4, 2, -14, -3, -18, -10, 18, 27, -45, -6, 9, 43, -46, 20, -23, 30, 27, -19, -43, 19, -28, 8, 37, -13, -8, -28, -21, 15, -40, 43, -36, -54, 44, 6, 13, -30, -53, -35, 31, -2, -9, -53, -18, 3, 16, 53, -2, -24, -12, -5, -15, 18, -7, -1, -28, -11, 18, -3, 38, 29, -12, 25, -83, -30, -51, -29, -24, 7, -3, -19, 47, 19, -24, -53, -10, 11, -18, 14, -3, -12, -50, -8, -10, 33, 16, -7, 26, -37, -4, 18, -26, 23, -11, 39, 16, 27, 6, -48, -38, -11, -34, -23, -37, -82, 6, 33, -46, 2, -24, 0, 24, -38, -20, -11, 8, 40, 2, -13, 18, 17, 39, 12, -5, -7, -42, 31, 20, -1, -32, 28, -2, 34, -22, -68, -7, -62, -33, 6, -46, 6, -24, 33, 5, -6, 39, 4, -28, -10, -79, 8, -29, -33, 7, 2, -19, -5, 1, 19, -15, -29, 32, 14, -34, -19, -40, 18, -16, 3, -1, 12, 5, 27, 0, 18, -62, -7, -24, -75, -22, 43, 21, 29, -20, -65, -4, 12, -49, 9, -36, 9, -44, 37, 1, -12, -22, -67, -1, -26, 21, -13, 44, -34, -45, 41, 2, 19, -27, 50, -23, -98, -19, 16, -11, -6, -36, -67, -11, -16, -48, -41, -8, -3, -1, 6, 6, 12, -13, 18, -13, 127, 6, 0, 9, -21, -4, 26, 44, 30, 4, 3, -23, -1, 43, -48, 18, -16, -16, 24, -18, -4, 3 }; -signed char f21[2304] = { 1, -10, -2, -6, -44, -16, 17, -21, -1, -1, -12, 3, -4, 2, 12, 16, -25, 8, -38, 1, -1, -25, 7, 15, -1, -17, 17, -25, 39, 5, -35, 43, -1, -21, 25, 11, -1, -5, 8, -8, 19, 26, -10, 6, 24, 10, 29, 15, -3, 19, -8, 0, 27, 7, -11, 0, 4, -11, -19, 6, 17, 10, 1, 0, 20, -2, -6, -9, -4, 4, 11, -2, -9, -6, -9, -24, -29, -3, -4, -5, -11, 3, 45, 13, -11, 51, 25, -10, 33, 25, 20, 13, 8, 15, 5, -5, 0, 5, -8, 2, 19, 30, -13, 23, 21, -12, 16, 17, -10, 9, 5, -25, 25, 18, -16, 19, 12, -23, -17, -4, 1, -25, -20, -7, -1, -9, -26, 25, -20, -22, 38, -11, -20, 7, -5, 16, 11, -21, 10, -3, -37, 12, 27, -25, 5, 1, 4, 12, 32, -11, 16, 13, -8, 33, 34, -6, -6, -6, -10, -11, -15, 21, -13, -25, -1, 24, 7, -6, -12, 9, -8, -21, -32, 32, -26, -52, 29, 30, 21, 31, 16, 28, 17, 13, 31, 27, 10, -5, 11, -4, 4, -2, -4, -3, 2, 12, 6, -18, 13, -18, -25, -6, -20, -5, 23, 55, 27, -5, -43, -24, -20, -38, -18, 5, -1, 2, 3, 10, 16, -19, -23, 5, 22, 32, 8, -6, -42, 1, -16, -32, -4, -1, -8, -6, -10, -4, 21, -1, -22, -1, 1, -12, -3, 13, 15, 29, 18, 25, 30, 29, 33, 34, -23, -55, -10, -24, -49, -2, 22, 14, 4, -8, -4, -3, -25, -16, -5, 9, -3, 4, 0, -30, 3, 8, -22, -4, 11, 14, -1, 7, -10, 2, 6, -1, -5, 21, 15, -1, 3, -7, -17, 5, -4, -4, -9, 14, 12, 2, 8, 8, 5, 1, 6, 0, -9, -15, 15, -15, 4, -6, -7, 0, -4, 9, -20, 27, 32, 0, 4, -34, 10, -9, -42, 25, 8, -13, -9, -14, -12, -5, -4, 22, 24, -22, -32, -12, -36, -28, -16, -13, -1, -8, 23, 13, 20, 42, 23, -11, 28, 29, 11, 34, 10, 12, -5, -52, -1, 3, 3, 15, 34, 36, 16, 10, 10, 10, -2, 5, 0, -40, -5, 2, -34, -46, 8, -8, -30, 11, -25, 29, 16, -42, -19, 28, -4, -32, -2, -3, -31, -7, -22, -15, 8, 15, 17, 32, 3, -3, 13, -41, -46, -9, -5, -2, 9, -2, 17, 6, 41, 81, 9, 23, 27, 2, 12, 14, 5, -23, -30, 1, 5, -11, 7, -13, -24, 5, 5, -15, -3, 8, 2, 16, 7, -7, -11, -22, -8, -19, -12, 13, -3, 4, -8, -20, 18, 0, 6, -19, -15, 9, 13, 2, -4, -2, 8, -3, -6, 21, 18, -10, 4, 3, 22, -45, 1, 22, -5, 1, 20, -18, -31, -1, -28, -23, -12, -7, -6, 2, 14, -2, -12, -2, 39, -9, -7, 4, -3, 21, -10, 28, 20, -11, 6, 5, 12, 8, -14, -34, 22, 2, 5, -3, -5, 12, 2, 8, 5, -13, -9, -13, -14, 6, -1, -36, -22, -1, 34, 17, -2, 29, 5, -10, -19, -35, 6, -17, 1, -10, -11, 17, -2, -10, 19, 22, -14, 7, 23, 0, -7, 6, -13, -13, -16, 2, 11, 7, 12, 25, 20, -9, 45, 16, -44, 4, 8, -19, 7, -12, 0, 3, 10, -5, -6, -4, -8, -11, -4, 0, 24, 6, 12, -6, -10, -6, -10, -14, -6, 9, 7, 11, 23, 3, 14, 2, -5, -78, -94, -27, -37, 44, 26, 46, 127, -3, -4, -7, 18, -9, 26, 9, 4, 33, -10, 2, -19, -4, -2, -3, 4, 3, 20, 17, 10, -9, -2, -11, -10, -7, -1, 20, 0, -15, -25, -3, -7, -15, -10, 6, 11, 4, 1, 2, -2, 2, 5, 2, 6, 1, 0, 7, 11, 3, 11, -1, -1, -2, 7, 1, 6, -16, 8, -20, -6, 2, 3, 19, 8, 3, -19, -3, -14, 5, -5, 0, 14, -1, 11, -12, -8, -1, -8, -9, -18, -11, 12, -11, -15, -15, -17, -14, -5, -19, -7, 8, -10, -21, -6, -4, -12, -4, 5, 12, 6, -10, 6, 18, -31, -2, -7, -18, -13, -11, -18, -38, -7, -19, 2, -4, 22, 39, -3, -1, 11, -5, 24, -1, -11, 7, -25, 6, 8, -8, -11, 10, -13, 16, -4, -4, 10, 15, -14, 0, 15, -26, 9, 21, -9, -7, 22, -15, -17, 23, -29, -12, 32, -17, 0, 1, 1, -23, 15, 17, -25, 19, 24, -18, 9, 3, -15, 10, -2, -14, 10, 11, -16, -2, -3, -19, 17, -7, -20, 10, 1, -6, -2, -8, 6, 15, 9, -9, -3, 11, 0, 4, -2, -4, 3, 2, -11, 5, -3, -15, 9, -5, -7, 6, 0, 0, -2, -4, -7, 10, 3, 10, 15, 2, 14, 19, 6, -10, 4, 6, 7, -1, -3, 0, -1, -4, -2, 6, 7, -14, 10, -4, 7, 7, -6, 1, -2, -9, -17, 13, -9, -10, 23, 3, -6, 18, -25, -3, 17, -11, -11, 7, -11, -7, -1, -14, 4, 6, -31, 10, 8, -22, 8, -12, 3, 23, -21, -4, 15, -26, 14, 14, 15, 20, 10, -36, -73, -36, -21, -22, -13, -14, -14, -1, -14, -17, 11, 33, 31, 26, -2, 34, 13, -38, -64, -2, -19, -21, 19, 28, 30, 15, 6, -23, 13, 13, -12, 12, 11, 12, -14, -18, 2, -10, 15, 21, 5, -12, -9, 1, 7, -12, -4, 8, 11, 11, 19, 24, 0, -1, 31, 29, -17, -7, 17, 7, -2, -12, -10, -8, 0, -4, -1, -1, 12, 26, 7, 16, 13, 25, 3, 1, 12, 1, 16, -2, 5, 8, 3, 11, 9, -1, 2, 3, 34, -20, -50, -7, -3, -10, -13, 27, 30, 43, 17, 34, 44, -13, -13, -6, 12, 15, 11, 6, 11, 16, -4, -3, 2, 7, -1, -2, 30, 9, -1, -4, 3, -4, 5, 8, -9, -19, -54, -19, 13, -25, -8, 45, 24, 31, -18, -42, -13, 14, 1, -9, 15, 32, 22, 8, 3, 3, 6, -14, -16, 4, 42, -14, 8, 2, 7, -27, -36, 15, 26, 43, 7, 12, 16, 12, 18, 2, -5, -19, 0, 15, -23, 18, 12, 15, 16, 25, 3, 40, 18, 38, 49, 2, 37, 37, 16, -5, 1, 5, -29, -14, -10, -29, -46, 1, 18, 7, 9, 7, -1, 19, 7, 33, 5, 12, 33, 27, 13, 16, 10, 6, -4, 10, 9, 17, 10, -1, 1, 22, -16, -24, 3, 1, -14, -12, -17, -6, -6, -1, 7, 15, -6, -13, 4, -45, -31, 7, -32, -36, 5, -31, -17, 3, -43, -25, -6, -23, -3, -2, 1, 13, -3, -8, 18, 1, -1, 12, 15, -13, -4, 5, -29, -46, 9, -41, -37, -5, 3, -24, -5, -15, -47, -14, -11, -50, -5, 33, 26, 23, -8, -28, 10, 0, -14, 15, 5, -4, 21, -18, 2, 49, -11, 2, 18, 10, 5, 20, -2, -8, 1, 17, 6, 0, 2, -16, -6, 10, 11, 8, 28, 17, 11, -6, -12, 27, -8, 0, 11, 11, -3, 21, -7, 25, 9, -9, 29, -11, -6, 9, -25, -37, -36, -8, -18, -37, -16, -6, -33, 5, 1, 11, 27, -5, 14, 21, -17, 15, 15, -2, -26, 8, -15, -28, -1, -25, -28, 15, 8, 3, -6, -1, -4, 0, -13, 4, -3, 16, 18, 17, 11, 18, 10, -5, 19, 0, -25, -19, 4, -32, -19, 28, -28, -10, 31, 13, 2, 21, -24, -20, 21, -1, 18, 38, -22, -20, 26, -36, -20, 18, -42, 1, 11, 10, -1, 9, -15, -10, 22, -14, -4, 19, -12, -4, 13, -16, -29, 9, 2, -1, 17, -4, -4, 15, -25, -25, 6, -14, -37, 26, 22, 25, 6, 30, 40, 19, 3, 15, -3, 35, 27, 17, -19, 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-2, 16, -3, 22, 15, 34, 28, 1, 3, 17, 7, 14, 13, -8, -17, 5, 10, 27, -4, -12, 7, 11, -9, 21, 7, -2, 3, 9, -23, 33, -1, -38, 13, -3, -23, 2, 6, 12, -27, -19, 22, -25, -14, 3, 34, 0, -20, -14, 37, -32, -4, 21, -14, 5, -21, -20, 14, 5, -3, 12, 5, 7, -10, -2, 27, 31, -16, 17, 12, 1, -14, -19, 11, -6, 9, 17, -4, 3, 19, -5, 10, -6, -25, 20, 2, -31, 15, 12, -24, -1, 24, 3, 6, -13, -6, 7, 5, 0, -2, 7, -9, 7, 19, -49, 15, 11, -54, 31, 28, 13, 3, -5, 13, 2, 3, 22, -1, -15, -22, -3, 17, -7, 15, -2, 6, 18, 5, -16, 2, 5, 17, 11, 2, 27, 8, 0, -14, 9, 2, 0, 2, 8, 10, -8, -8, -18, -3, 19, -62, 34, 21, -58, 40, 14, -8, -7, 6, -18, 28, 12, 7, 16, -17, -1, -17, -1, -3, 19, -1, 13, 5, -3, 0, -18, 9, -7, -2, 2, -4, 10, 4, 32, 15, 23, 8, -22, 3, 4, -8, -6, 12, -62, -22, -10, -79, -14, -6, 0, 20, 11, 15, -4, 15, 7, -21, 6, 12, 1, 19, 43, 1, 16, 9, -2, 14, 22, 15, 2, -31, -9, -11, -10, -4, -8, -4, 3, 13, 27, 26, 25, 54, 17, 16, 25, 8, 0, 23, -3, 19, 30, 0, 18, 18, 6, -4, -41, -22, 4, -26, 0, -2, -13, -3, 3, 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-5, -5, 3, 0, 18, 15, 13, 29, 9, 12, 18, 25, 15, 10, -1, 4, 5, -3, 10, 3, 14, 10, 7, 25, 6, 24, 14, -15, 28, 0, -19, -1, 11, 5, 6, 24, -2, -6, 13, 16, -4, 7, 6, -10, -9, -3, -7, -13, 0, 0, 14, 12, -3, 17, 0, 3, 12, 8, -2, 15, 9, 6, 24, -3, 8, 21, 14, -9, 8, 1, 14, 2, -8, 15, 6, -5, -11, -3, -17, -7, -16, -21, 12, 2, -3, 18, -7, -15, 14, -27, 4, 5, -2, 25, -4, 38, 29, 7, 15, 28, 9, 17, 10, 11, 33, 28, -25, -18, 0, 22, -36, -1, 13, 29, 19, 10, 38, 17, -24, 17, 24, 30, -18, -23, 33, 2, -22, 19, 8, -5, -7, 30, 11, 6, 35, 21, 7, -4, 17, -9, -31, 1, -5, -29, -32, -3, 8, -26, 15, 41, 6, 17, 7, 29, 20, 11, 28, 15, 5, 4, 9, -19, 3, 9, -19, 2, 3, 26, 30, -6, -1, 25, 3, 6, 4, 15, 3, -12, -2, 9, 2, -1, -7, -4, 3, -31, 4, -8, -23, -14, -5, -12, -16, -7, -19, -4, -3, 7, -16, 7, 25, 3, 0, 0, -5, 5, -8, -5, 7, -3, -10, -34, 11, 25, -8, -6, 24, 18, 15, 6, 3, -29, -25, -2, 4, -31, -5, -6, -16, 18, -14, -8, 35, -20, -21, 25, 16, 1 }; -signed char f22[256] = { 6, -8, 78, 44, -5, -36, 43, 86, 31, -47, -21, 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44, 81, -30, -21, 18, 18, 84, 2, -20, -14, -17, -9, 24, 33, 40, -15, 31 }; -signed char f31[4608] = { 6, 16, 14, 28, 6, -9, 4, 4, -15, 10, -7, -1, 5, -6, 14, 12, -19, 46, 18, 2, -38, 8, -30, 17, 10, 6, 27, 11, -61, -21, -4, -26, 14, -25, -12, -16, 18, -1, 10, 6, 17, -1, 3, -9, 1, 10, -19, -19, 18, -17, 17, -22, -11, -7, -24, -25, 19, 9, -16, 24, 20, 4, 29, 16, -16, -42, -7, 20, -4, 12, -17, -1, 0, -18, 11, -15, -10, -6, 5, -7, 10, 10, 55, 41, 17, 4, -2, 12, 37, 18, 10, 44, 10, 16, 13, -2, 22, 4, 26, 8, -62, 21, -8, 49, 21, -12, -15, 0, -15, -43, -13, -21, 11, -8, -3, 44, -9, 11, 29, -27, -2, -16, -5, -12, -55, -19, 19, 11, 5, 4, -11, -4, 11, 45, 7, -19, -8, -11, -15, 9, -23, -4, -25, -12, 3, -1, -11, -11, -11, -19, -12, 5, -3, -21, -1, 56, 9, -4, 23, 16, -4, 15, -2, -44, 18, -20, -19, -37, -10, 17, -10, 31, -4, -2, 17, 23, 0, 1, 26, -1, 17, 14, 2, 25, 6, -5, 6, 16, 7, -7, 13, 12, -2, 9, 36, -9, -2, -19, 1, -39, 22, 12, 3, -32, -5, 1, -1, -24, 22, 9, 46, -1, 4, 30, -5, 25, -2, -17, -36, 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13, -11, 12, 8, -2, 26, 17, 9, -3, -5, 10, -5, -19, -3, -5, -6, 56, 7, 0, -3, -4, 4, 22, -33, 8 }; -signed char f32[1024] = { 44, -36, -52, -10, 7, -4, -25, 3, 25, -22, -6, 43, 5, 31, -17, 1, -12, 2, -41, -15, 63, -40, -53, -4, 9, -6, -18, -16, 42, 21, 14, 15, 18, -31, -38, 8, 30, 10, 38, 76, 13, 6, -19, -52, -68, 33, -22, 75, 62, -39, 35, 1, 26, -24, 83, -5, -13, 16, 87, -40, -82, -85, -45, 33, -17, -3, -117, 23, 125, -19, 2, -11, 14, 50, -13, -50, 20, -42, -32, -33, -29, -49, 4, 24, -42, -4, -47, -9, 34, 35, -56, -29, -61, 23, -21, 69, -48, -11, -21, -19, 38, 64, -35, -25, -59, -41, 9, 36, -28, 1, 33, 0, 38, 10, -24, 31, -33, -25, 50, -14, 1, -26, 23, 56, -9, -21, 32, 19, -14, -21, -90, -43, 73, -7, -25, 12, -10, 62, -68, 8, -13, -44, -45, 11, -42, -22, -35, 39, -63, 20, -30, -33, 55, -29, 2, -25, 14, 32, -51, 75, -21, -5, -12, 38, 8, 52, -11, -11, 58, 8, 9, -10, -4, -40, -21, 21, -43, 67, 7, 54, -26, -22, -31, 19, 34, -19, -68, -53, -27, -35, 15, 3, 72, 23, -30, -28, 39, 76, 7, 10, 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-11, 25, 70, -4, 21, 82, -18, 15, -24, -10, -9, 7, -16, -14, -27, -5, 29, 60, -2, -7, 40, -16, -48, -16, -9, -8, 1, 13, -12, 4, -1, 3, -23, 7, -9, 0, -6, -27, -9, 13, 7, -8, -13, 4, -24, -10, -18, 2, 7, 4, 14, 19, -19, 21, 21, 9, -19, 7, -23, -20, -1, -3, 11, 6, 9, -8, -3, 20, 13, 4, -7, -20, 2, 10, -28, -17, -20, 19, -20, 2, -27, -11, -26, -13, 16, -18, -13, 5, 36, 23, -8, 81, 95, 3, 35, 18, 2, -6, 0, 8, -7, 15, 19, -20, -4, 3, 5, -15, -12, -13, -4, -14, 2, 4, 4, -25, 0, 0, -5, -6, 3, -13, -4, 0, 11, 4, 5, -15, -10, -12, 4, -12, 21, -12, -15, -3, -19, 13, 0, 9, 3, -10, -12, 3, -36, -29, -9, 3, -26, -11, 13, -8, 1, 12, 16, -4, 1, 3, -10, -3, 1, -14, -17, 5, -12, 3, -10, 20, 1, 1, -4, -10, -20, 1, -2, 1, -13, -5, -3, -8, -4, 18, 1, 18, -18, -14, 11, 7, 15, 2, -10, 0, -1, -14, 11, -1, -10, 7, -21, 16, -16, 6, -6, 0, -17, -16, -68, -9, 0, -20, -25, -6, -15 }; -signed char f4loc[4608] = { 16, 4, 12, -12, -4, 6, -10, 9, 10, 15, -3, 7, -4, 6, -5, -3, 10, -10, 2, 7, -8, 5, 9, -1, 0, 7, 1, -4, 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-15, -21, -14, -17, 1, -19, -29, -29, -28, -24, -11, -16, -30, -6, -9, -54, -24, 6, -35, -4, 2, -11, -28, -23, -35, -13, -15, -11, -18, 4, 40, 8, 21, -14, 23, -16, 0, -11, 18, -20, 12, -1, -10, -6, 6, 7, 16, 14, 8, 2, 9, -3, 10, 8, -26, -25, 2, -25, 3, -1, -26, -27, -3, -13, 3, 13, -6, 11, -15, -5, 16, 6, 18, 21, 39, 2, -4, -26, -33, -24, 18, -21, -16, -1, -30, 13, -14, 6, 30, -21, 16, 14, 14, 22, -20, 16, 4, 11, -10, 13, 0, -26, -45, -24, -42, -23, -6, -13, -5, -19, -2, 27, 35, -26, -28, 8, -26, -28, 22 }; +signed char f11[864] = {-33, -37, -29, 66, 80, 56, -31, -45, -30, -38, -63, -37, 65, 105, 58, -35, -35, -30, -7, -67, -41, 61, 75, 92, -32, -50, -23, 27, -45, 15, 23, -42, -11, 47, 0, -35, 6, -78, -4, -11, -37, 8, 56, 33, -27, 30, -57, 11, 55, -56, 2, 72, 50, -5, -14, -3, -1, 16, 33, -9, -23, 23, -26, -16, 0, 14, 30, 19, -8, -17, -10, 5, 6, -23, 9, -17, 20, -5, -21, 21, -13, 37, 35, 45, 31, 31, 38, 39, 17, 27, -11, -37, -43, -57, -63, -75, -39, -55, -56, -25, 3, 1, 20, 38, 25, -4, 20, 20, -25, -30, 24, -30, -25, 7, -7, -12, 36, -27, -33, -10, -36, -35, 16, -8, 3, 23, 65, 50, -14, 74, 54, -46, -2, 17, -64, 7, -1, -24, 1, -23, 11, -16, 0, 18, -22, 3, 13, -11, 13, -5, 19, 16, 2, 15, -7, 4, -13, 4, -24, -15, 3, -25, -8, -13, -3, 4, 6, -1, 67, 22, -50, -26, -43, -11, 1, -4, 19, 74, 31, -31, -31, -23, 17, 3, 6, 4, 25, 3, -56, -25, 0, -8, -24, 5, -5, 12, -20, -11, -12, -13, -16, -1, 8, -15, 12, 1, -7, 19, 20, -24, -26, -7, 10, -6, 17, -24, -6, -16, 11, -18, 12, -11, -17, -83, -4, -11, 18, 17, 25, 19, 29, 2, -9, 6, 17, 43, -2, 1, 52, 32, -37, -9, -14, -1, 60, 33, -7, 7, -25, 2, -19, -54, -1, 30, -9, -30, -28, -61, 9, 3, -36, 40, 127, 117, -31, 44, -30, -48, -23, -49, -21, -34, -39, -9, -38, -17, 25, 7, 1, 19, -17, 5, -14, -12, -4, -15, -37, -21, -19, 61, -5, -5, 50, 54, -7, -2, 33, 24, 18, 23, -31, -63, -40, 16, 34, 26, -19, -15, -18, -40, -69, -44, 63, 74, 60, 54, 49, 37, -45, -85, -58, -2, 34, 17, -30, 32, -23, 35, 18, -66, 26, 23, -11, 18, 32, -4, 45, -3, -54, 14, 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-7, 8, -48, 19, 24, -38, -68, 42, 37, -29, -12, -31, 22, 98, -73, -36, 89, -12, -5, 26, -1, 5, 19, 20, 40, 29, -6, 39, 21, 30, 49, 32, 5, 40, 51, 20, 1, 26, -22, -44, -47, -60, -71, -66, 26, -31, -25, -2, -4, -24, 63, -3, 10, -27, -22, 12, 22, -8, -71, 50, -1, -18, -37, -44, 20, 58, 0, -61, 84, -1, 11, 2, -47, 54, -6, 65, 37, -31, -16, 45, -36, -33, -24, 1, 14, 44, -53, -23, 63, -19, -13, -24, -13, 4, 0, -39, -22, 45, 5, 7, 0, 19, -11, -4, 10, -10, -13, -13, 20, 18, -15, 16, 1, 14, -16, 10, -8, -15, -14, -20, -21, 9, -9, -22, -8, -19, 12, -14, -3, -15, 3, 7, 16, -30, 17, 17, -21, 25, 0, 16, -1, -28, -23, 19, 10, -28, -10, -31, 41, -8, 6, 14, -33, 39, -7, -43, -46, 12, -14, 40, -22, -2, 61, 16, -74, -33, 12, -21, 49, -6, 24, 57, -34, -77, -6, -5, -3, 26, -14, 25, 94, -4, 13, 36, -11, -41, 27, -23, -1, 12, -15, -37, 30, -53, -59, 61, -43, -19, 25, -34, -22, 57, -44, -46, 104, -22, 10, 92, 13, -50, 61, 35, 0, 20, -19, 10, -11, -11, 26, 82, 55, -4, -8, -5, -6, -44, -50, -8, 22, 57, 31, -17, -60, -24, -63, -36, -45, -44, 31, 19, -21, 16, 29, -8, 14, -52, -69, 18, 16, -32, 26, 28, 15, 12, -46, -77, -38, 53, -38, -2, 95, 62, 43, 22, 16, -18, -1, 57, 20, -48, 5, -37, -19, -31, -43, 1, 41, 14, -53, 9, -24, 64, -2, -4, 12, 64, 5, -55, 16, 3 }; +signed char f12[512] = {125, 0, -10, 11, 18, 19, -27, -26, -1, -2, 3, 87, 7, -64, 20, 24, -80, 13, -8, -18, -12, 2, 5, -26, 16, -15, 11, -11, 18, -43, -17, -44, 70, -43, -31, 3, 17, -16, -5, 6, -14, 26, -4, 72, 2, 69, 58, -27, -31, -3, -38, -20, 8, -17, 4, -38, 15, 22, -9, 1, -86, 8, -24, -9, 29, 5, -19, -122, -50, 14, 11, 18, 13, 18, -79, 14, -5, -29, 123, -6, -83, -18, -1, -19, -10, 16, -37, 3, -17, 2, -54, -4, -8, -10, -9, 7, 8, 10, 1, 61, -55, -24, -23, -20, 41, 12, -22, -4, 8, -22, -31, -28, -16, 29, -5, 11, 9, 10, 82, 8, -22, -23, 20, 7, 13, -11, 20, 13, -23, 21, 20, 10, -60, 14, -93, 4, -3, -104, 25, -55, -68, 7, 68, 13, -4, 13, 9, -1, -122, 0, 8, -47, -98, 18, -38, 4, 23, -40, 46, -49, -33, 33, 1, 3, -12, -6, -27, 23, -24, 12, 13, -14, 26, 28, 39, 4, 22, -59, -72, 24, 8, 70, -1, -70, 49, -17, 42, 9, -45, -44, 23, -94, -7, 45, 26, 4, -3, 18, -34, 17, -6, -33, -24, -30, -58, 2, 58, -18, -20, -1, 3, 10, 27, 3, 10, 16, 0, 5, 0, -58, -127, -13, 43, -26, 64, -24, -16, -13, -15, 5, 2, 15, -64, 22, 41, 9, -5, 25, -62, -17, -2, 31, 7, -17, 12, 34, -2, -4, -2, 14, -10, -7, -3, 48, -53, 10, -49, -64, -16, 2, 39, -19, -21, 9, -7, -1, -5, -21, 47, -15, 20, -17, -11, 98, 21, 25, 26, -46, 2, 49, -82, 14, -6, -1, -42, 21, -12, -21, -50, 62, 11, -5, 53, 6, -32, 2, -14, -34, -35, -38, 53, 23, 20, 33, 11, -73, -87, 27, -19, -83, -4, 4, 8, 11, -6, -55, 23, -15, -18, 19, -19, -74, -4, -1, -46, 6, -29, -18, 8, -23, 18, -39, -96, 16, 67, 22, 14, 41, 25, 18, -82, 68, -4, -61, -52, 11, 5, 39, -40, -1, -15, -27, -14, 37, 20, -5, -15, 10, -50, 14, -6, -106, -11, -47, 0, 22, 65, 33, 5, -48, -45, 16, -114, -109, 5, 21, -18, 25, -25, -3, 29, -29, 35, -17, -68, 9, 21, 44, 92, -10, -10, -10, 37, -22, 33, -57, -3, -8, 67, -13, -18, -1, -3, 17, -10, -13, -17, -9, 8, -20, 16, -5, 8, 11, 1, -30, -19, 10, 2, 9, 63, 15, -42, 21, 17, 63, -2, 18, -4, -6, 22, -15, 11, -65, -96, -16, 17, -26, -8, -21, -45, -22, -22, -81, -22, -85, -96, 21, 7, -54, -43, -3, 0, -14, -23, -8, 6, 5, -2, -3, 9, 21, 23, -3, -21, -5, -32, -22, -67, -41, 7, -51, 29, 11, 36, 57, 60, 0, -39, 0, -35, -69, 23, 12, -28, -16, 19, 21, 22, 40, 58, -17, -105, 10, 10, -9, 29, -48, 37, 16, 2, 30, -3, -9, -17, 23, 16, -86, 12, -59, -72, 19 }; +signed char f21[4608] = {18, 15, -6, 19, 27, -4, -14, -17, -22, 22, 25, -9, 3, 18, -8, -34, -50, -13, -16, -5, 7, -36, -34, 8, 23, 21, 49, 11, 21, 10, -34, -20, -5, 14, 16, 12, 0, 13, 15, 9, 46, 14, -6, 49, 0, 20, 28, 3, 24, 25, 14, 21, -40, -25, -7, -1, 0, -1, 7, 8, -16, -47, 13, 7, -4, 12, 3, 7, 4, 0, -9, 2, -2, -2, -2, -4, 5, -15, -37, -2, 1, -35, -36, -18, -6, -37, 15, -23, -18, 20, -3, 7, 6, -15, 12, 24, -23, -33, 15, 8, -10, -9, 38, -15, -13, 35, -11, -9, 4, 2, 15, 2, 6, 6, 6, 15, -7, 17, -9, -19, 3, -45, -30, -42, 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12, -51, -17, -11, 0, 1, 34, 20, 0, -17, -34, -11, 15, 6, -2, 22, -7, -29, -8, 11, 12, 18, 27, 2, -18, -8, 19, -4, -16, 22, 5, -2, -5, -2, -9, 1, -11, 3, -20, 6, -10, 3, -3, -1, 1, -8, 6, -2, 10, 3, 6, 28, 4, -24, 29, 9, -16, 5, 0, 2, -34, -29, 18, 15, -15, -44, 15, 10, 2, 36, 1, -32, -34, -15, -2, -7, 11, -1, -5, -17, 6, -4, -24, -9, 0, 11, 18, -5, 9, 5, 10, -46, -34, 5, 5, 9, 3, 31, 7, 45, 10, -11, 0, -9, -3, 0, 4, -5, 8, -29, -15, -1, -8, -7, -19, 0, 14, -13, -34, 30, 4, -1, 1, 6, 4, 12, 14, -7, -7, -7, -21, -27, 4, 3, 7, -12, -16, 5, 2, -7, -9, 3, -11, -17, 13, 21, -17, -5, 52, -13, 6, 38, 40, -27, -29, 19, 10, -45, -6, 8, -1, -9, 12, -1, -43, -17, -21, -54, 5, 2, 19, -5, 9, 13, 15, 7, -1, -6, -17, -15, 0, -6, -9, 12, -4, 21, -3, -14, 5, -1, -22, -10, 6, -5, -7, -2, -11, -3, 13, -32, -26, -4, -28, -22, -13, -15, -6, -1, -4, -4, 13, 21, 16, -7, -14, -12, 8, -17, -23, 6, -15, -5, 2, 11, 7, 8, 15, -4, 8, 21, 1, -8, -18, 14, -1, 18, -17, -29, 12, 10, 11, 9, 0, 9, 9, -6, -7, -1, -4, -5, -8, -5, 1, -4, -3, 16, 15, 5, 0, 9, 15, -9, 4, 36, 15, 8, 10, 0, 5, 1, 19, 5, 11, 0, -8, -7, 15, 4, 15, 2, -10, -7, -1, 13, 14, -12, 4, 9, -2, 0, 26, 7, -19, -2, 12, 10, 5, 0, -20, -24, 5, 12, 7, 5, 6, 20, 8, 1, 24, 13, 14, 14, 8, -12, -25, 0, -32, -30, 2, -14, 0, 6, 1, 3, 17, 2, 15, 16, -11, 2, 6, -1, -15, 17, 4, 4, 10, 4, 0, -1, -7, -15, 6, -16, -28, -4, -18, -24, 23, -25, -44, 5, -49, -56, 4, -41, -34, 0, 4, -25, 19, -18, -38, 10, -20, -34, -1, 7, -4, 6, -15, -4, 12, 17, 42, 1, 14, 25, 14, 20, 14, -13, -44, -35, -8, 14, 11, 16, 27, -3, 20, 2, -15, 11, 14, 2, 13, 18, 1, 10, 4, 0, -1, -3, -25, -5, -37, -33, 9, -14, 0, -2, -9, 1, -2, -5, 11, 0, 2, 8, 11, 11, 10, -8, -24, 3, -6, -1, -7, 8, 18, 3, -70, -94, -14, 26, 103, -6, -12, -8, -11, -13, 16, -19, 11, -3, 17, -1, -6, 4, -4, 5, 11, 17, 15, 15, -3, -1, -8, 6, 6, 2, -1, -11, -4, 13, 11, 5, -3, 1, -5, -6, 0, -7, 9, 0, 7, 21, 15, 11, 15, 2, 22, -6, -20, -7, 0, -9, 2, -7, 3, 0, 7, 20, 8, 11, 0, -4, 3, 11, 9, 12, 3, 1, -6, 13, 10, 2, 7, 4, -2, -1, 9, 20, 7, 4, 20, 5, 6, 4, 9, 15, -10, -53, -1, -23, 19, -13, 8, -1, 6, -17, -33, -12, -8, 39, -5, 12, 9, 1, 3, -2, 0, -5, 1, -3, 12, 9, 1, -19, -8, 2, 7, 8, -1, 11, -8, 2, 8, -7, 5, 10, -10, -1, 7, -12, 3, 17, -11, 19, 0, -2, 10, 10, -1, -1, -6, 6, 3, -33, 38, 13, -20, -24, 7, 10, 29, 12, 4, 16, 1, 13, 25, 26, 21, 43, 17, 14, 15, 9, 2, -39, -3, 5, -63, 3, 7, 0, 5, -11, -2, -1, -16, -5, -11, 7, 20, 14, -6, -6, 1, -19, 0, -4, 3, 13, -9, 8, 25, 27, 12, 54, 13, 13, 27, 7, -14, -15, -6, -12, -17, -10, -3, 13, 0, -7, 8, 7, 1, 36, 7, 13, 30, 7, -5, -53, -14, -17, -49, 3, 12, -10, 1, -21, 6, -4, 19, 10, -12, -7, 30, -11, 11, 17, 9, 21, 7, -18, -3, -12, -15, -7, -8, 7, -15, -5, 0, 8, 26, 12, -3, -35, -4, -10, -55, -5, -11, -15, 11 }; +signed char f22[512] = {-32, -15, -39, -50, -29, -29, -7, 16, 57, -21, -29, -2, -37, -7, -11, -33, 98, -6, 24, -11, 16, -39, -26, 7, 37, -16, 27, -5, -56, -50, -72, 16, -10, 24, -56, -25, -19, 11, -44, 20, 5, -23, -28, -40, 18, -36, -22, -25, 1, -62, -12, 24, 12, -40, -35, 17, 115, -63, 5, -35, -5, -8, -35, -38, 0, -54, 59, -43, -6, -43, 47, 11, -14, 21, 18, -1, 11, -52, 8, -34, -17, 16, -5, 17, -66, 6, 37, -3, 71, -9, -23, -38, -46, -17, 3, 12, -17, 4, 61, -5, 3, 31, -38, 30, -7, -38, -42, 5, 10, -32, -39, 37, -41, 26, 42, -19, 52, 29, -3, 67, -28, -3, -6, -22, -1, 21, 30, 35, -5, 15, -18, -6, 62, 5, -22, 14, -2, -31, -5, -24, 90, -10, -33, -17, -7, -2, -35, -12, -26, -37, -13, 9, 13, 45, -16, 17, -37, 4, 0, 20, 20, -5, -3, 40, 63, 21, -8, -17, 7, -38, 9, -11, -24, 7, -13, 0, 2, 36, 66, -18, -36, 25, -11, -38, 17, 1, 38, 17, 83, -31, 19, -39, -5, 10, -20, 35, 6, 24, 47, 60, -5, -41, 29, 98, -39, -17, -41, 32, 19, 57, -33, 8, 33, -27, -19, 22, -1, 59, -46, 11, -7, 44, -73, -35, 49, -46, -25, 5, -5, -8, 36, 30, 3, -11, 31, -16, -64, -42, -30, -3, -18, -10, -56, 0, -24, 34, -21, 41, 34, -38, -9, -23, -4, -15, 4, 85, -24, -27, -32, 18, -49, 0, 58, 2, -45, 30, 21, -30, 12, -18, 38, 40, 2, -57, 7, 58, -39, -43, -29, 111, 6, 64, -71, -67, 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+signed char f31[9216] = {14, 2, 9, 8, -8, -4, -5, -65, -29, -23, -9, 1, -15, -18, -10, 6, 13, 17, 29, 37, 27, 20, 38, -5, -29, -58, -22, -2, -30, -9, 30, 11, 9, 23, 19, 11, 8, 21, 3, 32, 33, 10, -18, -34, -20, -19, -6, -5, -1, 11, 18, -24, -37, 6, -15, -39, -33, -21, 20, -18, -9, -5, 22, -13, -3, 24, 7, -16, 9, -5, -29, -51, -6, -31, -11, 13, -23, -7, 41, 60, 28, 5, 20, 4, 27, 46, 3, -20, -19, -58, 10, -6, 4, -3, 35, 3, 9, 35, 13, 1, -1, -9, -12, -31, -7, -17, -32, -20, 1, -21, -11, 9, -2, 6, -15, -37, -1, 15, 19, 19, -32, -43, -3, 19, -22, 19, 9, -8, -7, 3, 4, 7, -25, -20, 5, 26, 6, 8, 10, -21, 18, 10, 11, 19, -19, -3, -8, -16, 25, 23, -18, 2, 2, 8, -17, -6, -15, -11, -3, -18, -18, -18, -42, -19, -18, -20, -5, -11, 12, 25, 2, -5, 23, 19, -11, 4, -11, -24, -18, -9, -20, -25, 7, -26, 52, -3, 18, -22, -12, 16, -17, 1, -29, 11, -9, -78, -8, 26, 3, 11, 11, 5, 7, 44, 25, 37, 22, 6, 32, 26, 16, 6, 22, 20, -13, 4, 19, -5, -15, 34, -16, -27, 27, -1, -29, -2, -19, -24, -8, 19, 7, 7, 44, 7, -1, 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-87, 34, 3, 87, -37, -1, 96, 82, 79, 7, 75, -94, -23, 22, -59, 82, -38, -73, -24, 36, 36, -57, 29, 56, -63, 13, -7, -76, -81, 2, 12, -60, 79, 48, -41, -39, -34, 11, -68, -58, 91, -76, -17, 77, 13, 9, -5, 67, -30, 24, 23, -26, 68, -97, -77, 10, 24, -75, -74, -91, 29, 54, 71, -98, -64, 10, 21, 68, 22, -26, -33 }; +signed char f63[147456] = {-40, -18, 12, 60, 8, 20, 8, 45, 14, -44, 71, -29, -42, -5, 51, -67, 33, -40, -15, 7, 76, 76, -1, 15, -48, -69, -81, -24, -3, 26, -12, -57, 62, 16, 31, -60, -21, -70, -22, 11, -58, -4, 53, -74, 51, 17, 11, 71, -65, 35, 22, 6, 50, 4, -61, 40, -22, -7, 9, 14, 48, 23, 32, 22, -54, 41, 9, -41, 24, 31, 52, -52, 39, -38, 41, -62, 21, -46, -50, 32, 34, -21, 57, 57, -47, -27, 4, -45, -32, -73, -60, 33, 15, 25, -27, -65, 22, 80, -4, 10, -31, -43, -36, 13, -16, 7, -40, 42, 15, -54, 25, -32, 12, -68, 81, -50, -40, -51, -53, -12, 42, 46, -51, 74, -36, 46, 18, 65, 61, 12, -22, 30, -33, 41, -40, 37, 36, 40, -12, -52, 12, -33, -59, -1, 12, -56, -52, -34, -20, 14, -1, 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-27, -20, 9, -23, -5, -16, 6, -14, -6, -16, 3, 12, 12, -6, -19, 3, 11, -14, -27, 13, 14, 9, 4, 17, -29, -14, -21, -39, -34, -36, -43, -16, -16, 8, 31, -24, -24, -25, 5, -37, -28, 4, 0, 0, -15, 13, 36, -5, 2, 67, 25, -7, -20, -1, -17, -23, 12, 6, 0, 2, -24, 6, -16, -53, -26, 2, -4, 26, -20, 2, 13, 19, -16, -14, 18, 4, 9, 3, -3, 30, -4, -19, -28, -23, -13, 17, 1, -12, 11, -39, -38, -1, -16, -43, -1, 20, -15, -11, 18, -21, -14, -19, -11, -15, 8, -12, 16, -22, 14, -18, 4, 8, -8, 28, 19, 12, 5, 3, -14, 4, 7, 10, 2, -3, -25, 2, -28, -23, 13, -4, 9, -22, 5, -14, 1, -29, 1, 32, 5, 13, 0, -26, 1, -9, -18, 6, -27, -4, -6, 10, 25, 3, 4, 15, -5, -25, -32, -15, -17, 17, -10, -5, 10, -16, 5, 2, -5, 8, -7, -21, -27, -26, -22, 18, 14, 40, -5, -2, 18, -7, -18, -2, -8, -11, 3 }; signed char * param_ppConvCoefInt8[24] = { f11,f12,f21,f22,f31,f32,f33,f41,f42,f43,f51,f52,f53,f61,f62,f63,f3loc,f3conf,f4loc,f4conf,f5loc,f5conf,f6loc,f6conf }; -float param_pConvCoefScales[24] = { 132.763774410310f, 114.362496728599f, 153.349241244898f, 144.797613004250f, 240.639291070002f, 231.400376069275f, 384.138818158766f, 478.622265695767f, 506.944787629033f, 823.086381895940f, 1019.846273009683f, 829.463048967252f, 1373.793570521072f, 1851.613835790018f, 1023.417162635276f, 1913.889541411641f, 291.105605285927f, 286.438017753269f, 326.832960591184f, 333.029397371316f, 424.189927893703f, 299.370635379834f, 631.160857480994f, 684.609571219045f }; -#endif \ No newline at end of file +float param_pConvCoefScales[24] = {119.163854027152f, 145.918188266835f, 142.686760212700f, 145.051064328629f, 243.606582102613f, 245.835386897372f, 377.969825834081f, 716.781785649858f, 429.900508405703f, 902.979546771917f, 1513.269159323885f, 784.683718303925f, 1543.645364721974f, 2091.005939787832f, 1123.524319631342f, 2271.573520741759f, 236.581549825987f, 347.449442605992f, 439.399462130653f, 413.757523715204f, 443.311817021608f, 401.061468926724f, 661.198567195378f, 669.229446823400f}; diff --git a/src/facedetectcnn-model.cpp b/src/facedetectcnn-model.cpp index 2da23e2..15fcd73 100644 --- a/src/facedetectcnn-model.cpp +++ b/src/facedetectcnn-model.cpp @@ -56,14 +56,9 @@ the use of this software, even if advised of the possibility of such damage. #define NUM_CONV_LAYER 24 -#if defined(_ENABLE_INT8_CONV) extern signed char * param_ppConvCoefInt8[NUM_CONV_LAYER]; -#else -extern float * param_ppConvCoefFloat[NUM_CONV_LAYER]; -#endif extern float param_pConvCoefScales[NUM_CONV_LAYER]; - typedef struct SConvInfo_{ int pad; int stride; @@ -76,35 +71,34 @@ typedef struct SConvInfo_{ Filters param_pFilters[NUM_CONV_LAYER]; //NUM_CONV_LAYER conv layers -int param_pMean[3] = { 104,117,123 }; -float param_pConv3Norm[32] = { 6.592306137084961f, 6.558613300323486f, 6.324647903442383f, 6.125812530517578f, 6.558647632598877f, 6.526843547821045f, 6.558306694030762f, 6.556366443634033f, 6.638453483581543f, 6.558482646942139f, 6.631646633148193f, 6.536031246185303f, 6.456478118896484f, 6.558736801147461f, 6.567635536193848f, 5.369370937347412f, 6.567112445831299f, 6.5589985847473145f, 6.602363586425781f, 6.558731555938721f, 6.419933795928955f, 6.138179779052734f, 6.250294208526611f, 6.562124252319336f, 6.579089164733887f, 6.553215503692627f, 6.191119194030762f, 3.9663331508636475f, 6.459974765777588f, 6.555095195770264f, 6.545119762420654f, 6.626718997955322f }; -float param_pConv4Norm[64] = { 5.24936056137085f, 5.249833106994629f, 5.201494216918945f, 5.252224445343018f, 5.2574462890625f, 5.226278305053711f, 5.258802890777588f, 5.254302024841309f, 5.1779465675354f, 5.24658203125f, 5.252774238586426f, 5.248640060424805f, 5.209632396697998f, 5.256057262420654f, 5.198976039886475f, 5.259532928466797f, 5.150023460388184f, 5.225643634796143f, 5.25822114944458f, 5.247387886047363f, 5.2590227127075195f, 5.25047492980957f, 5.264795303344727f, 5.24699592590332f, 5.249448299407959f, 5.205463409423828f, 5.252189636230469f, 5.255984783172607f, 5.209300994873047f, 5.261776447296143f, 5.251255035400391f, 5.254635810852051f, 5.25943660736084f, 5.248631477355957f, 5.285300254821777f, 5.254481792449951f, 5.248084545135498f, 5.249952793121338f, 5.234015941619873f, 5.263407230377197f, 5.245810508728027f, 5.2482500076293945f, 5.102107048034668f, 5.221688747406006f, 5.245429515838623f, 5.251964569091797f, 5.305455684661865f, 5.1277570724487305f, 5.28142786026001f, 5.235960006713867f, 5.205482482910156f, 5.251782417297363f, 5.182538032531738f, 5.27116584777832f, 5.198187351226807f, 5.248366832733154f, 5.254012584686279f, 5.251954555511475f, 5.259944438934326f, 5.248141288757324f, 5.183408737182617f, 5.265050888061523f, 5.24997615814209f, 5.262747287750244f }; -float param_pConv5Norm[128] = { 3.2798357009887695f, 3.279853582382202f, 3.316532850265503f, 3.2797908782958984f, 3.283681631088257f, 3.292001962661743f, 3.2796599864959717f, 3.301004409790039f, 3.279745101928711f, 3.2916154861450195f, 3.304828405380249f, 3.2860844135284424f, 3.2962393760681152f, 3.2914977073669434f, 3.311239719390869f, 3.2796378135681152f, 3.30684757232666f, 3.2795956134796143f, 3.3049705028533936f, 3.2912471294403076f, 3.2799410820007324f, 3.280548095703125f, 3.279585838317871f, 3.3002915382385254f, 3.351128339767456f, 3.285721778869629f, 3.308690071105957f, 3.286360025405884f, 3.286612033843994f, 3.323331117630005f, 3.280801296234131f, 3.279557228088379f, 3.325319290161133f, 3.292274236679077f, 3.284330129623413f, 3.3016438484191895f, 3.305274248123169f, 3.293483257293701f, 3.2965309619903564f, 3.343932628631592f, 3.2799618244171143f, 3.281886100769043f, 3.2855217456817627f, 3.283858299255371f, 3.299082040786743f, 3.295645236968994f, 3.291444778442383f, 3.279492139816284f, 3.2956502437591553f, 3.3284802436828613f, 3.2878544330596924f, 3.291749954223633f, 3.3015894889831543f, 3.2998600006103516f, 3.2887396812438965f, 3.2832252979278564f, 3.285311698913574f, 3.30757474899292f, 3.284590721130371f, 3.3025388717651367f, 3.2936882972717285f, 3.279754877090454f, 3.307007312774658f, 3.2958528995513916f, 3.3630170822143555f, 3.326841354370117f, 3.2800698280334473f, 3.2920491695404053f, 3.2991254329681396f, 3.309135913848877f, 3.2799878120422363f, 3.2878851890563965f, 3.302861452102661f, 3.315964698791504f, 3.279761791229248f, 3.3086979389190674f, 3.2836644649505615f, 3.29606294631958f, 3.2939038276672363f, 3.296156883239746f, 3.300607204437256f, 3.3293192386627197f, 3.2886781692504883f, 3.292102098464966f, 3.279629945755005f, 3.2798566818237305f, 3.2876806259155273f, 3.281590223312378f, 3.281094789505005f, 3.2978975772857666f, 3.2799761295318604f, 3.3351552486419678f, 3.2866907119750977f, 3.338275671005249f, 3.2797188758850098f, 3.280174493789673f, 3.296318531036377f, 3.281552314758301f, 3.2805323600769043f, 3.294194459915161f, 3.279611349105835f, 3.3100433349609375f, 3.2793779373168945f, 3.2797317504882812f, 3.2823593616485596f, 3.2944772243499756f, 3.280740976333618f, 3.2863688468933105f, 3.299750804901123f, 3.282517194747925f, 3.2863147258758545f, 3.286238193511963f, 3.280881881713867f, 3.2873098850250244f, 3.2873218059539795f, 3.2804245948791504f, 3.306823968887329f, 3.312803030014038f, 3.2798690795898438f, 3.288886785507202f, 3.3185698986053467f, 3.299873113632202f, 3.279698610305786f, 3.3057520389556885f, 3.3125710487365723f, 3.2796096801757812f, 3.2905843257904053f, 3.2902579307556152f }; -float param_pConv6Norm[128] = { 3.2981998920440674f, 3.2801780700683594f, 3.2922229766845703f, 3.2955803871154785f, 3.294820547103882f, 3.292754888534546f, 3.2865407466888428f, 3.286324977874756f, 3.2815260887145996f, 3.2840657234191895f, 3.282493829727173f, 3.333282470703125f, 3.289398193359375f, 3.2884361743927f, 3.2864975929260254f, 3.2839083671569824f, 3.2875373363494873f, 3.322467565536499f, 3.28637433052063f, 3.286494731903076f, 3.291119337081909f, 3.322329044342041f, 3.2931485176086426f, 3.279733896255493f, 3.2803969383239746f, 3.295511245727539f, 3.2980639934539795f, 3.280717134475708f, 3.2957653999328613f, 3.317964553833008f, 3.2798070907592773f, 3.3057520389556885f, 3.305992841720581f, 3.348924160003662f, 3.291982650756836f, 3.3030447959899902f, 3.288562774658203f, 3.2987096309661865f, 3.2800376415252686f, 3.336385488510132f, 3.2968106269836426f, 3.296558380126953f, 3.2796730995178223f, 3.286644458770752f, 3.2797164916992188f, 3.2899343967437744f, 3.3156163692474365f, 3.3474082946777344f, 3.291513442993164f, 3.3053133487701416f, 3.283517360687256f, 3.305210828781128f, 3.303983211517334f, 3.282759189605713f, 3.280306816101074f, 3.2799665927886963f, 3.285292387008667f, 3.3079776763916016f, 3.29679274559021f, 3.280120849609375f, 3.2796525955200195f, 3.28070068359375f, 3.2877960205078125f, 3.302424192428589f, 3.2920310497283936f, 3.286226511001587f, 3.2799699306488037f, 3.2863574028015137f, 3.324301242828369f, 3.291632890701294f, 3.294870376586914f, 3.289574146270752f, 3.297481060028076f, 3.304020881652832f, 3.2841410636901855f, 3.2966504096984863f, 3.280271530151367f, 3.2840888500213623f, 3.3100359439849854f, 3.282552719116211f, 3.2812161445617676f, 3.3106632232666016f, 3.2951345443725586f, 3.280796766281128f, 3.283688545227051f, 3.328918695449829f, 3.2930126190185547f, 3.291832685470581f, 3.2913215160369873f, 3.282050609588623f, 3.280987501144409f, 3.303039073944092f, 3.2960453033447266f, 3.2923476696014404f, 3.2912893295288086f, 3.283773422241211f, 3.308786392211914f, 3.294309377670288f, 3.2953407764434814f, 3.2821991443634033f, 3.2892892360687256f, 3.2869884967803955f, 3.3056561946868896f, 3.281874895095825f, 3.337285280227661f, 3.2868685722351074f, 3.2931010723114014f, 3.279754638671875f, 3.2797317504882812f, 3.3095757961273193f, 3.2800772190093994f, 3.299697160720825f, 3.2798924446105957f, 3.2848010063171387f, 3.2815306186676025f, 3.29996919631958f, 3.29498553276062f, 3.317089557647705f, 3.2876529693603516f, 3.2813613414764404f, 3.290708065032959f, 3.2828986644744873f, 3.2800583839416504f, 3.3065834045410156f, 3.287909746170044f, 3.2797391414642334f, 3.3226301670074463f, 3.323007345199585f }; +float param_pConv3Norm[64] = { 6.067704200744629f, 6.086832523345947f, 6.075444221496582f, 4.864563465118408f, 4.982297897338867f, 6.065876483917236f, 6.065804958343506f, 6.066604137420654f, 6.091238498687744f, 6.066442489624023f, 6.067852020263672f, 6.065065383911133f, 6.111854076385498f, 5.854933738708496f, 5.094123363494873f, 5.611084938049316f, 5.638108253479004f, 6.075394153594971f, 6.086704730987549f, 6.040279865264893f, 5.760599136352539f, 6.066614627838135f, 6.067028999328613f, 6.090893268585205f, 6.067197799682617f, 6.0027570724487305f, 6.122867107391357f, 5.662094593048096f, 6.065771102905273f, 6.100675106048584f, 6.06779670715332f, 6.067671298980713f, 6.066753387451172f, 5.737717151641846f, 4.2541680335998535f, 6.091390609741211f, 6.082061767578125f, 6.048476696014404f, 5.471676349639893f, 6.0827765464782715f, 5.785499572753906f, 6.0660271644592285f, 6.075671195983887f, 6.097797393798828f, 6.047597885131836f, 5.906824111938477f, 5.394303798675537f, 6.067469596862793f, 6.061819553375244f, 5.82515811920166f, 5.5635809898376465f, 6.114983081817627f, 5.813036918640137f, 6.084056854248047f, 5.945978164672852f, 5.97323751449585f, 6.09974479675293f, 5.745806694030762f, 5.416194915771484f, 5.367262363433838f, 6.10166072845459f, 6.092775344848633f, 6.067975044250488f, 5.756527900695801f }; +float param_pConv4Norm[128] = { 4.853384494781494f, 4.889324188232422f, 4.874561786651611f, 4.852031230926514f, 4.922458648681641f, 4.826481342315674f, 4.852572441101074f, 4.854414939880371f, 4.861706256866455f, 4.847333908081055f, 4.850497722625732f, 4.8718366622924805f, 4.846635818481445f, 4.853606700897217f, 4.8193840980529785f, 4.8496294021606445f, 4.852749824523926f, 4.856253147125244f, 4.873744964599609f, 4.82371187210083f, 4.8532867431640625f, 4.856527328491211f, 4.85302734375f, 4.852819919586182f, 4.8583760261535645f, 4.854391574859619f, 4.8661627769470215f, 4.872909069061279f, 4.860682010650635f, 4.870107173919678f, 4.864952087402344f, 4.8647074699401855f, 4.875209331512451f, 4.841148376464844f, 4.853340148925781f, 4.815730094909668f, 4.8606181144714355f, 4.864851474761963f, 4.853943347930908f, 4.857072353363037f, 4.865845203399658f, 4.83903169631958f, 4.839323997497559f, 4.853547096252441f, 4.861319065093994f, 4.854262351989746f, 4.85335111618042f, 4.848951816558838f, 4.857367038726807f, 4.8540472984313965f, 4.854204177856445f, 4.853390693664551f, 4.850429058074951f, 4.8542256355285645f, 4.84539270401001f, 4.872445106506348f, 4.8350419998168945f, 4.855948448181152f, 4.791098594665527f, 4.860246658325195f, 4.85080099105835f, 4.824630260467529f, 4.852724552154541f, 4.83675479888916f, 4.853806495666504f, 4.831771373748779f, 4.839821815490723f, 4.856444835662842f, 4.853408336639404f, 4.839377403259277f, 4.888157367706299f, 4.847803592681885f, 4.926937580108643f, 4.86123514175415f, 4.8530097007751465f, 4.843791484832764f, 4.8553466796875f, 4.862685203552246f, 4.836782932281494f, 4.829556941986084f, 4.865046977996826f, 4.949220180511475f, 4.853907585144043f, 4.858654022216797f, 4.852842330932617f, 4.852590084075928f, 4.853270053863525f, 4.857703685760498f, 4.831657409667969f, 4.831831455230713f, 4.835714817047119f, 4.873287677764893f, 4.861383438110352f, 4.849035263061523f, 4.826011657714844f, 4.859689712524414f, 4.840816497802734f, 4.861761093139648f, 4.822288513183594f, 4.901276588439941f, 4.821767330169678f, 4.8713788986206055f, 4.8542914390563965f, 4.85370397567749f, 4.84406042098999f, 4.8527445793151855f, 4.876774311065674f, 4.875062942504883f, 4.885288238525391f, 4.8349103927612305f, 4.853190898895264f, 4.860854148864746f, 4.837437152862549f, 4.8570475578308105f, 4.856215953826904f, 4.8456034660339355f, 4.860386371612549f, 4.851935386657715f, 4.846256256103516f, 4.8642497062683105f, 4.861553192138672f, 4.850989818572998f, 4.867442607879639f, 4.852613925933838f, 4.856968879699707f, 4.855709552764893f, 4.85834264755249f, 4.854826927185059f }; +float param_pConv5Norm[128] = { 3.079456090927124f, 3.039461135864258f, 3.0351147651672363f, 3.037583589553833f, 3.048778533935547f, 3.0725064277648926f, 3.0622661113739014f, 3.064093828201294f, 3.048044443130493f, 3.036616086959839f, 3.1058716773986816f, 3.048161745071411f, 3.058666467666626f, 3.045867919921875f, 3.043402671813965f, 3.053333282470703f, 3.045125722885132f, 3.04072642326355f, 3.050602436065674f, 3.0699708461761475f, 3.0647435188293457f, 3.0702977180480957f, 3.061201810836792f, 3.0459437370300293f, 3.0622904300689697f, 3.067882776260376f, 3.0438363552093506f, 3.0610761642456055f, 3.044037103652954f, 3.0485501289367676f, 3.0330629348754883f, 3.048556327819824f, 3.0330440998077393f, 3.0585777759552f, 3.0413095951080322f, 3.0553483963012695f, 3.0651161670684814f, 3.0932703018188477f, 3.0698587894439697f, 3.067293167114258f, 3.040133237838745f, 3.0340847969055176f, 3.042184591293335f, 3.044334650039673f, 3.033623695373535f, 3.0592658519744873f, 3.0473034381866455f, 3.0568149089813232f, 3.0512683391571045f, 3.0338335037231445f, 3.0865068435668945f, 3.0527186393737793f, 3.035172700881958f, 3.0331568717956543f, 3.0637283325195312f, 3.0530037879943848f, 3.041080951690674f, 3.054175853729248f, 3.045405387878418f, 3.060072898864746f, 3.052427291870117f, 3.0673320293426514f, 3.0618369579315186f, 3.056979179382324f, 3.0732641220092773f, 3.049957275390625f, 3.033128261566162f, 3.0706357955932617f, 3.060206651687622f, 3.058335304260254f, 3.0502429008483887f, 3.033816337585449f, 3.0882437229156494f, 3.039879083633423f, 3.064310312271118f, 3.0645105838775635f, 3.0497498512268066f, 3.0598857402801514f, 3.0345287322998047f, 3.0501859188079834f, 3.0384273529052734f, 3.0725810527801514f, 3.089094638824463f, 3.074632406234741f, 3.0437800884246826f, 3.05190110206604f, 3.0339508056640625f, 3.033148765563965f, 3.0332436561584473f, 3.0683743953704834f, 3.075549602508545f, 3.057891845703125f, 3.0438103675842285f, 3.037956476211548f, 3.0511362552642822f, 3.0402984619140625f, 3.0735790729522705f, 3.068497896194458f, 3.039294719696045f, 3.0881500244140625f, 3.047109842300415f, 3.0450427532196045f, 3.0337936878204346f, 3.035036087036133f, 3.0530760288238525f, 3.034095048904419f, 3.074824571609497f, 3.0406930446624756f, 3.0669476985931396f, 3.049168109893799f, 3.076056480407715f, 3.046610116958618f, 3.083836317062378f, 3.080292224884033f, 3.0445592403411865f, 3.0415334701538086f, 3.057447671890259f, 3.0389440059661865f, 3.058000326156616f, 3.033161163330078f, 3.0331573486328125f, 3.044421911239624f, 3.039666175842285f, 3.054525375366211f, 3.098982572555542f, 3.0593252182006836f, 3.038623094558716f, 3.0782241821289062f }; +float param_pConv6Norm[128] = { 3.0690505504608154f, 3.0386440753936768f, 3.0784153938293457f, 3.064088821411133f, 3.058891773223877f, 3.036539316177368f, 3.0579793453216553f, 3.053611993789673f, 3.050058603286743f, 3.0333425998687744f, 3.097348213195801f, 3.06592059135437f, 3.068408489227295f, 3.0698347091674805f, 3.0762569904327393f, 3.054143190383911f, 3.054262399673462f, 3.0519206523895264f, 3.0427865982055664f, 3.046510934829712f, 3.054152250289917f, 3.101977586746216f, 3.0366146564483643f, 3.06791615486145f, 3.044861316680908f, 3.05081844329834f, 3.0827019214630127f, 3.046961784362793f, 3.0690267086029053f, 3.073493242263794f, 3.036958932876587f, 3.0742595195770264f, 3.0346856117248535f, 3.044112205505371f, 3.0804336071014404f, 3.0559825897216797f, 3.039485454559326f, 3.0536017417907715f, 3.035005569458008f, 3.0331103801727295f, 3.0532901287078857f, 3.041443347930908f, 3.075648307800293f, 3.033519744873047f, 3.057023525238037f, 3.0344996452331543f, 3.069003105163574f, 3.041029453277588f, 3.055204153060913f, 3.0332140922546387f, 3.058738946914673f, 3.033165454864502f, 3.041567802429199f, 3.0333807468414307f, 3.0331976413726807f, 3.0732359886169434f, 3.0440356731414795f, 3.0499985218048096f, 3.0332562923431396f, 3.0454604625701904f, 3.044590473175049f, 3.0364391803741455f, 3.034428596496582f, 3.040388584136963f, 3.0335984230041504f, 3.0358216762542725f, 3.037226676940918f, 3.0331900119781494f, 3.033982992172241f, 3.0461173057556152f, 3.0425851345062256f, 3.0333290100097656f, 3.0733861923217773f, 3.0609347820281982f, 3.0564520359039307f, 3.0463502407073975f, 3.066772699356079f, 3.0337183475494385f, 3.0710198879241943f, 3.0378026962280273f, 3.0489232540130615f, 3.0642757415771484f, 3.062023878097534f, 3.0553982257843018f, 3.0353715419769287f, 3.0331521034240723f, 3.033599615097046f, 3.038581609725952f, 3.0468997955322266f, 3.070789098739624f, 3.0823018550872803f, 3.045168399810791f, 3.050402879714966f, 3.04396390914917f, 3.053098678588867f, 3.073054790496826f, 3.036590814590454f, 3.0514144897460938f, 3.067574977874756f, 3.0603926181793213f, 3.04386568069458f, 3.069430112838745f, 3.0422122478485107f, 3.0464704036712646f, 3.0503995418548584f, 3.03448486328125f, 3.047955274581909f, 3.034306287765503f, 3.034529447555542f, 3.0360774993896484f, 3.0508804321289062f, 3.0470383167266846f, 3.05137038230896f, 3.0441949367523193f, 3.065099000930786f, 3.0329136848449707f, 3.0651464462280273f, 3.075334310531616f, 3.03696870803833f, 3.033154249191284f, 3.0385067462921143f, 3.0376787185668945f, 3.0448710918426514f, 3.0523617267608643f, 3.0374507904052734f, 3.0359244346618652f, 3.040173292160034f, 3.03313946723938f }; SConvInfo param_pConvInfo[NUM_CONV_LAYER] = { //conv1_1 - { 1, 2, 3, 3, 3, 16 }, + { 1, 2, 3, 3, 3, 32 }, //conv1_2 - { 0, 1, 1, 1, 16, 16 }, + { 0, 1, 1, 1, 32, 16 }, //conv2_1 - { 1, 1, 3, 3, 16, 16 }, + { 1, 1, 3, 3, 16, 32 }, //conv2_2 - { 0, 1, 1, 1, 16, 16 }, + { 0, 1, 1, 1, 32, 16 }, //conv3_1 - { 1, 1, 3, 3, 16, 32 }, + { 1, 1, 3, 3, 16, 64 }, //conv2_2 - { 0, 1, 1, 1, 32, 32 }, + { 0, 1, 1, 1, 64, 32 }, //conv3_3 - { 1, 1, 3, 3, 32, 32 }, - //conv4_1 { 1, 1, 3, 3, 32, 64 }, + //conv4_1 + { 1, 1, 3, 3, 64, 128 }, //conv4_2 - { 0, 1, 1, 1, 64, 64 }, + { 0, 1, 1, 1, 128, 64 }, //conv4_3 - { 1, 1, 3, 3, 64, 64 }, + { 1, 1, 3, 3, 64, 128 }, //conv5_1 - { 1, 1, 3, 3, 64,128 }, + { 1, 1, 3, 3, 128,128 }, //conv5_2 { 0, 1, 1, 1,128,128 }, //conv5_3 @@ -116,13 +110,13 @@ SConvInfo param_pConvInfo[NUM_CONV_LAYER] = { //conv6_3 { 1, 1, 3, 3,128,128 }, //loc3 - { 1, 1, 3, 3, 32, 12 }, + { 1, 1, 3, 3, 64, 12 }, //conf3 - { 1, 1, 3, 3, 32, 6 }, + { 1, 1, 3, 3, 64, 6 }, //loc4 - { 1, 1, 3, 3, 64, 8 }, + { 1, 1, 3, 3, 128, 8 }, //conf4 - { 1, 1, 3, 3, 64, 4 }, + { 1, 1, 3, 3, 128, 4 }, //loc5 { 1, 1, 3, 3,128, 8 }, //conf5 @@ -147,17 +141,11 @@ void init_parameters() for(int ff = 0; ff < param_pConvInfo[i].num; ff++) { - CDataBlob * b3x3 = new CDataBlob(param_pConvInfo[i].width, param_pConvInfo[i].height, param_pConvInfo[i].channels); - CDataBlob * b1x1 = new CDataBlob(); -#if defined(_ENABLE_INT8_CONV) - b3x3->setInt8DataFromCaffeFormat(param_ppConvCoefInt8[i] + ff * offset, - param_pConvInfo[i].width, param_pConvInfo[i].height, param_pConvInfo[i].channels); - blob2vector(b3x3, b1x1, false); -#else - b3x3->setFloatDataFromCaffeFormat(param_ppConvCoefFloat[i] + ff * offset, + CDataBlob * b3x3 = new CDataBlob(param_pConvInfo[i].width, param_pConvInfo[i].height, param_pConvInfo[i].channels); + CDataBlob * b1x1 = new CDataBlob(); + b3x3->setInt8DataFromCaffeFormat(param_ppConvCoefInt8[i] + ff * offset, param_pConvInfo[i].width, param_pConvInfo[i].height, param_pConvInfo[i].channels); - blob2vector(b3x3, b1x1, true); -#endif + blob2vector(b3x3, b1x1); delete b3x3; b3x3 = 0; param_pFilters[i].filters.push_back(b1x1); @@ -173,14 +161,9 @@ void init_parameters() for(int ff = 0; ff < param_pConvInfo[i].num; ff++) { - CDataBlob * b = new CDataBlob(param_pConvInfo[i].width, param_pConvInfo[i].height, param_pConvInfo[i].channels); -#if defined(_ENABLE_INT8_CONV) + CDataBlob * b = new CDataBlob(param_pConvInfo[i].width, param_pConvInfo[i].height, param_pConvInfo[i].channels); b->setInt8DataFromCaffeFormat(param_ppConvCoefInt8[i] + ff * offset, param_pConvInfo[i].width, param_pConvInfo[i].height, param_pConvInfo[i].channels); -#else - b->setFloatDataFromCaffeFormat(param_ppConvCoefFloat[i] + ff * offset, - param_pConvInfo[i].width, param_pConvInfo[i].height, param_pConvInfo[i].channels); -#endif param_pFilters[i].filters.push_back(b); } } @@ -188,14 +171,18 @@ void init_parameters() vector objectdetect_cnn(unsigned char * rgbImageData, int width, int height, int step) { - CDataBlob inputImage; - CDataBlob pConvDataBlobs[NUM_CONV_LAYER]; - CDataBlob pool1, pool2, pool3, pool4, pool5; - CDataBlob conv3norm, conv4norm, conv5norm, conv6norm; - CDataBlob conv3priorbox, conv4priorbox, conv5priorbox, conv6priorbox; - CDataBlob conv3priorbox_flat, conv4priorbox_flat, conv5priorbox_flat, conv6priorbox_flat, mbox_priorbox; - CDataBlob conv3loc_flat, conv4loc_flat, conv5loc_flat, conv6loc_flat, mbox_loc; - CDataBlob conv3conf_flat, conv4conf_flat, conv5conf_flat, conv6conf_flat, mbox_conf; + CDataBlob inputImage; + CDataBlob pConvDataBlobs[NUM_CONV_LAYER-8]; + CDataBlob pConvDataBlobsBranch[8]; + CDataBlob pool1, pool2, pool3, pool4, pool5; + CDataBlob conv3priorbox, conv4priorbox, conv5priorbox, conv6priorbox; + CDataBlob conv3priorbox_flat, conv4priorbox_flat, conv5priorbox_flat, conv6priorbox_flat, mbox_priorbox; + CDataBlob conv3loc_flat, conv4loc_flat, conv5loc_flat, conv6loc_flat; + CDataBlob conv3loc_flat_float, conv4loc_flat_float, conv5loc_flat_float, conv6loc_flat_float; + CDataBlob mbox_loc_float; ; + CDataBlob conv3conf_flat, conv4conf_flat, conv5conf_flat, conv6conf_flat; + CDataBlob conv3conf_flat_float, conv4conf_flat_float, conv5conf_flat_float, conv6conf_flat_float; + CDataBlob mbox_conf_float; double total = 0.0; double t = 0.0; @@ -213,26 +200,20 @@ vector objectdetect_cnn(unsigned char * rgbImageData, int width, int h TIME_START; //inputImage.setDataFromImage(rgbImageData, width, height, 3, step, param_pMean); - inputImage.setDataFrom3x3S2P1to1x1S1P0FromImage(rgbImageData, width, height, 3, step, param_pMean); + inputImage.setDataFrom3x3S2P1to1x1S1P0FromImage(rgbImageData, width, height, 3, step); TIME_END("convert data"); /***************CONV1*********************/ int convidx = 0; TIME_START; - convolution(&inputImage, param_pFilters + convidx, pConvDataBlobs + convidx); + convolution_relu(&inputImage, param_pFilters + convidx, pConvDataBlobs + convidx); TIME_END("conv11"); - TIME_START; - relu(pConvDataBlobs+convidx); - TIME_END("relu11"); convidx++; TIME_START; - convolution(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); + convolution_relu(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); TIME_END("conv12"); - TIME_START; - relu(pConvDataBlobs+convidx); - TIME_END("relu12"); TIME_START; maxpooling2x2S2(pConvDataBlobs+convidx, &pool1); @@ -240,136 +221,96 @@ vector objectdetect_cnn(unsigned char * rgbImageData, int width, int h /***************CONV2*********************/ convidx++; -TIME_START; - convolution(&pool1, param_pFilters+convidx, pConvDataBlobs+convidx); -TIME_END("conv21"); -TIME_START; - relu(pConvDataBlobs+convidx); -TIME_END("relu21"); + TIME_START; + convolution_relu(&pool1, param_pFilters+convidx, pConvDataBlobs+convidx); + TIME_END("conv21"); -convidx++; -TIME_START; - convolution(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); -TIME_END("conv22"); -TIME_START - relu(pConvDataBlobs+convidx); -TIME_END("relu22"); + convidx++; + TIME_START; + convolution_relu(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); + TIME_END("conv22"); -TIME_START; + TIME_START; maxpooling2x2S2(pConvDataBlobs+convidx, &pool2); -TIME_END("pool2"); + TIME_END("pool2"); /***************CONV3*********************/ convidx++; -TIME_START; - convolution(&pool2, param_pFilters+convidx, pConvDataBlobs+convidx); -TIME_END("conv31"); -TIME_START; - relu(pConvDataBlobs+convidx); -TIME_END("relu31"); + TIME_START; + convolution_relu(&pool2, param_pFilters+convidx, pConvDataBlobs+convidx); + TIME_END("conv31"); -convidx++; -TIME_START; - convolution(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); -TIME_END("conv32"); -TIME_START; - relu(pConvDataBlobs+convidx); -TIME_END("relu32"); + convidx++; + TIME_START; + convolution_relu(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); + TIME_END("conv32"); -convidx++; -TIME_START; - convolution(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); -TIME_END("conv33"); -TIME_START; - relu(pConvDataBlobs+convidx); -TIME_END("relu33"); + convidx++; + TIME_START; + convolution_relu(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); + TIME_END("conv33"); -TIME_START; + TIME_START; maxpooling2x2S2(pConvDataBlobs+convidx, &pool3); -TIME_END("pool3"); + TIME_END("pool3"); /***************CONV4*********************/ convidx++; -TIME_START; - convolution(&pool3, param_pFilters+convidx, pConvDataBlobs+convidx); -TIME_END("conv41"); -TIME_START - relu(pConvDataBlobs+convidx); -TIME_END("relu41"); + TIME_START; + convolution_relu(&pool3, param_pFilters+convidx, pConvDataBlobs+convidx); + TIME_END("conv41"); -convidx++; -TIME_START; - convolution(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); -TIME_END("conv42"); -TIME_START; - relu(pConvDataBlobs+convidx); -TIME_END("relu42"); + convidx++; + TIME_START; + convolution_relu(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); + TIME_END("conv42"); -convidx++; -TIME_START; - convolution(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); -TIME_END("conv43"); -TIME_START; - relu(pConvDataBlobs+convidx); -TIME_END("relu43"); + convidx++; + TIME_START; + convolution_relu(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); + TIME_END("conv43"); -TIME_START; + TIME_START; maxpooling2x2S2(pConvDataBlobs+convidx, &pool4); -TIME_END("pool4"); + TIME_END("pool4"); /***************CONV5*********************/ convidx++; -TIME_START; - convolution(&pool4, param_pFilters+convidx, pConvDataBlobs+convidx); -TIME_END("conv51"); -TIME_START; - relu(pConvDataBlobs+convidx); -TIME_END("relu51"); + TIME_START; + convolution_relu(&pool4, param_pFilters+convidx, pConvDataBlobs+convidx); + TIME_END("conv51"); -convidx++; -TIME_START; - convolution(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); -TIME_END("conv52"); -TIME_START - relu(pConvDataBlobs+convidx); -TIME_END("relu52"); + convidx++; + TIME_START; + convolution_relu(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); + TIME_END("conv52"); -convidx++; -TIME_START; - convolution(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); -TIME_END("conv53"); -TIME_START; - relu(pConvDataBlobs+convidx); -TIME_END("relu53"); + convidx++; + TIME_START; + convolution_relu(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); + TIME_END("conv53"); -TIME_START; + TIME_START; maxpooling2x2S2(pConvDataBlobs+convidx, &pool5); -TIME_END("pool5"); + TIME_END("pool5"); /***************CONV6*********************/ convidx++; -TIME_START; - convolution(&pool5, param_pFilters+convidx, pConvDataBlobs+convidx); -TIME_END("conv61"); -TIME_START; - relu(pConvDataBlobs+convidx); -TIME_END("relu61"); + TIME_START; + convolution_relu(&pool5, param_pFilters+convidx, pConvDataBlobs+convidx); + TIME_END("conv61"); -convidx++; -TIME_START; - convolution(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); -TIME_END("conv62"); -TIME_START - relu(pConvDataBlobs+convidx); -TIME_END("relu62"); + convidx++; + TIME_START; + convolution_relu(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); + TIME_END("conv62"); + + + convidx++; + TIME_START; + convolution_relu(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); + TIME_END("conv63"); -convidx++; -TIME_START; - convolution(pConvDataBlobs+convidx-1, param_pFilters+convidx, pConvDataBlobs+convidx); -TIME_END("conv63"); -TIME_START; - relu(pConvDataBlobs+convidx); -TIME_END("relu63"); /***************PRIORBOX3*********************/ int conv3idx = 6; @@ -379,12 +320,12 @@ TIME_END("relu63"); convidx++; TIME_START - convolution(pConvDataBlobs+ conv3idx, param_pFilters+convidx, pConvDataBlobs+convidx); + convolution(pConvDataBlobs+ conv3idx, param_pFilters+convidx, pConvDataBlobsBranch + 0); TIME_END("prior3 loc"); convidx++; TIME_START; - convolution(pConvDataBlobs+ conv3idx, param_pFilters+convidx, pConvDataBlobs+convidx); + convolution(pConvDataBlobs+ conv3idx, param_pFilters+convidx, pConvDataBlobsBranch + 1); TIME_END("prior3 conf"); TIME_START; @@ -400,12 +341,12 @@ TIME_END("relu63"); convidx++; TIME_START - convolution(pConvDataBlobs + conv4idx, param_pFilters + convidx, pConvDataBlobs + convidx); + convolution(pConvDataBlobs + conv4idx, param_pFilters + convidx, pConvDataBlobsBranch + 2); TIME_END("prior4 loc"); convidx++; TIME_START; - convolution(pConvDataBlobs + conv4idx, param_pFilters + convidx, pConvDataBlobs + convidx); + convolution(pConvDataBlobs + conv4idx, param_pFilters + convidx, pConvDataBlobsBranch + 3); TIME_END("prior4 conf"); TIME_START; @@ -421,12 +362,12 @@ TIME_END("relu63"); convidx++; TIME_START - convolution(pConvDataBlobs + conv5idx, param_pFilters + convidx, pConvDataBlobs + convidx); + convolution(pConvDataBlobs + conv5idx, param_pFilters + convidx, pConvDataBlobsBranch + 4); TIME_END("prior5 loc"); convidx++; TIME_START; - convolution(pConvDataBlobs + conv5idx, param_pFilters + convidx, pConvDataBlobs + convidx); + convolution(pConvDataBlobs + conv5idx, param_pFilters + convidx, pConvDataBlobsBranch + 5); TIME_END("prior5 conf"); TIME_START; @@ -442,12 +383,12 @@ TIME_END("relu63"); convidx++; TIME_START - convolution(pConvDataBlobs + conv6idx, param_pFilters + convidx, pConvDataBlobs + convidx); + convolution(pConvDataBlobs + conv6idx, param_pFilters + convidx, pConvDataBlobsBranch + 6); TIME_END("prior6 loc"); convidx++; TIME_START; - convolution(pConvDataBlobs + conv6idx, param_pFilters + convidx, pConvDataBlobs + convidx); + convolution(pConvDataBlobs + conv6idx, param_pFilters + convidx, pConvDataBlobsBranch + 7); TIME_END("prior6 conf"); TIME_START; @@ -458,39 +399,48 @@ TIME_END("relu63"); TIME_START; - blob2vector(&conv3priorbox, &conv3priorbox_flat, true); - blob2vector(pConvDataBlobs + 16, &conv3loc_flat, true); - blob2vector(pConvDataBlobs + 17, &conv3conf_flat, true); + blob2vector(&conv3priorbox, &conv3priorbox_flat); + blob2vector(pConvDataBlobsBranch + 0, &conv3loc_flat); + blob2vector(pConvDataBlobsBranch + 1, &conv3conf_flat); - blob2vector(&conv4priorbox, &conv4priorbox_flat, true); - blob2vector(pConvDataBlobs + 18, &conv4loc_flat, true); - blob2vector(pConvDataBlobs + 19, &conv4conf_flat, true); + blob2vector(&conv4priorbox, &conv4priorbox_flat); + blob2vector(pConvDataBlobsBranch + 2, &conv4loc_flat); + blob2vector(pConvDataBlobsBranch + 3, &conv4conf_flat); - blob2vector(&conv5priorbox, &conv5priorbox_flat, true); - blob2vector(pConvDataBlobs + 20, &conv5loc_flat, true); - blob2vector(pConvDataBlobs + 21, &conv5conf_flat, true); + blob2vector(&conv5priorbox, &conv5priorbox_flat); + blob2vector(pConvDataBlobsBranch + 4, &conv5loc_flat); + blob2vector(pConvDataBlobsBranch + 5, &conv5conf_flat); - blob2vector(&conv6priorbox, &conv6priorbox_flat, true); - blob2vector(pConvDataBlobs + 22, &conv6loc_flat, true); - blob2vector(pConvDataBlobs + 23, &conv6conf_flat, true); + blob2vector(&conv6priorbox, &conv6priorbox_flat); + blob2vector(pConvDataBlobsBranch + 6, &conv6loc_flat); + blob2vector(pConvDataBlobsBranch + 7, &conv6conf_flat); TIME_END("prior flat"); - +TIME_START + convertInt2Float(&conv3loc_flat, &conv3loc_flat_float); + convertInt2Float(&conv4loc_flat, &conv4loc_flat_float); + convertInt2Float(&conv5loc_flat, &conv5loc_flat_float); + convertInt2Float(&conv6loc_flat, &conv6loc_flat_float); + convertInt2Float(&conv3conf_flat, &conv3conf_flat_float); + convertInt2Float(&conv4conf_flat, &conv4conf_flat_float); + convertInt2Float(&conv5conf_flat, &conv5conf_flat_float); + convertInt2Float(&conv6conf_flat, &conv6conf_flat_float); +TIME_END("convert int to float"); TIME_START concat4(&conv3priorbox_flat, &conv4priorbox_flat, &conv5priorbox_flat, &conv6priorbox_flat, &mbox_priorbox); - concat4(&conv3loc_flat, &conv4loc_flat, &conv5loc_flat, &conv6loc_flat, &mbox_loc); - concat4(&conv3conf_flat, &conv4conf_flat, &conv5conf_flat, &conv6conf_flat, &mbox_conf); + concat4(&conv3loc_flat_float, &conv4loc_flat_float, &conv5loc_flat_float, &conv6loc_flat_float, &mbox_loc_float); + concat4(&conv3conf_flat_float, &conv4conf_flat_float, &conv5conf_flat_float, &conv6conf_flat_float, &mbox_conf_float); TIME_END("concat prior") TIME_START - softmax1vector2class(&mbox_conf); + softmax1vector2class(&mbox_conf_float); TIME_END("softmax") - CDataBlob facesInfo; + CDataBlob facesInfo; TIME_START; - detection_output(&mbox_priorbox, &mbox_loc, &mbox_conf, 0.3f, 0.5f, 100, 50, &facesInfo); + detection_output(&mbox_priorbox, &mbox_loc_float, &mbox_conf_float, 0.3f, 0.5f, 100, 50, &facesInfo); TIME_END("detection output") @@ -499,11 +449,11 @@ TIME_END("softmax") std::vector faces; for (int i = 0; i < facesInfo.width; i++) { - float score = facesInfo.getElementFloat(i, 0, 0); - float bbxmin = facesInfo.getElementFloat(i, 0, 1); - float bbymin = facesInfo.getElementFloat(i, 0, 2); - float bbxmax = facesInfo.getElementFloat(i, 0, 3); - float bbymax = facesInfo.getElementFloat(i, 0, 4); + float score = facesInfo.getElement(i, 0, 0); + float bbxmin = facesInfo.getElement(i, 0, 1); + float bbymin = facesInfo.getElement(i, 0, 2); + float bbxmax = facesInfo.getElement(i, 0, 3); + float bbymax = facesInfo.getElement(i, 0, 4); FaceRect r; r.score = score; //r.x = int(bbxmin * width + 0.5f); @@ -527,9 +477,6 @@ TIME_END("softmax") int * facedetect_cnn(unsigned char * result_buffer, //buffer memory for storing face detection results, !!its size must be 0x20000 Bytes!! unsigned char * rgb_image_data, int width, int height, int step) //input image, it must be RGB (three-channel) image! { -#ifdef __CALL_LIMIT__ - static int call_count = 0; -#endif if (!result_buffer) { @@ -563,14 +510,6 @@ int * facedetect_cnn(unsigned char * result_buffer, //buffer memory for storing p[3] = (short)faces[i].h; p[4] = (short)(faces[i].score * faces[i].score * 100); } -#ifdef __CALL_LIMIT__ - if(call_count>1814403) - { - memset(result_buffer, 0 , 4+284*num_faces); - } - else - call_count++; -#endif TIME_END("call detection"); return pCount; diff --git a/src/facedetectcnn.cpp b/src/facedetectcnn.cpp index d5b4400..12a3001 100644 --- a/src/facedetectcnn.cpp +++ b/src/facedetectcnn.cpp @@ -43,12 +43,6 @@ the use of this software, even if advised of the possibility of such damage. #include //for FLT_EPSION #include //for stable_sort, sort -#if defined( __WIN__) || defined(_WINDOWS) -#define SSE_256ELEMENT(vec, idx) vec.m256_f32[(idx)] -#else -#define SSE_256ELEMENT(vec, idx) vec[(idx)] -#endif - #if !defined(_ENABLE_OPENMP_SIMD) && ((defined(_OPENMP) && (_OPENMP >= 201307L))) # define _ENABLE_OPENMP_SIMD #elif defined(__cilk) @@ -93,102 +87,18 @@ void myFree_(void* ptr) } - -inline float dotProductFloatChGeneral(float* p1, float * p2, int num, int lengthInBytes) +inline int dotProductUint8Int8(unsigned char * p1, signed char * p2, int num, int lengthInBytes) { -#if defined(_ENABLE_NEON) && !defined(_ENABLE_INT8_CONV) - float sum = 0.0f; - float32x4_t a, b; - float32x4_t result_vec; - - result_vec = vdupq_n_f32(0); //zeros - for (int i = 0; i < num; i += 4) - { - a = vld1q_f32(p1 + i); - b = vld1q_f32(p2 + i); - result_vec = vmlaq_f32(result_vec, a, b); - } - sum += vgetq_lane_f32(result_vec, 0); - sum += vgetq_lane_f32(result_vec, 1); - sum += vgetq_lane_f32(result_vec, 2); - sum += vgetq_lane_f32(result_vec, 3); - - return sum; -#elif defined(_ENABLE_AVX2) && !defined(_ENABLE_INT8_CONV) - float sum = 0; - int end = lengthInBytes / sizeof(float); - - __m256 sumvec = _mm256_setzero_ps(); - __m256 avec, bvec; - for (int i = 0; i < end; i += 8) - { - avec = _mm256_load_ps(p1 + i); - bvec = _mm256_load_ps(p2 + i); - //_mm256_fmadd_ps needs FMA support - //but _mm256_add_ps and _mm256_mul_ps only need AVX - - //sumvec = _mm256_add_ps(sumvec, _mm256_mul_ps(avec, bvec)); - - //fmadd is faster than add+mul - sumvec = _mm256_fmadd_ps(avec, bvec, sumvec); - - //Note: _mm256_dp_ps is much slower than the previou line of code - } - sumvec = _mm256_hadd_ps(sumvec, sumvec); - sumvec = _mm256_hadd_ps(sumvec, sumvec); - sum += SSE_256ELEMENT(sumvec, 0); - sum += SSE_256ELEMENT(sumvec, 4); - - return sum; - -#else - float sum = 0; - -#if defined(_ENABLE_OPENMP_SIMD) -#pragma omp simd reduction(+:sum) -#endif - for (int i = 0; i < num; i++) - { - sum += (p1[i] * p2[i]); - } - return sum; -#endif -} - -inline int dotProductInt8ChGeneral(signed char * p1, signed char * p2, int num, int lengthInBytes) -{ -#if defined(_ENABLE_NEON) && defined(_ENABLE_INT8_CONV) - //int sum = 0; - //int16x8_t a, b; - //int16x8_t result_vec; - //int32x4_t d; - // - - //result_vec = vdupq_n_s16(0); //zeros - //for (int i = 0; i < num; i += 8) - //{ - // a = vld1q_s16(p1 + i); - // b = vld1q_s16(p2 + i); - // result_vec = vmlaq_s16(result_vec, a, b); - //} - //d = vpaddlq_s16(result_vec); - //sum += vgetq_lane_s32(d, 0); - //sum += vgetq_lane_s32(d, 1); - //sum += vgetq_lane_s32(d, 2); - //sum += vgetq_lane_s32(d, 3); - - //return sum; - int sum = 0; +#if defined(_ENABLE_NEON) int8x8x2_t a, b; int16x8_t result_vec; int32x4_t d; - - result_vec = vdupq_n_s16(0); //zeros + for (int i = 0; i < num; i += 16) { - a = vld2_s8(p1 + i); + a = vld2_s8((signed char*)p1 + i); b = vld2_s8(p2 + i); result_vec = vmlal_s8(result_vec, a.val[0], b.val[0]); result_vec = vmlal_s8(result_vec, a.val[1], b.val[1]); @@ -199,69 +109,40 @@ inline int dotProductInt8ChGeneral(signed char * p1, signed char * p2, int num, sum += vgetq_lane_s32(d, 2); sum += vgetq_lane_s32(d, 3); - return sum; - -#elif defined(_ENABLE_AVX2) && defined(_ENABLE_INT8_CONV) - int sum = 0; - int i = 0; - - short sumarray[16]; - - __m256i temp_sum; - __m128i ac, bc; - __m256i as, bs; - for (; i < num; i += 16) - { - ac = _mm_load_si128((__m128i*)(p1 + i)); - bc = _mm_load_si128((__m128i*)(p2 + i)); - as = _mm256_cvtepi8_epi16(ac); - bs = _mm256_cvtepi8_epi16(bc); - temp_sum = _mm256_mullo_epi16(as, bs); - temp_sum = _mm256_hadd_epi16(temp_sum, temp_sum); - temp_sum = _mm256_hadd_epi16(temp_sum, temp_sum); - //temp_sum = _mm256_hadd_epi16(temp_sum, temp_sum); - _mm256_store_si256((__m256i*)sumarray, temp_sum); - //sum += ((int)(sumarray[0]) + (int)(sumarray[8])); - sum += ((int)(sumarray[0]) + (int)(sumarray[1]) + +(int)(sumarray[8]) + (int)(sumarray[9])); - } - return sum; +#elif defined(_ENABLE_AVX2) + int sumarray[8]; + __m256i sum_int16x16; + __m256i tmp_int32x8; + __m256i a_uint8x32, b_int8x32; + __m256i ones16 = _mm256_set1_epi16(1); + __m256i sum_int32x8 = _mm256_setzero_si256(); + for (int i = 0; i < num; i += 32) + { + a_uint8x32 = _mm256_load_si256((__m256i const *)(p1 + i)); + b_int8x32 = _mm256_load_si256((__m256i const *)(p2 + i)); + sum_int16x16 = _mm256_maddubs_epi16(a_uint8x32, b_int8x32); + tmp_int32x8 = _mm256_madd_epi16(sum_int16x16, ones16); + sum_int32x8 = _mm256_add_epi32(sum_int32x8, tmp_int32x8); + } + sum_int32x8 = _mm256_hadd_epi32(sum_int32x8, sum_int32x8); + sum_int32x8 = _mm256_hadd_epi32(sum_int32x8, sum_int32x8); + _mm256_store_si256((__m256i*)sumarray, sum_int32x8); + sum += (sumarray[0] + sumarray[4]); #else - int sum = 0; - #if defined(_ENABLE_OPENMP_SIMD) #pragma omp simd reduction(+:sum) #endif for (int i = 0; i < num; i++) { - sum += ( int(p1[i]) * int(p2[i])); + sum += (int(p1[i]) * int(p2[i])); } - return sum; -#endif -} -bool convolutionFloat1x1P0S1(const CDataBlob *inputData, const Filters* filters, CDataBlob *outputData) -{ -#if defined(_OPENMP) -#pragma omp parallel for #endif - for (int row = 0; row < outputData->height; row++) - { - for (int col = 0; col < outputData->width; col++) - { - float * pOut = (outputData->data_float + (row*outputData->width + col)*outputData->floatChannelStepInByte / sizeof(float)); - float * pIn = (inputData->data_float + (row*inputData->width + col)*inputData->floatChannelStepInByte / sizeof(float)); - for (int ch = 0; ch < outputData->channels; ch++) - { - float * pF = (float*)(filters->filters[ch]->data_float); - pOut[ch] = dotProductFloatChGeneral(pIn, pF, inputData->channels, inputData->floatChannelStepInByte); - } - } - } - return true; + return sum; } -bool convolutionInt81x1P0S1(const CDataBlob *inputData, const Filters* filters, CDataBlob *outputData) +bool convolution1x1P0S1(const CDataBlob *inputData, const Filters* filters, CDataBlob *outputData) { #if defined(_OPENMP) #pragma omp parallel for @@ -270,12 +151,12 @@ bool convolutionInt81x1P0S1(const CDataBlob *inputData, const Filters* filters, { for (int col = 0; col < outputData->width; col++) { - float * pOut = (outputData->data_float + (row*outputData->width + col)*outputData->floatChannelStepInByte / sizeof(float)); - signed char * pIn = (inputData->data_int8 + (row*inputData->width + col)*inputData->int8ChannelStepInByte / sizeof(char)); + int * pOut = (outputData->data + (row*outputData->width + col)*outputData->channelStep / sizeof(int)); + unsigned char * pIn = (inputData->data + (row*inputData->width + col)*inputData->channelStep / sizeof(unsigned char)); for (int ch = 0; ch < outputData->channels; ch++) { - signed char * pF = (filters->filters[ch]->data_int8); - pOut[ch] = (float)dotProductInt8ChGeneral(pIn, pF, inputData->channels, inputData->int8ChannelStepInByte); + signed char * pF = (filters->filters[ch]->data); + pOut[ch] = dotProductUint8Int8(pIn, pF, inputData->channels, inputData->channelStep); } } } @@ -283,79 +164,14 @@ bool convolutionInt81x1P0S1(const CDataBlob *inputData, const Filters* filters, } - -bool convolutionFloat3x3P1ChGeneral(const CDataBlob *inputData, const Filters* filters, CDataBlob *outputData) -{ -#if defined(_OPENMP) -#pragma omp parallel for -#endif - for (int row = 0; row < outputData->height; row++) - { - int elementStepInFloat = inputData->floatChannelStepInByte/sizeof(float); - int stride = filters->stride; - int src_centery = row * stride; - for (int col = 0; col < outputData->width; col++) - { - int srcx_start = col * stride - 1; - int srcx_end = srcx_start + 3; - srcx_start = MAX(0, srcx_start); - srcx_end = MIN(srcx_end, inputData->width); - int num_pixels = srcx_end - srcx_start; - int num_pixels_infloat = (srcx_end - srcx_start) * elementStepInFloat; - - for (int ch = 0; ch < outputData->channels; ch++) - { - int srcy = src_centery - 1; - - float * pIn = (inputData->data_float + (srcy *inputData->width + srcx_start)*elementStepInFloat); - float * pF = (filters->filters[ch]->data_float) + (srcx_start - col*stride + 1) * elementStepInFloat; - float * pOut = (outputData->data_float + (row*outputData->width + col)*outputData->floatChannelStepInByte / sizeof(float)); - pOut[ch] = 0; //the new created blob is not zeros, clear it first - - { - if (srcy >= 0) - { - pOut[ch] += dotProductFloatChGeneral(pIn, - pF, - num_pixels_infloat, - num_pixels_infloat * sizeof(float)); - } - } - { - srcy++; - { - pIn += (inputData->width * elementStepInFloat); - pOut[ch] += dotProductFloatChGeneral(pIn, - pF + ( 3 * elementStepInFloat), - num_pixels_infloat, - num_pixels_infloat * sizeof(float)); - } - } - { - srcy++; - if (srcy < inputData->height) - { - pIn += (inputData->width * elementStepInFloat); - pOut[ch] += dotProductFloatChGeneral(pIn, - pF + ( 6 * elementStepInFloat ), - num_pixels_infloat, - num_pixels_infloat * sizeof(float)); - } - } - } - } - } - return true; -} - -bool convolutionInt83x3P1ChGeneral(const CDataBlob *inputData, const Filters* filters, CDataBlob *outputData) +bool convolution3x3P0(const CDataBlob *inputData, const Filters* filters, CDataBlob *outputData) { #if defined(_OPENMP) #pragma omp parallel for #endif for (int row = 0; row < outputData->height; row++) { - int elementStep = inputData->int8ChannelStepInByte; + int elementStep = inputData->channelStep; int stride = filters->stride; int src_centery = row * stride; for (int col = 0; col < outputData->width; col++) @@ -370,15 +186,15 @@ bool convolutionInt83x3P1ChGeneral(const CDataBlob *inputData, const Filters* fi { int srcy = src_centery - 1; - signed char * pIn = (inputData->data_int8 + (srcy *inputData->width + srcx_start)*elementStep); - signed char * pF = (filters->filters[ch]->data_int8) + ( (srcx_start - col*stride + 1)) * elementStep; - float * pOut = (outputData->data_float + (row*outputData->width + col)*outputData->floatChannelStepInByte / sizeof(float)); + unsigned char * pIn = (inputData->data + (srcy *inputData->width + srcx_start) * elementStep); + signed char * pF = (filters->filters[ch]->data) + ( (srcx_start - col*stride + 1)) * elementStep; + int * pOut = (outputData->data + (row*outputData->width + col)*outputData->channelStep / sizeof(int)); pOut[ch] = 0;//the new created blob is not zeros, clear it first { if (srcy >= 0) { - pOut[ch] += dotProductInt8ChGeneral(pIn, + pOut[ch] += dotProductUint8Int8(pIn, pF, num_pixels_inbytes, num_pixels_inbytes); @@ -388,7 +204,7 @@ bool convolutionInt83x3P1ChGeneral(const CDataBlob *inputData, const Filters* fi srcy++; { pIn += (inputData->width * elementStep); - pOut[ch] += dotProductInt8ChGeneral(pIn, + pOut[ch] += dotProductUint8Int8(pIn, pF + (3 * elementStep), num_pixels_inbytes, num_pixels_inbytes); @@ -399,7 +215,7 @@ bool convolutionInt83x3P1ChGeneral(const CDataBlob *inputData, const Filters* fi if (srcy < inputData->height) { pIn += (inputData->width * elementStep); - pOut[ch] += dotProductInt8ChGeneral(pIn, + pOut[ch] += dotProductUint8Int8(pIn, pF + (6 * elementStep), num_pixels_inbytes, num_pixels_inbytes); @@ -411,138 +227,10 @@ bool convolutionInt83x3P1ChGeneral(const CDataBlob *inputData, const Filters* fi return true; } -bool convertFloat2Int8(CDataBlob * dataBlob) -{ - if (dataBlob->data_float == NULL || dataBlob->data_int8 == NULL) - { - cerr << __FUNCTION__ << ": The input data is null." << endl; - return false; - } - float maxval = -FLT_MAX; -#if defined(_ENABLE_NEON) - float32x4_t maxvalvec = vdupq_n_f32(-FLT_MAX); - float32x4_t scalevec; -#elif defined(_ENABLE_AVX2) - //__m256 maxvalvec = _mm256_set1_ps(-FLT_MAX); - __m256 scalevec; -#endif - - float scale = 1.f; - - if (dataBlob->int8_data_valid) - return true; - - for (int row = 0; row < dataBlob->height; row++) - { - for (int col = 0; col < dataBlob->width; col++) - { - float * pF = (dataBlob->data_float + (row*dataBlob->width + col)*dataBlob->floatChannelStepInByte / sizeof(float)); - -#if defined(_ENABLE_NEON) - for (int ch = 0; ch < dataBlob->channels; ch+=4) - { - float32x4_t a; - a = vld1q_f32(pF + ch); - a = vabsq_f32(a); - maxvalvec = vmaxq_f32(maxvalvec, a); - } -#else - -#if defined(_ENABLE_OPENMP_SIMD) -#pragma omp simd reduction(max:maxval) -#endif - for (int ch = 0; ch < dataBlob->channels; ch++) - { - float tmp; - //tmp = fabs(pF[ch]); - //maxval = MAX(maxval, tmp); - tmp = pF[ch]; - tmp = tmp * ((tmp > 0) * 2 - 1); - maxval = MAX(maxval, tmp); - } -#endif - } - } -#if defined(_ENABLE_NEON) - { - float tmp; - tmp = vgetq_lane_f32(maxvalvec, 0); - maxval = MAX(maxval, tmp); - tmp = vgetq_lane_f32(maxvalvec, 1); - maxval = MAX(maxval, tmp); - tmp = vgetq_lane_f32(maxvalvec, 2); - maxval = MAX(maxval, tmp); - tmp = vgetq_lane_f32(maxvalvec, 3); - maxval = MAX(maxval, tmp); - } -#endif - scale = 127.f / (maxval + FLT_EPSILON); - -#if defined(_ENABLE_NEON) - scalevec = vdupq_n_f32(scale); -#elif defined(_ENABLE_AVX2) - scalevec = _mm256_set1_ps(scale); -#endif - -#if defined(_OPENMP) -#pragma omp parallel for -#endif - for (int row = 0; row < dataBlob->height; row++) - { - for (int col = 0; col < dataBlob->width; col++) - { - float * pF = (dataBlob->data_float + (row*dataBlob->width + col)*dataBlob->floatChannelStepInByte / sizeof(float)); - signed char * pI = (dataBlob->data_int8 + (row*dataBlob->width + col)*dataBlob->int8ChannelStepInByte / sizeof(char)); - -#if defined(_ENABLE_NEON) - for (int ch = 0; ch < dataBlob->channels; ch+=4) - { - float tmp; - float32x4_t a = vld1q_f32(pF + ch); - float32x4_t resultvec = vmulq_f32(a, scalevec); - - ////becuase Floating-point to integer conversions "vcvtq_s32_f32" use round towards zero. - ////but we need round to nearest - ////so we cannot use the following NEON instructions - //int32x4_t int32resultvec = vcvtq_s32_f32(resultvec); - //int16x4_t int16resultvec = vqmovn_s32(int32resultvec); - //vst1_s16(pI + ch, int16resultvec); - - tmp = vgetq_lane_f32(resultvec, 0); - pI[ch] = (signed char)(tmp + ((tmp>0) - 0.5f)); - tmp = vgetq_lane_f32(resultvec, 1); - pI[ch+1] = (signed char)(tmp + ((tmp>0) - 0.5f)); - tmp = vgetq_lane_f32(resultvec, 2); - pI[ch+2] = (signed char)(tmp + ((tmp>0) - 0.5f)); - tmp = vgetq_lane_f32(resultvec, 3); - pI[ch+3] = (signed char)(tmp + ((tmp>0) - 0.5f)); - } -#else -#if defined(_ENABLE_OPENMP_SIMD) -#pragma omp simd -#endif - for (int ch = 0; ch < dataBlob->channels; ch++) - { - float tmp; - //pI[ch] = (signed char)round(pF[ch] * scale); - //to speedup round() using the following code - tmp = pF[ch]; - pI[ch] = (signed char)(tmp * scale + ((tmp>0)-0.5f)); - } -#endif - } - } - dataBlob->int8float_scale = scale; - dataBlob->int8_data_valid = true; - - return true; -} - - -bool convolution(CDataBlob *inputData, const Filters* filters, CDataBlob *outputData) +bool convolution(CDataBlob *inputData, const Filters* filters, CDataBlob *outputData) { - if (inputData->data_float == NULL || inputData->data_int8 == NULL) + if (inputData->data == NULL) { cerr << __FUNCTION__ << ": The input data is null." << endl; return false; @@ -630,58 +318,104 @@ bool convolution(CDataBlob *inputData, const Filters* filters, CDataBlob *output outputData->create(outputW, outputH, outputC); - /* + if (filterW == 1 && filterH == 1) //1x1 filters { - float maxval = -FLT_MAX; - float minval = FLT_MAX; + convolution1x1P0S1(inputData, filters, outputData); + } + else if (filterW == 3 && filterH == 3) //3x3 filters + { + convolution3x3P0(inputData, filters, outputData); + } + + outputData->scale = inputData->scale * filters->scale; + + return true; +} + +bool convolution_relu(CDataBlob *inputData, const Filters* filters, CDataBlob *outputData) +{ + CDataBlob tmpOutputData; + bool bFlag = convolution(inputData, filters, &tmpOutputData); + if (bFlag == false) + return false; - for (int row = 0; row < inputData->height; row++) + //set negative values to zeros, + //and find the max value + int nMaxValue = 0; +#if defined(_ENABLE_NEON) +#elif defined(_ENABLE_AVX2) + __m256i max_int32x8 = _mm256_setzero_si256(); +#endif + + for (int row = 0; row < tmpOutputData.height; row++) + { + for (int col = 0; col < tmpOutputData.width; col++) { - for (int col = 0; col < inputData->width; col++) + int * pData = (tmpOutputData.data + (row*tmpOutputData.width + col)*tmpOutputData.channelStep / sizeof(int)); +#if defined(_ENABLE_NEON) + float32x4_t a, bzeros; + float32x4_t result_vec; + + bzeros = vdupq_n_f32(0); //zeros + for (int ch = 0; ch < inputOutputData->channels; ch += 4) { - float * pF = (inputData->data_float + (row*inputData->width + col)*inputData->floatChannelStepInByte / sizeof(float)); - for (int ch = 0; ch < inputData->channels; ch++) - { - maxval = MAX(maxval, pF[ch]); - minval = MIN(minval, pF[ch]); - } + a = vld1q_f32(pData + ch); + result_vec = vmaxq_f32(a, bzeros); + vst1q_f32(pData + ch, result_vec); } - } - cout << "\t\t\t\tconv range [min, max]=[" << minval << ", " << maxval << "]" << endl; - } - */ -#if defined(_ENABLE_INT8_CONV) - convertFloat2Int8(inputData); -#endif +#elif defined(_ENABLE_AVX2) + __m256i a, bzeros; + bzeros = _mm256_setzero_si256(); //zeros - if (filterW == 1 && filterH == 1) //1x1 filters - { -#if defined(_ENABLE_INT8_CONV) - convolutionInt81x1P0S1(inputData, filters, outputData); + for (int ch = 0; ch < tmpOutputData.channels; ch += 8) + { + a = _mm256_load_si256((__m256i const *)(pData + ch)); + a = _mm256_max_epi32(a, bzeros); + max_int32x8 = _mm256_max_epi32(a, max_int32x8); + _mm256_store_si256((__m256i *)(pData + ch), a); + } #else - convolutionFloat1x1P0S1(inputData, filters, outputData); + for (int ch = 0; ch < tmpOutputData.channels; ch++) + { + pData[ch] = MAX(pData[ch], 0); + nMaxValue = MAX(pData[ch], nMaxValue); + } #endif + } } - else if (filterW == 3 && filterH == 3) //3x3 filters - { -#if defined(_ENABLE_INT8_CONV) - convolutionInt83x3P1ChGeneral(inputData, filters, outputData); -#else - convolutionFloat3x3P1ChGeneral(inputData, filters, outputData); +#if defined(_ENABLE_NEON) +#elif defined(_ENABLE_AVX2) + int maxarray_int32x8[8]; + _mm256_store_si256((__m256i *)maxarray_int32x8, max_int32x8); + for(int i=0; i < 8; i++) + nMaxValue = MAX(maxarray_int32x8[i], nMaxValue); #endif - } -#if defined(_ENABLE_INT8_CONV) - scale(outputData, 1.0f / (inputData->int8float_scale * filters->scale)); -#endif + //scale the data to uint8 or int8 + float fCurrentScale = (_MAX_UINT8_VALUE) / float(nMaxValue); + outputData->create(tmpOutputData.width, tmpOutputData.height, tmpOutputData.channels); + outputData->scale = tmpOutputData.scale * fCurrentScale; - return true; + for (int row = 0; row < outputData->height; row++) + { + for (int col = 0; col < outputData->width; col++) + { + int * pInt32Data = (tmpOutputData.data + (row*tmpOutputData.width + col)*tmpOutputData.channelStep / sizeof(int)); + unsigned char * pUInt8Data = (outputData->data + (row*outputData->width + col)*outputData->channelStep / sizeof(unsigned char)); + + for (int ch = 0; ch < outputData->channels; ch++) + { + pUInt8Data[ch] = (unsigned char)(pInt32Data[ch] * fCurrentScale +0.499f); + } + } + } + return true; } //only 2X2 S2 is supported -bool maxpooling2x2S2(const CDataBlob *inputData, CDataBlob *outputData) +bool maxpooling2x2S2(const CDataBlob *inputData, CDataBlob *outputData) { - if (inputData->data_float == NULL) + if (inputData->data == NULL) { cerr << __FUNCTION__ << ": The input data is null." << endl; return false; @@ -696,10 +430,10 @@ bool maxpooling2x2S2(const CDataBlob *inputData, CDataBlob *outputData) return false; } - int elementStep = inputData->floatChannelStepInByte / sizeof(float); - int lineElementStep = inputData->width * elementStep; + int lineElementStep = inputData->width * inputData->channelStep; outputData->create(outputW, outputH, outputC); + outputData->scale = inputData->scale; for (int row = 0; row < outputData->height; row++) { @@ -716,41 +450,31 @@ bool maxpooling2x2S2(const CDataBlob *inputData, CDataBlob *outputData) for (int fy = hstart; fy < hend; fy++) for (int fx = wstart; fx < wend; fx++) { - inputMatOffsetsInElement[elementCount++] = (fy *inputData->width + fx) * inputData->floatChannelStepInByte / sizeof(float); + inputMatOffsetsInElement[elementCount++] = (fy *inputData->width + fx) * inputData->channelStep / sizeof(unsigned char); } - float * pOut = outputData->data_float + (row*outputData->width + col) * outputData->floatChannelStepInByte / sizeof(float); - float * pIn = inputData->data_float; + unsigned char * pOut = outputData->data + (row*outputData->width + col) * outputData->channelStep / sizeof(unsigned char); + unsigned char * pIn = inputData->data; #if defined(_ENABLE_NEON) - for (int ch = 0; ch < outputData->channels; ch += 4) - { - float32x4_t a; - float32x4_t maxval = vld1q_f32(pIn + ch + inputMatOffsetsInElement[0]); - for (int el = 1; el < elementCount; el++) - { - a = vld1q_f32(pIn + ch + inputMatOffsetsInElement[el]); - maxval = vmaxq_f32(maxval, a); - } - vst1q_f32(pOut + ch, maxval); - } + #elif defined(_ENABLE_AVX2) for (int ch = 0; ch < outputData->channels; ch += 8) { - __m256 a; - __m256 maxval = _mm256_load_ps(pIn + ch + inputMatOffsetsInElement[0]); + __m256i a; + __m256i maxval_uint8x32 = _mm256_load_si256((__m256i const *)(pIn + ch + inputMatOffsetsInElement[0])); for (int el = 1; el < elementCount; el++) { - a = _mm256_load_ps(pIn + ch + inputMatOffsetsInElement[el]); - maxval = _mm256_max_ps(maxval, a); + a = _mm256_load_si256((__m256i const *)(pIn + ch + inputMatOffsetsInElement[el])); + maxval_uint8x32 = _mm256_max_epu8(maxval_uint8x32, a); } - _mm256_store_ps(pOut + ch, maxval); + _mm256_store_si256((__m256i *)(pOut + ch), maxval_uint8x32); } #else for (int ch = 0; ch < outputData->channels; ch++) { - float maxval = pIn[ch + inputMatOffsetsInElement[0]]; + unsigned char maxval = pIn[ch + inputMatOffsetsInElement[0]]; #if defined(_ENABLE_OPENMP_SIMD) #pragma omp simd reduction(max:maxval) #endif @@ -768,10 +492,10 @@ bool maxpooling2x2S2(const CDataBlob *inputData, CDataBlob *outputData) } - -bool concat4(const CDataBlob *inputData1, const CDataBlob *inputData2, const CDataBlob *inputData3, const CDataBlob *inputData4, CDataBlob *outputData) +template +bool concat4(const CDataBlob *inputData1, const CDataBlob *inputData2, const CDataBlob *inputData3, const CDataBlob *inputData4, CDataBlob *outputData) { - if ((inputData1->data_float == NULL) || (inputData2->data_float == NULL) || (inputData3->data_float == NULL) || (inputData4->data_float == NULL)) + if ((inputData1->data == NULL) || (inputData2->data == NULL) || (inputData3->data == NULL) || (inputData4->data == NULL)) { cerr << __FUNCTION__ << ": The input data is null." << endl; return false; @@ -803,122 +527,116 @@ bool concat4(const CDataBlob *inputData1, const CDataBlob *inputData2, const CDa { for (int col = 0; col < outputData->width; col++) { - float * pOut = (outputData->data_float + (row*outputData->width + col)*outputData->floatChannelStepInByte / sizeof(float)); - float * pIn1 = (inputData1->data_float + (row*inputData1->width + col)*inputData1->floatChannelStepInByte / sizeof(float)); - float * pIn2 = (inputData2->data_float + (row*inputData2->width + col)*inputData2->floatChannelStepInByte / sizeof(float)); - float * pIn3 = (inputData3->data_float + (row*inputData3->width + col)*inputData3->floatChannelStepInByte / sizeof(float)); - float * pIn4 = (inputData4->data_float + (row*inputData4->width + col)*inputData4->floatChannelStepInByte / sizeof(float)); - - memcpy(pOut, pIn1, sizeof(float)* inputData1->channels); - memcpy(pOut + inputData1->channels, pIn2, sizeof(float)* inputData2->channels); - memcpy(pOut + inputData1->channels + inputData2->channels, pIn3, sizeof(float)* inputData3->channels); - memcpy(pOut + inputData1->channels + inputData2->channels + inputData3->channels, pIn4, sizeof(float)* inputData4->channels); + T * pOut = (outputData->data + (row*outputData->width + col)*outputData->channelStep / sizeof(T)); + T * pIn1 = (inputData1->data + (row*inputData1->width + col)*inputData1->channelStep / sizeof(T)); + T * pIn2 = (inputData2->data + (row*inputData2->width + col)*inputData2->channelStep / sizeof(T)); + T * pIn3 = (inputData3->data + (row*inputData3->width + col)*inputData3->channelStep / sizeof(T)); + T * pIn4 = (inputData4->data + (row*inputData4->width + col)*inputData4->channelStep / sizeof(T)); + + memcpy(pOut, pIn1, sizeof(T)* inputData1->channels); + memcpy(pOut + inputData1->channels, pIn2, sizeof(T)* inputData2->channels); + memcpy(pOut + inputData1->channels + inputData2->channels, pIn3, sizeof(T)* inputData3->channels); + memcpy(pOut + inputData1->channels + inputData2->channels + inputData3->channels, pIn4, sizeof(T)* inputData4->channels); } } return true; } +template bool concat4(const CDataBlob *inputData1, const CDataBlob *inputData2, const CDataBlob *inputData3, const CDataBlob *inputData4, CDataBlob *outputData); -bool scale(CDataBlob * dataBlob, float scale) +bool convertInt2Float(CDataBlob * inputData, CDataBlob * outputData) { - if (dataBlob->data_float == NULL || dataBlob->data_int8 == NULL) + if (inputData == NULL || outputData == NULL) { - cerr << __FUNCTION__ << ": The input data is null." << endl; + cerr << __FUNCTION__ << ": The input or output data is null." << endl; return false; } - for (int row = 0; row < dataBlob->height; row++) + outputData->create(inputData->width, inputData->height, inputData->channels); + float s = 1.0f / inputData->scale; + for (int row = 0; row < outputData->height; row++) { - for (int col = 0; col < dataBlob->width; col++) + for (int col = 0; col < outputData->width; col++) { - float * pF = (dataBlob->data_float + (row*dataBlob->width + col)*dataBlob->floatChannelStepInByte / sizeof(float)); -#if defined(_ENABLE_NEON) - float32x4_t a, bscale; - float32x4_t result_vec; + int * pInData = (inputData->data + (row*inputData->width + col)*inputData->channelStep / sizeof(int)); + float * pOutData = (outputData->data + (row*outputData->width + col)*outputData->channelStep / sizeof(float)); - bscale = vdupq_n_f32(scale); - for (int ch = 0; ch < dataBlob->channels; ch+=4) - { - a = vld1q_f32(pF + ch); - result_vec = vmulq_f32(a, bscale); - vst1q_f32(pF + ch, result_vec); - } -#elif defined(_ENABLE_AVX2) - __m256 a, bscale; - - bscale = _mm256_set1_ps(scale); - for (int ch = 0; ch < dataBlob->channels; ch += 8) - { - a = _mm256_load_ps(pF + ch); - a = _mm256_mul_ps(a, bscale); - _mm256_store_ps(pF + ch, a); - } - -#else -#if defined(_ENABLE_OPENMP_SIMD) -#pragma omp simd -#endif - for (int ch = 0; ch < dataBlob->channels; ch++) + for (int ch = 0; ch < outputData->channels; ch++) { - pF[ch] *= scale; + pOutData[ch] = pInData[ch] * s; } -#endif } } + return true; } -bool relu(const CDataBlob *inputOutputData) +bool normalize(CDataBlob * inputOutputData, float * pScale) { - if (inputOutputData->data_float == NULL) + if ((inputOutputData->data == NULL) || pScale == NULL) { cerr << __FUNCTION__ << ": The input data is null." << endl; return false; } + CDataBlob tmpData; + tmpData.create(inputOutputData->width, inputOutputData->height, inputOutputData->channels); + //normlize it, + //and find the max value + //because the input data is non-negative, so only the max value is needed + float fMaxValue = 0; for (int row = 0; row < inputOutputData->height; row++) { for (int col = 0; col < inputOutputData->width; col++) { - float * pData = (float*)(inputOutputData->data_float + (row*inputOutputData->width + col)*inputOutputData->floatChannelStepInByte / sizeof(float)); + unsigned char * pInData = (inputOutputData->data + (row*inputOutputData->width + col)*inputOutputData->channelStep / sizeof(unsigned char)); + float * pTmpData = (float*)(tmpData.data + (row*tmpData.width + col)*tmpData.channelStep / sizeof(float)); + float sum = FLT_EPSILON; + float s = 0; -#if defined(_ENABLE_NEON) - float32x4_t a, bzeros; - float32x4_t result_vec; +#if defined(_ENABLE_OPENMP_SIMD) +#pragma omp simd reduction(+:sum) +#endif + for (int ch = 0; ch < inputOutputData->channels; ch++) + sum += ( float(pInData[ch]) * float(pInData[ch])); - bzeros = vdupq_n_f32(0); //zeros - for (int ch = 0; ch < inputOutputData->channels; ch += 4) + s = 1.0f / sqrt(sum); +#if defined(_ENABLE_OPENMP_SIMD) +#pragma omp simd +#endif + for (int ch = 0; ch < inputOutputData->channels; ch++) { - a = vld1q_f32(pData + ch); - result_vec = vmaxq_f32(a, bzeros); - vst1q_f32(pData + ch, result_vec); + pTmpData[ch] = pInData[ch] * pScale[ch] * s; + fMaxValue = MAX(pTmpData[ch], fMaxValue); } -#elif defined(_ENABLE_AVX2) - __m256 a, bzeros; + } + } + + //scale the data to uint8 or int8 + float fCurrentScale = (_MAX_UINT8_VALUE) / float(fMaxValue); + inputOutputData->scale = fCurrentScale; + + for (int row = 0; row < inputOutputData->height; row++) + { + for (int col = 0; col < inputOutputData->width; col++) + { + float * pTmpData = (tmpData.data + (row*tmpData.width + col)*tmpData.channelStep / sizeof(float)); + unsigned char * pUInt8Data = (inputOutputData->data + (row*inputOutputData->width + col)*inputOutputData->channelStep / sizeof(unsigned char)); - bzeros = _mm256_setzero_ps(); //zeros - for (int ch = 0; ch < inputOutputData->channels; ch += 8) + for (int ch = 0; ch < inputOutputData->channels; ch++) { - a = _mm256_load_ps(pData + ch); - a = _mm256_max_ps(a, bzeros); - _mm256_store_ps(pData + ch, a); + pUInt8Data[ch] = (unsigned char)(pTmpData[ch] * fCurrentScale + 0.499f); } -#else -#if defined(_ENABLE_OPENMP_SIMD) -#pragma omp simd -#endif - for (int ch = 0; ch < inputOutputData->channels; ch++) - pData[ch] = MAX(pData[ch], 0); -#endif } } + return true; } -bool priorbox(const CDataBlob * featureData, const CDataBlob * imageData, int num_sizes, float * pWinSizes, CDataBlob * outputData) +bool priorbox(const CDataBlob * featureData, const CDataBlob * imageData, int num_sizes, float * pWinSizes, CDataBlob * outputData) { - if ((featureData->data_float == NULL) || - imageData->data_float == NULL|| + if ((featureData->data == NULL) || + imageData->data == NULL|| pWinSizes == NULL) { cerr << __FUNCTION__ << ": The input data is null." << endl; @@ -933,16 +651,15 @@ bool priorbox(const CDataBlob * featureData, const CDataBlob * imageData, int nu float step_w = static_cast(image_width) / feature_width; float step_h = static_cast(image_height) / feature_height; - float * output_data = outputData->data_float; + float * output_data = outputData->data; -// outputData->create(feature_width, feature_height, num_sizes * 4 * 2); outputData->create(feature_width, feature_height, num_sizes * 4); for (int h = 0; h < feature_height; ++h) { for (int w = 0; w < feature_width; ++w) { - float * pOut = (float*)(outputData->data_float + ( h * outputData->width + w) * outputData->floatChannelStepInByte / sizeof(float)); + float * pOut = (float*)(outputData->data + ( h * outputData->width + w) * outputData->channelStep / sizeof(float)); int idx = 0; //priorbox for (int s = 0; s < num_sizes; s++) @@ -966,100 +683,12 @@ bool priorbox(const CDataBlob * featureData, const CDataBlob * imageData, int nu } } - return true; } -bool normalize(CDataBlob * inputOutputData, float * pScale) +bool softmax1vector2class(CDataBlob *inputOutputData) { - if ((inputOutputData->data_float == NULL) || pScale == NULL) - { - cerr << __FUNCTION__ << ": The input data is null." << endl; - return false; - } - - - for (int row = 0; row < inputOutputData->height; row++) - { - for (int col = 0; col < inputOutputData->width; col++) - { - float * pData = (float*)(inputOutputData->data_float + (row*inputOutputData->width + col)*inputOutputData->floatChannelStepInByte / sizeof(float)); - float sum = FLT_EPSILON; - float s = 0; -#if defined(_ENABLE_NEON) - float32x4_t a, b, cscale; - float32x4_t result_vec; - for (int ch = 0; ch < inputOutputData->channels; ch += 4) - { - a = vld1q_f32(pData + ch); - result_vec = vmulq_f32(a, a); - sum += vgetq_lane_f32(result_vec, 0); - sum += vgetq_lane_f32(result_vec, 1); - sum += vgetq_lane_f32(result_vec, 2); - sum += vgetq_lane_f32(result_vec, 3); - } - - s = 1.0f/sqrt(sum); - cscale = vdupq_n_f32(s); - - for (int ch = 0; ch < inputOutputData->channels; ch += 4) - { - a = vld1q_f32(pData + ch); - b = vld1q_f32(pScale + ch); - - result_vec = vmulq_f32(a, b); - result_vec = vmulq_f32(result_vec, cscale); - vst1q_f32(pData + ch, result_vec); - } -#elif defined(_ENABLE_AVX2) - __m256 a, b, cscale; - __m256 result_vec; - for (int ch = 0; ch < inputOutputData->channels; ch += 8) - { - a = _mm256_load_ps(pData + ch); - a = _mm256_mul_ps(a, a); - a = _mm256_hadd_ps(a, a); - a = _mm256_hadd_ps(a, a); - sum += SSE_256ELEMENT(a, 0); - sum += SSE_256ELEMENT(a, 4); - } - - s = 1.0f / sqrt(sum); - cscale = _mm256_set1_ps(s); - - for (int ch = 0; ch < inputOutputData->channels; ch += 8) - { - a = _mm256_load_ps(pData + ch); - b = _mm256_load_ps(pScale + ch); - - result_vec = _mm256_mul_ps(a, b); - result_vec = _mm256_mul_ps(result_vec, cscale); - _mm256_store_ps(pData + ch, result_vec); - } -#else - -#if defined(_ENABLE_OPENMP_SIMD) -#pragma omp simd reduction(+:sum) -#endif - for (int ch = 0; ch < inputOutputData->channels; ch++) - sum += (pData[ch] * pData[ch]); - - s = 1.0f/sqrt(sum); -#if defined(_ENABLE_OPENMP_SIMD) -#pragma omp simd -#endif - for (int ch = 0; ch < inputOutputData->channels; ch++) - pData[ch] = pData[ch] * pScale[ch] * s; -#endif - } - } - return true; - -} - -bool softmax1vector2class(const CDataBlob *inputOutputData) -{ - if (inputOutputData->data_float == NULL) + if (inputOutputData == NULL ) { cerr << __FUNCTION__ << ": The input data is null." << endl; return false; @@ -1072,15 +701,15 @@ bool softmax1vector2class(const CDataBlob *inputOutputData) } int num = inputOutputData->channels; - float * pData = (inputOutputData->data_float); + float * pData = inputOutputData->data; -#if defined(_OPENMP) -#pragma omp parallel for -#endif +//#if defined(_OPENMP) +//#pragma omp parallel for +//#endif for(int i = 0; i < num; i+= 2) { float v1 = pData[i]; - float v2 = pData[i+1]; + float v2 = pData[i + 1]; float vm = MAX(v1, v2); v1 -= vm; v2 -= vm; @@ -1093,48 +722,35 @@ bool softmax1vector2class(const CDataBlob *inputOutputData) return true; } -bool blob2vector(const CDataBlob * inputData, CDataBlob * outputData, bool isFloat) +template +bool blob2vector(const CDataBlob * inputData, CDataBlob * outputData) { - if (inputData->data_float == NULL) + if (inputData->data == NULL || outputData == NULL) { - cerr << __FUNCTION__ << ": The input data is null." << endl; + cerr << __FUNCTION__ << ": The input or output data is null." << endl; return false; } outputData->create(1, 1, inputData->width * inputData->height * inputData->channels); + outputData->scale = inputData->scale; - if (isFloat) + int bytesOfAChannel = inputData->channels * sizeof(T); + T * pOut = outputData->data; + for (int row = 0; row < inputData->height; row++) { - int bytesOfAChannel = inputData->channels * sizeof(float); - float * pOut = outputData->data_float; - for (int row = 0; row < inputData->height; row++) + for (int col = 0; col < inputData->width; col++) { - for (int col = 0; col < inputData->width; col++) - { - float * pIn = (inputData->data_float + (row*inputData->width + col)*inputData->floatChannelStepInByte / sizeof(float)); - memcpy(pOut, pIn, bytesOfAChannel); - pOut += inputData->channels; - } - } - } - else - { - int bytesOfAChannel = inputData->channels * sizeof(char); - signed char * pOut = outputData->data_int8; - for (int row = 0; row < inputData->height; row++) - { - for (int col = 0; col < inputData->width; col++) - { - signed char * pIn = (inputData->data_int8 + (row*inputData->width + col)*inputData->int8ChannelStepInByte / sizeof(char)); - memcpy(pOut, pIn, bytesOfAChannel); - pOut += inputData->channels; - } + T * pIn = (inputData->data + (row*inputData->width + col)*inputData->channelStep / sizeof(T)); + memcpy(pOut, pIn, bytesOfAChannel); + pOut += inputData->channels; } } return true; - } +template bool blob2vector(const CDataBlob * inputData, CDataBlob * outputData); +template bool blob2vector(const CDataBlob * inputData, CDataBlob * outputData); +template bool blob2vector(const CDataBlob * inputData, CDataBlob * outputData); void IntersectBBox(const NormalizedBBox& bbox1, const NormalizedBBox& bbox2, NormalizedBBox* intersect_bbox) @@ -1184,9 +800,9 @@ bool SortScoreBBoxPairDescend(const pair& pair1, const } -bool detection_output(const CDataBlob * priorbox, const CDataBlob * loc, const CDataBlob * conf, float overlap_threshold, float confidence_threshold, int top_k, int keep_top_k, CDataBlob * outputData) +bool detection_output(const CDataBlob * priorbox, const CDataBlob * loc, const CDataBlob * conf, float overlap_threshold, float confidence_threshold, int top_k, int keep_top_k, CDataBlob * outputData) { - if (priorbox->data_float == NULL || loc->data_float == NULL || conf->data_float == NULL) + if (priorbox->data == NULL || loc->data == NULL || conf->data == NULL) { cerr << __FUNCTION__ << ": The input data is null." << endl; return 0; @@ -1195,13 +811,14 @@ bool detection_output(const CDataBlob * priorbox, const CDataBlob * loc, const C if (priorbox->channels != loc->channels || loc->channels != conf->channels*2 ) { cerr << __FUNCTION__ << ": The sizes of the inputs are not match." << endl; + cerr << "priorbox channels=" << priorbox->channels << ", loc channels=" << loc->channels << ", conf channels=" << conf->channels << endl; return 0; } float prior_variance[4] = {0.1f, 0.1f, 0.2f, 0.2f}; - float * pPriorBox = priorbox->data_float; - float * pLoc = loc->data_float; - float * pConf = conf->data_float; + float * pPriorBox = priorbox->data; + float * pLoc = loc->data; + float * pConf = conf->data; vector > score_bbox_vec; vector > final_score_bbox_vec; @@ -1296,7 +913,7 @@ bool detection_output(const CDataBlob * priorbox, const CDataBlob * loc, const C for (int fi = 0; fi < num_faces; fi++) { pair pp = final_score_bbox_vec[fi]; - float * pOut = (outputData->data_float + fi * outputData->floatChannelStepInByte / sizeof(float)); + float * pOut = (outputData->data + fi * outputData->channelStep / sizeof(float)); pOut[0] = pp.first; pOut[1] = pp.second.xmin; pOut[2] = pp.second.ymin; diff --git a/src/facedetectcnn.h b/src/facedetectcnn.h index 5d0e649..b646d7c 100644 --- a/src/facedetectcnn.h +++ b/src/facedetectcnn.h @@ -38,7 +38,7 @@ the use of this software, even if advised of the possibility of such damage. #pragma once -//#define _ENABLE_AVX2 //Please enable it if X64 CPU +#define _ENABLE_AVX2 //Please enable it if X64 CPU //#define _ENABLE_NEON //Please enable it if ARM CPU @@ -46,8 +46,6 @@ int * facedetect_cnn(unsigned char * result_buffer, //buffer memory for storing unsigned char * rgb_image_data, int width, int height, int step); //input image, it must be BGR (three channels) insteed of RGB image! - - //DO NOT EDIT the following code if you don't really understand it. #if defined(_ENABLE_AVX2) @@ -56,7 +54,13 @@ int * facedetect_cnn(unsigned char * result_buffer, //buffer memory for storing #if defined(_ENABLE_NEON) #include "arm_neon.h" -#define _ENABLE_INT8_CONV +//NEON does not support UINT8*INT8 dot product +//to conver the input data to range [0, 127], +//and then use INT8*INT8 dot product +#define _MAX_UINT8_VALUE 127 +#error NEON support is unfinished. +#else +#define _MAX_UINT8_VALUE 255 #endif #if defined(_ENABLE_AVX2) @@ -102,35 +106,28 @@ typedef struct FaceRect_ int h; }FaceRect; - +template class CDataBlob { public: - float * data_float; - signed char * data_int8; + T * data; int width; int height; int channels; - int floatChannelStepInByte; - int int8ChannelStepInByte; - float int8float_scale; - bool int8_data_valid; + int channelStep; + float scale; public: CDataBlob() { - data_float = 0; - data_int8 = 0; + data = 0; width = 0; height = 0; channels = 0; - floatChannelStepInByte = 0; - int8ChannelStepInByte = 0; - int8float_scale = 1.0f; - int8_data_valid = false; + channelStep = 0; + scale = 1.0f; } CDataBlob(int w, int h, int c) { - data_float = 0; - data_int8 = 0; + data = 0; create(w, h, c); } ~CDataBlob() @@ -140,13 +137,10 @@ class CDataBlob void setNULL() { - if (data_float) - myFree(&data_float); - if (data_int8) - myFree(&data_int8); - width = height = channels = floatChannelStepInByte = int8ChannelStepInByte = 0; - int8float_scale = 1.0f; - int8_data_valid = false; + if (data) + myFree(&data); + width = height = channels = channelStep = 0; + scale = 1.0f; } bool create(int w, int h, int c) { @@ -155,32 +149,16 @@ class CDataBlob width = w; height = h; channels = c; - //alloc space for float array - int remBytes = (sizeof(float)* channels) % (_MALLOC_ALIGN / 8); - if (remBytes == 0) - floatChannelStepInByte = channels * sizeof(float); - else - floatChannelStepInByte = (channels * sizeof(float)) + (_MALLOC_ALIGN / 8) - remBytes; - data_float = (float*)myAlloc(width * height * floatChannelStepInByte); //alloc space for int8 array - remBytes = (sizeof(char)* channels) % (_MALLOC_ALIGN / 8); + int remBytes = (sizeof(T)* channels) % (_MALLOC_ALIGN / 8); if (remBytes == 0) - int8ChannelStepInByte = channels * sizeof(char); + this->channelStep = channels * sizeof(T); else - int8ChannelStepInByte = (channels * sizeof(char)) + (_MALLOC_ALIGN / 8) - remBytes; - data_int8 = (signed char*)myAlloc(width * height * int8ChannelStepInByte); - - if (data_float == NULL) - { - cerr << "Cannot alloc memeory for float data blob: " - << width << "*" - << height << "*" - << channels << endl; - return false; - } + this->channelStep = (channels * sizeof(T)) + (_MALLOC_ALIGN / 8) - remBytes; + data = (T*)myAlloc(width * height * this->channelStep); - if (data_int8 == NULL) + if (data == NULL) { cerr << "Cannot alloc memeory for uint8 data blob: " << width << "*" @@ -189,8 +167,7 @@ class CDataBlob return false; } - //memset(data_float, 0, width * height * floatChannelStepInByte); - //memset(data_int8, 0, width * height * int8ChannelStepInByte); + //memset(data, 0, width * height * channelStep); //the following code is faster than memset //but not only the padding bytes are set to zero. @@ -202,13 +179,8 @@ class CDataBlob { for (int c = 0; c < this->width; c++) { - int pixel_end = this->floatChannelStepInByte / sizeof(float); - float * pF = (float*)(this->data_float + (r * this->width + c) * this->floatChannelStepInByte/sizeof(float)); - for (int ch = this->channels; ch < pixel_end; ch++) - pF[ch] = 0; - - pixel_end = this->int8ChannelStepInByte / sizeof(char); - char * pI = (char*)(this->data_int8 + (r * this->width + c) * this->int8ChannelStepInByte/sizeof(char)); + int pixel_end = this->channelStep / sizeof(T); + T * pI = (this->data + (r * this->width + c) * this->channelStep /sizeof(T)); for (int ch = this->channels; ch < pixel_end; ch++) pI[ch] = 0; } @@ -224,33 +196,13 @@ class CDataBlob cerr << "The input image data is null." << endl; return false; } - if (dataWidth != this->width || - dataHeight != this->height || - dataChannels != this->channels) - { - cerr << "The dim of the data can not match that of the Blob." << endl; - return false; - } - //create(dataWidth, dataHeight, dataChannels); - for(int row = 0; row < height; row++) - for (int col = 0; col < width; col++) - { - signed char * p = (this->data_int8 + (width * row + col) * int8ChannelStepInByte /sizeof(char)); - for (int ch = 0; ch < channels; ch++) - { - p[ch] = pData[ch * height * width + row * width + col]; - } - } - return true; - } - bool setFloatDataFromCaffeFormat(float * pData, int dataWidth, int dataHeight, int dataChannels) - { - if (pData == NULL) + if (typeid(signed char) != typeid(T)) { - cerr << "The input image data is null." << endl; + cerr << "Data must be signed char, the same with the source data." << endl; return false; } + if (dataWidth != this->width || dataHeight != this->height || dataChannels != this->channels) @@ -258,12 +210,11 @@ class CDataBlob cerr << "The dim of the data can not match that of the Blob." << endl; return false; } - //create(dataWidth, dataHeight, dataChannels); - for (int row = 0; row < height; row++) + for(int row = 0; row < height; row++) for (int col = 0; col < width; col++) { - float * p = (this->data_float + (width * row + col) * floatChannelStepInByte / sizeof(float)); + T * p = (this->data + (width * row + col) * channelStep /sizeof(T)); for (int ch = 0; ch < channels; ch++) { p[ch] = pData[ch * height * width + row * width + col]; @@ -272,47 +223,16 @@ class CDataBlob return true; } - bool setDataFromImage(const unsigned char * imgData, int imgWidth, int imgHeight, int imgChannels, int imgWidthStep, - int * pChannelMean) + bool setDataFrom3x3S2P1to1x1S1P0FromImage(const unsigned char * imgData, int imgWidth, int imgHeight, int imgChannels, int imgWidthStep) { if (imgData == NULL) { cerr << "The input image data is null." << endl; return false; } - if (pChannelMean == NULL) + if (typeid(unsigned char) != typeid(T)) { - cerr << "The mean values is null." << endl; - return false; - } - create(imgWidth, imgHeight, imgChannels); - -//#if defined(_OPENMP) -//#pragma omp parallel for -//#endif - for (int r = 0; r < imgHeight; r++) - { - for (int c = 0; c < imgWidth; c++) - { - const unsigned char * pImgData = imgData + imgWidthStep * r + imgChannels * c; - float * pBlobData = this->data_float + (this->width * r + c) * this->floatChannelStepInByte /sizeof(float); - for (int ch = 0; ch < imgChannels; ch++) - pBlobData[ch] = (float)(pImgData[ch] - pChannelMean[ch]); - } - } - return true; - } - bool setDataFrom3x3S2P1to1x1S1P0FromImage(const unsigned char * imgData, int imgWidth, int imgHeight, int imgChannels, int imgWidthStep, - int * pChannelMean) - { - if (imgData == NULL) - { - cerr << "The input image data is null." << endl; - return false; - } - if (pChannelMean == NULL) - { - cerr << "The mean values is null." << endl; + cerr << "Data must be unsigned char, the same with the source data." << endl; return false; } if (imgChannels != 3) @@ -324,7 +244,7 @@ class CDataBlob create((imgWidth+1)/2, (imgHeight+1)/2, 27); //since the pixel assignment cannot fill all the elements in the blob. //some elements in the blob should be initialized to 0 - memset(data_float, 0, width * height * floatChannelStepInByte); + memset(data, 0, width * height * channelStep); #if defined(_OPENMP) #pragma omp parallel for @@ -333,7 +253,7 @@ class CDataBlob { for (int c = 0; c < this->width; c++) { - float * pData = this->data_float + (r * this->width + c) * this->floatChannelStepInByte / sizeof(float); + T * pData = (unsigned char*)this->data + (r * this->width + c) * this->channelStep; for (int fy = -1; fy <= 1; fy++) { int srcy = r * 2 + fy; @@ -352,9 +272,9 @@ class CDataBlob int output_channel_offset = ((fy + 1) * 3 + fx + 1) * 3; //3x3 filters, 3-channel image - pData[output_channel_offset] = (float)(pImgData[0] - pChannelMean[0]); - pData[output_channel_offset+1] = (float)(pImgData[1] - pChannelMean[1]); - pData[output_channel_offset+2] = (float)(pImgData[2] - pChannelMean[2]); + pData[output_channel_offset] = (pImgData[0]); + pData[output_channel_offset+1] = (pImgData[1]); + pData[output_channel_offset+2] = (pImgData[2]); } @@ -363,43 +283,29 @@ class CDataBlob } return true; } - float getElementFloat(int x, int y, int channel) - { - if (this->data_float) - { - if (x >= 0 && x < this->width && - y >= 0 && y < this->height && - channel >= 0 && channel < this->channels) - { - float * p = (float*)(this->data_float + (y*this->width + x)*this->floatChannelStepInByte / sizeof(float)); - return p[channel]; - } - } - - return 0.f; - } - int getElementint8(int x, int y, int channel) + T getElement(int x, int y, int channel) { - if (this->data_int8 && this->int8_data_valid) + if (this->data) { if (x >= 0 && x < this->width && y >= 0 && y < this->height && channel >= 0 && channel < this->channels) { - signed char * p = this->data_int8 + (y*this->width + x)*this->int8ChannelStepInByte/sizeof(char); - return p[channel]; + T * p = this->data + (y*this->width + x)*this->channelStep/sizeof(T); + return (p[channel]); } } - return 0; + return (T)(0); } friend ostream &operator<<(ostream &output, const CDataBlob &dataBlob) { - output << "DataBlob Size (Width, Height, Channel) = (" + output << "DataBlob Size (Width, Height, Channel, scale) = (" << dataBlob.width - << ", " << dataBlob.height + << ", " << dataBlob.height << ", " << dataBlob.channels + << ", " << dataBlob.scale << ")" << endl; for (int ch = 0; ch < dataBlob.channels; ch++) { @@ -410,8 +316,13 @@ class CDataBlob output << "("; for (int col = 0; col < dataBlob.width; col++) { - float * p = (dataBlob.data_float + (dataBlob.width * row + col) * dataBlob.floatChannelStepInByte/sizeof(float)); - output << p[ch]; + T * p = (dataBlob.data + (dataBlob.width * row + col) * dataBlob.channelStep /sizeof(T) ); + + if(sizeof(T)<4) + output << (int)(p[ch]); + else + output << p[ch]; + if (col != dataBlob.width - 1) output << ", "; } @@ -425,22 +336,33 @@ class CDataBlob class Filters { public: - vector filters; + vector *> filters; int pad; int stride; float scale; //element * scale = original value }; -bool convolution(CDataBlob *inputData, const Filters* filters, CDataBlob *outputData); -bool maxpooling2x2S2(const CDataBlob *inputData, CDataBlob *outputData); -bool concat4(const CDataBlob *inputData1, const CDataBlob *inputData2, const CDataBlob *inputData3, const CDataBlob *inputData4, CDataBlob *outputData); -bool scale(CDataBlob * dataBlob, float scale); -bool relu(const CDataBlob *inputOutputData); -bool priorbox(const CDataBlob * featureData, const CDataBlob * imageData, int num_sizes, float * pWinSizes, CDataBlob * outputData); -bool normalize(CDataBlob * inputOutputData, float * pScale); -bool blob2vector(const CDataBlob * inputData, CDataBlob * outputData, bool isFloat); -bool detection_output(const CDataBlob * priorbox, const CDataBlob * loc, const CDataBlob * conf, float overlap_threshold, float confidence_threshold, int top_k, int keep_top_k, CDataBlob * outputData); +bool convertInt2Float(CDataBlob * inputData, CDataBlob * outputData); + +bool convolution(CDataBlob *inputData, const Filters* filters, CDataBlob *outputData); + +bool convolution_relu(CDataBlob *inputData, const Filters* filters, CDataBlob *outputData); + +bool maxpooling2x2S2(const CDataBlob *inputData, CDataBlob *outputData); + +bool normalize(CDataBlob * inputOutputData, float * pScale); + +bool priorbox(const CDataBlob * featureData, const CDataBlob * imageData, int num_sizes, float * pWinSizes, CDataBlob * outputData); + +template +bool concat4(const CDataBlob *inputData1, const CDataBlob *inputData2, const CDataBlob *inputData3, const CDataBlob *inputData4, CDataBlob *outputData); + /* the input data for softmax must be a vector, the data stored in a multi-channel blob with size 1x1 */ -bool softmax1vector2class(const CDataBlob *inputOutputData); +template +bool blob2vector(const CDataBlob * inputData, CDataBlob * outputData); + +bool softmax1vector2class(CDataBlob *inputOutputData); + +bool detection_output(const CDataBlob * priorbox, const CDataBlob * loc, const CDataBlob * conf, float overlap_threshold, float confidence_threshold, int top_k, int keep_top_k, CDataBlob * outputData); vector objectdetect_cnn(unsigned char * rgbImageData, int with, int height, int step);