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[submodule "third_party/zed-ros-wrapper"]
path = third_party/zed-ros-wrapper
url =
[submodule "third_party/kinova-ros"]
path = third_party/kinova-ros
url =
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# next_best_view_rl_benchmark
# next_best_view_rl
This project is an extension to work presented in this [repository]( The project aims to find the sequence of next-best-views of occluded and self-occluded objects using reinforcement learning algorithms: PPO, SAC, TD3, and A2C. We trained each algorithm separately on three different datasets, extracted from [TEOS dataset](, for five runs using random seed. We compared the performance of the algorithms during training and evaluation. We could find that SAC outperforms all the other algorithms.
# Setup
1. Clone the repository: `git clone --recurse-submodules ...`
2. in 'third_party/zed-ros-wrapper' verify branch `devel`: `git checkout devel`
3. `sudo apt-get update && sudo apt-get install cmake libopenmpi-dev python3-dev zlib1g-dev libosmesa6-dev libgl1-mesa-glx libglfw3'
4. Create virtual environment: `conda create --name nbv_env python=3.7`
5. Activate virual environment: 'conda activate nbv_env'
6. Download and unpack mjpro150 ([link]( and move the `mjpro150` folder into `~/.mujoco/`
7. Download and copy a license key ([link]( into `~/.mujoco/`
8. Add `LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/USER/.mujoco/mjpro150/bin` to your `.bashrc`
9. Install mujoco from ``
10. Install requirements: `pip install -r requirements.txt`
11. Install `pip install -e .`
# Execution
- modify configs in `config` with hydra framework
- to execute training of ppo algorithm
- start RL training: `python3 scripts/ algorithm=ppo`
- evaluate RL model: `python3 scripts/ algorithm=ppo`
## Getting started
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
Already a pro? Just edit this and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
## Add your files
- [ ] [Create]( or [upload]( files
- [ ] [Add files using the command line]( or push an existing Git repository with the following command:
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git remote add origin
git branch -M main
git push -uf origin main
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# Editing this README
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to []( for this template.
## Suggestions for a good README
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
## Name
Choose a self-explaining name for your project.
## Description
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SimpleNet in Pytorch
import torch.nn as nn
class Unit(nn.Module):
def __init__(self, in_channels, out_channels):
super(Unit, self).__init__()
self.conv = nn.Conv2d(in_channels=in_channels, kernel_size=3, out_channels=out_channels, stride=1, padding=1) = nn.BatchNorm2d(num_features=out_channels)
self.relu = nn.ReLU()
def forward(self, input):
output = self.conv(input)
output =
output = self.relu(output)
return output
class BasicNet(nn.Module):
def __init__(self, classes=10):
super(BasicNet, self).__init__()
# Create 14 layers of the unit with max pooling in between
self.unit1 = Unit(in_channels=3, out_channels=32)
self.unit2 = Unit(in_channels=32, out_channels=32)
self.unit3 = Unit(in_channels=32, out_channels=32)
self.pool1 = nn.MaxPool2d(kernel_size=2)
self.unit4 = Unit(in_channels=32, out_channels=64)
self.unit5 = Unit(in_channels=64, out_channels=64)
self.unit6 = Unit(in_channels=64, out_channels=64)
self.unit7 = Unit(in_channels=64, out_channels=64)
self.pool2 = nn.MaxPool2d(kernel_size=2)
self.unit8 = Unit(in_channels=64, out_channels=128)
self.unit9 = Unit(in_channels=128, out_channels=128)
self.unit10 = Unit(in_channels=128, out_channels=128)
self.unit11 = Unit(in_channels=128, out_channels=128)
self.pool3 = nn.MaxPool2d(kernel_size=2)
self.unit12 = Unit(in_channels=128, out_channels=128)
self.unit13 = Unit(in_channels=128, out_channels=128)
self.unit14 = Unit(in_channels=128, out_channels=128)
self.avgpool = nn.AvgPool2d(kernel_size=4)
# Add all the units into the Sequential layer in exact order = nn.Sequential(self.unit1, self.unit2, self.unit3, self.pool1, self.unit4, self.unit5, self.unit6
, self.unit7, self.pool2, self.unit8, self.unit9, self.unit10, self.unit11, self.pool3,
self.unit12, self.unit13, self.unit14, self.avgpool)
self.fc = nn.Linear(in_features=128*7*7, out_features=classes)
def forward(self, input):
output =
# print(output.shape)
output = output.view(output.size(0), -1)
# output = output.view(-1, 128)
output = self.fc(output)
return output
import torch
import torch.nn as nn
from torchvision import models
from classification.simplenet import SimpleNet
from classification.basicnet import BasicNet
class NetLoader:
def __init__(self, model_name, num_classes, resize_size=224,
feature_extract=True, use_pretrained=True, custom_pre_model=None):
self.model_name = model_name
self.num_classes = num_classes
self.resize_size = resize_size
self.feature_extract = feature_extract
self.use_pretrained = use_pretrained
self.custom_pre_model = custom_pre_model
self.model, self.input_size = self.initialize_model()
def get_model(self):
return self.model, self.input_size
def initialize_model(self):
# Initialize these variables which will be set in this if statement. Each of these
# variables is model specific.
model_ft = None
input_size = 0
if self.model_name == "basicnet":
""" BasicNet
model_ft = BasicNet(classes=self.num_classes)
input_size = self.resize_size
# TODO check for None
if self.use_pretrained and self.custom_pre_model:
checkpoint =torch.load(self.custom_pre_model)
elif self.model_name == "simplenet":
""" SimpleNet
model_ft = SimpleNet(classes=self.num_classes)
input_size = self.resize_size
if self.use_pretrained and self.custom_pre_model:
checkpoint = torch.load(self.custom_pre_model)
elif self.model_name == "resnet18":
""" Resnet18
model_ft = models.resnet18(pretrained=self.use_pretrained)
self.set_parameter_requires_grad(model_ft, self.feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, self.num_classes)
input_size = 224
elif self.model_name == "resnet34":
""" Resnet34
model_ft = models.resnet34(pretrained=self.use_pretrained)
self.set_parameter_requires_grad(model_ft, self.feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, self.num_classes)
input_size = 224
elif self.model_name == "resnet50":
""" Resnet50
model_ft = models.resnet50(pretrained=self.use_pretrained)
self.set_parameter_requires_grad(model_ft, self.feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, self.num_classes)
input_size = 224
elif self.model_name == "resnet101":
""" Resnet101
model_ft = models.resnet101(pretrained=self.use_pretrained)
self.set_parameter_requires_grad(model_ft, self.feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, self.num_classes)
input_size = 224
elif self.model_name == "resnet152":
""" Resnet152
model_ft = models.resnet152(pretrained=self.use_pretrained)
self.set_parameter_requires_grad(model_ft, self.feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, self.num_classes)
input_size = 224
elif self.model_name == "alexnet":
""" Alexnet
model_ft = models.alexnet(pretrained=self.use_pretrained)
self.set_parameter_requires_grad(model_ft, self.feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, self.num_classes)
input_size = 224
elif self.model_name == "vgg":
""" VGG11_bn
model_ft = models.vgg11_bn(pretrained=self.use_pretrained)
self.set_parameter_requires_grad(model_ft, self.feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, self.num_classes)
input_size = 224
elif self.model_name == "squeezenet":
""" Squeezenet
model_ft = models.squeezenet1_0(pretrained=self.use_pretrained)
self.set_parameter_requires_grad(model_ft, self.feature_extract)
model_ft.classifier[1] = nn.Conv2d(512, self.num_classes, kernel_size=(1,1), stride=(1,1))
model_ft.num_classes = self.num_classes
input_size = 224
elif self.model_name == "densenet":
""" Densenet
model_ft = models.densenet121(pretrained=self.use_pretrained)
self.set_parameter_requires_grad(model_ft, self.feature_extract)
num_ftrs = model_ft.classifier.in_features
model_ft.classifier = nn.Linear(num_ftrs, self.num_classes)
input_size = 224
elif self.model_name == "inception":
""" Inception v3
Be careful, expects (299,299) sized images and has auxiliary output
model_ft = models.inception_v3(pretrained= self.use_pretrained, aux_logits=False)
self.set_parameter_requires_grad(model_ft, self.feature_extract)
# Handle the auxilary net
# num_ftrs = model_ft.AuxLogits.fc.in_features
# model_ft.AuxLogits.fc = nn.Linear(num_ftrs, self.num_classes)
# Handle the primary net
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, self.num_classes)
input_size = 299
print("Invalid model name, exiting...")
return model_ft, input_size
def set_parameter_requires_grad(model, feature_extracting):
if feature_extracting:
for param in model.parameters():
param.requires_grad = False
import os
import numpy as np
import torch
from torchvision.transforms import transforms
from torch.autograd import Variable
from PIL import Image
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
import torch.nn as nn
from sklearn.manifold import TSNE
import umap.umap_ as umap
import umap.plot
import json
from classification.netloader import NetLoader
class Predictor:
def __init__(self, cfg, test=False):
self.cfg = cfg
#torch.backends.cudnn.deterministic = True
#torch.backends.cudnn.benchmark = False
self.print_debug = False
self.test = test
root_dir = self.cfg.system.project_path
if self.test:
self.model_dir_path = self.cfg.classificator.test_model_path
self.model_path = os.path.join(self.model_dir_path, self.cfg.classificator.test_model)
self.class_map_path = os.path.join(self.model_dir_path, self.cfg.classificator.class_map) =
self.resize_size = self.cfg.classificator.resize_size
self.activation = self.cfg.classificator.activation
self.model_dir_path = root_dir + self.cfg.model.path
self.model_path = os.path.join(self.model_dir_path, self.cfg.model.model)
self.class_map_path = os.path.join(self.model_dir_path, self.cfg.model.class_map) =
self.resize_size = self.cfg.model.resize_size
self.activation = self.cfg.model.activation
self.model_name = self.model_path.split("/")[-1].split(".")[0]
print("[Predictor] Model: %s: " % self.model_path)
self.class_map = json.load(open(self.class_map_path))
self.classes = len(self.class_map.keys())
netloader = NetLoader(,
self.model, self.resize = netloader.get_model()
self.softmax = nn.Softmax(dim=1)
self.sigmoid = nn.Sigmoid()
self.cuda_avail = torch.cuda.is_available()
if self.cuda_avail:
self.model = self.model.cuda()
checkpoint = torch.load(self.model_path)
def predict_image(self, image, resize=224, use_activation=True):
if isinstance(image, str):
image =
image = Image.fromarray(np.uint8(image)).convert("RGB")
elif isinstance(image, np.ndarray):
image = Image.fromarray(np.uint8(image)).convert("RGB")
# Define transformations for the image, should
transformation = transforms.Compose([
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
# Pre-process the image
image_tensor = transformation(image).float()
# Add an extra batch dimension since pytorch treats all images as batches
image_tensor = image_tensor.unsqueeze_(0)
if self.cuda_avail:
dtype = torch.cuda.FloatTensor
input = torch.autograd.Variable(image_tensor.type(dtype))
# Turn the input into a Variable
input = Variable(image_tensor)
# Predict the class of the image
output = self.model(input)
if use_activation:
if self.activation == "softmax":
output = self.softmax(output)
elif self.activation == "sigmoid":
output = self.sigmoid(output)
elif self.activation is None:
print("[Error] self.activation wrong defined, using None")
# print(output)
output_data =
if self.print_debug:
print(f"{output}: {output}")
return output_data[0]
def get_predicted_object(self, output_data):
index = output_data.argmax()
accuracy = output_data[index]
acc_rounded = float(str(round(accuracy)))