Merge remote-tracking branch 'origin/master'
This commit is contained in:
xinyang
@@ -92,8 +92,9 @@ void ArmorFinder::run(cv::Mat &src) {
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stateStandBy();
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}
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end:
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antiTop();
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// antiTop();
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if(target_box.rect != cv::Rect2d())
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sendBoxPosition(0);
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if (show_armor_box) { // 根据条件显示当前目标装甲板
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showArmorBox("box", src, target_box);
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cv::waitKey(1);
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@@ -27,10 +27,10 @@ static bool isValidLightBlob(const std::vector<cv::Point> &contour, const cv::Ro
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// 此函数可以有性能优化.
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static uint8_t get_blob_color(const cv::Mat &src, const cv::RotatedRect &blobPos) {
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auto region = blobPos.boundingRect();
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region.x -= fmax(2, region.width * 0.1);
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region.y -= fmax(2, region.height * 0.05);
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region.width += 2 * fmax(2, region.width * 0.1);
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region.height += 2 * fmax(2, region.height * 0.05);
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region.x -= fmax(3, region.width * 0.1);
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region.y -= fmax(3, region.height * 0.05);
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region.width += 2 * fmax(3, region.width * 0.1);
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region.height += 2 * fmax(3, region.height * 0.05);
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region &= cv::Rect(0, 0, src.cols, src.rows);
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cv::Mat roi = src(region);
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int red_cnt = 0, blue_cnt = 0;
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24
main.cpp
24
main.cpp
@@ -30,10 +30,10 @@ using namespace std;
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McuData mcu_data = { // 单片机端回传结构体
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0, // 当前云台yaw角
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0, // 当前云台pitch角
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BIG_ENERGY_STATE, // 当前状态，自瞄-大符-小符
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ARMOR_STATE, // 当前状态，自瞄-大符-小符
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0, // 云台角度标记位
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1, // 是否启用数字识别
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ENEMY_RED, // 敌方颜色
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ENEMY_BLUE, // 敌方颜色
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0, // 能量机关x轴补偿量
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0, // 能量机关y轴补偿量
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};
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@@ -63,11 +63,11 @@ int main(int argc, char *argv[]) {
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while (true) {
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// 打开视频源
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if (from_camera) {
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video_gimbal = new CameraWrapper(ARMOR_CAMERA_GAIN, 2/*, "armor"*/);
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video_chassis = new CameraWrapper(ENERGY_CAMERA_GAIN, 2/*, "energy"*/);
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video_gimbal = new CameraWrapper(ARMOR_CAMERA_EXPOSURE, ARMOR_CAMERA_GAIN, 2/*, "armor"*/);
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video_chassis = new CameraWrapper(ENERGY_CAMERA_EXPOSURE, ENERGY_CAMERA_GAIN, 2/*, "energy"*/);
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} else {
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video_gimbal = new VideoWrapper("/home/sun/项目/RM_auto-aim/gimbal_video/57.avi");
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video_chassis = new VideoWrapper("/home/sun/项目/RM_auto-aim/gimbal_video/57.avi");
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video_gimbal = new VideoWrapper(PROJECT_DIR"/26.avi");
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video_chassis = new VideoWrapper(PROJECT_DIR"/26.avi");
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}
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if (video_gimbal->init()) {
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LOGM("video_gimbal source initialization successfully.");
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@@ -100,7 +100,7 @@ int main(int argc, char *argv[]) {
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destroyAllWindows();
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if (from_camera) {
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delete video_gimbal;
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video_gimbal = new CameraWrapper(ENERGY_CAMERA_GAIN, 2/*, "armor"*/);
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video_gimbal = new CameraWrapper(ENERGY_CAMERA_EXPOSURE, ENERGY_CAMERA_GAIN, 2/*, "armor"*/);
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if (video_gimbal->init()) {
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LOGM("video_gimbal source initialization successfully.");
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} else {
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@@ -122,15 +122,15 @@ int main(int argc, char *argv[]) {
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if (!from_camera) extract(gimbal_src, chassis_src);
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if (save_video) saveVideos(gimbal_src, chassis_src);//保存视频
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if (show_origin) showOrigin(gimbal_src, chassis_src);//显示原始图像
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// energy.runBig(gimbal_src, chassis_src);
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energy.runBig(gimbal_src);
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energy.runBig(gimbal_src, chassis_src);
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// energy.runBig(gimbal_src);
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} else if (mcu_data.state == SMALL_ENERGY_STATE) {
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if (last_state != SMALL_ENERGY_STATE) {
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LOGM(STR_CTR(WORD_GREEN, "Start Small Energy!"));
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destroyAllWindows();
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if (from_camera) {
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delete video_gimbal;
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video_gimbal = new CameraWrapper(ENERGY_CAMERA_GAIN, 2/*, "armor"*/);
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video_gimbal = new CameraWrapper(ENERGY_CAMERA_EXPOSURE, ENERGY_CAMERA_GAIN, 2/*, "armor"*/);
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if (video_gimbal->init()) {
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LOGM("video_gimbal source initialization successfully.");
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} else {
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@@ -154,7 +154,7 @@ int main(int argc, char *argv[]) {
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destroyAllWindows();
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if (from_camera) {
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delete video_gimbal;
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video_gimbal = new CameraWrapper(ARMOR_CAMERA_GAIN, 2/*, "armor"*/);
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video_gimbal = new CameraWrapper(ARMOR_CAMERA_EXPOSURE, ARMOR_CAMERA_GAIN, 2/*, "armor"*/);
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if (video_gimbal->init()) {
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LOGM("video_gimbal source initialization successfully.");
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} else {
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@@ -164,7 +164,7 @@ int main(int argc, char *argv[]) {
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}
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last_state = mcu_data.state;
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CNT_TIME(STR_CTR(WORD_GREEN, "read img"), {
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if (!checkReconnect(video_gimbal->read(gimbal_src))) continue;
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if(!checkReconnect(video_gimbal->read(gimbal_src))) continue;
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});
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#ifdef GIMBAL_FLIP_MODE
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flip(gimbal_src, gimbal_src, GIMBAL_FLIP_MODE);
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@@ -41,8 +41,9 @@ private:
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RoundQueue<cv::Mat, 2> src_queue;
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public:
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int gain;
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int exposure;
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CameraWrapper(int gain, int camera_mode=1, const std::string &n="NULL");
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CameraWrapper(int exposure, int gain, int camera_mode=1, const std::string &n="NULL");
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~CameraWrapper() final;
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bool init() final;
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@@ -8,8 +8,12 @@
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#warning "Without config.h"
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#endif
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#ifndef CAMERA_EXPOSURE
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#define CAMERA_EXPOSURE (10)
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#ifndef ARMOR_CAMERA_EXPOSURE
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#define ARMOR_CAMERA_EXPOSURE (10)
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#endif
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#ifndef ENERGY_CAMERA_EXPOSURE
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#define ENERGY_CAMERA_EXPOSURE (10)
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#endif
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#ifndef CAMERA_RED_GAIN
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@@ -77,14 +77,16 @@ cv::VideoWriter initVideoWriter(const std::string &filename_prefix) {
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bool checkReconnect(bool is_camera_0_connect, bool is_camera_1_connect) {
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if (!is_camera_0_connect) {
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int curr_gain = ((CameraWrapper* )video_gimbal)->gain;
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int curr_exposure = ((CameraWrapper* )video_gimbal)->exposure;
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delete video_gimbal;
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video_gimbal = new CameraWrapper(curr_gain, 0/*, "armor"*/);
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video_gimbal = new CameraWrapper(curr_exposure, curr_gain, 0/*, "armor"*/);
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is_camera_0_connect = video_gimbal->init();
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}
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if (!is_camera_1_connect) {
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int curr_gain = ((CameraWrapper* )video_gimbal)->gain;
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int curr_exposure = ((CameraWrapper* )video_gimbal)->exposure;
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delete video_chassis;
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video_chassis = new CameraWrapper(curr_gain, 0/*, "energy"*/);
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video_chassis = new CameraWrapper(curr_exposure, curr_gain, 0/*, "energy"*/);
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is_camera_1_connect = video_chassis->init();
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}
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return is_camera_0_connect && is_camera_1_connect;
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@@ -93,8 +95,9 @@ bool checkReconnect(bool is_camera_0_connect, bool is_camera_1_connect) {
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bool checkReconnect(bool is_camera_connect) {
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if (!is_camera_connect) {
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int curr_gain = ((CameraWrapper* )video_gimbal)->gain;
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int curr_exposure = ((CameraWrapper* )video_gimbal)->exposure;
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delete video_gimbal;
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video_gimbal = new CameraWrapper(curr_gain, 0/*, "armor"*/);
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video_gimbal = new CameraWrapper(curr_exposure, curr_gain, 0/*, "armor"*/);
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is_camera_connect = video_gimbal->init();
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}
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return is_camera_connect;
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@@ -11,7 +11,7 @@
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using namespace std;
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using namespace cv;
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CameraWrapper::CameraWrapper(int gain, int camera_mode, const std::string &n) :
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CameraWrapper::CameraWrapper(int exposure, int gain, int camera_mode, const std::string &n) :
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name(n),
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init_done(false),
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mode(camera_mode),
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@@ -20,7 +20,8 @@ CameraWrapper::CameraWrapper(int gain, int camera_mode, const std::string &n) :
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iplImage(nullptr),
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rgb_buffer(nullptr),
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channel(3),
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gain(gain){
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gain(gain),
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exposure(exposure){
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}
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void cameraCallback(CameraHandle hCamera, BYTE *pFrameBuffer, tSdkFrameHead* pFrameHead,PVOID pContext){
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@@ -85,7 +86,7 @@ bool CameraWrapper::init() {
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CameraReadParameterFromFile(h_camera, PROJECT_DIR"/others/MV-UB31-Group0.config");
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CameraLoadParameter(h_camera, PARAMETER_TEAM_A);
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CameraSetAeState(h_camera, false);
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CameraSetExposureTime(h_camera, CAMERA_EXPOSURE * 1000);
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CameraSetExposureTime(h_camera, exposure * 1000);
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CameraSetAnalogGain(h_camera, gain);
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#endif
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double t;
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@@ -8,8 +8,10 @@ import forward
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import cv2
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import numpy as np
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import mvsdk
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print("Finish!")
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def save_kernal(fp, val):
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print(val.shape[2], file=fp)
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print(val.shape[3], file=fp)
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@@ -49,24 +51,24 @@ def save_para(folder, paras, names, info):
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fp.close()
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STEPS = 50000
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BATCH = 30
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LEARNING_RATE_BASE = 0.0005
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STEPS = 100000
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BATCH = 40
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LEARNING_RATE_BASE = 0.0002
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LEARNING_RATE_DECAY = 0.99
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MOVING_AVERAGE_DECAY = 0.99
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|
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def train(dataset, show_bar=False):
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x = tf.placeholder(tf.float32, [None, generate.SRC_ROWS, generate.SRC_COLS, generate.SRC_CHANNELS])
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y_= tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES])
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y_ = tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES])
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keep_rate = tf.placeholder(tf.float32)
|
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nodes, vars, vars_name = forward.forward(x, 0.01)
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nodes, vars, vars_name = forward.forward(x, 0.01, keep_rate)
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y = nodes[-1]
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ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
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# ce = tf.nn.weighted_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1), pos_weight=1)
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# ce = tf.nn.weighted_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1), pos_weight=1)
|
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cem = tf.reduce_mean(ce)
|
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loss= cem + tf.add_n(tf.get_collection("losses"))
|
||||
loss = cem + tf.add_n(tf.get_collection("losses"))
|
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|
||||
global_step = tf.Variable(0, trainable=False)
|
||||
learning_rate = tf.train.exponential_decay(
|
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@@ -92,31 +94,33 @@ def train(dataset, show_bar=False):
|
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|
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bar = tqdm(range(STEPS), ascii=True, dynamic_ncols=True)
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for i in bar:
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images_samples, labels_samples = dataset.sample_train_sets(BATCH)
|
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images_samples, labels_samples = dataset.sample_train_sets(BATCH, 0.03)
|
||||
|
||||
_, loss_value, step = sess.run(
|
||||
[train_op, loss, global_step],
|
||||
feed_dict={x: images_samples, y_: labels_samples, keep_rate:0.3}
|
||||
feed_dict={x: images_samples, y_: labels_samples, keep_rate: 0.3}
|
||||
)
|
||||
|
||||
if step % 500 == 0:
|
||||
test_images, test_labels = dataset.sample_test_sets(6000)
|
||||
test_acc, output = sess.run([accuracy, y], feed_dict={x: test_images, y_: test_labels, keep_rate:1.0})
|
||||
test_images, test_labels = dataset.sample_test_sets(10000)
|
||||
test_acc, output = sess.run([accuracy, y],
|
||||
feed_dict={x: test_images, y_: test_labels, keep_rate: 1.0})
|
||||
output = np.argmax(output, axis=1)
|
||||
real = np.argmax(test_labels, axis=1)
|
||||
print("=============test-set===============")
|
||||
for n in range(forward.OUTPUT_NODES):
|
||||
print("label: %d, precise: %f, recall: %f" %
|
||||
(n, np.mean(real[output==n]==n), np.mean(output[real==n]==n)))
|
||||
(n, np.mean(real[output == n] == n), np.mean(output[real == n] == n)))
|
||||
|
||||
train_images, train_labels = dataset.sample_train_sets(6000)
|
||||
train_acc, output = sess.run([accuracy, y], feed_dict={x: train_images, y_: train_labels, keep_rate:1.0})
|
||||
train_images, train_labels = dataset.sample_train_sets(10000)
|
||||
train_acc, output = sess.run([accuracy, y],
|
||||
feed_dict={x: train_images, y_: train_labels, keep_rate: 1.0})
|
||||
output = np.argmax(output, axis=1)
|
||||
real = np.argmax(train_labels, axis=1)
|
||||
print("=============train-set===============")
|
||||
for n in range(forward.OUTPUT_NODES):
|
||||
print("label: %d, precise: %f, recall: %f" %
|
||||
(n, np.mean(real[output==n]==n), np.mean(output[real==n]==n)))
|
||||
(n, np.mean(real[output == n] == n), np.mean(output[real == n] == n)))
|
||||
print("\n")
|
||||
if train_acc >= 0.99 and test_acc >= 0.99:
|
||||
vars_val = sess.run(vars)
|
||||
@@ -131,8 +135,8 @@ def train(dataset, show_bar=False):
|
||||
|
||||
# pred = sess.run(y, feed_dict={x: test_images, keep_rate:1.0})
|
||||
|
||||
# nodes_val = sess.run(nodes, feed_dict={x:test_images})
|
||||
# return vars_val, nodes_val
|
||||
# nodes_val = sess.run(nodes, feed_dict={x:test_images})
|
||||
# return vars_val, nodes_val
|
||||
DevList = mvsdk.CameraEnumerateDevice()
|
||||
nDev = len(DevList)
|
||||
if nDev < 1:
|
||||
@@ -150,7 +154,7 @@ def train(dataset, show_bar=False):
|
||||
try:
|
||||
hCamera = mvsdk.CameraInit(DevInfo, -1, -1)
|
||||
except mvsdk.CameraException as e:
|
||||
print("CameraInit Failed({}): {}".format(e.error_code, e.message) )
|
||||
print("CameraInit Failed({}): {}".format(e.error_code, e.message))
|
||||
return
|
||||
|
||||
# 获取相机特性描述
|
||||
@@ -192,24 +196,25 @@ def train(dataset, show_bar=False):
|
||||
# 把pFrameBuffer转换成opencv的图像格式以进行后续算法处理
|
||||
frame_data = (mvsdk.c_ubyte * FrameHead.uBytes).from_address(pFrameBuffer)
|
||||
frame = np.frombuffer(frame_data, dtype=np.uint8)
|
||||
frame = frame.reshape((FrameHead.iHeight, FrameHead.iWidth, 1 if FrameHead.uiMediaType == mvsdk.CAMERA_MEDIA_TYPE_MONO8 else 3) )
|
||||
frame = frame.reshape((FrameHead.iHeight, FrameHead.iWidth,
|
||||
1 if FrameHead.uiMediaType == mvsdk.CAMERA_MEDIA_TYPE_MONO8 else 3))
|
||||
|
||||
frame = cv2.resize(frame, (640,480), interpolation = cv2.INTER_LINEAR)
|
||||
frame = cv2.resize(frame, (640, 480), interpolation=cv2.INTER_LINEAR)
|
||||
cv2.imshow("Press q to end", frame)
|
||||
if (cv2.waitKey(1)&0xFF) == ord(' '):
|
||||
if (cv2.waitKey(1) & 0xFF) == ord(' '):
|
||||
roi = cv2.selectROI("roi", frame)
|
||||
roi = frame[roi[1]:roi[1]+roi[3], roi[0]:roi[0]+roi[2]]
|
||||
roi = frame[roi[1]:roi[1] + roi[3], roi[0]:roi[0] + roi[2]]
|
||||
print(roi)
|
||||
cv2.imshow("box", roi)
|
||||
image = cv2.resize(roi, (48, 36))
|
||||
image = image.astype(np.float32) / 255.0
|
||||
out = sess.run(y, feed_dict={x:[image]})
|
||||
out = sess.run(y, feed_dict={x: [image]})
|
||||
print(out)
|
||||
print(np.argmax(out))
|
||||
|
||||
except mvsdk.CameraException as e:
|
||||
if e.error_code != mvsdk.CAMERA_STATUS_TIME_OUT:
|
||||
print("CameraGetImageBuffer failed({}): {}".format(e.error_code, e.message) )
|
||||
print("CameraGetImageBuffer failed({}): {}".format(e.error_code, e.message))
|
||||
|
||||
# 关闭相机
|
||||
mvsdk.CameraUnInit(hCamera)
|
||||
|
||||
@@ -35,16 +35,16 @@ CONV1_OUTPUT_CHANNELS = 4
|
||||
CONV2_KERNAL_SIZE = 3
|
||||
|
||||
# 第二层卷积输出通道数
|
||||
CONV2_OUTPUT_CHANNELS = 6
|
||||
CONV2_OUTPUT_CHANNELS = 8
|
||||
|
||||
# 第三层卷积核大小
|
||||
CONV3_KERNAL_SIZE = 3
|
||||
|
||||
# 第三层卷积输出通道数
|
||||
CONV3_OUTPUT_CHANNELS = 8
|
||||
CONV3_OUTPUT_CHANNELS = 16
|
||||
|
||||
# 第一层全连接宽度
|
||||
FC1_OUTPUT_NODES = 50
|
||||
FC1_OUTPUT_NODES = 60
|
||||
|
||||
# 第二层全连接宽度（输出标签类型数）
|
||||
FC2_OUTPUT_NODES = 15
|
||||
@@ -113,4 +113,3 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
|
||||
nodes.extend([fc2])
|
||||
|
||||
return nodes, vars, vars_name
|
||||
|
||||
|
||||
@@ -4,6 +4,7 @@ import cv2
|
||||
import random
|
||||
from tqdm import tqdm
|
||||
from forward import OUTPUT_NODES
|
||||
|
||||
# 原图像行数
|
||||
SRC_ROWS = 36
|
||||
|
||||
@@ -40,38 +41,64 @@ class DataSet:
|
||||
files = os.listdir(dir)
|
||||
for file in tqdm(files, postfix={"loading id": i}, dynamic_ncols=True):
|
||||
if file[-3:] == "jpg":
|
||||
sample = self.file2nparray("%s/%s" % (dir, file))
|
||||
label = self.id2label(i)
|
||||
if random.random() > 0.7:
|
||||
self.train_samples.append(self.file2nparray("%s/%s" % (dir, file)))
|
||||
self.train_labels.append(self.id2label(i))
|
||||
self.train_samples.append(sample)
|
||||
self.train_labels.append(label)
|
||||
if i == 0:
|
||||
tmp = sample.copy()
|
||||
tmp = tmp[:, :, ::-1]
|
||||
self.train_samples.append(tmp)
|
||||
self.train_labels.append(label)
|
||||
else:
|
||||
self.test_samples.append(self.file2nparray("%s/%s" % (dir, file)))
|
||||
self.test_labels.append(self.id2label(i))
|
||||
tmp = sample.copy()
|
||||
tmp = 1.2 * tmp
|
||||
tmp = np.where(tmp > 1, 1, tmp)
|
||||
tmp = np.where(tmp < 0, 0, tmp)
|
||||
self.train_samples.append(tmp)
|
||||
self.train_labels.append(label)
|
||||
tmp = sample.copy()
|
||||
tmp = 0.8 * tmp
|
||||
tmp = np.where(tmp > 1, 1, tmp)
|
||||
tmp = np.where(tmp < 0, 0, tmp)
|
||||
self.train_samples.append(tmp)
|
||||
self.train_labels.append(label)
|
||||
else:
|
||||
self.test_samples.append(sample)
|
||||
self.test_labels.append(label)
|
||||
self.train_samples = np.array(self.train_samples)
|
||||
self.train_labels = np.array(self.train_labels)
|
||||
self.test_samples = np.array(self.test_samples)
|
||||
self.test_labels = np.array(self.test_labels)
|
||||
return sets
|
||||
|
||||
def sample_train_sets(self, length):
|
||||
def sample_train_sets(self, length, std=0.0):
|
||||
samples = []
|
||||
labels = []
|
||||
for i in range(length):
|
||||
id = random.randint(0, len(self.train_samples)-1)
|
||||
id = random.randint(0, len(self.train_samples) - 1)
|
||||
samples.append(self.train_samples[id])
|
||||
labels.append(self.train_labels[id])
|
||||
return np.array(samples), np.array(labels)
|
||||
samples = np.array(samples).copy()
|
||||
samples += np.random.normal(0, std, samples.shape)
|
||||
labels = np.array(labels)
|
||||
return samples, labels
|
||||
|
||||
def sample_test_sets(self, length):
|
||||
def sample_test_sets(self, length, std=0.0):
|
||||
samples = []
|
||||
labels = []
|
||||
for i in range(length):
|
||||
id = random.randint(0, len(self.test_samples)-1)
|
||||
id = random.randint(0, len(self.test_samples) - 1)
|
||||
samples.append(self.test_samples[id])
|
||||
labels.append(self.test_labels[id])
|
||||
return np.array(samples), np.array(labels)
|
||||
samples = np.array(samples).copy()
|
||||
samples += np.random.normal(0, std, samples.shape)
|
||||
labels = np.array(labels)
|
||||
return samples, labels
|
||||
|
||||
def all_train_sets(self):
|
||||
def all_train_sets(self, std=0.0):
|
||||
return self.train_samples[:], self.train_labels[:]
|
||||
|
||||
def all_test_sets(self):
|
||||
def all_test_sets(self, std=0.0):
|
||||
return self.test_samples[:], self.test_labels[:]
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
4
|
||||
-0.028037319
|
||||
0.5437552
|
||||
0.26716635
|
||||
0.15804483
|
||||
0.021338634
|
||||
-0.28292254
|
||||
0.39307028
|
||||
0.46493095
|
||||
|
||||
@@ -2,303 +2,303 @@
|
||||
4
|
||||
5
|
||||
5
|
||||
0.2558705
|
||||
0.50322264
|
||||
0.5455292
|
||||
0.3266633
|
||||
0.45495397
|
||||
0.33181077
|
||||
0.4608274
|
||||
0.44561535
|
||||
0.30626073
|
||||
0.5907795
|
||||
0.45085132
|
||||
0.32935262
|
||||
0.33333394
|
||||
0.42217836
|
||||
0.47149
|
||||
0.26239944
|
||||
0.106058985
|
||||
0.4186091
|
||||
0.33606878
|
||||
0.47154978
|
||||
0.29413304
|
||||
0.2689295
|
||||
0.043931372
|
||||
0.41913906
|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
1.4682909
|
||||
1.3129491
|
||||
2.1375504
|
||||
2.6406689
|
||||
2.372072
|
||||
1.4637381
|
||||
1.306143
|
||||
2.2453067
|
||||
2.5116432
|
||||
2.3163383
|
||||
1.5179764
|
||||
1.1999305
|
||||
1.7840445
|
||||
2.1370907
|
||||
1.8682098
|
||||
1.1948502
|
||||
0.7644319
|
||||
1.183056
|
||||
1.2842283
|
||||
1.0330013
|
||||
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|
||||
0.357544
|
||||
0.4590763
|
||||
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|
||||
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|
||||
0.15203553
|
||||
0.66836417
|
||||
0.83691925
|
||||
1.0917046
|
||||
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|
||||
0.4185427
|
||||
0.8069128
|
||||
1.1880662
|
||||
1.2418958
|
||||
0.98703665
|
||||
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|
||||
0.8052862
|
||||
1.1458472
|
||||
1.0837129
|
||||
0.8163929
|
||||
0.5508064
|
||||
0.84074473
|
||||
1.0375632
|
||||
1.0029732
|
||||
0.73068154
|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
0.8512386
|
||||
1.06337
|
||||
0.82065773
|
||||
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|
||||
0.51960915
|
||||
1.0729233
|
||||
1.5161965
|
||||
1.220796
|
||||
0.4743648
|
||||
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|
||||
1.3462025
|
||||
1.4488021
|
||||
1.060846
|
||||
0.3372449
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
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|
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|
||||
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
||||
1586
tools/para/conv3_w
1586
tools/para/conv3_w
File diff suppressed because it is too large
Load Diff
112
tools/para/fc1_b
112
tools/para/fc1_b
@@ -1,51 +1,61 @@
|
||||
50
|
||||
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|
||||
54396
tools/para/fc1_w
54396
tools/para/fc1_w
File diff suppressed because it is too large
Load Diff
@@ -1,16 +1,16 @@
|
||||
15
|
||||
0.6056967
|
||||
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||||
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||||
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||||
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||||
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|
||||
|
||||
1652
tools/para/fc2_w
1652
tools/para/fc2_w
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user