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

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 model?@foamliu

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

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

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?

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?

/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.

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.

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'

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,

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.