diff --git a/README.md b/README.md new file mode 100755 index 0000000..4ed6c69 --- /dev/null +++ b/README.md @@ -0,0 +1,161 @@ +# CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/centersnap-single-shot-multi-object-3d-shape/6d-pose-estimation-using-rgbd-on-camera25)](https://paperswithcode.com/sota/6d-pose-estimation-using-rgbd-on-camera25?p=centersnap-single-shot-multi-object-3d-shape) + +This repository is the pytorch implementation of our paper: + + + + +**CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation**
+[__***Muhammad Zubair Irshad***__](https://zubairirshad.com), [Thomas Kollar](http://www.tkollar.com/site/), [Michael Laskey](https://www.linkedin.com/in/michael-laskey-4b087ba2/), [Kevin Stone](https://www.linkedin.com/in/kevin-stone-51171270/), [Zsolt Kira](https://faculty.cc.gatech.edu/~zk15/)
+International Conference on Robotics and Automation (ICRA), 2022
+ +[[Project Page](https://zubair-irshad.github.io/projects/CenterSnap.html)] [[arXiv](https://arxiv.org/abs/2203.01929)] [[PDF](https://arxiv.org/pdf/2203.01929.pdf)] [[Video](https://www.youtube.com/watch?v=Bg5vi6DSMdM)] [[Poster](https://zubair-irshad.github.io/projects/resources/Poster%7CCenterSnap%7CICRA2022.pdf)] + +[![Explore CenterSnap in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/zubair-irshad/CenterSnap/blob/master/notebook/explore_CenterSnap.ipynb)
+ + +

+ +

+ +

+ +

+ +## Citation + +If you find this repository useful, please consider citing: + +``` +@inproceedings{irshad2022centersnap, + title={CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation}, + author={Muhammad Zubair Irshad and Thomas Kollar and Michael Laskey and Kevin Stone and Zsolt Kira}, + journal={IEEE International Conference on Robotics and Automation (ICRA)}, + year={2022}, + url={https://arxiv.org/abs/2203.01929}, +} +``` + +### Contents +
+ +
+ +## 💻 Environment + +Create a python 3.8 virtual environment and install requirements: + +```bash +cd $CenterSnap_Repo +conda create -y --prefix ./env python=3.8 +conda activate ./env/ +./env/bin/python -m pip install --upgrade pip +./env/bin/python -m pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html +``` +The code was built and tested on **cuda 10.2** + +## 📊 Dataset + +1. Download pre-processed dataset + +We recommend downloading the preprocessed dataset to train and evaluate CenterSnap model. Download and untar [Synthetic](https://tri-robotics-public.s3.amazonaws.com/centersnap/CAMERA.tar.gz) (868GB) and [Real](https://tri-robotics-public.s3.amazonaws.com/centersnap/Real.tar.gz) (70GB) datasets. These files contains all the training and validation you need to replicate our results. + +``` +cd $CenterSnap_REPO/data +wget https://tri-robotics-public.s3.amazonaws.com/centersnap/CAMERA.tar.gz +tar -xzvf CAMERA.tar.gz + +wget https://tri-robotics-public.s3.amazonaws.com/centersnap/Real.tar.gz +tar -xzvf Real.tar.gz +``` + +The data directory structure should follow: + +``` +data +├── CAMERA +│ ├── train +│ └── val_subset +├── Real +│ ├── train +└── └── test +``` + +2. To prepare your own dataset, we provide additional scripts under [prepare_data](https://github.com/zubair-irshad/CenterSnap/tree/master/prepare_data). + +## ✨ Training and Inference + +1. Train on NOCS Synthetic (requires 13GB GPU memory): +```bash +./runner.sh net_train.py @configs/net_config.txt +``` + +Note than *runner.sh* is equivalent to using *python* to run the script. Additionally it sets up the PYTHONPATH and CenterSnap Enviornment Path automatically. + +2. Finetune on NOCS Real Train (Note that good results can be obtained after finetuning on the Real train set for only a few epochs i.e. 1-5): +```bash +./runner.sh net_train.py @configs/net_config_real_resume.txt --checkpoint \path\to\best\checkpoint +``` + +3. Inference on a NOCS Real Test Subset + +

+ +

+ +Download a small NOCS Real subset from [[here](https://www.dropbox.com/s/yfenvre5fhx3oda/nocs_test_subset.tar.gz?dl=1)] + +```bash +./runner.sh inference/inference_real.py @configs/net_config.txt --data_dir path_to_nocs_test_subset --checkpoint checkpoint_path_here +``` + +You should see the **visualizations** saved in ```results/CenterSnap```. Change the --ouput_path in *config.txt to save them to a different folder + +4. Optional (Shape Auto-Encoder Pre-training) + +We provide pretrained model for shape auto-encoder to be used for data collection and inference. Although our codebase doesn't require separately training the shape auto-encoder, if you would like to do so, we provide additional scripts under **external/shape_pretraining** + + +## 📝 FAQ + +**1.** I am getting ```no cuda GPUs available``` while running colab. + +- Ans: Make sure to follow this instruction to activate GPUs in colab: + +``` +Make sure that you have enabled the GPU under Runtime-> Change runtime type! +``` + +**2.** I am getting ```raise RuntimeError('received %d items of ancdata' % +RuntimeError: received 0 items of ancdata``` + +- Ans: Increase ulimit to 2048 or 8096 via ```uimit -n 2048``` + +**3.** I am getting ``` RuntimeError: CUDA error: no kernel image is available for execution on the device``` or ``` You requested GPUs: [0] But your machine only has: [] ``` + +- Ans: Check your pytorch installation with your cuda installation. Try the following: + + +1. Installing cuda 10.2 and running the same script in requirements.txt + +2. Installing the relevant pytorch cuda version i.e. changing this line in the requirements.txt + +``` +torch==1.7.1 +torchvision==0.8.2 +``` + +**4.** I am seeing zero val metrics in ***wandb*** +- Ans: Make sure you threshold the metrics. Since pytorch lightning's first validation check metric is high, it seems like all other metrics are zero. Please threshold manually to remove the outlier metric in wandb to see actual metrics. + +## Acknowledgments +* This code is built upon the implementation from [SimNet](https://github.com/ToyotaResearchInstitute/simnet) + +## Licenses +* The source code is released under the [MIT license](https://opensource.org/licenses/MIT). diff --git a/configs/inference.txt b/configs/inference.txt new file mode 100755 index 0000000..dc07951 --- /dev/null +++ b/configs/inference.txt @@ -0,0 +1,25 @@ +--checkpoint=../nocs_test_subset/checkpoint/centersnap_real.ckpt +--max_steps=380000 +--model_file=models/panoptic_net.py +--model_name=res_fpn +--output=results/CenterSnap +--train_path=file://data/Real/train +--train_batch_size=32 +--train_num_workers=10 +--val_path=file://data/Real/test +--val_batch_size=32 +--val_num_workers=10 +--optim_learning_rate=0.0006 +--optim_momentum=0.9 +--optim_weight_decay=1e-4 +--optim_poly_exp=0.9 +--optim_warmup_epochs=1 +--loss_seg_mult=1.0 +--loss_depth_mult=1.0 +--loss_vertex_mult=0.1 +--loss_rotation_mult=0.1 +--loss_heatmap_mult=100.0 +--loss_latent_emb_mult=0.1 +--loss_abs_pose_mult=0.1 +--loss_z_centroid_mult=0.1 +--wandb_name=NOCS_Inference_Real diff --git a/configs/net_config.txt b/configs/net_config.txt new file mode 100755 index 0000000..902b61e --- /dev/null +++ b/configs/net_config.txt @@ -0,0 +1,24 @@ +--max_steps=380000 +--model_file=models/panoptic_net.py +--model_name=res_fpn +--output=results/CenterSnap_TrainSynthetic +--train_path=file://data/CAMERA/train +--train_batch_size=32 +--train_num_workers=10 +--val_path=file://data/CAMERA/val_subset +--val_batch_size=32 +--val_num_workers=10 +--optim_learning_rate=0.0006 +--optim_momentum=0.9 +--optim_weight_decay=1e-4 +--optim_poly_exp=0.9 +--optim_warmup_epochs=1 +--loss_seg_mult=1.0 +--loss_depth_mult=1.0 +--loss_vertex_mult=0.1 +--loss_rotation_mult=0.1 +--loss_heatmap_mult=100.0 +--loss_latent_emb_mult=0.1 +--loss_abs_pose_mult=0.1 +--loss_z_centroid_mult=0.1 +--wandb_name=NOCS_Train_Synthetic diff --git a/configs/net_config_real_resume.txt b/configs/net_config_real_resume.txt new file mode 100755 index 0000000..6184669 --- /dev/null +++ b/configs/net_config_real_resume.txt @@ -0,0 +1,25 @@ +--max_steps=240000 +--finetune_real=True +--model_file=models/panoptic_net.py +--model_name=res_fpn +--output=results/CenterSnap_FinetuneReal +--train_path=file://data/Real/train +--train_batch_size=32 +--train_num_workers=5 +--val_path=file://data/Real/test +--val_batch_size=32 +--val_num_workers=5 +--optim_learning_rate=0.0006 +--optim_momentum=0.9 +--optim_weight_decay=1e-4 +--optim_poly_exp=0.9 +--optim_warmup_epochs=1 +--loss_seg_mult=1.0 +--loss_depth_mult=1.0 +--loss_vertex_mult=0.1 +--loss_rotation_mult=0.1 +--loss_heatmap_mult=100.0 +--loss_latent_emb_mult=0.1 +--loss_abs_pose_mult=0.1 +--loss_z_centroid_mult=0.1 +--wandb_name=NOCS_Real_Finetune diff --git a/demo/Method_CS.gif b/demo/Method_CS.gif new file mode 100644 index 0000000..5ea7382 Binary files /dev/null and b/demo/Method_CS.gif differ diff --git a/demo/POSE_CS.gif b/demo/POSE_CS.gif new file mode 100644 index 0000000..1f9bbb7 Binary files /dev/null and b/demo/POSE_CS.gif differ diff --git a/demo/Pytorch_logo.png b/demo/Pytorch_logo.png new file mode 100644 index 0000000..7992605 Binary files /dev/null and b/demo/Pytorch_logo.png differ diff --git a/demo/reconstruction.gif b/demo/reconstruction.gif new file mode 100644 index 0000000..2abd7c0 Binary files /dev/null and b/demo/reconstruction.gif differ diff --git a/demo/tri-logo.png b/demo/tri-logo.png new file mode 100644 index 0000000..eaacf5b Binary files /dev/null and b/demo/tri-logo.png differ diff --git a/env/.gitignore b/env/.gitignore new file mode 100644 index 0000000..94548af --- /dev/null +++ b/env/.gitignore @@ -0,0 +1,3 @@ +* +*/ +!.gitignore diff --git a/external/shape_pretraining/README.md b/external/shape_pretraining/README.md new file mode 100644 index 0000000..c24b9d2 --- /dev/null +++ b/external/shape_pretraining/README.md @@ -0,0 +1,41 @@ +## Shape Autoencoder Pre-training
+Shape pretraining code is adapted from [object-deformnet](https://github.com/mentian/object-deformnet). + +### Install dependencies + +``` +conda activate ./env/ +cd $CenterSnap_Repo +conda install -c bottler nvidiacub +conda install -c conda-forge -c fvcore -c iopath fvcore iopath +./env/bin/python -m pip install "git+https://github.com/facebookresearch/pytorch3d.git@v0.4.0" +``` + +### Dataset Prepration +1. Download [object models](http://download.cs.stanford.edu/orion/nocs/obj_models.zip) provided by [NOCS](https://github.com/hughw19/NOCS_CVPR2019) + +2. Download NOCS [preprocess data](https://www.dropbox.com/s/8im9fzopo71h6yw/nocs_preprocess.tar.gz?dl=1) + +Unzip and organize these files in $CenterSnap/data as follows: +``` +data +├── obj_models + ├── train + ├── val + ├── real_train + ├── real_test + ├── mug_meta.pkl +``` + +2. Prepare data: + +``` +./runner.sh external/shape_pretraining/shape_data.py --obj_model_dir \path\to\object-model\dir +``` +A file would generate in ***obj_models*** folder named ***ShapeNetCore_2048.h5*** + +3. Train shape auto-encoder: +``` +cd external/shape_pretraining +./runner.sh external/shape_pretraining\train_ae.py --h5_file \path\to\h5_file +``` diff --git a/external/shape_pretraining/dataset/__pycache__/shape_dataset.cpython-38.pyc b/external/shape_pretraining/dataset/__pycache__/shape_dataset.cpython-38.pyc new file mode 100644 index 0000000..d5133a7 Binary files /dev/null and b/external/shape_pretraining/dataset/__pycache__/shape_dataset.cpython-38.pyc differ diff --git a/external/shape_pretraining/dataset/shape_dataset.py b/external/shape_pretraining/dataset/shape_dataset.py new file mode 100644 index 0000000..284150d --- /dev/null +++ b/external/shape_pretraining/dataset/shape_dataset.py @@ -0,0 +1,38 @@ +import h5py +import numpy as np +import torch.utils.data as data + +class ShapeDataset(data.Dataset): + def __init__(self, h5_file, mode, n_points=1024, augment=False): + assert (mode == 'train' or mode == 'val'), 'Mode must be "train" or "val".' + self.mode = mode + self.n_points = n_points + self.augment = augment + # load data from h5py file + with h5py.File(h5_file, 'r') as f: + self.length = f[self.mode].attrs['len'] + self.data = f[self.mode]['data'][:] + self.label = f[self.mode]['label'][:] + # augmentation parameters + self.sigma = 0.01 + self.clip = 0.02 + self.shift_range = 0.02 + + def __len__(self): + return self.length + + def __getitem__(self, index): + xyz = self.data[index] + label = self.label[index] - 1 # data saved indexed from 1 + # randomly downsample + np_data = xyz.shape[0] + assert np_data >= self.n_points, 'Not enough points in shape.' + idx = np.random.choice(np_data, self.n_points) + xyz = xyz[idx, :] + # data augmentation + if self.augment: + jitter = np.clip(self.sigma*np.random.randn(self.n_points, 3), -self.clip, self.clip) + xyz[:, :3] += jitter + shift = np.random.uniform(-self.shift_range, self.shift_range, (1, 3)) + xyz[:, :3] += shift + return xyz, label \ No newline at end of file diff --git a/external/shape_pretraining/model/__pycache__/auto_encoder.cpython-38.pyc b/external/shape_pretraining/model/__pycache__/auto_encoder.cpython-38.pyc new file mode 100644 index 0000000..e604fe7 Binary files /dev/null and b/external/shape_pretraining/model/__pycache__/auto_encoder.cpython-38.pyc differ diff --git a/external/shape_pretraining/model/auto_encoder.py b/external/shape_pretraining/model/auto_encoder.py new file mode 100644 index 0000000..9407525 --- /dev/null +++ b/external/shape_pretraining/model/auto_encoder.py @@ -0,0 +1,75 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class PointCloudEncoder(nn.Module): + def __init__(self, emb_dim): + super(PointCloudEncoder, self).__init__() + self.conv1 = nn.Conv1d(3, 64, 1) + self.conv2 = nn.Conv1d(64, 128, 1) + self.conv3 = nn.Conv1d(256, 256, 1) + self.conv4 = nn.Conv1d(256, 1024, 1) + self.fc = nn.Linear(1024, emb_dim) + + def forward(self, xyz): + """ + Args: + xyz: (B, 3, N) + + """ + np = xyz.size()[2] + x = F.relu(self.conv1(xyz)) + x = F.relu(self.conv2(x)) + global_feat = F.adaptive_max_pool1d(x, 1) + x = torch.cat((x, global_feat.repeat(1, 1, np)), dim=1) + x = F.relu(self.conv3(x)) + x = F.relu(self.conv4(x)) + x = torch.squeeze(F.adaptive_max_pool1d(x, 1), dim=2) + embedding = self.fc(x) + return embedding + + +class PointCloudDecoder(nn.Module): + def __init__(self, emb_dim, n_pts): + super(PointCloudDecoder, self).__init__() + self.fc1 = nn.Linear(emb_dim, 512) + self.fc2 = nn.Linear(512, 1024) + self.fc3 = nn.Linear(1024, 3*n_pts) + + def forward(self, embedding): + """ + Args: + embedding: (B, 512) + + """ + bs = embedding.size()[0] + out = F.relu(self.fc1(embedding)) + out = F.relu(self.fc2(out)) + out = self.fc3(out) + out_pc = out.view(bs, -1, 3) + return out_pc + + +class PointCloudAE(nn.Module): + def __init__(self, emb_dim=512, n_pts=1024): + super(PointCloudAE, self).__init__() + self.encoder = PointCloudEncoder(emb_dim) + self.decoder = PointCloudDecoder(emb_dim, n_pts) + + def forward(self, in_pc, emb=None): + """ + Args: + in_pc: (B, N, 3) + emb: (B, 512) + + Returns: + emb: (B, emb_dim) + out_pc: (B, n_pts, 3) + + """ + if emb is None: + xyz = in_pc.permute(0, 2, 1) + emb = self.encoder(xyz) + out_pc = self.decoder(emb) + return emb, out_pc diff --git a/external/shape_pretraining/shape_data.py b/external/shape_pretraining/shape_data.py new file mode 100644 index 0000000..c565e70 --- /dev/null +++ b/external/shape_pretraining/shape_data.py @@ -0,0 +1,143 @@ +import os +import sys +import h5py +import glob +import numpy as np +import _pickle as cPickle +import argparse +from utils.shape_utils import sample_points_from_mesh + +def save_model_to_hdf5(obj_model_dir, n_points, fps=False, include_distractors=False, with_normal=False): + """ Save object models (point cloud) to HDF5 file. + Dataset used to train the auto-encoder. + Only use models from ShapeNetCore. + Background objects are not inlcuded as default. We did not observe that it helps + to train the auto-encoder. + """ + catId_to_synsetId = {1: '02876657', 2: '02880940', 3: '02942699', 4: '02946921', 5: '03642806', 6: '03797390'} + distractors_synsetId = ['00000000', '02954340', '02992529', '03211117'] + with open(os.path.join(obj_model_dir, 'mug_meta.pkl'), 'rb') as f: + mug_meta = cPickle.load(f) + # read all the paths to models + print('Sampling points from mesh model ...') + if with_normal: + train_data = np.zeros((3000, n_points, 6), dtype=np.float32) + val_data = np.zeros((500, n_points, 6), dtype=np.float32) + else: + train_data = np.zeros((3000, n_points, 3), dtype=np.float32) + val_data = np.zeros((500, n_points, 3), dtype=np.float32) + train_label = [] + val_label = [] + train_count = 0 + val_count = 0 + # CAMERA + for subset in ['train', 'val']: + for catId in range(1, 7): + synset_dir = os.path.join(obj_model_dir, subset, catId_to_synsetId[catId]) + inst_list = sorted(os.listdir(synset_dir)) + for instance in inst_list: + path_to_mesh_model = os.path.join(synset_dir, instance, 'model.obj') + if instance == 'b9be7cfe653740eb7633a2dd89cec754' or instance =='d3b53f56b4a7b3b3c9f016d57db96408': + continue + model_points = sample_points_from_mesh(path_to_mesh_model, n_points, with_normal, fps=fps, ratio=2) + model_points = model_points * np.array([[1.0, 1.0, -1.0]]) + if catId == 6: + shift = mug_meta[instance][0] + scale = mug_meta[instance][1] + model_points = scale * (model_points + shift) + if subset == 'train': + train_data[train_count] = model_points + train_label.append(catId) + train_count += 1 + else: + val_data[val_count] = model_points + val_label.append(catId) + val_count += 1 + # distractors + if include_distractors: + for synsetId in distractors_synsetId: + synset_dir = os.path.join(obj_model_dir, subset, synsetId) + inst_list = sorted(os.listdir(synset_dir)) + for instance in inst_list: + path_to_mesh_model = os.path.join(synset_dir, instance, 'model.obj') + model_points = sample_points_from_mesh(path_to_mesh_model, n_points, with_normal, fps=fps, ratio=2) + # TODO: check whether need to flip z-axis, currently not used + model_points = model_points * np.array([[1.0, 1.0, -1.0]]) + if subset == 'train': + train_data[train_count] = model_points + train_label.append(0) + train_count += 1 + else: + val_data[val_count] = model_points + val_label.append(0) + val_count += 1 + # Real + for subset in ['real_train', 'real_test']: + path_to_mesh_models = glob.glob(os.path.join(obj_model_dir, subset, '*.obj')) + for inst_path in sorted(path_to_mesh_models): + instance = os.path.basename(inst_path).split('.')[0] + if instance.startswith('bottle'): + catId = 1 + elif instance.startswith('bowl'): + catId = 2 + elif instance.startswith('camera'): + catId = 3 + elif instance.startswith('can'): + catId = 4 + elif instance.startswith('laptop'): + catId = 5 + elif instance.startswith('mug'): + catId = 6 + else: + raise NotImplementedError + model_points = sample_points_from_mesh(inst_path, n_points, with_normal, fps=fps, ratio=2) + bbox_file = inst_path.replace('.obj', '.txt') + bbox_dims = np.loadtxt(bbox_file) + model_points /= np.linalg.norm(bbox_dims) + if catId == 6: + shift = mug_meta[instance][0] + scale = mug_meta[instance][1] + model_points = scale * (model_points + shift) + if subset == 'real_train': + train_data[train_count] = model_points + train_label.append(catId) + train_count += 1 + else: + val_data[val_count] = model_points + val_label.append(catId) + val_count += 1 + + num_train_instances = len(train_label) + num_val_instances = len(val_label) + assert num_train_instances == train_count + assert num_val_instances == val_count + train_data = train_data[:num_train_instances] + val_data = val_data[:num_val_instances] + train_label = np.array(train_label, dtype=np.uint8) + val_label = np.array(val_label, dtype=np.uint8) + print('{} shapes found in train dataset'.format(num_train_instances)) + print('{} shapes found in val dataset'.format(num_val_instances)) + + # write to HDF5 file + print('Writing data to HDF5 file ...') + if with_normal: + filename = 'ShapeNetCore_{}_with_normal.h5'.format(n_points) + else: + filename = 'ShapeNetCore_{}.h5'.format(n_points) + hfile = h5py.File(os.path.join(obj_model_dir, filename), 'w') + train_dataset = hfile.create_group('train') + train_dataset.attrs.create('len', num_train_instances) + train_dataset.create_dataset('data', data=train_data, compression='gzip', dtype='float32') + train_dataset.create_dataset('label', data=train_label, compression='gzip', dtype='uint8') + val_dataset = hfile.create_group('val') + val_dataset.attrs.create('len', num_val_instances) + val_dataset.create_dataset('data', data=val_data, compression='gzip', dtype='float32') + val_dataset.create_dataset('label', data=val_label, compression='gzip', dtype='uint8') + hfile.close() + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--obj_model_dir', type=str, required=True) + args = parser.parse_args() + obj_model_dir = args.obj_model_dir + save_model_to_hdf5(obj_model_dir, n_points=2048, fps=True) \ No newline at end of file diff --git a/external/shape_pretraining/train_ae.py b/external/shape_pretraining/train_ae.py new file mode 100644 index 0000000..c19741f --- /dev/null +++ b/external/shape_pretraining/train_ae.py @@ -0,0 +1,110 @@ +import os +import time +import argparse +import torch +import wandb +from external.shape_pretraining.model.auto_encoder import PointCloudAE +from pytorch3d.loss import chamfer_distance +from external.shape_pretraining.dataset.shape_dataset import ShapeDataset +from utils.shape_utils import setup_logger +import wandb +wandb.init(project="train_ae") + +parser = argparse.ArgumentParser() +parser.add_argument('--num_point', type=int, default=2048, help='number of points, needed if use points') +parser.add_argument('--emb_dim', type=int, default=128, help='dimension of latent embedding [default: 512]') +parser.add_argument('--h5_file', type=str, default='obj_models_dir/ShapeNetCore_2048.h5', help='h5 file') +parser.add_argument('--batch_size', type=int, default=256, help='batch size') +parser.add_argument('--num_workers', type=int, default=10, help='number of data loading workers') +parser.add_argument('--gpu', type=str, default='0', help='GPU to use') +parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate') +parser.add_argument('--start_epoch', type=int, default=1, help='which epoch to start') +parser.add_argument('--max_epoch', type=int, default=50, help='max number of epochs to train') +parser.add_argument('--resume_model', type=str, default='', help='resume from saved model') +parser.add_argument('--result_dir', type=str, default='results/ae_2048', help='directory to save train results') +opt = parser.parse_args() + +opt.repeat_epoch = 30 +opt.decay_step = 5000 +opt.decay_rate = [1.0, 0.6, 0.3, 0.1] + + +def train_net(): + if not os.path.exists(opt.result_dir): + os.makedirs(opt.result_dir) + logger = setup_logger('train_log', os.path.join(opt.result_dir, 'log.txt')) + for key, value in vars(opt).items(): + logger.info(key + ': ' + str(value)) + os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu + # model & loss + estimator = PointCloudAE(opt.emb_dim, opt.num_point) + estimator.cuda() + if opt.resume_model != '': + estimator.load_state_dict(torch.load(opt.resume_model)) + # dataset + train_dataset = ShapeDataset(opt.h5_file, mode='train', augment=True) + train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.batch_size, + shuffle=True, num_workers=opt.num_workers) + val_dataset = ShapeDataset(opt.h5_file, mode='val', augment=False) + val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=opt.batch_size, + shuffle=False, num_workers=opt.num_workers) + # train + st_time = time.time() + global_step = ((train_dataset.length + opt.batch_size - 1) // opt.batch_size) * opt.repeat_epoch * (opt.start_epoch - 1) + print("train_dataset",train_dataset.length) + print("global step", global_step) + decay_count = -1 + for epoch in range(opt.start_epoch, opt.max_epoch+1): + # train one epoch + logger.info('Time {0}'.format(time.strftime("%Hh %Mm %Ss", time.gmtime(time.time() - st_time)) + \ + ', ' + 'Epoch %02d' % epoch + ', ' + 'Training started')) + # create optimizer and adjust learning rate if needed + if global_step // opt.decay_step > decay_count: + decay_count += 1 + if decay_count < len(opt.decay_rate): + current_lr = opt.lr * opt.decay_rate[decay_count] + optimizer = torch.optim.Adam(estimator.parameters(), lr=current_lr) + batch_idx = 0 + estimator.train() + for rep in range(opt.repeat_epoch): + for i, data in enumerate(train_dataloader): + # label must be zero_indexed + batch_xyz, batch_label = data + batch_xyz = batch_xyz[:, :, :3].cuda() + optimizer.zero_grad() + embedding, point_cloud = estimator(batch_xyz) + loss, _ = chamfer_distance(point_cloud, batch_xyz) + wandb.log({'train_loss': loss}) + loss.backward() + optimizer.step() + global_step += 1 + batch_idx += 1 + # write results to tensorboard + # tb_writer.add_summary(summary, global_step) + if batch_idx % 100 == 0: + logger.info('Batch {0} Loss:{1:f}'.format(batch_idx, loss)) + logger.info('>>>>>>>>----------Epoch {:02d} train finish---------<<<<<<<<'.format(epoch)) + # evaluate one epoch + logger.info('Time {0}'.format(time.strftime("%Hh %Mm %Ss", time.gmtime(time.time() - st_time)) + \ + ', ' + 'Epoch %02d' % epoch + ', ' + 'Testing started')) + estimator.eval() + val_loss = 0.0 + for i, data in enumerate(val_dataloader, 1): + batch_xyz, batch_label = data + batch_xyz = batch_xyz[:, :, :3].cuda() + embedding, point_cloud = estimator(batch_xyz) + loss, _ = chamfer_distance(point_cloud, batch_xyz) + # loss, _, _ = criterion(point_cloud, batch_xyz) + val_loss += loss.item() + logger.info('Batch {0} Loss:{1:f}'.format(i, loss)) + val_loss = val_loss / i + wandb.log({'val_loss': val_loss}) + # summary = tf.Summary(value=[tf.Summary.Value(tag='val_loss', simple_value=val_loss)]) + # tb_writer.add_summary(summary, global_step) + logger.info('Epoch {0:02d} test average loss: {1:06f}'.format(epoch, val_loss)) + logger.info('>>>>>>>>----------Epoch {:02d} test finish---------<<<<<<<<'.format(epoch)) + # save model after each epoch + torch.save(estimator.state_dict(), '{0}/model_{1:02d}.pth'.format(opt.result_dir, epoch)) + +if __name__ == '__main__': + train_net() \ No newline at end of file diff --git a/inference/inference_real.py b/inference/inference_real.py new file mode 100755 index 0000000..b33de73 --- /dev/null +++ b/inference/inference_real.py @@ -0,0 +1,159 @@ +import argparse +import pathlib +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +import open3d as o3d +import matplotlib.pyplot as plt +import os +import time +import pytorch_lightning as pl +import _pickle as cPickle +from simnet.lib.net import common +from simnet.lib import camera +from simnet.lib.net.panoptic_trainer import PanopticModel +from simnet.lib.net.models.auto_encoder import PointCloudAE +from utils.nocs_utils import load_img_NOCS, create_input_norm +from utils.viz_utils import depth2inv, viz_inv_depth +from utils.transform_utils import get_gt_pointclouds, transform_coordinates_3d, calculate_2d_projections +from utils.transform_utils import project +from utils.viz_utils import save_projected_points, draw_bboxes, line_set_mesh + +import time +def get_auto_encoder(model_path): + emb_dim = 128 + n_pts = 2048 + ae = PointCloudAE(emb_dim, n_pts) + ae.cuda() + ae.load_state_dict(torch.load(model_path)) + ae.eval() + return ae + +def inference( + hparams, + data_dir, + output_path, + min_confidence=0.1, + use_gpu=True, +): + model = PanopticModel(hparams, 0, None, None) + model.eval() + if use_gpu: + model.cuda() + data_path = open(os.path.join(data_dir, 'Real', 'test_list_subset.txt')).read().splitlines() + _CAMERA = camera.NOCS_Real() + min_confidence = 0.50 + + for i, img_path in enumerate(data_path): + img_full_path = os.path.join(data_dir, 'Real', img_path) + color_path = img_full_path + '_color.png' + if not os.path.exists(color_path): + continue + depth_full_path = img_full_path + '_depth.png' + img_vis = cv2.imread(color_path) + left_linear, depth, actual_depth = load_img_NOCS(color_path, depth_full_path) + input = create_input_norm(left_linear, depth) + input = input[None, :, :, :] + if use_gpu: + input = input.to(torch.device('cuda:0')) + with torch.no_grad(): + _, _, _ , pose_output = model.forward(input) + latent_emb_outputs, abs_pose_outputs, img_output, _, _ = pose_output.compute_pointclouds_and_poses(min_confidence,is_target = False) + + auto_encoder_path = os.path.join(data_dir, 'ae_checkpoints', 'model_50_nocs.pth') + ae = get_auto_encoder(auto_encoder_path) + cv2.imwrite( + str(output_path / f'{i}_image.png'), + np.copy(np.copy(img_vis)) + ) + cv2.imwrite( + str(output_path / f'{i}_peaks_output.png'), + np.copy(img_output) + ) + depth_vis = depth2inv(torch.tensor(depth).unsqueeze(0).unsqueeze(0)) + depth_vis = viz_inv_depth(depth_vis) + depth_vis = depth_vis*255.0 + cv2.imwrite( + str(output_path / f'{i}_depth_vis.png'), + np.copy(depth_vis) + ) + write_pcd = False + rotated_pcds = [] + points_2d = [] + box_obb = [] + axes = [] + + for j in range(len(latent_emb_outputs)): + emb = latent_emb_outputs[j] + emb = latent_emb_outputs[j] + emb = torch.FloatTensor(emb).unsqueeze(0) + emb = emb.cuda() + _, shape_out = ae(None, emb) + shape_out = shape_out.cpu().detach().numpy()[0] + + rotated_pc, rotated_box, _ = get_gt_pointclouds(abs_pose_outputs[j], shape_out, camera_model = _CAMERA) + pcd = o3d.geometry.PointCloud() + pcd.points = o3d.utility.Vector3dVector(rotated_pc) + filename_rotated = str(output_path) + '/pcd_rotated'+str(i)+str(j)+'.ply' + if write_pcd: + o3d.io.write_point_cloud(filename_rotated, pcd) + else: + rotated_pcds.append(pcd) + + mesh_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.1, origin=[0, 0, 0]) + T = abs_pose_outputs[j].camera_T_object + mesh_frame = mesh_frame.transform(T) + rotated_pcds.append(mesh_frame) + cylinder_segments = line_set_mesh(rotated_box) + for k in range(len(cylinder_segments)): + rotated_pcds.append(cylinder_segments[k]) + + + points_mesh = camera.convert_points_to_homopoints(rotated_pc.T) + points_2d_mesh = project(_CAMERA.K_matrix, points_mesh) + points_2d_mesh = points_2d_mesh.T + points_2d.append(points_2d_mesh) + #2D output + points_obb = camera.convert_points_to_homopoints(np.array(rotated_box).T) + points_2d_obb = project(_CAMERA.K_matrix, points_obb) + points_2d_obb = points_2d_obb.T + box_obb.append(points_2d_obb) + xyz_axis = 0.3*np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0]]).transpose() + sRT = abs_pose_outputs[j].camera_T_object @ abs_pose_outputs[j].scale_matrix + transformed_axes = transform_coordinates_3d(xyz_axis, sRT) + projected_axes = calculate_2d_projections(transformed_axes, _CAMERA.K_matrix[:3,:3]) + axes.append(projected_axes) + + if not write_pcd: + o3d.visualization.draw_geometries(rotated_pcds) + save_projected_points(np.copy(img_vis), points_2d, str(output_path), i) + + colors_box = [(63, 237, 234)] + im = np.array(np.copy(img_vis)).copy() + for k in range(len(colors_box)): + for points_2d, axis in zip(box_obb, axes): + points_2d = np.array(points_2d) + im = draw_bboxes(im, points_2d, axis, colors_box[k]) + box_plot_name = str(output_path)+'/box3d'+str(i)+'.png' + cv2.imwrite( + box_plot_name, + np.copy(im) + ) + print("done with image: ", i ) + +if __name__ == '__main__': + parser = argparse.ArgumentParser(fromfile_prefix_chars='@') + common.add_train_args(parser) + app_group = parser.add_argument_group('app') + app_group.add_argument('--app_output', default='inference_best', type=str) + app_group.add_argument('--result_name', default='centersnap_nocs', type=str) + app_group.add_argument('--data_dir', default='nocs_data', type=str) + + hparams = parser.parse_args() + print(hparams) + result_name = hparams.result_name + path = 'data/'+result_name + output_path = pathlib.Path(path) / hparams.app_output + output_path.mkdir(parents=True, exist_ok=True) + inference(hparams, hparams.data_dir, output_path) diff --git a/net_train.py b/net_train.py new file mode 100755 index 0000000..598f484 --- /dev/null +++ b/net_train.py @@ -0,0 +1,99 @@ +import os + +os.environ['PYTHONHASHSEED'] = str(1) +import argparse +from importlib.machinery import SourceFileLoader +import sys + +import random + +random.seed(12345) +import numpy as np + +np.random.seed(12345) +import torch + +torch.manual_seed(12345) + +import wandb + +import pytorch_lightning as pl +from pytorch_lightning.callbacks import ModelCheckpoint +from pytorch_lightning import loggers + +from simnet.lib.net import common +from simnet.lib import datapoint +from simnet.lib import camera +from simnet.lib.net.panoptic_trainer import PanopticModel + +_GPU_TO_USE = 0 + + +class EvalMethod(): + + def __init__(self): + + self.eval_3d = None + self.camera_model = camera.NOCS_Camera() + + def process_sample(self, pose_outputs, box_outputs, seg_outputs, detections_gt, scene_name): + return True + + def process_all_dataset(self, log): + return True + # log['all 3Dmap'] = self.eval_3d.process_all_3D_dataset() + + def draw_detections( + self,seg_outputs,left_image_np, llog, prefix + ): + seg_vis = seg_outputs.get_visualization_img(np.copy(left_image_np)) + llog[f'{prefix}/seg'] = wandb.Image(seg_vis, caption=prefix) + +if __name__ == "__main__": + parser = argparse.ArgumentParser(fromfile_prefix_chars='@') + common.add_train_args(parser) + hparams = parser.parse_args() + train_ds = datapoint.make_dataset(hparams.train_path) + samples_per_epoch = len(train_ds.list()) + samples_per_step = hparams.train_batch_size + steps = hparams.max_steps + steps_per_epoch = samples_per_epoch // samples_per_step + epochs = int(np.ceil(steps / steps_per_epoch)) + actual_steps = epochs * steps_per_epoch + print('Samples per epoch', samples_per_epoch) + print('Steps per epoch', steps_per_epoch) + print('Target steps:', steps) + print('Actual steps:', actual_steps) + print('Epochs:', epochs) + + model = PanopticModel(hparams, epochs, train_ds, EvalMethod()) + model_checkpoint = ModelCheckpoint(filepath=hparams.output, save_top_k=-1, period=1, mode='max') + wandb_logger = loggers.WandbLogger(name=hparams.wandb_name, project='CenterSnap') + + if hparams.finetune_real: + trainer = pl.Trainer( + max_nb_epochs=epochs, + early_stop_callback=None, + gpus=[_GPU_TO_USE], + checkpoint_callback=model_checkpoint, + val_check_interval=1.0, + logger=wandb_logger, + default_save_path=hparams.output, + use_amp=False, + print_nan_grads=True, + resume_from_checkpoint=hparams.checkpoint + ) + else: + trainer = pl.Trainer( + max_nb_epochs=epochs, + early_stop_callback=None, + gpus=[_GPU_TO_USE], + checkpoint_callback=model_checkpoint, + val_check_interval=1.0, + logger=wandb_logger, + default_save_path=hparams.output, + use_amp=False, + print_nan_grads=True, + ) + + trainer.fit(model) diff --git a/notebook/.ipynb_checkpoints/explore_CenterSnap-checkpoint.ipynb b/notebook/.ipynb_checkpoints/explore_CenterSnap-checkpoint.ipynb new file mode 100644 index 0000000..a9f51f5 --- /dev/null +++ b/notebook/.ipynb_checkpoints/explore_CenterSnap-checkpoint.ipynb @@ -0,0 +1,245635 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "99fd0ad9", + "metadata": {}, + "source": [ + "# CenterSnap Exploration\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1c307d51", + "metadata": {}, + "source": [ + "This is a colab to explore CenterSnap inference properties, proposed in our work [Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation](https://zubair-irshad.github.io/projects/CenterSnap.html).\n", + "#### Make sure that you have enabled the GPU under Runtime-> Change runtime type!\n", + "\n", + "We will perform inference using the learnt model and then visualize qualitative results!\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ab91ae4", + "metadata": {}, + "outputs": [], + "source": [ + "#Run this on Google Colab\n", + "!git clone https://github.com/zubair-irshad/CenterSnap.git\n", + "!pip install --upgrade pip\n", + "!cd CenterSnap && pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html\n", + "!pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 -f https://download.pytorch.org/whl/torch_stable.html\n", + "!cd CenterSnap && wget https://www.dropbox.com/s/yfenvre5fhx3oda/nocs_test_subset.tar.gz?dl=1 -O nocs_test_subset.tar.gz && tar -xzvf nocs_test_subset.tar.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "93972c59", + "metadata": {}, + "outputs": [], + "source": [ + "import argparse\n", + "import pathlib\n", + "import cv2\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn.functional as F\n", + "import open3d as o3d\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "import time\n", + "import pytorch_lightning as pl\n", + "import _pickle as cPickle\n", + "import os, sys\n", + "sys.path.append('CenterSnap')\n", + "from simnet.lib.net import common\n", + "from simnet.lib import camera\n", + "from simnet.lib.net.panoptic_trainer import PanopticModel\n", + "from simnet.lib.net.models.auto_encoder import PointCloudAE\n", + "from utils.nocs_utils import load_img_NOCS, create_input_norm\n", + "from utils.viz_utils import depth2inv, viz_inv_depth\n", + "from utils.transform_utils import get_gt_pointclouds, transform_coordinates_3d, calculate_2d_projections\n", + "from utils.transform_utils import project\n", + "from utils.viz_utils import save_projected_points, draw_bboxes, line_set_mesh, display_gird, draw_geometries, show_projected_points" + ] + }, + { + "cell_type": "markdown", + "id": "f9cbd124", + "metadata": {}, + "source": [ + "## 1. Instantiate CenterSnap Model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d956ded1", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using model class from: /home/zubair/CenterSnap/simnet/lib/net/models/panoptic_net.py\n", + "Restoring from checkpoint: ../nocs_test_subset/checkpoint/centersnap_real.ckpt\n", + "test/scene_1/0006\n" + ] + } + ], + "source": [ + "sys.argv = ['', '@CenterSnap/configs/net_config.txt']\n", + "parser = argparse.ArgumentParser(fromfile_prefix_chars='@')\n", + "common.add_train_args(parser)\n", + "app_group = parser.add_argument_group('app')\n", + "app_group.add_argument('--app_output', default='inference', type=str)\n", + "app_group.add_argument('--result_name', default='centersnap_nocs', type=str)\n", + "app_group.add_argument('--data_dir', default='CenterSnap/nocs_test_subset', type=str)\n", + "hparams = parser.parse_args()\n", + "min_confidence = 0.50\n", + "use_gpu=True\n", + "hparams.checkpoint = 'CenterSnap/nocs_test_subset/checkpoint/centersnap_real.ckpt'\n", + "model = PanopticModel(hparams, 0, None, None)\n", + "model.eval()\n", + "if use_gpu:\n", + " model.cuda()\n", + "data_path = open(os.path.join(hparams.data_dir, 'Real', 'test_list_subset.txt')).read().splitlines()\n", + "_CAMERA = camera.NOCS_Real()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bb52a406", + "metadata": {}, + "outputs": [], + "source": [ + "def get_auto_encoder(model_path):\n", + " emb_dim = 128\n", + " n_pts = 2048\n", + " ae = PointCloudAE(emb_dim, n_pts)\n", + " ae.cuda()\n", + " ae.load_state_dict(torch.load(model_path))\n", + " ae.eval()\n", + " return ae" + ] + }, + { + "cell_type": "markdown", + "id": "74d9563a", + "metadata": {}, + "source": [ + "## 2. Perform inference using NOCS Real Subset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "806c7413", + "metadata": {}, + "outputs": [], + "source": [ + "#num from 0 to 3 (small subset of data)\n", + "num = 0\n", + "img_full_path = os.path.join(hparams.data_dir, 'Real', data_path[num])\n", + "img_vis = cv2.imread(img_full_path + '_color.png')\n", + "\n", + "left_linear, depth, actual_depth = load_img_NOCS(img_full_path + '_color.png' , img_full_path + '_depth.png')\n", + "input = create_input_norm(left_linear, depth)[None, :, :, :]\n", + "\n", + "auto_encoder_path = os.path.join(hparams.data_dir, 'ae_checkpoints', 'model_50_nocs.pth')\n", + "ae = get_auto_encoder(auto_encoder_path)\n", + " \n", + "if use_gpu:\n", + " input = input.to(torch.device('cuda:0'))\n", + "_, _, _ , pose_output = model.forward(input)\n", + "with torch.no_grad():\n", + " latent_emb_outputs, abs_pose_outputs, peak_output, _, _ = pose_output.compute_pointclouds_and_poses(min_confidence,is_target = False)" + ] + }, + { + "cell_type": "markdown", + "id": "d077e606", + "metadata": {}, + "source": [ + "### 2.1 Visualize Peaks output and Depth output" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "92b7807d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(480, 640, 3)\n" + ] + }, + { + "data": { + "image/png": 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0])\n", + " T = abs_pose_outputs[j].camera_T_object\n", + " mesh_frame = mesh_frame.transform(T)\n", + " rotated_pcds.append(mesh_frame)\n", + " cylinder_segments = line_set_mesh(rotated_box)\n", + " for k in range(len(cylinder_segments)):\n", + " rotated_pcds.append(cylinder_segments[k])\n", + " points_mesh = camera.convert_points_to_homopoints(rotated_pc.T)\n", + " points_2d_mesh = project(_CAMERA.K_matrix, points_mesh)\n", + " points_2d_mesh = points_2d_mesh.T\n", + " points_2d.append(points_2d_mesh)\n", + " #2D output\n", + " points_obb = camera.convert_points_to_homopoints(np.array(rotated_box).T)\n", + " points_2d_obb = project(_CAMERA.K_matrix, points_obb)\n", + " points_2d_obb = points_2d_obb.T\n", + " box_obb.append(points_2d_obb)\n", + " xyz_axis = 0.3*np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0]]).transpose()\n", + " sRT = abs_pose_outputs[j].camera_T_object @ abs_pose_outputs[j].scale_matrix\n", + " transformed_axes = transform_coordinates_3d(xyz_axis, sRT)\n", + " projected_axes = calculate_2d_projections(transformed_axes, _CAMERA.K_matrix[:3,:3])\n", + " axes.append(projected_axes)\n", + "\n", + "draw_geometries(rotated_pcds)" + ] + }, + { + "cell_type": "markdown", + "id": "37bc0fac", + "metadata": {}, + "source": [ + "## 2.3 Project 3D Pointclouds and 3D bounding boxes on 2D image" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d8070cda", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "color_img = np.copy(img_vis) \n", + "projected_points_img = show_projected_points(color_img, points_2d)\n", + "colors_box = [(63, 237, 234)]\n", + "im = np.array(np.copy(img_vis)).copy()\n", + "for k in range(len(colors_box)):\n", + " for points_2d, axis in zip(box_obb, axes):\n", + " points_2d = np.array(points_2d)\n", + " im = draw_bboxes(im, points_2d, axis, colors_box[k])\n", + "\n", + "plt.gca().invert_yaxis()\n", + "plt.axis('off')\n", + "plt.imshow(im[...,::-1])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a357d168", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "centersnap", + "language": "python", + "name": "centersnap" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebook/explore_CenterSnap.ipynb b/notebook/explore_CenterSnap.ipynb new file mode 100644 index 0000000..a9f51f5 --- /dev/null +++ b/notebook/explore_CenterSnap.ipynb @@ -0,0 +1,245635 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "99fd0ad9", + "metadata": {}, + "source": [ + "# CenterSnap Exploration\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1c307d51", + "metadata": {}, + "source": [ + "This is a colab to explore CenterSnap inference properties, proposed in our work [Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation](https://zubair-irshad.github.io/projects/CenterSnap.html).\n", + "#### Make sure that you have enabled the GPU under Runtime-> Change runtime type!\n", + "\n", + "We will perform inference using the learnt model and then visualize qualitative results!\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ab91ae4", + "metadata": {}, + "outputs": [], + "source": [ + "#Run this on Google Colab\n", + "!git clone https://github.com/zubair-irshad/CenterSnap.git\n", + "!pip install --upgrade pip\n", + "!cd CenterSnap && pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html\n", + "!pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 -f https://download.pytorch.org/whl/torch_stable.html\n", + "!cd CenterSnap && wget https://www.dropbox.com/s/yfenvre5fhx3oda/nocs_test_subset.tar.gz?dl=1 -O nocs_test_subset.tar.gz && tar -xzvf nocs_test_subset.tar.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "93972c59", + "metadata": {}, + "outputs": [], + "source": [ + "import argparse\n", + "import pathlib\n", + "import cv2\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn.functional as F\n", + "import open3d as o3d\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "import time\n", + "import pytorch_lightning as pl\n", + "import _pickle as cPickle\n", + "import os, sys\n", + "sys.path.append('CenterSnap')\n", + "from simnet.lib.net import common\n", + "from simnet.lib import camera\n", + "from simnet.lib.net.panoptic_trainer import PanopticModel\n", + "from simnet.lib.net.models.auto_encoder import PointCloudAE\n", + "from utils.nocs_utils import load_img_NOCS, create_input_norm\n", + "from utils.viz_utils import depth2inv, viz_inv_depth\n", + "from utils.transform_utils import get_gt_pointclouds, transform_coordinates_3d, calculate_2d_projections\n", + "from utils.transform_utils import project\n", + "from utils.viz_utils import save_projected_points, draw_bboxes, line_set_mesh, display_gird, draw_geometries, show_projected_points" + ] + }, + { + "cell_type": "markdown", + "id": "f9cbd124", + "metadata": {}, + "source": [ + "## 1. Instantiate CenterSnap Model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d956ded1", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using model class from: /home/zubair/CenterSnap/simnet/lib/net/models/panoptic_net.py\n", + "Restoring from checkpoint: ../nocs_test_subset/checkpoint/centersnap_real.ckpt\n", + "test/scene_1/0006\n" + ] + } + ], + "source": [ + "sys.argv = ['', '@CenterSnap/configs/net_config.txt']\n", + "parser = argparse.ArgumentParser(fromfile_prefix_chars='@')\n", + "common.add_train_args(parser)\n", + "app_group = parser.add_argument_group('app')\n", + "app_group.add_argument('--app_output', default='inference', type=str)\n", + "app_group.add_argument('--result_name', default='centersnap_nocs', type=str)\n", + "app_group.add_argument('--data_dir', default='CenterSnap/nocs_test_subset', type=str)\n", + "hparams = parser.parse_args()\n", + "min_confidence = 0.50\n", + "use_gpu=True\n", + "hparams.checkpoint = 'CenterSnap/nocs_test_subset/checkpoint/centersnap_real.ckpt'\n", + "model = PanopticModel(hparams, 0, None, None)\n", + "model.eval()\n", + "if use_gpu:\n", + " model.cuda()\n", + "data_path = open(os.path.join(hparams.data_dir, 'Real', 'test_list_subset.txt')).read().splitlines()\n", + "_CAMERA = camera.NOCS_Real()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bb52a406", + "metadata": {}, + "outputs": [], + "source": [ + "def get_auto_encoder(model_path):\n", + " emb_dim = 128\n", + " n_pts = 2048\n", + " ae = PointCloudAE(emb_dim, n_pts)\n", + " ae.cuda()\n", + " ae.load_state_dict(torch.load(model_path))\n", + " ae.eval()\n", + " return ae" + ] + }, + { + "cell_type": "markdown", + "id": "74d9563a", + "metadata": {}, + "source": [ + "## 2. Perform inference using NOCS Real Subset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "806c7413", + "metadata": {}, + "outputs": [], + "source": [ + "#num from 0 to 3 (small subset of data)\n", + "num = 0\n", + "img_full_path = os.path.join(hparams.data_dir, 'Real', data_path[num])\n", + "img_vis = cv2.imread(img_full_path + '_color.png')\n", + "\n", + "left_linear, depth, actual_depth = load_img_NOCS(img_full_path + '_color.png' , img_full_path + '_depth.png')\n", + "input = create_input_norm(left_linear, depth)[None, :, :, :]\n", + "\n", + "auto_encoder_path = os.path.join(hparams.data_dir, 'ae_checkpoints', 'model_50_nocs.pth')\n", + "ae = get_auto_encoder(auto_encoder_path)\n", + " \n", + "if use_gpu:\n", + " input = input.to(torch.device('cuda:0'))\n", + "_, _, _ , pose_output = model.forward(input)\n", + "with torch.no_grad():\n", + " latent_emb_outputs, abs_pose_outputs, peak_output, _, _ = pose_output.compute_pointclouds_and_poses(min_confidence,is_target = False)" + ] + }, + { + "cell_type": "markdown", + "id": "d077e606", + "metadata": {}, + "source": [ + "### 2.1 Visualize Peaks output and Depth output" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "92b7807d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(480, 640, 3)\n" + ] + }, + { + "data": { + "image/png": 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0])\n", + " T = abs_pose_outputs[j].camera_T_object\n", + " mesh_frame = mesh_frame.transform(T)\n", + " rotated_pcds.append(mesh_frame)\n", + " cylinder_segments = line_set_mesh(rotated_box)\n", + " for k in range(len(cylinder_segments)):\n", + " rotated_pcds.append(cylinder_segments[k])\n", + " points_mesh = camera.convert_points_to_homopoints(rotated_pc.T)\n", + " points_2d_mesh = project(_CAMERA.K_matrix, points_mesh)\n", + " points_2d_mesh = points_2d_mesh.T\n", + " points_2d.append(points_2d_mesh)\n", + " #2D output\n", + " points_obb = camera.convert_points_to_homopoints(np.array(rotated_box).T)\n", + " points_2d_obb = project(_CAMERA.K_matrix, points_obb)\n", + " points_2d_obb = points_2d_obb.T\n", + " box_obb.append(points_2d_obb)\n", + " xyz_axis = 0.3*np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0]]).transpose()\n", + " sRT = abs_pose_outputs[j].camera_T_object @ abs_pose_outputs[j].scale_matrix\n", + " transformed_axes = transform_coordinates_3d(xyz_axis, sRT)\n", + " projected_axes = calculate_2d_projections(transformed_axes, _CAMERA.K_matrix[:3,:3])\n", + " axes.append(projected_axes)\n", + "\n", + "draw_geometries(rotated_pcds)" + ] + }, + { + "cell_type": "markdown", + "id": "37bc0fac", + "metadata": {}, + "source": [ + "## 2.3 Project 3D Pointclouds and 3D bounding boxes on 2D image" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d8070cda", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "color_img = np.copy(img_vis) \n", + "projected_points_img = show_projected_points(color_img, points_2d)\n", + "colors_box = [(63, 237, 234)]\n", + "im = np.array(np.copy(img_vis)).copy()\n", + "for k in range(len(colors_box)):\n", + " for points_2d, axis in zip(box_obb, axes):\n", + " points_2d = np.array(points_2d)\n", + " im = draw_bboxes(im, points_2d, axis, colors_box[k])\n", + "\n", + "plt.gca().invert_yaxis()\n", + "plt.axis('off')\n", + "plt.imshow(im[...,::-1])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a357d168", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "centersnap", + "language": "python", + "name": "centersnap" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/prepare_data/README.md b/prepare_data/README.md new file mode 100644 index 0000000..0ec9769 --- /dev/null +++ b/prepare_data/README.md @@ -0,0 +1,50 @@ +## Preparing NOCS dataset
+ +This Readme should be used if you are preparing [NOCS](https://github.com/hughw19/NOCS_CVPR2019) dataset in the format requried by CenterSnap training and inference or if you would like to train CenterSnap with your custom dataset on a custom camera model. + +Note: The data generation script is currently not optimized for speed i.e. it might take a long time to generate the complete data. Please refer to [Datasets](https://github.com/zubair-irshad/CenterSnap/#-dataset) to download preprocessed dataset. Please manually use parallel python scripts to process the data faster using the below script. + + +Download [camera_train](http://download.cs.stanford.edu/orion/nocs/camera_train.zip), [camera_val](http://download.cs.stanford.edu/orion/nocs/camera_val25K.zip), +[real_train](http://download.cs.stanford.edu/orion/nocs/real_train.zip), [real_test](http://download.cs.stanford.edu/orion/nocs/real_test.zip), +[ground-truth annotations](http://download.cs.stanford.edu/orion/nocs/gts.zip), +[camera_composed_depth](http://download.cs.stanford.edu/orion/nocs/camera_composed_depth.zip), [mesh models](http://download.cs.stanford.edu/orion/nocs/obj_models.zip) +provided by [NOCS](https://github.com/hughw19/NOCS_CVPR2019) and [nocs preprocess data](https://www.dropbox.com/s/8im9fzopo71h6yw/nocs_preprocess.tar.gz?dl=1).
+Unzip and organize these files in $CenterSnap_Repo/data as follows: +``` +data +├── CAMERA +│ ├── train +│ └── val +├── Real +│ ├── train +│ └── test +├── camera_full_depths +│ ├── train +│ └── val +├── gts +│ ├── val +│ └── real_test +├── auto_encoder_model +│ ├── model_50_nocs.pth +└── obj_models + ├── train + ├── val + ├── real_train + ├── real_test + ├── camera_train.pkl + ├── camera_val.pkl + ├── real_train.pkl + ├── real_test.pkl + └── mug_meta.pkl +``` +Run python scripts to prepare the datasets. + +``` +cd $CenterSnap repo +./runner.sh prepare_data/generate_data_nocs.py --data_dir absolute_path\to\downloaded\NOCS\data +``` + +**Acknowledgement**
+The data processing script is adapted from [object-deformnet](https://github.com/mentian/object-deformnet) + diff --git a/prepare_data/generate_data_nocs.py b/prepare_data/generate_data_nocs.py new file mode 100755 index 0000000..9259858 --- /dev/null +++ b/prepare_data/generate_data_nocs.py @@ -0,0 +1,567 @@ +import os +import pathlib +import math +import glob +import cv2 +import numpy as np +import torch +import torchvision.transforms as transforms +import _pickle as cPickle +from tqdm import tqdm +from utils.nocs_utils import load_depth, align_nocs_to_depth +from simnet.lib import camera +from simnet.lib import transform +from simnet.lib import datapoint +from simnet.lib.net.pre_processing import obb_inputs +from simnet.lib.depth_noise import DepthManager +from simnet.lib.net.models.auto_encoder import PointCloudAE +from PIL import Image +import argparse + +def create_img_list(data_dir): + """ Create train/val/test data list for CAMERA and Real. """ + # CAMERA dataset + for subset in ['train', 'val']: + img_list = [] + img_dir = os.path.join(data_dir, 'CAMERA', subset) + folder_list = [name for name in os.listdir(img_dir) if os.path.isdir(os.path.join(img_dir, name))] + for i in range(10*len(folder_list)): + folder_id = int(i) // 10 + img_id = int(i) % 10 + img_path = os.path.join(subset, '{:05d}'.format(folder_id), '{:04d}'.format(img_id)) + img_list.append(img_path) + with open(os.path.join(data_dir, 'CAMERA', subset+'_list_all.txt'), 'w') as f: + for img_path in img_list: + f.write("%s\n" % img_path) + # Real dataset + for subset in ['train', 'test']: + img_list = [] + img_dir = os.path.join(data_dir, 'Real', subset) + folder_list = [name for name in sorted(os.listdir(img_dir)) if os.path.isdir(os.path.join(img_dir, name))] + for folder in folder_list: + img_paths = glob.glob(os.path.join(img_dir, folder, '*_color.png')) + img_paths = sorted(img_paths) + for img_full_path in img_paths: + img_name = os.path.basename(img_full_path) + img_ind = img_name.split('_')[0] + img_path = os.path.join(subset, folder, img_ind) + img_list.append(img_path) + with open(os.path.join(data_dir, 'Real', subset+'_list_all.txt'), 'w') as f: + for img_path in img_list: + f.write("%s\n" % img_path) + print('Write all data paths to file done!') + +def align_rotation(R): + """ Align rotations for symmetric objects. + Args: + sRT: 4 x 4 + """ + + theta_x = R[0, 0] + R[2, 2] + theta_y = R[0, 2] - R[2, 0] + r_norm = math.sqrt(theta_x**2 + theta_y**2) + s_map = np.array([[theta_x/r_norm, 0.0, -theta_y/r_norm], + [0.0, 1.0, 0.0 ], + [theta_y/r_norm, 0.0, theta_x/r_norm]]) + rotation = R @ s_map + return rotation + +def process_data(img_path, depth): + """ Load instance masks for the objects in the image. """ + mask_path = img_path + '_mask.png' + mask = cv2.imread(mask_path)[:, :, 2] + mask = np.array(mask, dtype=np.int32) + all_inst_ids = sorted(list(np.unique(mask))) + assert all_inst_ids[-1] == 255 + del all_inst_ids[-1] # remove background + num_all_inst = len(all_inst_ids) + h, w = mask.shape + + coord_path = img_path + '_coord.png' + coord_map = cv2.imread(coord_path)[:, :, :3] + coord_map = coord_map[:, :, (2, 1, 0)] + # flip z axis of coord map + coord_map = np.array(coord_map, dtype=np.float32) / 255 + coord_map[:, :, 2] = 1 - coord_map[:, :, 2] + + class_ids = [] + instance_ids = [] + model_list = [] + masks = np.zeros([h, w, num_all_inst], dtype=np.uint8) + coords = np.zeros((h, w, num_all_inst, 3), dtype=np.float32) + bboxes = np.zeros((num_all_inst, 4), dtype=np.int32) + + meta_path = img_path + '_meta.txt' + with open(meta_path, 'r') as f: + i = 0 + for line in f: + line_info = line.strip().split(' ') + inst_id = int(line_info[0]) + cls_id = int(line_info[1]) + # background objects and non-existing objects + if cls_id == 0 or (inst_id not in all_inst_ids): + continue + if len(line_info) == 3: + model_id = line_info[2] # Real scanned objs + else: + model_id = line_info[3] # CAMERA objs + # remove one mug instance in CAMERA train due to improper model + if model_id == 'b9be7cfe653740eb7633a2dd89cec754' or model_id == 'd3b53f56b4a7b3b3c9f016d57db96408': + continue + # process foreground objects + inst_mask = np.equal(mask, inst_id) + # bounding box + horizontal_indicies = np.where(np.any(inst_mask, axis=0))[0] + vertical_indicies = np.where(np.any(inst_mask, axis=1))[0] + assert horizontal_indicies.shape[0], print(img_path) + x1, x2 = horizontal_indicies[[0, -1]] + y1, y2 = vertical_indicies[[0, -1]] + # x2 and y2 should not be part of the box. Increment by 1. + x2 += 1 + y2 += 1 + # object occupies full image, rendering error, happens in CAMERA dataset + if np.any(np.logical_or((x2-x1) > 600, (y2-y1) > 440)): + return None, None, None, None, None, None + # not enough valid depth observation + final_mask = np.logical_and(inst_mask, depth > 0) + if np.sum(final_mask) < 64: + continue + class_ids.append(cls_id) + instance_ids.append(inst_id) + model_list.append(model_id) + masks[:, :, i] = inst_mask + coords[:, :, i, :] = np.multiply(coord_map, np.expand_dims(inst_mask, axis=-1)) + bboxes[i] = np.array([y1, x1, y2, x2]) + i += 1 + # no valid foreground objects + if i == 0: + return None, None, None, None, None, None + + masks = masks[:, :, :i] + coords = np.clip(coords[:, :, :i, :], 0, 1) + bboxes = bboxes[:i, :] + + return masks, coords, class_ids, instance_ids, model_list, bboxes + + +def annotate_camera_train(data_dir, estimator): + DATASET_NAME = 'NOCS_Data' + DATASET_DIR = pathlib.Path(f'data/{DATASET_NAME}') + DATASET_DIR.mkdir(parents=True, exist_ok=True) + _DATASET = datapoint.make_dataset(f'file://{DATASET_DIR}/CAMERA/train') + + _camera = camera.NOCS_Camera() + """ Generate gt labels for CAMERA train data. """ + camera_train = open(os.path.join(data_dir, 'CAMERA', 'train_list_all.txt')).read().splitlines() + intrinsics = np.array([[577.5, 0, 319.5], [0, 577.5, 239.5], [0, 0, 1]]) + # meta info for re-label mug category + with open(os.path.join(data_dir, 'obj_models/mug_meta.pkl'), 'rb') as f: + mug_meta = cPickle.load(f) + + #TEST MODELS + obj_model_dir = os.path.join(data_dir, 'obj_models') + with open(os.path.join(obj_model_dir, 'camera_train.pkl'), 'rb') as f: + obj_models = cPickle.load(f) + # i = len(camera_train)//20 + # num = 11 + # camera_train = camera_train[(num)*i:(num+1)*i] + valid_img_list = [] + for img_path in tqdm(camera_train): + img_full_path = os.path.join(data_dir, 'CAMERA', img_path) + depth_composed_path = img_path+'_composed.png' + depth_full_path = os.path.join(data_dir,'camera_full_depths', depth_composed_path) + all_exist = os.path.exists(img_full_path + '_color.png') and \ + os.path.exists(img_full_path + '_coord.png') and \ + os.path.exists(img_full_path + '_depth.png') and \ + os.path.exists(img_full_path + '_mask.png') and \ + os.path.exists(img_full_path + '_meta.txt') + if not all_exist: + continue + print("depth_full_path", depth_full_path) + depth = load_depth(depth_full_path) + masks, coords, class_ids, instance_ids, model_list, bboxes = process_data(img_full_path, depth) + if instance_ids is None: + continue + # Umeyama alignment of GT NOCS map with depth image + scales, rotations, translations, error_messages, _ = \ + align_nocs_to_depth(masks, coords, depth, intrinsics, instance_ids, img_path) + if error_messages: + continue + # re-label for mug category + for i in range(len(class_ids)): + if class_ids[i] == 6: + T0 = mug_meta[model_list[i]][0] + s0 = mug_meta[model_list[i]][1] + T = translations[i] - scales[i] * rotations[i] @ T0 + s = scales[i] / s0 + scales[i] = s + translations[i] = T + ### GET CENTERPOINT DATAPOINTS + #get latent embeddings + model_points = [obj_models[model_list[i]].astype(np.float32) for i in range(len(class_ids))] + latent_embeddings = get_latent_embeddings(model_points, estimator) + #get poses + abs_poses=[] + seg_mask = np.zeros([_camera.height, _camera.width]) + masks_list = [] + for i in range(len(class_ids)): + R = rotations[i] + T = translations[i] + s = scales[i] + sym_ids = [0, 1, 3] + cat_id = np.array(class_ids)[i] - 1 + if cat_id in sym_ids: + R = align_rotation(R) + + scale_matrix = np.eye(4) + scale_mat = s*np.eye(3, dtype=float) + scale_matrix[0:3, 0:3] = scale_mat + camera_T_object = np.eye(4) + camera_T_object[:3,:3] = R + camera_T_object[:3,3] = T + seg_mask[masks[:,:,i] > 0] = np.array(class_ids)[i] + # print("class id name", np.array(class_ids)[i]) + # plt.imshow(masks[:,:,i]) + # plt.show() + masks_list.append(masks[:,:,i]) + abs_poses.append(transform.Pose(camera_T_object=camera_T_object, scale_matrix=scale_matrix)) + obb_datapoint = obb_inputs.compute_nocs_network_targets(masks_list, latent_embeddings, abs_poses,_camera.height, _camera.width) + color_img = cv2.imread(img_full_path + '_color.png') + colorjitter = transforms.ColorJitter(0.5, 0.3, 0.5, 0.05) + rgb_img = colorjitter(Image.fromarray(color_img)) + jitter_img = colorjitter(rgb_img) + color_img = np.asarray(jitter_img) + depth_array = np.array(depth, dtype=np.float32)/255.0 + DM = DepthManager() + noisy_depth = DM.prepare_depth_data(depth_array) + stereo_datapoint = datapoint.Stereo(left_color=color_img, right_color=noisy_depth) + panoptic_datapoint = datapoint.Panoptic( + stereo=stereo_datapoint, + depth=noisy_depth, + segmentation=seg_mask, + object_poses=[obb_datapoint], + boxes=[], + detections=[] + ) + _DATASET.write(panoptic_datapoint) + ### Finish writing datapoint + +def annotate_real_train(data_dir, estimator): + + DATASET_NAME = 'NOCS_Data' + DATASET_DIR = pathlib.Path(f'data/{DATASET_NAME}') + DATASET_DIR.mkdir(parents=True, exist_ok=True) + _DATASET = datapoint.make_dataset(f'file://{DATASET_DIR}/Real/train') + + """ Generate gt labels for Real train data through PnP. """ + _camera = camera.NOCS_Real() + real_train = open(os.path.join(data_dir, 'Real/train_list_all.txt')).read().splitlines() + intrinsics = np.array([[591.0125, 0, 322.525], [0, 590.16775, 244.11084], [0, 0, 1]]) + # scale factors for all instances + scale_factors = {} + path_to_size = glob.glob(os.path.join(data_dir, 'obj_models/real_train', '*_norm.txt')) + for inst_path in sorted(path_to_size): + instance = os.path.basename(inst_path).split('.')[0] + bbox_dims = np.loadtxt(inst_path) + scale_factors[instance] = np.linalg.norm(bbox_dims) + # meta info for re-label mug category + with open(os.path.join(data_dir, 'obj_models/mug_meta.pkl'), 'rb') as f: + mug_meta = cPickle.load(f) + #TEST MODELS + obj_model_dir = os.path.join(data_dir, 'obj_models') + with open(os.path.join(obj_model_dir, 'real_train.pkl'), 'rb') as f: + obj_models = cPickle.load(f) + + valid_img_list = [] + # real_train = np.array(real_train)[np.array([2, 500, 1000, 1500, 1700, 1300, 2000, 2300, 2350, 2750])].tolist() + for img_path in tqdm(real_train): + img_full_path = os.path.join(data_dir, 'Real', img_path) + all_exist = os.path.exists(img_full_path + '_color.png') and \ + os.path.exists(img_full_path + '_coord.png') and \ + os.path.exists(img_full_path + '_depth.png') and \ + os.path.exists(img_full_path + '_mask.png') and \ + os.path.exists(img_full_path + '_meta.txt') + if not all_exist: + continue + depth_full_path = img_full_path+'_depth.png' + depth = load_depth(depth_full_path) + masks, coords, class_ids, instance_ids, model_list, bboxes = process_data(img_full_path, depth) + if instance_ids is None: + continue + # compute pose + num_insts = len(class_ids) + scales = np.zeros(num_insts) + rotations = np.zeros((num_insts, 3, 3)) + translations = np.zeros((num_insts, 3)) + for i in range(num_insts): + s = scale_factors[model_list[i]] + mask = masks[:, :, i] + idxs = np.where(mask) + coord = coords[:, :, i, :] + coord_pts = s * (coord[idxs[0], idxs[1], :] - 0.5) + coord_pts = coord_pts[:, :, None] + img_pts = np.array([idxs[1], idxs[0]]).transpose() + img_pts = img_pts[:, :, None].astype(float) + distCoeffs = np.zeros((4, 1)) # no distoration + retval, rvec, tvec = cv2.solvePnP(coord_pts, img_pts, intrinsics, distCoeffs) + assert retval + R, _ = cv2.Rodrigues(rvec) + T = np.squeeze(tvec) + # re-label for mug category + if class_ids[i] == 6: + T0 = mug_meta[model_list[i]][0] + s0 = mug_meta[model_list[i]][1] + T = T - s * R @ T0 + s = s / s0 + scales[i] = s + rotations[i] = R + translations[i] = T + + ### GET CENTERPOINT DATAPOINTS + #get latent embeddings + model_points = [obj_models[model_list[i]].astype(np.float32) for i in range(len(class_ids))] + latent_embeddings = get_latent_embeddings(model_points, estimator) + #get poses + abs_poses=[] + class_num=1 + seg_mask = np.zeros([_camera.height, _camera.width]) + masks_list = [] + for i in range(len(class_ids)): + R = rotations[i] + T = translations[i] + s = scales[i] + sym_ids = [0, 1, 3] + cat_id = np.array(class_ids)[i] - 1 + if cat_id in sym_ids: + R = align_rotation(R) + scale_matrix = np.eye(4) + scale_mat = s*np.eye(3, dtype=float) + scale_matrix[0:3, 0:3] = scale_mat + camera_T_object = np.eye(4) + camera_T_object[:3,:3] = R + camera_T_object[:3,3] = T + seg_mask[masks[:,:,i] > 0] = np.array(class_ids)[i] + class_num += 1 + masks_list.append(masks[:,:,i]) + abs_poses.append(transform.Pose(camera_T_object=camera_T_object, scale_matrix=scale_matrix)) + + obb_datapoint = obb_inputs.compute_nocs_network_targets(masks_list, latent_embeddings, abs_poses,_camera.height, _camera.width) + + color_img = cv2.imread(img_full_path + '_color.png') + colorjitter = transforms.ColorJitter(0.8, 0.5, 0.5, 0.05) + rgb_img = colorjitter(Image.fromarray(color_img)) + jitter_img = colorjitter(rgb_img) + color_img = np.asarray(jitter_img) + depth_array = np.array(depth, dtype=np.float32)/255.0 + DM = DepthManager() + noisy_depth = DM.prepare_depth_data(depth_array) + stereo_datapoint = datapoint.Stereo(left_color=color_img, right_color=noisy_depth) + panoptic_datapoint = datapoint.Panoptic( + stereo=stereo_datapoint, + depth=depth_array, + segmentation=seg_mask, + object_poses=[obb_datapoint], + boxes=[], + detections=[] + ) + _DATASET.write(panoptic_datapoint) + ### Finish writing datapoint + +def annotate_test_data(data_dir, estimator, source, subset): + """ Generate gt labels for test data. + Properly copy handle_visibility provided by NOCS gts. + """ + DATASET_NAME = 'NOCS_Data' + DATASET_DIR = pathlib.Path(f'data/{DATASET_NAME}') + DATASET_DIR.mkdir(parents=True, exist_ok=True) + _DATASET = datapoint.make_dataset(f'file://{DATASET_DIR}/{source}/{subset}') + + _camera = camera.NOCS_Real() + camera_val = open(os.path.join(data_dir, 'CAMERA', 'val_list_all.txt')).read().splitlines() + real_test = open(os.path.join(data_dir, 'Real', 'test_list_all.txt')).read().splitlines() + camera_intrinsics = np.array([[577.5, 0, 319.5], [0, 577.5, 239.5], [0, 0, 1]]) + real_intrinsics = np.array([[591.0125, 0, 322.525], [0, 590.16775, 244.11084], [0, 0, 1]]) + # compute model size + model_file_path = ['obj_models/camera_val.pkl', 'obj_models/real_test.pkl'] + models = {} + for path in model_file_path: + with open(os.path.join(data_dir, path), 'rb') as f: + models.update(cPickle.load(f)) + model_sizes = {} + for key in models.keys(): + model_sizes[key] = 2 * np.amax(np.abs(models[key]), axis=0) + # meta info for re-label mug category + with open(os.path.join(data_dir, 'obj_models/mug_meta.pkl'), 'rb') as f: + mug_meta = cPickle.load(f) + + #TEST MODELS + # obj_model_dir = os.path.join(data_dir, 'obj_models') + # with open(os.path.join(obj_model_dir, 'real_test.pkl'), 'rb') as f: + # obj_models = cPickle.load(f) + + if source == 'CAMERA': + subset_meta = [('CAMERA', camera_val, camera_intrinsics, 'val')] + else: + subset_meta = [('Real', real_test, real_intrinsics, 'test')] + # subset_meta = [('CAMERA', camera_val, camera_intrinsics, 'val'), ('Real', real_test, real_intrinsics, 'test')] + for source, img_list, intrinsics, subset in subset_meta: + valid_img_list = [] + # img_list = np.array(img_list)[np.array([2, 500, 1000, 1500, 1700, 1300, 2000, 2300, 2350, 2750])].tolist() + for img_path in tqdm(img_list): + img_full_path = os.path.join(data_dir, source, img_path) + all_exist = os.path.exists(img_full_path + '_color.png') and \ + os.path.exists(img_full_path + '_coord.png') and \ + os.path.exists(img_full_path + '_depth.png') and \ + os.path.exists(img_full_path + '_mask.png') and \ + os.path.exists(img_full_path + '_meta.txt') + if not all_exist: + continue + depth_full_path_real = img_full_path + '_depth.png' + depth = load_depth(depth_full_path_real) + masks, coords, class_ids, instance_ids, model_list, bboxes = process_data(img_full_path, depth) + if instance_ids is None: + continue + num_insts = len(instance_ids) + # match each instance with NOCS ground truth to properly assign gt_handle_visibility + nocs_dir = os.path.join(os.path.dirname(data_dir), 'results/nocs_results') + if source == 'CAMERA': + nocs_path = os.path.join(nocs_dir, 'val', 'results_val_{}_{}.pkl'.format( + img_path.split('/')[-2], img_path.split('/')[-1])) + else: + nocs_path = os.path.join(nocs_dir, 'real_test', 'results_test_{}_{}.pkl'.format( + img_path.split('/')[-2], img_path.split('/')[-1])) + with open(nocs_path, 'rb') as f: + nocs = cPickle.load(f) + gt_class_ids = nocs['gt_class_ids'] + gt_bboxes = nocs['gt_bboxes'] + gt_sRT = nocs['gt_RTs'] + gt_handle_visibility = nocs['gt_handle_visibility'] + map_to_nocs = [] + for i in range(num_insts): + gt_match = -1 + for j in range(len(gt_class_ids)): + if gt_class_ids[j] != class_ids[i]: + continue + if np.sum(np.abs(bboxes[i] - gt_bboxes[j])) > 5: + continue + # match found + gt_match = j + break + # check match validity + assert gt_match > -1, print(img_path, instance_ids[i], 'no match for instance') + assert gt_match not in map_to_nocs, print(img_path, instance_ids[i], 'duplicate match') + map_to_nocs.append(gt_match) + # copy from ground truth, re-label for mug category + handle_visibility = gt_handle_visibility[map_to_nocs] + sizes = np.zeros((num_insts, 3)) + poses = np.zeros((num_insts, 4, 4)) + scales = np.zeros(num_insts) + rotations = np.zeros((num_insts, 3, 3)) + translations = np.zeros((num_insts, 3)) + for i in range(num_insts): + gt_idx = map_to_nocs[i] + sizes[i] = model_sizes[model_list[i]] + sRT = gt_sRT[gt_idx] + s = np.cbrt(np.linalg.det(sRT[:3, :3])) + R = sRT[:3, :3] / s + T = sRT[:3, 3] + # re-label mug category + if class_ids[i] == 6: + T0 = mug_meta[model_list[i]][0] + s0 = mug_meta[model_list[i]][1] + T = T - s * R @ T0 + s = s / s0 + # used for test during training + scales[i] = s + rotations[i] = R + translations[i] = T + # used for evaluation + sRT = np.identity(4, dtype=np.float32) + sRT[:3, :3] = s * R + sRT[:3, 3] = T + poses[i] = sRT + + ### GET CENTERPOINT DATAPOINTS + #get latent embeddings + model_points = [models[model_list[i]].astype(np.float32) for i in range(len(class_ids))] + latent_embeddings = get_latent_embeddings(model_points, estimator) + #get poses + abs_poses=[] + class_num=1 + seg_mask = np.zeros([_camera.height, _camera.width]) + masks_list = [] + for i in range(len(class_ids)): + R = rotations[i] + T = translations[i] + s = scales[i] + sym_ids = [0, 1, 3] + cat_id = np.array(class_ids)[i] - 1 + if cat_id in sym_ids: + R = align_rotation(R) + scale_matrix = np.eye(4) + scale_mat = s*np.eye(3, dtype=float) + scale_matrix[0:3, 0:3] = scale_mat + camera_T_object = np.eye(4) + camera_T_object[:3,:3] = R + camera_T_object[:3,3] = T + seg_mask[masks[:,:,i] > 0] = np.array(class_ids)[i] + class_num += 1 + masks_list.append(masks[:,:,i]) + abs_poses.append(transform.Pose(camera_T_object=camera_T_object, scale_matrix=scale_matrix)) + obb_datapoint = obb_inputs.compute_nocs_network_targets(masks_list, latent_embeddings, abs_poses,_camera.height, _camera.width) + + color_img = cv2.imread(img_full_path + '_color.png') + depth_array = np.array(depth, dtype=np.float32)/255.0 + + DM = DepthManager() + noisy_depth = DM.prepare_depth_data(depth_array) + stereo_datapoint = datapoint.Stereo(left_color=color_img, right_color=noisy_depth) + panoptic_datapoint = datapoint.Panoptic( + stereo=stereo_datapoint, + depth=noisy_depth, + segmentation=seg_mask, + object_poses=[obb_datapoint], + boxes=[], + detections=[] + ) + _DATASET.write(panoptic_datapoint) + ### Finish writing datapoint + +def get_latent_embeddings(point_clouds, estimator): + latent_embeddings =[] + for i in range(len(point_clouds)): + batch_xyz = torch.from_numpy(point_clouds[i]).to(device="cuda", dtype=torch.float) + batch_xyz = batch_xyz.unsqueeze(0) + emb, _ = estimator(batch_xyz) + emb = emb.squeeze(0).cpu().detach().numpy() + latent_embeddings.append(emb) + return latent_embeddings + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', type=str, required=True) + args = parser.parse_args() + data_dir = args.data_dir + + emb_dim = 128 + n_cat = 57 + n_pts = 2048 + + model_path = os.path.join(data_dir, 'auto_encoder_model', 'model_50_nocs.pth') + estimator = PointCloudAE(emb_dim, n_pts) + estimator.cuda() + estimator.load_state_dict(torch.load(model_path)) + estimator.eval() + + print("Generating image lists") + create_img_list(data_dir) + print("Image lists generated...\n") + print("Generating Camera Train data...") + annotate_camera_train(data_dir, estimator) + print("Generating Real Train data...") + annotate_real_train(data_dir, estimator) + print("Generating Camera Val data...") + annotate_test_data(data_dir, estimator, 'CAMERA', 'val') + print("Generating Real Test data...") + annotate_test_data(data_dir, estimator, 'Real', 'test') \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..d6d5811 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,84 @@ +open3d +absl-py==0.13.0 +backcall==0.2.0 +boto3==1.18.1 +botocore==1.21.1 +cachetools==4.2.2 +certifi==2021.5.30 +charset-normalizer==2.0.3 +click==8.0.1 +colour==0.1.5 +configparser==5.0.2 +cycler==0.10.0 +decorator==4.4.2 +docker-pycreds==0.4.0 +future==0.18.2 +gitdb==4.0.7 +GitPython==3.1.18 +google-auth==1.33.0 +google-auth-oauthlib==0.4.4 +greenlet==1.1.0 +grpcio==1.38.1 +idna==3.2 +imageio==2.9.0 +ipython==7.25.0 +ipython-genutils==0.2.0 +jedi==0.18.0 +jmespath==0.10.0 +kiwisolver==1.3.1 +Markdown==3.3.4 +matplotlib==3.1.3 +matplotlib-inline==0.1.2 +msgpack==1.0.2 +networkx==2.5.1 +numpy==1.21.0 +oauthlib==3.1.1 +opencv-python==4.5.3.56 +parso==0.8.2 +pathtools==0.1.2 +pexpect==4.8.0 +pickleshare==0.7.5 +Pillow==8.3.1 +promise==2.3 +prompt-toolkit==3.0.19 +protobuf==3.17.3 +psutil==5.8.0 +ptyprocess==0.7.0 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +Pygments==2.9.0 +pynvim==0.4.3 +pyparsing==2.4.7 +python-dateutil==2.8.2 +pytorch-lightning==0.7.5 +PyWavelets==1.1.1 +PyYAML==5.4.1 +requests==2.26.0 +requests-oauthlib==1.3.0 +rsa==4.7.2 +s3transfer==0.5.0 +scikit-image==0.18.2 +scipy==1.7.0 +sentry-sdk==1.3.0 +shortuuid==1.0.1 +six==1.16.0 +smmap==4.0.0 +subprocess32==3.5.4 +tensorboard==2.5.0 +tensorboard-data-server==0.6.1 +tensorboard-plugin-wit==1.8.0 +tifffile==2021.7.2 +fvcore +torch==1.7.1 +torchvision==0.8.2 +tqdm==4.61.2 +traitlets==5.0.5 +urllib3==1.26.6 +wandb==0.11.0 +wcwidth==0.2.5 +Werkzeug==2.0.1 +ydiff==1.2 +zstandard==0.15.2 +plotly==5.6.0 +argparse +h5py \ No newline at end of file diff --git a/runner.sh b/runner.sh new file mode 100755 index 0000000..c4a01c9 --- /dev/null +++ b/runner.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +set -Eeuo pipefail + +SCRIPT_DIR=$(dirname $(readlink -f $0)) +export PYTHONPATH=$(readlink -f "${SCRIPT_DIR}") +export PYTHONPATH=$PYTHONPATH:$(readlink -f "${SCRIPT_DIR}/export") +export OPENBLAS_NUM_THREADS=1 +export PYOPENGL_PLATFORM=egl + +$SCRIPT_DIR/env/bin/python $@ diff --git a/simnet/.gitignore b/simnet/.gitignore new file mode 100644 index 0000000..0d20b64 --- /dev/null +++ b/simnet/.gitignore @@ -0,0 +1 @@ +*.pyc diff --git a/simnet/lib/camera.py b/simnet/lib/camera.py new file mode 100755 index 0000000..c01136d --- /dev/null +++ b/simnet/lib/camera.py @@ -0,0 +1,126 @@ +import copy +import numpy as np +from simnet.lib import transform + +CLIPPING_PLANE_NEAR = 0.4 +SCALE_FACTOR = 4 + +class NOCS_Camera(): + def __init__(self, height=480, width=640, scale_factor=1.): + + self.RT_matrix = transform.Transform.from_aa(axis=transform.X_AXIS, + angle_deg=180.0).matrix + self.height = int(height / scale_factor) + self.width = int(width / scale_factor) + self.f_x = 577.5 + self.f_y = 577.5 + self.c_x = 319.5 + self.c_y = 239.5 + self.stereo_baseline = 0.119559 + self._set_intrinsics( + np.array([ + [self.f_x, 0., self.c_x, 0.0], + [0., self.f_y, self.c_y, 0.0], + [0., 0., 1., 0.0], + [0., 0., 0., 1.], + ])) + def _set_intrinsics(self, intrinsics_matrix): + assert intrinsics_matrix.shape[0] == 4 + assert intrinsics_matrix.shape[1] == 4 + self.K_matrix = intrinsics_matrix + self.proj_matrix = self.K_matrix @ self.RT_matrix + +class NOCS_Real(): + def __init__(self, height=480, width=640, scale_factor=1.): + self.RT_matrix = transform.Transform.from_aa(axis=transform.X_AXIS, + angle_deg=180.0).matrix + self.height = int(height / scale_factor) + self.width = int(width / scale_factor) + self.f_x = 591.0125 + self.f_y = 590.16775 + self.c_x = 322.525 + self.c_y = 244.11084 + self.stereo_baseline = 0.119559 + self._set_intrinsics( + np.array([ + [self.f_x, 0., self.c_x, 0.0], + [0., self.f_y, self.c_y, 0.0], + [0., 0., 1., 0.0], + [0., 0., 0., 1.], + ])) + def _set_intrinsics(self, intrinsics_matrix): + assert intrinsics_matrix.shape[0] == 4 + assert intrinsics_matrix.shape[1] == 4 + self.K_matrix = intrinsics_matrix + self.proj_matrix = self.K_matrix @ self.RT_matrix + +def disp_to_depth(disp): + hfov = np.deg2rad(100.) + width = 2560. + fx = 0.5 * width / np.tan(0.5 * hfov) + b = 100e-3 # 100mm == 10cm + with np.errstate(divide='ignore'): + depth = b * fx / disp + valid = (depth >= 0.01) & (depth <= 100.0) & np.isfinite(depth) + depth[~valid] = 0. + return depth + + +def depth_to_disp(depth, K_matrix, baseline): + fx = K_matrix[0, 0] + b = baseline + valid = (depth > 300e-10) & (depth < 10e5) + disp = b * fx / depth + disp[~valid] = 0. + return disp + + +def convert_homopixels_to_pixels(pixels): + """Project 4d homogenous pixels (4xN) to 2d pixels (2xN)""" + assert len(pixels.shape) == 2 + assert pixels.shape[0] == 4 + pixels_3d = pixels[:3, :] / pixels[3:4, :] + pixels_2d = pixels_3d[:2, :] / pixels_3d[2:3, :] + assert pixels_2d.shape[1] == pixels.shape[1] + assert pixels_2d.shape[0] == 2 + return pixels_2d + + +def convert_pixels_to_homopixels(pixels, depths): + """Project 2d pixels (2xN) and depths (meters, 1xN) to 4d pixels (4xN)""" + assert len(pixels.shape) == 2 + assert pixels.shape[0] == 2 + assert len(depths.shape) == 2 + assert depths.shape[1] == pixels.shape[1] + assert depths.shape[0] == 1 + pixels_4d = np.concatenate([ + depths * pixels, + depths, + np.ones_like(depths), + ], axis=0) + assert pixels_4d.shape[0] == 4 + assert pixels_4d.shape[1] == pixels.shape[1] + return pixels_4d + + +def convert_points_to_homopoints(points): + """Project 3d points (3xN) to 4d homogenous points (4xN)""" + assert len(points.shape) == 2 + assert points.shape[0] == 3 + points_4d = np.concatenate([ + points, + np.ones((1, points.shape[1])), + ], axis=0) + assert points_4d.shape[1] == points.shape[1] + assert points_4d.shape[0] == 4 + return points_4d + + +def convert_homopoints_to_points(points_4d): + """Project 4d homogenous points (4xN) to 3d points (3xN)""" + assert len(points_4d.shape) == 2 + assert points_4d.shape[0] == 4 + points_3d = points_4d[:3, :] / points_4d[3:4, :] + assert points_3d.shape[1] == points_3d.shape[1] + assert points_3d.shape[0] == 3 + return points_3d diff --git a/simnet/lib/color_stuff.py b/simnet/lib/color_stuff.py new file mode 100644 index 0000000..5401c50 --- /dev/null +++ b/simnet/lib/color_stuff.py @@ -0,0 +1,33 @@ +import numpy as np +import colour + + +def get_colors(num_colors): + assert num_colors > 0 + + colors = list(colour.Color("purple").range_to(colour.Color("green"), num_colors)) + color_rgb = 255 * np.array([np.array(a.get_rgb()) for a in colors]) + color_rgb = [a.astype(np.int) for a in color_rgb] + return color_rgb + + +def get_panoptic_colors(): + color_list = ["yellow", "blue", "green", "red", "purple"] + color_rgb = np.array([[255, 254, 0], [ 0, 0, 255], [ 0, 128, 0],[255, 0, 0], [128, 0, 127]]) + return color_rgb + + +def get_unique_colors(num_colors): + ''' + Gives a the specified number of unique colors + Args: + num_colors: an int specifying the number of colors + Returs: + A list of rgb colors in the range of (0,255) + ''' + color_rgb = get_colors(num_colors) + + if (num_colors != len(np.unique(color_rgb, axis=0))): + raise ValueError('Colors returned are not unique.') + + return color_rgb \ No newline at end of file diff --git a/simnet/lib/datapoint.py b/simnet/lib/datapoint.py new file mode 100755 index 0000000..e417dbe --- /dev/null +++ b/simnet/lib/datapoint.py @@ -0,0 +1,597 @@ +import dataclasses +import operator +import time +import pathlib +import pickle +import shortuuid +import urllib3 +import boto3 +import numpy as np +import zstandard as zstd + + +def get_uid(): + return shortuuid.uuid() + +@dataclasses.dataclass +class Transform: + angle_degrees: float + rotation_axis: list + +@dataclasses.dataclass +class Category: + name: str + scale_range: list = None + initial_transform: Transform = None + + +# Struct for Pose Prediction +@dataclasses.dataclass +class Pose: + heat_map: np.ndarray + vertex_target: np.ndarray + z_centroid: np.ndarray + + +# Struct for Keypoint Prediction +@dataclasses.dataclass +class Keypoint: + heat_map: np.ndarray + +@dataclasses.dataclass +class OBB: + heat_map: np.ndarray + vertex_target: np.ndarray + z_centroid: np.ndarray + cov_matrices: np.ndarray + latent_emb: np.ndarray + abs_pose: np.ndarray + +@dataclasses.dataclass +class ABSPOSE: + heat_map: np.ndarray + latent_emb: np.ndarray + abs_pose: np.ndarray + + +#Struct for Stereo Representation +@dataclasses.dataclass +class Stereo: + left_color: np.ndarray + right_color: np.ndarray + + +# Struct for 2D bounding box +@dataclasses.dataclass +class Box: + heat_map: np.ndarray + vertex_target: np.ndarray + + +# Application Specific Datapoints Should be specified here. +@dataclasses.dataclass +class Panoptic: + stereo: Stereo + depth: np.ndarray + segmentation: np.ndarray + object_poses: list + boxes: list + detections: list + keypoints: list = dataclasses.field(default_factory=list) + instance_mask: np.ndarray = None + scene_name: str = 'sim' + uid: str = dataclasses.field(default_factory=get_uid) + + +# Application Specific Datapoints Should be specified here. +@dataclasses.dataclass +class RobotMask: + stereo: Stereo + depth: np.ndarray + segmentation: np.ndarray + uid: str = dataclasses.field(default_factory=get_uid) + + +# End Applications Here +def compress_datapoint(x): + buf = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL) + cctx = zstd.ZstdCompressor() + cbuf = cctx.compress(buf) + return cbuf + + +def decompress_datapoint(cbuf): + cctx = zstd.ZstdDecompressor() + buf = cctx.decompress(cbuf) + x = pickle.loads(buf) + return x + + +def make_dataset(uri): + if ',' in uri: + datasets = [] + for uri in uri.split(','): + datasets.append(make_one_dataset(uri)) + return ConcatDataset(datasets) + return make_one_dataset(uri) + + +def make_one_dataset(uri): + # parse parameters + uri, _, raw_params = uri.partition('?') + dataset = make_one_simple_dataset(uri) + if not raw_params: + return dataset + + params = {} + for raw_param in raw_params.split('&'): + k, _, v = raw_param.partition('=') + assert k and v + assert k not in params + params[k] = v + + return FilterDataset(dataset, params) + + +def make_one_simple_dataset(uri): + if uri.startswith('s3://'): + path = uri.partition('s3://')[2] + bucket, _, dataset_path = path.partition('/') + return RemoteDataset(bucket, dataset_path) + + if uri.startswith('file://'): + path = uri.partition('file://')[2] + dataset_path = pathlib.Path(path) + return LocalDataset(dataset_path) + + raise ValueError(f'uri must start with `s3://` or `file://`. uri={uri}') + + +def _datapoint_path(dataset_path, uid): + return f'{dataset_path}/{uid}.pickle.zstd' + + +class FilterDataset: + + def __init__(self, dataset, params): + self.dataset = dataset + self.params = params + self.max_samples = None + for key in params: + if key == 'samples': + self.max_samples = int(params[key]) + else: + raise ValueError(f'Unknown param in dataset args: {key}') + + def list(self): + handles = self.dataset.list() + if self.max_samples is not None: + handles = handles[:self.max_samples] + return handles + + def write(self, datapoint): + raise ValueError('Cannot write to concat dataset') + + +class ConcatDataset: + + def __init__(self, datasets): + self.datasets = datasets + + def list(self): + handles = [] + for dataset in self.datasets: + handles.extend(dataset.list()) + return handles + + def write(self, datapoint): + raise ValueError('Cannot write to concat dataset') + + +class RemoteDataset: + + def __init__(self, bucket, path): + self.s3 = boto3.resource('s3') + self.bucket = bucket + self.dataset_path = path + assert not path.endswith('/') + self._cache_list = None + + def list(self): + if self._cache_list is not None: + return self._cache_list + bucket = self.s3.Bucket(self.bucket) + handles = [] + for obj in bucket.objects.filter(Prefix=self.dataset_path + '/'): + path = obj.key + if not path.endswith('.pickle.zstd'): + continue + uid = path.rpartition('/')[2].partition('.pickle.zstd')[0] + handles.append(RemoteReadHandle(self.bucket, self.dataset_path, uid)) + x = sorted(handles, key=operator.attrgetter('uid')) + self._cache_list = x + return x + + def write(self, datapoint): + buf = compress_datapoint(datapoint) + path = _datapoint_path(self.dataset_path, datapoint.uid) + self.s3.Bucket(self.bucket).put_object(Key=path, Body=buf) + + +class RemoteDataset: + + def __init__(self, bucket, path): + self.s3 = boto3.resource('s3') + self.bucket = bucket + self.dataset_path = path + assert not path.endswith('/') + self._cache_list = None + + def list(self): + if self._cache_list is not None: + return self._cache_list + bucket = self.s3.Bucket(self.bucket) + handles = [] + for obj in bucket.objects.filter(Prefix=self.dataset_path + '/'): + path = obj.key + if not path.endswith('.pickle.zstd'): + continue + uid = path.rpartition('/')[2].partition('.pickle.zstd')[0] + handles.append(RemoteReadHandle(self.bucket, self.dataset_path, uid)) + x = sorted(handles, key=operator.attrgetter('uid')) + self._cache_list = x + return x + + def write(self, datapoint): + buf = compress_datapoint(datapoint) + path = _datapoint_path(self.dataset_path, datapoint.uid) + self.s3.Bucket(self.bucket).put_object(Key=path, Body=buf) + + +class RemoteReadHandle: + + def __init__(self, bucket, dataset_path, uid): + self.bucket = bucket + self.dataset_path = dataset_path + self.uid = uid + + def read(self): + # Lazily initialize s3 resource due to pytorch data loaders + attempt = 0 + while True: + success = False + try: + if False: + session = boto3.session.Session() + s3 = session.resource('s3') + else: + s3 = boto3.resource('s3') + path = _datapoint_path(self.dataset_path, self.uid) + rsp = s3.Object(self.bucket, path).get() + buf = rsp['Body'].read() + success = True + except urllib3.exceptions.ProtocolError as exc: + print('RETRY: urllib3.exceptions.ProtocolError, attempt:', attempt) + attempt += 1 + delay = retry_delay(attempt) + print('Sleeping before retry:', delay) + time.sleep(delay) + if success: + break + dp = decompress_datapoint(buf) + # TODO: remove this, once old datasets without UID are out of use + if not hasattr(dp, 'uid'): + dp.uid = self.uid + assert dp.uid == self.uid + return dp + + +def retry_delay(attempt): + """Exponential backoff with maximum delay and partial jitter.""" + delay = 100e-3 * (2.**attempt) # exponential back off + delay = min(10.0, delay) # with maximum + delay = delay * np.random.uniform(0.5, 1) # partial jitter + return delay + + +class LocalDataset: + + def __init__(self, dataset_path): + if not dataset_path.exists(): + print('New dataset directory:', dataset_path) + dataset_path.mkdir(parents=True) + assert dataset_path.is_dir() + self.dataset_path = dataset_path + + def list(self): + handles = [] + for path in self.dataset_path.glob('*.pickle.zstd'): + uid = path.name.partition('.')[0] + handles.append(LocalReadHandle(self.dataset_path, uid)) + return sorted(handles, key=operator.attrgetter('uid')) + + def write(self, datapoint): + path = _datapoint_path(self.dataset_path, datapoint.uid) + buf = compress_datapoint(datapoint) + with open(path, 'wb') as fh: + fh.write(buf) + + +class LocalReadHandle: + + def __init__(self, dataset_path, uid): + self.dataset_path = dataset_path + self.uid = uid + + def read(self): + path = _datapoint_path(self.dataset_path, self.uid) + with open(path, 'rb') as fh: + dp = decompress_datapoint(fh.read()) + # TODO: remove this, once old datasets without UID are out of use + if not hasattr(dp, 'uid'): + dp.uid = self.uid + assert dp.uid == self.uid + return dp + + +TURBO_COLORMAP_DATA_NP = np.array([ + [0.18995, 0.07176, 0.23217], + [0.19483, 0.08339, 0.26149], + [0.19956, 0.09498, 0.29024], + [0.20415, 0.10652, 0.31844], + [0.20860, 0.11802, 0.34607], + [0.21291, 0.12947, 0.37314], + [0.21708, 0.14087, 0.39964], + [0.22111, 0.15223, 0.42558], + [0.22500, 0.16354, 0.45096], + [0.22875, 0.17481, 0.47578], + [0.23236, 0.18603, 0.50004], + [0.23582, 0.19720, 0.52373], + [0.23915, 0.20833, 0.54686], + [0.24234, 0.21941, 0.56942], + [0.24539, 0.23044, 0.59142], + [0.24830, 0.24143, 0.61286], + [0.25107, 0.25237, 0.63374], + [0.25369, 0.26327, 0.65406], + [0.25618, 0.27412, 0.67381], + [0.25853, 0.28492, 0.69300], + [0.26074, 0.29568, 0.71162], + [0.26280, 0.30639, 0.72968], + [0.26473, 0.31706, 0.74718], + [0.26652, 0.32768, 0.76412], + [0.26816, 0.33825, 0.78050], + [0.26967, 0.34878, 0.79631], + [0.27103, 0.35926, 0.81156], + [0.27226, 0.36970, 0.82624], + [0.27334, 0.38008, 0.84037], + [0.27429, 0.39043, 0.85393], + [0.27509, 0.40072, 0.86692], + [0.27576, 0.41097, 0.87936], + [0.27628, 0.42118, 0.89123], + [0.27667, 0.43134, 0.90254], + [0.27691, 0.44145, 0.91328], + [0.27701, 0.45152, 0.92347], + [0.27698, 0.46153, 0.93309], + [0.27680, 0.47151, 0.94214], + [0.27648, 0.48144, 0.95064], + [0.27603, 0.49132, 0.95857], + [0.27543, 0.50115, 0.96594], + [0.27469, 0.51094, 0.97275], + [0.27381, 0.52069, 0.97899], + [0.27273, 0.53040, 0.98461], + [0.27106, 0.54015, 0.98930], + [0.26878, 0.54995, 0.99303], + [0.26592, 0.55979, 0.99583], + [0.26252, 0.56967, 0.99773], + [0.25862, 0.57958, 0.99876], + [0.25425, 0.58950, 0.99896], + [0.24946, 0.59943, 0.99835], + [0.24427, 0.60937, 0.99697], + [0.23874, 0.61931, 0.99485], + [0.23288, 0.62923, 0.99202], + [0.22676, 0.63913, 0.98851], + [0.22039, 0.64901, 0.98436], + [0.21382, 0.65886, 0.97959], + [0.20708, 0.66866, 0.97423], + [0.20021, 0.67842, 0.96833], + [0.19326, 0.68812, 0.96190], + [0.18625, 0.69775, 0.95498], + [0.17923, 0.70732, 0.94761], + [0.17223, 0.71680, 0.93981], + [0.16529, 0.72620, 0.93161], + [0.15844, 0.73551, 0.92305], + [0.15173, 0.74472, 0.91416], + [0.14519, 0.75381, 0.90496], + [0.13886, 0.76279, 0.89550], + [0.13278, 0.77165, 0.88580], + [0.12698, 0.78037, 0.87590], + [0.12151, 0.78896, 0.86581], + [0.11639, 0.79740, 0.85559], + [0.11167, 0.80569, 0.84525], + [0.10738, 0.81381, 0.83484], + [0.10357, 0.82177, 0.82437], + [0.10026, 0.82955, 0.81389], + [0.09750, 0.83714, 0.80342], + [0.09532, 0.84455, 0.79299], + [0.09377, 0.85175, 0.78264], + [0.09287, 0.85875, 0.77240], + [0.09267, 0.86554, 0.76230], + [0.09320, 0.87211, 0.75237], + [0.09451, 0.87844, 0.74265], + [0.09662, 0.88454, 0.73316], + [0.09958, 0.89040, 0.72393], + [0.10342, 0.89600, 0.71500], + [0.10815, 0.90142, 0.70599], + [0.11374, 0.90673, 0.69651], + [0.12014, 0.91193, 0.68660], + [0.12733, 0.91701, 0.67627], + [0.13526, 0.92197, 0.66556], + [0.14391, 0.92680, 0.65448], + [0.15323, 0.93151, 0.64308], + [0.16319, 0.93609, 0.63137], + [0.17377, 0.94053, 0.61938], + [0.18491, 0.94484, 0.60713], + [0.19659, 0.94901, 0.59466], + [0.20877, 0.95304, 0.58199], + [0.22142, 0.95692, 0.56914], + [0.23449, 0.96065, 0.55614], + [0.24797, 0.96423, 0.54303], + [0.26180, 0.96765, 0.52981], + [0.27597, 0.97092, 0.51653], + [0.29042, 0.97403, 0.50321], + [0.30513, 0.97697, 0.48987], + [0.32006, 0.97974, 0.47654], + [0.33517, 0.98234, 0.46325], + [0.35043, 0.98477, 0.45002], + [0.36581, 0.98702, 0.43688], + [0.38127, 0.98909, 0.42386], + [0.39678, 0.99098, 0.41098], + [0.41229, 0.99268, 0.39826], + 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[0.80473, 0.92452, 0.20459], + [0.81410, 0.91861, 0.20552], + [0.82333, 0.91253, 0.20663], + [0.83241, 0.90627, 0.20788], + [0.84133, 0.89986, 0.20926], + [0.85010, 0.89328, 0.21074], + [0.85868, 0.88655, 0.21230], + [0.86709, 0.87968, 0.21391], + [0.87530, 0.87267, 0.21555], + [0.88331, 0.86553, 0.21719], + [0.89112, 0.85826, 0.21880], + [0.89870, 0.85087, 0.22038], + [0.90605, 0.84337, 0.22188], + [0.91317, 0.83576, 0.22328], + [0.92004, 0.82806, 0.22456], + [0.92666, 0.82025, 0.22570], + [0.93301, 0.81236, 0.22667], + [0.93909, 0.80439, 0.22744], + [0.94489, 0.79634, 0.22800], + [0.95039, 0.78823, 0.22831], + [0.95560, 0.78005, 0.22836], + [0.96049, 0.77181, 0.22811], + [0.96507, 0.76352, 0.22754], + [0.96931, 0.75519, 0.22663], + [0.97323, 0.74682, 0.22536], + [0.97679, 0.73842, 0.22369], + [0.98000, 0.73000, 0.22161], + [0.98289, 0.72140, 0.21918], + [0.98549, 0.71250, 0.21650], + [0.98781, 0.70330, 0.21358], + [0.98986, 0.69382, 0.21043], + [0.99163, 0.68408, 0.20706], + [0.99314, 0.67408, 0.20348], + [0.99438, 0.66386, 0.19971], + [0.99535, 0.65341, 0.19577], + [0.99607, 0.64277, 0.19165], + [0.99654, 0.63193, 0.18738], + [0.99675, 0.62093, 0.18297], + [0.99672, 0.60977, 0.17842], + [0.99644, 0.59846, 0.17376], + [0.99593, 0.58703, 0.16899], + [0.99517, 0.57549, 0.16412], + [0.99419, 0.56386, 0.15918], + [0.99297, 0.55214, 0.15417], + [0.99153, 0.54036, 0.14910], + [0.98987, 0.52854, 0.14398], + [0.98799, 0.51667, 0.13883], + [0.98590, 0.50479, 0.13367], + [0.98360, 0.49291, 0.12849], + [0.98108, 0.48104, 0.12332], + [0.97837, 0.46920, 0.11817], + [0.97545, 0.45740, 0.11305], + [0.97234, 0.44565, 0.10797], + [0.96904, 0.43399, 0.10294], + [0.96555, 0.42241, 0.09798], + [0.96187, 0.41093, 0.09310], + [0.95801, 0.39958, 0.08831], + [0.95398, 0.38836, 0.08362], + [0.94977, 0.37729, 0.07905], + [0.94538, 0.36638, 0.07461], + [0.94084, 0.35566, 0.07031], + [0.93612, 0.34513, 0.06616], + [0.93125, 0.33482, 0.06218], + [0.92623, 0.32473, 0.05837], + [0.92105, 0.31489, 0.05475], + [0.91572, 0.30530, 0.05134], + [0.91024, 0.29599, 0.04814], + [0.90463, 0.28696, 0.04516], + [0.89888, 0.27824, 0.04243], + [0.89298, 0.26981, 0.03993], + [0.88691, 0.26152, 0.03753], + [0.88066, 0.25334, 0.03521], + [0.87422, 0.24526, 0.03297], + [0.86760, 0.23730, 0.03082], + [0.86079, 0.22945, 0.02875], + [0.85380, 0.22170, 0.02677], + [0.84662, 0.21407, 0.02487], + [0.83926, 0.20654, 0.02305], + [0.83172, 0.19912, 0.02131], + [0.82399, 0.19182, 0.01966], + [0.81608, 0.18462, 0.01809], + [0.80799, 0.17753, 0.01660], + [0.79971, 0.17055, 0.01520], + [0.79125, 0.16368, 0.01387], + [0.78260, 0.15693, 0.01264], + [0.77377, 0.15028, 0.01148], + [0.76476, 0.14374, 0.01041], + [0.75556, 0.13731, 0.00942], + [0.74617, 0.13098, 0.00851], + [0.73661, 0.12477, 0.00769], + [0.72686, 0.11867, 0.00695], + [0.71692, 0.11268, 0.00629], + [0.70680, 0.10680, 0.00571], + [0.69650, 0.10102, 0.00522], + [0.68602, 0.09536, 0.00481], + [0.67535, 0.08980, 0.00449], + [0.66449, 0.08436, 0.00424], + [0.65345, 0.07902, 0.00408], + [0.64223, 0.07380, 0.00401], + [0.63082, 0.06868, 0.00401], + [0.61923, 0.06367, 0.00410], + [0.60746, 0.05878, 0.00427], + [0.59550, 0.05399, 0.00453], + [0.58336, 0.04931, 0.00486], + [0.57103, 0.04474, 0.00529], + [0.55852, 0.04028, 0.00579], + [0.54583, 0.03593, 0.00638], + [0.53295, 0.03169, 0.00705], + [0.51989, 0.02756, 0.00780], + [0.50664, 0.02354, 0.00863], + [0.49321, 0.01963, 0.00955], + [0.47960, 0.01583, 0.01055], +]) + +if __name__ == '__main__': + main() diff --git a/simnet/lib/depth_noise.py b/simnet/lib/depth_noise.py new file mode 100755 index 0000000..29beebf --- /dev/null +++ b/simnet/lib/depth_noise.py @@ -0,0 +1,277 @@ +import numpy as np +import IPython +import cv2 +import scipy.misc as sm +import scipy.ndimage.filters as sf +import scipy.ndimage.morphology as snm +import scipy.stats as ss +import skimage.draw as sd +from skimage import exposure +import imageio +import matplotlib.pyplot as plt + + +class DepthManager(): + def __init__(self, config=None): + + self.depth_config_ = { + 'multiplicative_denoising': False, + 'gamma_shape': 0.1, + + 'image_dropout': True, + 'dropout_poisson_mean': 3000, + 'dropout_radius_shape': 0.8, + 'dropout_radius_scale': 0.8, + + + 'gradient_dropout': True, + 'gradient_dropout_sigma': 1, + 'gradient_dropout_shape': .8, + 'gradient_dropout_scale': .8, + + 'border_distortion': True, + 'border_grad_sigma': 1, + 'border_grad_thresh': 0.1, + 'border_poisson_mean': 1000, + 'border_radius_shape': 0.75, + 'border_radius_scale': 0.75, + + 'gaussian_process_denoising': True, + 'gaussian_process_sigma': 0.05, + 'gaussian_process_scaling_factor': 40, + + + } + # self.depth_config_ = { + # 'multiplicative_denoising': False, + # 'gamma_shape': 0.1, + + # 'image_dropout': True, + # 'dropout_poisson_mean': 1500, + # 'dropout_radius_shape': 0.5, + # 'dropout_radius_scale': 0.5, + + + # 'gradient_dropout': True, + # 'gradient_dropout_sigma': 1, + # 'gradient_dropout_shape': .01, + # 'gradient_dropout_scale': .01, + + # 'border_distortion': True, + # 'border_grad_sigma': 1, + # 'border_grad_thresh': 0.01, + # 'border_poisson_mean': 1000, + # 'border_radius_shape': 0.5, + # 'border_radius_scale': 0.5, + + # 'gaussian_process_denoising': True, + # 'gaussian_process_sigma': 0.05, + # 'gaussian_process_scaling_factor': 50, + + + # } + + + + def resize_image_and_label(self, depth_image): + # Resizes to 256,256 , which is the resolution of our network. This needs to be changed for you guys potentially. + height, width, c = depth_image.shape + # Resize image. + depth_image = cv2.resize(depth_image,(256, 256)) + + def depth_255_image(self, d_image): + # Scale to 255 (Note this handles the case where zero values are marked as invalid) + mask = d_image > 0 + d_image_scale = (d_image[mask] - np.min(d_image[mask])) / ( + np.max(d_image[mask]) - np.min(d_image[mask])) + d_image[mask] = d_image_scale * 254 + 1 + d_image[mask] = 255.0 - d_image[mask] + w, h = d_image.shape + new_d_image = np.zeros([w, h, 3]) + for i in range(3): + new_d_image[:, :, i] = d_image + return new_d_image.astype(np.uint8) + + def convert_depth_image_to_gray_scale(self, depth_image): + # Turns a depth image to a 3-channel uint8 RGB image with repeated channels. + depth_image = self.depth_255_image(depth_image) + + def prepare_depth_data(self, orig_depth_image): + # This is the main function which takes in a rendered depth image which is type float with shape [W,H,1] + di = np.copy(orig_depth_image) + + # for i in range(1, 20, 2): + # self.depth_config_['gamma_shape'] = i + depth_image = self.distort(np.copy(di)) + # plt.imshow(depth_image, cmap='gray') + # plt.show() + return depth_image + # import IPython; IPython.embed() + # imageio.imsave("depth_img.jpg", depth_image) + # import IPython; IPython.embed() + # self.convert_depth_image_to_gray_scale(depth_image) + # self.resize_image_and_label(depth_image) + # return depth_image + + def distort(self, d_img): + """ Adds noise to a single image """ + width, height = d_img.shape + + # denoising and synthetic data generation + if self.depth_config_['multiplicative_denoising']: + gamma_scale = 1.0 / self.depth_config_['gamma_shape'] + gamma_shape = self.depth_config_['gamma_shape'] + mult_samples = ss.gamma.rvs(gamma_shape, scale=gamma_scale, size=1) + mult_samples = mult_samples[:, np.newaxis] + d_img = d_img * np.tile(mult_samples, [width, height]) + + # dropout a region around the areas of the image with high gradient + if self.depth_config_['gradient_dropout']: + grad_mag = sf.gaussian_gradient_magnitude( + d_img, sigma=self.depth_config_['gradient_dropout_sigma']) + thresh = ss.gamma.rvs( + self.depth_config_['gradient_dropout_shape'], + scale=self.depth_config_['gradient_dropout_scale'], + size=100).mean() + # print(thresh) + + high_gradient_px = np.where(grad_mag > thresh) + # import IPython; IPython.embed() + d_img[high_gradient_px[0], high_gradient_px[1]] = 0.0 + + + # randomly dropout borders of the image for robustness + if self.depth_config_['border_distortion']: + grad_mag = sf.gaussian_gradient_magnitude( + d_img, sigma=self.depth_config_['border_grad_sigma']) + high_gradient_px = np.where( + grad_mag > self.depth_config_['border_grad_thresh']) + high_gradient_px = np.c_[high_gradient_px[0], high_gradient_px[1]] + num_nonzero = high_gradient_px.shape[0] + num_dropout_regions = ss.poisson.rvs( + self.depth_config_['border_poisson_mean']) + + # sample ellipses + if num_nonzero == 0: + return d_img + dropout_centers = np.random.choice( + num_nonzero, size=num_dropout_regions) + x_radii = ss.gamma.rvs( + self.depth_config_['border_radius_shape'], + scale=self.depth_config_['border_radius_scale'], + size=num_dropout_regions) + y_radii = ss.gamma.rvs( + self.depth_config_['border_radius_shape'], + scale=self.depth_config_['border_radius_scale'], + size=num_dropout_regions) + # set interior pixels to zero or one + for j in range(num_dropout_regions): + ind = dropout_centers[j] + dropout_center = high_gradient_px[ind, :] + x_radius = x_radii[j] + y_radius = y_radii[j] + dropout_px_y, dropout_px_x = sd.ellipse( + dropout_center[0], + dropout_center[1], + y_radius, + x_radius, + shape=d_img.shape) + d_img[dropout_px_y, dropout_px_x] = 0.0 + + + # randomly dropout regions of the image for robustness + if self.depth_config_['image_dropout']: + nonzero_px = np.where(d_img > 0) + nonzero_px = np.c_[nonzero_px[0], nonzero_px[1]] + num_nonzero = nonzero_px.shape[0] + num_dropout_regions = ss.poisson.rvs( + self.depth_config_['dropout_poisson_mean']) + if num_nonzero == 0: + return d_img + # sample ellipses + dropout_centers = np.random.choice( + num_nonzero, size=num_dropout_regions) + x_radii = ss.gamma.rvs( + self.depth_config_['dropout_radius_shape'], + scale=self.depth_config_['dropout_radius_scale'], + size=num_dropout_regions) + y_radii = ss.gamma.rvs( + self.depth_config_['dropout_radius_shape'], + scale=self.depth_config_['dropout_radius_scale'], + size=num_dropout_regions) + + # set interior pixels to zero + for j in range(num_dropout_regions): + ind = dropout_centers[j] + dropout_center = nonzero_px[ind, :] + x_radius = x_radii[j] + y_radius = y_radii[j] + dropout_px_y, dropout_px_x = sd.ellipse( + dropout_center[0], + dropout_center[1], + y_radius, + x_radius, + shape=d_img.shape) + d_img[dropout_px_y, dropout_px_x] = 0.0 + + # add correlated Gaussian noise + if self.depth_config_['gaussian_process_denoising']: + gp_rescale_factor = self.depth_config_[ + 'gaussian_process_scaling_factor'] + gp_sample_height = int(height / gp_rescale_factor) + gp_sample_width = int(width / gp_rescale_factor) + gp_num_pix = gp_sample_height * gp_sample_width + gp_sigma = self.depth_config_['gaussian_process_sigma'] + gp_noise = ss.norm.rvs( + scale=gp_sigma, size=gp_num_pix).reshape( + gp_sample_height, gp_sample_width) + gp_noise = cv2.resize( + gp_noise, (height, width)) + d_img[d_img > 0] += gp_noise[d_img > 0] + + # randomly dropout borders of the image for robustness + if self.depth_config_['border_distortion']: + # plt.imshow(d_img) + # plt.show() + high_gradient_px = np.where( + d_img < 20) + high_gradient_px = np.c_[high_gradient_px[0], high_gradient_px[1]] + num_nonzero = high_gradient_px.shape[0] + num_dropout_regions = ss.poisson.rvs( + self.depth_config_['border_poisson_mean']) + + # sample ellipses + if num_nonzero > 0: + dropout_centers = np.random.choice( + num_nonzero, size=num_dropout_regions) + x_radii = ss.gamma.rvs( + self.depth_config_['border_radius_shape'], + scale=self.depth_config_['border_radius_scale'], + size=num_dropout_regions) + y_radii = ss.gamma.rvs( + self.depth_config_['border_radius_shape'], + scale=self.depth_config_['border_radius_scale'], + size=num_dropout_regions) + + # set interior pixels to zero or one + for j in range(num_dropout_regions): + ind = dropout_centers[j] + dropout_center = high_gradient_px[ind, :] + x_radius = x_radii[j] + y_radius = y_radii[j] + dropout_px_y, dropout_px_x = sd.ellipse( + dropout_center[0], + dropout_center[1], + y_radius, + x_radius, + shape=d_img.shape) + d_img[dropout_px_y, dropout_px_x] = 0.0 + + + + return d_img + +if __name__ == '__main__': + im = np.load("depth_img.npy") + DM = DepthManager() + DM.prepare_depth_data(im) diff --git a/simnet/lib/net/common.py b/simnet/lib/net/common.py new file mode 100755 index 0000000..0025c31 --- /dev/null +++ b/simnet/lib/net/common.py @@ -0,0 +1,173 @@ +import pathlib +from importlib.machinery import SourceFileLoader +import numpy as np +import torch +from torch.utils.data import ConcatDataset, DataLoader +from simnet.lib.net.init.default_init import default_init +from simnet.lib.net.dataset import Dataset + + +def add_dataset_args(parser, prefix): + group = parser.add_argument_group("{}_dataset".format(prefix)) + group.add_argument("--{}_path".format(prefix), type=str, required=True) + #group.add_argument("--{}_fraction".format(prefix), type=str, default=None) + group.add_argument("--{}_batch_size".format(prefix), default=16, type=int) + group.add_argument("--{}_num_workers".format(prefix), default=7, type=int) + #group.add_argument("--{}_random_crop".format(prefix), default=None, type=int, nargs=2) + + +def add_train_args(parser): + parser.add_argument("--max_steps", type=int, required=True) + parser.add_argument("--output", type=str, required=True) + + add_dataset_args(parser, "train") + add_dataset_args(parser, "val") + + optim_group = parser.add_argument_group("optim") + optim_group.add_argument("--optim_type", default='sgd', type=str) + optim_group.add_argument("--optim_learning_rate", default=0.02, type=float) + optim_group.add_argument("--optim_momentum", default=0.9, type=float) + optim_group.add_argument("--optim_weight_decay", default=1e-4, type=float) + optim_group.add_argument("--optim_poly_exp", default=0.9, type=float) + optim_group.add_argument("--optim_warmup_epochs", default=None, type=int) + parser.add_argument("--model_file", type=str, required=True) + parser.add_argument("--model_name", type=str, required=True) + parser.add_argument("--checkpoint", default=None, type=str) + parser.add_argument("--finetune_real", default=False, type=bool) + parser.add_argument("--wandb_name", type=str, required=True) + # Ignore Mask Search. + parser.add_argument("--min_height", default=0.0, type=float) + parser.add_argument("--min_occlusion", default=0.0, type=float) + parser.add_argument("--min_truncation", default=0.0, type=float) + # Backbone configs + parser.add_argument("--model_norm", default='BN', type=str) + parser.add_argument("--num_filters_scale", default=4, type=int) + + # Loss weights + parser.add_argument("--frozen_stereo_checkpoint", default=None, type=str) + parser.add_argument("--loss_seg_mult", default=1.0, type=float) + parser.add_argument("--loss_depth_mult", default=1.0, type=float) + parser.add_argument("--loss_heatmap_mult", default=100.0, type=float) + parser.add_argument("--loss_vertex_mult", default=0.1, type=float) + parser.add_argument("--loss_z_centroid_mult", default=0.1, type=float) + parser.add_argument("--loss_rotation_mult", default=0.1, type=float) + parser.add_argument("--loss_keypoint_mult", default=0.1, type=float) + parser.add_argument("--loss_latent_emb_mult", default=0.1, type=float) + parser.add_argument("--loss_abs_pose_mult", default=0.1, type=float) + # Stereo Stem Args + parser.add_argument( + "--loss_disparity_stdmean_scaled", + action="store_true", + help="If true, the loss will be scaled based on the standard deviation and mean of the " + "ground truth disparities" + ) + parser.add_argument("--cost_volume_downsample_factor", default=4, type=int) + parser.add_argument("--max_disparity", default=90, type=int) + parser.add_argument( + "--fe_features", + default=16, + type=int, + help="Number of output features in feature extraction stage" + ) + parser.add_argument( + "--fe_internal_features", + default=32, + type=int, + help="Number of features in the first block of the feature extraction" + ) + # keypoint head args + parser.add_argument("--num_keypoints", default=1, type=int) + + + +def get_config_value(hparams, prefix, key): + full_key = "{}_{}".format(prefix, key) + if hasattr(hparams, full_key): + return getattr(hparams, full_key) + else: + return None + + +def get_loader(hparams, prefix, preprocess_func=None, datapoint_dataset=None): + datasets = [] + path = get_config_value(hparams, prefix, 'path') + datasets.append( + Dataset( + path, hparams, preprocess_image_func=preprocess_func, datapoint_dataset=datapoint_dataset + ) + ) + batch_size = get_config_value(hparams, prefix, "batch_size") + + collate_fn = simnet_collate + if prefix == 'train': + return DataLoader( + ConcatDataset(datasets), + batch_size=batch_size, + collate_fn=collate_fn, + num_workers=get_config_value(hparams, prefix, "num_workers"), + pin_memory=False, + drop_last=True, + shuffle=True + ) + else: + return DataLoader( + ConcatDataset(datasets), + batch_size=batch_size, + collate_fn=collate_fn, + num_workers=get_config_value(hparams, prefix, "num_workers"), + pin_memory=False, + drop_last=True, + shuffle=False + ) + + +def simnet_collate(batch): + # list of elements per patch + # Each element is a tuple of (stereo,imgs) + targets = [] + for ii in range(len(batch[0])): + targets.append([batch_element[ii] for batch_element in batch]) + stacked_images = torch.stack(targets[0]) + return stacked_images, targets[1], targets[2], targets[3], targets[4], targets[5] + + +def prune_state_dict(state_dict): + for key in list(state_dict.keys()): + state_dict[key[6:]] = state_dict.pop(key) + return state_dict + + +def keep_only_stereo_weights(state_dict): + pruned_state_dict = {} + for key in list(state_dict.keys()): + if 'stereo' in key: + pruned_state_dict[key] = state_dict[key] + return pruned_state_dict + + +def get_model(hparams): + model_path = (pathlib.Path(__file__).parent / hparams.model_file).resolve() + print('Using model class from:', model_path) + net_module = SourceFileLoader(hparams.model_name, str(model_path)).load_module() + net_attr = getattr(net_module, hparams.model_name) + model = net_attr(hparams) + model.apply(default_init) + # For large models use imagenet weights. + # This speeds up training and can give a +2 mAP score on car detections + if hparams.num_filters_scale == 1: + model.load_imagenet_weights() + + if hparams.frozen_stereo_checkpoint is not None: + print('Restoring stereo weights from checkpoint:', hparams.frozen_stereo_checkpoint) + state_dict = torch.load(hparams.frozen_stereo_checkpoint, map_location='cpu')['state_dict'] + state_dict = prune_state_dict(state_dict) + state_dict = keep_only_stereo_weights(state_dict) + model.load_state_dict(state_dict, strict=False) + + if hparams.checkpoint is not None: + print('Restoring from checkpoint:', hparams.checkpoint) + state_dict = torch.load(hparams.checkpoint, map_location='cpu')['state_dict'] + state_dict = prune_state_dict(state_dict) + model.load_state_dict(state_dict, strict=False) + + return model diff --git a/simnet/lib/net/dataset.py b/simnet/lib/net/dataset.py new file mode 100755 index 0000000..1984bfd --- /dev/null +++ b/simnet/lib/net/dataset.py @@ -0,0 +1,77 @@ +# Copyright 2019 Toyota Research Institute. All rights reserved. + +import os +import random +import pathlib +import cv2 +import numpy as np +import torch +import IPython +import torch.nn.functional as F +from torch.utils.data import Dataset +import torchvision.transforms as transforms +from simnet.lib import datapoint +from simnet.lib.net.post_processing.segmentation_outputs import SegmentationOutput +from simnet.lib.net.post_processing.depth_outputs import DepthOutput +from simnet.lib.net.post_processing.abs_pose_outputs import OBBOutput + +def extract_left_numpy_img(anaglyph): + anaglyph_np = np.ascontiguousarray(anaglyph.cpu().numpy()) + anaglyph_np = anaglyph_np.transpose((1, 2, 0)) + left_img = anaglyph_np[..., 0:3] * 255.0 + return left_img + +def extract_right_numpy_img(anaglyph): + anaglyph_np = np.ascontiguousarray(anaglyph.cpu().numpy()) + anaglyph_np = anaglyph_np.transpose((1, 2, 0)) + left_img = anaglyph_np[..., 3:6] * 255.0 + return left_img + +def create_anaglyph(stereo_dp): + height, width, _ = stereo_dp.left_color.shape + image = torch.zeros(4, height, width, dtype=torch.float32) + cv2.normalize(stereo_dp.left_color, stereo_dp.left_color, 0, 255, cv2.NORM_MINMAX) + + rgb = stereo_dp.left_color* 1. / 255.0 + norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + rgb = norm(torch.from_numpy(rgb.astype(np.float32).transpose((2,0,1)))) + + if len(stereo_dp.right_color.shape) == 2: + depth = stereo_dp.right_color + depth = torch.from_numpy(depth.astype(np.float32)) + + image[0:3, :] = rgb + image[3, :] = depth + return image + +class Dataset(Dataset): + def __init__(self, dataset_uri, hparams, preprocess_image_func=None, datapoint_dataset=None): + super().__init__() + if datapoint_dataset is None: + datapoint_dataset = datapoint.make_dataset(dataset_uri) + self.datapoint_handles = datapoint_dataset.list() + # No need to shuffle, already shufled based on random uids + self.hparams = hparams + if preprocess_image_func is None: + self.preprocces_image_func = create_anaglyph + else: + self.preprocces_image_func = preprocess_image_func + + def __len__(self): + return len(self.datapoint_handles) + + def __getitem__(self, idx): + dp = self.datapoint_handles[idx].read() + anaglyph = self.preprocces_image_func(dp.stereo) + segmentation_target = SegmentationOutput(dp.segmentation, self.hparams) + segmentation_target.convert_to_torch_from_numpy() + depth_target = DepthOutput(dp.depth, self.hparams) + depth_target.convert_to_torch_from_numpy() + pose_target = None + for pose_dp in dp.object_poses: + pose_target = OBBOutput( + pose_dp.heat_map, pose_dp.latent_emb, pose_dp.abs_pose, self.hparams + ) + pose_target.convert_to_torch_from_numpy() + scene_name = dp.scene_name + return anaglyph, segmentation_target, depth_target, pose_target, dp.detections, scene_name diff --git a/simnet/lib/net/functions/__init__.py b/simnet/lib/net/functions/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/simnet/lib/net/functions/learning_rate.py b/simnet/lib/net/functions/learning_rate.py new file mode 100755 index 0000000..814bb25 --- /dev/null +++ b/simnet/lib/net/functions/learning_rate.py @@ -0,0 +1,28 @@ +# Copyright 2018 Toyota Research Institute. All rights reserved. +# +# Originally from Koichiro Yamaguchi's pixwislab repo mirrored at: +# https://github.awsinternal.tri.global/driving/pixwislab + + +def lambda_learning_rate_poly(max_epochs, exponent): + """Make a function for computing learning rate by "poly" policy. + + This policy does a polynomial decay of the learning rate over the epochs + of training. + + Args: + max_epochs (int): max numbers of epochs + exponent (float): exponent value + """ + return lambda epoch: pow((1.0 - epoch / max_epochs), exponent) + + +def lambda_warmup(warmup_period, warmup_factor, wrapped_lambda): + + def warmup(epoch, warmup_period, warmup_factor): + if epoch > warmup_period: + return 1.0 + else: + return warmup_factor + (1.0 - warmup_factor) * (epoch / warmup_period) + + return lambda epoch: warmup(epoch, warmup_period, warmup_factor) * wrapped_lambda(epoch) diff --git a/simnet/lib/net/init/__init__.py b/simnet/lib/net/init/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/simnet/lib/net/init/default_init.py b/simnet/lib/net/init/default_init.py new file mode 100755 index 0000000..acac9f8 --- /dev/null +++ b/simnet/lib/net/init/default_init.py @@ -0,0 +1,25 @@ +import torch.nn as nn + + +def default_init(module): + """Initialize parameters of the module. + + For convolution, weights are initialized by Kaiming method and + biases are initialized to zero. + For batch normalization, scales and biases are set to 1 and 0, + respectively. + """ + if isinstance(module, nn.Conv2d): + nn.init.kaiming_normal_(module.weight.data) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Conv3d): + nn.init.kaiming_normal_(module.weight.data) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.BatchNorm2d): + module.weight.data.fill_(1) + module.bias.data.zero_() + elif isinstance(module, nn.BatchNorm3d): + module.weight.data.fill_(1) + module.bias.data.zero_() diff --git a/simnet/lib/net/losses.py b/simnet/lib/net/losses.py new file mode 100755 index 0000000..ca1b851 --- /dev/null +++ b/simnet/lib/net/losses.py @@ -0,0 +1,67 @@ +# Copyright 2019 Toyota Research Institute. All rights reserved. + +import torch +import torch.nn as nn +import numpy as np +import IPython + + +class MaskedL1Loss(nn.Module): + + def __init__(self, centroid_threshold=0.3, downscale_factor=8): + super().__init__() + self.loss = nn.L1Loss(reduction='none') + self.centroid_threshold = centroid_threshold + self.downscale_factor = downscale_factor + + def forward(self, output, target, valid_mask): + ''' + output: [N,16,H,W] + target: [N,16,H,W] + valid_mask: [N,H,W] + ''' + valid_count = torch.sum( + valid_mask[:, ::self.downscale_factor, ::self.downscale_factor] > self.centroid_threshold + ) + loss = self.loss(output, target) + if len(output.shape) == 4: + loss = torch.sum(loss, dim=1) + loss[valid_mask[:, ::self.downscale_factor, ::self.downscale_factor] < self.centroid_threshold + ] = 0.0 + if valid_count == 0: + return torch.sum(loss) + return torch.sum(loss) / valid_count + + +class MSELoss(nn.Module): + + def __init__(self): + super().__init__() + self.loss = nn.MSELoss(reduction='none') + + def forward(self, output, target): + ''' + output: [N,H,W] + target: [N,H,W] + ignore_mask: [N,H,W] + ''' + loss = self.loss(output, target) + return torch.mean(loss) + + +class MaskedMSELoss(nn.Module): + + def __init__(self): + super().__init__() + self.loss = nn.MSELoss(reduction='none') + + def forward(self, output, target, ignore_mask): + ''' + output: [N,H,W] + target: [N,H,W] + ignore_mask: [N,H,W] + ''' + valid_sum = torch.sum(torch.logical_not(ignore_mask)) + loss = self.loss(output, target) + loss[ignore_mask > 0] = 0.0 + return torch.sum(loss) / valid_sum diff --git a/simnet/lib/net/models/__init__.py b/simnet/lib/net/models/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/simnet/lib/net/models/auto_encoder.py b/simnet/lib/net/models/auto_encoder.py new file mode 100755 index 0000000..698948f --- /dev/null +++ b/simnet/lib/net/models/auto_encoder.py @@ -0,0 +1,73 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class PointCloudEncoder(nn.Module): + def __init__(self, emb_dim): + super(PointCloudEncoder, self).__init__() + self.conv1 = nn.Conv1d(3, 64, 1) + self.conv2 = nn.Conv1d(64, 128, 1) + self.conv3 = nn.Conv1d(256, 256, 1) + self.conv4 = nn.Conv1d(256, 1024, 1) + self.fc = nn.Linear(1024, emb_dim) + + def forward(self, xyz): + """ + Args: + xyz: (B, 3, N) + """ + np = xyz.size()[2] + x = F.relu(self.conv1(xyz)) + x = F.relu(self.conv2(x)) + global_feat = F.adaptive_max_pool1d(x, 1) + x = torch.cat((x, global_feat.repeat(1, 1, np)), dim=1) + x = F.relu(self.conv3(x)) + x = F.relu(self.conv4(x)) + x = torch.squeeze(F.adaptive_max_pool1d(x, 1), dim=2) + embedding = self.fc(x) + return embedding + + +class PointCloudDecoder(nn.Module): + def __init__(self, emb_dim, n_pts): + super(PointCloudDecoder, self).__init__() + self.fc1 = nn.Linear(emb_dim, 512) + self.fc2 = nn.Linear(512, 1024) + self.fc3 = nn.Linear(1024, 3*n_pts) + + def forward(self, embedding): + """ + Args: + embedding: (B, 512) + + """ + bs = embedding.size()[0] + out = F.relu(self.fc1(embedding)) + out = F.relu(self.fc2(out)) + out = self.fc3(out) + out_pc = out.view(bs, -1, 3) + return out_pc + + +class PointCloudAE(nn.Module): + def __init__(self, emb_dim=512, n_pts=1024): + super(PointCloudAE, self).__init__() + self.encoder = PointCloudEncoder(emb_dim) + self.decoder = PointCloudDecoder(emb_dim, n_pts) + + def forward(self, in_pc, emb=None): + """ + Args: + in_pc: (B, N, 3) + emb: (B, 512) + + Returns: + emb: (B, emb_dim) + out_pc: (B, n_pts, 3) + + """ + if emb is None: + xyz = in_pc.permute(0, 2, 1) + emb = self.encoder(xyz) + out_pc = self.decoder(emb) + return emb, out_pc diff --git a/simnet/lib/net/models/basic_stem.py b/simnet/lib/net/models/basic_stem.py new file mode 100755 index 0000000..71e4154 --- /dev/null +++ b/simnet/lib/net/models/basic_stem.py @@ -0,0 +1,30 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from simnet.lib.net.models.layers.residual_blocks import PreactBasicResidualBlock +from simnet.lib.net.models import panoptic_backbone + +#RGB-D +class RGBDStem(nn.Module): + + def __init__(self, hparams, in_channels=3): + super().__init__() + self.rgb_stem = panoptic_backbone.BasicStem(in_channels=in_channels, out_channels=32) + self.depth_stem = panoptic_backbone.BasicStem(in_channels=1, out_channels=1) + self.disp_features = PreactBasicResidualBlock( + 1, 32, stride=1, dilation_rate=5, add_preact=True, add_last_norm=False + ) + + def forward(self, stacked_img, robot_joint_angles=None): + small_disp = self.depth_stem.forward(stacked_img[:, 3:], None) + left_rgb_features = self.rgb_stem.forward(stacked_img[:, 0:3], robot_joint_angles) + disp_features = self.disp_features(small_disp) + return torch.cat((disp_features, left_rgb_features), axis=1), small_disp + + @property + def out_channels(self): + return self.rgb_stem.out_channels + + @property + def stride(self): + return 4 # = stride 2 conv -> stride 2 max pool \ No newline at end of file diff --git a/simnet/lib/net/models/layers/__init__.py b/simnet/lib/net/models/layers/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/simnet/lib/net/models/layers/cost_volume.py b/simnet/lib/net/models/layers/cost_volume.py new file mode 100755 index 0000000..20f7840 --- /dev/null +++ b/simnet/lib/net/models/layers/cost_volume.py @@ -0,0 +1,85 @@ +# Copyright 2020 Toyota Research Institute. All rights reserved. + +import torch +import torch.nn as nn + + +@torch.jit.script +def cost_volume(left, right, num_disparities: int, is_right: bool): + batch_size, channels, height, width = left.shape + + output = torch.zeros((batch_size, channels, num_disparities, height, width), + dtype=left.dtype, + device=left.device) + + for i in range(num_disparities): + if not is_right: + output[:, :, i, :, i:] = left[:, :, :, i:] * right[:, :, :, :width - i] + else: + output[:, :, i, :, :width - i] = left[:, :, :, i:] * right[:, :, :, :width - i] + + return output + + +class CostVolume(nn.Module): + """Compute cost volume using cross correlation of left and right feature maps""" + + def __init__(self, num_disparities, is_right=False): + super().__init__() + self.num_disparities = num_disparities + self.is_right = is_right + + def forward(self, left, right): + if torch.jit.is_scripting(): + return cost_volume(left, right, self.num_disparities, self.is_right) + else: + return self.forward_with_amp(left, right) + + @torch.jit.unused + def forward_with_amp(self, left, right): + """This operation is unstable at float16, so compute at float32 even when using mixed precision""" + with torch.cuda.amp.autocast(enabled=False): + left = left.to(torch.float32) + right = right.to(torch.float32) + output = cost_volume(left, right, self.num_disparities, self.is_right) + output = torch.clamp(output, -1e3, 1e3) + return output + + +@torch.jit.script +def dot_product_cost_volume(left, right, num_disparities: int, is_right: bool): + batch_size, channels, height, width = left.shape + + output = torch.zeros((batch_size, num_disparities, height, width), + dtype=left.dtype, + device=left.device) + + for i in range(num_disparities): + if not is_right: + output[:, i, :, i:] = (left[:, :, :, i:] * right[:, :, :, :width - i]).mean(dim=1) + else: + output[:, i, :, width - i] = (left[:, :, :, i:] * right[:, :, :, :width - i]).mean(dim=1) + + return output + + +class DotProductCostVolume(nn.Module): + """Compute cost volume using dot product of left and right feature maps""" + + def __init__(self, num_disparities, is_right=False): + super().__init__() + self.num_disparities = num_disparities + self.is_right = is_right + + def forward(self, left, right): + return dot_product_cost_volume(left, right, self.num_disparities, self.is_right) + + @torch.jit.unused + def forward_with_amp(self, left, right): + """This operation is unstable at float16, so compute at float32 even when using mixed precision""" + with torch.cuda.amp.autocast(enabled=False): + left = left.to(torch.float32) + right = right.to(torch.float32) + output = dot_product_cost_volume(left, right, self.num_disparities, self.is_right) + output = torch.clamp(output, -1e3, 1e3) + return output diff --git a/simnet/lib/net/models/layers/hdc_functions.py b/simnet/lib/net/models/layers/hdc_functions.py new file mode 100755 index 0000000..cd1a4f2 --- /dev/null +++ b/simnet/lib/net/models/layers/hdc_functions.py @@ -0,0 +1,43 @@ +# Copyright 2018-2020 Toyota Research Institute. All rights reserved. +# +# Originally from Koichiro Yamaguchi's pixwislab repo mirrored at: +# https://github.awsinternal.tri.global/driving/pixwislab + +import torch.nn as nn + + +def hdc_resnet_group(block_func, in_channels, base_channels, num_blocks, dilation_rates): + """Make a group of pre-activation residual blocks with Hybrid Dilated + Convolution (HDC). + + "Understanding Convolution for Semantic Segmentation", + https://arxiv.org/abs/1702.08502. + + Args: + block_func (ResidualBlock): Function of a residual block. + in_channels (int): The number of input channels. + base_channels (int): The number of base channels of the residual block. + num_blocks (int): The number of residual blocks. + dilation_rates (list): List of dilation rates. + + Returns: + Module of a group of residual blocks. + """ + assert block_func.preact() + + num_rates = len(dilation_rates) + residual_blocks = [ + block_func(in_channels, base_channels, dilation_rate=dilation_rates[0], add_preact=False) + ] + in_channels = block_func.expansion() * base_channels + for idx in range(1, num_blocks): + residual_blocks.append( + block_func( + in_channels, + base_channels, + dilation_rate=dilation_rates[idx % num_rates], + add_preact=True, + add_last_norm=idx == num_blocks - 1 + ) + ) + return nn.Sequential(*residual_blocks) diff --git a/simnet/lib/net/models/layers/matchability.py b/simnet/lib/net/models/layers/matchability.py new file mode 100755 index 0000000..40c0200 --- /dev/null +++ b/simnet/lib/net/models/layers/matchability.py @@ -0,0 +1,33 @@ +# Copyright 2020 Toyota Research Institute. All rights reserved. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +# + + +@torch.jit.script +def matchability(input): + softmin = F.softmin(input, dim=1) + log_softmin = F.log_softmax(-input, dim=1) + output = torch.sum(softmin * log_softmin, dim=1, keepdim=True) + return output + + +class Matchability(nn.Module): + """Compute disparity matchability value from https://arxiv.org/abs/2008.04800""" + + def forward(self, input): + if torch.jit.is_scripting(): + # Torchscript generation can't handle mixed precision, so always compute at float32. + return matchability(input) + else: + return self.forward_with_amp(input) + + @torch.jit.unused + def forward_with_amp(self, input): + """This operation is unstable at float16, so compute at float32 even when using mixed precision""" + with torch.cuda.amp.autocast(enabled=False): + input = input.to(torch.float32) + return matchability(input) diff --git a/simnet/lib/net/models/layers/residual_blocks.py b/simnet/lib/net/models/layers/residual_blocks.py new file mode 100755 index 0000000..abf0303 --- /dev/null +++ b/simnet/lib/net/models/layers/residual_blocks.py @@ -0,0 +1,179 @@ +# Copyright 2018-2020 Toyota Research Institute. All rights reserved. +# +# Originally from Koichiro Yamaguchi's pixwislab repo mirrored at: +# https://github.awsinternal.tri.global/driving/pixwislab + +import torch.nn as nn + + +def resnet_shortcut(in_channels, out_channels, stride, preact=False): + """Shortcut layer for residual block. + + When the numbers of input and output channels are the same and stride is + equal to 1, no layer is made. + + Args: + in_channels (int): The number of input channels. + out_channels (int): The number of output channels. + stride (int): Stride of the residual block. + preact (bool, optional): If True, make a shortcut for pre-activation + residual block. + + Returns: + Module of shortcut layers. + """ + if stride == 1 and in_channels == out_channels: + return None + + if preact: + return nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False) + else: + return nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(out_channels) + ) + + +class ResidualBlock(nn.Module): + """Base class for residual block.""" + + @classmethod + def expansion(cls): + """Expansion rate.""" + raise NotImplementedError + + @classmethod + def preact(cls): + """Pre-activation flag.""" + raise NotImplementedError + + +class PreactBasicResidualBlock(ResidualBlock): + """Pre-activation basic residual block.""" + + def __init__( + self, + in_channels, + base_channels, + stride=1, + dilation_rate=1, + add_preact=True, + add_last_norm=False + ): + """ + Args: + in_channels (int): The number of input channels. + base_channels (int): The number of output channels. + stride (int, optional): Stride of the residual block. + dilation_rate (int, optional): Dilation rate of the residual block. + add_preact (bool, optional): If True, add pre-activation. + add_last_norm (bool, optional): If True, add batch normalization + after the last convolution. + """ + super().__init__() + if add_preact: + self.preact_bn = nn.BatchNorm2d(in_channels) + else: + self.preact_bn = None + self.conv_shortcut = resnet_shortcut(in_channels, base_channels, stride, preact=True) + self.conv1 = nn.Conv2d( + in_channels, + base_channels, + kernel_size=3, + stride=stride, + padding=dilation_rate, + dilation=dilation_rate, + bias=False + ) + self.bn1 = nn.BatchNorm2d(base_channels) + self.relu = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d( + base_channels, + base_channels, + kernel_size=3, + padding=dilation_rate, + dilation=dilation_rate, + bias=False + ) + self.bn_last = nn.BatchNorm2d(base_channels) if add_last_norm else None + + @classmethod + def expansion(cls): + """Expansion rate, which is a ratio of the number of the output + channels to the number of the base channels in the residual block. + + Returns: + Expansion rate (= 1). + """ + return 1 + + @classmethod + def preact(cls): + """Pre-activation flag. + + Returns: + Flag (= True). + """ + return True + + def forward(self, inputs): + """Forward computation. + + Args: + inputs (Tensor): Input tensor. + + Returns: + Output tensor. + """ + if self.conv_shortcut is None: + shortcut_inputs = inputs + else: + shortcut_inputs = self.conv_shortcut(inputs) + + if self.preact_bn is not None: + inputs = self.relu(self.preact_bn(inputs)) + outputs = self.relu(self.bn1(self.conv1(inputs))) + outputs = self.conv2(outputs) + + outputs += shortcut_inputs + + if self.bn_last is not None: + outputs = self.relu(self.bn_last(outputs)) + return outputs + + +def preact_resnet_group( + block_func, in_channels, base_channels, num_blocks, stride=1, dilation_rate=1 +): + """Make a group of pre-activation residual blocks. + + Args: + block_func (ResidualBlock): Function of a residual block. + in_channels (int): The number of input channels. + base_channels (int): The number of base channels of the residual block. + num_blocks (int): The number of residual blocks. + stride (int, optional): Stride of the first residual block. + dilation_rate (int, optional): Dilation rate of residual blocks. + + Returns: + Module of a group of residual blocks. + """ + assert block_func.preact() + + residual_blocks = [ + block_func( + in_channels, base_channels, stride=stride, dilation_rate=dilation_rate, add_preact=False + ) + ] + in_channels = block_func.expansion() * base_channels + for idx in range(1, num_blocks): + residual_blocks.append( + block_func( + in_channels, + base_channels, + dilation_rate=dilation_rate, + add_preact=True, + add_last_norm=idx == num_blocks - 1 + ) + ) + return nn.Sequential(*residual_blocks) diff --git a/simnet/lib/net/models/layers/soft_argmin.py b/simnet/lib/net/models/layers/soft_argmin.py new file mode 100755 index 0000000..34eab51 --- /dev/null +++ b/simnet/lib/net/models/layers/soft_argmin.py @@ -0,0 +1,23 @@ +# Copyright 2020 Toyota Research Institute. All rights reserved. + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +@torch.jit.script +def soft_argmin(input): + _, channels, _, _ = input.shape + + softmin = F.softmin(input, dim=1) + index_tensor = torch.arange(0, channels, dtype=softmin.dtype, + device=softmin.device).view(1, channels, 1, 1) + output = torch.sum(softmin * index_tensor, dim=1, keepdim=True) + return output + + +class SoftArgmin(nn.Module): + """Compute soft argmin operation for given cost volume""" + + def forward(self, input): + return soft_argmin(input) diff --git a/simnet/lib/net/models/layers/transition_blocks.py b/simnet/lib/net/models/layers/transition_blocks.py new file mode 100755 index 0000000..8f1d9b0 --- /dev/null +++ b/simnet/lib/net/models/layers/transition_blocks.py @@ -0,0 +1,41 @@ +# Copyright 2018-2020 Toyota Research Institute. All rights reserved. +# +# Originally from Koichiro Yamaguchi's pixwislab repo mirrored at: +# https://github.awsinternal.tri.global/driving/pixwislab + +import torch.nn as nn + + +class TransitionBlock(nn.Module): + """Transition block for changing resolution or the number of channels.""" + + def __init__(self, in_channels, out_channels, stride): + """ + Args: + in_channels (int): The number of input channels. + out_channels (int): The number of output channels. + stride (int): Stride (1 or 2). + """ + assert stride in (1, 2) + assert not (in_channels == out_channels and stride == 1) + super().__init__() + + if stride == 1: + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) + else: + self.conv = nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False + ) + self.bn = nn.BatchNorm2d(out_channels) + self.relu = nn.ReLU(inplace=True) + + def forward(self, inputs): + """Forward computation. + + Args: + inputs (Tensor): Input tensor. + + Returns: + Output tensor. + """ + return self.relu(self.bn(self.conv(inputs))) diff --git a/simnet/lib/net/models/panoptic_backbone.py b/simnet/lib/net/models/panoptic_backbone.py new file mode 100755 index 0000000..3a97773 --- /dev/null +++ b/simnet/lib/net/models/panoptic_backbone.py @@ -0,0 +1,2005 @@ +import abc +import math +import collections + +import numpy as np +import torch +import torch.nn as nn +from torch.nn import functional as F +import fvcore.nn.weight_init as weight_init + +import logging +loger = logging.getLogger('lightning') +TORCH_VERSION = (1, 4) + +#Model cfg +#Hard code parameters for now here. +MODEL_RESNETS_DEPTH = 50 +MODEL_RESNETS_OUT_FEATURES = ['res2', 'res3', 'res4', 'res5'] +MODEL_META_ARCHITECTURE = "PanopticFPN" +# https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md +# COCO panoptic weights: +# https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514569/model_final_c10459.pkl +#MODEL_WEIGHTS = '/data/good_ckpts/pretrained_cocopanoptic_c10459_pkl' + +USE_STEREO_STEM = False + +NUM_BLOCKS_PER_STAGE = [3, 4, 6, 3] +# NUM_BLOCKS_PER_STAGE = [2, 2, 2, 2] +USE_TOP_BLOCK_P6 = False + +MODEL_FPN_IN_FEATURES = ['res2', 'res3', 'res4', 'res5'] +# Options: FrozenBN, GN, "SyncBN", "BN" +MODEL_RESNETS_NORM = "GN" # originally FrozenBN +# from: detectron2/modeling/backbone/resnet.py +MODEL_BACKBONE_FREEZE_AT = -1 # originally 2 +# Parameters for seg fpn head. +if USE_TOP_BLOCK_P6: + MODEL_SEM_SEG_HEAD_IN_FEATURES = ['p2', 'p3', 'p4', 'p5', 'p6'] +else: + MODEL_SEM_SEG_HEAD_IN_FEATURES = ['p2', 'p3', 'p4', 'p5'] + +MODEL_POSE_HEAD_IN_FEATURES = ['p3', 'p4', 'p5'] +#MODEL_SEM_SEG_HEAD_CONVS_DIM = 128 // SCALE +MODEL_SEM_SEG_HEAD_IGNORE_VALUE = 255 +MODEL_SEM_SEG_HEAD_COMMON_STRIDE = 4 +MODEL_POSE_HEAD_COMMON_STRIDE = 8 +#MODEL_SEM_SEG_HEAD_NORM = "GN" +MODEL_SEM_SEG_HEAD_LOSS_WEIGHT = 1.0 + +MODEL_RESNETS_NUM_GROUPS = 1 +# Output width of res2. Scaling this parameters will scale the width of all 1x1 convs in ResNet +# For R18 and R34, this needs to be set to 64 +MODEL_RESNETS_STEM_OUT_CHANNELS = 64 # // SCALE + +# Baseline width of each group. +# Scaling this parameters will scale the width of all bottleneck layers. +#MODEL_RESNETS_RES2_OUT_CHANNELS = 256 // SCALE +MODEL_RESNETS_RES2_OUT_CHANNELS_MAX = 19999 + +#MODEL_RESNETS_WIDTH_PER_GROUP = max(64 // SCALE, 32) +MODEL_RESNETS_WIDTH_PER_GROUP_MAX = 19999 + +# Place the stride 2 conv on the 1x1 filter +# Use True only for the original MSRA ResNet; use False for C2 and Torch models +MODEL_RESNETS_STRIDE_IN_1X1 = True + +# Apply dilation in stage "res5" +MODEL_RESNETS_RES5_DILATION = 1 + +#MODEL_FPN_NORM = 'GN' +# Types for fusing the FPN top-down and lateral features. Can be either "sum" or "avg" +MODEL_FPN_FUSE_TYPE = "sum" + + +def main(): + module = SimnetResFPNGrasp() + +def c2_normal_fill(module: nn.Module) -> None: + """ + Initialize `module.weight` using the "MSRAFill" implemented in Caffe2. + Also initializes `module.bias` to 0. + + Args: + module (torch.nn.Module): module to initialize. + """ + # nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu") + + latent_size = 128 + nn.init.normal_( + module.weight, + 0.0, + 1.0 / math.sqrt(latent_size), + ) + + if module.bias is not None: + # pyre-fixme[6]: Expected `Tensor` for 1st param but got `Union[nn.Module, + # torch.Tensor]`. + nn.init.constant_(module.bias, 0) + + + +def build_resnet_fpn_backbone(input_shape, stereo_stem, model_norm='BN', num_filters_scale=4): + # For now, we are just hardcode the global parameters. We plan to depracte dectron2 dependence this quarter. + MODEL_FPN_NORM = model_norm + MODEL_FPN_OUT_CHANNELS = 256 // num_filters_scale + + if USE_TOP_BLOCK_P6: + top_block = LastLevelMaxPool() + elif False: + in_channels_p6p7 = bottom_up.output_shape()["res5"].channels + top_block = LastLevelP6P7(in_channels_p6p7, out_channels) + else: + top_block = None + + bottom_up = build_resnet_backbone( + input_shape, stereo_stem, model_norm=model_norm, num_filters_scale=num_filters_scale + ) + in_features = MODEL_FPN_IN_FEATURES + out_channels = MODEL_FPN_OUT_CHANNELS + backbone = FPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + norm=MODEL_FPN_NORM, + top_block=top_block, + fuse_type=MODEL_FPN_FUSE_TYPE, + ) + return backbone + + +def get_group_gn(dim, dim_per_gp, num_groups): + """get number of groups used by GroupNorm, based on number of channels.""" + assert dim_per_gp == -1 or num_groups == -1, \ + "GroupNorm: can only specify G or C/G." + + if dim_per_gp > 0: + assert dim % dim_per_gp == 0, \ + "dim: {}, dim_per_gp: {}".format(dim, dim_per_gp) + group_gn = dim // dim_per_gp + else: + assert dim % num_groups == 0, \ + "dim: {}, num_groups: {}".format(dim, num_groups) + group_gn = num_groups + + return group_gn + + +def group_norm(out_channels, affine=True, divisor=1): + out_channels = out_channels // divisor + dim_per_gp = -1 // divisor + num_groups = 16 // divisor + eps = 1e-5 # default: 1e-5 + return torch.nn.GroupNorm( + get_group_gn(out_channels, dim_per_gp, num_groups), out_channels, eps, affine + ) + + +class FPNXconvFeatureExtractor(nn.Module): + """ + Heads for FPN for classification + """ + + def __init__(self, in_channels, num_stacked_convs=4, conv_head_dim=16, use_gn=False): + super(FPNXconvFeatureExtractor, self).__init__() + xconvs = [] + for ix in range(num_stacked_convs): + xconvs.append( + nn.Conv2d( + in_channels, + conv_head_dim, + kernel_size=3, + stride=1, + padding=1, + bias=False if use_gn else True + ) + ) + in_channels = conv_head_dim + if use_gn: + xconvs.append(group_norm(in_channels)) + xconvs.append(nn.ReLU(inplace=True)) + + self.add_module("xconvs", nn.Sequential(*xconvs)) + for modules in [ + self.xconvs, + ]: + for l in modules.modules(): + if isinstance(l, nn.Conv2d): + torch.nn.init.normal_(l.weight, std=0.01) + if not use_gn: + torch.nn.init.constant_(l.bias, 0) + + def forward(self, x): + x = self.xconvs(x) + return x + + +class SemSegFPNHead(nn.Module): + """ + A semantic segmentation head described in detail in the Panoptic Feature Pyramid Networks paper + (https://arxiv.org/abs/1901.02446). It takes FPN features as input and merges information from + all levels of the FPN into single output. + """ + + def __init__(self, input_shape, num_classes, model_norm='BN', num_filters_scale=4): + super().__init__() + MODEL_SEM_SEG_HEAD_NORM = model_norm + MODEL_SEM_SEG_HEAD_CONVS_DIM = 128 // num_filters_scale + + self.in_features = MODEL_SEM_SEG_HEAD_IN_FEATURES + feature_strides = {k: v.stride for k, v in input_shape.items()} + feature_channels = {k: v.channels for k, v in input_shape.items()} + self_ignore_value = MODEL_SEM_SEG_HEAD_IGNORE_VALUE + conv_dims = MODEL_SEM_SEG_HEAD_CONVS_DIM + self.common_stride = MODEL_SEM_SEG_HEAD_COMMON_STRIDE + norm = MODEL_SEM_SEG_HEAD_NORM + self.bilinear_upsample = nn.Upsample( + scale_factor=self.common_stride, mode="bilinear", align_corners=False + ) + + self.scale_heads = [] + for in_feature in self.in_features: + head_ops = [] + head_length = max(1, int(np.log2(feature_strides[in_feature]) - np.log2(self.common_stride))) + for k in range(head_length): + norm_module = get_norm(norm, conv_dims) + conv = Conv2d( + feature_channels[in_feature] if k == 0 else conv_dims, + conv_dims, + kernel_size=3, + stride=1, + padding=1, + bias=not norm, + norm=norm_module, + activation=F.relu, + ) + weight_init.c2_msra_fill(conv) + head_ops.append(conv) + if feature_strides[in_feature] != self.common_stride: + head_ops.append(nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)) + self.scale_heads.append(nn.Sequential(*head_ops)) + self.add_module(in_feature, self.scale_heads[-1]) + self.predictor = Conv2d(conv_dims, num_classes, kernel_size=1, stride=1, padding=0) + weight_init.c2_msra_fill(self.predictor) + + def forward(self, features, targets=None): + """ + Returns: + In training, returns (None, dict of losses) + In inference, returns (predictions, {}) + """ + x = self.layers(features) + x = self.bilinear_upsample(x) + return x + + def layers(self, features): + for i, f in enumerate(self.in_features): + if i == 0: + x = self.scale_heads[i](F.relu(features[f])) + else: + x = x - -self.scale_heads[i](F.relu(features[f])) + x = self.predictor(x) + return x + + def losses(self, predictions, targets): + predictions = F.interpolate( + predictions, scale_factor=self.common_stride, mode="bilinear", align_corners=False + ) + loss = F.cross_entropy(predictions, targets, reduction="mean", ignore_index=self.ignore_value) + return loss + + +class PoseFPNHead(nn.Module): + """ + A semantic segmentation head described in detail in the Panoptic Feature Pyramid Networks paper + (https://arxiv.org/abs/1901.02446). It takes FPN features as input and merges information from + all levels of the FPN into single output. + """ + + def __init__(self, input_shape, num_classes, model_norm='BN', num_filters_scale=4): + super().__init__() + MODEL_SEM_SEG_HEAD_NORM = model_norm + MODEL_SEM_SEG_HEAD_CONVS_DIM = 128 // num_filters_scale + self.in_features = MODEL_POSE_HEAD_IN_FEATURES + feature_strides = {k: v.stride for k, v in input_shape.items()} + feature_channels = {k: v.channels for k, v in input_shape.items()} + self_ignore_value = MODEL_SEM_SEG_HEAD_IGNORE_VALUE + conv_dims = MODEL_SEM_SEG_HEAD_CONVS_DIM + self.common_stride = MODEL_POSE_HEAD_COMMON_STRIDE + norm = MODEL_SEM_SEG_HEAD_NORM + + self.scale_heads = [] + for in_feature in self.in_features: + head_ops = [] + head_length = max(1, int(np.log2(feature_strides[in_feature]) - np.log2(self.common_stride))) + + # loger.info("head length", head_length) + # loger.info("substraction value",int(np.log2(feature_strides[in_feature]) - np.log2(self.common_stride))) + # print("head length", head_length) + for k in range(head_length): + norm_module = get_norm(norm, conv_dims) + conv = Conv2d( + feature_channels[in_feature] if k == 0 else conv_dims, + conv_dims, + kernel_size=3, + stride=1, + padding=1, + bias=not norm, + norm=norm_module, + activation=F.relu, + ) + # c2_normal_fill(conv) + weight_init.c2_msra_fill(conv) + head_ops.append(conv) + if feature_strides[in_feature] != self.common_stride: + head_ops.append(nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)) + self.scale_heads.append(nn.Sequential(*head_ops)) + self.add_module(in_feature, self.scale_heads[-1]) + self.predictor = Conv2d(conv_dims, num_classes, kernel_size=1, stride=1, padding=0) + # c2_normal_fill(self.predictor) + weight_init.c2_msra_fill(self.predictor) + + def forward(self, features, targets=None): + """ + Returns: + In training, returns (None, dict of losses) + In inference, returns (predictions, {}) + """ + x = self.layers(features) + return x + + def layers(self, features): + for i, f in enumerate(self.in_features): + if i == 0: + x = self.scale_heads[i](F.relu(features[f])) + else: + x = x - -self.scale_heads[i](F.relu(features[f])) + x = self.predictor(x) + return x + + +class PoseFPNHead_Latent(nn.Module): + """ + A semantic segmentation head described in detail in the Panoptic Feature Pyramid Networks paper + (https://arxiv.org/abs/1901.02446). It takes FPN features as input and merges information from + all levels of the FPN into single output. + """ + + def __init__(self, input_shape, num_classes, model_norm='BN', num_filters_scale=4): + super().__init__() + MODEL_SEM_SEG_HEAD_NORM = model_norm + MODEL_SEM_SEG_HEAD_CONVS_DIM = 128 // num_filters_scale + self.in_features = MODEL_POSE_HEAD_IN_FEATURES + feature_strides = {k: v.stride for k, v in input_shape.items()} + feature_channels = {k: v.channels for k, v in input_shape.items()} + self_ignore_value = MODEL_SEM_SEG_HEAD_IGNORE_VALUE + conv_dims = MODEL_SEM_SEG_HEAD_CONVS_DIM + self.common_stride = MODEL_POSE_HEAD_COMMON_STRIDE + norm = MODEL_SEM_SEG_HEAD_NORM + + self.scale_heads = [] + for in_feature in self.in_features: + head_ops = [] + head_length = max(1, int(np.log2(feature_strides[in_feature]) - np.log2(self.common_stride))) + + # loger.info("head length", head_length) + # loger.info("substraction value",int(np.log2(feature_strides[in_feature]) - np.log2(self.common_stride))) + # print("head length", head_length) + # head_length = 4 + for k in range(head_length): + + # print("k", k) + # print("feature_channels[in_feature]", feature_channels[in_feature]) + norm_module = get_norm(norm, conv_dims) + conv = Conv2d( + feature_channels[in_feature] if k == 0 else conv_dims, + conv_dims, + kernel_size=3, + stride=1, + padding=1, + bias=not norm, + norm=norm_module, + activation=F.relu, + ) + # c2_normal_fill(conv) + weight_init.c2_msra_fill(conv) + head_ops.append(conv) + + for _ in range(32): + conv_head = Conv2d(conv_dims, conv_dims, kernel_size=3, stride=1, padding=1, bias=not norm, norm=norm_module, activation=F.relu,) + weight_init.c2_msra_fill(conv_head) + head_ops.append(conv_head) + + if feature_strides[in_feature] != self.common_stride: + head_ops.append(nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)) + self.scale_heads.append(nn.Sequential(*head_ops)) + self.add_module(in_feature, self.scale_heads[-1]) + self.predictor = Conv2d(conv_dims, num_classes, kernel_size=1, stride=1, padding=0) + # c2_normal_fill(self.predictor) + weight_init.c2_msra_fill(self.predictor) + + def forward(self, features, targets=None): + """ + Returns: + In training, returns (None, dict of losses) + In inference, returns (predictions, {}) + """ + x = self.layers(features) + return x + + def layers(self, features): + for i, f in enumerate(self.in_features): + # print("i, f", i, f) + + # print("features[f])", features[f].shape) + if i == 0: + x = self.scale_heads[i](F.relu(features[f])) + else: + x = x - -self.scale_heads[i](F.relu(features[f])) + # print("x", x.shape) + x = self.predictor(x) + # print("x after predictor", x.shape) + # print("======================\n\n") + return x + + +class ShapeSpec(collections.namedtuple("_ShapeSpec", ["channels", "height", "width", "stride"])): + """ + A simple structure that contains basic shape specification about a tensor. + It is often used as the auxiliary inputs/outputs of models, + to obtain the shape inference ability among pytorch modules. + + Attributes: + channels: + height: + width: + stride: + """ + + def __new__(cls, *, channels=None, height=None, width=None, stride=None): + return super().__new__(cls, channels, height, width, stride) + + +class Backbone(nn.Module, metaclass=abc.ABCMeta): + + def __init__(self): + """ + The `__init__` method of any subclass can specify its own set of arguments. + """ + super().__init__() + + @abc.abstractmethod + def forward(self): + """ + Subclasses must override this method, but adhere to the same return type. + + Returns: + dict[str->Tensor]: mapping from feature name (e.g., "res2") to tensor + """ + pass + + @property + def size_divisibility(self): + """ + Some backbones require the input height and width to be divisible by a + specific integer. This is typically true for encoder / decoder type networks + with lateral connection (e.g., FPN) for which feature maps need to match + dimension in the "bottom up" and "top down" paths. Set to 0 if no specific + input size divisibility is required. + """ + return 0 + + def output_shape(self): + """ + Returns: + dict[str->ShapeSpec] + """ + # this is a backward-compatible default + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) for name in self._out_features + } + + +def get_norm(norm, out_channels): + """ + Args: + norm (str or callable): either one of BN, SyncBN, FrozenBN, GN; + or a callable that takes a channel number and returns + the normalization layer as a nn.Module. + + Returns: + nn.Module or None: the normalization layer + """ + if out_channels == 32: + N = 16 + else: + N = 32 + if isinstance(norm, str): + if len(norm) == 0: + return None + norm = { + "BN": BatchNorm2d, + #"SyncBN": NaiveSyncBatchNorm, + #"FrozenBN": FrozenBatchNorm2d, + "GN": lambda channels: nn.GroupNorm(N, channels), + #"nnSyncBN": nn.SyncBatchNorm, # keep for debugging + }[norm] + return norm(out_channels) + + +def cat(tensors, dim=0): + """ + Efficient version of torch.cat that avoids a copy if there is only a single element in a list + """ + assert isinstance(tensors, (list, tuple)) + if len(tensors) == 1: + return tensors[0] + return torch.cat(tensors, dim) + + +class _NewEmptyTensorOp(torch.autograd.Function): + + @staticmethod + def forward(ctx, x, new_shape): + ctx.shape = x.shape + return x.new_empty(new_shape) + + @staticmethod + def backward(ctx, grad): + shape = ctx.shape + return _NewEmptyTensorOp.apply(grad, shape), None + + +class Conv2d(torch.nn.Conv2d): + """ + A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and more features. + """ + + def __init__(self, *args, **kwargs): + """ + Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`: + + Args: + norm (nn.Module, optional): a normalization layer + activation (callable(Tensor) -> Tensor): a callable activation function + + It assumes that norm layer is used before activation. + """ + norm = kwargs.pop("norm", None) + activation = kwargs.pop("activation", None) + super().__init__(*args, **kwargs) + + self.norm = norm + self.activation = activation + + def forward(self, x): + if x.numel() == 0 and self.training: + # https://github.com/pytorch/pytorch/issues/12013 + assert not isinstance( + self.norm, torch.nn.SyncBatchNorm + ), "SyncBatchNorm does not support empty inputs!" + + if x.numel() == 0 and TORCH_VERSION <= (1, 4): + assert not isinstance( + self.norm, torch.nn.GroupNorm + ), "GroupNorm does not support empty inputs in PyTorch <=1.4!" + # When input is empty, we want to return a empty tensor with "correct" shape, + # So that the following operations will not panic + # if they check for the shape of the tensor. + # This computes the height and width of the output tensor + output_shape = [(i + 2 * p - (di * (k - 1) + 1)) // s + 1 for i, p, di, k, s in + zip(x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride)] + output_shape = [x.shape[0], self.weight.shape[0]] + output_shape + empty = _NewEmptyTensorOp.apply(x, output_shape) + if self.training: + # This is to make DDP happy. + # DDP expects all workers to have gradient w.r.t the same set of parameters. + _dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0 + return empty + _dummy + else: + return empty + + x = super().forward(x) + if self.norm is not None: + x = self.norm(x) + if self.activation is not None: + x = self.activation(x) + return x + + +if TORCH_VERSION > (1, 4): + ConvTranspose2d = torch.nn.ConvTranspose2d +else: + + class ConvTranspose2d(torch.nn.ConvTranspose2d): + """ + A wrapper around :class:`torch.nn.ConvTranspose2d` to support zero-size tensor. + """ + + def forward(self, x): + if x.numel() > 0: + return super(ConvTranspose2d, self).forward(x) + # get output shape + + # When input is empty, we want to return a empty tensor with "correct" shape, + # So that the following operations will not panic + # if they check for the shape of the tensor. + # This computes the height and width of the output tensor + output_shape = [(i - 1) * d - 2 * p + (di * (k - 1) + 1) + op for i, p, di, k, d, op in zip( + x.shape[-2:], + self.padding, + self.dilation, + self.kernel_size, + self.stride, + self.output_padding, + )] + output_shape = [x.shape[0], self.out_channels] + output_shape + # This is to make DDP happy. + # DDP expects all workers to have gradient w.r.t the same set of parameters. + _dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0 + return _NewEmptyTensorOp.apply(x, output_shape) + _dummy + + +if TORCH_VERSION > (1, 4): + BatchNorm2d = torch.nn.BatchNorm2d +else: + + class BatchNorm2d(torch.nn.BatchNorm2d): + """ + A wrapper around :class:`torch.nn.BatchNorm2d` to support zero-size tensor. + """ + + def forward(self, x): + if x.numel() > 0: + return super(BatchNorm2d, self).forward(x) + # get output shape + output_shape = x.shape + return _NewEmptyTensorOp.apply(x, output_shape) + + +if False: # not yet fixed in pytorch + Linear = torch.nn.Linear +else: + + class Linear(torch.nn.Linear): + """ + A wrapper around :class:`torch.nn.Linear` to support empty inputs and more features. + Because of https://github.com/pytorch/pytorch/issues/34202 + """ + + def forward(self, x): + if x.numel() == 0: + output_shape = [x.shape[0], self.weight.shape[0]] + + empty = _NewEmptyTensorOp.apply(x, output_shape) + if self.training: + # This is to make DDP happy. + # DDP expects all workers to have gradient w.r.t the same set of parameters. + _dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0 + return empty + _dummy + else: + return empty + + x = super().forward(x) + return x + + +def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): + """ + A wrapper around :func:`torch.nn.functional.interpolate` to support zero-size tensor. + """ + if input.numel() > 0: + return torch.nn.functional.interpolate( + input, size, scale_factor, mode, align_corners=align_corners + ) + + def _check_size_scale_factor(dim): + if size is None and scale_factor is None: + raise ValueError("either size or scale_factor should be defined") + if size is not None and scale_factor is not None: + raise ValueError("only one of size or scale_factor should be defined") + if (scale_factor is not None and isinstance(scale_factor, tuple) and len(scale_factor) != dim): + raise ValueError( + "scale_factor shape must match input shape. " + "Input is {}D, scale_factor size is {}".format(dim, len(scale_factor)) + ) + + def _output_size(dim): + _check_size_scale_factor(dim) + if size is not None: + return size + scale_factors = _ntuple(dim)(scale_factor) + # math.floor might return float in py2.7 + return [int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)] + + output_shape = tuple(_output_size(2)) + output_shape = input.shape[:-2] + output_shape + return _NewEmptyTensorOp.apply(input, output_shape) + + +class FPN(Backbone): + """ + This module implements Feature Pyramid Network. + It creates pyramid features built on top of some input feature maps. + """ + + def __init__( + self, bottom_up, in_features, out_channels, norm="", top_block=None, fuse_type="sum" + ): + """ + Args: + bottom_up (Backbone): module representing the bottom up subnetwork. + Must be a subclass of :class:`Backbone`. The multi-scale feature + maps generated by the bottom up network, and listed in `in_features`, + are used to generate FPN levels. + in_features (list[str]): names of the input feature maps coming + from the backbone to which FPN is attached. For example, if the + backbone produces ["res2", "res3", "res4"], any *contiguous* sublist + of these may be used; order must be from high to low resolution. + out_channels (int): number of channels in the output feature maps. + norm (str): the normalization to use. + top_block (nn.Module or None): if provided, an extra operation will + be performed on the output of the last (smallest resolution) + FPN output, and the result will extend the result list. The top_block + further downsamples the feature map. It must have an attribute + "num_levels", meaning the number of extra FPN levels added by + this block, and "in_feature", which is a string representing + its input feature (e.g., p5). + fuse_type (str): types for fusing the top down features and the lateral + ones. It can be "sum" (default), which sums up element-wise; or "avg", + which takes the element-wise mean of the two. + """ + super(FPN, self).__init__() + assert isinstance(bottom_up, Backbone) + + # Feature map strides and channels from the bottom up network (e.g. ResNet) + input_shapes = bottom_up.output_shape() + in_strides = [input_shapes[f].stride for f in in_features] + in_channels = [input_shapes[f].channels for f in in_features] + + _assert_strides_are_log2_contiguous(in_strides) + lateral_convs = [] + output_convs = [] + + use_bias = norm == "" + for idx, in_channels in enumerate(in_channels): + lateral_norm = get_norm(norm, out_channels) + output_norm = get_norm(norm, out_channels) + + lateral_conv = Conv2d( + in_channels, out_channels, kernel_size=1, bias=use_bias, norm=lateral_norm + ) + output_conv = Conv2d( + out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + ) + weight_init.c2_xavier_fill(lateral_conv) + weight_init.c2_xavier_fill(output_conv) + stage = int(math.log2(in_strides[idx])) + self.add_module("fpn_lateral{}".format(stage), lateral_conv) + self.add_module("fpn_output{}".format(stage), output_conv) + + lateral_convs.append(lateral_conv) + output_convs.append(output_conv) + # Place convs into top-down order (from low to high resolution) + # to make the top-down computation in forward clearer. + self.lateral_convs = lateral_convs[::-1] + self.output_convs = output_convs[::-1] + self.top_block = top_block + self.in_features = in_features + self.bottom_up = bottom_up + # Return feature names are "p", like ["p2", "p3", ..., "p6"] + self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in in_strides} + # top block output feature maps. + if self.top_block is not None: + for s in range(stage, stage + self.top_block.num_levels): + self._out_feature_strides["p{}".format(s + 1)] = 2**(s + 1) + + self._out_features = list(self._out_feature_strides.keys()) + self._out_feature_channels = {k: out_channels for k in self._out_features} + self._size_divisibility = in_strides[-1] + assert fuse_type in {"avg", "sum"} + self._fuse_type = fuse_type + self.nearest_two_upsample = nn.Upsample(scale_factor=2, mode="nearest") + + @property + def size_divisibility(self): + return self._size_divisibility + + def forward(self, x): + """ + Args: + input (dict[str->Tensor]): mapping feature map name (e.g., "res5") to + feature map tensor for each feature level in high to low resolution order. + + Returns: + dict[str->Tensor]: + mapping from feature map name to FPN feature map tensor + in high to low resolution order. Returned feature names follow the FPN + paper convention: "p", where stage has stride = 2 ** stage e.g., + ["p2", "p3", ..., "p6"]. + """ + # Reverse feature maps into top-down order (from low to high resolution) + bottom_up_features = self.bottom_up(x) + x = [bottom_up_features[f] for f in self.in_features[::-1]] + results = [] + prev_features = self.lateral_convs[0](x[0]) + results.append(self.output_convs[0](prev_features)) + for features, lateral_conv, output_conv in zip( + x[1:], self.lateral_convs[1:], self.output_convs[1:] + ): + top_down_features = self.nearest_two_upsample(prev_features) + lateral_features = lateral_conv(features) + prev_features = lateral_features + top_down_features + if self._fuse_type == "avg": + prev_features /= 2 + results.insert(0, output_conv(prev_features)) + + if self.top_block is not None: + top_block_in_feature = bottom_up_features.get(self.top_block.in_feature, None) + if top_block_in_feature is None: + top_block_in_feature = results[self._out_features.index(self.top_block.in_feature)] + results.extend(self.top_block(top_block_in_feature)) + assert len(self._out_features) == len(results) + return dict(zip(self._out_features, results)), bottom_up_features['disp_small'] + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) for name in self._out_features + } + + +def _assert_strides_are_log2_contiguous(strides): + """ + Assert that each stride is 2x times its preceding stride, i.e. "contiguous in log2". + """ + for i, stride in enumerate(strides[1:], 1): + assert stride == 2 * strides[i - 1], "Strides {} {} are not log2 contiguous".format( + stride, strides[i - 1] + ) + + +class LastLevelMaxPool(nn.Module): + """ + This module is used in the original FPN to generate a downsampled + P6 feature from P5. + """ + + def __init__(self): + super().__init__() + self.num_levels = 1 + self.in_feature = "p5" + + def forward(self, x): + return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] + + +class LastLevelP6P7(nn.Module): + """ + This module is used in RetinaNet to generate extra layers, P6 and P7 from + C5 feature. + """ + + def __init__(self, in_channels, out_channels, in_feature="res5"): + super().__init__() + self.num_levels = 2 + self.in_feature = in_feature + self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1) + self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1) + for module in [self.p6, self.p7]: + weight_init.c2_xavier_fill(module) + + def forward(self, c5): + p6 = self.p6(c5) + p7 = self.p7(F.relu(p6)) + return [p6, p7] + + +class ResNetBlockBase(nn.Module): + + def __init__(self, in_channels, out_channels, stride): + """ + The `__init__` method of any subclass should also contain these arguments. + + Args: + in_channels (int): + out_channels (int): + stride (int): + """ + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.stride = stride + + def freeze(self): + for p in self.parameters(): + p.requires_grad = False + FrozenBatchNorm2d.convert_frozen_batchnorm(self) + return self + + +class BasicBlock(ResNetBlockBase): + + def __init__(self, in_channels, out_channels, *, stride=1, norm="BN"): + """ + The standard block type for ResNet18 and ResNet34. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + stride (int): Stride for the first conv. + norm (str or callable): A callable that takes the number of + channels and returns a `nn.Module`, or a pre-defined string + (one of {"FrozenBN", "BN", "GN"}). + """ + super().__init__(in_channels, out_channels, stride) + + if in_channels != out_channels: + self.shortcut = Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=stride, + bias=False, + norm=get_norm(norm, out_channels), + ) + else: + self.shortcut = None + + self.conv1 = Conv2d( + in_channels, + out_channels, + kernel_size=3, + stride=stride, + padding=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + self.conv2 = Conv2d( + out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + for layer in [self.conv1, self.conv2, self.shortcut]: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + + def forward(self, x): + out = self.conv1(x) + out = F.relu_(out) + out = self.conv2(out) + + if self.shortcut is not None: + shortcut = self.shortcut(x) + else: + shortcut = x + + out += shortcut + out = F.relu_(out) + return out + + +class BottleneckBlock(ResNetBlockBase): + + def __init__( + self, + in_channels, + out_channels, + *, + bottleneck_channels, + stride=1, + num_groups=1, + norm="BN", + stride_in_1x1=False, + dilation=1, + ): + """ + Args: + norm (str or callable): a callable that takes the number of + channels and return a `nn.Module`, or a pre-defined string + (one of {"FrozenBN", "BN", "GN"}). + stride_in_1x1 (bool): when stride==2, whether to put stride in the + first 1x1 convolution or the bottleneck 3x3 convolution. + """ + super().__init__(in_channels, out_channels, stride) + + if in_channels != out_channels: + self.shortcut = Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=stride, + bias=False, + norm=get_norm(norm, out_channels), + ) + else: + self.shortcut = None + + # The original MSRA ResNet models have stride in the first 1x1 conv + # The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have + # stride in the 3x3 conv + stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride) + + self.conv1 = Conv2d( + in_channels, + bottleneck_channels, + kernel_size=1, + stride=stride_1x1, + bias=False, + norm=get_norm(norm, bottleneck_channels), + ) + + self.conv2 = Conv2d( + bottleneck_channels, + bottleneck_channels, + kernel_size=3, + stride=stride_3x3, + padding=1 * dilation, + bias=False, + groups=num_groups, + dilation=dilation, + norm=get_norm(norm, bottleneck_channels), + ) + + self.conv3 = Conv2d( + bottleneck_channels, + out_channels, + kernel_size=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + + # Zero-initialize the last normalization in each residual branch, + # so that at the beginning, the residual branch starts with zeros, + # and each residual block behaves like an identity. + # See Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour": + # "For BN layers, the learnable scaling coefficient γ is initialized + # to be 1, except for each residual block's last BN + # where γ is initialized to be 0." + + # nn.init.constant_(self.conv3.norm.weight, 0) + # TODO this somehow hurts performance when training GN models from scratch. + # Add it as an option when we need to use this code to train a backbone. + + def forward(self, x): + out = self.conv1(x) + out = F.relu_(out) + + out = self.conv2(out) + out = F.relu_(out) + + out = self.conv3(out) + + if self.shortcut is not None: + shortcut = self.shortcut(x) + else: + shortcut = x + + out += shortcut + out = F.relu_(out) + return out + + +def make_stage(block_class, num_blocks, first_stride, **kwargs): + """ + Create a resnet stage by creating many blocks. + + Args: + block_class (class): a subclass of ResNetBlockBase + num_blocks (int): + first_stride (int): the stride of the first block. The other blocks will have stride=1. + A `stride` argument will be passed to the block constructor. + kwargs: other arguments passed to the block constructor. + + Returns: + list[nn.Module]: a list of block module. + """ + blocks = [] + for i in range(num_blocks): + blocks.append(block_class(stride=first_stride if i == 0 else 1, **kwargs)) + kwargs["in_channels"] = kwargs["out_channels"] + return blocks + + +class JointAngleStem(nn.Module): + + def __init__(self, in_channels=3, out_channels=64, norm="BN"): + super().__init__() + self.conv1 = Conv2d( + in_channels, + out_channels, + kernel_size=7, + stride=2, + padding=3, + bias=False, + norm=get_norm(norm, out_channels), + ) + + D_in = 50 + hidden = 256 + self.D_out = out_channels + self.l1 = torch.nn.Linear(D_in, hidden) + self.b1 = torch.nn.BatchNorm1d(hidden, momentum=0.1) + self.l2 = torch.nn.Linear(hidden, hidden) + self.b2 = torch.nn.BatchNorm1d(hidden, momentum=0.1) + self.l3 = torch.nn.Linear(hidden, self.D_out) + + weight_init.c2_msra_fill(self.conv1) + + def forward(self, x, robot_joint_angles): + x = self.conv1(x) + x = F.relu_(x) + x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) + + b, c, h, w = x.size() + + ja = robot_joint_angles + ja = F.relu(self.b1(self.l1(ja))) + ja = F.relu(self.b2(self.l2(ja))) + ja = self.l3(ja) + + ja = ja.view(-1, self.D_out, 1, 1).expand(-1, -1, h, w) + return torch.cat([x, ja], dim=-3) + + @property + def out_channels(self): + return self.conv1.out_channels + + @property + def stride(self): + return 4 # = stride 2 conv -> stride 2 max pool + + +class StereoStem(nn.Module): + + def __init__(self, in_channels=3, out_channels=64, norm="BN"): + """ + Args: + norm (str or callable): a callable that takes the number of + channels and return a `nn.Module`, or a pre-defined string + (one of {"FrozenBN", "BN", "GN"}). + """ + super().__init__() + assert in_channels == 3 + self.conv1 = Conv2d( + 1, 32, kernel_size=7, stride=2, padding=3, bias=False, norm=get_norm(norm, 32) + ) + self.conv2 = Conv2d( + 1, 32, kernel_size=7, stride=2, padding=3, bias=False, norm=get_norm(norm, 32) + ) + self.conv3 = Conv2d( + 1, 64, kernel_size=7, stride=2, padding=3, bias=False, norm=get_norm(norm, 64) + ) + weight_init.c2_msra_fill(self.conv1) + weight_init.c2_msra_fill(self.conv2) + weight_init.c2_msra_fill(self.conv3) + + def forward(self, x): + assert x.shape[1] == 3 + c1 = x[:, 0:1, :, :] + c2 = x[:, 1:2, :, :] + c3 = x[:, 2:3, :, :] + c1 = F.max_pool2d(F.relu_(self.conv1(c1)), kernel_size=3, stride=2, padding=1) + c2 = F.max_pool2d(F.relu_(self.conv2(c2)), kernel_size=3, stride=2, padding=1) + c3 = F.max_pool2d(F.relu_(self.conv3(c3)), kernel_size=3, stride=2, padding=1) + diff = (c1 - c2).abs() + both = c1 + c2 + lr = torch.cat((diff, both), 1) + return lr + c3 + + @property + def out_channels(self): + return self.conv1.out_channels + + @property + def stride(self): + return 4 # = stride 2 conv -> stride 2 max pool + + +class BasicStem(nn.Module): + + def __init__(self, in_channels=3, out_channels=64, norm="BN"): + """ + Args: + norm (str or callable): a callable that takes the number of + channels and return a `nn.Module`, or a pre-defined string + (one of {"FrozenBN", "BN", "GN"}). + """ + super().__init__() + self.conv1 = Conv2d( + in_channels, + out_channels, + kernel_size=7, + stride=2, + padding=3, + bias=False, + norm=get_norm(norm, out_channels), + ) + weight_init.c2_msra_fill(self.conv1) + + def forward(self, x, robot_joint_angles): + x = self.conv1(x) + x = F.relu_(x) + x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) + return x + + @property + def out_channels(self): + return self.conv1.out_channels + + @property + def stride(self): + return 4 # = stride 2 conv -> stride 2 max pool + + +class ResNet(Backbone): + + def __init__(self, stem, stages, num_classes=None, out_features=None): + """ + Args: + stem (nn.Module): a stem module + stages (list[list[ResNetBlock]]): several (typically 4) stages, + each contains multiple :class:`ResNetBlockBase`. + num_classes (None or int): if None, will not perform classification. + out_features (list[str]): name of the layers whose outputs should + be returned in forward. Can be anything in "stem", "linear", or "res2" ... + If None, will return the output of the last layer. + """ + super(ResNet, self).__init__() + self.stem_new = stem + self.num_classes = num_classes + + current_stride = self.stem_new.stride + self._out_feature_strides = {"stem": current_stride} + self._out_feature_channels = {"stem": self.stem_new.out_channels} + + self.stages_and_names = [] + for i, blocks in enumerate(stages): + for block in blocks: + assert isinstance(block, ResNetBlockBase), block + curr_channels = block.out_channels + stage = nn.Sequential(*blocks) + name = "res" + str(i + 2) + self.add_module(name, stage) + self.stages_and_names.append((stage, name)) + self._out_feature_strides[name] = current_stride = int( + current_stride * np.prod([k.stride for k in blocks]) + ) + self._out_feature_channels[name] = blocks[-1].out_channels + + if num_classes is not None: + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.linear = nn.Linear(curr_channels, num_classes) + + # Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour": + # "The 1000-way fully-connected layer is initialized by + # drawing weights from a zero-mean Gaussian with standard deviation of 0.01." + nn.init.normal_(self.linear.weight, std=0.01) + name = "linear" + + if out_features is None: + out_features = [name] + self._out_features = out_features + assert len(self._out_features) + children = [x[0] for x in self.named_children()] + for out_feature in self._out_features: + assert out_feature in children, "Available children: {}".format(", ".join(children)) + + def forward(self, x, robot_joint_angles=None): + outputs = {} + x, disp_small = self.stem_new(x, robot_joint_angles) + outputs['disp_small'] = disp_small + if "stem" in self._out_features: + outputs["stem"] = x + for stage, name in self.stages_and_names: + x = stage(x) + if name in self._out_features: + outputs[name] = x + if self.num_classes is not None: + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.linear(x) + if "linear" in self._out_features: + outputs["linear"] = x + return outputs + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) for name in self._out_features + } + + +def build_resnet_backbone(input_shape, stereo_stem, model_norm='BN', num_filters_scale=4): + """ + Create a ResNet instance from config. + + Returns: + ResNet: a :class:`ResNet` instance. + """ + # need registration of new blocks/stems? + MODEL_RESNETS_RES2_OUT_CHANNELS = 256 // num_filters_scale + MODEL_RESNETS_WIDTH_PER_GROUP = max(64 // num_filters_scale, 32) + MODEL_RESNETS_NORM = model_norm + + norm = MODEL_RESNETS_NORM + #stem = BasicStem( + # in_channels=input_shape.channels, + # out_channels=MODEL_RESNETS_STEM_OUT_CHANNELS, + # norm=norm, + #) + stem = stereo_stem + stem_out_channels = MODEL_RESNETS_STEM_OUT_CHANNELS + + # fmt: off + out_features = MODEL_RESNETS_OUT_FEATURES + depth = MODEL_RESNETS_DEPTH + num_groups = MODEL_RESNETS_NUM_GROUPS + width_per_group = MODEL_RESNETS_WIDTH_PER_GROUP + bottleneck_channels = num_groups * width_per_group + in_channels = stem_out_channels + out_channels = MODEL_RESNETS_RES2_OUT_CHANNELS + stride_in_1x1 = MODEL_RESNETS_STRIDE_IN_1X1 + res5_dilation = MODEL_RESNETS_RES5_DILATION + # fmt: on + assert res5_dilation in {1, 2}, "res5_dilation cannot be {}.".format(res5_dilation) + + num_blocks_per_stage = { + 18: [2, 2, 2, 2], + 34: [3, 4, 6, 3], + 50: NUM_BLOCKS_PER_STAGE, + 101: [3, 4, 23, 3], + 152: [3, 8, 36, 3], + }[depth] + + if depth in [18, 34]: + assert out_channels == 64, "Must set MODEL.RESNETS.RES2_OUT_CHANNELS = 64 for R18/R34" + assert res5_dilation == 1, "Must set MODEL.RESNETS.RES5_DILATION = 1 for R18/R34" + assert num_groups == 1, "Must set MODEL.RESNETS.NUM_GROUPS = 1 for R18/R34" + + stages = [] + + # Avoid creating variables without gradients + # It consumes extra memory and may cause allreduce to fail + out_stage_idx = [{"res2": 2, "res3": 3, "res4": 4, "res5": 5}[f] for f in out_features] + max_stage_idx = max(out_stage_idx) + for idx, stage_idx in enumerate(range(2, max_stage_idx + 1)): + dilation = res5_dilation if stage_idx == 5 else 1 + first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2 + stage_kargs = { + "num_blocks": num_blocks_per_stage[idx], + "first_stride": first_stride, + "in_channels": in_channels, + "out_channels": out_channels, + "norm": norm, + } + # Use BasicBlock for R18 and R34. + if depth in [18, 34]: + stage_kargs["block_class"] = BasicBlock + else: + stage_kargs["bottleneck_channels"] = bottleneck_channels + stage_kargs["stride_in_1x1"] = stride_in_1x1 + stage_kargs["dilation"] = dilation + stage_kargs["num_groups"] = num_groups + stage_kargs["block_class"] = BottleneckBlock + blocks = make_stage(**stage_kargs) + # print('stage', idx) + # print(stage_kargs) + # print() + in_channels = out_channels + out_channels *= 2 + bottleneck_channels *= 2 + + out_channels = min(MODEL_RESNETS_RES2_OUT_CHANNELS_MAX, out_channels) + bottleneck_channels = min( + MODEL_RESNETS_WIDTH_PER_GROUP_MAX * MODEL_RESNETS_NUM_GROUPS, bottleneck_channels + ) + + stages.append(blocks) + return ResNet(stem, stages, out_features=out_features) + + +def output_shape(backbone): + """ + Returns: + dict[str->ShapeSpec] + """ + # this is a backward-compatible default + return { + name: ShapeSpec( + channels=backbone._out_feature_channels[name], stride=backbone._out_feature_strides[name] + ) for name in backbone._out_features + } + + +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# from .config import CfgNode as CN + +# ----------------------------------------------------------------------------- +# Convention about Training / Test specific parameters +# ----------------------------------------------------------------------------- +# Whenever an argument can be either used for training or for testing, the +# corresponding name will be post-fixed by a _TRAIN for a training parameter, +# or _TEST for a test-specific parameter. +# For example, the number of images during training will be +# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be +# IMAGES_PER_BATCH_TEST + +# ----------------------------------------------------------------------------- +# Config definition +# ----------------------------------------------------------------------------- + +# _C = CN() +# +# # The version number, to upgrade from old configs to new ones if any +# # changes happen. It's recommended to keep a VERSION in your config file. +# _C.VERSION = 2 +# +# _C.MODEL = CN() +# _C.MODEL.LOAD_PROPOSALS = False +# _C.MODEL.MASK_ON = False +# _C.MODEL.KEYPOINT_ON = False +# _C.MODEL.DEVICE = "cuda" +# _C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN" +# +# # Path (possibly with schema like catalog:// or detectron2://) to a checkpoint file +# # to be loaded to the model. You can find available models in the model zoo. +# _C.MODEL.WEIGHTS = "" +# +# # Values to be used for image normalization (BGR order, since INPUT.FORMAT defaults to BGR). +# # To train on images of different number of channels, just set different mean & std. +# # Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675] +# _C.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675] +# # When using pre-trained models in Detectron1 or any MSRA models, +# # std has been absorbed into its conv1 weights, so the std needs to be set 1. +# # Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std) +# _C.MODEL.PIXEL_STD = [1.0, 1.0, 1.0] +# +# +# # ----------------------------------------------------------------------------- +# # INPUT +# # ----------------------------------------------------------------------------- +# _C.INPUT = CN() +# # Size of the smallest side of the image during training +# _C.INPUT.MIN_SIZE_TRAIN = (800,) +# # Sample size of smallest side by choice or random selection from range give by +# # INPUT.MIN_SIZE_TRAIN +# _C.INPUT.MIN_SIZE_TRAIN_SAMPLING = "choice" +# # Maximum size of the side of the image during training +# _C.INPUT.MAX_SIZE_TRAIN = 1333 +# # Size of the smallest side of the image during testing. Set to zero to disable resize in testing. +# _C.INPUT.MIN_SIZE_TEST = 800 +# # Maximum size of the side of the image during testing +# _C.INPUT.MAX_SIZE_TEST = 1333 +# +# # `True` if cropping is used for data augmentation during training +# _C.INPUT.CROP = CN({"ENABLED": False}) +# # Cropping type: +# # - "relative" crop (H * CROP.SIZE[0], W * CROP.SIZE[1]) part of an input of size (H, W) +# # - "relative_range" uniformly sample relative crop size from between [CROP.SIZE[0], [CROP.SIZE[1]]. +# # and [1, 1] and use it as in "relative" scenario. +# # - "absolute" crop part of an input with absolute size: (CROP.SIZE[0], CROP.SIZE[1]). +# _C.INPUT.CROP.TYPE = "relative_range" +# # Size of crop in range (0, 1] if CROP.TYPE is "relative" or "relative_range" and in number of +# # pixels if CROP.TYPE is "absolute" +# _C.INPUT.CROP.SIZE = [0.9, 0.9] +# +# +# # Whether the model needs RGB, YUV, HSV etc. +# # Should be one of the modes defined here, as we use PIL to read the image: +# # https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes +# # with BGR being the one exception. One can set image format to BGR, we will +# # internally use RGB for conversion and flip the channels over +# _C.INPUT.FORMAT = "BGR" +# # The ground truth mask format that the model will use. +# # Mask R-CNN supports either "polygon" or "bitmask" as ground truth. +# _C.INPUT.MASK_FORMAT = "polygon" # alternative: "bitmask" +# +# +# # ----------------------------------------------------------------------------- +# # Dataset +# # ----------------------------------------------------------------------------- +# _C.DATASETS = CN() +# # List of the dataset names for training. Must be registered in DatasetCatalog +# _C.DATASETS.TRAIN = () +# # List of the pre-computed proposal files for training, which must be consistent +# # with datasets listed in DATASETS.TRAIN. +# _C.DATASETS.PROPOSAL_FILES_TRAIN = () +# # Number of top scoring precomputed proposals to keep for training +# _C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN = 2000 +# # List of the dataset names for testing. Must be registered in DatasetCatalog +# _C.DATASETS.TEST = () +# # List of the pre-computed proposal files for test, which must be consistent +# # with datasets listed in DATASETS.TEST. +# _C.DATASETS.PROPOSAL_FILES_TEST = () +# # Number of top scoring precomputed proposals to keep for test +# _C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST = 1000 +# +# # ----------------------------------------------------------------------------- +# # DataLoader +# # ----------------------------------------------------------------------------- +# _C.DATALOADER = CN() +# # Number of data loading threads +# _C.DATALOADER.NUM_WORKERS = 4 +# # If True, each batch should contain only images for which the aspect ratio +# # is compatible. This groups portrait images together, and landscape images +# # are not batched with portrait images. +# _C.DATALOADER.ASPECT_RATIO_GROUPING = True +# # Options: TrainingSampler, RepeatFactorTrainingSampler +# _C.DATALOADER.SAMPLER_TRAIN = "TrainingSampler" +# # Repeat threshold for RepeatFactorTrainingSampler +# _C.DATALOADER.REPEAT_THRESHOLD = 0.0 +# # if True, the dataloader will filter out images that have no associated +# # annotations at train time. +# _C.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True +# +# # ---------------------------------------------------------------------------- # +# # Backbone options +# # ---------------------------------------------------------------------------- # +# _C.MODEL.BACKBONE = CN() +# +# _C.MODEL.BACKBONE.NAME = "build_resnet_backbone" +# # Freeze the first several stages so they are not trained. +# # There are 5 stages in ResNet. The first is a convolution, and the following +# # stages are each group of residual blocks. +# _C.MODEL.BACKBONE.FREEZE_AT = 2 +# +# +# # ---------------------------------------------------------------------------- # +# # FPN options +# # ---------------------------------------------------------------------------- # +# _C.MODEL.FPN = CN() +# # Names of the input feature maps to be used by FPN +# # They must have contiguous power of 2 strides +# # e.g., ["res2", "res3", "res4", "res5"] +# _C.MODEL.FPN.IN_FEATURES = [] +# _C.MODEL.FPN.OUT_CHANNELS = 256 +# +# # Options: "" (no norm), "GN" +# _C.MODEL.FPN.NORM = "" +# +# # Types for fusing the FPN top-down and lateral features. Can be either "sum" or "avg" +# _C.MODEL.FPN.FUSE_TYPE = "sum" +# +# +# # ---------------------------------------------------------------------------- # +# # Proposal generator options +# # ---------------------------------------------------------------------------- # +# _C.MODEL.PROPOSAL_GENERATOR = CN() +# # Current proposal generators include "RPN", "RRPN" and "PrecomputedProposals" +# _C.MODEL.PROPOSAL_GENERATOR.NAME = "RPN" +# # Proposal height and width both need to be greater than MIN_SIZE +# # (a the scale used during training or inference) +# _C.MODEL.PROPOSAL_GENERATOR.MIN_SIZE = 0 +# +# +# # ---------------------------------------------------------------------------- # +# # Anchor generator options +# # ---------------------------------------------------------------------------- # +# _C.MODEL.ANCHOR_GENERATOR = CN() +# # The generator can be any name in the ANCHOR_GENERATOR registry +# _C.MODEL.ANCHOR_GENERATOR.NAME = "DefaultAnchorGenerator" +# # Anchor sizes (i.e. sqrt of area) in absolute pixels w.r.t. the network input. +# # Format: list[list[int]]. SIZES[i] specifies the list of sizes +# # to use for IN_FEATURES[i]; len(SIZES) == len(IN_FEATURES) must be true, +# # or len(SIZES) == 1 is true and size list SIZES[0] is used for all +# # IN_FEATURES. +# _C.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64, 128, 256, 512]] +# # Anchor aspect ratios. For each area given in `SIZES`, anchors with different aspect +# # ratios are generated by an anchor generator. +# # Format: list[list[int]]. ASPECT_RATIOS[i] specifies the list of aspect ratios +# # to use for IN_FEATURES[i]; len(ASPECT_RATIOS) == len(IN_FEATURES) must be true, +# # or len(ASPECT_RATIOS) == 1 is true and aspect ratio list ASPECT_RATIOS[0] is used +# # for all IN_FEATURES. +# _C.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.5, 1.0, 2.0]] +# # Anchor angles. +# # list[float], the angle in degrees, for each input feature map. +# # ANGLES[i] specifies the list of angles for IN_FEATURES[i]. +# _C.MODEL.ANCHOR_GENERATOR.ANGLES = [[-90, 0, 90]] +# # Relative offset between the center of the first anchor and the top-left corner of the image +# # Units: fraction of feature map stride (e.g., 0.5 means half stride) +# # Allowed values are floats in [0, 1) range inclusive. +# # Recommended value is 0.5, although it is not expected to affect model accuracy. +# _C.MODEL.ANCHOR_GENERATOR.OFFSET = 0.0 +# +# # ---------------------------------------------------------------------------- # +# # RPN options +# # ---------------------------------------------------------------------------- # +# _C.MODEL.RPN = CN() +# _C.MODEL.RPN.HEAD_NAME = "StandardRPNHead" # used by RPN_HEAD_REGISTRY +# +# # Names of the input feature maps to be used by RPN +# # e.g., ["p2", "p3", "p4", "p5", "p6"] for FPN +# _C.MODEL.RPN.IN_FEATURES = ["res4"] +# # Remove RPN anchors that go outside the image by BOUNDARY_THRESH pixels +# # Set to -1 or a large value, e.g. 100000, to disable pruning anchors +# _C.MODEL.RPN.BOUNDARY_THRESH = -1 +# # IOU overlap ratios [BG_IOU_THRESHOLD, FG_IOU_THRESHOLD] +# # Minimum overlap required between an anchor and ground-truth box for the +# # (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD +# # ==> positive RPN example: 1) +# # Maximum overlap allowed between an anchor and ground-truth box for the +# # (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD +# # ==> negative RPN example: 0) +# # Anchors with overlap in between (BG_IOU_THRESHOLD <= IoU < FG_IOU_THRESHOLD) +# # are ignored (-1) +# _C.MODEL.RPN.IOU_THRESHOLDS = [0.3, 0.7] +# _C.MODEL.RPN.IOU_LABELS = [0, -1, 1] +# # Total number of RPN examples per image +# _C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256 +# # Target fraction of foreground (positive) examples per RPN minibatch +# _C.MODEL.RPN.POSITIVE_FRACTION = 0.5 +# # Weights on (dx, dy, dw, dh) for normalizing RPN anchor regression targets +# _C.MODEL.RPN.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0) +# # The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1. +# _C.MODEL.RPN.SMOOTH_L1_BETA = 0.0 +# _C.MODEL.RPN.LOSS_WEIGHT = 1.0 +# # Number of top scoring RPN proposals to keep before applying NMS +# # When FPN is used, this is *per FPN level* (not total) +# _C.MODEL.RPN.PRE_NMS_TOPK_TRAIN = 12000 +# _C.MODEL.RPN.PRE_NMS_TOPK_TEST = 6000 +# # Number of top scoring RPN proposals to keep after applying NMS +# # When FPN is used, this limit is applied per level and then again to the union +# # of proposals from all levels +# # NOTE: When FPN is used, the meaning of this config is different from Detectron1. +# # It means per-batch topk in Detectron1, but per-image topk here. +# # See "modeling/rpn/rpn_outputs.py" for details. +# _C.MODEL.RPN.POST_NMS_TOPK_TRAIN = 2000 +# _C.MODEL.RPN.POST_NMS_TOPK_TEST = 1000 +# # NMS threshold used on RPN proposals +# _C.MODEL.RPN.NMS_THRESH = 0.7 +# +# # ---------------------------------------------------------------------------- # +# # ROI HEADS options +# # ---------------------------------------------------------------------------- # +# _C.MODEL.ROI_HEADS = CN() +# _C.MODEL.ROI_HEADS.NAME = "Res5ROIHeads" +# # Number of foreground classes +# _C.MODEL.ROI_HEADS.NUM_CLASSES = 80 +# # Names of the input feature maps to be used by ROI heads +# # Currently all heads (box, mask, ...) use the same input feature map list +# # e.g., ["p2", "p3", "p4", "p5"] is commonly used for FPN +# _C.MODEL.ROI_HEADS.IN_FEATURES = ["res4"] +# # IOU overlap ratios [IOU_THRESHOLD] +# # Overlap threshold for an RoI to be considered background (if < IOU_THRESHOLD) +# # Overlap threshold for an RoI to be considered foreground (if >= IOU_THRESHOLD) +# _C.MODEL.ROI_HEADS.IOU_THRESHOLDS = [0.5] +# _C.MODEL.ROI_HEADS.IOU_LABELS = [0, 1] +# # RoI minibatch size *per image* (number of regions of interest [ROIs]) +# # Total number of RoIs per training minibatch = +# # ROI_HEADS.BATCH_SIZE_PER_IMAGE * SOLVER.IMS_PER_BATCH +# # E.g., a common configuration is: 512 * 16 = 8192 +# _C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512 +# # Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0) +# _C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25 +# +# # Only used on test mode +# +# # Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to +# # balance obtaining high recall with not having too many low precision +# # detections that will slow down inference post processing steps (like NMS) +# # A default threshold of 0.0 increases AP by ~0.2-0.3 but significantly slows down +# # inference. +# _C.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05 +# # Overlap threshold used for non-maximum suppression (suppress boxes with +# # IoU >= this threshold) +# _C.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.5 +# # If True, augment proposals with ground-truth boxes before sampling proposals to +# # train ROI heads. +# _C.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT = True +# +# # ---------------------------------------------------------------------------- # +# # Box Head +# # ---------------------------------------------------------------------------- # +# _C.MODEL.ROI_BOX_HEAD = CN() +# # C4 don't use head name option +# # Options for non-C4 models: FastRCNNConvFCHead, +# _C.MODEL.ROI_BOX_HEAD.NAME = "" +# # Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets +# # These are empirically chosen to approximately lead to unit variance targets +# _C.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10.0, 10.0, 5.0, 5.0) +# # The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1. +# _C.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA = 0.0 +# _C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14 +# _C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0 +# # Type of pooling operation applied to the incoming feature map for each RoI +# _C.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2" +# +# _C.MODEL.ROI_BOX_HEAD.NUM_FC = 0 +# # Hidden layer dimension for FC layers in the RoI box head +# _C.MODEL.ROI_BOX_HEAD.FC_DIM = 1024 +# _C.MODEL.ROI_BOX_HEAD.NUM_CONV = 0 +# # Channel dimension for Conv layers in the RoI box head +# _C.MODEL.ROI_BOX_HEAD.CONV_DIM = 256 +# # Normalization method for the convolution layers. +# # Options: "" (no norm), "GN", "SyncBN". +# _C.MODEL.ROI_BOX_HEAD.NORM = "" +# # Whether to use class agnostic for bbox regression +# _C.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG = False +# # If true, RoI heads use bounding boxes predicted by the box head rather than proposal boxes. +# _C.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES = False +# +# # ---------------------------------------------------------------------------- # +# # Cascaded Box Head +# # ---------------------------------------------------------------------------- # +# _C.MODEL.ROI_BOX_CASCADE_HEAD = CN() +# # The number of cascade stages is implicitly defined by the length of the following two configs. +# _C.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS = ( +# (10.0, 10.0, 5.0, 5.0), +# (20.0, 20.0, 10.0, 10.0), +# (30.0, 30.0, 15.0, 15.0), +# ) +# _C.MODEL.ROI_BOX_CASCADE_HEAD.IOUS = (0.5, 0.6, 0.7) +# +# +# # ---------------------------------------------------------------------------- # +# # Mask Head +# # ---------------------------------------------------------------------------- # +# _C.MODEL.ROI_MASK_HEAD = CN() +# _C.MODEL.ROI_MASK_HEAD.NAME = "MaskRCNNConvUpsampleHead" +# _C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14 +# _C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0 +# _C.MODEL.ROI_MASK_HEAD.NUM_CONV = 0 # The number of convs in the mask head +# _C.MODEL.ROI_MASK_HEAD.CONV_DIM = 256 +# # Normalization method for the convolution layers. +# # Options: "" (no norm), "GN", "SyncBN". +# _C.MODEL.ROI_MASK_HEAD.NORM = "" +# # Whether to use class agnostic for mask prediction +# _C.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK = False +# # Type of pooling operation applied to the incoming feature map for each RoI +# _C.MODEL.ROI_MASK_HEAD.POOLER_TYPE = "ROIAlignV2" +# +# +# # ---------------------------------------------------------------------------- # +# # Keypoint Head +# # ---------------------------------------------------------------------------- # +# _C.MODEL.ROI_KEYPOINT_HEAD = CN() +# _C.MODEL.ROI_KEYPOINT_HEAD.NAME = "KRCNNConvDeconvUpsampleHead" +# _C.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION = 14 +# _C.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO = 0 +# _C.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS = tuple(512 for _ in range(8)) +# _C.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS = 17 # 17 is the number of keypoints in COCO. +# +# # Images with too few (or no) keypoints are excluded from training. +# _C.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE = 1 +# # Normalize by the total number of visible keypoints in the minibatch if True. +# # Otherwise, normalize by the total number of keypoints that could ever exist +# # in the minibatch. +# # The keypoint softmax loss is only calculated on visible keypoints. +# # Since the number of visible keypoints can vary significantly between +# # minibatches, this has the effect of up-weighting the importance of +# # minibatches with few visible keypoints. (Imagine the extreme case of +# # only one visible keypoint versus N: in the case of N, each one +# # contributes 1/N to the gradient compared to the single keypoint +# # determining the gradient direction). Instead, we can normalize the +# # loss by the total number of keypoints, if it were the case that all +# # keypoints were visible in a full minibatch. (Returning to the example, +# # this means that the one visible keypoint contributes as much as each +# # of the N keypoints.) +# _C.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS = True +# # Multi-task loss weight to use for keypoints +# # Recommended values: +# # - use 1.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is True +# # - use 4.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is False +# _C.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT = 1.0 +# # Type of pooling operation applied to the incoming feature map for each RoI +# _C.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE = "ROIAlignV2" +# +# # ---------------------------------------------------------------------------- # +# # Semantic Segmentation Head +# # ---------------------------------------------------------------------------- # +# _C.MODEL.SEM_SEG_HEAD = CN() +# _C.MODEL.SEM_SEG_HEAD.NAME = "SemSegFPNHead" +# _C.MODEL.SEM_SEG_HEAD.IN_FEATURES = ["p2", "p3", "p4", "p5"] +# # Label in the semantic segmentation ground truth that is ignored, i.e., no loss is calculated for +# # the correposnding pixel. +# _C.MODEL.SEM_SEG_HEAD.IGNORE_VALUE = 255 +# # Number of classes in the semantic segmentation head +# _C.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 54 +# # Number of channels in the 3x3 convs inside semantic-FPN heads. +# _C.MODEL.SEM_SEG_HEAD.CONVS_DIM = 128 +# # Outputs from semantic-FPN heads are up-scaled to the COMMON_STRIDE stride. +# _C.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4 +# # Normalization method for the convolution layers. Options: "" (no norm), "GN". +# _C.MODEL.SEM_SEG_HEAD.NORM = "GN" +# _C.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT = 1.0 +# +# _C.MODEL.PANOPTIC_FPN = CN() +# # Scaling of all losses from instance detection / segmentation head. +# _C.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT = 1.0 +# +# # options when combining instance & semantic segmentation outputs +# _C.MODEL.PANOPTIC_FPN.COMBINE = CN({"ENABLED": True}) +# _C.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH = 0.5 +# _C.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT = 4096 +# _C.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = 0.5 +# +# +# # ---------------------------------------------------------------------------- # +# # RetinaNet Head +# # ---------------------------------------------------------------------------- # +# _C.MODEL.RETINANET = CN() +# +# # This is the number of foreground classes. +# _C.MODEL.RETINANET.NUM_CLASSES = 80 +# +# _C.MODEL.RETINANET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"] +# +# # Convolutions to use in the cls and bbox tower +# # NOTE: this doesn't include the last conv for logits +# _C.MODEL.RETINANET.NUM_CONVS = 4 +# +# # IoU overlap ratio [bg, fg] for labeling anchors. +# # Anchors with < bg are labeled negative (0) +# # Anchors with >= bg and < fg are ignored (-1) +# # Anchors with >= fg are labeled positive (1) +# _C.MODEL.RETINANET.IOU_THRESHOLDS = [0.4, 0.5] +# _C.MODEL.RETINANET.IOU_LABELS = [0, -1, 1] +# +# # Prior prob for rare case (i.e. foreground) at the beginning of training. +# # This is used to set the bias for the logits layer of the classifier subnet. +# # This improves training stability in the case of heavy class imbalance. +# _C.MODEL.RETINANET.PRIOR_PROB = 0.01 +# +# # Inference cls score threshold, only anchors with score > INFERENCE_TH are +# # considered for inference (to improve speed) +# _C.MODEL.RETINANET.SCORE_THRESH_TEST = 0.05 +# _C.MODEL.RETINANET.TOPK_CANDIDATES_TEST = 1000 +# _C.MODEL.RETINANET.NMS_THRESH_TEST = 0.5 +# +# # Weights on (dx, dy, dw, dh) for normalizing Retinanet anchor regression targets +# _C.MODEL.RETINANET.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0) +# +# # Loss parameters +# _C.MODEL.RETINANET.FOCAL_LOSS_GAMMA = 2.0 +# _C.MODEL.RETINANET.FOCAL_LOSS_ALPHA = 0.25 +# _C.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA = 0.1 +# +# +# # ---------------------------------------------------------------------------- # +# # ResNe[X]t options (ResNets = {ResNet, ResNeXt} +# # Note that parts of a resnet may be used for both the backbone and the head +# # These options apply to both +# # ---------------------------------------------------------------------------- # +# _C.MODEL.RESNETS = CN() +# +# _C.MODEL.RESNETS.DEPTH = 50 +# _C.MODEL.RESNETS.OUT_FEATURES = ["res4"] # res4 for C4 backbone, res2..5 for FPN backbone +# +# # Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt +# _C.MODEL.RESNETS.NUM_GROUPS = 1 +# +# # Options: FrozenBN, GN, "SyncBN", "BN" +# _C.MODEL.RESNETS.NORM = "FrozenBN" +# +# # Baseline width of each group. +# # Scaling this parameters will scale the width of all bottleneck layers. +# _C.MODEL.RESNETS.WIDTH_PER_GROUP = 64 +# +# # Place the stride 2 conv on the 1x1 filter +# # Use True only for the original MSRA ResNet; use False for C2 and Torch models +# _C.MODEL.RESNETS.STRIDE_IN_1X1 = True +# +# # Apply dilation in stage "res5" +# _C.MODEL.RESNETS.RES5_DILATION = 1 +# +# # Output width of res2. Scaling this parameters will scale the width of all 1x1 convs in ResNet +# # For R18 and R34, this needs to be set to 64 +# _C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256 +# _C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64 +# +# +# +# # ---------------------------------------------------------------------------- # +# # Solver +# # ---------------------------------------------------------------------------- # +# _C.SOLVER = CN() +# +# # See detectron2/solver/build.py for LR scheduler options +# _C.SOLVER.LR_SCHEDULER_NAME = "WarmupMultiStepLR" +# +# _C.SOLVER.MAX_ITER = 40000 +# +# _C.SOLVER.BASE_LR = 0.001 +# +# _C.SOLVER.MOMENTUM = 0.9 +# +# _C.SOLVER.WEIGHT_DECAY = 0.0001 +# # The weight decay that's applied to parameters of normalization layers +# # (typically the affine transformation) +# _C.SOLVER.WEIGHT_DECAY_NORM = 0.0 +# +# _C.SOLVER.GAMMA = 0.1 +# # The iteration number to decrease learning rate by GAMMA. +# _C.SOLVER.STEPS = (30000,) +# +# _C.SOLVER.WARMUP_FACTOR = 1.0 / 1000 +# _C.SOLVER.WARMUP_ITERS = 1000 +# _C.SOLVER.WARMUP_METHOD = "linear" +# +# # Save a checkpoint after every this number of iterations +# _C.SOLVER.CHECKPOINT_PERIOD = 5000 +# +# # Number of images per batch across all machines. +# # If we have 16 GPUs and IMS_PER_BATCH = 32, +# # each GPU will see 2 images per batch. +# _C.SOLVER.IMS_PER_BATCH = 16 +# +# # Detectron v1 (and previous detection code) used a 2x higher LR and 0 WD for +# # biases. This is not useful (at least for recent models). You should avoid +# # changing these and they exist only to reproduce Detectron v1 training if +# # desired. +# _C.SOLVER.BIAS_LR_FACTOR = 1.0 +# _C.SOLVER.WEIGHT_DECAY_BIAS = _C.SOLVER.WEIGHT_DECAY +# +# # Gradient clipping +# _C.SOLVER.CLIP_GRADIENTS = CN({"ENABLED": False}) +# # Type of gradient clipping, currently 2 values are supported: +# # - "value": the absolute values of elements of each gradients are clipped +# # - "norm": the norm of the gradient for each parameter is clipped thus +# # affecting all elements in the parameter +# _C.SOLVER.CLIP_GRADIENTS.CLIP_TYPE = "value" +# # Maximum absolute value used for clipping gradients +# _C.SOLVER.CLIP_GRADIENTS.CLIP_VALUE = 1.0 +# # Floating point number p for L-p norm to be used with the "norm" +# # gradient clipping type; for L-inf, please specify .inf +# _C.SOLVER.CLIP_GRADIENTS.NORM_TYPE = 2.0 +# +# # ---------------------------------------------------------------------------- # +# # Specific test options +# # ---------------------------------------------------------------------------- # +# _C.TEST = CN() +# # For end-to-end tests to verify the expected accuracy. +# # Each item is [task, metric, value, tolerance] +# # e.g.: [['bbox', 'AP', 38.5, 0.2]] +# _C.TEST.EXPECTED_RESULTS = [] +# # The period (in terms of steps) to evaluate the model during training. +# # Set to 0 to disable. +# _C.TEST.EVAL_PERIOD = 0 +# # The sigmas used to calculate keypoint OKS. See http://cocodataset.org/#keypoints-eval +# # When empty it will use the defaults in COCO. +# # Otherwise it should have the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS. +# _C.TEST.KEYPOINT_OKS_SIGMAS = [] +# # Maximum number of detections to return per image during inference (100 is +# # based on the limit established for the COCO dataset). +# _C.TEST.DETECTIONS_PER_IMAGE = 100 +# +# _C.TEST.AUG = CN({"ENABLED": False}) +# _C.TEST.AUG.MIN_SIZES = (400, 500, 600, 700, 800, 900, 1000, 1100, 1200) +# _C.TEST.AUG.MAX_SIZE = 4000 +# _C.TEST.AUG.FLIP = True +# +# _C.TEST.PRECISE_BN = CN({"ENABLED": False}) +# _C.TEST.PRECISE_BN.NUM_ITER = 200 +# +# # ---------------------------------------------------------------------------- # +# # Misc options +# # ---------------------------------------------------------------------------- # +# # Directory where output files are written +# _C.OUTPUT_DIR = "./output" +# # Set seed to negative to fully randomize everything. +# # Set seed to positive to use a fixed seed. Note that a fixed seed does not +# # guarantee fully deterministic behavior. +# _C.SEED = -1 +# # Benchmark different cudnn algorithms. +# # If input images have very different sizes, this option will have large overhead +# # for about 10k iterations. It usually hurts total time, but can benefit for certain models. +# # If input images have the same or similar sizes, benchmark is often helpful. +# _C.CUDNN_BENCHMARK = False +# # The period (in terms of steps) for minibatch visualization at train time. +# # Set to 0 to disable. +# _C.VIS_PERIOD = 0 +# +# # global config is for quick hack purposes. +# # You can set them in command line or config files, +# # and access it with: +# # +# # from detectron2.config import global_cfg +# # print(global_cfg.HACK) +# # +# # Do not commit any configs into it. +# _C.GLOBAL = CN() +# _C.GLOBAL.HACK = 1.0 + +if __name__ == '__main__': + main() diff --git a/simnet/lib/net/models/panoptic_net.py b/simnet/lib/net/models/panoptic_net.py new file mode 100755 index 0000000..f5f2f48 --- /dev/null +++ b/simnet/lib/net/models/panoptic_net.py @@ -0,0 +1,188 @@ +import numpy as np +import torch.nn as nn + +from simnet.lib.net.models.panoptic_backbone import SemSegFPNHead, PoseFPNHead, ShapeSpec, output_shape, build_resnet_fpn_backbone +from simnet.lib.net.models.basic_stem import RGBDStem +from simnet.lib.net.post_processing import segmentation_outputs +from simnet.lib.net.post_processing import depth_outputs +from simnet.lib.net.post_processing import pose_outputs +from simnet.lib.net.post_processing import abs_pose_outputs +from simnet.lib.net.post_processing import box_outputs +from simnet.lib.net.post_processing import keypoint_outputs + + +def res_fpn(hparams): + return PanopticNet(hparams) + +class DepthHead(nn.Module): + def __init__(self, backbone_output_shape, hparams): + super().__init__() + self.head = SemSegFPNHead( + backbone_output_shape, + num_classes=1, + model_norm=hparams.model_norm, + num_filters_scale=hparams.num_filters_scale + ) + self.hparams = hparams + def forward(self, features): + depth_pred = self.head.forward(features).squeeze(dim=1) + return depth_outputs.DepthOutput(depth_pred, self.hparams) + + +class SegmentationHead(nn.Module): + def __init__(self, backbone_output_shape, num_classes, hparams): + super().__init__() + self.head = SemSegFPNHead( + backbone_output_shape, + num_classes=num_classes, + model_norm=hparams.model_norm, + num_filters_scale=hparams.num_filters_scale + ) + self.hparams = hparams + def forward(self, features): + pred = self.head.forward(features) + return segmentation_outputs.SegmentationOutput(pred, self.hparams) + + +class PoseHead(nn.Module): + + def __init__(self, backbone_output_shape, hparams): + super().__init__() + self.hparams = hparams + self.heatmap_head = SemSegFPNHead( + backbone_output_shape, + num_classes=1, + model_norm=hparams.model_norm, + num_filters_scale=hparams.num_filters_scale + ) + self.vertex_head = PoseFPNHead( + backbone_output_shape, + num_classes=16, + model_norm=hparams.model_norm, + num_filters_scale=hparams.num_filters_scale + ) + self.z_centroid_head = PoseFPNHead( + backbone_output_shape, + num_classes=1, + model_norm=hparams.model_norm, + num_filters_scale=hparams.num_filters_scale + ) + + def forward(self, features): + z_centroid_output = self.z_centroid_head.forward(features).squeeze(dim=1) + heatmap_output = self.heatmap_head.forward(features).squeeze(dim=1) + vertex_output = self.vertex_head.forward(features) + return pose_outputs.PoseOutput(heatmap_output, vertex_output, z_centroid_output, self.hparams) + + +class OBBHead(nn.Module): + def __init__(self, backbone_output_shape, hparams): + super().__init__() + self.hparams = hparams + self.heatmap_head = SemSegFPNHead( + backbone_output_shape, + num_classes=1, + model_norm=hparams.model_norm, + num_filters_scale=hparams.num_filters_scale + ) + self.latent_embedding_head = PoseFPNHead( + backbone_output_shape, + num_classes=128, + model_norm=hparams.model_norm, + num_filters_scale=hparams.num_filters_scale + ) + self.abs_pose_head = PoseFPNHead( + backbone_output_shape, + num_classes=13, + model_norm=hparams.model_norm, + num_filters_scale=hparams.num_filters_scale + ) + def forward(self, features): + heatmap_output = self.heatmap_head.forward(features).squeeze(dim=1) + latent_emb_output = self.latent_embedding_head.forward(features) + abs_pose_output = self.abs_pose_head.forward(features) + return abs_pose_outputs.OBBOutput( + heatmap_output, latent_emb_output, abs_pose_output, self.hparams + ) + +class BoxHead(nn.Module): + + def __init__(self, backbone_output_shape, hparams): + super().__init__() + self.hparams = hparams + self.heatmap_head = SemSegFPNHead( + backbone_output_shape, + num_classes=1, + model_norm=hparams.model_norm, + num_filters_scale=hparams.num_filters_scale + ) + self.vertex_head = SemSegFPNHead( + backbone_output_shape, + num_classes=4, + model_norm=hparams.model_norm, + num_filters_scale=hparams.num_filters_scale + ) + + def forward(self, features): + heatmap_output = self.heatmap_head.forward(features).squeeze(dim=1) + vertex_output = self.vertex_head.forward(features) + return box_outputs.BoxOutput(heatmap_output, vertex_output, self.hparams) + + +class KeypointHead(nn.Module): + + def __init__(self, backbone_output_shape, hparams): + super().__init__() + self.hparams = hparams + self.num_keypoints = hparams.num_keypoints + self.heatmap_head = SemSegFPNHead( + backbone_output_shape, + num_classes=self.num_keypoints, + model_norm=hparams.model_norm, + num_filters_scale=hparams.num_filters_scale + ) + self.activation = nn.Sigmoid() + + def forward(self, features): + heatmap_output = self.heatmap_head.forward(features).squeeze(dim=1) + heatmap_output = self.activation(heatmap_output) + return keypoint_outputs.KeypointOutput(heatmap_output, self.hparams) + +class PanopticNet(nn.Module): + + def __init__(self, hparams): + super().__init__() + self.hparams = hparams + input_shape = ShapeSpec(channels=3, height=512, width=640) + stereo_stem = RGBDStem(hparams) + self.backbone = build_resnet_fpn_backbone( + input_shape, + stereo_stem, + model_norm=hparams.model_norm, + num_filters_scale=hparams.num_filters_scale + ) + shape = output_shape(self.backbone) + # Add depth head. + self.depth_head = DepthHead(shape, hparams) + # Add segmentation head. 6+1 categories for NOCS + self.seg_head = SegmentationHead(shape, 7, hparams) + self.pose_head = OBBHead(shape, hparams) + self.box_head = BoxHead(shape, hparams) + self.keypoint_head = KeypointHead(shape, hparams) + + + def forward(self, image): + #fine tune only + # self.backbone.eval() + # with torch.no_grad(): + # features, small_disp_output = self.backbone.forward(image) + features, small_disp_output = self.backbone.forward(image) + small_disp_output = small_disp_output.squeeze(dim=1) + + if self.hparams.frozen_stereo_checkpoint is not None: + small_disp_output = small_disp_output.detach() + small_depth_output = depth_outputs.DepthOutput(small_disp_output, self.hparams) + seg_output = self.seg_head.forward(features) + depth_output = self.depth_head.forward(features) + pose_output = self.pose_head.forward(features) + return seg_output, depth_output, small_depth_output, pose_output diff --git a/simnet/lib/net/models/specs.json b/simnet/lib/net/models/specs.json new file mode 100644 index 0000000..cf82d4f --- /dev/null +++ b/simnet/lib/net/models/specs.json @@ -0,0 +1,43 @@ +{ + "Description" : "shape representation for bottles", + "DataSource" : "data", + "TrainSplit" : "examples/splits/bottle_train.json", + "TestSplit" : "examples/splits/bottle_test.json", + "NetworkArch" : "deep_sdf_decoder", + "NetworkSpecs" : { + "dims" : [ 512, 512, 512, 512, 512, 512, 512, 512 ], + "dropout" : [0, 1, 2, 3, 4, 5, 6, 7], + "dropout_prob" : 0.2, + "norm_layers" : [0, 1, 2, 3, 4, 5, 6, 7], + "latent_in" : [4], + "xyz_in_all" : false, + "use_tanh" : false, + "latent_dropout" : false, + "weight_norm" : true + }, + "CodeLength" : 128, + "NumEpochs" : 2001, + "SnapshotFrequency" : 1000, + "AdditionalSnapshots" : [ 100, 500 ], + "LearningRateSchedule" : [ + { + "Type" : "Step", + "Initial" : 0.0005, + "Interval" : 500, + "Factor" : 0.5 + }, + { + "Type" : "Step", + "Initial" : 0.001, + "Interval" : 500, + "Factor" : 0.5 + }], + "SamplesPerScene" : 16384, + "ScenesPerBatch" : 64, + "DataLoaderThreads" : 16, + "ClampingDistance" : 0.1, + "CodeRegularization" : true, + "CodeRegularizationLambda" : 1e-4, + "CodeBound" : 1.0 + } + \ No newline at end of file diff --git a/simnet/lib/net/panoptic_trainer.py b/simnet/lib/net/panoptic_trainer.py new file mode 100755 index 0000000..80fd16b --- /dev/null +++ b/simnet/lib/net/panoptic_trainer.py @@ -0,0 +1,137 @@ +import os +import copy + +os.environ['PYTHONHASHSEED'] = str(1) +from importlib.machinery import SourceFileLoader +import random +random.seed(123456) +import numpy as np +np.random.seed(123456) +import torch +import wandb +import pytorch_lightning as pl + +from simnet.lib.net import common +from simnet.lib.net.dataset import extract_left_numpy_img +from simnet.lib.net.functions.learning_rate import lambda_learning_rate_poly, lambda_warmup + +_GPU_TO_USE = 0 +class PanopticModel(pl.LightningModule): + def __init__( + self, hparams, epochs=None, train_dataset=None, eval_metric=None, preprocess_func=None + ): + super().__init__() + + self.hparams = hparams + self.epochs = epochs + self.train_dataset = train_dataset + + self.model = common.get_model(hparams) + self.eval_metrics = eval_metric + self.preprocess_func = preprocess_func + + def forward(self, image): + seg_output, depth_output, small_depth_output, pose_output = self.model( + image + ) + return seg_output, depth_output, small_depth_output, pose_output + + def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i, second_order_closure=None): + super().optimizer_step(epoch_nb, batch_nb, optimizer, optimizer_i, second_order_closure) + if batch_nb == 0: + for param_group in optimizer.param_groups: + learning_rate = param_group['lr'] + + def training_step(self, batch, batch_idx): + image, seg_target, depth_target, pose_targets, _, _ = batch + seg_output, depth_output, small_depth_output, pose_outputs = self.forward( + image + ) + log = {} + prefix = 'train' + + loss = depth_output.compute_loss(copy.deepcopy(depth_target), log, f'{prefix}_detailed/loss/refined_disp') + if self.hparams.frozen_stereo_checkpoint is None: + loss = loss + small_depth_output.compute_loss(depth_target, log, f'{prefix}_detailed/loss/train_cost_volume_disp') + loss = loss + seg_output.compute_loss(seg_target, log, f'{prefix}_detailed/loss/seg') + if pose_targets[0] is not None: + loss = loss + pose_outputs.compute_loss(pose_targets, log, f'{prefix}_detailed/pose') + log['train/loss/total'] = loss + + if (batch_idx % 200) == 0: + with torch.no_grad(): + llog = {} + prefix = 'train' + left_image_np = extract_left_numpy_img(image[0]) + logger = self.logger.experiment + seg_pred_vis = seg_output.get_visualization_img(np.copy(left_image_np)) + llog[f'{prefix}/seg'] = wandb.Image(seg_pred_vis, caption=prefix) + depth_vis = depth_output.get_visualization_img(np.copy(left_image_np)) + llog[f'{prefix}/disparity'] = wandb.Image(depth_vis, caption=prefix) + small_depth_vis = small_depth_output.get_visualization_img(np.copy(left_image_np)) + llog[f'{prefix}/small_disparity'] = wandb.Image(small_depth_vis, caption=prefix) + logger.log(llog) + return {'loss': loss, 'log': log} + + def validation_step(self, batch, batch_idx): + image, seg_target, depth_target, pose_targets, detections_gt, scene_name = batch + dt = [di.depth_pred.cuda().unsqueeze(0) for di in depth_target] + dt = torch.stack(dt) + real_image = torch.cat([image[:,:3,:,:], dt], dim=1) + seg_output, depth_output, small_depth_output, pose_outputs = self.forward( + real_image + ) + log = {} + logger = self.logger.experiment + with torch.no_grad(): + prefix_loss = 'validation' + loss = depth_output.compute_loss(copy.deepcopy(depth_target), log, f'{prefix_loss}_detailed/loss/refined_disp') + if self.hparams.frozen_stereo_checkpoint is None: + loss = loss + small_depth_output.compute_loss(depth_target, log, f'{prefix_loss}_detailed_loss/train_cost_volume_disp') + loss = loss + seg_output.compute_loss(seg_target, log, f'{prefix_loss}_detailed/loss/seg') + if pose_targets[0] is not None: + loss = loss + pose_outputs.compute_loss(pose_targets, log, f'{prefix_loss}_detailed/pose') + log['validation/loss/total'] = loss.item() + if batch_idx < 5 or scene_name[0] == 'fmk': + llog = {} + left_image_np = extract_left_numpy_img(image[0]) + prefix = f'val/{batch_idx}' + depth_vis = depth_output.get_visualization_img(np.copy(left_image_np)) + llog[f'{prefix}/disparity'] = wandb.Image(depth_vis, caption=prefix) + small_depth_vis = small_depth_output.get_visualization_img(np.copy(left_image_np)) + llog[f'{prefix}/small_disparity'] = wandb.Image(small_depth_vis, caption=prefix) + self.eval_metrics.draw_detections( + seg_output,left_image_np, llog, prefix + ) + logger.log(llog) + return log + + def validation_epoch_end(self, outputs): + self.trainer.checkpoint_callback.save_best_only = False + mean_dict = {} + for key in outputs[0].keys(): + mean_dict[key] = np.mean([d[key] for d in outputs], axis=0) + logger = self.logger.experiment + logger.log(mean_dict) + log = {} + return {'log': log} + + @pl.data_loader + def train_dataloader(self): + return common.get_loader( + self.hparams, + "train", + preprocess_func=self.preprocess_func, + datapoint_dataset=self.train_dataset + ) + @pl.data_loader + def val_dataloader(self): + return common.get_loader(self.hparams, "val", preprocess_func=self.preprocess_func) + + def configure_optimizers(self): + optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.optim_learning_rate) + lr_lambda = lambda_learning_rate_poly(self.epochs, self.hparams.optim_poly_exp) + if self.hparams.optim_warmup_epochs is not None and self.hparams.optim_warmup_epochs > 0: + lr_lambda = lambda_warmup(self.hparams.optim_warmup_epochs, 0.2, lr_lambda) + scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_lambda) + return [optimizer], [scheduler] diff --git a/simnet/lib/net/post_processing/abs_pose_outputs.py b/simnet/lib/net/post_processing/abs_pose_outputs.py new file mode 100755 index 0000000..a9179c5 --- /dev/null +++ b/simnet/lib/net/post_processing/abs_pose_outputs.py @@ -0,0 +1,142 @@ +import numpy as np +import torch +import torch.nn as nn +from simnet.lib.net.post_processing import pose_outputs +from simnet.lib.net import losses +import torch.nn.functional as F + +_mask_l1_loss = losses.MaskedL1Loss() +_mse_loss = losses.MSELoss() + +class OBBOutput: + def __init__(self, heatmap, latent_emb, abs_pose_field, hparams): + self.heatmap = heatmap + self.latent_emb = latent_emb + self.abs_pose_field = abs_pose_field + self.is_numpy = False + self.hparams = hparams + + def convert_to_numpy_from_torch(self): + self.heatmap = np.ascontiguousarray(self.heatmap.cpu().numpy()) + + #latent emb + self.latent_emb = np.ascontiguousarray(self.latent_emb.cpu().numpy()) + self.latent_emb = self.latent_emb.transpose((0, 2, 3, 1)) + self.latent_emb = self.latent_emb / 100.0 + # self.latent_emb = self.latent_emb / 10.0 + + #abs pose emb + self.abs_pose_field = np.ascontiguousarray(self.abs_pose_field.cpu().numpy()) + self.abs_pose_field = self.abs_pose_field.transpose((0, 2, 3, 1)) + self.abs_pose_field = self.abs_pose_field / 100.0 + self.is_numpy = True + + def convert_to_torch_from_numpy(self): + #latent embedding + self.latent_emb = self.latent_emb.transpose((2, 0, 1)) + self.latent_emb = 100.0 * self.latent_emb + # self.latent_emb = 10.0 * self.latent_emb + self.latent_emb = torch.from_numpy(np.ascontiguousarray(self.latent_emb)).float() + + #abs pose + self.abs_pose_field = self.abs_pose_field.transpose((2, 0, 1)) + self.abs_pose_field = 100.0 * self.abs_pose_field + self.abs_pose_field = torch.from_numpy(np.ascontiguousarray(self.abs_pose_field)).float() + self.heatmap = torch.from_numpy(np.ascontiguousarray(self.heatmap)).float() + + def nms_heatmap(self, heat, kernel=3): + pad = (kernel - 1) // 2 + hmax = nn.functional.max_pool2d(heat, (kernel, kernel), stride=1, padding=pad) + keep = (hmax == heat).float() + return heat * keep + + def compute_pointclouds_and_poses(self, min_confidence, is_target): + if is_target: + heatmap = np.ascontiguousarray(self.heatmap.cpu().numpy()) + + latent_emb = np.ascontiguousarray(self.latent_emb.cpu().numpy()) + latent_emb = latent_emb.transpose((1, 2, 0)) + latent_emb = latent_emb / 100.0 + + abs_pose_field = np.ascontiguousarray(self.abs_pose_field.cpu().numpy()) + abs_pose_field = abs_pose_field.transpose((1, 2, 0)) + abs_pose_field = abs_pose_field / 100.0 + latent_embeddings, abs_pose_outputs, img, scores, indices = compute_pointclouds_and_poses(heatmap, latent_emb, abs_pose_field, min_confidence) + else: + if not self.is_numpy: + self.convert_to_numpy_from_torch() + latent_embeddings, abs_pose_outputs, img, scores, indices = compute_pointclouds_and_poses(np.copy(self.heatmap[0]), np.copy(self.latent_emb[0]), np.copy(self.abs_pose_field[0]), min_confidence) + return latent_embeddings, abs_pose_outputs, img, scores, indices + + def get_latent_embeddings(self, is_target): + if is_target: + heatmap = np.ascontiguousarray(self.heatmap.cpu().numpy()) + latent_emb = np.ascontiguousarray(self.latent_emb.cpu().numpy()) + latent_emb = latent_emb.transpose((1, 2, 0)) + latent_emb = latent_emb / 100.0 + latent_embeddings, img,scores, indices = compute_point_cloud_embeddings(heatmap, latent_emb) + else: + if not self.is_numpy: + self.convert_to_numpy_from_torch() + latent_embeddings,img,scores, indices = compute_point_cloud_embeddings(np.copy(self.heatmap[0]), np.copy(self.latent_emb[0])) + return latent_embeddings,img,scores, indices + + def compute_loss(self, obb_targets, log, prefix): + if self.is_numpy: + raise ValueError("Output is not in torch mode") + + heatmap_target = torch.stack([obb_target.heatmap for obb_target in obb_targets]) + + # Move to GPU + heatmap_target = heatmap_target.to(torch.device('cuda:0')) + + if obb_targets[0].latent_emb is not None: + latent_emb_target = torch.stack([obb_target.latent_emb for obb_target in obb_targets]) + latent_emb_target = latent_emb_target.to(torch.device('cuda:0')) + + latent_emb_loss = _mask_l1_loss(latent_emb_target, self.latent_emb, heatmap_target) + log[f'{prefix}/latent_emb'] = latent_emb_loss.item() + + heatmap_loss = _mse_loss(heatmap_target, self.heatmap) + log[f'{prefix}/heatmap'] = heatmap_loss.item() + + #Naively regressing the absolute pose + if obb_targets[0].abs_pose_field is not None: + abs_pose_target = torch.stack([obb_target.abs_pose_field for obb_target in obb_targets]) + abs_pose_target = abs_pose_target.to(torch.device('cuda:0')) + + # svd rotation loss + abs_rotation_loss = _mask_l1_loss(abs_pose_target[:,:9,:,:], self.abs_pose_field[:,:9,:,:], heatmap_target) + log[f'{prefix}/abs_svd_rotation'] = abs_rotation_loss.item() + + # svd translation_ scale loss + abs_trans_scale_loss = _mask_l1_loss(abs_pose_target[:,9:,:,:], self.abs_pose_field[:,9:,:,:], heatmap_target) + log[f'{prefix}/abs_svd_translation+scale'] = abs_trans_scale_loss.item() + + abs_pose_loss = abs_rotation_loss + abs_trans_scale_loss + log[f'{prefix}/abs_svd_pose'] = abs_pose_loss.item() + return self.hparams.loss_heatmap_mult * heatmap_loss + self.hparams.loss_latent_emb_mult * latent_emb_loss + self.hparams.loss_abs_pose_mult * abs_pose_loss + +def compute_point_cloud_embeddings(heatmap_output, latent_emb_output, min_confidence): + peaks = pose_outputs.extract_peaks_from_centroid_sorted(np.copy(heatmap_output), min_confidence) + #peaks_image = None + peaks_image = pose_outputs.draw_peaks(np.copy(heatmap_output), np.copy(peaks)) + latent_embs, indices, scores = pose_outputs.extract_latent_emb_from_peaks( + np.copy(heatmap_output), np.copy(peaks), np.copy(latent_emb_output) + ) + return latent_embs, peaks, peaks_image,scores, indices + +def compute_pointclouds_and_poses( + heatmap_output, + latent_emb_output, + abs_pose_output, + min_confidence +): + latent_embeddings , peaks, img,scores, indices = compute_point_cloud_embeddings(np.copy(heatmap_output), np.copy(latent_emb_output), min_confidence) + abs_pose_outputs = pose_outputs.extract_abs_pose_from_peaks(np.copy(peaks), abs_pose_output) + return latent_embeddings, abs_pose_outputs, img, scores, indices + +def compute_detected_pc_and_poses(index_centers, heatmap_output, latent_emb_output, abs_pose_output): + latent_embs, indices, scores = pose_outputs.extract_latent_emb_from_peaks(np.copy(heatmap_output), np.copy(index_centers), np.copy(latent_emb_output)) + abs_pose_outputs = pose_outputs.extract_abs_pose_from_peaks(np.copy(index_centers), abs_pose_output) + return latent_embs, abs_pose_outputs \ No newline at end of file diff --git a/simnet/lib/net/post_processing/box_outputs.py b/simnet/lib/net/post_processing/box_outputs.py new file mode 100644 index 0000000..fb819db --- /dev/null +++ b/simnet/lib/net/post_processing/box_outputs.py @@ -0,0 +1,92 @@ +import numpy as np +import cv2 +import IPython +import torch +import torch.nn as nn +from simnet.lib.net import losses + +_mask_l1_loss = losses.MaskedL1Loss(downscale_factor=1) +_mse_loss = losses.MaskedMSELoss() + + +class BoxOutput: + + def __init__(self, heatmap, vertex_field, hparams, ignore_mask=None): + self.heatmap = heatmap + self.vertex_field = vertex_field + self.ignore_mask = ignore_mask + self.is_numpy = False + self.hparams = hparams + + # Converters for torch to numpy + def convert_to_numpy_from_torch(self): + self.heatmap = np.ascontiguousarray(self.heatmap.cpu().numpy()) + self.vertex_field = np.ascontiguousarray(self.vertex_field.cpu().numpy()) + self.vertex_field = self.vertex_field.transpose((0, 2, 3, 1)) + self.vertex_field = self.vertex_field / 100.0 + self.is_numpy = True + + def convert_to_torch_from_numpy(self): + self.vertex_field = self.vertex_field.transpose((2, 0, 1)) + self.vertex_field = 100.0 * self.vertex_field + self.vertex_field = torch.from_numpy(np.ascontiguousarray(self.vertex_field)).float() + self.ignore_mask = torch.from_numpy(np.ascontiguousarray(self.ignore_mask)).bool() + self.heatmap = torch.from_numpy(np.ascontiguousarray(self.heatmap)).float() + self.is_numpy = False + + def compute_loss(self, pose_targets, log): + if self.is_numpy: + raise ValueError("Output is not in torch mode") + vertex_target = torch.stack([pose_target.vertex_field for pose_target in pose_targets]) + heatmap_target = torch.stack([pose_target.heatmap for pose_target in pose_targets]) + ignore_target = torch.stack([pose_target.ignore_mask for pose_target in pose_targets]) + + # Move to GPU + heatmap_target = heatmap_target.to(torch.device('cuda:0')) + vertex_target = vertex_target.to(torch.device('cuda:0')) + ignore_target = ignore_target.to(torch.device('cuda:0')) + + vertex_loss = _mask_l1_loss(vertex_target, self.vertex_field, heatmap_target) + log['vertex_loss'] = vertex_loss + heatmap_loss = _mse_loss(self.heatmap, heatmap_target, ignore_target) + log['heatmap'] = heatmap_loss + return self.hparams.loss_vertex_mult * vertex_loss + self.hparams.loss_heatmap_mult * heatmap_loss + +def extract_vertices_from_peaks(peaks, vertex_fields, c_img, scale_factor=1): + assert peaks.shape[1] == 2 + assert vertex_fields.shape[2] == 4 + height = vertex_fields.shape[0] * scale_factor + width = vertex_fields.shape[1] * scale_factor + vertex_fields[:, :, ::2] = (1.0 - vertex_fields[:, :, ::2]) * (2 * height) - height + vertex_fields[:, :, 1::2] = (1.0 - vertex_fields[:, :, 1::2]) * (2 * width) - width + bboxes = [] + for ii in range(peaks.shape[0]): + bbox = get_bbox_from_vertex(vertex_fields, peaks[ii, :], scale_factor=scale_factor) + bboxes.append(bbox) + return bboxes + + +def get_bbox_from_vertex(vertex_fields, index, scale_factor=64): + assert index.shape[0] == 2 + index[0] = int(index[0] / scale_factor) + index[1] = int(index[1] / scale_factor) + bbox = vertex_fields[index[0], index[1], :] + bbox = [[bbox[0], bbox[1]], [bbox[2], bbox[3]]] + bbox = scale_factor * (index) - bbox + return bbox + + +def draw_2d_boxes_with_colors(img, bboxes, colors): + for bbox, color in zip(bboxes, colors): + pt1 = (int(bbox[0][1]), int(bbox[0][0])) + pt2 = (int(bbox[1][1]), int(bbox[1][0])) + img = cv2.rectangle(img, pt1, pt2, color, 2) + return img + + +def draw_2d_boxes(img, bboxes): + for bbox in bboxes: + pt1 = (int(bbox[0][1]), int(bbox[0][0])) + pt2 = (int(bbox[1][1]), int(bbox[1][0])) + img = cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2) + return img diff --git a/simnet/lib/net/post_processing/depth_outputs.py b/simnet/lib/net/post_processing/depth_outputs.py new file mode 100755 index 0000000..b5db93b --- /dev/null +++ b/simnet/lib/net/post_processing/depth_outputs.py @@ -0,0 +1,175 @@ +import numpy as np +import cv2 +import IPython +import torch +import torch.nn as nn + +from simnet.lib import datapoint +from torch.nn import functional as F +from simnet.lib.net import losses + +_mse_loss = losses.MSELoss() +_MAX_DISP = 128 + + +class DepthOutput: + + def __init__(self, depth_pred, hparams): + self.depth_pred = depth_pred + self.is_numpy = False + self.disp_loss = DisparityLoss(_MAX_DISP, False) + self.loss = nn.SmoothL1Loss(reduction="none") + self.hparams = hparams + + # Converters for torch to numpy + def convert_to_numpy_from_torch(self): + self.depth_pred = np.ascontiguousarray(self.depth_pred.cpu().numpy()) + self.is_numpy = True + + def convert_to_torch_from_numpy(self): + self.depth_pred[self.depth_pred > _MAX_DISP] = _MAX_DISP - 1 + self.depth_pred = torch.from_numpy(np.ascontiguousarray(self.depth_pred)).float() + self.is_numpy = False + + def get_visualization_img(self, left_img_np, corner_scale=1, raw_disp=True): + if not self.is_numpy: + self.convert_to_numpy_from_torch() + disp = self.depth_pred[0] + + if raw_disp: + return disp_map_visualize(disp) + disp_scaled = disp[::corner_scale, ::corner_scale] + left_img_np[:disp_scaled.shape[0], -disp_scaled.shape[1]:] = disp_map_visualize(disp_scaled) + return left_img_np + + def compute_loss(self, depth_targets, log, name): + if self.is_numpy: + raise ValueError("Output is not in torch mode") + depth_target_stacked = [] + for depth_target in depth_targets: + depth_target_stacked.append(depth_target.depth_pred) + depth_target_batch = torch.stack(depth_target_stacked) + depth_target_batch = depth_target_batch.to(torch.device('cuda:0')) + depth_loss = self.disp_loss(self.depth_pred, depth_target_batch) + log[name] = depth_loss.item() + return self.hparams.loss_depth_mult * depth_loss + + +class DisparityLoss(nn.Module): + """Smooth L1-loss for disparity with check for valid ground truth""" + + def __init__(self, max_disparity, stdmean_scaled): + super().__init__() + + self.max_disparity = max_disparity + self.stdmean_scaled = stdmean_scaled + self.loss = nn.SmoothL1Loss(reduction="none") + + def forward(self, disparity, disparity_gt, right=False, low_range_div=None): + # Scale ground truth disparity based on output scale. + scale_factor = disparity_gt.shape[2] // disparity.shape[2] + disparity_gt = downsample_disparity(disparity_gt, scale_factor) + max_disparity = self.max_disparity / scale_factor + if low_range_div is not None: + max_disparity /= low_range_div + batch_size, _, _ = disparity.shape + loss = torch.zeros(1, dtype=disparity.dtype, device=disparity.device) + # Not all batch elements may have ground truth for disparity, so we compute the loss for each batch element + # individually. + valid_count = 0 + for batch_idx in range(batch_size): + if torch.sum(disparity_gt[batch_idx, :, :]) < 1e-3: + continue + + single_loss = self.loss(disparity[batch_idx, :, :], disparity_gt[batch_idx, :, :]) + valid_count += 1 + + if self.stdmean_scaled: + # Scale loss by standard deviation and mean of ground truth to reduce influence of very high disparities. + gt_std, gt_mean = torch.std_mean(disparity_gt[batch_idx, :, :]) + loss += torch.mean(single_loss) / (gt_mean + 2.0 * gt_std) + else: + # Scale loss by scale factor due to difference of expected magnitude of disparity at different scales. + loss += torch.mean(single_loss) * scale_factor + # Avoid potential divide by 0. + if valid_count > 0: + return loss / batch_size + else: + return loss + +def downsample_disparity(disparity, factor): + """Downsample disparity using a min-pool operation + + Input can be either a Numpy array or Torch tensor. + """ + with torch.no_grad(): + # Convert input to tensor at the appropriate number of dimensions if needed. + is_numpy = type(disparity) == np.ndarray + if is_numpy: + disparity = torch.from_numpy(disparity) + new_dims = 4 - len(disparity.shape) + for i in range(new_dims): + disparity = disparity.unsqueeze(0) + + disparity = F.max_pool2d(disparity, kernel_size=factor, stride=factor) / factor + + # Convert output disparity back into same format and number of dimensions as input. + for i in range(new_dims): + disparity = disparity.squeeze(0) + if is_numpy: + disparity = disparity.numpy() + return disparity + +def get_disparity_valid_mask(disparity, max_disparity, right=False): + """Generate mask where disparity is valid based on the given max_disparity""" + IGNORE_EDGE = False + result = torch.logical_and(disparity > 1e-3, disparity < (max_disparity - 1 - 1e-3)) + if IGNORE_EDGE: + width = disparity.shape[-1] + edge_mask = torch.arange(width, dtype=disparity.dtype, device=disparity.device) - 1 + if right: + edge_mask = torch.flip(edge_mask, (0,)) + edge_mask = edge_mask.expand_as(disparity) + valid_edge = disparity < edge_mask + result = torch.logical_and(result, valid_edge) + return result + + +def turbo_vis(heatmap, normalize=False, uint8_output=False): + assert len(heatmap.shape) == 2 + if normalize: + heatmap = heatmap.astype(np.float32) + heatmap -= np.min(heatmap) + heatmap /= np.max(heatmap) + assert heatmap.dtype != np.uint8 + + x = heatmap + x = x.clip(0, 1) + a = (x * 255).astype(int) + b = (a + 1).clip(max=255) + f = x * 255.0 - a + turbo_map = datapoint.TURBO_COLORMAP_DATA_NP[::-1] + pseudo_color = (turbo_map[a] + (turbo_map[b] - turbo_map[a]) * f[..., np.newaxis]) + pseudo_color[heatmap < 0.0] = 0.0 + pseudo_color[heatmap > 1.0] = 1.0 + if uint8_output: + pseudo_color = (pseudo_color * 255).astype(np.uint8) + return pseudo_color + + +def disp_map_visualize(x, max_disp=_MAX_DISP): + min_disp = -10000 + assert len(x.shape) == 2 + x = x.astype(np.float64) + valid = ((x < max_disp) & np.isfinite(x) & (x> min_disp)) + if valid.sum() == 0 or x.sum() ==0 or x[valid].sum() ==0: + return np.zeros_like(x).astype(np.uint8) + if np.min(x[valid]) == np.max(x[valid]): + return np.zeros_like(x).astype(np.uint8) + x -= np.min(x[valid]) + x /= np.max(x[valid]) + x = 1. - x + x[~valid] = 0. + x = turbo_vis(x) + x = (x * 255).astype(np.uint8) + return x[:, :, ::-1] diff --git a/simnet/lib/net/post_processing/keypoint_outputs.py b/simnet/lib/net/post_processing/keypoint_outputs.py new file mode 100644 index 0000000..e745e4a --- /dev/null +++ b/simnet/lib/net/post_processing/keypoint_outputs.py @@ -0,0 +1,111 @@ +import numpy as np +import cv2 +import IPython +import torch +import torch.nn as nn +from skimage.feature import peak_local_max +LOSS = nn.BCELoss() +# LOSS = nn.MSELoss() + + +class KeypointOutput: + + def __init__(self, heatmap, hparams, ignore_mask=None): + self.heatmap = heatmap + self.first_heatmap = None + self.ignore_mask = ignore_mask + self.is_numpy = False + self.hparams = hparams + self.loss = LOSS + self.num_keypoints = hparams.num_keypoints + self.all_keypoints = None + + # Converters for torch to numpy + def convert_to_numpy_from_torch(self): + self.heatmap = np.ascontiguousarray(self.heatmap.cpu().numpy()) + self.is_numpy = True + + def convert_to_torch_from_numpy(self): + self.heatmap = torch.from_numpy(np.ascontiguousarray(self.heatmap)).float() + self.is_numpy = False + + def evaluation_metrics(self, targ_kp_output): + targ_keypoints = targ_kp_output.get_keypoints() + results = {} + for i in range(len(targ_keypoints)): + results[i] = {} + for confidence in np.linspace(0, 1.01, 10): + all_keypoints = self.get_keypoints(min_confidence=confidence) + for i, (pred_class, targ_class) in enumerate(zip(all_keypoints, targ_keypoints)): + tp, fp, fn = evaluate_keypoints(pred_class, targ_class) + precision = tp / (tp + fp) if tp + fp > 0 else 1 + recall = tp / (tp + fn) if tp + fn > 0 else 1 + results[i][confidence] = (precision, recall) + return results + + def get_visualization_img(self, left_img): + if not self.is_numpy: + self.convert_to_numpy_from_torch() + if self.num_keypoints == 1: + self.first_heatmap = self.heatmap[:, None, :, :][0] + else: + self.first_heatmap = self.heatmap[0] + return vis_network_outputs(self.first_heatmap, left_img) + + def compute_loss(self, keypoint_targets, log): + if self.is_numpy: + raise ValueError("Output is not in torch mode") + heatmap_target = torch.stack([ + torch.squeeze(keypoint_target.heatmap) for keypoint_target in keypoint_targets + ]) + + # Move to GPU + heatmap_target = heatmap_target.to(torch.device('cuda:0')) + + heatmap_loss = self.loss(self.heatmap, heatmap_target) + log['keypoint'] = heatmap_loss + return self.hparams.loss_keypoint_mult * heatmap_loss + + +def vis_network_outputs(heatmaps, left_img, idx=0): + heatmap_vis = [] + gray_img = cv2.cvtColor(left_img, cv2.COLOR_BGR2GRAY) + for heatmap in heatmaps: + img = np.copy(gray_img) + heatmap /= np.max(heatmap) + img = cv2.addWeighted(heatmap.astype(float), 0.999, img.astype(float), 0.001, 0) + img /= img.max() / 255 + heatmap_vis.append(img[:, :, np.newaxis].astype(np.float32)) + return heatmap_vis + +def extract_peaks_from_heatmap(heatmap, min_distance=40, min_confidence=0.3): + peaks = peak_local_max( + heatmap, + min_distance=min_distance, + threshold_abs=min_confidence, + exclude_border=False, + num_peaks=2 + ) + return peaks + +def evaluate_keypoints(pred_kp, targ_kp, distance_threshold=20): + true_positives = 0 + false_negatives = 0 + counted = [] + all_distances = [] + for px in pred_kp.pixels: + distances = np.linalg.norm(targ_kp.pixels - px, axis=1) + all_distances.append(distances) + all_distances = np.array(all_distances) + for i in range(len(targ_kp.pixels)): + if len(all_distances) == 0: + break + closest = all_distances[:, i].argmin() # prediction that is closest + if all_distances[closest, i] < distance_threshold: + true_positives += 1 # correct prediction + all_distances[closest] = 1e10 # don't let this prediction be a positive for anything else + else: + false_negatives += 1 # no prediction was sufficiently close + false_positives = len(pred_kp.pixels) - true_positives + false_negatives = len(targ_kp.pixels) - true_positives + return true_positives, false_positives, false_negatives diff --git a/simnet/lib/net/post_processing/pose_outputs.py b/simnet/lib/net/post_processing/pose_outputs.py new file mode 100755 index 0000000..ff5a994 --- /dev/null +++ b/simnet/lib/net/post_processing/pose_outputs.py @@ -0,0 +1,189 @@ +import numpy as np +import cv2 +import torch +import torch.nn as nn + +from skimage.feature import peak_local_max +from simnet.lib import transform +from simnet.lib.net import losses + +_mask_l1_loss = losses.MaskedL1Loss() +_mse_loss = losses.MSELoss() + + +class PoseOutput: + + def __init__(self, heatmap, vertex_field, z_centroid_field, hparams): + self.heatmap = heatmap + self.vertex_field = vertex_field + self.z_centroid_field = z_centroid_field + self.is_numpy = False + self.hparams = hparams + + # Converters for torch to numpy + def convert_to_numpy_from_torch(self): + self.heatmap = np.ascontiguousarray(self.heatmap.cpu().numpy()) + self.vertex_field = np.ascontiguousarray(self.vertex_field.cpu().numpy()) + self.vertex_field = self.vertex_field.transpose((0, 2, 3, 1)) + self.vertex_field = self.vertex_field / 100.0 + self.z_centroid_field = np.ascontiguousarray(self.z_centroid_field.cpu().numpy()) + self.z_centroid_field = self.z_centroid_field / 100.0 + 1.0 + self.is_numpy = True + + def convert_to_torch_from_numpy(self): + self.vertex_field = self.vertex_field.transpose((2, 0, 1)) + self.vertex_field = 100.0 * self.vertex_field + self.vertex_field = torch.from_numpy(np.ascontiguousarray(self.vertex_field)).float() + self.heatmap = torch.from_numpy(np.ascontiguousarray(self.heatmap)).float() + # Normalize z_centroid by 1. + self.z_centroid_field = 100.0 * (self.z_centroid_field - 1.0) + self.z_centroid_field = torch.from_numpy(np.ascontiguousarray(self.z_centroid_field)).float() + self.is_numpy = False + + def get_visualization_img(self, left_img): + if not self.is_numpy: + self.convert_to_numpy_from_torch() + return draw_pose_from_outputs( + self.heatmap[0], self.vertex_field[0], self.z_centroid_field[0], left_img + ) + + def compute_loss(self, pose_targets, log): + if self.is_numpy: + raise ValueError("Output is not in torch mode") + vertex_target = torch.stack([pose_target.vertex_field for pose_target in pose_targets]) + z_centroid_field_target = torch.stack([ + pose_target.z_centroid_field for pose_target in pose_targets + ]) + heatmap_target = torch.stack([pose_target.heatmap for pose_target in pose_targets]) + + # Move to GPU + heatmap_target = heatmap_target.to(torch.device('cuda:0')) + vertex_target = vertex_target.to(torch.device('cuda:0')) + z_centroid_field_target = z_centroid_field_target.to(torch.device('cuda:0')) + + vertex_loss = _mask_l1_loss(vertex_target, self.vertex_field, heatmap_target) + log['vertex_loss'] = vertex_loss + z_centroid_loss = _mask_l1_loss(z_centroid_field_target, self.z_centroid_field, heatmap_target) + log['z_centroid'] = z_centroid_loss + + heatmap_loss = _mse_loss(heatmap_target, self.heatmap) + log['heatmap'] = heatmap_loss + return self.hparams.loss_vertex_mult * vertex_loss + self.hparams.loss_heatmap_mult * heatmap_loss + self.hparams.loss_z_centroid_mult * z_centroid_loss + +def _gather_feat(feat, ind, mask=None): + dim = feat.size(2) + ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim) + feat = feat.gather(1, ind) + if mask is not None: + mask = mask.unsqueeze(2).expand_as(feat) + feat = feat[mask] + feat = feat.view(-1, dim) + return feat + +def _topk(scores, K=10): + batch, cat, height, width = scores.size() + topk_scores, topk_inds = torch.topk(scores.view(batch, cat, -1), K) + topk_inds = topk_inds % (height * width) + topk_ys = (topk_inds / width).int().float() + topk_xs = (topk_inds % width).int().float() + + topk_score, topk_ind = torch.topk(topk_scores.view(batch, -1), K) + topk_clses = (topk_ind / K).int() + topk_inds = _gather_feat(topk_inds.view(batch, -1, 1), topk_ind).view(batch, K) + topk_ys = _gather_feat(topk_ys.view(batch, -1, 1), topk_ind).view(batch, K) + topk_xs = _gather_feat(topk_xs.view(batch, -1, 1), topk_ind).view(batch, K) + + ind = topk_scores>0.70 + topk_ys = topk_ys[ind.squeeze(0)] + topk_xs = topk_xs[ind.squeeze(0)] + return topk_score, topk_inds, topk_clses, topk_ys, topk_xs + +def nms_heatmap(heat, kernel=3): + pad = (kernel - 1) // 2 + hmax = nn.functional.max_pool2d(heat, (kernel, kernel), stride=1, padding=pad) + centers_keep = torch.abs(hmax - heat) < 1e-6 + return heat * centers_keep + +def extract_peaks_from_centroid_nms(centroid_heatmap, min_distance=5, min_confidence=0.3): + centroid_heatmap = torch.tensor(centroid_heatmap).unsqueeze(0).unsqueeze(0) + centroid_heatmap = nms_heatmap(centroid_heatmap) + topk_scores, topk_inds,_,topk_ys, topk_xs = _topk(centroid_heatmap) + peaks = np.array([topk_ys.numpy(), topk_xs.numpy()]) + peaks = np.transpose(peaks) + peaks = find_nearest(peaks,[0,0]) + return peaks, topk_scores, topk_inds, topk_ys, topk_xs + +def extract_peaks_from_centroid(centroid_heatmap, min_distance=10, min_confidence=0.20): + peaks = peak_local_max(centroid_heatmap, min_distance=min_distance, threshold_abs=min_confidence) + peaks = peaks[peaks[:,1].argsort()] + return peaks + +def find_nearest(peaks,value): + newList = np.linalg.norm(peaks-value, axis=1) + return peaks[np.argsort(newList)] + +#confidence is 0.3 for CAM and 0.15 for Real +def extract_peaks_from_centroid_sorted(centroid_heatmap,min_confidence=0.15, min_distance=10): + peaks = peak_local_max(centroid_heatmap, min_distance=min_distance, threshold_abs=min_confidence) + peaks = find_nearest(peaks,[0,0]) + return peaks + +def extract_latent_emb_from_peaks(heatmap_output, peaks, latent_emb_output, scale_factor=8): + assert peaks.shape[1] == 2 + latent_embeddings = [] + indices = [] + scores = [] + for ii in range(peaks.shape[0]): + index = np.zeros([2]) + index[0] = int(peaks[ii, 0] / scale_factor) + index[1] = int(peaks[ii, 1] / scale_factor) + index = index.astype(np.int) + latent_emb = latent_emb_output[index[0], index[1], :] + latent_embeddings.append(latent_emb) + indices.append(index*scale_factor) + scores.append(heatmap_output[peaks[ii, 0], peaks[ii, 1]]) + return latent_embeddings, indices, scores + +def extract_abs_pose_from_peaks(peaks, abs_pose_output, scale_factor=8): + assert peaks.shape[1] == 2 + abs_poses = [] + scales = [] + for ii in range(peaks.shape[0]): + index = np.zeros([2]) + index[0] = int(peaks[ii, 0] / scale_factor) + index[1] = int(peaks[ii, 1] / scale_factor) + index = index.astype(np.int) + abs_pose_values = abs_pose_output[index[0], index[1], :] + rotation_matrix = np.array([[abs_pose_values[0], abs_pose_values[1], abs_pose_values[2]], + [abs_pose_values[3], abs_pose_values[4], abs_pose_values[5]], + [abs_pose_values[6], abs_pose_values[7], abs_pose_values[8]]]) + translation_vector = np.array([abs_pose_values[9], abs_pose_values[10], abs_pose_values[11]]) + + transformation_mat = np.eye(4) + transformation_mat[:3,:3] = rotation_matrix + transformation_mat[:3,3] = translation_vector + + scale = abs_pose_values[12] + scale_matrix = np.eye(4) + scale_mat = scale*np.eye(3, dtype=float) + scale_matrix[0:3, 0:3] = scale_mat + scales.append(scale_matrix) + + abs_poses.append(transform.Pose( + camera_T_object=transformation_mat, scale_matrix=scale_matrix + )) + return abs_poses + +def draw_peaks(centroid_target, peaks): + centroid_target = np.clip(centroid_target, 0.0, 1.0) * 255.0 + heatmap_img = cv2.applyColorMap(centroid_target.astype(np.uint8), cv2.COLORMAP_JET) + for ii in range(peaks.shape[0]): + point = (int(peaks[ii, 1]), int(peaks[ii, 0])) + heatmap_img = cv2.putText(heatmap_img,str(ii), + point, + cv2.FONT_HERSHEY_SIMPLEX, + 1, + (255,255,255), + 2) + cv2.line(heatmap_img, (point), ([0,0]), (0, 255, 0), thickness=3, lineType=8) + return heatmap_img \ No newline at end of file diff --git a/simnet/lib/net/post_processing/segmentation_outputs.py b/simnet/lib/net/post_processing/segmentation_outputs.py new file mode 100755 index 0000000..27f5de5 --- /dev/null +++ b/simnet/lib/net/post_processing/segmentation_outputs.py @@ -0,0 +1,79 @@ +import numpy as np +import cv2 +import IPython +import torch +import torch.nn as nn +from torch.nn import functional as F + +from simnet.lib import color_stuff + +# Panoptic Segmentation Colors + + +class SegmentationOutput: + + def __init__(self, seg_pred, hparams): + self.seg_pred = seg_pred + self.is_numpy = False + self.hparams = hparams + + # Converters for torch to numpy + def convert_to_numpy_from_torch(self): + self.seg_pred = np.ascontiguousarray(self.seg_pred.cpu().numpy()) + self.is_numpy = True + + def convert_to_torch_from_numpy(self): + self.seg_pred = torch.from_numpy(np.ascontiguousarray(self.seg_pred)).long() + self.is_numpy = False + + def get_visualization_img(self, left_image): + if not self.is_numpy: + self.convert_to_numpy_from_torch() + return draw_segmentation_mask(left_image, self.seg_pred[0]) + + def get_prediction(self): + if not self.is_numpy: + self.convert_to_numpy_from_torch() + return self.seg_pred[0] + + def compute_loss(self, seg_targets, log, name): + if self.is_numpy: + raise ValueError("Output is not in torch mode") + seg_target_stacked = [] + for seg_target in seg_targets: + seg_target_stacked.append(seg_target.seg_pred) + seg_target_batch = torch.stack(seg_target_stacked) + seg_target_batch = seg_target_batch.to(torch.device('cuda:0')) + seg_loss = F.cross_entropy(self.seg_pred, seg_target_batch, reduction="mean", ignore_index=-100) + # log['segmentation'] = seg_loss + log[name] = seg_loss.item() + return self.hparams.loss_seg_mult * seg_loss + + +def draw_segmentation_mask_gt(color_img, seg_mask, num_classes=5): + assert len(seg_mask.shape) == 2 + seg_mask = seg_mask.astype(np.uint8) + colors = color_stuff.get_panoptic_colors() + color_img = color_img_to_gray(color_img) + for ii, color in zip(range(num_classes), colors): + colored_mask = np.zeros([seg_mask.shape[0], seg_mask.shape[1], 3]) + colored_mask[seg_mask == ii, :] = color + color_img = cv2.addWeighted( + color_img.astype(np.uint8), 0.9, colored_mask.astype(np.uint8), 0.4, 0 + ) + return cv2.cvtColor(color_img, cv2.COLOR_BGR2RGB) + +def color_img_to_gray(image): + gray_scale_img = np.zeros(image.shape) + img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + for i in range(3): + gray_scale_img[:, :, i] = img + gray_scale_img[:, :, i] = img + return gray_scale_img + +def draw_segmentation_mask(color_img, seg_mask): + assert len(seg_mask.shape) == 3 + num_classes = seg_mask.shape[0] + # Convert to predictions + seg_mask_predictions = np.argmax(seg_mask, axis=0) + return draw_segmentation_mask_gt(color_img, seg_mask_predictions, num_classes=num_classes) diff --git a/simnet/lib/net/pre_processing/obb_inputs.py b/simnet/lib/net/pre_processing/obb_inputs.py new file mode 100755 index 0000000..aa289a7 --- /dev/null +++ b/simnet/lib/net/pre_processing/obb_inputs.py @@ -0,0 +1,98 @@ +import numpy as np +from simnet.lib import datapoint +from scipy.stats import multivariate_normal + +_HEATMAP_THRESHOLD = 0.3 +_DOWNSCALE_VALUE = 8 +_PEAK_CONCENTRATION = 0.8 + +def compute_heatmaps_from_masks(masks): + heatmaps = [compute_heatmap_from_mask(mask) for mask in masks] + return heatmaps + +def compute_heatmap_from_mask(mask): + if np.sum(mask) == 0: + raise ValueError('Mask is empty') + coords = np.indices(mask.shape) + coords = coords.reshape([2, -1]).T + mask_f = mask.flatten() + indices = coords[np.where(mask_f > 0)] + mean_value = np.floor(np.average(indices, axis=0)) + cov = np.cov((indices - mean_value).T) + cov = cov * _PEAK_CONCENTRATION + multi_var = multivariate_normal(mean=mean_value, cov=cov) + density = multi_var.pdf(coords) + heat_map = np.zeros(mask.shape) + heat_map[coords[:, 0], coords[:, 1]] = density + return heat_map / np.max(heat_map) + +def compute_nocs_network_targets(masks, pointembs, poses, height, width): + if len(masks) == 0: + height_d = int(height / _DOWNSCALE_VALUE) + width_d = int(width / _DOWNSCALE_VALUE) + return datapoint.ABSPOSE( + heat_map=np.zeros([height, width]), + latent_emb=np.zeros([height_d, width_d, 128]), + abs_pose = np.zeros([height_d, width_d, 13]) + ) + heatmaps = compute_heatmaps_from_masks(masks) + latent_emb_target = compute_latent_emb(masks, pointembs, heatmaps) + abs_pose_target = compute_nocs_abspose_field(poses, heatmaps) + return datapoint.ABSPOSE( + heat_map=np.max(heatmaps, axis=0), + latent_emb= latent_emb_target, + abs_pose = abs_pose_target + ) + +def compute_rotation_field(obbs, heat_maps, threshold=0.3): + cov_target = np.zeros([len(obbs), heat_maps[0].shape[0], heat_maps[0].shape[1], 6]) + heatmap_indices = np.argmax(np.array(heat_maps), axis=0) + for obb, ii in zip(obbs, range(len(heat_maps))): + mask = (heatmap_indices == ii) + cov_matrix = obb.cov_matrix + cov_mat_values = np.array([ + cov_matrix[0, 0], cov_matrix[1, 1], cov_matrix[2, 2], cov_matrix[0, 1], cov_matrix[0, 2], + cov_matrix[1, 2] + ]) + cov_target[ii, mask] = cov_mat_values + return np.sum(cov_target, axis=0)[::_DOWNSCALE_VALUE, ::_DOWNSCALE_VALUE] + +def compute_latent_emb(obbs, point_embs, heat_maps): + latent_emb_target = np.zeros([len(obbs), heat_maps[0].shape[0], heat_maps[0].shape[1], 128]) + heatmap_indices = np.argmax(np.array(heat_maps), axis=0) + for emb, ii in zip(point_embs, range(len(heat_maps))): + mask = (heatmap_indices == ii) + latent_emb_target[ii, mask] = emb + sum = np.sum(latent_emb_target, axis=0)[::_DOWNSCALE_VALUE, ::_DOWNSCALE_VALUE] + return sum + +def compute_abspose_field(poses, heat_maps,camera_model, threshold=0.3): + abs_pose_target = np.zeros([len(poses), heat_maps[0].shape[0], heat_maps[0].shape[1], 13]) + heatmap_indices = np.argmax(np.array(heat_maps), axis=0) + for pose, ii in zip(poses, range(len(heat_maps))): + mask = (heatmap_indices == ii) + actual_abs_pose = camera_model.RT_matrix @ pose.camera_T_object + rotation_matrix = actual_abs_pose[:3,:3] + translation_vector = actual_abs_pose[:3,3] + scale = pose.scale_matrix[0,0] + abs_pose_values = np.array([ + rotation_matrix[0, 0], rotation_matrix[0, 1], rotation_matrix[0, 2], rotation_matrix[1, 0], rotation_matrix[1, 1], rotation_matrix[1, 2], rotation_matrix[2, 0], + rotation_matrix[2, 1], rotation_matrix[2, 2], translation_vector[0], translation_vector[1], translation_vector[2], scale + ]) + abs_pose_target[ii, mask] = abs_pose_values + return np.sum(abs_pose_target, axis=0)[::_DOWNSCALE_VALUE, ::_DOWNSCALE_VALUE] + +def compute_nocs_abspose_field(poses, heat_maps, threshold=0.3): + abs_pose_target = np.zeros([len(poses), heat_maps[0].shape[0], heat_maps[0].shape[1], 13]) + heatmap_indices = np.argmax(np.array(heat_maps), axis=0) + for pose, ii in zip(poses, range(len(heat_maps))): + mask = (heatmap_indices == ii) + rotation_matrix = pose.camera_T_object[:3,:3] + translation_vector = pose.camera_T_object[:3,3] + scale = pose.scale_matrix[0,0] + abs_pose_values = np.array([ + rotation_matrix[0, 0], rotation_matrix[0, 1], rotation_matrix[0, 2], rotation_matrix[1, 0], rotation_matrix[1, 1], rotation_matrix[1, 2], rotation_matrix[2, 0], + rotation_matrix[2, 1], rotation_matrix[2, 2], translation_vector[0], translation_vector[1], translation_vector[2], scale + ]) + abs_pose_target[ii, mask] = abs_pose_values + return np.sum(abs_pose_target, axis=0)[::_DOWNSCALE_VALUE, ::_DOWNSCALE_VALUE] \ No newline at end of file diff --git a/simnet/lib/transform.py b/simnet/lib/transform.py new file mode 100644 index 0000000..a0399ea --- /dev/null +++ b/simnet/lib/transform.py @@ -0,0 +1,113 @@ +import dataclasses + +import numpy as np + +X_AXIS = np.array([1., 0., 0.]) +Y_AXIS = np.array([0., 1., 0.]) +Z_AXIS = np.array([0., 0., 1.]) + + +@dataclasses.dataclass +class Pose: + camera_T_object: np.ndarray + scale_matrix: np.ndarray = np.eye(4) + + +class Transform: + + def __init__(self, matrix=None): + if matrix is None: + self.matrix = np.eye(4) + else: + self.matrix = matrix + self.is_concrete = True + + def apply_transform(self, transform): + assert self.is_concrete + assert isinstance(transform, Transform) + self.matrix = self.matrix @ transform.matrix + + def inverse(self): + assert self.is_concrete + return Transform(matrix=np.linalg.inv(self.matrix)) + + def __repr__(self): + assert self.matrix.shape == (4, 4) + if self.is_SE3(): + return f'Transform(translate={self.translation})' + else: + return f'Transform(IS_NOT_SE3,matrix={self.matrix})' + + def is_SE3(self): + return matrixIsSE3(self.matrix) + + @property + def translation(self): + return self.matrix[:3, 3] + + @translation.setter + def translation(self, value): + assert value.shape == (3,) + self.matrix[:3, 3] = value + + @property + def rotation(self): + return self.matrix[:3, :3] + + @rotation.setter + def rotation(self, value): + assert value.shape == (3, 3) + self.matrix[:3, :3] = value + + @classmethod + def from_aa(cls, axis=X_AXIS, angle_deg=0., translation=None): + assert axis.shape == (3,) + matrix = np.eye(4) + if angle_deg != 0.: + matrix[:3, :3] = axis_angle_to_rotation_matrix(axis, np.deg2rad(angle_deg)) + if translation is not None: + translation = np.array(translation) + assert translation.shape == (3,) + matrix[:3, 3] = translation + return cls(matrix=matrix) + + +def matrixIsSE3(matrix): + if not np.allclose(matrix[3, :], np.array([0., 0., 0., 1.])): + return False + rot = matrix[:3, :3] + if not np.allclose(rot @ rot.T, np.eye(3)): + return False + if not np.isclose(np.linalg.det(rot), 1.): + return False + return True + + +def find_closest_SE3(matrix): + matrix = np.copy(matrix) + assert np.allclose(matrix[3, :], np.array([0., 0., 0., 1.])) + rotation = matrix[:3, :3] + u, s, vh = np.linalg.svd(rotation) + matrix[:3, :3] = u @ vh + assert matrixIsSE3(matrix) + return matrix + + +def axis_angle_to_rotation_matrix(axis, theta): + """Return the rotation matrix associated with counterclockwise rotation about + the given axis by theta radians. + Args: + axis: a list which specifies a unit axis + theta: an angle in radians, for which to rotate around by + Returns: + A 3x3 rotation matrix + """ + axis = np.asarray(axis) + axis = axis / np.sqrt(np.dot(axis, axis)) + a = np.cos(theta / 2.0) + b, c, d = -axis * np.sin(theta / 2.0) + aa, bb, cc, dd = a * a, b * b, c * c, d * d + bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d + return np.array([[aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)], + [2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)], + [2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc]]) \ No newline at end of file diff --git a/utils/__pycache__/nocs_utils.cpython-38.pyc b/utils/__pycache__/nocs_utils.cpython-38.pyc new file mode 100644 index 0000000..6efe78b Binary files /dev/null and b/utils/__pycache__/nocs_utils.cpython-38.pyc differ diff --git a/utils/__pycache__/transform_utils.cpython-38.pyc b/utils/__pycache__/transform_utils.cpython-38.pyc new file mode 100644 index 0000000..0991ca7 Binary files /dev/null and b/utils/__pycache__/transform_utils.cpython-38.pyc differ diff --git a/utils/__pycache__/viz_utils.cpython-38.pyc b/utils/__pycache__/viz_utils.cpython-38.pyc new file mode 100644 index 0000000..acbccb4 Binary files /dev/null and b/utils/__pycache__/viz_utils.cpython-38.pyc differ diff --git a/utils/nocs_eval_utils.py b/utils/nocs_eval_utils.py new file mode 100755 index 0000000..8e972b1 --- /dev/null +++ b/utils/nocs_eval_utils.py @@ -0,0 +1,895 @@ +""" + Evaluation-related codes are modified from + https://github.com/hughw19/NOCS_CVPR2019 +""" +import logging +import os +import math +import cv2 +import numpy as np +import matplotlib.pyplot as plt +import _pickle as cPickle +from numpy.core.fromnumeric import size +from tqdm import tqdm +from simnet.lib import camera + + +def setup_logger(logger_name, log_file, level=logging.INFO): + logger = logging.getLogger(logger_name) + formatter = logging.Formatter('%(asctime)s : %(message)s') + fileHandler = logging.FileHandler(log_file, mode='a') + fileHandler.setFormatter(formatter) + logger.setLevel(level) + logger.addHandler(fileHandler) + streamHandler = logging.StreamHandler() + streamHandler.setFormatter(formatter) + logger.addHandler(streamHandler) + return logger + + +def load_obj(path_to_file): + """ Load obj file. + + Args: + path_to_file: path + + Returns: + vertices: ndarray + faces: ndarray, index of triangle vertices + + """ + vertices = [] + faces = [] + with open(path_to_file, 'r') as f: + for line in f: + if line[:2] == 'v ': + vertex = line[2:].strip().split(' ') + vertex = [float(xyz) for xyz in vertex] + vertices.append(vertex) + elif line[0] == 'f': + face = line[1:].replace('//', '/').strip().split(' ') + face = [int(idx.split('/')[0])-1 for idx in face] + faces.append(face) + else: + continue + vertices = np.asarray(vertices) + faces = np.asarray(faces) + return vertices, faces + + +def create_sphere(): + # 642 verts, 1280 faces, + verts, faces = load_obj('assets/sphere_mesh_template.obj') + return verts, faces + + +def random_point(face_vertices): + """ Sampling point using Barycentric coordiante. + + """ + r1, r2 = np.random.random(2) + sqrt_r1 = np.sqrt(r1) + point = (1 - sqrt_r1) * face_vertices[0, :] + \ + sqrt_r1 * (1 - r2) * face_vertices[1, :] + \ + sqrt_r1 * r2 * face_vertices[2, :] + + return point + + +def pairwise_distance(A, B): + """ Compute pairwise distance of two point clouds.point + + Args: + A: n x 3 numpy array + B: m x 3 numpy array + + Return: + C: n x m numpy array + + """ + diff = A[:, :, None] - B[:, :, None].T + C = np.sqrt(np.sum(diff**2, axis=1)) + + return C + + +def uniform_sample(vertices, faces, n_samples, with_normal=False): + """ Sampling points according to the area of mesh surface. + + """ + sampled_points = np.zeros((n_samples, 3), dtype=float) + normals = np.zeros((n_samples, 3), dtype=float) + faces = vertices[faces] + vec_cross = np.cross(faces[:, 1, :] - faces[:, 0, :], + faces[:, 2, :] - faces[:, 0, :]) + face_area = 0.5 * np.linalg.norm(vec_cross, axis=1) + cum_area = np.cumsum(face_area) + for i in range(n_samples): + face_id = np.searchsorted(cum_area, np.random.random() * cum_area[-1]) + sampled_points[i] = random_point(faces[face_id, :, :]) + normals[i] = vec_cross[face_id] + normals = normals / np.linalg.norm(normals, axis=1, keepdims=True) + if with_normal: + sampled_points = np.concatenate((sampled_points, normals), axis=1) + return sampled_points + + +def farthest_point_sampling(points, n_samples): + """ Farthest point sampling. + + """ + selected_pts = np.zeros((n_samples,), dtype=int) + dist_mat = pairwise_distance(points, points) + # start from first point + pt_idx = 0 + dist_to_set = dist_mat[:, pt_idx] + for i in range(n_samples): + selected_pts[i] = pt_idx + dist_to_set = np.minimum(dist_to_set, dist_mat[:, pt_idx]) + pt_idx = np.argmax(dist_to_set) + return selected_pts + + +def sample_points_from_mesh(path, n_pts, with_normal=False, fps=False, ratio=2): + """ Uniformly sampling points from mesh model. + + Args: + path: path to OBJ file. + n_pts: int, number of points being sampled. + with_normal: return points with normal, approximated by mesh triangle normal + fps: whether to use fps for post-processing, default False. + ratio: int, if use fps, sample ratio*n_pts first, then use fps to sample final output. + + Returns: + points: n_pts x 3, n_pts x 6 if with_normal = True + + """ + vertices, faces = load_obj(path) + if fps: + points = uniform_sample(vertices, faces, ratio*n_pts, with_normal) + pts_idx = farthest_point_sampling(points[:, :3], n_pts) + points = points[pts_idx] + else: + points = uniform_sample(vertices, faces, n_pts, with_normal) + return points + + +def load_depth(depth_path): + """ Load depth image from img_path. """ + # depth_path = img_path + '_depth.png' + depth = cv2.imread(depth_path, -1) + if len(depth.shape) == 3: + # This is encoded depth image, let's convert + # NOTE: RGB is actually BGR in opencv + depth16 = depth[:, :, 1]*256 + depth[:, :, 2] + depth16 = np.where(depth16==32001, 0, depth16) + depth16 = depth16.astype(np.uint16) + elif len(depth.shape) == 2 and depth.dtype == 'uint16': + depth16 = depth + else: + assert False, '[ Error ]: Unsupported depth type.' + return depth16 + + +def get_bbox(bbox): + """ Compute square image crop window. """ + y1, x1, y2, x2 = bbox + img_width = 480 + img_length = 640 + window_size = (max(y2-y1, x2-x1) // 40 + 1) * 40 + window_size = min(window_size, 440) + center = [(y1 + y2) // 2, (x1 + x2) // 2] + rmin = center[0] - int(window_size / 2) + rmax = center[0] + int(window_size / 2) + cmin = center[1] - int(window_size / 2) + cmax = center[1] + int(window_size / 2) + if rmin < 0: + delt = -rmin + rmin = 0 + rmax += delt + if cmin < 0: + delt = -cmin + cmin = 0 + cmax += delt + if rmax > img_width: + delt = rmax - img_width + rmax = img_width + rmin -= delt + if cmax > img_length: + delt = cmax - img_length + cmax = img_length + cmin -= delt + return rmin, rmax, cmin, cmax + + +def compute_sRT_errors(sRT1, sRT2): + """ + Args: + sRT1: [4, 4]. homogeneous affine transformation + sRT2: [4, 4]. homogeneous affine transformation + + Returns: + R_error: angle difference in degree, + T_error: Euclidean distance + IoU: relative scale error + + """ + try: + assert np.array_equal(sRT1[3, :], sRT2[3, :]) + assert np.array_equal(sRT1[3, :], np.array([0, 0, 0, 1])) + except AssertionError: + print(sRT1[3, :], sRT2[3, :]) + + s1 = np.cbrt(np.linalg.det(sRT1[:3, :3])) + R1 = sRT1[:3, :3] / s1 + T1 = sRT1[:3, 3] + s2 = np.cbrt(np.linalg.det(sRT2[:3, :3])) + R2 = sRT2[:3, :3] / s2 + T2 = sRT2[:3, 3] + R12 = R1 @ R2.transpose() + R_error = np.arccos(np.clip((np.trace(R12)-1)/2, -1.0, 1.0)) * 180 / np.pi + T_error = np.linalg.norm(T1 - T2) + IoU = np.abs(s1 - s2) / s2 + + return R_error, T_error, IoU + + +############################################################ +# Evaluation +############################################################ + +def get_3d_bbox(size, shift=0): + """ + Args: + size: [3] or scalar + shift: [3] or scalar + Returns: + bbox_3d: [3, N] + + """ + + # bbox_3d = np.array([[+size[0] / 2, +size[1] / 2, +size[2] / 2], + # [+size[0] / 2, +size[1] / 2, -size[2] / 2], + # [-size[0] / 2, +size[1] / 2, +size[2] / 2], + # [-size[0] / 2, +size[1] / 2, -size[2] / 2], + # [+size[0] / 2, -size[1] / 2, +size[2] / 2], + # [+size[0] / 2, -size[1] / 2, -size[2] / 2], + # [-size[0] / 2, -size[1] / 2, +size[2] / 2], + # [-size[0] / 2, -size[1] / 2, -size[2] / 2]]) + shift + + bbox_3d = np.array([[0,0,0], + [-size[0] / 2, -size[1] / 2, -size[2] / 2], + [-size[0] / 2, -size[1] / 2, +size[2] / 2], + [-size[0] / 2, +size[1] / 2, -size[2] / 2], + [-size[0] / 2, +size[1] / 2, +size[2] / 2], + [+size[0] / 2, -size[1] / 2, -size[2] / 2], + [+size[0] / 2, -size[1] / 2, +size[2] / 2], + [+size[0] / 2, +size[1] / 2, -size[2] / 2], + [+size[0] / 2, +size[1] / 2, +size[2] / 2]]) + shift + + bbox_3d = bbox_3d.transpose() + return bbox_3d + + +# def transform_coordinates_3d(coordinates, sRT): +# """ +# Args: +# coordinates: [3, N] +# sRT: [4, 4] + +# Returns: +# new_coordinates: [3, N] + +# """ +# assert coordinates.shape[0] == 3 +# coordinates = np.vstack([coordinates, np.ones((1, coordinates.shape[1]), dtype=np.float32)]) +# new_coordinates = sRT @ coordinates +# new_coordinates = new_coordinates[:3, :] / new_coordinates[3, :] +# return new_coordinates + +def transform_coordinates_3d(coordinates, RT): + """ + Args: + coordinates: [3, N] + sRT: [4, 4] + + Returns: + new_coordinates: [3, N] + + """ + # assert coordinates.shape[0] == 3 + # coordinates = np.vstack([coordinates, np.ones((1, coordinates.shape[1]), dtype=np.float32)]) + # new_coordinates = sRT @ coordinates + # new_coordinates = new_coordinates[:3, :] / new_coordinates[3, :] + + unit_box_homopoints = camera.convert_points_to_homopoints(coordinates) + # morphed_box_homopoints = pose.camera_T_object @ (pose.scale_matrix @ unit_box_homopoints) + morphed_box_homopoints = RT @ unit_box_homopoints + new_coordinates = camera.convert_homopoints_to_points(morphed_box_homopoints) + + return new_coordinates + + +def compute_3d_IoU(sRT_1, sRT_2, size_1, size_2, class_name_1, class_name_2, handle_visibility): + """ Computes IoU overlaps between two 3D bboxes. """ + def asymmetric_3d_iou(sRT_1, sRT_2, size_1, size_2): + + # print("sizes", size_1, size_2) + # print("SRT", sRT_1, sRT_1) + noc_cube_1 = get_3d_bbox(size_1, 0) + bbox_3d_1 = transform_coordinates_3d(noc_cube_1, sRT_1) + noc_cube_2 = get_3d_bbox(size_2, 0) + bbox_3d_2 = transform_coordinates_3d(noc_cube_2, sRT_2) + + # print("box 3d 1", bbox_3d_1) + # print("box 3d 2", bbox_3d_2) + + bbox_1_max = np.amax(bbox_3d_1, axis=0) + bbox_1_min = np.amin(bbox_3d_1, axis=0) + bbox_2_max = np.amax(bbox_3d_2, axis=0) + bbox_2_min = np.amin(bbox_3d_2, axis=0) + + overlap_min = np.maximum(bbox_1_min, bbox_2_min) + overlap_max = np.minimum(bbox_1_max, bbox_2_max) + # intersections and union + if np.amin(overlap_max - overlap_min) < 0: + intersections = 0 + else: + intersections = np.prod(overlap_max - overlap_min) + union = np.prod(bbox_1_max - bbox_1_min) + np.prod(bbox_2_max - bbox_2_min) - intersections + + # print("overlap_max: ", overlap_max) + # print("overlap_min: ", overlap_min) + # print("insterection, union", intersections, union) + overlaps = intersections / union + return overlaps + + if sRT_1 is None or sRT_2 is None: + return -1 + if (class_name_1 in ['bottle', 'bowl', 'can'] and class_name_1 == class_name_2) or \ + (class_name_1 == 'mug' and class_name_1 == class_name_2 and handle_visibility==0): + def y_rotation_matrix(theta): + return np.array([[ np.cos(theta), 0, np.sin(theta), 0], + [ 0, 1, 0, 0], + [-np.sin(theta), 0, np.cos(theta), 0], + [ 0, 0, 0, 1]]) + n = 20 + max_iou = 0 + for i in range(n): + rotated_RT_1 = sRT_1 @ y_rotation_matrix(2 * math.pi * i / float(n)) + max_iou = max(max_iou, asymmetric_3d_iou(rotated_RT_1, sRT_2, size_1, size_2)) + else: + max_iou = asymmetric_3d_iou(sRT_1, sRT_2, size_1, size_2) + + return max_iou + + +def compute_IoU_matches(gt_class_ids, gt_sRT, gt_size, gt_handle_visibility, + pred_class_ids, pred_sRT, pred_size, + synset_names, iou_3d_thresholds, score_threshold=0): + """ Find matches between NOCS prediction and ground truth instances. + + Args: + size: 3D bounding box size + bboxes: 2D bounding boxes + + Returns: + gt_matches: 2-D array. For each GT box it has the index of the matched predicted box. + pred_matches: 2-D array. For each predicted box, it has the index of the matched ground truth box. + overlaps: IoU overlaps. + indices: + + """ + num_pred = len(pred_class_ids) + num_gt = len(gt_class_ids) + # indices = np.zeros(0) + indices = np.arange(len(pred_class_ids)) + # if num_pred: + # # Sort predictions by score from high to low + # indices = np.argsort(pred_size)[::-1] + # pred_class_ids = pred_class_ids[indices].copy() + # pred_size = pred_size[indices].copy() + # pred_sRT = pred_sRT[indices].copy() + # compute IoU overlaps [pred_bboxs gt_bboxs] + overlaps = np.zeros((num_pred, num_gt), dtype=np.float32) + for i in range(num_pred): + for j in range(num_gt): + # print("pred SRT", pred_sRT[i]) + # print("gt SRT",gt_sRT[j]) + # print("\n") + # print("pred_size[i, :]", pred_size[i, :]) + # print("gt_size[j]", gt_size[j]) + overlaps[i, j] = compute_3d_IoU(pred_sRT[i], gt_sRT[j], pred_size[i, :], gt_size[j], + synset_names[pred_class_ids[i]], synset_names[gt_class_ids[j]], gt_handle_visibility[j]) + # print("\n") + # print("overlaps", overlaps) + # loop through predictions and find matching ground truth boxes + num_iou_3d_thres = len(iou_3d_thresholds) + pred_matches = -1 * np.ones([num_iou_3d_thres, num_pred]) + gt_matches = -1 * np.ones([num_iou_3d_thres, num_gt]) + for s, iou_thres in enumerate(iou_3d_thresholds): + for i in range(pred_sRT.shape[0]): + # Find best matching ground truth box + # 1. Sort matches by score + sorted_ixs = np.argsort(overlaps[i])[::-1] + # 2. Remove low scores + low_score_idx = np.where(overlaps[i, sorted_ixs] < score_threshold)[0] + if low_score_idx.size > 0: + sorted_ixs = sorted_ixs[:low_score_idx[0]] + # 3. Find the match + for j in sorted_ixs: + # If ground truth box is already matched, go to next one + if gt_matches[s, j] > -1: + continue + # If we reach IoU smaller than the threshold, end the loop + iou = overlaps[i, j] + if iou < iou_thres: + break + # Do we have a match? + if not pred_class_ids[i] == gt_class_ids[j]: + continue + if iou > iou_thres: + gt_matches[s, j] = i + pred_matches[s, i] = j + break + # print("pred match", pred_matches.shape) + # print("------------\n") + return gt_matches, pred_matches, overlaps, indices + + +def compute_RT_errors(sRT_1, sRT_2, class_id, handle_visibility, synset_names): + """ + Args: + sRT_1: [4, 4]. homogeneous affine transformation + sRT_2: [4, 4]. homogeneous affine transformation + + Returns: + theta: angle difference of R in degree + shift: l2 difference of T in centimeter + """ + # make sure the last row is [0, 0, 0, 1] + if sRT_1 is None or sRT_2 is None: + return -1 + try: + assert np.array_equal(sRT_1[3, :], sRT_2[3, :]) + assert np.array_equal(sRT_1[3, :], np.array([0, 0, 0, 1])) + except AssertionError: + print(sRT_1[3, :], sRT_2[3, :]) + exit() + + R1 = sRT_1[:3, :3] / np.cbrt(np.linalg.det(sRT_1[:3, :3])) + T1 = sRT_1[:3, 3] + R2 = sRT_2[:3, :3] / np.cbrt(np.linalg.det(sRT_2[:3, :3])) + T2 = sRT_2[:3, 3] + # symmetric when rotating around y-axis + if synset_names[class_id] in ['bottle', 'can', 'bowl'] or \ + (synset_names[class_id] == 'mug' and handle_visibility == 0): + y = np.array([0, 1, 0]) + y1 = R1 @ y + y2 = R2 @ y + cos_theta = y1.dot(y2) / (np.linalg.norm(y1) * np.linalg.norm(y2)) + else: + R = R1 @ R2.transpose() + cos_theta = (np.trace(R) - 1) / 2 + + theta = np.arccos(np.clip(cos_theta, -1.0, 1.0)) * 180 / np.pi + shift = np.linalg.norm(T1 - T2) * 100 + result = np.array([theta, shift]) + + return result + + +def compute_RT_overlaps(gt_class_ids, gt_sRT, gt_handle_visibility, pred_class_ids, pred_sRT, synset_names): + """ Finds overlaps between prediction and ground truth instances. + + Returns: + overlaps: + + """ + num_pred = len(pred_class_ids) + num_gt = len(gt_class_ids) + overlaps = np.zeros((num_pred, num_gt, 2)) + + for i in range(num_pred): + for j in range(num_gt): + overlaps[i, j, :] = compute_RT_errors(pred_sRT[i], gt_sRT[j], gt_class_ids[j], + gt_handle_visibility[j], synset_names) + return overlaps + + +def compute_RT_matches(overlaps, pred_class_ids, gt_class_ids, degree_thres_list, shift_thres_list): + num_degree_thres = len(degree_thres_list) + num_shift_thres = len(shift_thres_list) + num_pred = len(pred_class_ids) + num_gt = len(gt_class_ids) + + pred_matches = -1 * np.ones((num_degree_thres, num_shift_thres, num_pred)) + gt_matches = -1 * np.ones((num_degree_thres, num_shift_thres, num_gt)) + + if num_pred == 0 or num_gt == 0: + return gt_matches, pred_matches + + assert num_pred == overlaps.shape[0] + assert num_gt == overlaps.shape[1] + assert overlaps.shape[2] == 2 + + for d, degree_thres in enumerate(degree_thres_list): + for s, shift_thres in enumerate(shift_thres_list): + for i in range(num_pred): + # Find best matching ground truth box + # 1. Sort matches by scores from low to high + sum_degree_shift = np.sum(overlaps[i, :, :], axis=-1) + sorted_ixs = np.argsort(sum_degree_shift) + # 2. Find the match + for j in sorted_ixs: + # If ground truth box is already matched, go to next one + if gt_matches[d, s, j] > -1 or pred_class_ids[i] != gt_class_ids[j]: + continue + # If we reach IoU smaller than the threshold, end the loop + if overlaps[i, j, 0] > degree_thres or overlaps[i, j, 1] > shift_thres: + continue + gt_matches[d, s, j] = i + pred_matches[d, s, i] = j + break + + return gt_matches, pred_matches + + +def compute_ap_and_acc(pred_matches, gt_matches): + # sort the scores from high to low + # assert pred_matches.shape[0] == pred_scores.shape[0] + # score_indices = np.argsort(pred_scores)[::-1] + # # pred_scores = pred_scores[score_indices] + # pred_matches = pred_matches[score_indices] + + precisions = np.cumsum(pred_matches > -1) / (np.arange(len(pred_matches)) + 1) + recalls = np.cumsum(pred_matches > -1).astype(np.float32) / len(gt_matches) + # Pad with start and end values to simplify the math + precisions = np.concatenate([[0], precisions, [0]]) + recalls = np.concatenate([[0], recalls, [1]]) + # Ensure precision values decrease but don't increase. This way, the + # precision value at each recall threshold is the maximum it can be + # for all following recall thresholds, as specified by the VOC paper. + for i in range(len(precisions) - 2, -1, -1): + precisions[i] = np.maximum(precisions[i], precisions[i + 1]) + # compute mean AP over recall range + indices = np.where(recalls[:-1] != recalls[1:])[0] + 1 + ap = np.sum((recalls[indices] - recalls[indices - 1]) * precisions[indices]) + # accuracy + acc = np.sum(pred_matches > -1) / len(pred_matches) + + return ap, acc + + +def compute_mAP(pred_results, out_dir, degree_thresholds=[180], shift_thresholds=[100], + iou_3d_thresholds=[0.1], iou_pose_thres=0.1, use_matches_for_pose=False): + """ Compute mean Average Precision. + + Returns: + iou_aps: + pose_aps: + iou_acc: + pose_acc: + + """ + synset_names = ['BG', 'bottle', 'bowl', 'camera', 'can', 'laptop', 'mug'] + num_classes = len(synset_names) + degree_thres_list = list(degree_thresholds) + [360] + num_degree_thres = len(degree_thres_list) + shift_thres_list = list(shift_thresholds) + [100] + num_shift_thres = len(shift_thres_list) + iou_thres_list = list(iou_3d_thresholds) + num_iou_thres = len(iou_thres_list) + + if use_matches_for_pose: + assert iou_pose_thres in iou_thres_list + + # pre-allocate more than enough memory + iou_aps = np.zeros((num_classes + 1, num_iou_thres)) + iou_acc = np.zeros((num_classes + 1, num_iou_thres)) + iou_pred_matches_all = [np.zeros((num_iou_thres, 30000)) for _ in range(num_classes)] + # iou_pred_scores_all = [np.zeros((num_iou_thres, 30000)) for _ in range(num_classes)] + iou_gt_matches_all = [np.zeros((num_iou_thres, 30000)) for _ in range(num_classes)] + iou_pred_count = [0 for _ in range(num_classes)] + iou_gt_count = [0 for _ in range(num_classes)] + + pose_aps = np.zeros((num_classes + 1, num_degree_thres, num_shift_thres)) + pose_acc = np.zeros((num_classes + 1, num_degree_thres, num_shift_thres)) + pose_pred_matches_all = [np.zeros((num_degree_thres, num_shift_thres, 30000)) for _ in range(num_classes)] + # pose_pred_scores_all = [np.zeros((num_degree_thres, num_shift_thres, 30000)) for _ in range(num_classes)] + pose_gt_matches_all = [np.zeros((num_degree_thres, num_shift_thres, 30000)) for _ in range(num_classes)] + pose_pred_count = [0 for _ in range(num_classes)] + pose_gt_count = [0 for _ in range(num_classes)] + + # loop over results to gather pred matches and gt matches for iou and pose metrics + progress = 0 + for progress, result in enumerate(tqdm(pred_results)): + gt_class_ids = result['gt_class_ids'].astype(np.int32) + # print("gt class ids", gt_class_ids) + gt_sRT = np.array(result['gt_RTs']) + gt_size = np.array(result['gt_scales']) + gt_handle_visibility = result['gt_handle_visibility'] + + pred_class_ids = result['pred_class_ids'] + # print("pred_class ids", pred_class_ids) + # print("=====================\n") + pred_sRT = np.array(result['pred_RTs']) + pred_size = result['pred_scales'] + # pred_scores = result['pred_scores'] + # pred_scores = [] + + if len(gt_class_ids) == 0 and len(pred_class_ids) == 0: + continue + + for cls_id in range(1, num_classes): + # print("class id", cls_id) + # print("gt class ids", gt_class_ids) + # print("gt class ids = class id", gt_class_ids==cls_id) + cls_gt_class_ids = gt_class_ids[gt_class_ids==cls_id] if len(gt_class_ids) else np.zeros(0) + cls_gt_sRT = gt_sRT[gt_class_ids==cls_id] if len(gt_class_ids) else np.zeros((0, 4, 4)) + cls_gt_size = gt_size[gt_class_ids==cls_id] if len(gt_class_ids) else np.zeros((0, 3)) + + # print("cls_gt_class_ids", cls_gt_class_ids) + # print("clas gt_size", cls_gt_size) + if synset_names[cls_id] != 'mug': + cls_gt_handle_visibility = np.ones_like(cls_gt_class_ids) + else: + cls_gt_handle_visibility = gt_handle_visibility[gt_class_ids==cls_id] if len(gt_class_ids) else np.ones(0) + + # print("pred class ids", pred_class_ids) + # print("pred class ids = class id", pred_class_ids==cls_id) + cls_pred_class_ids = pred_class_ids[pred_class_ids==cls_id] if len(pred_class_ids) else np.zeros(0) + cls_pred_sRT = pred_sRT[pred_class_ids==cls_id] if len(pred_class_ids) else np.zeros((0, 4, 4)) + cls_pred_size = pred_size[pred_class_ids==cls_id] if len(pred_class_ids) else np.zeros((0, 3)) + # cls_pred_scores = pred_scores[pred_class_ids==cls_id] if len(pred_scores) else np.zeros(0) + + # calculate the overlap between each gt instance and pred instance + iou_cls_gt_match, iou_cls_pred_match, _, iou_pred_indices = \ + compute_IoU_matches(cls_gt_class_ids, cls_gt_sRT, cls_gt_size, cls_gt_handle_visibility, + cls_pred_class_ids, cls_pred_sRT, cls_pred_size, + synset_names, iou_thres_list) + + # print("IOU CLASS pred matches", iou_cls_pred_match) + if len(iou_pred_indices): + cls_pred_class_ids = cls_pred_class_ids[iou_pred_indices] + cls_pred_sRT = cls_pred_sRT[iou_pred_indices] + # cls_pred_scores = cls_pred_scores[iou_pred_indices] + + num_pred = iou_cls_pred_match.shape[1] + pred_start = iou_pred_count[cls_id] + pred_end = pred_start + num_pred + iou_pred_count[cls_id] = pred_end + iou_pred_matches_all[cls_id][:, pred_start:pred_end] = iou_cls_pred_match + # cls_pred_scores_tile = np.tile(cls_pred_scores, (num_iou_thres, 1)) + # assert cls_pred_scores_tile.shape[1] == num_pred + # iou_pred_scores_all[cls_id][:, pred_start:pred_end] = cls_pred_scores_tile + num_gt = iou_cls_gt_match.shape[1] + gt_start = iou_gt_count[cls_id] + gt_end = gt_start + num_gt + iou_gt_count[cls_id] = gt_end + iou_gt_matches_all[cls_id][:, gt_start:gt_end] = iou_cls_gt_match + + if use_matches_for_pose: + thres_ind = list(iou_thres_list).index(iou_pose_thres) + iou_thres_pred_match = iou_cls_pred_match[thres_ind, :] + cls_pred_class_ids = cls_pred_class_ids[iou_thres_pred_match > -1] if len(iou_thres_pred_match) > 0 else np.zeros(0) + cls_pred_sRT = cls_pred_sRT[iou_thres_pred_match > -1] if len(iou_thres_pred_match) > 0 else np.zeros((0, 4, 4)) + # cls_pred_scores = cls_pred_scores[iou_thres_pred_match > -1] if len(iou_thres_pred_match) > 0 else np.zeros(0) + iou_thres_gt_match = iou_cls_gt_match[thres_ind, :] + cls_gt_class_ids = cls_gt_class_ids[iou_thres_gt_match > -1] if len(iou_thres_gt_match) > 0 else np.zeros(0) + cls_gt_sRT = cls_gt_sRT[iou_thres_gt_match > -1] if len(iou_thres_gt_match) > 0 else np.zeros((0, 4, 4)) + cls_gt_handle_visibility = cls_gt_handle_visibility[iou_thres_gt_match > -1] if len(iou_thres_gt_match) > 0 else np.zeros(0) + + RT_overlaps = compute_RT_overlaps(cls_gt_class_ids, cls_gt_sRT, cls_gt_handle_visibility, + cls_pred_class_ids, cls_pred_sRT, synset_names) + pose_cls_gt_match, pose_cls_pred_match = compute_RT_matches(RT_overlaps, cls_pred_class_ids, cls_gt_class_ids, + degree_thres_list, shift_thres_list) + num_pred = pose_cls_pred_match.shape[2] + pred_start = pose_pred_count[cls_id] + pred_end = pred_start + num_pred + pose_pred_count[cls_id] = pred_end + pose_pred_matches_all[cls_id][:, :, pred_start:pred_end] = pose_cls_pred_match + # cls_pred_scores_tile = np.tile(cls_pred_scores, (num_degree_thres, num_shift_thres, 1)) + # assert cls_pred_scores_tile.shape[2] == num_pred + # pose_pred_scores_all[cls_id][:, :, pred_start:pred_end] = cls_pred_scores_tile + num_gt = pose_cls_gt_match.shape[2] + gt_start = pose_gt_count[cls_id] + gt_end = gt_start + num_gt + pose_gt_count[cls_id] = gt_end + pose_gt_matches_all[cls_id][:, :, gt_start:gt_end] = pose_cls_gt_match + + # trim zeros + for cls_id in range(num_classes): + # IoU + iou_pred_matches_all[cls_id] = iou_pred_matches_all[cls_id][:, :iou_pred_count[cls_id]] + # iou_pred_scores_all[cls_id] = iou_pred_scores_all[cls_id][:, :iou_pred_count[cls_id]] + iou_gt_matches_all[cls_id] = iou_gt_matches_all[cls_id][:, :iou_gt_count[cls_id]] + # pose + pose_pred_matches_all[cls_id] = pose_pred_matches_all[cls_id][:, :, :pose_pred_count[cls_id]] + # pose_pred_scores_all[cls_id] = pose_pred_scores_all[cls_id][:, :, :pose_pred_count[cls_id]] + pose_gt_matches_all[cls_id] = pose_gt_matches_all[cls_id][:, :, :pose_gt_count[cls_id]] + + # compute 3D IoU mAP + for cls_id in range(1, num_classes): + for s, iou_thres in enumerate(iou_thres_list): + iou_aps[cls_id, s], iou_acc[cls_id, s] = compute_ap_and_acc(iou_pred_matches_all[cls_id][s, :], + # iou_pred_scores_all[cls_id][s, :], + iou_gt_matches_all[cls_id][s, :]) + iou_aps[-1, :] = np.mean(iou_aps[1:-1, :], axis=0) + iou_acc[-1, :] = np.mean(iou_acc[1:-1, :], axis=0) + # compute pose mAP + for i, degree_thres in enumerate(degree_thres_list): + for j, shift_thres in enumerate(shift_thres_list): + for cls_id in range(1, num_classes): + cls_pose_pred_matches_all = pose_pred_matches_all[cls_id][i, j, :] + cls_pose_gt_matches_all = pose_gt_matches_all[cls_id][i, j, :] + # cls_pose_pred_scores_all = pose_pred_scores_all[cls_id][i, j, :] + pose_aps[cls_id, i, j], pose_acc[cls_id, i, j] = compute_ap_and_acc(cls_pose_pred_matches_all, + # cls_pose_pred_scores_all, + cls_pose_gt_matches_all) + pose_aps[-1, i, j] = np.mean(pose_aps[1:-1, i, j]) + pose_acc[-1, i, j] = np.mean(pose_acc[1:-1, i, j]) + + # save results to pkl + result_dict = {} + result_dict['iou_thres_list'] = iou_thres_list + result_dict['degree_thres_list'] = degree_thres_list + result_dict['shift_thres_list'] = shift_thres_list + result_dict['iou_aps'] = iou_aps + result_dict['pose_aps'] = pose_aps + result_dict['iou_acc'] = iou_acc + result_dict['pose_acc'] = pose_acc + pkl_path = os.path.join(out_dir, 'mAP_Acc.pkl') + with open(pkl_path, 'wb') as f: + cPickle.dump(result_dict, f) + return iou_aps, pose_aps, iou_acc, pose_acc + + +def plot_mAP(iou_aps, pose_aps, out_dir, iou_thres_list, degree_thres_list, shift_thres_list): + """ Draw iou 3d AP vs. iou thresholds. + """ + + labels = ['bottle', 'bowl', 'camera', 'can', 'laptop', 'mug', 'mean', 'nocs'] + colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:pink', 'tab:olive', 'tab:purple', 'tab:red', 'tab:gray'] + styles = ['-', '-', '-', '-', '-', '-', '--', ':'] + + fig, (ax_iou, ax_degree, ax_shift) = plt.subplots(1, 3, figsize=(8, 3.5)) + # IoU subplot + ax_iou.set_title('3D IoU', fontsize=10) + ax_iou.set_ylabel('Average Precision') + ax_iou.set_ylim(0, 100) + ax_iou.set_xlabel('Percent') + ax_iou.set_xlim(0, 100) + ax_iou.xaxis.set_ticks([0, 25, 50, 75, 100]) + ax_iou.grid() + for i in range(1, iou_aps.shape[0]): + ax_iou.plot(100*np.array(iou_thres_list), 100*iou_aps[i, :], + color=colors[i-1], linestyle=styles[i-1], label=labels[i-1]) + # rotation subplot + ax_degree.set_title('Rotation', fontsize=10) + ax_degree.set_ylim(0, 100) + ax_degree.yaxis.set_ticklabels([]) + ax_degree.set_xlabel('Degree') + ax_degree.set_xlim(0, 60) + ax_degree.xaxis.set_ticks([0, 20, 40, 60]) + ax_degree.grid() + for i in range(1, pose_aps.shape[0]): + ax_degree.plot(np.array(degree_thres_list), 100*pose_aps[i, :len(degree_thres_list), -1], + color=colors[i-1], linestyle=styles[i-1], label=labels[i-1]) + # translation subplot + ax_shift.set_title('Translation', fontsize=10) + ax_shift.set_ylim(0, 100) + ax_shift.yaxis.set_ticklabels([]) + ax_shift.set_xlabel('Centimeter') + ax_shift.set_xlim(0, 10) + ax_shift.xaxis.set_ticks([0, 5, 10]) + ax_shift.grid() + for i in range(1, pose_aps.shape[0]): + ax_shift.plot(np.array(shift_thres_list), 100*pose_aps[i, -1, :len(shift_thres_list)], + color=colors[i-1], linestyle=styles[i-1], label=labels[i-1]) + ax_shift.legend(loc='lower right', fontsize='small') + plt.tight_layout() + # plt.show() + plt.savefig(os.path.join(out_dir, 'mAP.png')) + plt.close(fig) + return + + +def calculate_2d_projections(coordinates_3d, intrinsics): + """ + Args: + coordinates_3d: [3, N] + intrinsics: [3, 3] + + Returns: + projected_coordinates: [N, 2] + """ + projected_coordinates = intrinsics @ coordinates_3d + projected_coordinates = projected_coordinates[:2, :] / projected_coordinates[2, :] + projected_coordinates = projected_coordinates.transpose() + projected_coordinates = np.array(projected_coordinates, dtype=np.int32) + + return projected_coordinates + + +def align_rotation(sRT): + """ Align rotations for symmetric objects. + Args: + sRT: 4 x 4 + """ + s = np.cbrt(np.linalg.det(sRT[:3, :3])) + R = sRT[:3, :3] / s + T = sRT[:3, 3] + + theta_x = R[0, 0] + R[2, 2] + theta_y = R[0, 2] - R[2, 0] + r_norm = math.sqrt(theta_x**2 + theta_y**2) + s_map = np.array([[theta_x/r_norm, 0.0, -theta_y/r_norm], + [0.0, 1.0, 0.0 ], + [theta_y/r_norm, 0.0, theta_x/r_norm]]) + rotation = R @ s_map + aligned_sRT = np.identity(4, dtype=np.float32) + aligned_sRT[:3, :3] = s * rotation + aligned_sRT[:3, 3] = T + return aligned_sRT + + +def draw_bboxes(img, img_pts, color): + img_pts = np.int32(img_pts).reshape(-1, 2) + # draw ground layer in darker color + color_ground = (int(color[0]*0.3), int(color[1]*0.3), int(color[2]*0.3)) + for i, j in zip([4, 5, 6, 7], [5, 7, 4, 6]): + img = cv2.line(img, tuple(img_pts[i]), tuple(img_pts[j]), color_ground, 2) + # draw pillars in minor darker color + color_pillar = (int(color[0]*0.6), int(color[1]*0.6), int(color[2]*0.6)) + for i, j in zip(range(4), range(4, 8)): + img = cv2.line(img, tuple(img_pts[i]), tuple(img_pts[j]), color_pillar, 2) + # draw top layer in original color + for i, j in zip([0, 1, 2, 3], [1, 3, 0, 2]): + img = cv2.line(img, tuple(img_pts[i]), tuple(img_pts[j]), color, 2) + + return img + + +def draw_detections(img, out_dir, data_name, img_id, intrinsics, pred_sRT, pred_size, pred_class_ids, + gt_sRT, gt_size, gt_class_ids, nocs_sRT, nocs_size, nocs_class_ids, draw_gt=True, draw_nocs=True): + """ Visualize pose predictions. + """ + out_path = os.path.join(out_dir, '{}_{}_pred.png'.format(data_name, img_id)) + + # draw nocs results - BLUE color + if draw_nocs: + for i in range(nocs_sRT.shape[0]): + if nocs_class_ids[i] in [1, 2, 4]: + sRT = align_rotation(nocs_sRT[i, :, :]) + else: + sRT = nocs_sRT[i, :, :] + bbox_3d = get_3d_bbox(nocs_size[i, :], 0) + transformed_bbox_3d = transform_coordinates_3d(bbox_3d, sRT) + projected_bbox = calculate_2d_projections(transformed_bbox_3d, intrinsics) + img = draw_bboxes(img, projected_bbox, (255, 0, 0)) + # darw ground truth - GREEN color + if draw_gt: + for i in range(gt_sRT.shape[0]): + if gt_class_ids[i] in [1, 2, 4]: + sRT = align_rotation(gt_sRT[i, :, :]) + else: + sRT = gt_sRT[i, :, :] + bbox_3d = get_3d_bbox(gt_size[i, :], 0) + transformed_bbox_3d = transform_coordinates_3d(bbox_3d, sRT) + projected_bbox = calculate_2d_projections(transformed_bbox_3d, intrinsics) + img = draw_bboxes(img, projected_bbox, (0, 255, 0)) + # darw prediction - RED color + for i in range(pred_sRT.shape[0]): + if pred_class_ids[i] in [1, 2, 4]: + sRT = align_rotation(pred_sRT[i, :, :]) + else: + sRT = pred_sRT[i, :, :] + bbox_3d = get_3d_bbox(pred_size[i, :], 0) + transformed_bbox_3d = transform_coordinates_3d(bbox_3d, sRT) + projected_bbox = calculate_2d_projections(transformed_bbox_3d, intrinsics) + img = draw_bboxes(img, projected_bbox, (0, 0, 255)) + + cv2.imwrite(out_path, img) + # cv2.imshow('vis', img) + # cv2.waitKey(0) diff --git a/utils/nocs_eval_utils_od.py b/utils/nocs_eval_utils_od.py new file mode 100755 index 0000000..3e9d66b --- /dev/null +++ b/utils/nocs_eval_utils_od.py @@ -0,0 +1,838 @@ +""" + Evaluation-related codes are modified from + https://github.com/hughw19/NOCS_CVPR2019 +""" +import logging +import os +import math +import cv2 +import numpy as np +import matplotlib.pyplot as plt +import _pickle as cPickle +from tqdm import tqdm + + +def setup_logger(logger_name, log_file, level=logging.INFO): + logger = logging.getLogger(logger_name) + formatter = logging.Formatter('%(asctime)s : %(message)s') + fileHandler = logging.FileHandler(log_file, mode='a') + fileHandler.setFormatter(formatter) + logger.setLevel(level) + logger.addHandler(fileHandler) + streamHandler = logging.StreamHandler() + streamHandler.setFormatter(formatter) + logger.addHandler(streamHandler) + return logger + + +def load_obj(path_to_file): + """ Load obj file. + + Args: + path_to_file: path + + Returns: + vertices: ndarray + faces: ndarray, index of triangle vertices + + """ + vertices = [] + faces = [] + with open(path_to_file, 'r') as f: + for line in f: + if line[:2] == 'v ': + vertex = line[2:].strip().split(' ') + vertex = [float(xyz) for xyz in vertex] + vertices.append(vertex) + elif line[0] == 'f': + face = line[1:].replace('//', '/').strip().split(' ') + face = [int(idx.split('/')[0])-1 for idx in face] + faces.append(face) + else: + continue + vertices = np.asarray(vertices) + faces = np.asarray(faces) + return vertices, faces + + +def create_sphere(): + # 642 verts, 1280 faces, + verts, faces = load_obj('assets/sphere_mesh_template.obj') + return verts, faces + + +def random_point(face_vertices): + """ Sampling point using Barycentric coordiante. + + """ + r1, r2 = np.random.random(2) + sqrt_r1 = np.sqrt(r1) + point = (1 - sqrt_r1) * face_vertices[0, :] + \ + sqrt_r1 * (1 - r2) * face_vertices[1, :] + \ + sqrt_r1 * r2 * face_vertices[2, :] + + return point + + +def pairwise_distance(A, B): + """ Compute pairwise distance of two point clouds.point + + Args: + A: n x 3 numpy array + B: m x 3 numpy array + + Return: + C: n x m numpy array + + """ + diff = A[:, :, None] - B[:, :, None].T + C = np.sqrt(np.sum(diff**2, axis=1)) + + return C + + +def uniform_sample(vertices, faces, n_samples, with_normal=False): + """ Sampling points according to the area of mesh surface. + + """ + sampled_points = np.zeros((n_samples, 3), dtype=float) + normals = np.zeros((n_samples, 3), dtype=float) + faces = vertices[faces] + vec_cross = np.cross(faces[:, 1, :] - faces[:, 0, :], + faces[:, 2, :] - faces[:, 0, :]) + face_area = 0.5 * np.linalg.norm(vec_cross, axis=1) + cum_area = np.cumsum(face_area) + for i in range(n_samples): + face_id = np.searchsorted(cum_area, np.random.random() * cum_area[-1]) + sampled_points[i] = random_point(faces[face_id, :, :]) + normals[i] = vec_cross[face_id] + normals = normals / np.linalg.norm(normals, axis=1, keepdims=True) + if with_normal: + sampled_points = np.concatenate((sampled_points, normals), axis=1) + return sampled_points + + +def farthest_point_sampling(points, n_samples): + """ Farthest point sampling. + + """ + selected_pts = np.zeros((n_samples,), dtype=int) + dist_mat = pairwise_distance(points, points) + # start from first point + pt_idx = 0 + dist_to_set = dist_mat[:, pt_idx] + for i in range(n_samples): + selected_pts[i] = pt_idx + dist_to_set = np.minimum(dist_to_set, dist_mat[:, pt_idx]) + pt_idx = np.argmax(dist_to_set) + return selected_pts + + +def sample_points_from_mesh(path, n_pts, with_normal=False, fps=False, ratio=2): + """ Uniformly sampling points from mesh model. + + Args: + path: path to OBJ file. + n_pts: int, number of points being sampled. + with_normal: return points with normal, approximated by mesh triangle normal + fps: whether to use fps for post-processing, default False. + ratio: int, if use fps, sample ratio*n_pts first, then use fps to sample final output. + + Returns: + points: n_pts x 3, n_pts x 6 if with_normal = True + + """ + vertices, faces = load_obj(path) + if fps: + points = uniform_sample(vertices, faces, ratio*n_pts, with_normal) + pts_idx = farthest_point_sampling(points[:, :3], n_pts) + points = points[pts_idx] + else: + points = uniform_sample(vertices, faces, n_pts, with_normal) + return points + + +def load_depth(depth_path): + """ Load depth image from img_path. """ + # depth_path = img_path + '_depth.png' + depth = cv2.imread(depth_path, -1) + if len(depth.shape) == 3: + # This is encoded depth image, let's convert + # NOTE: RGB is actually BGR in opencv + depth16 = depth[:, :, 1]*256 + depth[:, :, 2] + depth16 = np.where(depth16==32001, 0, depth16) + depth16 = depth16.astype(np.uint16) + elif len(depth.shape) == 2 and depth.dtype == 'uint16': + depth16 = depth + else: + assert False, '[ Error ]: Unsupported depth type.' + return depth16 + + +def get_bbox(bbox): + """ Compute square image crop window. """ + y1, x1, y2, x2 = bbox + img_width = 480 + img_length = 640 + window_size = (max(y2-y1, x2-x1) // 40 + 1) * 40 + window_size = min(window_size, 440) + center = [(y1 + y2) // 2, (x1 + x2) // 2] + rmin = center[0] - int(window_size / 2) + rmax = center[0] + int(window_size / 2) + cmin = center[1] - int(window_size / 2) + cmax = center[1] + int(window_size / 2) + if rmin < 0: + delt = -rmin + rmin = 0 + rmax += delt + if cmin < 0: + delt = -cmin + cmin = 0 + cmax += delt + if rmax > img_width: + delt = rmax - img_width + rmax = img_width + rmin -= delt + if cmax > img_length: + delt = cmax - img_length + cmax = img_length + cmin -= delt + return rmin, rmax, cmin, cmax + + +def compute_sRT_errors(sRT1, sRT2): + """ + Args: + sRT1: [4, 4]. homogeneous affine transformation + sRT2: [4, 4]. homogeneous affine transformation + + Returns: + R_error: angle difference in degree, + T_error: Euclidean distance + IoU: relative scale error + + """ + try: + assert np.array_equal(sRT1[3, :], sRT2[3, :]) + assert np.array_equal(sRT1[3, :], np.array([0, 0, 0, 1])) + except AssertionError: + print(sRT1[3, :], sRT2[3, :]) + + s1 = np.cbrt(np.linalg.det(sRT1[:3, :3])) + R1 = sRT1[:3, :3] / s1 + T1 = sRT1[:3, 3] + s2 = np.cbrt(np.linalg.det(sRT2[:3, :3])) + R2 = sRT2[:3, :3] / s2 + T2 = sRT2[:3, 3] + R12 = R1 @ R2.transpose() + R_error = np.arccos(np.clip((np.trace(R12)-1)/2, -1.0, 1.0)) * 180 / np.pi + T_error = np.linalg.norm(T1 - T2) + IoU = np.abs(s1 - s2) / s2 + + return R_error, T_error, IoU + + +############################################################ +# Evaluation +############################################################ + +def get_3d_bbox(size, shift=0): + """ + Args: + size: [3] or scalar + shift: [3] or scalar + Returns: + bbox_3d: [3, N] + + """ + bbox_3d = np.array([[+size[0] / 2, +size[1] / 2, +size[2] / 2], + [+size[0] / 2, +size[1] / 2, -size[2] / 2], + [-size[0] / 2, +size[1] / 2, +size[2] / 2], + [-size[0] / 2, +size[1] / 2, -size[2] / 2], + [+size[0] / 2, -size[1] / 2, +size[2] / 2], + [+size[0] / 2, -size[1] / 2, -size[2] / 2], + [-size[0] / 2, -size[1] / 2, +size[2] / 2], + [-size[0] / 2, -size[1] / 2, -size[2] / 2]]) + shift + bbox_3d = bbox_3d.transpose() + return bbox_3d + + +def transform_coordinates_3d(coordinates, sRT): + """ + Args: + coordinates: [3, N] + sRT: [4, 4] + + Returns: + new_coordinates: [3, N] + + """ + assert coordinates.shape[0] == 3 + coordinates = np.vstack([coordinates, np.ones((1, coordinates.shape[1]), dtype=np.float32)]) + new_coordinates = sRT @ coordinates + new_coordinates = new_coordinates[:3, :] / new_coordinates[3, :] + return new_coordinates + + +def compute_3d_IoU(sRT_1, sRT_2, size_1, size_2, class_name_1, class_name_2, handle_visibility): + """ Computes IoU overlaps between two 3D bboxes. """ + def asymmetric_3d_iou(sRT_1, sRT_2, size_1, size_2): + noc_cube_1 = get_3d_bbox(size_1, 0) + bbox_3d_1 = transform_coordinates_3d(noc_cube_1, sRT_1) + noc_cube_2 = get_3d_bbox(size_2, 0) + bbox_3d_2 = transform_coordinates_3d(noc_cube_2, sRT_2) + + bbox_1_max = np.amax(bbox_3d_1, axis=0) + bbox_1_min = np.amin(bbox_3d_1, axis=0) + bbox_2_max = np.amax(bbox_3d_2, axis=0) + bbox_2_min = np.amin(bbox_3d_2, axis=0) + + overlap_min = np.maximum(bbox_1_min, bbox_2_min) + overlap_max = np.minimum(bbox_1_max, bbox_2_max) + + # intersections and union + if np.amin(overlap_max - overlap_min) < 0: + intersections = 0 + else: + intersections = np.prod(overlap_max - overlap_min) + union = np.prod(bbox_1_max - bbox_1_min) + np.prod(bbox_2_max - bbox_2_min) - intersections + overlaps = intersections / union + return overlaps + + if sRT_1 is None or sRT_2 is None: + return -1 + + if (class_name_1 in ['bottle', 'bowl', 'can'] and class_name_1 == class_name_2) or \ + (class_name_1 == 'mug' and class_name_1 == class_name_2 and handle_visibility==0): + def y_rotation_matrix(theta): + return np.array([[ np.cos(theta), 0, np.sin(theta), 0], + [ 0, 1, 0, 0], + [-np.sin(theta), 0, np.cos(theta), 0], + [ 0, 0, 0, 1]]) + n = 100 + max_iou = 0 + for i in range(n): + rotated_RT_1 = sRT_1 @ y_rotation_matrix(2 * math.pi * i / float(n)) + max_iou = max(max_iou, asymmetric_3d_iou(rotated_RT_1, sRT_2, size_1, size_2)) + # print("max iou", max_iou) + else: + max_iou = asymmetric_3d_iou(sRT_1, sRT_2, size_1, size_2) + # print("max iou", max_iou) + + return max_iou + + +def compute_IoU_matches(gt_class_ids, gt_sRT, gt_size, gt_handle_visibility, + pred_class_ids, pred_sRT, pred_size, pred_scores, + synset_names, iou_3d_thresholds, score_threshold=0): + """ Find matches between NOCS prediction and ground truth instances. + + Args: + size: 3D bounding box size + bboxes: 2D bounding boxes + + Returns: + gt_matches: 2-D array. For each GT box it has the index of the matched predicted box. + pred_matches: 2-D array. For each predicted box, it has the index of the matched ground truth box. + overlaps: IoU overlaps. + indices: + + """ + num_pred = len(pred_class_ids) + num_gt = len(gt_class_ids) + indices = np.zeros(0) + if num_pred: + # Sort predictions by score from high to low + indices = np.argsort(pred_scores)[::-1] + pred_class_ids = pred_class_ids[indices].copy() + pred_size = pred_size[indices].copy() + pred_sRT = pred_sRT[indices].copy() + # compute IoU overlaps [pred_bboxs gt_bboxs] + overlaps = np.zeros((num_pred, num_gt), dtype=np.float32) + for i in range(num_pred): + for j in range(num_gt): + overlaps[i, j] = compute_3d_IoU(pred_sRT[i], gt_sRT[j], pred_size[i, :], gt_size[j], + synset_names[pred_class_ids[i]], synset_names[gt_class_ids[j]], gt_handle_visibility[j]) + # loop through predictions and find matching ground truth boxes + num_iou_3d_thres = len(iou_3d_thresholds) + pred_matches = -1 * np.ones([num_iou_3d_thres, num_pred]) + gt_matches = -1 * np.ones([num_iou_3d_thres, num_gt]) + for s, iou_thres in enumerate(iou_3d_thresholds): + for i in range(indices.shape[0]): + # Find best matching ground truth box + # 1. Sort matches by score + sorted_ixs = np.argsort(overlaps[i])[::-1] + # 2. Remove low scores + low_score_idx = np.where(overlaps[i, sorted_ixs] < score_threshold)[0] + if low_score_idx.size > 0: + sorted_ixs = sorted_ixs[:low_score_idx[0]] + # 3. Find the match + for j in sorted_ixs: + # If ground truth box is already matched, go to next one + if gt_matches[s, j] > -1: + continue + # If we reach IoU smaller than the threshold, end the loop + iou = overlaps[i, j] + if iou < iou_thres: + break + # Do we have a match? + if not pred_class_ids[i] == gt_class_ids[j]: + continue + if iou > iou_thres: + gt_matches[s, j] = i + pred_matches[s, i] = j + break + return gt_matches, pred_matches, overlaps, indices + + +def compute_RT_errors(sRT_1, sRT_2, class_id, handle_visibility, synset_names): + """ + Args: + sRT_1: [4, 4]. homogeneous affine transformation + sRT_2: [4, 4]. homogeneous affine transformation + + Returns: + theta: angle difference of R in degree + shift: l2 difference of T in centimeter + """ + # make sure the last row is [0, 0, 0, 1] + if sRT_1 is None or sRT_2 is None: + return -1 + try: + assert np.array_equal(sRT_1[3, :], sRT_2[3, :]) + assert np.array_equal(sRT_1[3, :], np.array([0, 0, 0, 1])) + except AssertionError: + print(sRT_1[3, :], sRT_2[3, :]) + exit() + + R1 = sRT_1[:3, :3] / np.cbrt(np.linalg.det(sRT_1[:3, :3])) + T1 = sRT_1[:3, 3] + R2 = sRT_2[:3, :3] / np.cbrt(np.linalg.det(sRT_2[:3, :3])) + T2 = sRT_2[:3, 3] + # symmetric when rotating around y-axis + if synset_names[class_id] in ['bottle', 'can', 'bowl'] or \ + (synset_names[class_id] == 'mug' and handle_visibility == 0): + y = np.array([0, 1, 0]) + y1 = R1 @ y + y2 = R2 @ y + cos_theta = y1.dot(y2) / (np.linalg.norm(y1) * np.linalg.norm(y2)) + else: + R = R1 @ R2.transpose() + cos_theta = (np.trace(R) - 1) / 2 + + theta = np.arccos(np.clip(cos_theta, -1.0, 1.0)) * 180 / np.pi + shift = np.linalg.norm(T1 - T2) * 100 + result = np.array([theta, shift]) + + return result + + +def compute_RT_overlaps(gt_class_ids, gt_sRT, gt_handle_visibility, pred_class_ids, pred_sRT, synset_names): + """ Finds overlaps between prediction and ground truth instances. + + Returns: + overlaps: + + """ + num_pred = len(pred_class_ids) + num_gt = len(gt_class_ids) + overlaps = np.zeros((num_pred, num_gt, 2)) + + for i in range(num_pred): + for j in range(num_gt): + overlaps[i, j, :] = compute_RT_errors(pred_sRT[i], gt_sRT[j], gt_class_ids[j], + gt_handle_visibility[j], synset_names) + + # print("RT overlaps", overlaps) + return overlaps + + +def compute_RT_matches(overlaps, pred_class_ids, gt_class_ids, degree_thres_list, shift_thres_list): + num_degree_thres = len(degree_thres_list) + num_shift_thres = len(shift_thres_list) + num_pred = len(pred_class_ids) + num_gt = len(gt_class_ids) + + pred_matches = -1 * np.ones((num_degree_thres, num_shift_thres, num_pred)) + gt_matches = -1 * np.ones((num_degree_thres, num_shift_thres, num_gt)) + + if num_pred == 0 or num_gt == 0: + return gt_matches, pred_matches + + assert num_pred == overlaps.shape[0] + assert num_gt == overlaps.shape[1] + assert overlaps.shape[2] == 2 + + for d, degree_thres in enumerate(degree_thres_list): + for s, shift_thres in enumerate(shift_thres_list): + for i in range(num_pred): + # Find best matching ground truth box + # 1. Sort matches by scores from low to high + sum_degree_shift = np.sum(overlaps[i, :, :], axis=-1) + sorted_ixs = np.argsort(sum_degree_shift) + # 2. Find the match + for j in sorted_ixs: + # If ground truth box is already matched, go to next one + if gt_matches[d, s, j] > -1 or pred_class_ids[i] != gt_class_ids[j]: + continue + # If we reach IoU smaller than the threshold, end the loop + if overlaps[i, j, 0] > degree_thres or overlaps[i, j, 1] > shift_thres: + continue + gt_matches[d, s, j] = i + pred_matches[d, s, i] = j + break + + return gt_matches, pred_matches + + +def compute_ap_and_acc(pred_matches, pred_scores, gt_matches): + # sort the scores from high to low + assert pred_matches.shape[0] == pred_scores.shape[0] + score_indices = np.argsort(pred_scores)[::-1] + # pred_scores = pred_scores[score_indices] + pred_matches = pred_matches[score_indices] + precisions = np.cumsum(pred_matches > -1) / (np.arange(len(pred_matches)) + 1) + recalls = np.cumsum(pred_matches > -1).astype(np.float32) / len(gt_matches) + # Pad with start and end values to simplify the math + precisions = np.concatenate([[0], precisions, [0]]) + recalls = np.concatenate([[0], recalls, [1]]) + # Ensure precision values decrease but don't increase. This way, the + # precision value at each recall threshold is the maximum it can be + # for all following recall thresholds, as specified by the VOC paper. + for i in range(len(precisions) - 2, -1, -1): + precisions[i] = np.maximum(precisions[i], precisions[i + 1]) + # compute mean AP over recall range + indices = np.where(recalls[:-1] != recalls[1:])[0] + 1 + ap = np.sum((recalls[indices] - recalls[indices - 1]) * precisions[indices]) + # accuracy + acc = np.sum(pred_matches > -1) / len(pred_matches) + + return ap, acc + + +def compute_mAP(pred_results, out_dir, degree_thresholds=[180], shift_thresholds=[100], + iou_3d_thresholds=[0.1], iou_pose_thres=0.1, use_matches_for_pose=False): + """ Compute mean Average Precision. + + Returns: + iou_aps: + pose_aps: + iou_acc: + pose_acc: + + """ + synset_names = ['BG', 'bottle', 'bowl', 'camera', 'can', 'laptop', 'mug'] + num_classes = len(synset_names) + degree_thres_list = list(degree_thresholds) + [360] + num_degree_thres = len(degree_thres_list) + shift_thres_list = list(shift_thresholds) + [100] + num_shift_thres = len(shift_thres_list) + iou_thres_list = list(iou_3d_thresholds) + num_iou_thres = len(iou_thres_list) + + if use_matches_for_pose: + assert iou_pose_thres in iou_thres_list + + # pre-allocate more than enough memory + iou_aps = np.zeros((num_classes + 1, num_iou_thres)) + iou_acc = np.zeros((num_classes + 1, num_iou_thres)) + iou_pred_matches_all = [np.zeros((num_iou_thres, 30000)) for _ in range(num_classes)] + iou_pred_scores_all = [np.zeros((num_iou_thres, 30000)) for _ in range(num_classes)] + iou_gt_matches_all = [np.zeros((num_iou_thres, 30000)) for _ in range(num_classes)] + iou_pred_count = [0 for _ in range(num_classes)] + iou_gt_count = [0 for _ in range(num_classes)] + + pose_aps = np.zeros((num_classes + 1, num_degree_thres, num_shift_thres)) + pose_acc = np.zeros((num_classes + 1, num_degree_thres, num_shift_thres)) + pose_pred_matches_all = [np.zeros((num_degree_thres, num_shift_thres, 30000)) for _ in range(num_classes)] + pose_pred_scores_all = [np.zeros((num_degree_thres, num_shift_thres, 30000)) for _ in range(num_classes)] + pose_gt_matches_all = [np.zeros((num_degree_thres, num_shift_thres, 30000)) for _ in range(num_classes)] + pose_pred_count = [0 for _ in range(num_classes)] + pose_gt_count = [0 for _ in range(num_classes)] + + # loop over results to gather pred matches and gt matches for iou and pose metrics + progress = 0 + for progress, result in enumerate(tqdm(pred_results)): + gt_class_ids = result['gt_class_ids'].astype(np.int32) + gt_sRT = np.array(result['gt_RTs']) + gt_size = np.array(result['gt_scales']) + gt_handle_visibility = result['gt_handle_visibility'] + + pred_class_ids = result['pred_class_ids'] + pred_sRT = np.array(result['pred_RTs']) + pred_size = result['pred_scales'] + pred_scores = result['pred_scores'] + + if len(gt_class_ids) == 0 and len(pred_class_ids) == 0: + continue + + for cls_id in range(1, num_classes): + # print("class_id, name", cls_id, synset_names[cls_id]) + # get gt and predictions in this class + cls_gt_class_ids = gt_class_ids[gt_class_ids==cls_id] if len(gt_class_ids) else np.zeros(0) + cls_gt_sRT = gt_sRT[gt_class_ids==cls_id] if len(gt_class_ids) else np.zeros((0, 4, 4)) + cls_gt_size = gt_size[gt_class_ids==cls_id] if len(gt_class_ids) else np.zeros((0, 3)) + if synset_names[cls_id] != 'mug': + cls_gt_handle_visibility = np.ones_like(cls_gt_class_ids) + else: + cls_gt_handle_visibility = gt_handle_visibility[gt_class_ids==cls_id] if len(gt_class_ids) else np.ones(0) + + cls_pred_class_ids = pred_class_ids[pred_class_ids==cls_id] if len(pred_class_ids) else np.zeros(0) + cls_pred_sRT = pred_sRT[pred_class_ids==cls_id] if len(pred_class_ids) else np.zeros((0, 4, 4)) + + # print("cls_pred_size", pred_size) + # print("pred_size[pred_class_ids==cls_id]", pred_size[pred_class_ids==cls_id]) + cls_pred_size = pred_size[pred_class_ids==cls_id] if len(pred_class_ids) else np.zeros((0, 3)) + # print("pred_class_ids==cls_id", pred_class_ids==cls_id) + # print("pred scores", pred_scores) + # print("pred_scores[pred_class_ids==cls_id]", pred_scores[pred_class_ids==cls_id]) + cls_pred_scores = pred_scores[pred_class_ids==cls_id] if len(pred_class_ids) else np.zeros(0) + + # calculate the overlap between each gt instance and pred instance + iou_cls_gt_match, iou_cls_pred_match, _, iou_pred_indices = \ + compute_IoU_matches(cls_gt_class_ids, cls_gt_sRT, cls_gt_size, cls_gt_handle_visibility, + cls_pred_class_ids, cls_pred_sRT, cls_pred_size, cls_pred_scores, + synset_names, iou_thres_list) + if len(iou_pred_indices): + cls_pred_class_ids = cls_pred_class_ids[iou_pred_indices] + cls_pred_sRT = cls_pred_sRT[iou_pred_indices] + cls_pred_scores = cls_pred_scores[iou_pred_indices] + + num_pred = iou_cls_pred_match.shape[1] + pred_start = iou_pred_count[cls_id] + pred_end = pred_start + num_pred + iou_pred_count[cls_id] = pred_end + iou_pred_matches_all[cls_id][:, pred_start:pred_end] = iou_cls_pred_match + cls_pred_scores_tile = np.tile(cls_pred_scores, (num_iou_thres, 1)) + assert cls_pred_scores_tile.shape[1] == num_pred + iou_pred_scores_all[cls_id][:, pred_start:pred_end] = cls_pred_scores_tile + num_gt = iou_cls_gt_match.shape[1] + gt_start = iou_gt_count[cls_id] + gt_end = gt_start + num_gt + iou_gt_count[cls_id] = gt_end + iou_gt_matches_all[cls_id][:, gt_start:gt_end] = iou_cls_gt_match + + if use_matches_for_pose: + thres_ind = list(iou_thres_list).index(iou_pose_thres) + iou_thres_pred_match = iou_cls_pred_match[thres_ind, :] + cls_pred_class_ids = cls_pred_class_ids[iou_thres_pred_match > -1] if len(iou_thres_pred_match) > 0 else np.zeros(0) + cls_pred_sRT = cls_pred_sRT[iou_thres_pred_match > -1] if len(iou_thres_pred_match) > 0 else np.zeros((0, 4, 4)) + cls_pred_scores = cls_pred_scores[iou_thres_pred_match > -1] if len(iou_thres_pred_match) > 0 else np.zeros(0) + iou_thres_gt_match = iou_cls_gt_match[thres_ind, :] + cls_gt_class_ids = cls_gt_class_ids[iou_thres_gt_match > -1] if len(iou_thres_gt_match) > 0 else np.zeros(0) + cls_gt_sRT = cls_gt_sRT[iou_thres_gt_match > -1] if len(iou_thres_gt_match) > 0 else np.zeros((0, 4, 4)) + cls_gt_handle_visibility = cls_gt_handle_visibility[iou_thres_gt_match > -1] if len(iou_thres_gt_match) > 0 else np.zeros(0) + + RT_overlaps = compute_RT_overlaps(cls_gt_class_ids, cls_gt_sRT, cls_gt_handle_visibility, + cls_pred_class_ids, cls_pred_sRT, synset_names) + pose_cls_gt_match, pose_cls_pred_match = compute_RT_matches(RT_overlaps, cls_pred_class_ids, cls_gt_class_ids, + degree_thres_list, shift_thres_list) + num_pred = pose_cls_pred_match.shape[2] + pred_start = pose_pred_count[cls_id] + pred_end = pred_start + num_pred + pose_pred_count[cls_id] = pred_end + pose_pred_matches_all[cls_id][:, :, pred_start:pred_end] = pose_cls_pred_match + cls_pred_scores_tile = np.tile(cls_pred_scores, (num_degree_thres, num_shift_thres, 1)) + assert cls_pred_scores_tile.shape[2] == num_pred + pose_pred_scores_all[cls_id][:, :, pred_start:pred_end] = cls_pred_scores_tile + num_gt = pose_cls_gt_match.shape[2] + gt_start = pose_gt_count[cls_id] + gt_end = gt_start + num_gt + pose_gt_count[cls_id] = gt_end + pose_gt_matches_all[cls_id][:, :, gt_start:gt_end] = pose_cls_gt_match + # print("===============\n") + + # trim zeros + for cls_id in range(num_classes): + # IoU + iou_pred_matches_all[cls_id] = iou_pred_matches_all[cls_id][:, :iou_pred_count[cls_id]] + iou_pred_scores_all[cls_id] = iou_pred_scores_all[cls_id][:, :iou_pred_count[cls_id]] + iou_gt_matches_all[cls_id] = iou_gt_matches_all[cls_id][:, :iou_gt_count[cls_id]] + # pose + pose_pred_matches_all[cls_id] = pose_pred_matches_all[cls_id][:, :, :pose_pred_count[cls_id]] + pose_pred_scores_all[cls_id] = pose_pred_scores_all[cls_id][:, :, :pose_pred_count[cls_id]] + pose_gt_matches_all[cls_id] = pose_gt_matches_all[cls_id][:, :, :pose_gt_count[cls_id]] + + # compute 3D IoU mAP + for cls_id in range(1, num_classes): + for s, iou_thres in enumerate(iou_thres_list): + iou_aps[cls_id, s], iou_acc[cls_id, s] = compute_ap_and_acc(iou_pred_matches_all[cls_id][s, :], + iou_pred_scores_all[cls_id][s, :], + iou_gt_matches_all[cls_id][s, :]) + iou_aps[-1, :] = np.mean(iou_aps[1:-1, :], axis=0) + iou_acc[-1, :] = np.mean(iou_acc[1:-1, :], axis=0) + # compute pose mAP + for i, degree_thres in enumerate(degree_thres_list): + for j, shift_thres in enumerate(shift_thres_list): + for cls_id in range(1, num_classes): + cls_pose_pred_matches_all = pose_pred_matches_all[cls_id][i, j, :] + cls_pose_gt_matches_all = pose_gt_matches_all[cls_id][i, j, :] + cls_pose_pred_scores_all = pose_pred_scores_all[cls_id][i, j, :] + pose_aps[cls_id, i, j], pose_acc[cls_id, i, j] = compute_ap_and_acc(cls_pose_pred_matches_all, + cls_pose_pred_scores_all, + cls_pose_gt_matches_all) + pose_aps[-1, i, j] = np.mean(pose_aps[1:-1, i, j]) + pose_acc[-1, i, j] = np.mean(pose_acc[1:-1, i, j]) + # save results to pkl + result_dict = {} + result_dict['iou_thres_list'] = iou_thres_list + result_dict['degree_thres_list'] = degree_thres_list + result_dict['shift_thres_list'] = shift_thres_list + result_dict['iou_aps'] = iou_aps + result_dict['pose_aps'] = pose_aps + result_dict['iou_acc'] = iou_acc + result_dict['pose_acc'] = pose_acc + pkl_path = os.path.join(out_dir, 'mAP_Acc.pkl') + with open(pkl_path, 'wb') as f: + cPickle.dump(result_dict, f) + return iou_aps, pose_aps, iou_acc, pose_acc + + +def plot_mAP(iou_aps, pose_aps, out_dir, iou_thres_list, degree_thres_list, shift_thres_list): + """ Draw iou 3d AP vs. iou thresholds. + """ + + labels = ['bottle', 'bowl', 'camera', 'can', 'laptop', 'mug', 'mean', 'nocs'] + colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:pink', 'tab:olive', 'tab:purple', 'tab:red', 'tab:gray'] + styles = ['-', '-', '-', '-', '-', '-', '--', ':'] + + fig, (ax_iou, ax_degree, ax_shift) = plt.subplots(1, 3, figsize=(8, 3.5)) + # IoU subplot + ax_iou.set_title('3D IoU', fontsize=10) + ax_iou.set_ylabel('Average Precision') + ax_iou.set_ylim(0, 100) + ax_iou.set_xlabel('Percent') + ax_iou.set_xlim(0, 100) + ax_iou.xaxis.set_ticks([0, 25, 50, 75, 100]) + ax_iou.grid() + for i in range(1, iou_aps.shape[0]): + ax_iou.plot(100*np.array(iou_thres_list), 100*iou_aps[i, :], + color=colors[i-1], linestyle=styles[i-1], label=labels[i-1]) + # rotation subplot + ax_degree.set_title('Rotation', fontsize=10) + ax_degree.set_ylim(0, 100) + ax_degree.yaxis.set_ticklabels([]) + ax_degree.set_xlabel('Degree') + ax_degree.set_xlim(0, 60) + ax_degree.xaxis.set_ticks([0, 20, 40, 60]) + ax_degree.grid() + for i in range(1, pose_aps.shape[0]): + ax_degree.plot(np.array(degree_thres_list), 100*pose_aps[i, :len(degree_thres_list), -1], + color=colors[i-1], linestyle=styles[i-1], label=labels[i-1]) + # translation subplot + ax_shift.set_title('Translation', fontsize=10) + ax_shift.set_ylim(0, 100) + ax_shift.yaxis.set_ticklabels([]) + ax_shift.set_xlabel('Centimeter') + ax_shift.set_xlim(0, 10) + ax_shift.xaxis.set_ticks([0, 5, 10]) + ax_shift.grid() + for i in range(1, pose_aps.shape[0]): + ax_shift.plot(np.array(shift_thres_list), 100*pose_aps[i, -1, :len(shift_thres_list)], + color=colors[i-1], linestyle=styles[i-1], label=labels[i-1]) + ax_shift.legend(loc='lower right', fontsize='small') + plt.tight_layout() + # plt.show() + plt.savefig(os.path.join(out_dir, 'mAP.png')) + plt.close(fig) + return + + +def calculate_2d_projections(coordinates_3d, intrinsics): + """ + Args: + coordinates_3d: [3, N] + intrinsics: [3, 3] + + Returns: + projected_coordinates: [N, 2] + """ + projected_coordinates = intrinsics @ coordinates_3d + projected_coordinates = projected_coordinates[:2, :] / projected_coordinates[2, :] + projected_coordinates = projected_coordinates.transpose() + projected_coordinates = np.array(projected_coordinates, dtype=np.int32) + + return projected_coordinates + + +def align_rotation(sRT): + """ Align rotations for symmetric objects. + Args: + sRT: 4 x 4 + """ + s = np.cbrt(np.linalg.det(sRT[:3, :3])) + R = sRT[:3, :3] / s + T = sRT[:3, 3] + + theta_x = R[0, 0] + R[2, 2] + theta_y = R[0, 2] - R[2, 0] + r_norm = math.sqrt(theta_x**2 + theta_y**2) + s_map = np.array([[theta_x/r_norm, 0.0, -theta_y/r_norm], + [0.0, 1.0, 0.0 ], + [theta_y/r_norm, 0.0, theta_x/r_norm]]) + rotation = R @ s_map + aligned_sRT = np.identity(4, dtype=np.float32) + aligned_sRT[:3, :3] = s * rotation + aligned_sRT[:3, 3] = T + return aligned_sRT + + +def draw_bboxes(img, img_pts, color): + img_pts = np.int32(img_pts).reshape(-1, 2) + # draw ground layer in darker color + color_ground = (int(color[0]*0.3), int(color[1]*0.3), int(color[2]*0.3)) + for i, j in zip([4, 5, 6, 7], [5, 7, 4, 6]): + img = cv2.line(img, tuple(img_pts[i]), tuple(img_pts[j]), color_ground, 2) + # draw pillars in minor darker color + color_pillar = (int(color[0]*0.6), int(color[1]*0.6), int(color[2]*0.6)) + for i, j in zip(range(4), range(4, 8)): + img = cv2.line(img, tuple(img_pts[i]), tuple(img_pts[j]), color_pillar, 2) + # draw top layer in original color + for i, j in zip([0, 1, 2, 3], [1, 3, 0, 2]): + img = cv2.line(img, tuple(img_pts[i]), tuple(img_pts[j]), color, 2) + + return img + + +def draw_detections(img, out_dir, data_name, img_id, intrinsics, pred_sRT, pred_size, pred_class_ids, + gt_sRT, gt_size, gt_class_ids, nocs_sRT, nocs_size, nocs_class_ids, draw_gt=True, draw_nocs=True): + """ Visualize pose predictions. + """ + out_path = os.path.join(out_dir, '{}_{}_pred.png'.format(data_name, img_id)) + + # draw nocs results - BLUE color + if draw_nocs: + for i in range(nocs_sRT.shape[0]): + if nocs_class_ids[i] in [1, 2, 4]: + sRT = align_rotation(nocs_sRT[i, :, :]) + else: + sRT = nocs_sRT[i, :, :] + bbox_3d = get_3d_bbox(nocs_size[i, :], 0) + transformed_bbox_3d = transform_coordinates_3d(bbox_3d, sRT) + projected_bbox = calculate_2d_projections(transformed_bbox_3d, intrinsics) + img = draw_bboxes(img, projected_bbox, (255, 0, 0)) + # darw ground truth - GREEN color + if draw_gt: + for i in range(gt_sRT.shape[0]): + if gt_class_ids[i] in [1, 2, 4]: + sRT = align_rotation(gt_sRT[i, :, :]) + else: + sRT = gt_sRT[i, :, :] + bbox_3d = get_3d_bbox(gt_size[i, :], 0) + transformed_bbox_3d = transform_coordinates_3d(bbox_3d, sRT) + projected_bbox = calculate_2d_projections(transformed_bbox_3d, intrinsics) + img = draw_bboxes(img, projected_bbox, (0, 255, 0)) + # darw prediction - RED color + for i in range(pred_sRT.shape[0]): + if pred_class_ids[i] in [1, 2, 4]: + sRT = align_rotation(pred_sRT[i, :, :]) + else: + sRT = pred_sRT[i, :, :] + bbox_3d = get_3d_bbox(pred_size[i, :], 0) + transformed_bbox_3d = transform_coordinates_3d(bbox_3d, sRT) + projected_bbox = calculate_2d_projections(transformed_bbox_3d, intrinsics) + img = draw_bboxes(img, projected_bbox, (0, 0, 255)) + + cv2.imwrite(out_path, img) + # cv2.imshow('vis', img) + # cv2.waitKey(0) diff --git a/utils/nocs_utils.py b/utils/nocs_utils.py new file mode 100644 index 0000000..0e21cde --- /dev/null +++ b/utils/nocs_utils.py @@ -0,0 +1,296 @@ +import torch +import numpy as np +import cv2 +import torchvision.transforms as transforms + + +def load_depth(depth_path): + """ Load depth image from img_path. """ + # depth_path = depth_path + '_depth.png' + # print("depth_path", depth_path) + depth = cv2.imread(depth_path, -1) + if len(depth.shape) == 3: + # This is encoded depth image, let's convert + # NOTE: RGB is actually BGR in opencv + depth16 = depth[:, :, 1]*256 + depth[:, :, 2] + depth16 = np.where(depth16==32001, 0, depth16) + depth16 = depth16.astype(np.uint16) + elif len(depth.shape) == 2 and depth.dtype == 'uint16': + depth16 = depth + else: + assert False, '[ Error ]: Unsupported depth type.' + return depth16 + +def load_img_NOCS(color, depth): + left_img = cv2.imread(color) + actual_depth = load_depth(depth) + right_img = np.array(actual_depth, dtype=np.float32)/255.0 + return left_img, right_img, actual_depth + +def create_input_w_depth(left_color,right_color ): + height, width, _ = left_color.shape + image = np.zeros([height, width, 4], dtype=np.uint8) + cv2.normalize(left_color, left_color, 0, 255, cv2.NORM_MINMAX) + # cv2.normalize(stereo_dp.right_color, stereo_dp.right_color, 0, 255, cv2.NORM_MINMAX) + image[..., 0:3] = left_color + image = image * 1. / 255.0 + if len(right_color.shape ) == 2: + image[..., 3] = right_color + # print(image.shape) + image = image.transpose((2, 0, 1)) + return torch.from_numpy(np.ascontiguousarray(image)).float() + +def create_input_norm(left_color,right_color): + height, width, _ = left_color.shape + image = torch.zeros(4, height, width, dtype=torch.float32) + cv2.normalize(left_color, left_color, 0, 255, cv2.NORM_MINMAX) + + rgb = left_color* 1. / 255.0 + norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + rgb = norm(torch.from_numpy(rgb.astype(np.float32).transpose((2,0,1)))) + + if len(right_color.shape) == 2: + depth = right_color + depth = torch.from_numpy(depth.astype(np.float32)) + + image[0:3, :] = rgb + image[3, :] = depth + return image + + +def process_data(img_path, depth): + """ Load instance masks for the objects in the image. """ + mask_path = img_path + '_mask.png' + mask = cv2.imread(mask_path)[:, :, 2] + mask = np.array(mask, dtype=np.int32) + all_inst_ids = sorted(list(np.unique(mask))) + assert all_inst_ids[-1] == 255 + del all_inst_ids[-1] # remove background + num_all_inst = len(all_inst_ids) + h, w = mask.shape + + coord_path = img_path + '_coord.png' + coord_map = cv2.imread(coord_path)[:, :, :3] + coord_map = coord_map[:, :, (2, 1, 0)] + # flip z axis of coord map + coord_map = np.array(coord_map, dtype=np.float32) / 255 + coord_map[:, :, 2] = 1 - coord_map[:, :, 2] + + class_ids = [] + instance_ids = [] + model_list = [] + masks = np.zeros([h, w, num_all_inst], dtype=np.uint8) + coords = np.zeros((h, w, num_all_inst, 3), dtype=np.float32) + bboxes = np.zeros((num_all_inst, 4), dtype=np.int32) + + meta_path = img_path + '_meta.txt' + with open(meta_path, 'r') as f: + i = 0 + for line in f: + line_info = line.strip().split(' ') + inst_id = int(line_info[0]) + cls_id = int(line_info[1]) + # background objects and non-existing objects + if cls_id == 0 or (inst_id not in all_inst_ids): + continue + if len(line_info) == 3: + model_id = line_info[2] # Real scanned objs + else: + model_id = line_info[3] # CAMERA objs + # remove one mug instance in CAMERA train due to improper model + if model_id == 'b9be7cfe653740eb7633a2dd89cec754' or model_id == 'd3b53f56b4a7b3b3c9f016d57db96408': + continue + # process foreground objects + inst_mask = np.equal(mask, inst_id) + # bounding box + horizontal_indicies = np.where(np.any(inst_mask, axis=0))[0] + vertical_indicies = np.where(np.any(inst_mask, axis=1))[0] + assert horizontal_indicies.shape[0], print(img_path) + x1, x2 = horizontal_indicies[[0, -1]] + y1, y2 = vertical_indicies[[0, -1]] + # x2 and y2 should not be part of the box. Increment by 1. + x2 += 1 + y2 += 1 + # object occupies full image, rendering error, happens in CAMERA dataset + if np.any(np.logical_or((x2-x1) > 600, (y2-y1) > 440)): + return None, None, None, None, None, None + # not enough valid depth observation + final_mask = np.logical_and(inst_mask, depth > 0) + if np.sum(final_mask) < 64: + continue + class_ids.append(cls_id) + instance_ids.append(inst_id) + model_list.append(model_id) + masks[:, :, i] = inst_mask + coords[:, :, i, :] = np.multiply(coord_map, np.expand_dims(inst_mask, axis=-1)) + bboxes[i] = np.array([y1, x1, y2, x2]) + i += 1 + # no valid foreground objects + if i == 0: + return None, None, None, None, None, None + + masks = masks[:, :, :i] + coords = np.clip(coords[:, :, :i, :], 0, 1) + bboxes = bboxes[:i, :] + + return masks, coords, class_ids, instance_ids, model_list, bboxes + +""" + RANSAC for Similarity Transformation Estimation + Modified from https://github.com/hughw19/NOCS_CVPR2019 + Originally Written by Srinath Sridhar +""" +import time +import numpy as np + + +def estimateSimilarityUmeyama(SourceHom, TargetHom): + # Copy of original paper is at: http://web.stanford.edu/class/cs273/refs/umeyama.pdf + SourceCentroid = np.mean(SourceHom[:3, :], axis=1) + TargetCentroid = np.mean(TargetHom[:3, :], axis=1) + nPoints = SourceHom.shape[1] + CenteredSource = SourceHom[:3, :] - np.tile(SourceCentroid, (nPoints, 1)).transpose() + CenteredTarget = TargetHom[:3, :] - np.tile(TargetCentroid, (nPoints, 1)).transpose() + CovMatrix = np.matmul(CenteredTarget, np.transpose(CenteredSource)) / nPoints + if np.isnan(CovMatrix).any(): + print('nPoints:', nPoints) + print(SourceHom.shape) + print(TargetHom.shape) + raise RuntimeError('There are NANs in the input.') + + U, D, Vh = np.linalg.svd(CovMatrix, full_matrices=True) + d = (np.linalg.det(U) * np.linalg.det(Vh)) < 0.0 + if d: + D[-1] = -D[-1] + U[:, -1] = -U[:, -1] + # rotation + Rotation = np.matmul(U, Vh) + # scale + varP = np.var(SourceHom[:3, :], axis=1).sum() + Scale = 1 / varP * np.sum(D) + # translation + Translation = TargetHom[:3, :].mean(axis=1) - SourceHom[:3, :].mean(axis=1).dot(Scale*Rotation.T) + # transformation matrix + OutTransform = np.identity(4) + OutTransform[:3, :3] = Scale * Rotation + OutTransform[:3, 3] = Translation + + return Scale, Rotation, Translation, OutTransform + + +def estimateSimilarityTransform(source: np.array, target: np.array, verbose=False): + """ Add RANSAC algorithm to account for outliers. + + """ + assert source.shape[0] == target.shape[0], 'Source and Target must have same number of points.' + SourceHom = np.transpose(np.hstack([source, np.ones([source.shape[0], 1])])) + TargetHom = np.transpose(np.hstack([target, np.ones([target.shape[0], 1])])) + # Auto-parameter selection based on source heuristics + # Assume source is object model or gt nocs map, which is of high quality + SourceCentroid = np.mean(SourceHom[:3, :], axis=1) + nPoints = SourceHom.shape[1] + CenteredSource = SourceHom[:3, :] - np.tile(SourceCentroid, (nPoints, 1)).transpose() + SourceDiameter = 2 * np.amax(np.linalg.norm(CenteredSource, axis=0)) + InlierT = SourceDiameter / 10.0 # 0.1 of source diameter + maxIter = 128 + confidence = 0.99 + + if verbose: + print('Inlier threshold: ', InlierT) + print('Max number of iterations: ', maxIter) + + BestInlierRatio = 0 + BestInlierIdx = np.arange(nPoints) + for i in range(0, maxIter): + # Pick 5 random (but corresponding) points from source and target + RandIdx = np.random.randint(nPoints, size=5) + Scale, _, _, OutTransform = estimateSimilarityUmeyama(SourceHom[:, RandIdx], TargetHom[:, RandIdx]) + PassThreshold = Scale * InlierT # propagate inlier threshold to target scale + Diff = TargetHom - np.matmul(OutTransform, SourceHom) + ResidualVec = np.linalg.norm(Diff[:3, :], axis=0) + InlierIdx = np.where(ResidualVec < PassThreshold)[0] + nInliers = InlierIdx.shape[0] + InlierRatio = nInliers / nPoints + # update best hypothesis + if InlierRatio > BestInlierRatio: + BestInlierRatio = InlierRatio + BestInlierIdx = InlierIdx + if verbose: + print('Iteration: ', i) + print('Inlier ratio: ', BestInlierRatio) + # early break + if (1 - (1 - BestInlierRatio ** 5) ** i) > confidence: + break + + if(BestInlierRatio < 0.1): + print('[ WARN ] - Something is wrong. Small BestInlierRatio: ', BestInlierRatio) + return None, None, None, None + + SourceInliersHom = SourceHom[:, BestInlierIdx] + TargetInliersHom = TargetHom[:, BestInlierIdx] + Scale, Rotation, Translation, OutTransform = estimateSimilarityUmeyama(SourceInliersHom, TargetInliersHom) + + if verbose: + print('BestInlierRatio:', BestInlierRatio) + print('Rotation:\n', Rotation) + print('Translation:\n', Translation) + print('Scale:', Scale) + + return Scale, Rotation, Translation, OutTransform + + +def backproject(depth, intrinsics, instance_mask): + """ Back-projection, use opencv camera coordinate frame. + + """ + cam_fx = intrinsics[0, 0] + cam_fy = intrinsics[1, 1] + cam_cx = intrinsics[0, 2] + cam_cy = intrinsics[1, 2] + + non_zero_mask = (depth > 0) + final_instance_mask = np.logical_and(instance_mask, non_zero_mask) + idxs = np.where(final_instance_mask) + + z = depth[idxs[0], idxs[1]] + x = (idxs[1] - cam_cx) * z / cam_fx + y = (idxs[0] - cam_cy) * z / cam_fy + pts = np.stack((x, y, z), axis=1) + + return pts, idxs + + +def align_nocs_to_depth(masks, coords, depth, intrinsics, instance_ids, img_path, verbose=False): + num_instances = len(instance_ids) + error_messages = '' + elapses = [] + scales = np.zeros(num_instances) + rotations = np.zeros((num_instances, 3, 3)) + translations = np.zeros((num_instances, 3)) + + for i in range(num_instances): + mask = masks[:, :, i] + coord = coords[:, :, i, :] + pts, idxs = backproject(depth, intrinsics, mask) + coord_pts = coord[idxs[0], idxs[1], :] - 0.5 + try: + start = time.time() + s, R, T, outtransform = estimateSimilarityTransform(coord_pts, pts, False) + elapsed = time.time() - start + if verbose: + print('elapsed: ', elapsed) + elapses.append(elapsed) + except Exception as e: + message = '[ Error ] aligning instance {} in {} fails. Message: {}.'.format(instance_ids[i], img_path, str(e)) + print(message) + error_messages += message + '\n' + s = 1.0 + R = np.eye(3) + T = np.zeros(3) + outtransform = np.identity(4, dtype=np.float32) + + scales[i] = s / 1000.0 + rotations[i, :, :] = R + translations[i, :] = T / 1000.0 + + return scales, rotations, translations, error_messages, elapses \ No newline at end of file diff --git a/utils/shape_utils.py b/utils/shape_utils.py new file mode 100644 index 0000000..fac671a --- /dev/null +++ b/utils/shape_utils.py @@ -0,0 +1,131 @@ +import numpy as np +import logging + +def setup_logger(logger_name, log_file, level=logging.INFO): + logger = logging.getLogger(logger_name) + formatter = logging.Formatter('%(asctime)s : %(message)s') + fileHandler = logging.FileHandler(log_file, mode='a') + fileHandler.setFormatter(formatter) + logger.setLevel(level) + logger.addHandler(fileHandler) + streamHandler = logging.StreamHandler() + streamHandler.setFormatter(formatter) + logger.addHandler(streamHandler) + return logger + + +def random_point(face_vertices): + """ Sampling point using Barycentric coordiante. + """ + r1, r2 = np.random.random(2) + sqrt_r1 = np.sqrt(r1) + point = (1 - sqrt_r1) * face_vertices[0, :] + \ + sqrt_r1 * (1 - r2) * face_vertices[1, :] + \ + sqrt_r1 * r2 * face_vertices[2, :] + + return point + +def load_obj(path_to_file): + """ Load obj file. + + Args: + path_to_file: path + + Returns: + vertices: ndarray + faces: ndarray, index of triangle vertices + + """ + vertices = [] + faces = [] + with open(path_to_file, 'r') as f: + for line in f: + if line[:2] == 'v ': + vertex = line[2:].strip().split(' ') + vertex = [float(xyz) for xyz in vertex] + vertices.append(vertex) + elif line[0] == 'f': + face = line[1:].replace('//', '/').strip().split(' ') + face = [int(idx.split('/')[0])-1 for idx in face] + faces.append(face) + else: + continue + vertices = np.asarray(vertices) + faces = np.asarray(faces) + return vertices, faces + +def uniform_sample(vertices, faces, n_samples, with_normal=False): + """ Sampling points according to the area of mesh surface. + + """ + sampled_points = np.zeros((n_samples, 3), dtype=float) + normals = np.zeros((n_samples, 3), dtype=float) + faces = vertices[faces] + vec_cross = np.cross(faces[:, 1, :] - faces[:, 0, :], + faces[:, 2, :] - faces[:, 0, :]) + face_area = 0.5 * np.linalg.norm(vec_cross, axis=1) + cum_area = np.cumsum(face_area) + for i in range(n_samples): + face_id = np.searchsorted(cum_area, np.random.random() * cum_area[-1]) + sampled_points[i] = random_point(faces[face_id, :, :]) + normals[i] = vec_cross[face_id] + normals = normals / np.linalg.norm(normals, axis=1, keepdims=True) + if with_normal: + sampled_points = np.concatenate((sampled_points, normals), axis=1) + return sampled_points + +def pairwise_distance(A, B): + """ Compute pairwise distance of two point clouds.point + + Args: + A: n x 3 numpy array + B: m x 3 numpy array + + Return: + C: n x m numpy array + + """ + diff = A[:, :, None] - B[:, :, None].T + C = np.sqrt(np.sum(diff**2, axis=1)) + + return C + + +def farthest_point_sampling(points, n_samples): + """ Farthest point sampling. + + """ + selected_pts = np.zeros((n_samples,), dtype=int) + dist_mat = pairwise_distance(points, points) + # start from first point + pt_idx = 0 + dist_to_set = dist_mat[:, pt_idx] + for i in range(n_samples): + selected_pts[i] = pt_idx + dist_to_set = np.minimum(dist_to_set, dist_mat[:, pt_idx]) + pt_idx = np.argmax(dist_to_set) + return selected_pts + + +def sample_points_from_mesh(path, n_pts, with_normal=False, fps=False, ratio=2): + """ Uniformly sampling points from mesh model. + + Args: + path: path to OBJ file. + n_pts: int, number of points being sampled. + with_normal: return points with normal, approximated by mesh triangle normal + fps: whether to use fps for post-processing, default False. + ratio: int, if use fps, sample ratio*n_pts first, then use fps to sample final output. + + Returns: + points: n_pts x 3, n_pts x 6 if with_normal = True + + """ + vertices, faces = load_obj(path) + if fps: + points = uniform_sample(vertices, faces, ratio*n_pts, with_normal) + pts_idx = farthest_point_sampling(points[:, :3], n_pts) + points = points[pts_idx] + else: + points = uniform_sample(vertices, faces, n_pts, with_normal) + return points diff --git a/utils/transform_utils.py b/utils/transform_utils.py new file mode 100644 index 0000000..ce7fc18 --- /dev/null +++ b/utils/transform_utils.py @@ -0,0 +1,185 @@ +import math +import numpy as np +from simnet.lib import camera +import torch + +def align_rotation(R): + """ Align rotations for symmetric objects. + Args: + sRT: 4 x 4 + """ + + theta_x = R[0, 0] + R[2, 2] + theta_y = R[0, 2] - R[2, 0] + r_norm = math.sqrt(theta_x**2 + theta_y**2) + s_map = np.array([[theta_x/r_norm, 0.0, -theta_y/r_norm], + [0.0, 1.0, 0.0 ], + [theta_y/r_norm, 0.0, theta_x/r_norm]]) + rotation = R @ s_map + return rotation + + +def get_scaled_pc(scale_matrix, pc): + pc_homopoints = camera.convert_points_to_homopoints(pc.T) + scaled_homopoints = (scale_matrix @ pc_homopoints) + scaled_homopoints = camera.convert_homopoints_to_points(scaled_homopoints).T + return scaled_homopoints + + +def project(K, p_3d): + projections_2d = np.zeros((2, p_3d.shape[1]), dtype='float32') + p_2d = np.dot(K, p_3d) + projections_2d[0, :] = p_2d[0, :]/p_2d[2, :] + projections_2d[1, :] = p_2d[1, :]/p_2d[2, :] + return projections_2d + + +def get_gt_pointclouds(pose, pc, camera_model=None, sizes = None +): + if sizes is not None: + pc_homopoints = camera.convert_points_to_homopoints(pc.T) + morphed_pc_homopoints = pose @ pc_homopoints + morphed_pc_homopoints = camera.convert_homopoints_to_points(morphed_pc_homopoints).T + else: + pc_homopoints = camera.convert_points_to_homopoints(pc.T) + morphed_pc_homopoints = pose.camera_T_object @ (pose.scale_matrix @ pc_homopoints) + morphed_pc_homopoints = camera.convert_homopoints_to_points(morphed_pc_homopoints).T + + if sizes is not None: + size = sizes + box = get_3d_bbox(size) + unit_box_homopoints = camera.convert_points_to_homopoints(box.T) + morphed_box_homopoints = pose @ unit_box_homopoints + morphed_box_points = camera.convert_homopoints_to_points(morphed_box_homopoints).T + else: + pc_hp = camera.convert_points_to_homopoints(pc.T) + scaled_homopoints = (pose.scale_matrix @ pc_hp) + scaled_homopoints = camera.convert_homopoints_to_points(scaled_homopoints).T + size = 2 * np.amax(np.abs(scaled_homopoints), axis=0) + box = get_3d_bbox(size) + unit_box_homopoints = camera.convert_points_to_homopoints(box.T) + morphed_box_homopoints = pose.camera_T_object @ unit_box_homopoints + morphed_box_points = camera.convert_homopoints_to_points(morphed_box_homopoints).T + return morphed_pc_homopoints, morphed_box_points, size + +def get_3d_bbox(size, shift=0): + """ + Args: + size: [3] or scalar + shift: [3] or scalar + Returns: + bbox_3d: [3, N] + + """ + bbox_3d = np.array([[+size[0] / 2, +size[1] / 2, +size[2] / 2], + [+size[0] / 2, +size[1] / 2, -size[2] / 2], + [-size[0] / 2, +size[1] / 2, +size[2] / 2], + [-size[0] / 2, +size[1] / 2, -size[2] / 2], + [+size[0] / 2, -size[1] / 2, +size[2] / 2], + [+size[0] / 2, -size[1] / 2, -size[2] / 2], + [-size[0] / 2, -size[1] / 2, +size[2] / 2], + [-size[0] / 2, -size[1] / 2, -size[2] / 2]]) + shift + return bbox_3d + + +def get_2d_box(pose,pc, camera_model): + unit_box_homopoints = camera.convert_points_to_homopoints(pc.T) + morphed_homopoints = pose.camera_T_object @ (pose.scale_matrix @ unit_box_homopoints) + morphed_pixels = camera.convert_homopixels_to_pixels(camera_model.K_matrix @ morphed_homopoints).T + bbox = [ + np.array([np.min(morphed_pixels[:, 0]), + np.min(morphed_pixels[:, 1])]), + np.array([np.max(morphed_pixels[:, 0]), + np.max(morphed_pixels[:, 1])]) + ] + return bbox + +def rgbd_size(pc): + size = 2 * np.amax(np.abs(pc), axis=0) + return size + + +def transform_coordinates_3d(coordinates, RT): + """ + Input: + coordinates: [3, N] + RT: [4, 4] + Return + new_coordinates: [3, N] + + """ + assert coordinates.shape[0] == 3 + coordinates = np.vstack([coordinates, np.ones((1, coordinates.shape[1]), dtype=np.float32)]) + new_coordinates = RT @ coordinates + new_coordinates = new_coordinates[:3, :]/new_coordinates[3, :] + return new_coordinates + + +def calculate_2d_projections(coordinates_3d, intrinsics): + """ + Input: + coordinates: [3, N] + intrinsics: [3, 3] + Return + projected_coordinates: [N, 2] + """ + projected_coordinates = intrinsics @ coordinates_3d + projected_coordinates = projected_coordinates[:2, :] / projected_coordinates[2, :] + projected_coordinates = projected_coordinates.transpose() + projected_coordinates = np.array(projected_coordinates, dtype=np.int32) + + return projected_coordinates + + +def rgbd_size(pc): + size = 2 * np.amax(np.abs(pc), axis=0) + return size + + + +def compute_sRT_errors(s1, R1, T1, s2, R2, T2): + """ + Args: + sRT1: [4, 4]. homogeneous affine transformation + sRT2: [4, 4]. homogeneous affine transformation + + Returns: + R_error: angle difference in degree, + T_error: Euclidean distance + IoU: relative scale error + + """ + R12 = R1 @ R2.transpose() + R_error = np.arccos(np.clip((np.trace(R12)-1)/2, -1.0, 1.0)) * 180 / np.pi + T_error = np.linalg.norm(T1 - T2) + IoU = np.abs(s1 - s2) / s2 + return R_error, T_error, IoU + +def compute_geodesic_distance_from_two_matrices(m1, m2): + batch=m1.shape[0] + m = torch.bmm(m1, m2.transpose(1,2)) #batch*3*3 + + cos = (m[:,0,0] + m[:,1,1] + m[:,2,2] - 1 )/2 + cos = torch.min(cos, torch.ones(batch) ) + cos = torch.max(cos, torch.ones(batch)*-1 ) + + + theta = torch.acos(cos) + #theta = torch.min(theta, 2*np.pi - theta) + return theta + +def symmetric_orthogonalization(x): + """Maps 9D input vectors onto SO(3) via symmetric orthogonalization. + + x: should have size [batch_size, 9] + + Output has size [batch_size, 3, 3], where each inner 3x3 matrix is in SO(3). + """ + m = x.view(-1, 3, 3) + u, s, v = torch.svd(m) + vt = torch.transpose(v, 1, 2) + det = torch.det(torch.matmul(u, vt)) + det = det.view(-1, 1, 1) + vt = torch.cat((vt[:, :2, :], vt[:, -1:, :] * det), 1) + r = torch.matmul(u, vt) + return r \ No newline at end of file diff --git a/utils/viz_utils.py b/utils/viz_utils.py new file mode 100644 index 0000000..9357f85 --- /dev/null +++ b/utils/viz_utils.py @@ -0,0 +1,598 @@ +import matplotlib.pyplot as plt +import numpy as np +from simnet.lib import camera +import matplotlib.patches as patches +import cv2 +import random +import open3d as o3d +import torch +from matplotlib.cm import get_cmap +import copy +import colorsys +import plotly.graph_objects as go + +def align_vector_to_another(a=np.array([0, 0, 1]), b=np.array([1, 0, 0])): + """ + Aligns vector a to vector b with axis angle rotation + """ + if np.array_equal(a, b): + return None, None + axis_ = np.cross(a, b) + axis_ = axis_ / np.linalg.norm(axis_) + angle = np.arccos(np.dot(a, b)) + + return axis_, angle + + +def normalized(a, axis=-1, order=2): + """Normalizes a numpy array of points""" + l2 = np.atleast_1d(np.linalg.norm(a, order, axis)) + l2[l2 == 0] = 1 + return a / np.expand_dims(l2, axis), l2 + + +class LineMesh(object): + def __init__(self, points, lines=None, colors=[0, 1, 0], radius=0.15): + """Creates a line represented as sequence of cylinder triangular meshes + + Arguments: + points {ndarray} -- Numpy array of ponts Nx3. + + Keyword Arguments: + lines {list[list] or None} -- List of point index pairs denoting line segments. If None, implicit lines from ordered pairwise points. (default: {None}) + colors {list} -- list of colors, or single color of the line (default: {[0, 1, 0]}) + radius {float} -- radius of cylinder (default: {0.15}) + """ + self.points = np.array(points) + self.lines = np.array( + lines) if lines is not None else self.lines_from_ordered_points(self.points) + self.colors = np.array(colors) + self.radius = radius + self.cylinder_segments = [] + + self.create_line_mesh() + + @staticmethod + def lines_from_ordered_points(points): + lines = [[i, i + 1] for i in range(0, points.shape[0] - 1, 1)] + return np.array(lines) + + def create_line_mesh(self): + first_points = self.points[self.lines[:, 0], :] + second_points = self.points[self.lines[:, 1], :] + line_segments = second_points - first_points + line_segments_unit, line_lengths = normalized(line_segments) + + z_axis = np.array([0, 0, 1]) + # Create triangular mesh cylinder segments of line + for i in range(line_segments_unit.shape[0]): + line_segment = line_segments_unit[i, :] + line_length = line_lengths[i] + # get axis angle rotation to allign cylinder with line segment + axis, angle = align_vector_to_another(z_axis, line_segment) + # Get translation vector + translation = first_points[i, :] + line_segment * line_length * 0.5 + # create cylinder and apply transformations + cylinder_segment = o3d.geometry.TriangleMesh.create_cylinder( + self.radius, line_length) + cylinder_segment = cylinder_segment.translate( + translation, relative=False) + if axis is not None: + axis_a = axis * angle + cylinder_segment = cylinder_segment.rotate( + R=o3d.geometry.get_rotation_matrix_from_axis_angle(axis_a), + center=cylinder_segment.get_center()) + # cylinder_segment = cylinder_segment.rotate( + # axis_a, center=True, type=o3d.geometry.RotationType.AxisAngle) + # color cylinder + color = self.colors if self.colors.ndim == 1 else self.colors[i, :] + cylinder_segment.paint_uniform_color(color) + + self.cylinder_segments.append(cylinder_segment) + + def add_line(self, vis): + """Adds this line to the visualizer""" + for cylinder in self.cylinder_segments: + vis.add_geometry(cylinder) + + def remove_line(self, vis): + """Removes this line from the visualizer""" + for cylinder in self.cylinder_segments: + vis.remove_geometry(cylinder) + +def line_set_mesh(points_array): + open_3d_lines = [ + [0, 1], + [7,3], + [1, 3], + [2, 0], + [3, 2], + [0, 4], + [1, 5], + [2, 6], + # [4, 7], + [7, 6], + [6, 4], + [4, 5], + [5, 7], + ] + colors = random_colors(len(open_3d_lines)) + open_3d_lines = np.array(open_3d_lines) + line_set = LineMesh(points_array, open_3d_lines,colors=colors, radius=0.001) + line_set = line_set.cylinder_segments + return line_set + + +def line_set(points_array): + open_3d_lines = [ + [0, 1], + [7,3], + [1, 3], + [2, 0], + [3, 2], + [0, 4], + [1, 5], + [2, 6], + # [4, 7], + [7, 6], + [6, 4], + [4, 5], + [5, 7], + ] + # colors = [[1, 0, 0] for i in range(len(lines))] + colors = random_colors(len(open_3d_lines)) + line_set = o3d.geometry.LineSet( + points=o3d.utility.Vector3dVector(points_array), + lines=o3d.utility.Vector2iVector(open_3d_lines), + ) + line_set.colors = o3d.utility.Vector3dVector(colors) + return line_set + + +def visualize_projected_points(color_img, pcd_array, box_obb, _DEBUG_FILE_PATH, uid): + open_3d_lines = [ + [5, 3], + [6, 4], + [0, 2], + [1, 7], + [0, 3], + [1, 6], + [2, 5], + [4, 7], + [0, 1], + [6, 3], + [4, 5], + [2, 7], + ] + edges_corners = [[0, 1], [0, 2], [0, 4], [1, 3], [1, 5], [2, 3], [2, 6], [3, 7], [4, 5], [4, 6], [5, 7], [6, 7]] + plt.xlim((0, color_img.shape[1])) + plt.ylim((0, color_img.shape[0])) + # Projections + color = ['g', 'y', 'b', 'r', 'm', 'c', '#3a7c00', '#3a7cd9', '#8b7cd9', '#211249'] + for i, points_2d_mesh in enumerate(pcd_array): + plt.scatter(points_2d_mesh[:,0], points_2d_mesh[:,1], color=color[i], s=2) + # for points in points_2d_mesh: + + plt.gca().invert_yaxis() + plt.axis('off') + plt.imshow(color_img) + # plt_name = name+'plot_points' + + # # plt.show() + # # plt.savefig('/home/zubairirshad/fleet/simnet/data/simnet/zubair_ZED2_test_pose_4/'+plt_name+'img.png', bbox_inches='tight') + + for points_2d in box_obb: + for edge in open_3d_lines: + plt.plot(points_2d[edge, 0], points_2d[edge, 1], color='b', linewidth=1.0) + + plt.savefig(str(_DEBUG_FILE_PATH / f'{uid}_projection.png')) + # plt.axis('off') + # plt.show() + # plt.close('all') + + +def visualize_projected_points_only(color_img, pcd_array): + open_3d_lines = [ + [5, 3], + [6, 4], + [0, 2], + [1, 7], + [0, 3], + [1, 6], + [2, 5], + [4, 7], + [0, 1], + [6, 3], + [4, 5], + [2, 7], + ] + edges_corners = [[0, 1], [0, 2], [0, 4], [1, 3], [1, 5], [2, 3], [2, 6], [3, 7], [4, 5], [4, 6], [5, 7], [6, 7]] + plt.xlim((0, color_img.shape[1])) + plt.ylim((0, color_img.shape[0])) + # Projections + color = ['g', 'y', 'b', 'r', 'm', 'c', '#3a7c00', '#3a7cd9', '#8b7cd9', '#211249'] + for i, points_2d_mesh in enumerate(pcd_array): + plt.scatter(points_2d_mesh[:,0], points_2d_mesh[:,1], color=color[i], s=2) + plt.gca().invert_yaxis() + plt.axis('off') + plt.imshow(color_img) + plt.show() + +def show_projected_points(color_img, pcd_array): + open_3d_lines = [ + [5, 3], + [6, 4], + [0, 2], + [1, 7], + [0, 3], + [1, 6], + [2, 5], + [4, 7], + [0, 1], + [6, 3], + [4, 5], + [2, 7], + ] + fig = plt.figure() + edges_corners = [[0, 1], [0, 2], [0, 4], [1, 3], [1, 5], [2, 3], [2, 6], [3, 7], [4, 5], [4, 6], [5, 7], [6, 7]] + plt.xlim((0, color_img.shape[1])) + plt.ylim((0, color_img.shape[0])) + # Projections + plt.imshow(color_img[...,::-1]) + color = ['g', 'y', 'b', 'r', 'm', 'c', '#3a7c00', '#3a7cd9', '#8b7cd9', '#211249'] + for i, points_2d_mesh in enumerate(pcd_array): + plt.scatter(points_2d_mesh[:,0], points_2d_mesh[:,1], color=color[i], s=2) + plt.gca().invert_yaxis() + plt.axis('off') + fig.canvas.draw() + color_img = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8) + color_img = color_img.reshape(fig.canvas.get_width_height()[::-1] + (3,)) + plt.show() + return color_img + + +def save_projected_points(color_img, pcd_array, output_path, uid): + open_3d_lines = [ + [5, 3], + [6, 4], + [0, 2], + [1, 7], + [0, 3], + [1, 6], + [2, 5], + [4, 7], + [0, 1], + [6, 3], + [4, 5], + [2, 7], + ] + edges_corners = [[0, 1], [0, 2], [0, 4], [1, 3], [1, 5], [2, 3], [2, 6], [3, 7], [4, 5], [4, 6], [5, 7], [6, 7]] + plt.figure() + plt.xlim((0, color_img.shape[1])) + plt.ylim((0, color_img.shape[0])) + # Projections + color = ['g', 'y', 'b', 'r', 'm', 'c', '#3a7c00', '#3a7cd9', '#8b7cd9', '#211249'] + for i, points_2d_mesh in enumerate(pcd_array): + plt.scatter(points_2d_mesh[:,0], points_2d_mesh[:,1], color=color[i], s=2) + plt.gca().invert_yaxis() + plt.axis('off') + plt.imshow(color_img[:,:,::-1]) + plt.savefig(output_path +'/projection'+str(uid)+'.png') + +def random_colors(N, bright=True): + """ + Generate random colors. + To get visually distinct colors, generate them in HSV space then + convert to RGB. + """ + brightness = 1.0 if bright else 0.7 + hsv = [(i / N, 1, brightness) for i in range(N)] + colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv)) + # random.shuffle(colors) + return colors + +def visualize(detections, img, classes, seg_mask, object_key_to_name, filename): + colors = random_colors(len(classes)) + fig, ax = plt.subplots(1, figsize=(10,7.5)) + plt.axis('off') + ax.imshow(cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2RGB)) + if detections is not None: + # unique_labels = detections[:, -1].cpu().unique() + # n_cls_preds = len(unique_labels) + bbox_colors = random.sample(colors, len(classes)) + colored_mask = np.zeros([seg_mask.shape[0], seg_mask.shape[1], 3]) + for ii, color in zip(classes, colors): + colored_mask[seg_mask == ii, :] = color + # browse detections and draw bounding boxes + for (x1, y1, x2, y2), cls_pred, color in zip(detections, classes, bbox_colors): + box_h = (y2 - y1) + box_w = (x2 - x1) + # color = bbox_colors + bbox = patches.Rectangle((x1, y1), box_w, box_h, + linewidth=2, edgecolor=color, facecolor='none') + ax.add_patch(bbox) + text = object_key_to_name[cls_pred] + plt.text(x1, y1, s=text, + color='white', verticalalignment='top', + bbox={'color': color, 'pad': 0}) + plt.axis('off') + # save image + plt.imshow(colored_mask, alpha=0.5) + # plt.show() + # plt.show() + plt.savefig(filename) + plt.close() + + +def draw_bboxes(img, img_pts, axes, color): + img_pts = np.int32(img_pts).reshape(-1, 2) + # draw ground layer in darker color + + # color_ground = (int(color[0]*0.3), int(color[1]*0.3), int(color[2]*0.3)) + color_ground = (int(color[0]), int(color[1]), int(color[2])) + + for i, j in zip([4, 5, 6, 7], [5, 7, 4, 6]): + img = cv2.line(img, tuple(img_pts[i]), tuple(img_pts[j]), color_ground, 3) + # draw pillars in minor darker color + # color_pillar = (int(color[0]*0.6), int(color[1]*0.6), int(color[2]*0.6)) + color_pillar = (int(color[0]), int(color[1]), int(color[2])) + for i, j in zip(range(4), range(4, 8)): + img = cv2.line(img, tuple(img_pts[i]), tuple(img_pts[j]), color_pillar, 3) + # draw top layer in original color + for i, j in zip([0, 1, 2, 3], [1, 3, 0, 2]): + img = cv2.line(img, tuple(img_pts[i]), tuple(img_pts[j]), color, 3) + + # draw axes + img = cv2.arrowedLine(img, tuple(axes[0]), tuple(axes[1]), (0, 0, 255), 4) + img = cv2.arrowedLine(img, tuple(axes[0]), tuple(axes[3]), (255, 0, 0), 4) + img = cv2.arrowedLine(img, tuple(axes[0]), tuple(axes[2]), (0, 255, 0), 4) ## y last + + return img + +def custom_draw_geometry_with_rotation(pcd): + def rotate_view(vis): + opt = vis.get_render_option() + vis.create_window() + # vis.create_window(window_name=name, width=3000, height=3000) + opt.background_color = np.asarray([1, 1, 1]) + ctr = vis.get_view_control() + ctr.rotate(5.0, 0.0) + # return False + + o3d.visualization.draw_geometries_with_animation_callback(pcd, + rotate_view) + + +def is_tensor(data): + """Checks if data is a torch tensor.""" + return type(data) == torch.Tensor + +def depth2inv(depth): + """ + Invert a depth map to produce an inverse depth map + Parameters + ---------- + depth : torch.Tensor or list of torch.Tensor [B,1,H,W] + Depth map + Returns + ------- + inv_depth : torch.Tensor or list of torch.Tensor [B,1,H,W] + Inverse depth map + """ + inv_depth = 1. / depth.clamp(min=1e-6) + inv_depth[depth <= 0.] = 0. + return inv_depth + +def viz_inv_depth(inv_depth, normalizer=None, percentile=95, + colormap='plasma', filter_zeros=False): + """ + Converts an inverse depth map to a colormap for visualization. + Parameters + ---------- + inv_depth : torch.Tensor [B,1,H,W] + Inverse depth map to be converted + normalizer : float + Value for inverse depth map normalization + percentile : float + Percentile value for automatic normalization + colormap : str + Colormap to be used + filter_zeros : bool + If True, do not consider zero values during normalization + Returns + ------- + colormap : np.array [H,W,3] + Colormap generated from the inverse depth map + """ + # If a tensor is provided, convert to numpy + if is_tensor(inv_depth): + inv_depth = inv_depth.squeeze(0).squeeze(0) + # Squeeze if depth channel exists + # if len(inv_depth.shape) == 3: + # inv_depth = inv_depth.squeeze(0) + inv_depth = inv_depth.detach().cpu().numpy() + cm = get_cmap(colormap) + if normalizer is None: + normalizer = np.percentile( + inv_depth[inv_depth > 0] if filter_zeros else inv_depth, percentile) + inv_depth /= (normalizer + 1e-6) + return cm(np.clip(inv_depth, 0., 1.0))[:, :, :3] + + +def visualize_shape(filename,result_dir, shape_list): + """ Visualization and save image. + + Args: + name: window name + shape: list of geoemtries + + """ + vis = o3d.visualization.Visualizer() + vis.create_window(width=512, height=512, left=50, top=25) + for shape in shape_list: + vis.add_geometry(shape) + ctr = vis.get_view_control() + # ctr.rotate(-300.0, 150.0) + # if name == 'camera': + # ctr.translate(20.0, -20.0) # (horizontal right +, vertical down +) + # if name == 'laptop': + # ctr.translate(25.0, -60.0) + vis.run() + vis.capture_screen_image(os.path.join(result_dir,filename)) + vis.destroy_window() + +def custom_draw_geometry_with_rotation(pcd): + def rotate_view(vis): + opt = vis.get_render_option() + vis.create_window() + # vis.create_window(window_name=name, width=1920, height=1080) + opt.background_color = np.asarray([1, 1, 1]) + ctr = vis.get_view_control() + ctr.rotate(1.0, 0.0) + # return False + + o3d.visualization.draw_geometries_with_animation_callback(pcd, + rotate_view) + + +def draw_registration_result(source, target, transformation): + source_temp = copy.deepcopy(source) + target_temp = copy.deepcopy(target) + source_temp.paint_uniform_color([1, 0.706, 0]) + target_temp.paint_uniform_color([0, 0.651, 0.929]) + if not transformation is None: + source_temp.transform(transformation) + # o3d.visualization.draw_geometries([source_temp, target_temp]) + return source_temp + +def visualize_mrcnn_boxes(detections, img, classes, object_key_to_name, filename): + # print(classes) + colors = random_colors(len(classes)) + fig, ax = plt.subplots(1, figsize=(10,7.5)) + plt.axis('off') + ax.imshow(cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2RGB)) + # ax.imshow(img) + # plt.show() + # ax.show(img) + if detections is not None: + # unique_labels = detections[:, -1].cpu().unique() + # n_cls_preds = len(unique_labels) + bbox_colors = random.sample(colors, len(classes)) + # browse detections and draw bounding boxes + for (y1, x1, y2, x2), cls_pred,color in zip(detections,classes, bbox_colors): + box_h = (y2 - y1) + box_w = (x2 - x1) + # color = bbox_colors + bbox = patches.Rectangle((x1, y1), box_w, box_h, + linewidth=6, edgecolor=color, facecolor='none') + ax.add_patch(bbox) + text = object_key_to_name[cls_pred] + plt.text(x1, y1, s=text, + color='white', verticalalignment='top', + bbox={'color': color, 'pad': 0}) + plt.axis('off') + plt.savefig(filename) + plt.close() + +def resize_and_draw(name, img, scale=2): + dim = (img.shape[1] * scale, img.shape[0] * scale) + resized_img = cv2.resize(img, dim) + cv2.imshow(name, img) + +def im_resize(img): + img = cv2.resize(img, (int(img.shape[1] / 2), int(img.shape[0] / 2))) + return img + +def resize_upscale(img, scale=2): + dim = (img.shape[1] * scale, img.shape[0] * scale) + # resized_img = cv2.resize(img, dim) + return resized_img + + +def apply_mask(image, mask, color, alpha=0.5): + """Apply the given mask to the image. + """ + for c in range(3): + image[:, :, c] = np.where(mask == 1, + image[:, :, c] * + (1 - alpha) + alpha * color[c] * 255, + image[:, :, c]) + return image + +open_3d_lines = [ + [5, 3], + [6, 4], + [0, 2], + [1, 7], + [0, 3], + [1, 6], + [2, 5], + [4, 7], + [0, 1], + [6, 3], + [4, 5], + [2, 7], +] + +edges_corners = [[0, 1], [0, 2], [0, 4], [1, 3], [1, 5], [2, 3], [2, 6], [3, 7], [4, 5], [4, 6], [5, 7], [6, 7]] + +def display_gird(img_vis, depth, peaks_vis): + images=[] + images.append(np.copy(img_vis)[...,::-1]) + images.append(np.copy(peaks_vis)[...,::-1]) + depth_vis = depth2inv(torch.tensor(depth).unsqueeze(0).unsqueeze(0)) + depth_vis = viz_inv_depth(depth_vis) + images.append(np.copy(depth_vis)) + rows=3 + img_count = 0 + print(images[0].shape) + fig, axes = plt.subplots(ncols=rows, figsize=(15,15)) + for i in range(rows): + if img_count < len(images): + axes[i].imshow(images[img_count]) + img_count+=1 + +def draw_geometries(geometries): + graph_objects = [] + + for geometry in geometries: + geometry_type = geometry.get_geometry_type() + + if geometry_type == o3d.geometry.Geometry.Type.PointCloud: + points = np.asarray(geometry.points) + colors = None + if geometry.has_colors(): + colors = np.asarray(geometry.colors) + elif geometry.has_normals(): + colors = (0.5, 0.5, 0.5) + np.asarray(geometry.normals) * 0.5 + else: + geometry.paint_uniform_color((1.0, 0.0, 0.0)) + colors = np.asarray(geometry.colors) + + scatter_3d = go.Scatter3d(x=points[:,0], y=points[:,1], z=points[:,2], mode='markers', marker=dict(size=1, color=colors)) + graph_objects.append(scatter_3d) + + if geometry_type == o3d.geometry.Geometry.Type.TriangleMesh: + triangles = np.asarray(geometry.triangles) + vertices = np.asarray(geometry.vertices) + colors = None + if geometry.has_triangle_normals(): + colors = (0.5, 0.5, 0.5) + np.asarray(geometry.triangle_normals) * 0.5 + colors = tuple(map(tuple, colors)) + else: + colors = (1.0, 0.0, 0.0) + + mesh_3d = go.Mesh3d(x=vertices[:,0], y=vertices[:,1], z=vertices[:,2], i=triangles[:,0], j=triangles[:,1], k=triangles[:,2], facecolor=colors, opacity=0.50) + graph_objects.append(mesh_3d) + + fig = go.Figure( + data=graph_objects, + layout=dict( + scene=dict( + xaxis=dict(visible=False), + yaxis=dict(visible=False), + zaxis=dict(visible=False) + ) + ) + ) + fig.show() \ No newline at end of file