From 9b15c7336f0fd0cdcd8c6d1865dbe2032c9b8f06 Mon Sep 17 00:00:00 2001 From: Labhesh Valechha Date: Mon, 1 Mar 2021 21:52:17 +0530 Subject: [PATCH 1/2] opencv dnn Windows instructions --- OpenCV-dnn-gpu-support-Windows/CMakeLists.txt | 14 + .../OpenPoseVideo.cpp | 163 + .../OpenPoseVideo.py | 103 + OpenCV-dnn-gpu-support-Windows/README.md | 37 + OpenCV-dnn-gpu-support-Windows/getModels.sh | 13 + .../pose/coco/pose_deploy_linevec.prototxt | 2976 +++++++++++++++++ .../pose/mpi/pose_deploy_linevec.prototxt | 2975 ++++++++++++++++ ...se_deploy_linevec_faster_4_stages.prototxt | 2081 ++++++++++++ README.md | 1 + 9 files changed, 8363 insertions(+) create mode 100755 OpenCV-dnn-gpu-support-Windows/CMakeLists.txt create mode 100755 OpenCV-dnn-gpu-support-Windows/OpenPoseVideo.cpp create mode 100755 OpenCV-dnn-gpu-support-Windows/OpenPoseVideo.py create mode 100755 OpenCV-dnn-gpu-support-Windows/README.md create mode 100755 OpenCV-dnn-gpu-support-Windows/getModels.sh create mode 100755 OpenCV-dnn-gpu-support-Windows/pose/coco/pose_deploy_linevec.prototxt create mode 100755 OpenCV-dnn-gpu-support-Windows/pose/mpi/pose_deploy_linevec.prototxt create mode 100755 OpenCV-dnn-gpu-support-Windows/pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt diff --git a/OpenCV-dnn-gpu-support-Windows/CMakeLists.txt b/OpenCV-dnn-gpu-support-Windows/CMakeLists.txt new file mode 100755 index 000000000..fe6c92229 --- /dev/null +++ b/OpenCV-dnn-gpu-support-Windows/CMakeLists.txt @@ -0,0 +1,14 @@ +cmake_minimum_required(VERSION 2.8.12) + +PROJECT(openPose) + +find_package( OpenCV REQUIRED ) + +include_directories( ${OpenCV_INCLUDE_DIRS}) + +MACRO(add_example name) + ADD_EXECUTABLE(${name} ${name}.cpp) + TARGET_LINK_LIBRARIES(${name} ${OpenCV_LIBS}) +ENDMACRO() + +add_example(OpenPoseVideo) diff --git a/OpenCV-dnn-gpu-support-Windows/OpenPoseVideo.cpp b/OpenCV-dnn-gpu-support-Windows/OpenPoseVideo.cpp new file mode 100755 index 000000000..d92cbb769 --- /dev/null +++ b/OpenCV-dnn-gpu-support-Windows/OpenPoseVideo.cpp @@ -0,0 +1,163 @@ +#include +#include + +using namespace std; +using namespace cv; +using namespace cv::dnn; + +#define MPI + +#ifdef MPI +const int POSE_PAIRS[14][2] = +{ + {0,1}, {1,2}, {2,3}, + {3,4}, {1,5}, {5,6}, + {6,7}, {1,14}, {14,8}, {8,9}, + {9,10}, {14,11}, {11,12}, {12,13} +}; + +string protoFile = "pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt"; +string weightsFile = "pose/mpi/pose_iter_160000.caffemodel"; + +int nPoints = 15; +#endif + +#ifdef COCO +const int POSE_PAIRS[17][2] = +{ + {1,2}, {1,5}, {2,3}, + {3,4}, {5,6}, {6,7}, + {1,8}, {8,9}, {9,10}, + {1,11}, {11,12}, {12,13}, + {1,0}, {0,14}, + {14,16}, {0,15}, {15,17} +}; + +string protoFile = "pose/coco/pose_deploy_linevec.prototxt"; +string weightsFile = "pose/coco/pose_iter_440000.caffemodel"; + +int nPoints = 18; +#endif + +int main(int argc, char **argv) +{ + + cout << "USAGE : ./OpenPose " << endl; + cout << "USAGE : ./OpenPose " << endl; + + string device = "cpu"; + string videoFile = "sample_video.mp4"; + + // Take arguments from commmand line + if (argc == 2) + { + if((string)argv[1] == "gpu") + device = "gpu"; + else + videoFile = argv[1]; + } + else if (argc == 3) + { + videoFile = argv[1]; + if((string)argv[2] == "gpu") + device = "gpu"; + } + + int inWidth = 368; + int inHeight = 368; + float thresh = 0.01; + + cv::VideoCapture cap(videoFile); + + if (!cap.isOpened()) + { + cerr << "Unable to connect to camera" << endl; + return 1; + } + + Mat frame, frameCopy; + int frameWidth = cap.get(CAP_PROP_FRAME_WIDTH); + int frameHeight = cap.get(CAP_PROP_FRAME_HEIGHT); + + VideoWriter video("Output-Skeleton.avi",VideoWriter::fourcc('M','J','P','G'), 10, Size(frameWidth,frameHeight)); + + Net net = readNetFromCaffe(protoFile, weightsFile); + + if (device == "cpu") + { + cout << "Using CPU device" << endl; + net.setPreferableBackend(DNN_TARGET_CPU); + } + else if (device == "gpu") + { + cout << "Using GPU device" << endl; + net.setPreferableBackend(DNN_BACKEND_CUDA); + net.setPreferableTarget(DNN_TARGET_CUDA); + } + + double t=0; + while( waitKey(1) < 0) + { + double t = (double) cv::getTickCount(); + + cap >> frame; + frameCopy = frame.clone(); + Mat inpBlob = blobFromImage(frame, 1.0 / 255, Size(inWidth, inHeight), Scalar(0, 0, 0), false, false); + + net.setInput(inpBlob); + + Mat output = net.forward(); + + int H = output.size[2]; + int W = output.size[3]; + + // find the position of the body parts + vector points(nPoints); + for (int n=0; n < nPoints; n++) + { + // Probability map of corresponding body's part. + Mat probMap(H, W, CV_32F, output.ptr(0,n)); + + Point2f p(-1,-1); + Point maxLoc; + double prob; + minMaxLoc(probMap, 0, &prob, 0, &maxLoc); + if (prob > thresh) + { + p = maxLoc; + p.x *= (float)frameWidth / W ; + p.y *= (float)frameHeight / H ; + + circle(frameCopy, cv::Point((int)p.x, (int)p.y), 8, Scalar(0,255,255), -1); + cv::putText(frameCopy, cv::format("%d", n), cv::Point((int)p.x, (int)p.y), cv::FONT_HERSHEY_COMPLEX, 1.1, cv::Scalar(0, 0, 255), 2); + } + points[n] = p; + } + + int nPairs = sizeof(POSE_PAIRS)/sizeof(POSE_PAIRS[0]); + + for (int n = 0; n < nPairs; n++) + { + // lookup 2 connected body/hand parts + Point2f partA = points[POSE_PAIRS[n][0]]; + Point2f partB = points[POSE_PAIRS[n][1]]; + + if (partA.x<=0 || partA.y<=0 || partB.x<=0 || partB.y<=0) + continue; + + line(frame, partA, partB, Scalar(0,255,255), 8); + circle(frame, partA, 8, Scalar(0,0,255), -1); + circle(frame, partB, 8, Scalar(0,0,255), -1); + } + + t = ((double)cv::getTickCount() - t)/cv::getTickFrequency(); + cv::putText(frame, cv::format("time taken = %.3f sec", t), cv::Point(50, 50), cv::FONT_HERSHEY_COMPLEX, .8, cv::Scalar(255, 50, 0), 2); + imshow("Output-Skeleton", frame); + video.write(frame); + } + // When everything done, release the video capture and write object + cap.release(); + video.release(); + + return 0; +} diff --git a/OpenCV-dnn-gpu-support-Windows/OpenPoseVideo.py b/OpenCV-dnn-gpu-support-Windows/OpenPoseVideo.py new file mode 100755 index 000000000..161bcda99 --- /dev/null +++ b/OpenCV-dnn-gpu-support-Windows/OpenPoseVideo.py @@ -0,0 +1,103 @@ +import cv2 +import time +import numpy as np +import argparse + +parser = argparse.ArgumentParser(description='Run keypoint detection') +parser.add_argument("--device", default="cpu", help="Device to inference on") +parser.add_argument("--video_file", default="sample_video.mp4", help="Input Video") + +args = parser.parse_args() + +MODE = "MPI" + +if MODE == "COCO": + protoFile = "pose/coco/pose_deploy_linevec.prototxt" + weightsFile = "pose/coco/pose_iter_440000.caffemodel" + nPoints = 18 + POSE_PAIRS = [ [1,0],[1,2],[1,5],[2,3],[3,4],[5,6],[6,7],[1,8],[8,9],[9,10],[1,11],[11,12],[12,13],[0,14],[0,15],[14,16],[15,17]] + +elif MODE == "MPI" : + protoFile = "pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt" + weightsFile = "pose/mpi/pose_iter_160000.caffemodel" + nPoints = 15 + POSE_PAIRS = [[0,1], [1,2], [2,3], [3,4], [1,5], [5,6], [6,7], [1,14], [14,8], [8,9], [9,10], [14,11], [11,12], [12,13] ] + + +inWidth = 368 +inHeight = 368 +threshold = 0.1 + + +input_source = args.video_file +cap = cv2.VideoCapture(input_source) +hasFrame, frame = cap.read() + +vid_writer = cv2.VideoWriter('output.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 10, (frame.shape[1],frame.shape[0])) + +net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile) +if args.device == "cpu": + net.setPreferableBackend(cv2.dnn.DNN_TARGET_CPU) + print("Using CPU device") +elif args.device == "gpu": + net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA) + net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA) + print("Using GPU device") + +while cv2.waitKey(1) < 0: + t = time.time() + hasFrame, frame = cap.read() + frameCopy = np.copy(frame) + if not hasFrame: + cv2.waitKey() + break + + frameWidth = frame.shape[1] + frameHeight = frame.shape[0] + + inpBlob = cv2.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight), + (0, 0, 0), swapRB=False, crop=False) + net.setInput(inpBlob) + output = net.forward() + + H = output.shape[2] + W = output.shape[3] + # Empty list to store the detected keypoints + points = [] + + for i in range(nPoints): + # confidence map of corresponding body's part. + probMap = output[0, i, :, :] + + # Find global maxima of the probMap. + minVal, prob, minLoc, point = cv2.minMaxLoc(probMap) + + # Scale the point to fit on the original image + x = (frameWidth * point[0]) / W + y = (frameHeight * point[1]) / H + + if prob > threshold : + cv2.circle(frameCopy, (int(x), int(y)), 8, (0, 255, 255), thickness=-1, lineType=cv2.FILLED) + cv2.putText(frameCopy, "{}".format(i), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, lineType=cv2.LINE_AA) + + # Add the point to the list if the probability is greater than the threshold + points.append((int(x), int(y))) + else : + points.append(None) + + # Draw Skeleton + for pair in POSE_PAIRS: + partA = pair[0] + partB = pair[1] + + if points[partA] and points[partB]: + cv2.line(frame, points[partA], points[partB], (0, 255, 255), 3, lineType=cv2.LINE_AA) + cv2.circle(frame, points[partA], 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED) + cv2.circle(frame, points[partB], 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED) + + cv2.putText(frame, "time taken = {:.3f} sec".format(time.time() - t), (50, 50), cv2.FONT_HERSHEY_COMPLEX, .8, (255, 50, 0), 2, lineType=cv2.LINE_AA) + cv2.imshow('Output-Skeleton', frame) + + vid_writer.write(frame) + +vid_writer.release() diff --git a/OpenCV-dnn-gpu-support-Windows/README.md b/OpenCV-dnn-gpu-support-Windows/README.md new file mode 100755 index 000000000..123eb6e9b --- /dev/null +++ b/OpenCV-dnn-gpu-support-Windows/README.md @@ -0,0 +1,37 @@ +# Code for How to use OpenCV DNN Module with Nvidia GPU on Windows + +This repository contains the code for [How to use OpenCV DNN Module with Nvidia GPU on Windows](https://www.learnopencv.com/how-to-use-opencv-dnn-module-with-nvidia-gpu-on-windows) + +## Models +Download models from + +COCO : https://www.dropbox.com/s/2h2bv29a130sgrk/pose_iter_440000.caffemodel?dl=0 + +MPI : https://www.dropbox.com/s/drumc6dzllfed16/pose_iter_160000.caffemodel?dl=0" + +## Run Code: + +### C++ +``` +mkdir build +cd build +cmake .. +make +cd .. +./build/OpenPoseVideo +``` + +### Python +``` +python OpenPoseVideo.py --device= +``` + +# AI Courses by OpenCV + +Want to become an expert in AI? [AI Courses by OpenCV](https://opencv.org/courses/) is a great place to start. + + +

+ +

+ diff --git a/OpenCV-dnn-gpu-support-Windows/getModels.sh b/OpenCV-dnn-gpu-support-Windows/getModels.sh new file mode 100755 index 000000000..ce71c9e3a --- /dev/null +++ b/OpenCV-dnn-gpu-support-Windows/getModels.sh @@ -0,0 +1,13 @@ +# ------------------------- POSE MODELS ------------------------- + + +# Downloading the pose-model trained on COCO +COCO_POSE_URL="https://www.dropbox.com/s/2h2bv29a130sgrk/pose_iter_440000.caffemodel" +COCO_FOLDER="pose/coco/" +wget -c ${COCO_POSE_URL} -P ${COCO_FOLDER} + +# Downloading the pose-model trained on MPI +MPI_POSE_URL="https://www.dropbox.com/s/drumc6dzllfed16/pose_iter_160000.caffemodel" +MPI_FOLDER="pose/mpi/" +wget -c ${MPI_POSE_URL} -P ${MPI_FOLDER} + diff --git a/OpenCV-dnn-gpu-support-Windows/pose/coco/pose_deploy_linevec.prototxt b/OpenCV-dnn-gpu-support-Windows/pose/coco/pose_deploy_linevec.prototxt new file mode 100755 index 000000000..3afa0b539 --- /dev/null +++ b/OpenCV-dnn-gpu-support-Windows/pose/coco/pose_deploy_linevec.prototxt @@ -0,0 +1,2976 @@ +input: "image" +input_dim: 1 +input_dim: 3 +input_dim: 1 # This value will be defined at runtime +input_dim: 1 # This value will be defined at runtime +layer { + name: "conv1_1" + type: "Convolution" + bottom: "image" + top: "conv1_1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +layer { + name: "pool1_stage1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1_stage1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1_stage1" + top: "conv2_1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +layer { + name: "pool2_stage1" + type: "Pooling" + bottom: "conv2_2" + top: "pool2_stage1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2_stage1" + top: "conv3_1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} +layer { + name: "conv3_4" + type: "Convolution" + bottom: "conv3_3" + top: "conv3_4" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu3_4" + type: "ReLU" + bottom: "conv3_4" + top: "conv3_4" +} +layer { + name: "pool3_stage1" + type: "Pooling" + bottom: "conv3_4" + top: "pool3_stage1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3_stage1" + top: "conv4_1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3_CPM" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3_CPM" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu4_3_CPM" + type: "ReLU" + bottom: "conv4_3_CPM" + top: "conv4_3_CPM" +} +layer { + name: "conv4_4_CPM" + type: "Convolution" + bottom: "conv4_3_CPM" + top: "conv4_4_CPM" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu4_4_CPM" + type: "ReLU" + bottom: "conv4_4_CPM" + top: "conv4_4_CPM" +} +layer { + name: "conv5_1_CPM_L1" + type: "Convolution" + bottom: "conv4_4_CPM" + top: "conv5_1_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_1_CPM_L1" + type: "ReLU" + bottom: "conv5_1_CPM_L1" + top: "conv5_1_CPM_L1" +} +layer { + name: "conv5_1_CPM_L2" + type: "Convolution" + bottom: "conv4_4_CPM" + top: "conv5_1_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_1_CPM_L2" + type: "ReLU" + bottom: "conv5_1_CPM_L2" + top: "conv5_1_CPM_L2" +} +layer { + name: "conv5_2_CPM_L1" + type: "Convolution" + bottom: "conv5_1_CPM_L1" + top: "conv5_2_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_2_CPM_L1" + type: "ReLU" + bottom: "conv5_2_CPM_L1" + top: "conv5_2_CPM_L1" +} +layer { + name: "conv5_2_CPM_L2" + type: "Convolution" + bottom: "conv5_1_CPM_L2" + top: "conv5_2_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_2_CPM_L2" + type: "ReLU" + bottom: "conv5_2_CPM_L2" + top: "conv5_2_CPM_L2" +} +layer { + name: "conv5_3_CPM_L1" + type: "Convolution" + bottom: "conv5_2_CPM_L1" + top: "conv5_3_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_3_CPM_L1" + type: "ReLU" + bottom: "conv5_3_CPM_L1" + top: "conv5_3_CPM_L1" +} +layer { + name: "conv5_3_CPM_L2" + type: "Convolution" + bottom: "conv5_2_CPM_L2" + top: "conv5_3_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_3_CPM_L2" + type: "ReLU" + bottom: "conv5_3_CPM_L2" + top: "conv5_3_CPM_L2" +} +layer { + name: "conv5_4_CPM_L1" + type: "Convolution" + bottom: "conv5_3_CPM_L1" + top: "conv5_4_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_4_CPM_L1" + type: "ReLU" + bottom: "conv5_4_CPM_L1" + top: "conv5_4_CPM_L1" +} +layer { + name: "conv5_4_CPM_L2" + type: "Convolution" + bottom: "conv5_3_CPM_L2" + top: "conv5_4_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_4_CPM_L2" + type: "ReLU" + bottom: "conv5_4_CPM_L2" + top: "conv5_4_CPM_L2" +} +layer { + name: "conv5_5_CPM_L1" + type: "Convolution" + bottom: "conv5_4_CPM_L1" + top: "conv5_5_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 38 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "conv5_5_CPM_L2" + type: "Convolution" + bottom: "conv5_4_CPM_L2" + top: "conv5_5_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 19 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage2" + type: "Concat" + bottom: "conv5_5_CPM_L1" + bottom: "conv5_5_CPM_L2" + bottom: "conv4_4_CPM" + top: "concat_stage2" + concat_param { + axis: 1 + } +} +layer { + name: "Mconv1_stage2_L1" + type: "Convolution" + bottom: "concat_stage2" + top: "Mconv1_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage2_L1" + type: "ReLU" + bottom: "Mconv1_stage2_L1" + top: "Mconv1_stage2_L1" +} +layer { + name: "Mconv1_stage2_L2" + type: "Convolution" + bottom: "concat_stage2" + top: "Mconv1_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage2_L2" + type: "ReLU" + bottom: "Mconv1_stage2_L2" + top: "Mconv1_stage2_L2" +} +layer { + name: "Mconv2_stage2_L1" + type: "Convolution" + bottom: "Mconv1_stage2_L1" + top: "Mconv2_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage2_L1" + type: "ReLU" + bottom: "Mconv2_stage2_L1" + top: "Mconv2_stage2_L1" +} +layer { + name: "Mconv2_stage2_L2" + type: "Convolution" + bottom: "Mconv1_stage2_L2" + top: "Mconv2_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage2_L2" + type: "ReLU" + bottom: "Mconv2_stage2_L2" + top: "Mconv2_stage2_L2" +} +layer { + name: "Mconv3_stage2_L1" + type: "Convolution" + bottom: "Mconv2_stage2_L1" + top: "Mconv3_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage2_L1" + type: "ReLU" + bottom: "Mconv3_stage2_L1" + top: "Mconv3_stage2_L1" +} +layer { + name: "Mconv3_stage2_L2" + type: "Convolution" + bottom: "Mconv2_stage2_L2" + top: "Mconv3_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage2_L2" + type: "ReLU" + bottom: "Mconv3_stage2_L2" + top: "Mconv3_stage2_L2" +} +layer { + name: "Mconv4_stage2_L1" + type: "Convolution" + bottom: "Mconv3_stage2_L1" + top: "Mconv4_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage2_L1" + type: "ReLU" + bottom: "Mconv4_stage2_L1" + top: "Mconv4_stage2_L1" +} +layer { + name: "Mconv4_stage2_L2" + type: "Convolution" + bottom: "Mconv3_stage2_L2" + top: "Mconv4_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage2_L2" + type: "ReLU" + bottom: "Mconv4_stage2_L2" + top: "Mconv4_stage2_L2" +} +layer { + name: "Mconv5_stage2_L1" + type: "Convolution" + bottom: "Mconv4_stage2_L1" + top: "Mconv5_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage2_L1" + type: "ReLU" + bottom: "Mconv5_stage2_L1" + top: "Mconv5_stage2_L1" +} +layer { + name: "Mconv5_stage2_L2" + type: "Convolution" + bottom: "Mconv4_stage2_L2" + top: "Mconv5_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage2_L2" + type: "ReLU" + bottom: "Mconv5_stage2_L2" + top: "Mconv5_stage2_L2" +} +layer { + name: "Mconv6_stage2_L1" + type: "Convolution" + bottom: "Mconv5_stage2_L1" + top: "Mconv6_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage2_L1" + type: "ReLU" + bottom: "Mconv6_stage2_L1" + top: "Mconv6_stage2_L1" +} +layer { + name: "Mconv6_stage2_L2" + type: "Convolution" + bottom: "Mconv5_stage2_L2" + top: "Mconv6_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage2_L2" + type: "ReLU" + bottom: "Mconv6_stage2_L2" + top: "Mconv6_stage2_L2" +} +layer { + name: "Mconv7_stage2_L1" + type: "Convolution" + bottom: "Mconv6_stage2_L1" + top: "Mconv7_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 38 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mconv7_stage2_L2" + type: "Convolution" + bottom: "Mconv6_stage2_L2" + top: "Mconv7_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 19 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage3" + type: "Concat" + bottom: "Mconv7_stage2_L1" + bottom: "Mconv7_stage2_L2" + bottom: "conv4_4_CPM" + top: "concat_stage3" + concat_param { + axis: 1 + } +} +layer { + name: "Mconv1_stage3_L1" + type: "Convolution" + bottom: "concat_stage3" + top: "Mconv1_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage3_L1" + type: "ReLU" + bottom: "Mconv1_stage3_L1" + top: "Mconv1_stage3_L1" +} +layer { + name: "Mconv1_stage3_L2" + type: "Convolution" + bottom: "concat_stage3" + top: "Mconv1_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage3_L2" + type: "ReLU" + bottom: "Mconv1_stage3_L2" + top: "Mconv1_stage3_L2" +} +layer { + name: "Mconv2_stage3_L1" + type: "Convolution" + bottom: "Mconv1_stage3_L1" + top: "Mconv2_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage3_L1" + type: "ReLU" + bottom: "Mconv2_stage3_L1" + top: "Mconv2_stage3_L1" +} +layer { + name: "Mconv2_stage3_L2" + type: "Convolution" + bottom: "Mconv1_stage3_L2" + top: "Mconv2_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage3_L2" + type: "ReLU" + bottom: "Mconv2_stage3_L2" + top: "Mconv2_stage3_L2" +} +layer { + name: "Mconv3_stage3_L1" + type: "Convolution" + bottom: "Mconv2_stage3_L1" + top: "Mconv3_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage3_L1" + type: "ReLU" + bottom: "Mconv3_stage3_L1" + top: "Mconv3_stage3_L1" +} +layer { + name: "Mconv3_stage3_L2" + type: "Convolution" + bottom: "Mconv2_stage3_L2" + top: "Mconv3_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage3_L2" + type: "ReLU" + bottom: "Mconv3_stage3_L2" + top: "Mconv3_stage3_L2" +} +layer { + name: "Mconv4_stage3_L1" + type: "Convolution" + bottom: "Mconv3_stage3_L1" + top: "Mconv4_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage3_L1" + type: "ReLU" + bottom: "Mconv4_stage3_L1" + top: "Mconv4_stage3_L1" +} +layer { + name: "Mconv4_stage3_L2" + type: "Convolution" + bottom: "Mconv3_stage3_L2" + top: "Mconv4_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage3_L2" + type: "ReLU" + bottom: "Mconv4_stage3_L2" + top: "Mconv4_stage3_L2" +} +layer { + name: "Mconv5_stage3_L1" + type: "Convolution" + bottom: "Mconv4_stage3_L1" + top: "Mconv5_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage3_L1" + type: "ReLU" + bottom: "Mconv5_stage3_L1" + top: "Mconv5_stage3_L1" +} +layer { + name: "Mconv5_stage3_L2" + type: "Convolution" + bottom: "Mconv4_stage3_L2" + top: "Mconv5_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage3_L2" + type: "ReLU" + bottom: "Mconv5_stage3_L2" + top: "Mconv5_stage3_L2" +} +layer { + name: "Mconv6_stage3_L1" + type: "Convolution" + bottom: "Mconv5_stage3_L1" + top: "Mconv6_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage3_L1" + type: "ReLU" + bottom: "Mconv6_stage3_L1" + top: "Mconv6_stage3_L1" +} +layer { + name: "Mconv6_stage3_L2" + type: "Convolution" + bottom: "Mconv5_stage3_L2" + top: "Mconv6_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage3_L2" + type: "ReLU" + bottom: "Mconv6_stage3_L2" + top: "Mconv6_stage3_L2" +} +layer { + name: "Mconv7_stage3_L1" + type: "Convolution" + bottom: "Mconv6_stage3_L1" + top: "Mconv7_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 38 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mconv7_stage3_L2" + type: "Convolution" + bottom: "Mconv6_stage3_L2" + top: "Mconv7_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 19 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage4" + type: "Concat" + bottom: "Mconv7_stage3_L1" + bottom: "Mconv7_stage3_L2" + bottom: "conv4_4_CPM" + top: "concat_stage4" + concat_param { + axis: 1 + } +} +layer { + name: "Mconv1_stage4_L1" + type: "Convolution" + bottom: "concat_stage4" + top: "Mconv1_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage4_L1" + type: "ReLU" + bottom: "Mconv1_stage4_L1" + top: "Mconv1_stage4_L1" +} +layer { + name: "Mconv1_stage4_L2" + type: "Convolution" + bottom: "concat_stage4" + top: "Mconv1_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage4_L2" + type: "ReLU" + bottom: "Mconv1_stage4_L2" + top: "Mconv1_stage4_L2" +} +layer { + name: "Mconv2_stage4_L1" + type: "Convolution" + bottom: "Mconv1_stage4_L1" + top: "Mconv2_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage4_L1" + type: "ReLU" + bottom: "Mconv2_stage4_L1" + top: "Mconv2_stage4_L1" +} +layer { + name: "Mconv2_stage4_L2" + type: "Convolution" + bottom: "Mconv1_stage4_L2" + top: "Mconv2_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage4_L2" + type: "ReLU" + bottom: "Mconv2_stage4_L2" + top: "Mconv2_stage4_L2" +} +layer { + name: "Mconv3_stage4_L1" + type: "Convolution" + bottom: "Mconv2_stage4_L1" + top: "Mconv3_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage4_L1" + type: "ReLU" + bottom: "Mconv3_stage4_L1" + top: "Mconv3_stage4_L1" +} +layer { + name: "Mconv3_stage4_L2" + type: "Convolution" + bottom: "Mconv2_stage4_L2" + top: "Mconv3_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage4_L2" + type: "ReLU" + bottom: "Mconv3_stage4_L2" + top: "Mconv3_stage4_L2" +} +layer { + name: "Mconv4_stage4_L1" + type: "Convolution" + bottom: "Mconv3_stage4_L1" + top: "Mconv4_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage4_L1" + type: "ReLU" + bottom: "Mconv4_stage4_L1" + top: "Mconv4_stage4_L1" +} +layer { + name: "Mconv4_stage4_L2" + type: "Convolution" + bottom: "Mconv3_stage4_L2" + top: "Mconv4_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage4_L2" + type: "ReLU" + bottom: "Mconv4_stage4_L2" + top: "Mconv4_stage4_L2" +} +layer { + name: "Mconv5_stage4_L1" + type: "Convolution" + bottom: "Mconv4_stage4_L1" + top: "Mconv5_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage4_L1" + type: "ReLU" + bottom: "Mconv5_stage4_L1" + top: "Mconv5_stage4_L1" +} +layer { + name: "Mconv5_stage4_L2" + type: "Convolution" + bottom: "Mconv4_stage4_L2" + top: "Mconv5_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage4_L2" + type: "ReLU" + bottom: "Mconv5_stage4_L2" + top: "Mconv5_stage4_L2" +} +layer { + name: "Mconv6_stage4_L1" + type: "Convolution" + bottom: "Mconv5_stage4_L1" + top: "Mconv6_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage4_L1" + type: "ReLU" + bottom: "Mconv6_stage4_L1" + top: "Mconv6_stage4_L1" +} +layer { + name: "Mconv6_stage4_L2" + type: "Convolution" + bottom: "Mconv5_stage4_L2" + top: "Mconv6_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage4_L2" + type: "ReLU" + bottom: "Mconv6_stage4_L2" + top: "Mconv6_stage4_L2" +} +layer { + name: "Mconv7_stage4_L1" + type: "Convolution" + bottom: "Mconv6_stage4_L1" + top: "Mconv7_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 38 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mconv7_stage4_L2" + type: "Convolution" + bottom: "Mconv6_stage4_L2" + top: "Mconv7_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 19 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage5" + type: "Concat" + bottom: "Mconv7_stage4_L1" + bottom: "Mconv7_stage4_L2" + bottom: "conv4_4_CPM" + top: "concat_stage5" + concat_param { + axis: 1 + } +} +layer { + name: "Mconv1_stage5_L1" + type: "Convolution" + bottom: "concat_stage5" + top: "Mconv1_stage5_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage5_L1" + type: "ReLU" + bottom: "Mconv1_stage5_L1" + top: "Mconv1_stage5_L1" +} +layer { + name: "Mconv1_stage5_L2" + type: "Convolution" + bottom: "concat_stage5" + top: "Mconv1_stage5_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage5_L2" + type: "ReLU" + bottom: "Mconv1_stage5_L2" + top: "Mconv1_stage5_L2" +} +layer { + name: "Mconv2_stage5_L1" + type: "Convolution" + bottom: "Mconv1_stage5_L1" + top: "Mconv2_stage5_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage5_L1" + type: "ReLU" + bottom: "Mconv2_stage5_L1" + top: "Mconv2_stage5_L1" +} +layer { + name: "Mconv2_stage5_L2" + type: "Convolution" + bottom: "Mconv1_stage5_L2" + top: "Mconv2_stage5_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage5_L2" + type: "ReLU" + bottom: "Mconv2_stage5_L2" + top: "Mconv2_stage5_L2" +} +layer { + name: "Mconv3_stage5_L1" + type: "Convolution" + bottom: "Mconv2_stage5_L1" + top: "Mconv3_stage5_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage5_L1" + type: "ReLU" + bottom: "Mconv3_stage5_L1" + top: "Mconv3_stage5_L1" +} +layer { + name: "Mconv3_stage5_L2" + type: "Convolution" + bottom: "Mconv2_stage5_L2" + top: "Mconv3_stage5_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage5_L2" + type: "ReLU" + bottom: "Mconv3_stage5_L2" + top: "Mconv3_stage5_L2" +} +layer { + name: "Mconv4_stage5_L1" + type: "Convolution" + bottom: "Mconv3_stage5_L1" + top: "Mconv4_stage5_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage5_L1" + type: "ReLU" + bottom: "Mconv4_stage5_L1" + top: "Mconv4_stage5_L1" +} +layer { + name: "Mconv4_stage5_L2" + type: "Convolution" + bottom: "Mconv3_stage5_L2" + top: "Mconv4_stage5_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage5_L2" + type: "ReLU" + bottom: "Mconv4_stage5_L2" + top: "Mconv4_stage5_L2" +} +layer { + name: "Mconv5_stage5_L1" + type: "Convolution" + bottom: "Mconv4_stage5_L1" + top: "Mconv5_stage5_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage5_L1" + type: "ReLU" + bottom: "Mconv5_stage5_L1" + top: "Mconv5_stage5_L1" +} +layer { + name: "Mconv5_stage5_L2" + type: "Convolution" + bottom: "Mconv4_stage5_L2" + top: "Mconv5_stage5_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage5_L2" + type: "ReLU" + bottom: "Mconv5_stage5_L2" + top: "Mconv5_stage5_L2" +} +layer { + name: "Mconv6_stage5_L1" + type: "Convolution" + bottom: "Mconv5_stage5_L1" + top: "Mconv6_stage5_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage5_L1" + type: "ReLU" + bottom: "Mconv6_stage5_L1" + top: "Mconv6_stage5_L1" +} +layer { + name: "Mconv6_stage5_L2" + type: "Convolution" + bottom: "Mconv5_stage5_L2" + top: "Mconv6_stage5_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage5_L2" + type: "ReLU" + bottom: "Mconv6_stage5_L2" + top: "Mconv6_stage5_L2" +} +layer { + name: "Mconv7_stage5_L1" + type: "Convolution" + bottom: "Mconv6_stage5_L1" + top: "Mconv7_stage5_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 38 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mconv7_stage5_L2" + type: "Convolution" + bottom: "Mconv6_stage5_L2" + top: "Mconv7_stage5_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 19 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage6" + type: "Concat" + bottom: "Mconv7_stage5_L1" + bottom: "Mconv7_stage5_L2" + bottom: "conv4_4_CPM" + top: "concat_stage6" + concat_param { + axis: 1 + } +} +layer { + name: "Mconv1_stage6_L1" + type: "Convolution" + bottom: "concat_stage6" + top: "Mconv1_stage6_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage6_L1" + type: "ReLU" + bottom: "Mconv1_stage6_L1" + top: "Mconv1_stage6_L1" +} +layer { + name: "Mconv1_stage6_L2" + type: "Convolution" + bottom: "concat_stage6" + top: "Mconv1_stage6_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage6_L2" + type: "ReLU" + bottom: "Mconv1_stage6_L2" + top: "Mconv1_stage6_L2" +} +layer { + name: "Mconv2_stage6_L1" + type: "Convolution" + bottom: "Mconv1_stage6_L1" + top: "Mconv2_stage6_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage6_L1" + type: "ReLU" + bottom: "Mconv2_stage6_L1" + top: "Mconv2_stage6_L1" +} +layer { + name: "Mconv2_stage6_L2" + type: "Convolution" + bottom: "Mconv1_stage6_L2" + top: "Mconv2_stage6_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage6_L2" + type: "ReLU" + bottom: "Mconv2_stage6_L2" + top: "Mconv2_stage6_L2" +} +layer { + name: "Mconv3_stage6_L1" + type: "Convolution" + bottom: "Mconv2_stage6_L1" + top: "Mconv3_stage6_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage6_L1" + type: "ReLU" + bottom: "Mconv3_stage6_L1" + top: "Mconv3_stage6_L1" +} +layer { + name: "Mconv3_stage6_L2" + type: "Convolution" + bottom: "Mconv2_stage6_L2" + top: "Mconv3_stage6_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage6_L2" + type: "ReLU" + bottom: "Mconv3_stage6_L2" + top: "Mconv3_stage6_L2" +} +layer { + name: "Mconv4_stage6_L1" + type: "Convolution" + bottom: "Mconv3_stage6_L1" + top: "Mconv4_stage6_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage6_L1" + type: "ReLU" + bottom: "Mconv4_stage6_L1" + top: "Mconv4_stage6_L1" +} +layer { + name: "Mconv4_stage6_L2" + type: "Convolution" + bottom: "Mconv3_stage6_L2" + top: "Mconv4_stage6_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage6_L2" + type: "ReLU" + bottom: "Mconv4_stage6_L2" + top: "Mconv4_stage6_L2" +} +layer { + name: "Mconv5_stage6_L1" + type: "Convolution" + bottom: "Mconv4_stage6_L1" + top: "Mconv5_stage6_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage6_L1" + type: "ReLU" + bottom: "Mconv5_stage6_L1" + top: "Mconv5_stage6_L1" +} +layer { + name: "Mconv5_stage6_L2" + type: "Convolution" + bottom: "Mconv4_stage6_L2" + top: "Mconv5_stage6_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage6_L2" + type: "ReLU" + bottom: "Mconv5_stage6_L2" + top: "Mconv5_stage6_L2" +} +layer { + name: "Mconv6_stage6_L1" + type: "Convolution" + bottom: "Mconv5_stage6_L1" + top: "Mconv6_stage6_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage6_L1" + type: "ReLU" + bottom: "Mconv6_stage6_L1" + top: "Mconv6_stage6_L1" +} +layer { + name: "Mconv6_stage6_L2" + type: "Convolution" + bottom: "Mconv5_stage6_L2" + top: "Mconv6_stage6_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage6_L2" + type: "ReLU" + bottom: "Mconv6_stage6_L2" + top: "Mconv6_stage6_L2" +} +layer { + name: "Mconv7_stage6_L1" + type: "Convolution" + bottom: "Mconv6_stage6_L1" + top: "Mconv7_stage6_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 38 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mconv7_stage6_L2" + type: "Convolution" + bottom: "Mconv6_stage6_L2" + top: "Mconv7_stage6_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 19 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage7" + type: "Concat" + bottom: "Mconv7_stage6_L2" + bottom: "Mconv7_stage6_L1" + # top: "concat_stage7" + top: "net_output" + concat_param { + axis: 1 + } +} \ No newline at end of file diff --git a/OpenCV-dnn-gpu-support-Windows/pose/mpi/pose_deploy_linevec.prototxt b/OpenCV-dnn-gpu-support-Windows/pose/mpi/pose_deploy_linevec.prototxt new file mode 100755 index 000000000..bffc00ce5 --- /dev/null +++ b/OpenCV-dnn-gpu-support-Windows/pose/mpi/pose_deploy_linevec.prototxt @@ -0,0 +1,2975 @@ +input: "image" +input_dim: 1 +input_dim: 3 +input_dim: 1 # This value will be defined at runtime +input_dim: 1 # This value will be defined at runtime +layer { + name: "conv1_1" + type: "Convolution" + bottom: "image" + top: "conv1_1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +layer { + name: "pool1_stage1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1_stage1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1_stage1" + top: "conv2_1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +layer { + name: "pool2_stage1" + type: "Pooling" + bottom: "conv2_2" + top: "pool2_stage1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2_stage1" + top: "conv3_1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} +layer { + name: "conv3_4" + type: "Convolution" + bottom: "conv3_3" + top: "conv3_4" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu3_4" + type: "ReLU" + bottom: "conv3_4" + top: "conv3_4" +} +layer { + name: "pool3_stage1" + type: "Pooling" + bottom: "conv3_4" + top: "pool3_stage1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3_stage1" + top: "conv4_1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3_CPM" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3_CPM" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu4_3_CPM" + type: "ReLU" + bottom: "conv4_3_CPM" + top: "conv4_3_CPM" +} +layer { + name: "conv4_4_CPM" + type: "Convolution" + bottom: "conv4_3_CPM" + top: "conv4_4_CPM" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu4_4_CPM" + type: "ReLU" + bottom: "conv4_4_CPM" + top: "conv4_4_CPM" +} +layer { + name: "conv5_1_CPM_L1" + type: "Convolution" + bottom: "conv4_4_CPM" + top: "conv5_1_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_1_CPM_L1" + type: "ReLU" + bottom: "conv5_1_CPM_L1" + top: "conv5_1_CPM_L1" +} +layer { + name: "conv5_1_CPM_L2" + type: "Convolution" + bottom: "conv4_4_CPM" + top: "conv5_1_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_1_CPM_L2" + type: "ReLU" + bottom: "conv5_1_CPM_L2" + top: "conv5_1_CPM_L2" +} +layer { + name: "conv5_2_CPM_L1" + type: "Convolution" + bottom: "conv5_1_CPM_L1" + top: "conv5_2_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_2_CPM_L1" + type: "ReLU" + bottom: "conv5_2_CPM_L1" + top: "conv5_2_CPM_L1" +} +layer { + name: "conv5_2_CPM_L2" + type: "Convolution" + bottom: "conv5_1_CPM_L2" + top: "conv5_2_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_2_CPM_L2" + type: "ReLU" + bottom: "conv5_2_CPM_L2" + top: "conv5_2_CPM_L2" +} +layer { + name: "conv5_3_CPM_L1" + type: "Convolution" + bottom: "conv5_2_CPM_L1" + top: "conv5_3_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_3_CPM_L1" + type: "ReLU" + bottom: "conv5_3_CPM_L1" + top: "conv5_3_CPM_L1" +} +layer { + name: "conv5_3_CPM_L2" + type: "Convolution" + bottom: "conv5_2_CPM_L2" + top: "conv5_3_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_3_CPM_L2" + type: "ReLU" + bottom: "conv5_3_CPM_L2" + top: "conv5_3_CPM_L2" +} +layer { + name: "conv5_4_CPM_L1" + type: "Convolution" + bottom: "conv5_3_CPM_L1" + top: "conv5_4_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_4_CPM_L1" + type: "ReLU" + bottom: "conv5_4_CPM_L1" + top: "conv5_4_CPM_L1" +} +layer { + name: "conv5_4_CPM_L2" + type: "Convolution" + bottom: "conv5_3_CPM_L2" + top: "conv5_4_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_4_CPM_L2" + type: "ReLU" + bottom: "conv5_4_CPM_L2" + top: "conv5_4_CPM_L2" +} +layer { + name: "conv5_5_CPM_L1" + type: "Convolution" + bottom: "conv5_4_CPM_L1" + top: "conv5_5_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 28 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "conv5_5_CPM_L2" + type: "Convolution" + bottom: "conv5_4_CPM_L2" + top: "conv5_5_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage2" + type: "Concat" + bottom: "conv5_5_CPM_L1" + bottom: "conv5_5_CPM_L2" + bottom: "conv4_4_CPM" + top: "concat_stage2" + concat_param { + axis: 1 + } +} +layer { + name: "Mconv1_stage2_L1" + type: "Convolution" + bottom: "concat_stage2" + top: "Mconv1_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage2_L1" + type: "ReLU" + bottom: "Mconv1_stage2_L1" + top: "Mconv1_stage2_L1" +} +layer { + name: "Mconv1_stage2_L2" + type: "Convolution" + bottom: "concat_stage2" + top: "Mconv1_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage2_L2" + type: "ReLU" + bottom: "Mconv1_stage2_L2" + top: "Mconv1_stage2_L2" +} +layer { + name: "Mconv2_stage2_L1" + type: "Convolution" + bottom: "Mconv1_stage2_L1" + top: "Mconv2_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage2_L1" + type: "ReLU" + bottom: "Mconv2_stage2_L1" + top: "Mconv2_stage2_L1" +} +layer { + name: "Mconv2_stage2_L2" + type: "Convolution" + bottom: "Mconv1_stage2_L2" + top: "Mconv2_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage2_L2" + type: "ReLU" + bottom: "Mconv2_stage2_L2" + top: "Mconv2_stage2_L2" +} +layer { + name: "Mconv3_stage2_L1" + type: "Convolution" + bottom: "Mconv2_stage2_L1" + top: "Mconv3_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage2_L1" + type: "ReLU" + bottom: "Mconv3_stage2_L1" + top: "Mconv3_stage2_L1" +} +layer { + name: "Mconv3_stage2_L2" + type: "Convolution" + bottom: "Mconv2_stage2_L2" + top: "Mconv3_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage2_L2" + type: "ReLU" + bottom: "Mconv3_stage2_L2" + top: "Mconv3_stage2_L2" +} +layer { + name: "Mconv4_stage2_L1" + type: "Convolution" + bottom: "Mconv3_stage2_L1" + top: "Mconv4_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage2_L1" + type: "ReLU" + bottom: "Mconv4_stage2_L1" + top: "Mconv4_stage2_L1" +} +layer { + name: "Mconv4_stage2_L2" + type: "Convolution" + bottom: "Mconv3_stage2_L2" + top: "Mconv4_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage2_L2" + type: "ReLU" + bottom: "Mconv4_stage2_L2" + top: "Mconv4_stage2_L2" +} +layer { + name: "Mconv5_stage2_L1" + type: "Convolution" + bottom: "Mconv4_stage2_L1" + top: "Mconv5_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage2_L1" + type: "ReLU" + bottom: "Mconv5_stage2_L1" + top: "Mconv5_stage2_L1" +} +layer { + name: "Mconv5_stage2_L2" + type: "Convolution" + bottom: "Mconv4_stage2_L2" + top: "Mconv5_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage2_L2" + type: "ReLU" + bottom: "Mconv5_stage2_L2" + top: "Mconv5_stage2_L2" +} +layer { + name: "Mconv6_stage2_L1" + type: "Convolution" + bottom: "Mconv5_stage2_L1" + top: "Mconv6_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage2_L1" + type: "ReLU" + bottom: "Mconv6_stage2_L1" + top: "Mconv6_stage2_L1" +} +layer { + name: "Mconv6_stage2_L2" + type: "Convolution" + bottom: "Mconv5_stage2_L2" + top: "Mconv6_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage2_L2" + type: "ReLU" + bottom: "Mconv6_stage2_L2" + top: "Mconv6_stage2_L2" +} +layer { + name: "Mconv7_stage2_L1" + type: "Convolution" + bottom: "Mconv6_stage2_L1" + top: "Mconv7_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 28 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mconv7_stage2_L2" + type: "Convolution" + bottom: "Mconv6_stage2_L2" + top: "Mconv7_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage3" + type: "Concat" + bottom: "Mconv7_stage2_L1" + bottom: "Mconv7_stage2_L2" + bottom: "conv4_4_CPM" + top: "concat_stage3" + concat_param { + axis: 1 + } +} +layer { + name: "Mconv1_stage3_L1" + type: "Convolution" + bottom: "concat_stage3" + top: "Mconv1_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage3_L1" + type: "ReLU" + bottom: "Mconv1_stage3_L1" + top: "Mconv1_stage3_L1" +} +layer { + name: "Mconv1_stage3_L2" + type: "Convolution" + bottom: "concat_stage3" + top: "Mconv1_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage3_L2" + type: "ReLU" + bottom: "Mconv1_stage3_L2" + top: "Mconv1_stage3_L2" +} +layer { + name: "Mconv2_stage3_L1" + type: "Convolution" + bottom: "Mconv1_stage3_L1" + top: "Mconv2_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage3_L1" + type: "ReLU" + bottom: "Mconv2_stage3_L1" + top: "Mconv2_stage3_L1" +} +layer { + name: "Mconv2_stage3_L2" + type: "Convolution" + bottom: "Mconv1_stage3_L2" + top: "Mconv2_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage3_L2" + type: "ReLU" + bottom: "Mconv2_stage3_L2" + top: "Mconv2_stage3_L2" +} +layer { + name: "Mconv3_stage3_L1" + type: "Convolution" + bottom: "Mconv2_stage3_L1" + top: "Mconv3_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage3_L1" + type: "ReLU" + bottom: "Mconv3_stage3_L1" + top: "Mconv3_stage3_L1" +} +layer { + name: "Mconv3_stage3_L2" + type: "Convolution" + bottom: "Mconv2_stage3_L2" + top: "Mconv3_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage3_L2" + type: "ReLU" + bottom: "Mconv3_stage3_L2" + top: "Mconv3_stage3_L2" +} +layer { + name: "Mconv4_stage3_L1" + type: "Convolution" + bottom: "Mconv3_stage3_L1" + top: "Mconv4_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage3_L1" + type: "ReLU" + bottom: "Mconv4_stage3_L1" + top: "Mconv4_stage3_L1" +} +layer { + name: "Mconv4_stage3_L2" + type: "Convolution" + bottom: "Mconv3_stage3_L2" + top: "Mconv4_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage3_L2" + type: "ReLU" + bottom: "Mconv4_stage3_L2" + top: "Mconv4_stage3_L2" +} +layer { + name: "Mconv5_stage3_L1" + type: "Convolution" + bottom: "Mconv4_stage3_L1" + top: "Mconv5_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage3_L1" + type: "ReLU" + bottom: "Mconv5_stage3_L1" + top: "Mconv5_stage3_L1" +} +layer { + name: "Mconv5_stage3_L2" + type: "Convolution" + bottom: "Mconv4_stage3_L2" + top: "Mconv5_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage3_L2" + type: "ReLU" + bottom: "Mconv5_stage3_L2" + top: "Mconv5_stage3_L2" +} +layer { + name: "Mconv6_stage3_L1" + type: "Convolution" + bottom: "Mconv5_stage3_L1" + top: "Mconv6_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage3_L1" + type: "ReLU" + bottom: "Mconv6_stage3_L1" + top: "Mconv6_stage3_L1" +} +layer { + name: "Mconv6_stage3_L2" + type: "Convolution" + bottom: "Mconv5_stage3_L2" + top: "Mconv6_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage3_L2" + type: "ReLU" + bottom: "Mconv6_stage3_L2" + top: "Mconv6_stage3_L2" +} +layer { + name: "Mconv7_stage3_L1" + type: "Convolution" + bottom: "Mconv6_stage3_L1" + top: "Mconv7_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 28 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mconv7_stage3_L2" + type: "Convolution" + bottom: "Mconv6_stage3_L2" + top: "Mconv7_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage4" + type: "Concat" + bottom: "Mconv7_stage3_L1" + bottom: "Mconv7_stage3_L2" + bottom: "conv4_4_CPM" + top: "concat_stage4" + concat_param { + axis: 1 + } +} +layer { + name: "Mconv1_stage4_L1" + type: "Convolution" + bottom: "concat_stage4" + top: "Mconv1_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage4_L1" + type: "ReLU" + bottom: "Mconv1_stage4_L1" + top: "Mconv1_stage4_L1" +} +layer { + name: "Mconv1_stage4_L2" + type: "Convolution" + bottom: "concat_stage4" + top: "Mconv1_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage4_L2" + type: "ReLU" + bottom: "Mconv1_stage4_L2" + top: "Mconv1_stage4_L2" +} +layer { + name: "Mconv2_stage4_L1" + type: "Convolution" + bottom: "Mconv1_stage4_L1" + top: "Mconv2_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage4_L1" + type: "ReLU" + bottom: "Mconv2_stage4_L1" + top: "Mconv2_stage4_L1" +} +layer { + name: "Mconv2_stage4_L2" + type: "Convolution" + bottom: "Mconv1_stage4_L2" + top: "Mconv2_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage4_L2" + type: "ReLU" + bottom: "Mconv2_stage4_L2" + top: "Mconv2_stage4_L2" +} +layer { + name: "Mconv3_stage4_L1" + type: "Convolution" + bottom: "Mconv2_stage4_L1" + top: "Mconv3_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage4_L1" + type: "ReLU" + bottom: "Mconv3_stage4_L1" + top: "Mconv3_stage4_L1" +} +layer { + name: "Mconv3_stage4_L2" + type: "Convolution" + bottom: "Mconv2_stage4_L2" + top: "Mconv3_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage4_L2" + type: "ReLU" + bottom: "Mconv3_stage4_L2" + top: "Mconv3_stage4_L2" +} +layer { + name: "Mconv4_stage4_L1" + type: "Convolution" + bottom: "Mconv3_stage4_L1" + top: "Mconv4_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage4_L1" + type: "ReLU" + bottom: "Mconv4_stage4_L1" + top: "Mconv4_stage4_L1" +} +layer { + name: "Mconv4_stage4_L2" + type: "Convolution" + bottom: "Mconv3_stage4_L2" + top: "Mconv4_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage4_L2" + type: "ReLU" + bottom: "Mconv4_stage4_L2" + top: "Mconv4_stage4_L2" +} +layer { + name: "Mconv5_stage4_L1" + type: "Convolution" + bottom: "Mconv4_stage4_L1" + top: "Mconv5_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage4_L1" + type: "ReLU" + bottom: "Mconv5_stage4_L1" + top: "Mconv5_stage4_L1" +} +layer { + name: "Mconv5_stage4_L2" + type: "Convolution" + bottom: "Mconv4_stage4_L2" + top: "Mconv5_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage4_L2" + type: "ReLU" + bottom: "Mconv5_stage4_L2" + top: "Mconv5_stage4_L2" +} +layer { + name: "Mconv6_stage4_L1" + type: "Convolution" + bottom: "Mconv5_stage4_L1" + top: "Mconv6_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage4_L1" + type: "ReLU" + bottom: "Mconv6_stage4_L1" + top: "Mconv6_stage4_L1" +} +layer { + name: "Mconv6_stage4_L2" + type: "Convolution" + bottom: "Mconv5_stage4_L2" + top: "Mconv6_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage4_L2" + type: "ReLU" + bottom: "Mconv6_stage4_L2" + top: "Mconv6_stage4_L2" +} +layer { + name: "Mconv7_stage4_L1" + type: "Convolution" + bottom: "Mconv6_stage4_L1" + top: "Mconv7_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 28 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mconv7_stage4_L2" + type: "Convolution" + bottom: "Mconv6_stage4_L2" + top: "Mconv7_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage5" + type: "Concat" + bottom: "Mconv7_stage4_L1" + bottom: "Mconv7_stage4_L2" + bottom: "conv4_4_CPM" + top: "concat_stage5" + concat_param { + axis: 1 + } +} +layer { + name: "Mconv1_stage5_L1" + type: "Convolution" + bottom: "concat_stage5" + top: "Mconv1_stage5_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage5_L1" + type: "ReLU" + bottom: "Mconv1_stage5_L1" + top: "Mconv1_stage5_L1" +} +layer { + name: "Mconv1_stage5_L2" + type: "Convolution" + bottom: "concat_stage5" + top: "Mconv1_stage5_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage5_L2" + type: "ReLU" + bottom: "Mconv1_stage5_L2" + top: "Mconv1_stage5_L2" +} +layer { + name: "Mconv2_stage5_L1" + type: "Convolution" + bottom: "Mconv1_stage5_L1" + top: "Mconv2_stage5_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage5_L1" + type: "ReLU" + bottom: "Mconv2_stage5_L1" + top: "Mconv2_stage5_L1" +} +layer { + name: "Mconv2_stage5_L2" + type: "Convolution" + bottom: "Mconv1_stage5_L2" + top: "Mconv2_stage5_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage5_L2" + type: "ReLU" + bottom: "Mconv2_stage5_L2" + top: "Mconv2_stage5_L2" +} +layer { + name: "Mconv3_stage5_L1" + type: "Convolution" + bottom: "Mconv2_stage5_L1" + top: "Mconv3_stage5_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage5_L1" + type: "ReLU" + bottom: "Mconv3_stage5_L1" + top: "Mconv3_stage5_L1" +} +layer { + name: "Mconv3_stage5_L2" + type: "Convolution" + bottom: "Mconv2_stage5_L2" + top: "Mconv3_stage5_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage5_L2" + type: "ReLU" + bottom: "Mconv3_stage5_L2" + top: "Mconv3_stage5_L2" +} +layer { + name: "Mconv4_stage5_L1" + type: "Convolution" + bottom: "Mconv3_stage5_L1" + top: "Mconv4_stage5_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage5_L1" + type: "ReLU" + bottom: "Mconv4_stage5_L1" + top: "Mconv4_stage5_L1" +} +layer { + name: "Mconv4_stage5_L2" + type: "Convolution" + bottom: "Mconv3_stage5_L2" + top: "Mconv4_stage5_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage5_L2" + type: "ReLU" + bottom: "Mconv4_stage5_L2" + top: "Mconv4_stage5_L2" +} +layer { + name: "Mconv5_stage5_L1" + type: "Convolution" + bottom: "Mconv4_stage5_L1" + top: "Mconv5_stage5_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage5_L1" + type: "ReLU" + bottom: "Mconv5_stage5_L1" + top: "Mconv5_stage5_L1" +} +layer { + name: "Mconv5_stage5_L2" + type: "Convolution" + bottom: "Mconv4_stage5_L2" + top: "Mconv5_stage5_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage5_L2" + type: "ReLU" + bottom: "Mconv5_stage5_L2" + top: "Mconv5_stage5_L2" +} +layer { + name: "Mconv6_stage5_L1" + type: "Convolution" + bottom: "Mconv5_stage5_L1" + top: "Mconv6_stage5_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage5_L1" + type: "ReLU" + bottom: "Mconv6_stage5_L1" + top: "Mconv6_stage5_L1" +} +layer { + name: "Mconv6_stage5_L2" + type: "Convolution" + bottom: "Mconv5_stage5_L2" + top: "Mconv6_stage5_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage5_L2" + type: "ReLU" + bottom: "Mconv6_stage5_L2" + top: "Mconv6_stage5_L2" +} +layer { + name: "Mconv7_stage5_L1" + type: "Convolution" + bottom: "Mconv6_stage5_L1" + top: "Mconv7_stage5_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 28 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mconv7_stage5_L2" + type: "Convolution" + bottom: "Mconv6_stage5_L2" + top: "Mconv7_stage5_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage6" + type: "Concat" + bottom: "Mconv7_stage5_L1" + bottom: "Mconv7_stage5_L2" + bottom: "conv4_4_CPM" + top: "concat_stage6" + concat_param { + axis: 1 + } +} +layer { + name: "Mconv1_stage6_L1" + type: "Convolution" + bottom: "concat_stage6" + top: "Mconv1_stage6_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage6_L1" + type: "ReLU" + bottom: "Mconv1_stage6_L1" + top: "Mconv1_stage6_L1" +} +layer { + name: "Mconv1_stage6_L2" + type: "Convolution" + bottom: "concat_stage6" + top: "Mconv1_stage6_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage6_L2" + type: "ReLU" + bottom: "Mconv1_stage6_L2" + top: "Mconv1_stage6_L2" +} +layer { + name: "Mconv2_stage6_L1" + type: "Convolution" + bottom: "Mconv1_stage6_L1" + top: "Mconv2_stage6_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage6_L1" + type: "ReLU" + bottom: "Mconv2_stage6_L1" + top: "Mconv2_stage6_L1" +} +layer { + name: "Mconv2_stage6_L2" + type: "Convolution" + bottom: "Mconv1_stage6_L2" + top: "Mconv2_stage6_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage6_L2" + type: "ReLU" + bottom: "Mconv2_stage6_L2" + top: "Mconv2_stage6_L2" +} +layer { + name: "Mconv3_stage6_L1" + type: "Convolution" + bottom: "Mconv2_stage6_L1" + top: "Mconv3_stage6_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage6_L1" + type: "ReLU" + bottom: "Mconv3_stage6_L1" + top: "Mconv3_stage6_L1" +} +layer { + name: "Mconv3_stage6_L2" + type: "Convolution" + bottom: "Mconv2_stage6_L2" + top: "Mconv3_stage6_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage6_L2" + type: "ReLU" + bottom: "Mconv3_stage6_L2" + top: "Mconv3_stage6_L2" +} +layer { + name: "Mconv4_stage6_L1" + type: "Convolution" + bottom: "Mconv3_stage6_L1" + top: "Mconv4_stage6_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage6_L1" + type: "ReLU" + bottom: "Mconv4_stage6_L1" + top: "Mconv4_stage6_L1" +} +layer { + name: "Mconv4_stage6_L2" + type: "Convolution" + bottom: "Mconv3_stage6_L2" + top: "Mconv4_stage6_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage6_L2" + type: "ReLU" + bottom: "Mconv4_stage6_L2" + top: "Mconv4_stage6_L2" +} +layer { + name: "Mconv5_stage6_L1" + type: "Convolution" + bottom: "Mconv4_stage6_L1" + top: "Mconv5_stage6_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage6_L1" + type: "ReLU" + bottom: "Mconv5_stage6_L1" + top: "Mconv5_stage6_L1" +} +layer { + name: "Mconv5_stage6_L2" + type: "Convolution" + bottom: "Mconv4_stage6_L2" + top: "Mconv5_stage6_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage6_L2" + type: "ReLU" + bottom: "Mconv5_stage6_L2" + top: "Mconv5_stage6_L2" +} +layer { + name: "Mconv6_stage6_L1" + type: "Convolution" + bottom: "Mconv5_stage6_L1" + top: "Mconv6_stage6_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage6_L1" + type: "ReLU" + bottom: "Mconv6_stage6_L1" + top: "Mconv6_stage6_L1" +} +layer { + name: "Mconv6_stage6_L2" + type: "Convolution" + bottom: "Mconv5_stage6_L2" + top: "Mconv6_stage6_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage6_L2" + type: "ReLU" + bottom: "Mconv6_stage6_L2" + top: "Mconv6_stage6_L2" +} +layer { + name: "Mconv7_stage6_L1" + type: "Convolution" + bottom: "Mconv6_stage6_L1" + top: "Mconv7_stage6_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 28 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mconv7_stage6_L2" + type: "Convolution" + bottom: "Mconv6_stage6_L2" + top: "Mconv7_stage6_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage7" + type: "Concat" + bottom: "Mconv7_stage6_L2" + bottom: "Mconv7_stage6_L1" + top: "net_output" + concat_param { + axis: 1 + } +} diff --git a/OpenCV-dnn-gpu-support-Windows/pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt b/OpenCV-dnn-gpu-support-Windows/pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt new file mode 100755 index 000000000..008a75e4d --- /dev/null +++ b/OpenCV-dnn-gpu-support-Windows/pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt @@ -0,0 +1,2081 @@ +input: "image" +input_dim: 1 +input_dim: 3 +input_dim: 1 # This value will be defined at runtime +input_dim: 1 # This value will be defined at runtime +layer { + name: "conv1_1" + type: "Convolution" + bottom: "image" + top: "conv1_1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +layer { + name: "pool1_stage1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1_stage1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1_stage1" + top: "conv2_1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +layer { + name: "pool2_stage1" + type: "Pooling" + bottom: "conv2_2" + top: "pool2_stage1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2_stage1" + top: "conv3_1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} +layer { + name: "conv3_4" + type: "Convolution" + bottom: "conv3_3" + top: "conv3_4" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu3_4" + type: "ReLU" + bottom: "conv3_4" + top: "conv3_4" +} +layer { + name: "pool3_stage1" + type: "Pooling" + bottom: "conv3_4" + top: "pool3_stage1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3_stage1" + top: "conv4_1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +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 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3_CPM" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3_CPM" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu4_3_CPM" + type: "ReLU" + bottom: "conv4_3_CPM" + top: "conv4_3_CPM" +} +layer { + name: "conv4_4_CPM" + type: "Convolution" + bottom: "conv4_3_CPM" + top: "conv4_4_CPM" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu4_4_CPM" + type: "ReLU" + bottom: "conv4_4_CPM" + top: "conv4_4_CPM" +} +layer { + name: "conv5_1_CPM_L1" + type: "Convolution" + bottom: "conv4_4_CPM" + top: "conv5_1_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_1_CPM_L1" + type: "ReLU" + bottom: "conv5_1_CPM_L1" + top: "conv5_1_CPM_L1" +} +layer { + name: "conv5_1_CPM_L2" + type: "Convolution" + bottom: "conv4_4_CPM" + top: "conv5_1_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_1_CPM_L2" + type: "ReLU" + bottom: "conv5_1_CPM_L2" + top: "conv5_1_CPM_L2" +} +layer { + name: "conv5_2_CPM_L1" + type: "Convolution" + bottom: "conv5_1_CPM_L1" + top: "conv5_2_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_2_CPM_L1" + type: "ReLU" + bottom: "conv5_2_CPM_L1" + top: "conv5_2_CPM_L1" +} +layer { + name: "conv5_2_CPM_L2" + type: "Convolution" + bottom: "conv5_1_CPM_L2" + top: "conv5_2_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_2_CPM_L2" + type: "ReLU" + bottom: "conv5_2_CPM_L2" + top: "conv5_2_CPM_L2" +} +layer { + name: "conv5_3_CPM_L1" + type: "Convolution" + bottom: "conv5_2_CPM_L1" + top: "conv5_3_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_3_CPM_L1" + type: "ReLU" + bottom: "conv5_3_CPM_L1" + top: "conv5_3_CPM_L1" +} +layer { + name: "conv5_3_CPM_L2" + type: "Convolution" + bottom: "conv5_2_CPM_L2" + top: "conv5_3_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_3_CPM_L2" + type: "ReLU" + bottom: "conv5_3_CPM_L2" + top: "conv5_3_CPM_L2" +} +layer { + name: "conv5_4_CPM_L1" + type: "Convolution" + bottom: "conv5_3_CPM_L1" + top: "conv5_4_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_4_CPM_L1" + type: "ReLU" + bottom: "conv5_4_CPM_L1" + top: "conv5_4_CPM_L1" +} +layer { + name: "conv5_4_CPM_L2" + type: "Convolution" + bottom: "conv5_3_CPM_L2" + top: "conv5_4_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu5_4_CPM_L2" + type: "ReLU" + bottom: "conv5_4_CPM_L2" + top: "conv5_4_CPM_L2" +} +layer { + name: "conv5_5_CPM_L1" + type: "Convolution" + bottom: "conv5_4_CPM_L1" + top: "conv5_5_CPM_L1" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 28 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "conv5_5_CPM_L2" + type: "Convolution" + bottom: "conv5_4_CPM_L2" + top: "conv5_5_CPM_L2" + param { + lr_mult: 1.0 + decay_mult: 1 + } + param { + lr_mult: 2.0 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage2" + type: "Concat" + bottom: "conv5_5_CPM_L1" + bottom: "conv5_5_CPM_L2" + bottom: "conv4_4_CPM" + top: "concat_stage2" + concat_param { + axis: 1 + } +} +layer { + name: "Mconv1_stage2_L1" + type: "Convolution" + bottom: "concat_stage2" + top: "Mconv1_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage2_L1" + type: "ReLU" + bottom: "Mconv1_stage2_L1" + top: "Mconv1_stage2_L1" +} +layer { + name: "Mconv1_stage2_L2" + type: "Convolution" + bottom: "concat_stage2" + top: "Mconv1_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage2_L2" + type: "ReLU" + bottom: "Mconv1_stage2_L2" + top: "Mconv1_stage2_L2" +} +layer { + name: "Mconv2_stage2_L1" + type: "Convolution" + bottom: "Mconv1_stage2_L1" + top: "Mconv2_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage2_L1" + type: "ReLU" + bottom: "Mconv2_stage2_L1" + top: "Mconv2_stage2_L1" +} +layer { + name: "Mconv2_stage2_L2" + type: "Convolution" + bottom: "Mconv1_stage2_L2" + top: "Mconv2_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage2_L2" + type: "ReLU" + bottom: "Mconv2_stage2_L2" + top: "Mconv2_stage2_L2" +} +layer { + name: "Mconv3_stage2_L1" + type: "Convolution" + bottom: "Mconv2_stage2_L1" + top: "Mconv3_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage2_L1" + type: "ReLU" + bottom: "Mconv3_stage2_L1" + top: "Mconv3_stage2_L1" +} +layer { + name: "Mconv3_stage2_L2" + type: "Convolution" + bottom: "Mconv2_stage2_L2" + top: "Mconv3_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage2_L2" + type: "ReLU" + bottom: "Mconv3_stage2_L2" + top: "Mconv3_stage2_L2" +} +layer { + name: "Mconv4_stage2_L1" + type: "Convolution" + bottom: "Mconv3_stage2_L1" + top: "Mconv4_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage2_L1" + type: "ReLU" + bottom: "Mconv4_stage2_L1" + top: "Mconv4_stage2_L1" +} +layer { + name: "Mconv4_stage2_L2" + type: "Convolution" + bottom: "Mconv3_stage2_L2" + top: "Mconv4_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage2_L2" + type: "ReLU" + bottom: "Mconv4_stage2_L2" + top: "Mconv4_stage2_L2" +} +layer { + name: "Mconv5_stage2_L1" + type: "Convolution" + bottom: "Mconv4_stage2_L1" + top: "Mconv5_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage2_L1" + type: "ReLU" + bottom: "Mconv5_stage2_L1" + top: "Mconv5_stage2_L1" +} +layer { + name: "Mconv5_stage2_L2" + type: "Convolution" + bottom: "Mconv4_stage2_L2" + top: "Mconv5_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage2_L2" + type: "ReLU" + bottom: "Mconv5_stage2_L2" + top: "Mconv5_stage2_L2" +} +layer { + name: "Mconv6_stage2_L1" + type: "Convolution" + bottom: "Mconv5_stage2_L1" + top: "Mconv6_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage2_L1" + type: "ReLU" + bottom: "Mconv6_stage2_L1" + top: "Mconv6_stage2_L1" +} +layer { + name: "Mconv6_stage2_L2" + type: "Convolution" + bottom: "Mconv5_stage2_L2" + top: "Mconv6_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage2_L2" + type: "ReLU" + bottom: "Mconv6_stage2_L2" + top: "Mconv6_stage2_L2" +} +layer { + name: "Mconv7_stage2_L1" + type: "Convolution" + bottom: "Mconv6_stage2_L1" + top: "Mconv7_stage2_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 28 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mconv7_stage2_L2" + type: "Convolution" + bottom: "Mconv6_stage2_L2" + top: "Mconv7_stage2_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage3" + type: "Concat" + bottom: "Mconv7_stage2_L1" + bottom: "Mconv7_stage2_L2" + bottom: "conv4_4_CPM" + top: "concat_stage3" + concat_param { + axis: 1 + } +} +layer { + name: "Mconv1_stage3_L1" + type: "Convolution" + bottom: "concat_stage3" + top: "Mconv1_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage3_L1" + type: "ReLU" + bottom: "Mconv1_stage3_L1" + top: "Mconv1_stage3_L1" +} +layer { + name: "Mconv1_stage3_L2" + type: "Convolution" + bottom: "concat_stage3" + top: "Mconv1_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage3_L2" + type: "ReLU" + bottom: "Mconv1_stage3_L2" + top: "Mconv1_stage3_L2" +} +layer { + name: "Mconv2_stage3_L1" + type: "Convolution" + bottom: "Mconv1_stage3_L1" + top: "Mconv2_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage3_L1" + type: "ReLU" + bottom: "Mconv2_stage3_L1" + top: "Mconv2_stage3_L1" +} +layer { + name: "Mconv2_stage3_L2" + type: "Convolution" + bottom: "Mconv1_stage3_L2" + top: "Mconv2_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage3_L2" + type: "ReLU" + bottom: "Mconv2_stage3_L2" + top: "Mconv2_stage3_L2" +} +layer { + name: "Mconv3_stage3_L1" + type: "Convolution" + bottom: "Mconv2_stage3_L1" + top: "Mconv3_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage3_L1" + type: "ReLU" + bottom: "Mconv3_stage3_L1" + top: "Mconv3_stage3_L1" +} +layer { + name: "Mconv3_stage3_L2" + type: "Convolution" + bottom: "Mconv2_stage3_L2" + top: "Mconv3_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage3_L2" + type: "ReLU" + bottom: "Mconv3_stage3_L2" + top: "Mconv3_stage3_L2" +} +layer { + name: "Mconv4_stage3_L1" + type: "Convolution" + bottom: "Mconv3_stage3_L1" + top: "Mconv4_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage3_L1" + type: "ReLU" + bottom: "Mconv4_stage3_L1" + top: "Mconv4_stage3_L1" +} +layer { + name: "Mconv4_stage3_L2" + type: "Convolution" + bottom: "Mconv3_stage3_L2" + top: "Mconv4_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage3_L2" + type: "ReLU" + bottom: "Mconv4_stage3_L2" + top: "Mconv4_stage3_L2" +} +layer { + name: "Mconv5_stage3_L1" + type: "Convolution" + bottom: "Mconv4_stage3_L1" + top: "Mconv5_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage3_L1" + type: "ReLU" + bottom: "Mconv5_stage3_L1" + top: "Mconv5_stage3_L1" +} +layer { + name: "Mconv5_stage3_L2" + type: "Convolution" + bottom: "Mconv4_stage3_L2" + top: "Mconv5_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage3_L2" + type: "ReLU" + bottom: "Mconv5_stage3_L2" + top: "Mconv5_stage3_L2" +} +layer { + name: "Mconv6_stage3_L1" + type: "Convolution" + bottom: "Mconv5_stage3_L1" + top: "Mconv6_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage3_L1" + type: "ReLU" + bottom: "Mconv6_stage3_L1" + top: "Mconv6_stage3_L1" +} +layer { + name: "Mconv6_stage3_L2" + type: "Convolution" + bottom: "Mconv5_stage3_L2" + top: "Mconv6_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage3_L2" + type: "ReLU" + bottom: "Mconv6_stage3_L2" + top: "Mconv6_stage3_L2" +} +layer { + name: "Mconv7_stage3_L1" + type: "Convolution" + bottom: "Mconv6_stage3_L1" + top: "Mconv7_stage3_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 28 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mconv7_stage3_L2" + type: "Convolution" + bottom: "Mconv6_stage3_L2" + top: "Mconv7_stage3_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage4" + type: "Concat" + bottom: "Mconv7_stage3_L1" + bottom: "Mconv7_stage3_L2" + bottom: "conv4_4_CPM" + top: "concat_stage4" + concat_param { + axis: 1 + } +} +layer { + name: "Mconv1_stage4_L1" + type: "Convolution" + bottom: "concat_stage4" + top: "Mconv1_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage4_L1" + type: "ReLU" + bottom: "Mconv1_stage4_L1" + top: "Mconv1_stage4_L1" +} +layer { + name: "Mconv1_stage4_L2" + type: "Convolution" + bottom: "concat_stage4" + top: "Mconv1_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu1_stage4_L2" + type: "ReLU" + bottom: "Mconv1_stage4_L2" + top: "Mconv1_stage4_L2" +} +layer { + name: "Mconv2_stage4_L1" + type: "Convolution" + bottom: "Mconv1_stage4_L1" + top: "Mconv2_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage4_L1" + type: "ReLU" + bottom: "Mconv2_stage4_L1" + top: "Mconv2_stage4_L1" +} +layer { + name: "Mconv2_stage4_L2" + type: "Convolution" + bottom: "Mconv1_stage4_L2" + top: "Mconv2_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu2_stage4_L2" + type: "ReLU" + bottom: "Mconv2_stage4_L2" + top: "Mconv2_stage4_L2" +} +layer { + name: "Mconv3_stage4_L1" + type: "Convolution" + bottom: "Mconv2_stage4_L1" + top: "Mconv3_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage4_L1" + type: "ReLU" + bottom: "Mconv3_stage4_L1" + top: "Mconv3_stage4_L1" +} +layer { + name: "Mconv3_stage4_L2" + type: "Convolution" + bottom: "Mconv2_stage4_L2" + top: "Mconv3_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu3_stage4_L2" + type: "ReLU" + bottom: "Mconv3_stage4_L2" + top: "Mconv3_stage4_L2" +} +layer { + name: "Mconv4_stage4_L1" + type: "Convolution" + bottom: "Mconv3_stage4_L1" + top: "Mconv4_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage4_L1" + type: "ReLU" + bottom: "Mconv4_stage4_L1" + top: "Mconv4_stage4_L1" +} +layer { + name: "Mconv4_stage4_L2" + type: "Convolution" + bottom: "Mconv3_stage4_L2" + top: "Mconv4_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu4_stage4_L2" + type: "ReLU" + bottom: "Mconv4_stage4_L2" + top: "Mconv4_stage4_L2" +} +layer { + name: "Mconv5_stage4_L1" + type: "Convolution" + bottom: "Mconv4_stage4_L1" + top: "Mconv5_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage4_L1" + type: "ReLU" + bottom: "Mconv5_stage4_L1" + top: "Mconv5_stage4_L1" +} +layer { + name: "Mconv5_stage4_L2" + type: "Convolution" + bottom: "Mconv4_stage4_L2" + top: "Mconv5_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 3 + kernel_size: 7 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu5_stage4_L2" + type: "ReLU" + bottom: "Mconv5_stage4_L2" + top: "Mconv5_stage4_L2" +} +layer { + name: "Mconv6_stage4_L1" + type: "Convolution" + bottom: "Mconv5_stage4_L1" + top: "Mconv6_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage4_L1" + type: "ReLU" + bottom: "Mconv6_stage4_L1" + top: "Mconv6_stage4_L1" +} +layer { + name: "Mconv6_stage4_L2" + type: "Convolution" + bottom: "Mconv5_stage4_L2" + top: "Mconv6_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mrelu6_stage4_L2" + type: "ReLU" + bottom: "Mconv6_stage4_L2" + top: "Mconv6_stage4_L2" +} +layer { + name: "Mconv7_stage4_L1" + type: "Convolution" + bottom: "Mconv6_stage4_L1" + top: "Mconv7_stage4_L1" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 28 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "Mconv7_stage4_L2" + type: "Convolution" + bottom: "Mconv6_stage4_L2" + top: "Mconv7_stage4_L2" + param { + lr_mult: 4.0 + decay_mult: 1 + } + param { + lr_mult: 8.0 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 0 + kernel_size: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "concat_stage7" + type: "Concat" + bottom: "Mconv7_stage4_L2" + bottom: "Mconv7_stage4_L1" + top: "net_output" + concat_param { + axis: 1 + } +} diff --git a/README.md b/README.md index ee4b44b80..685031a82 100644 --- a/README.md +++ b/README.md @@ -12,6 +12,7 @@ Want to become an expert in AI? [AI Courses by OpenCV](https://opencv.org/course ## List of Blog Posts | Blog Post | | | ------------- |:-------------| +|[How to use OpenCV DNN Module with Nvidia GPU on Windows](https://learnopencv.com/how-to-use-opencv-dnn-module-with-nvidia-gpu-on-windows) | [Code](https://github.com/spmallick/learnopencv/tree/master/OpenCV-dnn-gpu-support-Windows) | |[Video Classification & Human Activity Recognition](https://learnopencv.com/introduction-to-video-classification-human-activity-recognition/) | [Code](https://github.com/spmallick/learnopencv/tree/master/video-classification-and-human-activity-recognition) |[Face Recognition with ArcFace](https://www.learnopencv.com/face-recognition-with-arcface/) | [Code](https://github.com/spmallick/learnopencv/tree/master/Face-Recognition-with-ArcFace) |[Background Subtraction with OpenCV and BGS Libraries](https://www.learnopencv.com/background-subtraction-with-opencv-and-bgs-libraries/) | [Code](https://github.com/spmallick/learnopencv/tree/master/Background-Subtraction) | From 579195bdc0d4b9bc36db97d572fe58e0270634dc Mon Sep 17 00:00:00 2001 From: Labhesh Valechha <55636313+xlabd@users.noreply.github.com> Date: Tue, 2 Mar 2021 09:55:46 +0530 Subject: [PATCH 2/2] Update README.md --- OpenCV-dnn-gpu-support-Windows/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/OpenCV-dnn-gpu-support-Windows/README.md b/OpenCV-dnn-gpu-support-Windows/README.md index 123eb6e9b..e315af984 100755 --- a/OpenCV-dnn-gpu-support-Windows/README.md +++ b/OpenCV-dnn-gpu-support-Windows/README.md @@ -15,10 +15,10 @@ MPI : https://www.dropbox.com/s/drumc6dzllfed16/pose_iter_160000.caffemodel?dl=0 ``` mkdir build cd build -cmake .. -make +cmake -G "Visual Studio 16 2019" .. +cmake --build . --config Release cd .. -./build/OpenPoseVideo +./build/Release/OpenPoseVideo ``` ### Python