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But first, please read -. \ No newline at end of file diff --git a/README.md b/README.md deleted file mode 100755 index 9f97006..0000000 --- a/README.md +++ /dev/null @@ -1,159 +0,0 @@ -# Welcome - -This is the codebase of the paper titled "Vector Detection Network: Pointer Detector for Robots Reading Analog Meters in the Wild". -We assume you download it with `git clone `, and the code folder `/VDN` is located in `~`. - -OS: Ubuntu 16.04 or 18.04 -Language: Python 3.6+ -Deep learning framework: PyTorch - -# Prerequisites - -## Hardware - -Make sure the PC is with 8 GB or more GRAM. - -Please make sure you have the correct version of the Nvidia driver installed, and that is compatible with your card. - -## Software - -We use Docker to ease the process of building the environment for running VDN. The installation of Docker can be done by: - -``` -wget -qO- https://get.docker.com/ | sh -systemctl enable docker.service -``` - -Meanwhile, you should also install [nvidia-docker][nv] plugin. To be brief, this is a quick guide: - -``` -# Add the package repositories -curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - - -distribution=$(. /etc/os-release;echo $ID$VERSION_ID) - -curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \ - sudo tee /etc/apt/sources.list.d/nvidia-docker.list - -sudo apt-get update - -# Install nvidia-docker2 and reload the Docker daemon configuration -sudo apt-get install -y nvidia-docker2 -sudo pkill -SIGHUP dockerd -``` - -Other than that, all software dependence can be handled within the Docker container. -A detailed software dependence list could be found in `VDN/Dockerfile`. -For anonymity concern, we do not provide our docker image, yet you may build one exactly as ours by: - -``` -cd ~/VDN -docker build --tag=vdn/vdn . -``` - -# File structure - -This repo is organized as follows: - -``` -. -+-- cfgs # The configurations of different network architectures -| -+-- compiled # Compiled third-party libraries -| -+-- data -| +-- demo -| +-- result -| -+-- libs # VDN libraries -| -+-- modules # The VDN class -| -+-- utils # Some handy utilities -| -+-- weights -| +-- pretrained -| -+-- .dockerignore -+-- .gitignore -+-- add_aliases.sh # Bash script for adding Docker shortcuts to bash_aliaes -+-- Dockerfile -+-- LICENSE -+-- README.md -| -+-- demo.py # Quick demo for a demonstration -+-- train.py # Training script of VDN -+-- test.py # Evaluation and experiments for VDN - -``` - -# Compile - -We have provided a bash script `add_aliases.sh` to insert some handy bash scripts within the file `~/.bash_aliases`. -It is recommended to do so in the root folder of this project: - -``` -bash add_aliases.sh -source ~/.bash_aliases -``` - -Then, before training or testing VDN, run this to compile the code: - -``` -vdn_compile -``` - -# The Pointer-10K dataset - -The Pointer-10K dataset referred to in our paper is publicly available for non-commercial usage. -If you are interested in the data, please contact us via email. The address will be released afterward. - - -# Basic Training - -Before training the VDN model, (i) make sure you have the Pointer-10K dataset located in `~/Database/Done/pointer_10k`. -(ii) Download the pre-trained ResNet model `resnet50-19c8e357.pth` for parameter initialization from -[torchvision](https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py) -and put it in `weights/pretrained/` (you may need to create the path manually). - -``` -# start the docker container -vdn_run - -# train -python train.py -``` - -# Run the demo - -To run the demo, put the trained model named as `vdn_best.pth.tar` into `weights/`, and -run the code below - -``` -# start the docker container -vdn_run - -# run the demo within the container -python demo.py -``` - -We provide the model trained by us: [download]() - -You can put your image into `VDN/data/demo`, and the algorithm will automatically find all images within the folder -and detect pointers in these images if any analog meters exist. Please note that VDN takes the image patches output -by a meter detector, with this the provided demo images should contain the whole dial face but not much background -nor only a part of the meters. - -The results of the demo will be output to automatically created folder `output/demo`. - -# Experiments - -You can use the `eval.py` script to perform the experiments conducted in the paper. -For example, to evaluate the performance of the default configuration (ResNet34 backbone -with 384x384 input size), just issue `python eval.py` in the root folder. Evaluations -of ResNet18 and ResNet50 could be executed in the `master` branch, -whereas Res2Net50 is evaluated in individual branch `res2net50`. - -The evaluation output could be found in `/VDN/output/eval-`. - - [nv]: \ No newline at end of file diff --git a/cfgs/resnet34/eval.yaml b/cfgs/resnet34/eval.yaml deleted file mode 100755 index 7691404..0000000 --- a/cfgs/resnet34/eval.yaml +++ /dev/null @@ -1,57 +0,0 @@ -GPUS: '0' -OUTPUT_DIR: '/VDN/output/eval-resnet34' -LOG_DIR: '/VDN/logs/eval-resnet34' -WORKERS: 4 - -DATASET: - DATASET: 'coco' - ROOT: '/Database/Done/pointer_10k' - TRAIN_SET: 'train_pointer' - TEST_SET: 'test_pointer' - ROT_FACTOR: 90 - SCALE_FACTOR: 0.02 -MODEL: - NAME: 'vdn_model' # This name should exist in /VDN/libs/models - PRETRAINED: '/VDN/weights/vdn_model_34_best.pth.tar' - IMAGE_SIZE: - - 384 # width - - 384 # height - NUM_JOINTS: 1 - EXTRA: - TARGET_TYPE: 'gaussian' - HEATMAP_SIZE: - - 96 - - 96 - SIGMA: 3 - FINAL_CONV_KERNEL: 1 - DECONV_WITH_BIAS: false - NUM_DECONV_LAYERS: 3 - NUM_DECONV_FILTERS: - - 256 - - 256 - - 256 - NUM_DECONV_KERNELS: - - 4 - - 4 - - 4 - NUM_LAYERS: 34 -LOSS: - USE_TARGET_WEIGHT: true -TEST: - BATCH_SIZE: 1 - COCO_BBOX_FILE: - BBOX_THRE: 1.0 - FLIP_TEST: false - IMAGE_THRE: 0.0 - IN_VIS_THRE: 0.2 - MODEL_FILE: '' - NMS_THRE: 1.0 - OKS_THRE: 0.9 - USE_GT_BBOX: true -DEBUG: - DEBUG: true - SAVE_BATCH_IMAGES_GT: true - SAVE_BATCH_IMAGES_PRED: true - SAVE_HEATMAPS_GT: true - SAVE_HEATMAPS_PRED: true - diff --git a/cfgs/resnet34/train.yaml b/cfgs/resnet34/train.yaml deleted file mode 100755 index 5e82741..0000000 --- a/cfgs/resnet34/train.yaml +++ /dev/null @@ -1,73 +0,0 @@ -GPUS: '0' -OUTPUT_DIR: '/VDN/output/train-resnet34' -LOG_DIR: '/VDN/logs' -WORKERS: 4 - -DATASET: - DATASET: 'coco' - ROOT: '/Database/Done/pointer_10k' - TRAIN_SET: 'train_pointer' - TEST_SET: 'val_pointer' - ROT_FACTOR: 90 - SCALE_FACTOR: 0.02 -MODEL: - NAME: 'vdn_model' - PRETRAINED: '/VDN/weights/pretrained/resnet34-333f7ec4.pth' - IMAGE_SIZE: - - 384 # width - - 384 # height - NUM_JOINTS: 1 - EXTRA: - TARGET_TYPE: 'gaussian' - HEATMAP_SIZE: - - 96 - - 96 - SIGMA: 3 - FINAL_CONV_KERNEL: 1 - DECONV_WITH_BIAS: false - NUM_DECONV_LAYERS: 3 - NUM_DECONV_FILTERS: - - 256 - - 256 - - 256 - NUM_DECONV_KERNELS: - - 4 - - 4 - - 4 - NUM_LAYERS: 34 -LOSS: - USE_TARGET_WEIGHT: true -TRAIN: - BATCH_SIZE: 2 - SHUFFLE: true - BEGIN_EPOCH: 0 - END_EPOCH: 160 - RESUME: false - OPTIMIZER: 'adam' - LR: 0.001 - LR_FACTOR: 0.1 - LR_STEP: - - 100 - - 140 - WD: 0.0001 - GAMMA1: 0.99 - GAMMA2: 0.0 - MOMENTUM: 0.9 - NESTEROV: false -TEST: - BATCH_SIZE: 1 - COCO_BBOX_FILE: - BBOX_THRE: 1.0 - FLIP_TEST: false - IMAGE_THRE: 0.0 - IN_VIS_THRE: 0.2 - MODEL_FILE: '' - NMS_THRE: 1.0 - OKS_THRE: 0.9 - USE_GT_BBOX: true -DEBUG: - DEBUG: true - SAVE_BATCH_IMAGES_GT: true - SAVE_BATCH_IMAGES_PRED: true - SAVE_HEATMAPS_GT: true - SAVE_HEATMAPS_PRED: true diff --git a/cfgs/resnet50/eval.yaml b/cfgs/resnet50/eval.yaml deleted file mode 100755 index d877f3c..0000000 --- a/cfgs/resnet50/eval.yaml +++ /dev/null @@ -1,55 +0,0 @@ -GPUS: '0' -OUTPUT_DIR: '/VDN/output/eval-resnet50' -LOG_DIR: '/VDN/logs/eval-resnet50' -WORKERS: 4 - -DATASET: - DATASET: 'coco' - ROOT: '/Database/Done/pointer_10k' - TEST_SET: 'test_pointer' - ROT_FACTOR: 90 - SCALE_FACTOR: 0.02 -MODEL: - NAME: 'vdn_model' # This name should exist in /VDN/libs/models - PRETRAINED: '/VDN/weights/vdn_model_50_best.pth.tar' - IMAGE_SIZE: - - 384 # width - - 384 # height - NUM_JOINTS: 1 - EXTRA: - TARGET_TYPE: 'gaussian' - HEATMAP_SIZE: - - 96 - - 96 - SIGMA: 3 - FINAL_CONV_KERNEL: 1 - DECONV_WITH_BIAS: false - NUM_DECONV_LAYERS: 3 - NUM_DECONV_FILTERS: - - 256 - - 256 - - 256 - NUM_DECONV_KERNELS: - - 4 - - 4 - - 4 - NUM_LAYERS: 50 -LOSS: - USE_TARGET_WEIGHT: true -TEST: - BATCH_SIZE: 1 - COCO_BBOX_FILE: - BBOX_THRE: 1.0 - FLIP_TEST: false - IMAGE_THRE: 0.0 - IN_VIS_THRE: 0.2 - MODEL_FILE: '' - NMS_THRE: 1.0 - OKS_THRE: 0.9 - USE_GT_BBOX: true -DEBUG: - DEBUG: true - SAVE_BATCH_IMAGES_GT: true - SAVE_BATCH_IMAGES_PRED: true - SAVE_HEATMAPS_GT: true - SAVE_HEATMAPS_PRED: true diff --git a/cfgs/resnet50/train.yaml b/cfgs/resnet50/train.yaml deleted file mode 100755 index a9035fe..0000000 --- a/cfgs/resnet50/train.yaml +++ /dev/null @@ -1,73 +0,0 @@ -GPUS: '0' -OUTPUT_DIR: '/VDN/output/train-resnet50' -LOG_DIR: '/VDN/logs' -WORKERS: 4 - -DATASET: - DATASET: 'coco' - ROOT: '/Database/Done/pointer_10k' - TRAIN_SET: 'train_pointer' - TEST_SET: 'val_pointer' - ROT_FACTOR: 90 - SCALE_FACTOR: 0.02 -MODEL: - NAME: 'vdn_model' - PRETRAINED: '/VDN/weights/pretrained/resnet50-19c8e357.pth' - IMAGE_SIZE: - - 384 # width - - 384 # height - NUM_JOINTS: 1 - EXTRA: - TARGET_TYPE: 'gaussian' - HEATMAP_SIZE: - - 96 - - 96 - SIGMA: 3 - FINAL_CONV_KERNEL: 1 - DECONV_WITH_BIAS: false - NUM_DECONV_LAYERS: 3 - NUM_DECONV_FILTERS: - - 256 - - 256 - - 256 - NUM_DECONV_KERNELS: - - 4 - - 4 - - 4 - NUM_LAYERS: 50 -LOSS: - USE_TARGET_WEIGHT: true -TRAIN: - BATCH_SIZE: 8 - SHUFFLE: true - BEGIN_EPOCH: 0 - END_EPOCH: 200 - RESUME: false - OPTIMIZER: 'adam' - LR: 0.001 - LR_FACTOR: 0.1 - LR_STEP: - - 140 - - 190 - WD: 0.0001 - GAMMA1: 0.99 - GAMMA2: 0.0 - MOMENTUM: 0.9 - NESTEROV: false -TEST: - BATCH_SIZE: 1 - COCO_BBOX_FILE: - BBOX_THRE: 1.0 - FLIP_TEST: false - IMAGE_THRE: 0.0 - IN_VIS_THRE: 0.2 - MODEL_FILE: '' - NMS_THRE: 1.0 - OKS_THRE: 0.9 - USE_GT_BBOX: true -DEBUG: - DEBUG: true - SAVE_BATCH_IMAGES_GT: false - SAVE_BATCH_IMAGES_PRED: false - SAVE_HEATMAPS_GT: false - SAVE_HEATMAPS_PRED: false diff --git a/demo.py b/demo.py index e693773..edaf556 100755 --- a/demo.py +++ b/demo.py @@ -11,11 +11,7 @@ class Test(unittest.TestCase): def test_demo_default(self): - """You can change the backbone to resnet34, resnet101, res2net50 - if those models have been trained. - - """ - VDN = vdn.VectorDetectionNetwork(backbone='resnet34') + VDN = vdn.VectorDetectionNetwork(backbone='resnet18') demo_data_dir = "./data/demo" if not os.path.exists(demo_data_dir): diff --git a/eval.py b/eval.py index ad5ed87..b640693 100755 --- a/eval.py +++ b/eval.py @@ -10,10 +10,7 @@ class Test(unittest.TestCase): def test_eval(self): - """Available backbones: resnet34 (default), resnet18, resnet50. - - """ - vdn_instance = vdn.VectorDetectionNetwork(backbone='resnet34') + vdn_instance = vdn.VectorDetectionNetwork(backbone='resnet18') vdn_instance.eval() diff --git a/libs/dataset/JointsDataset.py b/libs/dataset/JointsDataset.py index 310e449..9f3b830 100755 --- a/libs/dataset/JointsDataset.py +++ b/libs/dataset/JointsDataset.py @@ -114,6 +114,10 @@ def __getitem__(self, idx): s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf) r = np.clip(np.random.randn() * rf, -rf * 2, rf * 2) if random.random() <= 0.5 else 0 + # For ablation test 1 + # Apply different scales 0.5:1.75:0.25 + # s = 1.75 * s + trans = get_affine_transform(c, s, r, self.image_size) input_t = cv2.warpAffine(input_numpy, trans, (int(self.image_size[0]), int(self.image_size[1])), flags=cv2.INTER_LINEAR) @@ -131,6 +135,19 @@ def __getitem__(self, idx): joints_xyv[n, k, 0:2] = affine_transform(joints_xyv[n, k, 0:2], trans) joints_xyv[n, k, 2:4] = affine_transform(joints_xyv[n, k, 2:4], trans) + # For ablation test 2, apply mask on pointer tips or tails + # mask_sz = 3 * self.sigma + # tips = joints_xyv[..., 0:2] + # tails = joints_xyv[..., 2:4] + # for n in range(self.num_joints): + # for k in range(joints_xyv.shape[1]): + # x = tips[n][k][0] + # y = tips[n][k][1] + # try: + # input_t[:, y-mask_sz:y+mask_sz, x-mask_sz:x+mask_sz] = 0 + # except ValueError: + # pass + target_heatmap, target_vectormap = self.generate_target(joints_xyv) target_heatmap = torch.from_numpy(target_heatmap) @@ -152,7 +169,7 @@ def __getitem__(self, idx): def generate_target(self, joints_xyv: np.ndarray): """Get target heatmap and vectormap from joint labels. - :param joints_xyv: [num_joints, k, 5], k is the object number; 5 is for [head_x, head_y, tail_x, tail_y, vis] + :param joints_xyv: [num_joints, k, 5], k is the object number; 5 is for [tip_x, tip_y, tail_x, tail_y, vis] :return: target_heatmap: [num_joints, h, w] target_vectormap: [num_joints, 2, h, w]; 2 for (vx, vy) ∈ [-1, 1] """ @@ -199,7 +216,10 @@ def generate_target(self, joints_xyv: np.ndarray): # determine the heat value of the pixel affected by multiple peaks to be the maximum value prev_val = target_heatmap[n][img_y[0]:img_y[1], img_x[0]:img_x[1]] curr_val = g[g_y[0]:g_y[1], g_x[0]:g_x[1]] - max_val = np.maximum(prev_val, curr_val) + try: + max_val = np.maximum(prev_val, curr_val) + except ValueError: + continue target_heatmap[n][img_y[0]:img_y[1], img_x[0]:img_x[1]] = max_val dx = head_x - tail_x diff --git a/modules/vdn.py b/modules/vdn.py index bbdfc31..a9222ee 100755 --- a/modules/vdn.py +++ b/modules/vdn.py @@ -51,7 +51,7 @@ class VectorDetectionNetwork: """ """ - def __init__(self, train=False, backbone='resnet50'): + def __init__(self, train=False, backbone='resnet18'): if train: vdn_config = os.path.join(root_dir, f"cfgs/{backbone}/train.yaml") else: @@ -96,8 +96,6 @@ def train(self): } # define loss function (criterion) and optimizer - # crit_heatmap = lib_loss.JointsMSELoss(use_target_weight=cfgs.LOSS.USE_TARGET_WEIGHT).cuda() - # crit_vector = lib_loss.OrientsMSELoss().cuda() crit_heatmap = lib_loss.MSELoss().cuda() crit_vector = lib_loss.MSELoss().cuda() diff --git a/train.py b/train.py index c99df07..d19955b 100755 --- a/train.py +++ b/train.py @@ -10,10 +10,7 @@ class Test(unittest.TestCase): def test_train(self): - """Available backbones: resnet34 (default), resnet18, resnet50. - - """ - vdn_instance = vdn.VectorDetectionNetwork(train=True, backbone='resnet34') + vdn_instance = vdn.VectorDetectionNetwork(train=True, backbone='resnet18') vdn_instance.train()