Inclusion of a Shuffled String into Another String sliding window Instructions The function is given two strings s1 and s2. Determine if one of the permutations of characters of s1 is a substring of s2, return true / false. Examples checkInclusion(“ab”, “edabitbooo”) // true // “ab” is in s2. checkInclusion(“ab”, “edaoboat”) // false // neither […]
Tag: shuffle
06-MapReduce (3) Shuffle mechanism
Table of Contents 1. Definition 2. Partition 3. Practical operation of Partition partition case 1. need 2. Demand analysis 3. Write code 4. Sorting (WritableComparable) 1. Sorting classification ?2. Custom sorting WritableComparable 3. Practical operation of WritableComparable sorting case (full sorting) (1) Demand (2) Demand analysis (3)Write code 1) Build package 2) Write the FlowBean […]
Spark Shuffle Principle and Tuning
1. Shuffle principle 1. Introduction to spark shuffle When a shuffle occurs, a shuffle occurs when the following operators are used: Repartitioning operators: coalesce, repartition; Operators of byKey type: reduceByKey, groupByKey, aggregateByKey, foldByKey, combineByKey, sortByKey; Join type operators join, leftOuterJoin, cogroup, etc. Spark divides the job into multiple stages during the DAG scheduling phase. The […]
The latest improvements to the YOLOv8 series: YOLOv8 improves ShuffleNetV2, effectively increasing points!
Improve the YOLOv8 backbone network. For more detailed tutorials, please pay attention to Station B: AI Academic Calling Beast Detailed teaching on improvement methods. Discussion and exchange are welcome. 1. Introduction to ShuffleNetV2 (just have a rough understanding) 2. YOLOv8 improves ShuffleNetV2 teaching 2.1 The first step is to replace the yaml file 2.2 Create […]
Trino Exchange Shuffle compression optimization
Background After a round of tardigrade batch processing tpcds performance test, it was found that the performance in tardigrade mode is relatively poor. Looking at the machine load, each node has full IO of 4 local disks, and disk IO becomes a bottleneck. The currently selected model, Huawei Cloud d6.2xlarge.4, has a resource ratio of: […]
YOLOv5 algorithm improvement (9) – ShuffleNetV2 replacing the backbone network
Foreword:Hello everyone, I am Brother Tan. ShuffleNetV2 is a lightweight neural network architecture suitable for resource-constrained scenarios such as mobile devices and embedded devices. It is designed to provide efficient computing and reasoning capabilities on devices with limited computing resources. It uses Channel rearrangement operations and point-by-point group convolution are introduced to reduce the amount […]
pytorch reproduces ShuffleNetV2
import torch import torch.nn as nn from torch import Tensor from typing import List, Callable #Channel rearrangement def channel_shuffle(x: Tensor, groups: int) -> Tensor: batch_size, num_channels,height,width = x.size() channel_pre_group = num_channels // groups #reshape # [batch_size, num_channels, height, width] -> [batch_size, groups, channels_per_group, height, width] x = x.view(batch_size, groups, channel_pre_group, height, width) x = torch.transpose(x, […]
[torch.nn.PixelShuffle] and [torch.nn.UnpixelShuffle]
Article directory torch.nn.PixelShuffle? intuitive explanation official document torch.nn.PixelUnshuffle? intuitive explanation official document torch.nn.PixelShuffle Intuitive explanation PixelShuffle is an upsampling method that takes the shape of ( ? , C x r 2 , h , W ) (?, C\times r^2, H, W) Tensors of (?,C×r2,H,W) are rearranged into shapes of ( ? , C , […]
[Week 4] MobileNet_ShuffleNet
MobileNet v1 network The MobileNet network focuses on lightweight CNN networks in mobile or embedded devices. Compared with the traditional convolutional neural network, the model parameters and the amount of calculation are greatly reduced under the premise of a small decrease in accuracy. Compared with VGG16, the accuracy rate is reduced by 0.9%, but the […]
Combined with PixelShuffle operation, understanding of high-dimensional tensor reshape and transpose dimension exchange
PixelShuffle and tensor dimension reshaping operations Dimensionality swap from matrix transpose to high-dimensional tensor About PixelShuffle Understanding matrix transposition from the base, strides, and address properties of Array Extend the above understanding to high-dimensional tensor arrays and PixelShuffle operations The case of direct reshape without replacing dimensions Summarize Dimension exchange from matrix transpose to high-dimensional […]