#getnode

[waatai]

Godot Journal - stuck on Signals

Signals in C# are different and weird and trying to make them talk across scenes is causing me a headache. I'm in Main Tree. I Instantiate the startmenu. In the start menu is the button NewGame. I want to get the signal of that button being pressed so I can drop startmenu and pick up another scene.

I know line 23 is wrong, I'm just not sure how to make it right.

My understanding is I need to GetNode to pull it into this script, and then line 24 will know when it's pressed and can Do The Thing.

#godot #gamedev #beginner coding #pigeon witch

[rockergiirl]

so I just discovered Godot's signal system, while handy for calling functions on other scripts, is also jank as fuck apparently. I had this bug where if I assigned a label3d to a test dummy entity it would override the line in the player entity scene that connected the signal to the camera controller somehow

had to fix it by now needing to use godot's horrible GetNode system for getting the script reference because for some fucking reason you can't assign a script in editor, only nodes

the more I use godot the less confident I am that it can handle anything more complex than like pong

[scriptzol]

Which one of the following is a possible return value type of Mage::getConfig()--getNode()?

A. Array B. Integer C. String D. Object

#quiz #poll #developerquiz #magentoquiz #magento_extension_development #magento2_extension #magento_development_company #softwarequiz #scriptzolquiz #scriptzolpoll #scriptzol #scriptzolteam

[cittasolutions]

Comment your Answer:

Which one of the following is a possible return value type of Mage::getConfig()--getNode()?

a. Array b. Integer c. String d. Object

#Citta Cittasolutions development #Magento Magentodevelopment #webdeveloper Programmimg #Softwaredeveloper Hirededecateddeveloper #Hirewebdeveloper

[getnodeerfahrung-blog]

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#Getnode Erfahrung

[lewuathe]

Order Sensitive Heterogeneous Partitioning in Glow

Glow supports the heterogenous partitioning which allows us to split the input model into multiple segments according to the given device configuration.

A Glow backend sometimes has unsupported operators due to the limitation of the functionality. Additionally, some backend provides more performant execution for the specific operator. Graph partitioning gives us more chance to improve the performance and reliability by making the most of the available resources for the computation.

It is necessary to write the device configuration to achieve the heterogeneous partitioning as follows.

--- - name: Device1 backendName: CPU parameters: | "deviceID" : "0" - name: Device2 backendName: OpenCL parameters: | "nonSupportedNodes": "ResizeBilinear" "deviceID": "1"

This file instructs Glow to be aware of two platforms to run the partition. One is CPU on the host machine; the other is GPU device providing OpenCL API. Since OpenCL backend does not support ResizeBilinear, we marked it as nonSupportedNodes so that Glow will automatically put the operator on CPU instead.

But when I tried to partition the MobileNet v3 model in ONNX, I’ve got the following message.

$ ./bin/image-classifier \ -model=../../mobilenetv2-7.onnx \ -load-device-configs=../../heterogeneousConfig-bad.yaml \ -log-partition=true \ ../../glow/tests/images/imagenet/cat_285.png \ -model-input-name=input \ -onnx-define-symbol=batch_size,1 ... I0629 06:16:44.610828 23057 Partitioner.cpp:88] The number of partitions is : 1 I0629 06:16:44.610846 23057 PartitionerUtils.cpp:549] Partition 0: Name : ../../mobilenetv2-7.onnx_part1_part1 BackendKind : CPU context count : 1 total Memory : 14557376 input size: 602112 input count : 1 input only from peers count : 0 output size: 4000 constant size: 13951264

No partitioning looks happen, and all operators seem to be assigned to the CPU backend. That’s a bizarre situation.

After a while digging deeper into the code base, I found the cause in partitioner of Glow.

Expected<DAGListTy> Partitioner::backendBasedPartition( FunctionToBackendNameMap &funcToBackend, Function *F, std::vector<Backend *> &backends, CompilationContext &cctx) { NodeToFunctionMap mapping; llvm::DenseMap<Node *, std::string> nodeToBackendName; // For each node find a backend that supports it. for (auto &N : F->getNodes()) { for (auto &backend : backends) { // Find the first backend that supports this node. The order of backends // is important. The check flow is : // ... } // ... } }

The algorithm is first-come-first-served, which always prefers the backend coming first if it supports the operator. Accidentally, the CPU backend generally supports more operators than the OpenCL backend. Therefore, the configuration file having CPU first always leads to the single partition backed by CPU. To overcome the situation, we can reorder the backends listed in the configuration file.

--- - name: Device2 backendName: OpenCL parameters: | "nonSupportedNodes": "ResizeBilinear" "deviceID": "1" - name: Device1 backendName: CPU parameters: | "deviceID" : "0"

I’ve just switched the order of CPU and OpenCL devices.

I0629 06:31:54.274185 23240 Partitioner.cpp:88] The number of partitions is : 1 I0629 06:31:54.274202 23240 PartitionerUtils.cpp:549] Partition 0: Name : ../../mobilenetv2-7.onnx_part1_part1 BackendKind : OpenCL context count : 1 total Memory : 14557376 input size: 602112 input count : 1 input only from peers count : 0 output size: 4000 constant size: 13951264 I0629 06:31:54.274243 23240 PartitionerUtils.cpp:5

Now every operator is assigned to the OpenCL backend. If a model like FCN containing resize operator, we will get the following partitioning layout.

I0629 06:34:34.624155 23388 Partitioner.cpp:88] The number of partitions is : 3 I0629 06:34:34.624177 23388 PartitionerUtils.cpp:549] Partition 0: Name : ../../fcn.onnx_part1_part1 BackendKind : OpenCL context count : 1 total Memory : 217777448 input size: 602112 input count : 1 input only from peers count : 0 output size: 131712 constant size: 217043624 I0629 06:34:34.624246 23388 PartitionerUtils.cpp:570] LogicalDeviceIDs : 1 I0629 06:34:34.624260 23388 PartitionerUtils.cpp:549] Partition 1: Name : ../../fcn.onnx_part2_part1 BackendKind : CPU context count : 1 total Memory : 8561280 input size: 131712 input count : 2 input only from peers count : 0 output size: 8429568 constant size: 0 I0629 06:34:34.624302 23388 PartitionerUtils.cpp:570] LogicalDeviceIDs : 0 I0629 06:34:34.624315 23388 PartitionerUtils.cpp:549] Partition 2: Name : ../../fcn.onnx_part3_part1 BackendKind : OpenCL context count : 1 total Memory : 16859136 input size: 8429568 input count : 2 input only from peers count : 0 output size: 8429568 constant size: 0

One thing to note in this article is the order-sensitiveness of the device configuration in Glow. So we should put the weaker device first and the strong backend like Interpreter or CPU to increase the chance of balanced distribution of partitions.

Reference

Partitioner in Glow

source http://www.lewuathe.com/order-sensitive-heterogeneous-partioning-in-glow.html

[bernd1982]