diff --git a/armor/src/armor_finder/armor_finder.cpp b/armor/src/armor_finder/armor_finder.cpp index 800ef49..790b16d 100644 --- a/armor/src/armor_finder/armor_finder.cpp +++ b/armor/src/armor_finder/armor_finder.cpp @@ -92,8 +92,9 @@ void ArmorFinder::run(cv::Mat &src) { stateStandBy(); } end: - antiTop(); - +// antiTop(); + if(target_box.rect != cv::Rect2d()) + sendBoxPosition(0); if (show_armor_box) { // 根据条件显示当前目标装甲板 showArmorBox("box", src, target_box); cv::waitKey(1); diff --git a/armor/src/armor_finder/find/find_light_blobs.cpp b/armor/src/armor_finder/find/find_light_blobs.cpp index f983f07..f456c77 100644 --- a/armor/src/armor_finder/find/find_light_blobs.cpp +++ b/armor/src/armor_finder/find/find_light_blobs.cpp @@ -27,10 +27,10 @@ static bool isValidLightBlob(const std::vector &contour, const cv::Ro // 此函数可以有性能优化. static uint8_t get_blob_color(const cv::Mat &src, const cv::RotatedRect &blobPos) { auto region = blobPos.boundingRect(); - region.x -= fmax(2, region.width * 0.1); - region.y -= fmax(2, region.height * 0.05); - region.width += 2 * fmax(2, region.width * 0.1); - region.height += 2 * fmax(2, region.height * 0.05); + region.x -= fmax(3, region.width * 0.1); + region.y -= fmax(3, region.height * 0.05); + region.width += 2 * fmax(3, region.width * 0.1); + region.height += 2 * fmax(3, region.height * 0.05); region &= cv::Rect(0, 0, src.cols, src.rows); cv::Mat roi = src(region); int red_cnt = 0, blue_cnt = 0; diff --git a/main.cpp b/main.cpp index 10671d7..1e604fe 100644 --- a/main.cpp +++ b/main.cpp @@ -33,7 +33,7 @@ McuData mcu_data = { // 单片机端回传结构体 ARMOR_STATE, // 当前状态，自瞄-大符-小符 0, // 云台角度标记位 1, // 是否启用数字识别 - ENEMY_RED, // 敌方颜色 + ENEMY_BLUE, // 敌方颜色 0, // 能量机关x轴补偿量 0, // 能量机关y轴补偿量 }; @@ -63,11 +63,11 @@ int main(int argc, char *argv[]) { while (true) { // 打开视频源 if (from_camera) { - video_gimbal = new CameraWrapper(ARMOR_CAMERA_GAIN, 2/*, "armor"*/); - video_chassis = new CameraWrapper(ENERGY_CAMERA_GAIN, 2/*, "energy"*/); + video_gimbal = new CameraWrapper(ARMOR_CAMERA_EXPOSURE, ARMOR_CAMERA_GAIN, 2/*, "armor"*/); + video_chassis = new CameraWrapper(ENERGY_CAMERA_EXPOSURE, ENERGY_CAMERA_GAIN, 2/*, "energy"*/); } else { - video_gimbal = new VideoWrapper(PROJECT_DIR"/gimbal_video/0.avi"); - video_chassis = new VideoWrapper(PROJECT_DIR"/gimbal_video/0.avi"); + video_gimbal = new VideoWrapper(PROJECT_DIR"/26.avi"); + video_chassis = new VideoWrapper(PROJECT_DIR"/26.avi"); } if (video_gimbal->init()) { LOGM("video_gimbal source initialization successfully."); @@ -100,7 +100,7 @@ int main(int argc, char *argv[]) { destroyAllWindows(); if (from_camera) { delete video_gimbal; - video_gimbal = new CameraWrapper(ENERGY_CAMERA_GAIN, 2/*, "armor"*/); + video_gimbal = new CameraWrapper(ENERGY_CAMERA_EXPOSURE, ENERGY_CAMERA_GAIN, 2/*, "armor"*/); if (video_gimbal->init()) { LOGM("video_gimbal source initialization successfully."); } else { @@ -130,7 +130,7 @@ int main(int argc, char *argv[]) { destroyAllWindows(); if (from_camera) { delete video_gimbal; - video_gimbal = new CameraWrapper(ENERGY_CAMERA_GAIN, 2/*, "armor"*/); + video_gimbal = new CameraWrapper(ENERGY_CAMERA_EXPOSURE, ENERGY_CAMERA_GAIN, 2/*, "armor"*/); if (video_gimbal->init()) { LOGM("video_gimbal source initialization successfully."); } else { @@ -154,7 +154,7 @@ int main(int argc, char *argv[]) { destroyAllWindows(); if (from_camera) { delete video_gimbal; - video_gimbal = new CameraWrapper(ARMOR_CAMERA_GAIN, 2/*, "armor"*/); + video_gimbal = new CameraWrapper(ARMOR_CAMERA_EXPOSURE, ARMOR_CAMERA_GAIN, 2/*, "armor"*/); if (video_gimbal->init()) { LOGM("video_gimbal source initialization successfully."); } else { diff --git a/others/include/camera/camera_wrapper.h b/others/include/camera/camera_wrapper.h index a9bed24..7422fbb 100644 --- a/others/include/camera/camera_wrapper.h +++ b/others/include/camera/camera_wrapper.h @@ -41,8 +41,9 @@ private: RoundQueue src_queue; public: int gain; + int exposure; - CameraWrapper(int gain, int camera_mode=1, const std::string &n="NULL"); + CameraWrapper(int exposure, int gain, int camera_mode=1, const std::string &n="NULL"); ~CameraWrapper() final; bool init() final; diff --git a/others/include/config/setconfig.h b/others/include/config/setconfig.h index 0768d1c..c8bc35c 100644 --- a/others/include/config/setconfig.h +++ b/others/include/config/setconfig.h @@ -8,8 +8,12 @@ #warning "Without config.h" #endif -#ifndef CAMERA_EXPOSURE - #define CAMERA_EXPOSURE (10) +#ifndef ARMOR_CAMERA_EXPOSURE + #define ARMOR_CAMERA_EXPOSURE (10) +#endif + +#ifndef ENERGY_CAMERA_EXPOSURE + #define ENERGY_CAMERA_EXPOSURE (10) #endif #ifndef CAMERA_RED_GAIN diff --git a/others/src/additions.cpp b/others/src/additions.cpp index aff1734..cb543c3 100644 --- a/others/src/additions.cpp +++ b/others/src/additions.cpp @@ -77,14 +77,16 @@ cv::VideoWriter initVideoWriter(const std::string &filename_prefix) { bool checkReconnect(bool is_camera_0_connect, bool is_camera_1_connect) { if (!is_camera_0_connect) { int curr_gain = ((CameraWrapper* )video_gimbal)->gain; + int curr_exposure = ((CameraWrapper* )video_gimbal)->exposure; delete video_gimbal; - video_gimbal = new CameraWrapper(curr_gain, 0/*, "armor"*/); + video_gimbal = new CameraWrapper(curr_exposure, curr_gain, 0/*, "armor"*/); is_camera_0_connect = video_gimbal->init(); } if (!is_camera_1_connect) { int curr_gain = ((CameraWrapper* )video_gimbal)->gain; + int curr_exposure = ((CameraWrapper* )video_gimbal)->exposure; delete video_chassis; - video_chassis = new CameraWrapper(curr_gain, 0/*, "energy"*/); + video_chassis = new CameraWrapper(curr_exposure, curr_gain, 0/*, "energy"*/); is_camera_1_connect = video_chassis->init(); } return is_camera_0_connect && is_camera_1_connect; @@ -93,8 +95,9 @@ bool checkReconnect(bool is_camera_0_connect, bool is_camera_1_connect) { bool checkReconnect(bool is_camera_connect) { if (!is_camera_connect) { int curr_gain = ((CameraWrapper* )video_gimbal)->gain; + int curr_exposure = ((CameraWrapper* )video_gimbal)->exposure; delete video_gimbal; - video_gimbal = new CameraWrapper(curr_gain, 0/*, "armor"*/); + video_gimbal = new CameraWrapper(curr_exposure, curr_gain, 0/*, "armor"*/); is_camera_connect = video_gimbal->init(); } return is_camera_connect; diff --git a/others/src/camera/camera_wrapper.cpp b/others/src/camera/camera_wrapper.cpp index 963586d..16ba19e 100644 --- a/others/src/camera/camera_wrapper.cpp +++ b/others/src/camera/camera_wrapper.cpp @@ -11,7 +11,7 @@ using namespace std; using namespace cv; -CameraWrapper::CameraWrapper(int gain, int camera_mode, const std::string &n) : +CameraWrapper::CameraWrapper(int exposure, int gain, int camera_mode, const std::string &n) : name(n), init_done(false), mode(camera_mode), @@ -20,7 +20,8 @@ CameraWrapper::CameraWrapper(int gain, int camera_mode, const std::string &n) : iplImage(nullptr), rgb_buffer(nullptr), channel(3), - gain(gain){ + gain(gain), + exposure(exposure){ } void cameraCallback(CameraHandle hCamera, BYTE *pFrameBuffer, tSdkFrameHead* pFrameHead,PVOID pContext){ @@ -85,7 +86,7 @@ bool CameraWrapper::init() { CameraReadParameterFromFile(h_camera, PROJECT_DIR"/others/MV-UB31-Group0.config"); CameraLoadParameter(h_camera, PARAMETER_TEAM_A); CameraSetAeState(h_camera, false); - CameraSetExposureTime(h_camera, CAMERA_EXPOSURE * 1000); + CameraSetExposureTime(h_camera, exposure * 1000); CameraSetAnalogGain(h_camera, gain); #endif double t; diff --git a/tools/TrainCNN/backward.py b/tools/TrainCNN/backward.py index fc88244..c7f4357 100755 --- a/tools/TrainCNN/backward.py +++ b/tools/TrainCNN/backward.py @@ -8,8 +8,10 @@ import forward import cv2 import numpy as np import mvsdk + print("Finish!") + def save_kernal(fp, val): print(val.shape[2], file=fp) print(val.shape[3], file=fp) @@ -49,24 +51,24 @@ def save_para(folder, paras, names, info): fp.close() -STEPS = 50000 -BATCH = 30 -LEARNING_RATE_BASE = 0.0005 +STEPS = 100000 +BATCH = 40 +LEARNING_RATE_BASE = 0.0002 LEARNING_RATE_DECAY = 0.99 MOVING_AVERAGE_DECAY = 0.99 def train(dataset, show_bar=False): x = tf.placeholder(tf.float32, [None, generate.SRC_ROWS, generate.SRC_COLS, generate.SRC_CHANNELS]) - y_= tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES]) + y_ = tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES]) keep_rate = tf.placeholder(tf.float32) - nodes, vars, vars_name = forward.forward(x, 0.01) + nodes, vars, vars_name = forward.forward(x, 0.01, keep_rate) y = nodes[-1] - ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1)) -# ce = tf.nn.weighted_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1), pos_weight=1) + ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1)) + # ce = tf.nn.weighted_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1), pos_weight=1) cem = tf.reduce_mean(ce) - loss= cem + tf.add_n(tf.get_collection("losses")) + loss = cem + tf.add_n(tf.get_collection("losses")) global_step = tf.Variable(0, trainable=False) learning_rate = tf.train.exponential_decay( @@ -92,31 +94,33 @@ def train(dataset, show_bar=False): bar = tqdm(range(STEPS), ascii=True, dynamic_ncols=True) for i in bar: - images_samples, labels_samples = dataset.sample_train_sets(BATCH) + 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) @@ -219,9 +224,9 @@ def train(dataset, show_bar=False): if __name__ == "__main__": - # import os - # os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" - # os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + # import os + # os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" + # os.environ["CUDA_VISIBLE_DEVICES"] = "-1" dataset = generate.DataSet("/home/xinyang/Workspace/box_resize") train(dataset, show_bar=True) input("press enter to continue...") diff --git a/tools/TrainCNN/forward.py b/tools/TrainCNN/forward.py index cf9799d..522b262 100644 --- a/tools/TrainCNN/forward.py +++ b/tools/TrainCNN/forward.py @@ -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 @@ -62,8 +62,8 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)): [CONV1_KERNAL_SIZE, CONV1_KERNAL_SIZE, int(x.shape[3]), CONV1_OUTPUT_CHANNELS] ) conv1_b = get_bias([CONV1_OUTPUT_CHANNELS]) - conv1 = tf.nn.relu(tf.nn.bias_add(conv2d(x, conv1_w), conv1_b)) - pool1 = avg_pool_2x2(conv1) + conv1 = tf.nn.relu(tf.nn.bias_add(conv2d(x, conv1_w), conv1_b)) + pool1 = avg_pool_2x2(conv1) print("conv1: ", conv1.shape) print("pool1: ", pool1.shape) vars.extend([conv1_w, conv1_b]) @@ -100,7 +100,7 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)): fc1_w = get_weight([node, FC1_OUTPUT_NODES], regularizer) fc1_b = get_bias([FC1_OUTPUT_NODES]) - fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b) + fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b) vars.extend([fc1_w, fc1_b]) vars_name.extend(["fc1_w", "fc1_b"]) nodes.extend([fc1]) @@ -113,4 +113,3 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)): nodes.extend([fc2]) return nodes, vars, vars_name - diff --git a/tools/TrainCNN/generate.py b/tools/TrainCNN/generate.py index ffa2229..07c0d9c 100644 --- a/tools/TrainCNN/generate.py +++ b/tools/TrainCNN/generate.py @@ -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: + 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(self.file2nparray("%s/%s" % (dir, file))) - self.test_labels.append(self.id2label(i)) + 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[:] diff --git a/tools/para/conv1_b b/tools/para/conv1_b index 9024bfa..e9cdfb2 100644 --- a/tools/para/conv1_b +++ b/tools/para/conv1_b @@ -1,5 +1,5 @@ 4 --0.028037319 -0.5437552 -0.26716635 -0.15804483 +0.021338634 +-0.28292254 +0.39307028 +0.46493095 diff --git a/tools/para/conv1_w b/tools/para/conv1_w index 0d06cb0..0a4b9dd 100644 --- a/tools/para/conv1_w +++ b/tools/para/conv1_w @@ -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 -0.3930859 --0.42042395 --0.19382745 --0.37871224 --0.36034295 --0.35385096 --0.13182718 --0.15764016 --0.18180493 --0.35480255 --0.38093758 --0.26962438 --0.13433722 --0.11590976 --0.12100729 --0.41767576 --0.088450246 --0.15360384 --0.21754275 --0.33203712 --0.36672434 --0.33835274 --0.09806653 --0.25720394 --0.48649773 --0.28400043 -0.015362165 -0.17902431 -0.34508342 -0.30716935 -0.40274766 -0.14824653 -0.3363854 -0.61393374 -0.61485726 -0.185212 -0.27937973 -0.4085319 -0.56713074 -0.50924385 -0.14822921 --0.11108435 -0.1610854 -0.2802524 -0.04015192 -0.08895228 --0.19824773 --0.06844609 --0.069947205 --0.21471545 --0.21651222 --0.28975108 --0.36048082 --0.121744774 --0.32950944 --0.2776884 --0.38737568 --0.09360009 --0.116772 --0.2323865 --0.3063033 --0.09292303 --0.120976545 --0.098381124 --0.13898924 --0.24542217 --0.018138783 -0.1710496 -0.116988935 --0.30227587 --0.25962535 --0.20448786 --0.0741184 --0.021265341 --0.21863441 --0.24914841 -0.3989018 -0.38929248 -0.24901842 -0.28578317 -0.35204405 -0.21083018 -0.30775398 -0.17769343 -0.24103095 -0.43094385 -0.24827914 -0.12439884 -0.11712248 -0.3559909 -0.16364971 -0.038071744 -0.13404451 -0.12091028 --0.023739764 -0.08232509 --0.0009516679 -0.15716548 --0.10213259 -0.0639761 --0.10133095 --0.009949305 -0.09284781 -0.40186858 -0.25586432 -0.14342904 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a/tools/para/conv2_w +++ b/tools/para/conv2_w @@ -1,220 +1,292 @@ 4 -6 +8 3 3 --0.48382398 --0.36632606 --0.5114979 --0.14837296 --0.44867024 --0.4505499 --0.35961452 --0.28533116 --0.25498483 -0.21828032 -0.40261173 -0.4014606 -0.4331948 -0.57971007 -0.48511884 -0.4658404 -0.23746017 -0.25643173 --0.42512512 --0.32099026 --0.4071419 --0.07042966 --0.18999289 --0.41244265 --0.05782267 --0.26706457 --0.39842066 --0.42635453 --0.54127145 --0.50406104 --0.2970212 --0.2382909 --0.373955 --0.26274928 -0.09654497 -0.0068873395 -0.4851344 -0.71676046 -0.83653075 -0.36806846 -0.59301347 -0.43608147 -0.26763174 -0.24044617 -0.3138616 -0.32284674 -0.48771864 -0.47890294 -0.59924245 -0.58470404 -0.66103566 -0.4588577 -0.66345775 -0.5417458 -0.33620733 -0.6311169 -0.5712364 -0.27047473 -0.5663411 -0.62967324 -0.23675068 -0.18819211 -0.26856497 --0.24069026 -0.06490354 --0.20222004 -0.12556747 --0.081858225 --0.14404371 -0.15644343 -0.0056566484 --0.27573076 -0.6260534 -0.6114578 -0.4143709 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