Deep Image Matting implementation in PyTorch

Last update: Dec 27, 2022

Overview

Deep Image Matting

Deep Image Matting paper implementation in PyTorch.

Differences

"fc6" is dropped.
Indices pooling.

"fc6" is clumpy, over 100 millions parameters, makes the model hard to converge. I guess it is the reason why the model (paper) has to be trained stagewisely.

Performance

The Composition-1k testing dataset.
Evaluate with whole image.
SAD normalized by 1000.
Input image is normalized with mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].
Both erode and dialte to generate trimap.

Models	SAD	MSE	Download
paper-stage0	59.6	0.019
paper-stage1	54.6	0.017
paper-stage3	50.4	0.014
my-stage0	66.8	0.024	Link

Dependencies

Python 3.5.2
PyTorch 1.1.0

Dataset

Adobe Deep Image Matting Dataset

Follow the instruction to contact author for the dataset.

MSCOCO

Go to MSCOCO to download:

2014 Train images

PASCAL VOC

Go to PASCAL VOC to download:

VOC challenge 2008 training/validation data
The test data for the VOC2008 challenge

Usage

Data Pre-processing

Extract training images:

$ python pre_process.py

Train

$ python train.py

If you want to visualize during training, run in your terminal:

$ tensorboard --logdir runs

Experimental results

The Composition-1k testing dataset

Test:

$ python test.py

It prints out average SAD and MSE errors when finished.

The alphamatting.com dataset

Download the evaluation datasets: Go to the Datasets page and download the evaluation datasets. Make sure you pick the low-resolution dataset.
Extract evaluation images:

$ python extract.py

Evaluate:

$ python eval.py

Click to view whole images:

Image	Trimap1	Trimap2	Trimap3

Demo

Download pre-trained Deep Image Matting Link then run:

$ python demo.py

Image/Trimap	Output/GT	New BG/Compose

小小的赞助~

若对您有帮助可给予小小的赞助~

Comments

the frozen model named BEST_checkpoint.tar cannot be uncompressed

when I try to uncompress the frozen model it shows

tar: This does not look like a tar archive tar: Skipping to next header tar: Exiting with failure status due to previous errors

this means the .tar file is not complete

opened by banrenmasanxing 6
my own datasets are all full human body images

Hi,thanks for your excellent work.Now i prepare my own datasets.This datasets are consists of thounds of high resolution image(average 4000*4000).They are all full human body images.When i process these images,i meet a questions: When i crop the trimap(generated from alpha),often crop some places which are not include hair.Such as foot,leg.Is it ok to input these images into [email protected]

opened by lfxx 5
run demo.py question！

File "demo.py", line 84, in new_bgs = random.sample(new_bgs, 10) File "C:\Users\15432\AppData\Local\conda\conda\envs\python34\lib\random.py", line 324, in sample raise ValueError("Sample larger than population") ValueError: Sample larger than population

opened by kxcg99 5

Invalid BEST_checkpoint.tar ?

Hi, thank you for the code. I tried to download the pretrained model and extract it but it dosnt work.

tar xvf BEST_checkpoint.tar BEST_checkpoint

results in

tar: Ceci ne ressemble pas à une archive de type « tar »
tar: On saute à l'en-tête suivant
tar: BEST_checkpoint : non trouvé dans l'archive
tar: Arrêt avec code d'échec à cause des erreurs précédentes

anything i'm doing the wrong way ? or the provided tar is not valid ? kind reards

opened by flocreate 4

How can i get the Trimaps of my pictures?

Now, I got a model, I want to use it but I can't, because I have not the Trimaps of my pictures. Are there the script of code to build the Trimaps? How can i get the Trimaps of my pictures?

opened by huangjunxiong11 3
can not unpack the 'BEST_checkpoint.tar'

When i download the file "BEST_checkpoint.tar" successfully, i can't unpack it. Actually, when i try to unpack 'BEST_checkpoint.tar', it make an error. Is it my fault , or, Is the file mistaken?

opened by huangjunxiong11 3
Demo error

/Users/7plus/opt/anaconda3/lib/python3.7/site-packages/torch/serialization.py:435: SourceChangeWarning: source code of class 'torch.nn.parallel.data_parallel.DataParallel' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes. warnings.warn(msg, SourceChangeWarning) /Users/7plus/opt/anaconda3/lib/python3.7/site-packages/torch/serialization.py:435: SourceChangeWarning: source code of class 'torch.nn.modules.conv.Conv2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes. warnings.warn(msg, SourceChangeWarning) Traceback (most recent call last): File "demo.py", line 69, in checkpoint = torch.load(checkpoint) File "/Users/7plus/opt/anaconda3/lib/python3.7/site-packages/torch/serialization.py", line 368, in load return _load(f, map_location, pickle_module) File "/Users/7plus/opt/anaconda3/lib/python3.7/site-packages/torch/serialization.py", line 542, in _load result = unpickler.load() File "/Users/7plus/opt/anaconda3/lib/python3.7/site-packages/torch/serialization.py", line 505, in persistent_load data_type(size), location) File "/Users/7plus/opt/anaconda3/lib/python3.7/site-packages/torch/serialization.py", line 114, in default_restore_location result = fn(storage, location) File "/Users/7plus/opt/anaconda3/lib/python3.7/site-packages/torch/serialization.py", line 95, in _cuda_deserialize device = validate_cuda_device(location) File "/Users/7plus/opt/anaconda3/lib/python3.7/site-packages/torch/serialization.py", line 79, in validate_cuda_device raise RuntimeError('Attempting to deserialize object on a CUDA ' RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location='cpu' to map your storages to the CPU.

opened by Mlt123 3
Deep-Image-Matting-v2 implemetation on Android

Hi, Thanks for you work! its looking awesome output. I want to integrate your demo into android project. Is it possible to integrate model into android Project? If it possible, then How can i integrate this model into android project? Can you please give some suggestions? Thanks in advance.

opened by charlizesmith 3
unable to start training using pretrained weigths

whenever pre-trained weights are used for training the model using own dataset, the following error is occurring.

python3 train.py --batch-size 4 --checkpoint checkpoint/BEST_checkpoint.tar

/usr/local/lib/python3.5/dist-packages/torch/serialization.py:454: SourceChangeWarning: source code of class 'torch.nn.parallel.data_parallel.DataParallel' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes. warnings.warn(msg, SourceChangeWarning) /usr/local/lib/python3.5/dist-packages/torch/serialization.py:454: SourceChangeWarning: source code of class 'torch.nn.modules.conv.Conv2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes. warnings.warn(msg, SourceChangeWarning) /usr/local/lib/python3.5/dist-packages/torch/serialization.py:454: SourceChangeWarning: source code of class 'torch.nn.modules.batchnorm.BatchNorm2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes. warnings.warn(msg, SourceChangeWarning) /usr/local/lib/python3.5/dist-packages/torch/serialization.py:454: SourceChangeWarning: source code of class 'torch.nn.modules.activation.ReLU' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes. warnings.warn(msg, SourceChangeWarning) Traceback (most recent call last): File "train.py", line 180, in main() File "train.py", line 176, in main train_net(args) File "train.py", line 71, in train_net logger=logger) File "train.py", line 112, in train alpha_out = model(img) # [N, 3, 320, 320] File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 493, in call result = self.forward(*input, **kwargs) File "/usr/local/lib/python3.5/dist-packages/torch/nn/parallel/data_parallel.py", line 143, in forward if t.device != self.src_device_obj: File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 539, in getattr type(self).name, name)) AttributeError: 'DataParallel' object has no attribute 'src_device_obj'

opened by dev-srikanth 3
v2 didn't performance well as v1?

Hi, thanks for your pretrained model! I test both your v1 pretrained model and v2 pretrained model , v2 is much faster than v1 , but I found it didn't performance well as v1. the image: the origin tri map: the v1 output: the v2 output:

do you know what's the problem?

Thanks,

opened by MarSaKi 3
Questions about the PyTorch version and an issue in training regarding to the batch size

Hi,

Thank you for sharing your PyTorch version of reimplementation. Would you like to share the PyTorch version you used to development?

I am using PyTorch 1.0.1, CUDA 9, two RTX 2080 Ti to run the 'train.py' since I see you use Data Parallel module to support multi-GPUs training. However, I encountered and the trackbacks are here:

Traceback (most recent call last): File "train.py", line 171, in main() File "train.py", line 167, in main train_net(args) File "train.py", line 64, in train_net logger=logger) File "train.py", line 103, in train alpha_out = model(img) # [N, 3, 320, 320] File "/home/mingfu/anaconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call result = self.forward(*input, **kwargs) File "/home/mingfu/anaconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 143, in forward outputs = self.parallel_apply(replicas, inputs, kwargs) File "/home/mingfu/anaconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 153, in parallel_apply return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) File "/home/mingfu/anaconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/torch/nn/parallel/parallel_apply.py", line 83, in parallel_apply raise output File "/home/mingfu/anaconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/torch/nn/parallel/parallel_apply.py", line 59, in _worker output = module(*input, **kwargs) File "/home/mingfu/anaconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call result = self.forward(*input, **kwargs) File "/home/mingfu/Deep-Image-Matting-v2/models.py", line 127, in forward up4 = self.up4(up5, indices_4, unpool_shape4) File "/home/mingfu/anaconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call result = self.forward(*input, **kwargs) File "/home/mingfu/Deep-Image-Matting-v2/models.py", line 87, in forward outputs = self.conv(outputs) File "/home/mingfu/anaconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call result = self.forward(*input, **kwargs) File "/home/mingfu/Deep-Image-Matting-v2/models.py", line 43, in forward outputs = self.cbr_unit(inputs) File "/home/mingfu/anaconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call result = self.forward(*input, **kwargs) File "/home/mingfu/anaconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward input = module(input) File "/home/mingfu/anaconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call result = self.forward(*input, **kwargs) File "/home/mingfu/anaconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 320, in forward self.padding, self.dilation, self.groups) RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED

I have tested the DATA PARALLELISM using the example here and it works well.

opened by wuyujack 3

Releases(v1.0)

v1.0(Jul 17, 2019)

Source code(tar.gz)
Source code(zip)
BEST_checkpoint.tar(292.89 MB)

Owner

Yang Liu

Algorithm engineer

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