Real-Time Semantic Segmentation in Mobile device

Last update: Jan 1, 2023

Overview

Real-Time Semantic Segmentation in Mobile device

This project is an example project of semantic segmentation for mobile real-time app.

The architecture is inspired by MobileNetV2 and U-Net.

LFW, Labeled Faces in the Wild, is used as a Dataset.

The goal of this project is to detect hair segments with reasonable accuracy and speed in mobile device. Currently, it achieves 0.89 IoU.

About speed vs accuracy, more details are available at my post.

Example application

iOS
Android (TODO)

Requirements

Python 3.8
pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html
CoreML for iOS app.

About Model

At this time, there is only one model in this repository, MobileNetV2_unet. As a typical U-Net architecture, it has encoder and decoder parts, which consist of depthwise conv blocks proposed by MobileNets.

Input image is encoded to 1/32 size, and then decoded to 1/2. Finally, it scores the results and make it to original size.

Steps to training

Data Preparation

Data is available at LFW. To get mask images, refer issue #11 for more. After you got images and masks, put the images of faces and masks as shown below.

data/
  lfw/
    raw/
      images/
        0001.jpg
        0002.jpg
      masks/
        0001.ppm
        0002.ppm

Training

If you use 224 x 224 as input size, pre-trained weight of MobileNetV2 is available. It will be automatically downloaded when you train model with the following command.

cd src
python run_train.py params/002.yaml

Dice coefficient is used as a loss function.

Pretrained model

Input size	IoU	Download
224	0.89	Google Drive

Converting

As the purpose of this project is to make model run in mobile device, this repository contains some scripts to convert models for iOS and Android.

run_convert_coreml.py
- It converts trained PyTorch model into CoreML model for iOS app.

TBD

Report speed vs accuracy in mobile device.
Convert pytorch to Android using TesorFlow Light

Comments

Face segmentation

Hi

The ppm files of LFW dataset has face as green, hair as red & remaining area as blue.

Suppose if I want to train the model for face segmentation, what are the changes need to be done? Please let me know.

Regards Gopi. J

opened by gopi77 23
Any Inference Speed Record in CPU Mode

I am looking for a semantic segmentation model that could run fast in CPU mode. I appreciated your post offers a very detailed analysis on the runtime and accuracy for variants of MobileNetv2-UNet. However, I found that the mobile devices you did experiments on are all embedded with GPU device. I am more concerned with the runtime and accuracy performance on CPU mode (hopefully the model could run at FPS >= 10). Did you do any similar analysis before? Or are you confident that the model (MobileNetv2-UNet) could give a speed of at least 10FPS with a normal Macbook Air? (8GB memory, i7 CPU)

FYI, I tried your model (image size = 224, 224) in my laptop and found that the inference speed is ~0.3s. Not sure how I could optimize the speed

opened by riven314 9
LFW related

Hi

I see the below comments in Readme

Data is available at LFW. Put the images of faces and masks as shown below.

data/ raw/ images/ 0001.jpg 0002.jpg masks/ 0001.ppm 0002.ppm <<<<

But in the link http://vis-www.cs.umass.edu/lfw/part_labels/#download, I can download files like lfw-funneled.tgz. If I extract I get folders with person names & corresponding jpg files. Do I need to manually remove the folder structure & rearrange those files as *0001.jpg, *0002.jpg, etc... similar to the ppm files list ?

FYI, I got ppm files from https://drive.google.com/file/d/1TbQ24nIc3GGNWzV_GGX_D-1WpI2KOGii/view (will be great if you can share the method/code used to generate ppm files)

Regards Gopi. J

opened by gopi77 9
Have you tried the model on real world images?

Hi, I have tried running produced from the training on sample images from normal real world seances, and I'm getting very bad results and idea why? the training and validation scores seem alright?

opened by arnonkahani 4
How long to train the model?

I'm planning on mimicking your architecture on the cityscapes dataset. Do you mind sharing some details on the training phase? ( Hardware / how long).

opened by normandra 3
I trained with my own dataset input size is 224x224, but the output is 112 x 112

hi ,I would like to use your training method to train my own dataset and want to replace my own mlmodel to the iOS sample Hair mlmodel. However, when I done training and covert it to mlmoldel, input size is 224 x 224 color, output is 112x112 color. may I ask how to make the output same as the ios sample hair model mlmodel (MultiArray (Double 1 x 224 x 224))?

opened by jiangzhubo 3
ImportError: cannot import name 'relu6'

Hi,

with Keras 2.2.2, there is problem importing DepthwiseConv2D and relu6 in MobileUNet.py

DepthwiseConv2D can be probably imported from keras.layers, but I don't know where to find relu6.

opened by nemanjaq 3
Training Issue

when i run this command python train_full.py \ --img_file=/path/to/images.npy \ --mask_file=/path/to/masks.npy

i got this error:

Traceback (most recent call last): File "train_full.py", line 96, in <module> train(**vars(args)) File "train_full.py", line 21, in train train_gen, validation_gen, img_shape = load_data(img_file, mask_file) File "/home/lafi/Desktop/HairSegmentation/mobile-semantic-segmentation-master/data.py", line 60, in load_data train_img_gen.fit(images) File "/usr/local/lib/python2.7/dist-packages/keras/preprocessing/image.py", line 675, in fit 'Got array with shape: ' + str(x.shape)) ValueError: Input to.fit()should have rank 4. Got array with shape: (0,) Please how i can fix it Thank you Lafi

opened by shadow111 3
the last depthwise_conv_block(b18)

I noticed that the last depthwise_conv_block(b18) is annotated. You used a standard conv layer to replace it. This standard conv layer cost about 20% computation. So why not use depthwise_conv_block as above layers?

opened by stainless-steel-rat 3
why predict values are strange

I trained a model with my own data, the test output are as follows with "pred.py":

it seems there is something wrong, what's the reason?

I trained with my own data, my mask's pixel value is 0,1,2,3,4

opened by liangdashuang 3
output size of the converted model is wrong

Hi, I tried your coreml_converter.py script and the output layer (identifier 597) is: tensor_type { elem_type: FLOAT shape { dim { dim_value: 1 } dim { dim_value: 3 } dim { dim_value: 224 } dim { dim_value: 224 }

The identifier is 597 and not 595 as you told because I used your interpolate layer (was in # in your original code): x = interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)

Now, I have two questions: (1) why the output is 3x224x224 and not 1x224x224 ? (2) the interpolate layer is getting error when I am trying to convert. Is there another way to upload from 112x112 to 224x224?

Thanks.

opened by roeiherz 2
Bump certifi from 2020.11.8 to 2022.12.7
Bumps certifi from 2020.11.8 to 2022.12.7.

Commits

9e9e840 2022.12.07

b81bdb2 2022.09.24

939a28f 2022.09.14

aca828a 2022.06.15.2

de0eae1 Only use importlib.resources's new files() / Traversable API on Python ≥3.11 ...

b8eb5e9 2022.06.15.1

47fb7ab Fix deprecation warning on Python 3.11 (#199)

b0b48e0 fixes #198 -- update link in license

9d514b4 2022.06.15

4151e88 Add py.typed to MANIFEST.in to package in sdist (#196)

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dependencies
opened by dependabot[bot] 0
Bump pillow from 8.0.1 to 9.3.0
Bumps pillow from 8.0.1 to 9.3.0.

Release notes

Sourced from pillow's releases.

9.3.0

https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

Changes

Initialize libtiff buffer when saving #6699 [@radarhere]

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [@wiredfool]

Inline fname2char to fix memory leak #6329 [@nulano]

Fix memory leaks related to text features #6330 [@nulano]

Use double quotes for version check on old CPython on Windows #6695 [@hugovk]

GHA: replace deprecated set-output command with GITHUB_OUTPUT file #6697 [@nulano]

Remove backup implementation of Round for Windows platforms #6693 [@cgohlke]

Upload fribidi.dll to GitHub Actions #6532 [@nulano]

Fixed set_variation_by_name offset #6445 [@radarhere]

Windows build improvements #6562 [@nulano]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [@cgohlke]

Only use ASCII characters in C source file #6691 [@cgohlke]

Release Python GIL when converting images using matrix operations #6418 [@hmaarrfk]

Added ExifTags enums #6630 [@radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [@radarhere]

Added support for reading BMP images with RLE4 compression #6674 [@npjg]

Decode JPEG compressed BLP1 data in original mode #6678 [@radarhere]

pylint warnings #6659 [@marksmayo]

Added GPS TIFF tag info #6661 [@radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [@radarhere]

Do not attempt normalization if mode is already normal #6644 [@radarhere]

Fixed seeking to an L frame in a GIF #6576 [@radarhere]

Consider all frames when selecting mode for PNG save_all #6610 [@radarhere]

Don't reassign crc on ChunkStream close #6627 [@radarhere]

Raise a warning if NumPy failed to raise an error during conversion #6594 [@radarhere]

Only read a maximum of 100 bytes at a time in IMT header #6623 [@radarhere]

Show all frames in ImageShow #6611 [@radarhere]

Allow FLI palette chunk to not be first #6626 [@radarhere]

If first GIF frame has transparency for RGB_ALWAYS loading strategy, use RGBA mode #6592 [@radarhere]

Round box position to integer when pasting embedded color #6517 [@radarhere]

Removed EXIF prefix when saving WebP #6582 [@radarhere]

Pad IM palette to 768 bytes when saving #6579 [@radarhere]

Added DDS BC6H reading #6449 [@ShadelessFox]

Added support for opening WhiteIsZero 16-bit integer TIFF images #6642 [@JayWiz]

Raise an error when allocating translucent color to RGB palette #6654 [@jsbueno]

Moved mode check outside of loops #6650 [@radarhere]

Added reading of TIFF child images #6569 [@radarhere]

Improved ImageOps palette handling #6596 [@PososikTeam]

Defer parsing of palette into colors #6567 [@radarhere]

Apply transparency to P images in ImageTk.PhotoImage #6559 [@radarhere]

Use rounding in ImageOps contain() and pad() #6522 [@bibinhashley]

Fixed GIF remapping to palette with duplicate entries #6548 [@radarhere]

Allow remap_palette() to return an image with less than 256 palette entries #6543 [@radarhere]

Corrected BMP and TGA palette size when saving #6500 [@radarhere]

... (truncated)

Changelog

Sourced from pillow's changelog.

9.3.0 (2022-10-29)

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

Initialize libtiff buffer when saving #6699 [radarhere]

Inline fname2char to fix memory leak #6329 [nulano]

Fix memory leaks related to text features #6330 [nulano]

Use double quotes for version check on old CPython on Windows #6695 [hugovk]

Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

Fixed set_variation_by_name offset #6445 [radarhere]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

Added ExifTags enums #6630 [radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

Added GPS TIFF tag info #6661 [radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

Do not attempt normalization if mode is already normal #6644 [radarhere]

... (truncated)

Commits

d594f4c Update CHANGES.rst [ci skip]

909dc64 9.3.0 version bump

1a51ce7 Merge pull request #6699 from hugovk/security-libtiff_buffer

2444cdd Merge pull request #6700 from hugovk/security-samples_per_pixel-sec

744f455 Added release notes

0846bfa Add to release notes

799a6a0 Fix linting

00b25fd Hide UserWarning in logs

05b175e Tighter test case

13f2c5a Prevent DOS with large SAMPLESPERPIXEL in Tiff IFD

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dependencies
opened by dependabot[bot] 0
Bump joblib from 0.17.0 to 1.2.0
Bumps joblib from 0.17.0 to 1.2.0.

Changelog

Sourced from joblib's changelog.

Release 1.2.0

Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported. joblib/joblib#1327

Make sure that joblib works even when multiprocessing is not available, for instance with Pyodide joblib/joblib#1256

Avoid unnecessary warnings when workers and main process delete the temporary memmap folder contents concurrently. joblib/joblib#1263

Fix memory alignment bug for pickles containing numpy arrays. This is especially important when loading the pickle with mmap_mode != None as the resulting numpy.memmap object would not be able to correct the misalignment without performing a memory copy. This bug would cause invalid computation and segmentation faults with native code that would directly access the underlying data buffer of a numpy array, for instance C/C++/Cython code compiled with older GCC versions or some old OpenBLAS written in platform specific assembly. joblib/joblib#1254

Vendor cloudpickle 2.2.0 which adds support for PyPy 3.8+.

Vendor loky 3.3.0 which fixes several bugs including:

robustly forcibly terminating worker processes in case of a crash (joblib/joblib#1269);

avoiding leaking worker processes in case of nested loky parallel calls;

reliability spawn the correct number of reusable workers.

Release 1.1.0

Fix byte order inconsistency issue during deserialization using joblib.load in cross-endian environment: the numpy arrays are now always loaded to use the system byte order, independently of the byte order of the system that serialized the pickle. joblib/joblib#1181

Fix joblib.Memory bug with the ignore parameter when the cached function is a decorated function.

... (truncated)

Commits

5991350 Release 1.2.0

3fa2188 MAINT cleanup numpy warnings related to np.matrix in tests (#1340)

cea26ff CI test the future loky-3.3.0 branch (#1338)

8aca6f4 MAINT: remove pytest.warns(None) warnings in pytest 7 (#1264)

067ed4f XFAIL test_child_raises_parent_exits_cleanly with multiprocessing (#1339)

ac4ebd5 MAINT add back pytest warnings plugin (#1337)

a23427d Test child raises parent exits cleanly more reliable on macos (#1335)

ac09691 [MAINT] various test updates (#1334)

4a314b1 Vendor loky 3.2.0 (#1333)

bdf47e9 Make test_parallel_with_interactively_defined_functions_default_backend timeo...

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dependencies
opened by dependabot[bot] 0
Bump protobuf from 3.14.0 to 3.18.3
Bumps protobuf from 3.14.0 to 3.18.3.

Release notes

Sourced from protobuf's releases.

Protocol Buffers v3.18.3

C++

Reduce memory consumption of MessageSet parsing

This release addresses a Security Advisory for C++ and Python users

Protocol Buffers v3.16.1

Java

Improve performance characteristics of UnknownFieldSet parsing (#9371)

Protocol Buffers v3.18.2

Java

Improve performance characteristics of UnknownFieldSet parsing (#9371)

Protocol Buffers v3.18.1

Python

Update setup.py to reflect that we now require at least Python 3.5 (#8989)

Performance fix for DynamicMessage: force GetRaw() to be inlined (#9023)

Ruby

Update ruby_generator.cc to allow proto2 imports in proto3 (#9003)

Protocol Buffers v3.18.0

C++

Fix warnings raised by clang 11 (#8664)

Make StringPiece constructible from std::string_view (#8707)

Add missing capability attributes for LLVM 12 (#8714)

Stop using std::iterator (deprecated in C++17). (#8741)

Move field_access_listener from libprotobuf-lite to libprotobuf (#8775)

Fix #7047 Safely handle setlocale (#8735)

Remove deprecated version of SetTotalBytesLimit() (#8794)

Support arena allocation of google::protobuf::AnyMetadata (#8758)

Fix undefined symbol error around SharedCtor() (#8827)

Fix default value of enum(int) in json_util with proto2 (#8835)

Better Smaller ByteSizeLong

Introduce event filters for inject_field_listener_events

Reduce memory usage of DescriptorPool

For lazy fields copy serialized form when allowed.

Re-introduce the InlinedStringField class

v2 access listener

Reduce padding in the proto's ExtensionRegistry map.

GetExtension performance optimizations

Make tracker a static variable rather than call static functions

Support extensions in field access listener

Annotate MergeFrom for field access listener

Fix incomplete types for field access listener

Add map_entry/new_map_entry to SpecificField in MessageDifferencer. They record the map items which are different in MessageDifferencer's reporter.

Reduce binary size due to fieldless proto messages

TextFormat: ParseInfoTree supports getting field end location in addition to start.

... (truncated)

Commits

a902b39 No-op whitespace change

ae62acd Updating version.json and repo version numbers to: 18.3

f43ac49 Merge pull request #10542 from deannagarcia/3.18.x

9efdf55 Add missing includes

d1635e1 Apply patch

5b37c91 Update version.json with "lts": true (#10534)

c39d622 Merge pull request #10529 from protocolbuffers/deannagarcia-patch-5

f77d3b6 Update version.json

8178b06 Merge pull request #10503 from deannagarcia/3.18.x

24ca839 Add version file

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dependencies
opened by dependabot[bot] 0
Bump numpy from 1.19.4 to 1.22.0
Bumps numpy from 1.19.4 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.

A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.

NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.

New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.

A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release

fd66547 REL: Prepare for the NumPy 1.22.0 release.

125304b wip

c283859 Merge pull request #20682 from charris/backport-20416

5399c03 Merge pull request #20681 from charris/backport-20954

f9c45f8 Merge pull request #20680 from charris/backport-20663

794b36f Update armccompiler.py

d93b14e Update test_public_api.py

7662c07 Update init.py

311ab52 Update armccompiler.py

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opened by dependabot[bot] 0
Output data type is MultiArray after run_convert_coreml.py

Hi, after training is completed, I want the model to convert to .mlmodel format, but I got this kind of file below which output is not same as input data type:

Am I miss something? I have search coremltools API ref, it seems there has no way to config output in pytorch, does someone know how I can fix it?

opened by maromaSamsa 0

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Real-Time Semantic Segmentation in Mobile device

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Overview

Real-Time Semantic Segmentation in Mobile device

Example application

Requirements

About Model

Steps to training

Data Preparation

Training

Pretrained model

Converting

TBD

Comments

9.3.0

Changes

9.3.0 (2022-10-29)

Release 1.2.0

Release 1.1.0

Protocol Buffers v3.18.3

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Protocol Buffers v3.16.1

Java

Protocol Buffers v3.18.2

Java

Protocol Buffers v3.18.1

Python

Ruby

Protocol Buffers v3.18.0

C++

v1.22.0

NumPy 1.22.0 Release Notes

Expired deprecations

Deprecated numeric style dtype strings have been removed

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

Owner

TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation, CVPR2022

Real-Time-Student-Attendence-System - Real Time Student Attendence System

HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation Official PyTorch Implementation

FANet - Real-time Semantic Segmentation with Fast Attention

This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data

DFFNet: An IoT-perceptive Dual Feature Fusion Network for General Real-time Semantic Segmentation

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)

Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion (CVPR 2021)

Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

Code for Boundary-Aware Segmentation Network for Mobile and Web Applications

YolactEdge: Real-time Instance Segmentation on the Edge

OrienMask: Real-time Instance Segmentation with Discriminative Orientation Maps

implement of SwiftNet:Real-time Video Object Segmentation

A keras-based real-time model for medical image segmentation (CFPNet-M)

A lane detection integrated Real-time Instance Segmentation based on YOLACT (You Only Look At CoefficienTs)

TCNN Temporal convolutional neural network for real-time speech enhancement in the time domain

Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP

TorchDistiller - a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

Mae segmentation - Reproduction of semantic segmentation using masked autoencoder (mae)

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio