stride uber

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Linux performance tuning 3: considerations before partitioning and formatting

, ********************************** **************************************** * ***** test 1 data: [root @ shorttop132 ~] # Mkfs. ext3-B 1024/dev/sdb5mke2fs 1.41.12 (17-May-2010) Filesystem label = OS type: Linux Block size = 1024 (log = 0) fragment size = 1024 (log = 0) Stride = 0 blocks, Stripe width = 0 blocks 28112 inodes, 112392 blocks5619 blocks (5.00%) reserved for the super userFirst data block = 1 Maximum filesystem blocks = 6737100814 block

Android ApiDemos example (62): Graphics-& gt; CreateBitmap

This example introduces several static functions for creating Mutable Bitmap defined in Bitmap. [Java]// These three are initialized with colors []MBitmaps [0] = Bitmap. createBitmap (colors, 0, STRIDE, WIDTH, HEIGHT,Bitmap. Config. ARGB_8888 );MBitmaps [1] = Bitmap. createBitmap (colors, 0, STRIDE, WIDTH, HEIGHT,Bitmap. Config. RGB_565 );MBitmaps [2] = Bitmap. createBitmap (colors, 0,

R-cnn,spp-net, FAST-R-CNN,FASTER-R-CNN, YOLO, SSD series deep learning detection method combing

output characteristics in the position can correspond to the original image, For example, the bottom left wheel in the first figure shows the active area of "^" in its conv5 diagram, so based on this feature, the spp-net only needs to go through the forward convolution of the whole plot, after the conv5 features obtained, and then extract the corresponding proposal features with spp-net respectively).Spp-layer principle:In Rnn, the CONV5 is Pool5, and in Spp-net, the Spp-layer is substituted fo

Machine learning Exercises (2) __ Machine learning

1. Foreword Or the study is not solid ah, if there are any questions welcome to the message. Parsing may not be right, because I pushed it myself (covering my face). 2. Exercise 1 (convolution and Chihua) Enter a picture size of 200x200, followed by a layer of convolution (kernel size 5x5,padding 1,stride 2), pooling (kernel size 3x3,padding 0,stride 1), and another layer of convolution (kernel Size 3x3,pa

OpenGL Learning Footprints: Drawing a triangle

the function Glvertexattribpointer. API void Glvertexattribpointer (Gluint index,Glint size,Glenum type,Glboolean normalized,Glsizei Stride,Const GLVOID * pointer);1. The index for the vertex attribute is the index of the attribute in the vertex shader, and the index starts from 0.2. Parameter size each attribute data consists of several components. For example, each of the above vertices has a property of 3 float, and the size is 3. The number of co

These 20 design code selection cases are an eye-opener for you.

engineers can directly refer to and use. According to these three categories, the following selected a variety of other normative cases.   First, Brandguidelines brand norms  2. Nintendo character design specification (1993) Press the Buttons:mario, Kirby, and Samus Aran Shine in the Nintendo Character This is a very early design specifications, for each persona and the use of the scene are described, which for today's animation image design has a very important reference significance.

Different types of digital certificates

KeyStore (KeyStore) (I know a total of 5 kinds, JKS, JCEKS, PKCS12, Bks,uber). JKS's provider is Sun, which is available in every version of the JDK, and Jceks provider is the sunjce,1.4 we can use directly. Jceks is stronger at the security level than JKS, and the provider used is Jceks (recommended), especially on the private key in the protection KeyStore (using TripleDES). PKCS#12 is the public key encryption standard, which stipulates that all p

Examples of sws_scale () usage in FFMPEG

, uint8_t * src2, int stride1, int stride2, int W, int h ){Int X, Y;Uint64_t SSD = 0; // Printf ("% d/N", W, H ); For (y = 0; y For (x = 0; x Int d = src1 [x + y * stride1]-src2 [x + y * stride2];SSD + = D * D;// Printf ("% d", ABS (src1 [x + y * stride1]-src2 [x + y * stride2])/26 );}// Printf ("/N ");}Return SSD;} // Test by ref-> Src-> DST-> out compare out against ref// Ref out are yv12Static int dotest (uint8_t * ref [3], int refstride [3], int W, int H, int srcformat, int dstformat,Int s

Edit image pixels in WPF

pixels in the image. Its prototype is public virtual void copypixels (array pixels, int stride, int offset). [This is only a reload method ]. Array pixels: After this function is executed, the value in this variable is the image pixel data to be obtained. Before calling this function, you can declare a byte array (for example, byte [] imgpixels = new byte [number of pixels × number of elements contained in each pixel]); Int

OpenGL functions (GL)

glfloat * V)Void glcolor4iv (const glint * V)Void glcolor4sv (const glshort * V)Void glcolor4ubv (const glubyte * V)Void glcolor4uiv (const gluint * V)Void glcolor4usv (const glushort * V)Parameter: V specifies an array pointer whose values are red, green, blue, and Alpha.GlcolorpointerDefine color arrayVoid glcolorpointer (glint size, glenum type, glsizei stride, glsizei count, const glvoid * pointer)Parameter: size the number of parts of each color

Use Visual C # To process digital images (2)

[Introduction] This article uses a simple example to show you how to use Visual C # And GDI + to process digital images. Note: To compile Insecure code, set the "allow Insecure code block" attribute on the project property page to true, as shown in the following figure:The program implementation result of this function is as follows:(Before processing)(After processing)The algorithm of the gray () function is as follows: Public static

Carnegie ssd6 system-level programming exercise 1 Summary

I personally think that ssd6 Exercise 1 is the most classic topic in Carnegie history. I wanted to write a long article that everyone can understand, but I had no time to continue with the style of recording the history, list your experiences one by one. Experience 1. Understand the differences between C-language value transfer and address transfer at the Assembly level. Int main (INT argc, char * argv []) { Int start = 10; Int stride = 3; Int ke

Python notes Anatomy of the Python Slice (slicing) syntax

It is also easy to understand the following slicing behavior, even for novice python:>>> s = ' this_is_a_test ' >>> s[1:5] ' his_ 'Further, the following syntax and output are not difficult to understand:>>> s = ' this_is_a_test ' >>> s[:: 2] ' ti_sats 'So, what about the following?>>> s = ' this_is_a_test ' >>> s[::-1] ' tset_a_si_siht ' # # Why has s been reversed? >>> S[1:6:-1] ' # # Why just get an empty string? >>> s[6:1:-1] ' Si_si ' # # Why is this result?Do you think the result is a

[Caffe] Source analysis of the layer

outputs for the layer optional BOOL Bias_term = 2 [default = True]; Whether to have bias terms//Pad, kernel size, and stride are all given as a single value for equal//dimensions in height and width or as Y, X pairs. Optional UInt32 pad = 3 [default = 0]; The padding size (equal in Y, X) Optional UInt32 pad_h = 9 [default = 0]; The padding height optional uint32 pad_w = ten [default = 0]; The padding width optional uint32 kernel_size = 4; The kerne

YOLO v2 Algorithm Details--taking Li Yu's gluon code as an example __ algorithm

) with Autograd.record (): x = Net (x) out Put, cls_pred, score, XYWH = Yolo2_forward (x, 2, scales) with Autograd.pause (): Tid, Tscore, t box, Sample_weight = Yolo2_target (score, xywh, y, Scales, thresh=0.5) # Losses Loss1 = Sce_loss (cl S_pred, Tid, Sample_weight * class_weight) score_weight = nd.wheRe (sample_weight > 0, nd.ones_like (sample_weight) * Positive_weight, Nd.ones_like (sample_weight) * negative_weight) Loss2 = L1_loss (score, Tscore, Score_weig HT) LOSS3 = L1_loss (Xywh, Tbox,

TensorFlow realizes wgan-gp mnist picture Generation __tensorflow

Generate the Fight network Gan currently has a very good application in image generation and confrontation training, this article aims to do a simple tf wgan-gp mnist generation Tutorial, the code used is very simple, and we hope to learn together. The code is as follows: The use of the environment: TensorFlow 1.2.0 GPU acceleration, the CPU is also OK, is very slow, you can change the batchsize small, with a good CPU training some, and by the way to create image code department to change, My

Parameter calculation of convolution neural network

each filter, and of course, don't forget to add bias:5x5x3x6 + 6 = 456 Another need to calculate the size of the output after the convolution, from the following figure is very good to understand, with the formula directly calculated. where n is the input image of the size,f is the size,stride of the filter is the sliding step.And then from the last example in the diagram above, we can see that when the stride

Yolo How to train the classification network???

=1 height=416 width=416 channels=3 max_crop=428 min_crop=428 hue=.1 saturation=.75 exposure=.75 learning_rate=0.1 policy=poly power=4 max_ Batches = momentum=0.9 decay=0.0005 [convolutional] batch_normalize=1 filters=32 size=3 stride=1 pad=1 activation=leaky [Maxpool] size=2 stride=2 [ Convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activat

A word on the Python six Musketeers

ordinary for loop implementation can be, after all, convenient implementation of the function is the first, more than a few lines of code more than a few lines.Example: Yang Hui triangle:#coding =utf-8def yhtriangle (n):L=[1]Print LWhile n>0:L=[1]+[x+y for x, y in Zip (l[:],l[1:])]+[1]N-=1Print LYhtriangle (10)6. SlicingThe slice syntax is as follows:S[begin:end:stride]In contrast to the simple slice syntax, the extended slice simply adds a 3rd parameter, the step parameter (commonly referred t

Real-time style conversion and super-resolution reconstruction based on perceptual loss function __ Image processing

a style loss lstyle, respectively, to measure differences in content and style. For each of the input images x we have a content target YC a style target Ys, for style conversion, content target YC is input image x, output image y, should combine style ys to content x=yc. We train a network for each target style. For Tantu resolution reconstruction, input image X is a low-resolution input, the target content is a real high-resolution image, style reconstruction is not used. We train a network f

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