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#pragma once | |
#include "NVSceneCapturerUtils.generated.h" | |
USTRUCT(BlueprintType) | |
struct FCapturedObjectData | |
{ | |
GENERATED_USTRUCT_BODY() | |
public: | |
UPROPERTY(EditAnywhere, BlueprintReadWrite) |
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#pragma once | |
#include "NVSceneCapturerModule.h" | |
#include "Components/ActorComponent.h" | |
#include "Components/StaticMeshComponent.h" | |
#include "IImageWrapper.h" | |
#include "Runtime/Core/Public/Misc/FileHelper.h" | |
#include "Kismet/BlueprintFunctionLibrary.h" | |
#include "Serialization/JsonSerializerMacros.h" | |
#include "Serialization/JsonTypes.h" | |
#include "Paths.h" |
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{ | |
"SettingsVersion": 1.0, | |
"SimMode": "UrdfBot", | |
"LocalHostIp": "127.0.0.1", | |
"ViewMode": "GroundObserver", |
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/* | |
* Copyright (c) 2018 NVIDIA Corporation. All rights reserved. | |
* This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 | |
* International License. (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) | |
*/ | |
#pragma once | |
#include "DomainRandomizationDNNPCH.h" | |
#include "RandomComponentBase.h" |
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""" | |
This script will demonstrate how to use a pretrained model, in PyTorch, | |
to make predictions. Specifically, we will be using VGG16 with a cat | |
image. | |
References used to make this script: | |
PyTorch pretrained models doc: | |
http://pytorch.org/docs/master/torchvision/models.html | |
PyTorch image transforms example: | |
http://pytorch.org/tutorials/beginner/data_loading_tutorial.html#transforms |
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# returns the list of subexpressions which a given tape depends on directly | |
function list_subexpressions(nd::Vector{NodeData}) | |
# TODO: code overlap with compute_gradient_sparsity | |
indices = Set{Int}() | |
for k in 1:length(nd) | |
nod = nd[k] | |
if nod.nodetype == SUBEXPRESSION |
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<?xml version="1.0" ?> | |
<launch> | |
<arg name="paused" default="false"/> | |
<arg name="use_sim_time" default="true"/> | |
<arg name="gui" default="true"/> | |
<arg name="headless" default="false"/> | |
<arg name="debug" default="false"/> | |
<arg name="verbose" default="false"/> | |
<arg name="world_name" default="worlds/empty.world"/> <!-- Note: the world_name is with respect to GAZEBO_RESOURCE_PATH environmental variable --> |
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pa = Vpara(x_min=x_min,x_max=x_max,y_min=y_min,y_max=y_max,sm=0.01); | |
@NLconstraint(mdl, obs_con[j=1:Q,i=2:N+1], 1 <= ((x[i]-X_obs[j,i])^2)/((a[j]+sm)^2) + ((y[i]-Y_obs[j,i])^2)/((b[j]+sm)^2)) |
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Pkg.clone("https://github.com/huckl3b3rry87/PrettyPlots.jl") | |
Pkg.clone("https://github.com/huckl3b3rry87/VehicleModels.jl") |
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module DiffEqProblemLibrary | |
using DiffEqBase, ParameterizedFunctions, | |
FiniteElementDiffEq, AlgebraicDiffEq, JLD | |
print("1") | |
include("ode_premade_problems.jl") | |
print("2") | |
include("dae_premade_problems.jl") | |
print("3") | |
include("sde_premade_problems.jl") |
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