Often we spend lots of time calculating the Receptive field of a CNN model.This Module can calculate the receptive field, Output image size from a model object
Reason this release was yanked:
FC layer updates
Project description
This Project can help you calculating Receptive Field in every layer for CNN model
How to use ?
from rf_calc import receptive_field
model = models.GoogLeNet().to(device)
image_input_size = 224
RF = receptive_field(model,image_input_size)
Output
Kernel_size : Size of the convolving kernel.
Padding : Zero-padding added to both sides of the input image.
Stride : Stride of the convolution. Default: 1
Input_Img_size : Shape of image as input to the layer.
Output_Img_size : Shape of image as Output from the layer.
Receptive_field : Shape pf Receptive field in the layer.
=======================================Reciptive Field Calculator========================================
| | Kernel_size | Padding | Stride | Input_Img_size | Output_Img_size | Receptive_field |
|---:|:--------------|:----------|---------:|:-----------------|:------------------|:------------------|
| 0 | 7*7 | 3 | 2 | 224*224 | 112*112 | 7*7 |
| 1 | 3*3 | NO | 2 | 112*112 | 55*55 | 11*11 |
| 2 | 1*1 | NO | 1 | 55*55 | 55*55 | 11*11 |
| 3 | 3*3 | 1 | 1 | 55*55 | 55*55 | 19*19 |
| 4 | 3*3 | NO | 2 | 55*55 | 27*27 | 27*27 |
| 5 | 1*1 | NO | 1 | 27*27 | 27*27 | 27*27 |
| 6 | 1*1 | NO | 1 | 27*27 | 27*27 | 27*27 |
| 7 | 3*3 | 1 | 1 | 27*27 | 27*27 | 43*43 |
| 8 | 1*1 | NO | 1 | 27*27 | 27*27 | 43*43 |
| 9 | 3*3 | 1 | 1 | 27*27 | 27*27 | 59*59 |
| 10 | 3*3 | 1 | 1 | 27*27 | 27*27 | 75*75 |
| 11 | 1*1 | NO | 1 | 27*27 | 27*27 | 75*75 |
| 12 | 1*1 | NO | 1 | 27*27 | 27*27 | 75*75 |
| 13 | 1*1 | NO | 1 | 27*27 | 27*27 | 75*75 |
| 14 | 3*3 | 1 | 1 | 27*27 | 27*27 | 91*91 |
| 15 | 1*1 | NO | 1 | 27*27 | 27*27 | 91*91 |
| 16 | 3*3 | 1 | 1 | 27*27 | 27*27 | 107*107 |
| 17 | 3*3 | 1 | 1 | 27*27 | 27*27 | 123*123 |
| 18 | 1*1 | NO | 1 | 27*27 | 27*27 | 123*123 |
| 19 | 3*3 | NO | 2 | 27*27 | 13*13 | 139*139 |
| 20 | 1*1 | NO | 1 | 13*13 | 13*13 | 139*139 |
| 21 | 1*1 | NO | 1 | 13*13 | 13*13 | 139*139 |
| 22 | 3*3 | 1 | 1 | 13*13 | 13*13 | 171*171 |
| 23 | 1*1 | NO | 1 | 13*13 | 13*13 | 171*171 |
| 24 | 3*3 | 1 | 1 | 13*13 | 13*13 | 203*203 |
| 25 | 3*3 | 1 | 1 | 13*13 | 13*13 | 235*235 |
| 26 | 1*1 | NO | 1 | 13*13 | 13*13 | 235*235 |
| 27 | 1*1 | NO | 1 | 13*13 | 13*13 | 235*235 |
| 28 | 1*1 | NO | 1 | 13*13 | 13*13 | 235*235 |
| 29 | 3*3 | 1 | 1 | 13*13 | 13*13 | 267*267 |
| 30 | 1*1 | NO | 1 | 13*13 | 13*13 | 267*267 |
| 31 | 3*3 | 1 | 1 | 13*13 | 13*13 | 299*299 |
| 32 | 3*3 | 1 | 1 | 13*13 | 13*13 | 331*331 |
| 33 | 1*1 | NO | 1 | 13*13 | 13*13 | 331*331 |
| 34 | 1*1 | NO | 1 | 13*13 | 13*13 | 331*331 |
| 35 | 1*1 | NO | 1 | 13*13 | 13*13 | 331*331 |
| 36 | 3*3 | 1 | 1 | 13*13 | 13*13 | 363*363 |
| 37 | 1*1 | NO | 1 | 13*13 | 13*13 | 363*363 |
| 38 | 3*3 | 1 | 1 | 13*13 | 13*13 | 395*395 |
| 39 | 3*3 | 1 | 1 | 13*13 | 13*13 | 427*427 |
| 40 | 1*1 | NO | 1 | 13*13 | 13*13 | 427*427 |
| 41 | 1*1 | NO | 1 | 13*13 | 13*13 | 427*427 |
| 42 | 1*1 | NO | 1 | 13*13 | 13*13 | 427*427 |
| 43 | 3*3 | 1 | 1 | 13*13 | 13*13 | 459*459 |
| 44 | 1*1 | NO | 1 | 13*13 | 13*13 | 459*459 |
| 45 | 3*3 | 1 | 1 | 13*13 | 13*13 | 491*491 |
| 46 | 3*3 | 1 | 1 | 13*13 | 13*13 | 523*523 |
| 47 | 1*1 | NO | 1 | 13*13 | 13*13 | 523*523 |
| 48 | 1*1 | NO | 1 | 13*13 | 13*13 | 523*523 |
| 49 | 1*1 | NO | 1 | 13*13 | 13*13 | 523*523 |
| 50 | 3*3 | 1 | 1 | 13*13 | 13*13 | 555*555 |
| 51 | 1*1 | NO | 1 | 13*13 | 13*13 | 555*555 |
| 52 | 3*3 | 1 | 1 | 13*13 | 13*13 | 587*587 |
| 53 | 3*3 | 1 | 1 | 13*13 | 13*13 | 619*619 |
| 54 | 1*1 | NO | 1 | 13*13 | 13*13 | 619*619 |
| 55 | 2*2 | NO | 2 | 13*13 | 6*6 | 635*635 |
| 56 | 1*1 | NO | 1 | 6*6 | 6*6 | 635*635 |
| 57 | 1*1 | NO | 1 | 6*6 | 6*6 | 635*635 |
| 58 | 3*3 | 1 | 1 | 6*6 | 6*6 | 699*699 |
| 59 | 1*1 | NO | 1 | 6*6 | 6*6 | 699*699 |
| 60 | 3*3 | 1 | 1 | 6*6 | 6*6 | 763*763 |
| 61 | 3*3 | 1 | 1 | 6*6 | 6*6 | 827*827 |
| 62 | 1*1 | NO | 1 | 6*6 | 6*6 | 827*827 |
| 63 | 1*1 | NO | 1 | 6*6 | 6*6 | 827*827 |
| 64 | 1*1 | NO | 1 | 6*6 | 6*6 | 827*827 |
| 65 | 3*3 | 1 | 1 | 6*6 | 6*6 | 891*891 |
| 66 | 1*1 | NO | 1 | 6*6 | 6*6 | 891*891 |
| 67 | 3*3 | 1 | 1 | 6*6 | 6*6 | 955*955 |
| 68 | 3*3 | 1 | 1 | 6*6 | 6*6 | 1019*1019 |
| 69 | 1*1 | NO | 1 | 6*6 | 6*6 | 1019*1019 |
| 70 | 1*1 | NO | 1 | 6*6 | 6*6 | 1019*1019 |
| 71 | 1*1 | NO | 1 | 6*6 | 6*6 | 1019*1019 |
=========================================================================================================
About Receptive Field
What is Receptive Field ?
1> Local Receptive field Local receptive field is present in every layer. Local receptive will be the size of kernel used in the layer .For example if we have an image of size 19x19 and we are applying a 3x3 metric then local receptive field will be 3x3 in first layer.
2> Global Receptive field At every layer the part of image our kernel can see is global receptive field .For a 3x3 kernel convolution global receptive field will increase by 2 units ( there is a mathematical formula that we can cover in later chapters ). It means if you see the below code in every convolution step our model will be able to see 2 pixel more in each side of image .
Input image => kernel shape => Output Image -> local Receptive field -> Global Receptive field 19x19 => 3x3 => 17x17 -> 3x3 ->3x3 17x17 => 3x3 => 15x15 ->3x3 ->5x5 15x15 => 3x3 => 13x13 ->3x3 ->7x7 13x13 => 3x3 => 11x11 ->3x3 ->9x9 11x11 => 3x3 => 9x9 ->3x3 ->11x11 9x9 => 3x3 => 7x7 ->3x3 ->13x13 7x7 => 3x3 => 5x5 ->3x3 ->15x15 5x5 => 3x3 => 3x3 ->3x3 ->17x17 3x3 => 3x3 => 1x1 ->3x3 ->19x19
Read the article for better understanding. https://medium.com/@data.pruthiraj/building-blocks-of-computer-vision-and-cnn-f5acdbf3c0b7
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file rf_calc-0.0.5.tar.gz.
File metadata
- Download URL: rf_calc-0.0.5.tar.gz
- Upload date:
- Size: 5.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.8
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
1711e36062c24687ab8d5ceddde1e7554aaaa7936d580fe3c2f667e9332817bc
|
|
| MD5 |
63ef8f6e1d43d4d2273b3ac4af3f67c4
|
|
| BLAKE2b-256 |
443ff02cae9f0646e46b642959694f6a86776fded6a10261acff9858c98638d4
|
File details
Details for the file rf_calc-0.0.5-py3-none-any.whl.
File metadata
- Download URL: rf_calc-0.0.5-py3-none-any.whl
- Upload date:
- Size: 4.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.8
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
4b946b059da65db6c192613e8b97b9ae7711457e171ec03341baa5959f5cb300
|
|
| MD5 |
0f133f77a1381dd779d6db796f6bdd06
|
|
| BLAKE2b-256 |
ad3d8661c94977204f965b3a2582679aab77bc2c8b5f17084eb0c7005406a895
|
