Hi,

Can you please provide the testing pairs used for MPV dataset?

Thanks

first of all,thnx for useful repo.
I get error white running train_PFAFN_stage1.sh file

Ignoring "Error(s) in loading state_dict for ResUnetGenerator:
	size mismatch for model.model.0.weight: copying a param with shape torch.Size([64, 7, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 8, 3, 3])."

It looks like you’re trying to load pretrained weights into the model that has a different size than the original one.

Hello,
In the article, you did explain the refinement of flow in the Appearance flow estimation network. There are two places, which I did not understand when you said that you added residual flows but in code, as I actually see, that you did grid sample on flows. Could you please explain why did you do such an operation not only on the clothes to warp it but on the flows themselves?

Since the clothflow,this was the other job on appearance warping,awesome!

And how is your FID(10.09) calculated? The test dataset has 2032 clothing images, there are countless kinds of try-on possibilities.Maybe you have the file like the demo.txt you provided?

When im running "bash scripts/train_PBAFN_stage1.sh"
its giving me the following error

RuntimeError: cuda runtime error (10) : invalid device ordinal at ..\torch\csrc\cuda\Module.cpp:33

device ordinal are:0 NVIDIA GeForce MX13

urgent help needed!!

Hello ,

I have tested many pictures some of them works but most of them results as same in the picture attached

Thank you again

Hi! Thanks for your efforts.
Now I want to train the model with my own dataset, and I have 2 questions. First, is if the custom image with different size (like 800*800) ,do i need to change the network architecture? and how? Another one is the train label number is 14,but the number of CIHP_PGN label is 20.How can I solve it. thanks

I change some of the code and the script "train_PBAFN_stage1.sh". And my environment:
RTX2080Ti
pytorch = 1.1.0
cudatoolkit = 9.0
cupy = 6.0.0
Just the GPU differ from your's
and here are the key changes:
----------------------------------script---------------------------------------------
CUDA_VISIBLE_DEVICES=4 python -m train_PBAFN_stage1_oneGPU.py
--name PBAFN_stage1
--resize_or_crop None
--verbose --tf_log
--batchSize 1
--num_gpus 1
--gpu_ids 0
--label_nc 14

And I got the error:

First of all, thank you very much for your contribution. This work seems to work considerably better than ACGPN and other approaches tackling this task.

I wanted to issue that even if I followed the steps to run it, the results I got from running the test.sh are quite different from the ones you showcase. Here is an example:

Has anyone encountered similar misaligments? Do you believe that those are caused by mismatching of the dependency versions? I am aware that you comment about it in the markdown, but I would like to ask how slight differences impacts final output.

Thank you very much for your time and congratulations for your publication.

Edit: typo

When I use my own image test，the results like this，any suggestion? thx

I ran your given test script on the given checkpoints. It doesn't validate the results. Please help with the issue

RuntimeError: Given input size: (512x2x1). Calculated output size: (512x1x0). Output size is too small

@geyuying

I have a AMD CPU and a pair of 3090s. I am facing difficulty setting up the environment for this code.
I am facing issues with cupy installation as it requires older versions of cudatoolkit.
Can any one please help?

Can you please tell me how to segregate the test results into three individual images like a person, dress, and the resultant image into individual images?

thank you

Hi, first of all, great work!
Second, can you please provide supplementary materials as well as access to VITON-HD dataset

Hi,
Thanks for the great work.
When I was reading the paper and the code, I found the flow refinement is done by a sampling operation rather than addition as described in the paper (section 3.2). Can you please give some explanations about this?

Hi! That is a brilliant work!

I wonder the detail rule on FID evalutaion since there cannot find the GT img of the tryon output from test_pairs.txt. Therefore, it is confused to me that calculation of FID between the output and the dataset. Should I use test_pairs_same.txt (https://github.com/minar09/cp-vton-plus/blob/master/data/test_pairs_same.txt ) ?

Could you share your code/setup for edge/mask extraction?

I simply obtained similar results using opencv canny edge detection and contours filling, but wanted to be sure to match your setup as closely as possible for reproducibility and quality comparison.

Hello , Thanks for sharing the code, how can we train using our dataset? are you going to publish the train script?

I tried running the code in Colab, but the results weren't the same as yours. I think it is because of the environment issue. How can I create the same environment in Colab

This was one of the best. It's simple and fast compared current state of the art like ACGPN. Kudos for your great efforts. When will be training script releases to train some of my custom images

Thanks for your great work!

I have ran your code and the result was so good and the execution speed was quite fast.
I actually compared the speed and it was as shown below.

warp_model inference time: 0.016576[sec]
gen_model inference time: 0.007718[sec]

Therefore, it has the potential to be used in real time on mobile and other devices.
I have successfully run gen_model on cpu, but warp_model was not possible due to unimplemented parts only on CPU.

There are the following two unimplemented parts of CPU inference.

Is it possible to support CPU inference for warp_model?

You are using cupy_launch, but perhaps the following issue might be helpful.
sniklaus/pytorch-pwc#39

I only have one GPU, and now I have successfully run test.py.
But when I try to run train***.py, it will report this error:

So I added some parameters by searching for information, and then running, it will report this error.

I don’t know how to solve this error so far.How can I solve this problem?

Hi,

I reproduced all your steps and got the following output from the console:

`------------ Options -------------
batchSize: 1
data_type: 32
dataroot: dataset/
display_winsize: 512
fineSize: 512
gen_checkpoint: checkpoints/PFAFN/gen_model_final.pth
gpu_ids: [0]
input_nc: 3
isTrain: False
loadSize: 512
max_dataset_size: inf
nThreads: 1
name: demo
no_flip: False
norm: instance
output_nc: 3
phase: test
resize_or_crop: None
serial_batches: False
tf_log: False
use_dropout: False
verbose: False
warp_checkpoint: checkpoints/PFAFN/warp_model_final.pth
-------------- End ----------------
CustomDatasetDataLoader
dataset [AlignedDataset] was created
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)
(2): Sequential(
(0): Conv2d(49, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): Sequential(
(0): Conv2d(49, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): Sequential(
(0): Conv2d(49, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(netRefine): ModuleList(
(0): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
)
)
ResUnetGenerator(
(model): ResUnetSkipConnectionBlock(
(model): Sequential(
(0): Conv2d(7, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): ReLU(inplace)
(2): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(3): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): ResUnetSkipConnectionBlock(
(model): Sequential(
(0): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(5): ResUnetSkipConnectionBlock(
(model): Sequential(
(0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(5): ResUnetSkipConnectionBlock(
(model): Sequential(
(0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(5): ResUnetSkipConnectionBlock(
(model): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): ReLU(inplace)
(2): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(3): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): Upsample(scale_factor=2.0, mode=nearest)
(5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU(inplace)
(8): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(9): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(6): Upsample(scale_factor=2.0, mode=nearest)
(7): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(8): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(9): ReLU(inplace)
(10): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(11): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(6): Upsample(scale_factor=2.0, mode=nearest)
(7): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(9): ReLU(inplace)
(10): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(11): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(6): Upsample(scale_factor=2.0, mode=nearest)
(7): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(8): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(9): ReLU(inplace)
(10): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(11): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(5): Upsample(scale_factor=2.0, mode=nearest)
(6): Conv2d(128, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)------------ Options -------------
batchSize: 1
data_type: 32
dataroot: dataset/
display_winsize: 512
fineSize: 512
gen_checkpoint: checkpoints/PFAFN/gen_model_final.pth
gpu_ids: [0]
input_nc: 3
isTrain: False
loadSize: 512
max_dataset_size: inf
nThreads: 1
name: demo
no_flip: False
norm: instance
output_nc: 3
phase: test
resize_or_crop: None
serial_batches: False
tf_log: False
use_dropout: False
verbose: False
warp_checkpoint: checkpoints/PFAFN/warp_model_final.pth
-------------- End ----------------
CustomDatasetDataLoader
dataset [AlignedDataset] was created
6
AFWM(
(image_features): FeatureEncoder(
(encoders): ModuleList(
(0): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(1): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(2): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(3): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(4): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
)
)
(cond_features): FeatureEncoder(
(encoders): ModuleList(
(0): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(1): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(2): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(3): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(4): Sequential(
(0): DownSample(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
)
)
(1): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(2): ResBlock(
(block): Sequential(
(0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace)
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
)
)
(image_FPN): RefinePyramid(
(adaptive): ModuleList(
(0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
(4): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
)
(smooth): ModuleList(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(cond_FPN): RefinePyramid(
(adaptive): ModuleList(
(0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
(4): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
)
(smooth): ModuleList(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(aflow_net): AFlowNet(
(netMain): ModuleList(
(0): Sequential(
(0): Conv2d(49, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): Sequential(
(0): Conv2d(49, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): Sequential(
(0): Conv2d(49, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): Sequential(
(0): Conv2d(49, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): Sequential(
(0): Conv2d(49, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(netRefine): ModuleList(
(0): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): Sequential(
(0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
(2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.1)
(4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): LeakyReLU(negative_slope=0.1)
(6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
)
)
ResUnetGenerator(
(model): ResUnetSkipConnectionBlock(
(model): Sequential(
(0): Conv2d(7, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): ReLU(inplace)
(2): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(3): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): ResUnetSkipConnectionBlock(
(model): Sequential(
(0): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(5): ResUnetSkipConnectionBlock(
(model): Sequential(
(0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(5): ResUnetSkipConnectionBlock(
(model): Sequential(
(0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(5): ResUnetSkipConnectionBlock(
(model): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): ReLU(inplace)
(2): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(3): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): Upsample(scale_factor=2.0, mode=nearest)
(5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU(inplace)
(8): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(9): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(6): Upsample(scale_factor=2.0, mode=nearest)
(7): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(8): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(9): ReLU(inplace)
(10): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(11): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(6): Upsample(scale_factor=2.0, mode=nearest)
(7): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(9): ReLU(inplace)
(10): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(11): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(6): Upsample(scale_factor=2.0, mode=nearest)
(7): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(8): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(9): ReLU(inplace)
(10): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(11): ResidualBlock(
(relu): ReLU(inplace)
(block): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(5): Upsample(scale_factor=2.0, mode=nearest)
(6): Conv2d(128, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)

Traceback (most recent call last):
File "", line 1, in
File "D:\ANACONDA\envs\tryon\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "D:\ANACONDA\envs\tryon\lib\multiprocessing\spawn.py", line 114, in _main
prepare(preparation_data)
File "D:\ANACONDA\envs\tryon\lib\multiprocessing\spawn.py", line 225, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "D:\ANACONDA\envs\tryon\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
run_name="mp_main")
File "D:\ANACONDA\envs\tryon\lib\runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "D:\ANACONDA\envs\tryon\lib\runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "D:\ANACONDA\envs\tryon\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\JONY-\PF-AFN\PF-AFN_test\test.py", line 40, in
for i, data in enumerate(dataset, start=epoch_iter):
File "D:\ANACONDA\envs\tryon\lib\site-packages\torch\utils\data\dataloader.py", line 193, in iter
return _DataLoaderIter(self)
File "D:\ANACONDA\envs\tryon\lib\site-packages\torch\utils\data\dataloader.py", line 469, in init
w.start()
File "D:\ANACONDA\envs\tryon\lib\multiprocessing\process.py", line 105, in start
self._popen = self._Popen(self)
File "D:\ANACONDA\envs\tryon\lib\multiprocessing\context.py", line 223, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "D:\ANACONDA\envs\tryon\lib\multiprocessing\context.py", line 322, in _Popen
return Popen(process_obj)
File "D:\ANACONDA\envs\tryon\lib\multiprocessing\popen_spawn_win32.py", line 33, in init
prep_data = spawn.get_preparation_data(process_obj._name)
File "D:\ANACONDA\envs\tryon\lib\multiprocessing\spawn.py", line 143, in get_preparation_data
_check_not_importing_main()
File "D:\ANACONDA\envs\tryon\lib\multiprocessing\spawn.py", line 136, in _check_not_importing_main
is not going to be frozen to produce an executable.''')
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.

    This probably means that you are not using fork to start your
    child processes and you have forgotten to use the proper idiom
    in the main module:

        if __name__ == '__main__':
            freeze_support()
            ...

    The "freeze_support()" line can be omitted if the program
    is not going to be frozen to produce an executable.

Traceback (most recent call last):
File "D:\ANACONDA\envs\tryon\lib\site-packages\torch\utils\data\dataloader.py", line 511, in _try_get_batch
data = self.data_queue.get(timeout=timeout)
File "D:\ANACONDA\envs\tryon\lib\multiprocessing\queues.py", line 105, in get
raise Empty
queue.Empty

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "test.py", line 40, in
for i, data in enumerate(dataset, start=epoch_iter):
File "D:\ANACONDA\envs\tryon\lib\site-packages\torch\utils\data\dataloader.py", line 576, in next
idx, batch = self._get_batch()
File "D:\ANACONDA\envs\tryon\lib\site-packages\torch\utils\data\dataloader.py", line 553, in _get_batch
success, data = self._try_get_batch()
File "D:\ANACONDA\envs\tryon\lib\site-packages\torch\utils\data\dataloader.py", line 519, in _try_get_batch
raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str))
RuntimeError: DataLoader worker (pid(s) 3220) exited unexpectedly`

If you could offer a solution, It'll be really helpful. I'm trying to reproduce your results against CP-VTON+ and so far I have not been able.

When I run:
conda create -n tryon python=3.6
source activate tryon or conda activate tryon
conda install pytorch=1.1.0 torchvision=0.3.0 cudatoolkit=9.0 -c pytorch
conda install cupy or pip install cupy==6.0.0
pip install opencv-python

and test.sh I meet the problem:
Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.

When I modify align_corners=True in function interpolate it raise another problem:
align_corners option can only be set with the interpolating modes: linear | bilinear | bicubic | trilinear

How can I solve it? Thank you!

Hi! Thanks for your development - looks interesting.
Can you or somebody run website with this try-on online?

Hello, excuse me!
I use my own pictures, the effect is very poor

But with sample pictures, the effect will become very good!

I have shrunk this picture to the same size as the case picture. Same as the example below:

Can you give me a solution.
Thank you very much!

I want to train an images of 384 x 512. How do I customize that code?

I use this wonderful project with another CV project,whitch require the core ENV bellow:
torch 1.8.0+cu111
torchaudio 0.8.0
torchvision 0.9.0+cu111
cuda 0.0.1
cudatoolkit 11.1.1
cupy-cuda111 8.6.0
cupy 9.0.0
cupy-cuda111 8.6.0
The script [scripts/train_PBAFN_stage1.sh] return this error " RuntimeError: Given input size: (512x2x1). Calculated output size: (512x1x0). Output size is too small", can anyone help me,thank you!

Hello , i want to test the pretrained model using only CPU but asking always for cuda even that I disabled cuda device.

hi, thanks for your great job! can you provide the high resolution checkpoints of 512x384?

Thanks for the nice work!
And we are trying to train the PF-AFN in VITON dataset recently but got the below error in train_PBAFN_stage1.
"RuntimeError: Given input size: (512x2x1). Calculated output size: (512x1x0). Output size is too small" in line 150 of train_PBAFN_stage1.py.
It seems that the output's shape of 'self.slice4' in line 149 is 512x2x1, which is smaller than the pooling kernel size in VGG19.
We will appreciate it if you could tell me how to train the model correctly.
Thank you for sharing the excellent work again!

Thanks for your amazing work.
I tried the environment installation according to your readme.md. But I found a question: pythorch 1.1.0 is based on cudnn7.5.1, but cupy6.0 is based on a newer version of cudnn, so after the command 'conda install cupy', the version of cudnn changed, and pytorch could not be used anymore.

Traceback (most recent call last):
File "train_PBAFN_stage1.py", line 30, in
Traceback (most recent call last):
File "train_PBAFN_stage1.py", line 30, in
torch.cuda.set_device(opt.local_rank)
File "/usr/local/lib/python3.7/dist-packages/torch/cuda/init.py", line 261, in set_device
torch._C._cuda_setDevice(device)
RuntimeError: CUDA error: invalid device ordinal
torch.cuda.set_device(opt.local_rank)
File "/usr/local/lib/python3.7/dist-packages/torch/cuda/init.py", line 261, in set_device
torch._C._cuda_setDevice(device)
RuntimeError: CUDA error: invalid device ordinal
Traceback (most recent call last):
File "train_PBAFN_stage1.py", line 30, in
Traceback (most recent call last):
torch.cuda.set_device(opt.local_rank)
File "/usr/local/lib/python3.7/dist-packages/torch/cuda/init.py", line 261, in set_device
torch._C._cuda_setDevice(device)
RuntimeError: CUDA error: invalid device ordinal
Traceback (most recent call last):
File "train_PBAFN_stage1.py", line 30, in
File "train_PBAFN_stage1.py", line 30, in
Traceback (most recent call last):
torch.cuda.set_device(opt.local_rank)
File "/usr/local/lib/python3.7/dist-packages/torch/cuda/init.py", line 261, in set_device
torch._C._cuda_setDevice(device)
RuntimeError: CUDA error: invalid device ordinal
torch.cuda.set_device(opt.local_rank)
File "/usr/local/lib/python3.7/dist-packages/torch/cuda/init.py", line 261, in set_device
torch._C._cuda_setDevice(device)
RuntimeError: CUDA error: invalid device ordinal
File "train_PBAFN_stage1.py", line 30, in
Traceback (most recent call last):
File "train_PBAFN_stage1.py", line 30, in
torch.cuda.set_device(opt.local_rank)
File "/usr/local/lib/python3.7/dist-packages/torch/cuda/init.py", line 261, in set_device
torch.cuda.set_device(opt.local_rank)
File "/usr/local/lib/python3.7/dist-packages/torch/cuda/init.py", line 261, in set_device
torch._C._cuda_setDevice(device)
RuntimeError: CUDA error: invalid device ordinal
torch._C._cuda_setDevice(device)
RuntimeError: CUDA error: invalid device ordinal
Killing subprocess 253
Killing subprocess 254
Killing subprocess 255
Killing subprocess 256
Killing subprocess 257
Killing subprocess 258
Killing subprocess 259
Killing subprocess 260
Traceback (most recent call last):
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py", line 340, in
main()
File "/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py", line 326, in main
sigkill_handler(signal.SIGTERM, None) # not coming back
File "/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py", line 301, in sigkill_handler
raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd)
subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', 'train_PBAFN_stage1.py', '--local_rank=7', '--name', 'PBAFN_stage1', '--resize_or_crop', 'None', '--verbose', '--tf_log', '--batchSize', '1', '--num_gpus', '1', '--label_nc', '7', '--launcher', 'pytorch']' returned non-zero exit status 1.

My environment is 1 GPU. How do I adjust the training set?

When I try to run the training, I have the following errors:

It seems that this error is caused by my version of PyTorch, so is anyone able to run the training with higher version of pytorch?

Hi,
could you publish the script for edges extraction from the clothes images (built-in function in python) or point me in the direction of how you did that?

Hi, author. I want to generate training data using a custom dataset. I find that LIP has 20 classes and ATR has 18 classes.

LIP: 'Background', 'Hat', 'Hair', 'Glove', 'Sunglasses', 'Upper-clothes', 'Dress', 'Coat', 'Socks', 'Pants', 'Jumpsuits', 'Scarf', 'Skirt', 'Face', 'Left-arm', 'Right-arm', 'Left-leg', 'Right-leg', 'Left-shoe', 'Right-shoe'
ATR:'Background', 'Hat', 'Hair', 'Sunglasses', 'Upper-clothes', 'Skirt', 'Pants', 'Dress', 'Belt', 'Left-shoe', 'Right-shoe', 'Face', 'Left-leg', 'Right-leg', 'Left-arm', 'Right-arm', 'Bag', 'Scarf'.
I want to know what is num_classes and the label of your generated human parsing results. Thank~. @geyuying

@geyuying Hello, This is a wonderful repository you have provided us. I'm trying to train the Network on google colab, I was able to do the necessary modifications in code to bring it down to a single GPU. But there's a type mismatch while it runs iteration through .npy file formats. It's being perceived as int object. I request you to help me resolve the issue

/usr/local/lib/python3.7/site-packages/torchvision/transforms/transforms.py:281: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum.
"Argument interpolation should be of type InterpolationMode instead of int. "
Traceback (most recent call last):
File "train_PBAFN_stage1.py", line 79, in
for i, data in enumerate(train_loader):
File "/usr/local/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 521, in next
data = self._next_data()
File "/usr/local/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 561, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/usr/local/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/content/PF-AFN/PF-AFN_train/data/aligned_dataset.py", line 108, in getitem
dense_mask = transform_A(dense_mask)
File "/usr/local/lib/python3.7/site-packages/torchvision/transforms/transforms.py", line 60, in call
img = t(img)
File "/usr/local/lib/python3.7/site-packages/torchvision/transforms/transforms.py", line 430, in call
return self.lambd(img)
File "/content/PF-AFN/PF-AFN_train/data/base_dataset.py", line 64, in
transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.loadSize, method)))
File "/content/PF-AFN/PF-AFN_train/data/base_dataset.py", line 98, in __scale_width
ow, oh = img.size
TypeError: cannot unpack non-iterable int object

** Thank You**

On any custom dimensional image, I get this error.

Traceback (most recent call last): File "test.py", line 50, in <module> flow_out = warp_model(real_image.cuda(), clothes.cuda()) File "C:\Users\sjasu\anaconda3\envs\fyp\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "C:\Users\sjasu\Projects\fyp\PF-AFN\PF-AFN_test\models\afwm.py", line 192, in forward cond_pyramids = self.cond_FPN(self.cond_features(cond_input)) # maybe use nn.Sequential File "C:\Users\sjasu\anaconda3\envs\fyp\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "C:\Users\sjasu\Projects\fyp\PF-AFN\PF-AFN_test\models\afwm.py", line 103, in forward feature = feature + F.interpolate(last_feature, scale_factor=2, mode='nearest') RuntimeError: The size of tensor a (175) must match the size of tensor b (176) at non-singleton dimension 2

I tried to solve this by resizing the image to multiple of 192 x 256.
Here I resized to 384 x 512, this is the output I am getting ..

hi~
I'm using a single GPU to train the train_PBAFN_stage1.py, but I have this problem：TypeError: 'int' object is not iterable.
What is the reason for this problem?

Please, how does this code run with only cpu ? Thanks!

Thank you so much for implementing this research paper in such a convenient format for us! I just needed a little help!
I have a custom dataset with a directory for "clothes", "clothes-masks", "test-images", how do I go about testing it?
My approach, from what I understood in the code, was to add the images in the folders and then edit the demo.txt to form pairs accordingly? Is there an easier/more automated method for doing this? Thank you for your help!

Tried connecting through mail. Request to respond to our license query please

Is there a place to download checkpoints about PBAFN?

Hi!
First - congrats with acceptance to CVPR, great work! and thanks for the code.
Second: I was testing your pretrained model on various pairs of person/cloth, on official demo examples everything is great, however I found various artefacts for other examples (and I didn't find any failure cases in your paper).
Here's what I've noticed so far and have some questions for you, maybe you could help me:

• does the network (and how?) understand what part of the cloth is in front and which should be hidden (behind the neck)? In the image you can see that the back of t-shirt is put on top
  
• pretrained model has problems with arms in front of cloth, sometimes they disappear
  
  sometimes they have some kind of a line inside them
  
• problems with arms, they are morphing into long sleeves (here it's in the left)
  
  But most of the time, reconstruction of the arms is usually very natural, which is great.
• parts of original cloth are still visible
  
  or they are preserved in the new cloth, which looks weird
  

Have you studied these or any other cases? Maybe I'm doing something wrong?

Hi @geyuying

Thanks for your wonderful work! I want to train the network on our own dataset, to align the setting. Can you share the codes (or which github codes) used for extracting human pose (not densepose).

Thanks so much!

Hi, Thank about your project
I 'ad run test.py .
But I got runtime error like that:

Traceback (most recent call last):
File "", line 1, in
File "C:\Users\AT.conda\envs\tryon\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "C:\Users\AT.conda\envs\tryon\lib\multiprocessing\spawn.py", line 114, in _main
prepare(preparation_data)
File "C:\Users\AT.conda\envs\tryon\lib\multiprocessing\spawn.py", line 225, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\AT.conda\envs\tryon\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
run_name="mp_main")
File "C:\Users\AT.conda\envs\tryon\lib\runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "C:\Users\AT.conda\envs\tryon\lib\runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "C:\Users\AT.conda\envs\tryon\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\AT\Desktop\GAN\CUDA10\PF-AFN\PF-AFN_test\test.py", line 40, in
for i, data in enumerate(dataset, start=epoch_iter):
File "C:\Users\AT.conda\envs\tryon\lib\site-packages\torch\utils\data\dataloader.py", line 193, in iter
return _DataLoaderIter(self)
File "C:\Users\AT.conda\envs\tryon\lib\site-packages\torch\utils\data\dataloader.py", line 469, in init
w.start()
File "C:\Users\AT.conda\envs\tryon\lib\multiprocessing\process.py", line 105, in start
self._popen = self._Popen(self)
File "C:\Users\AT.conda\envs\tryon\lib\multiprocessing\context.py", line 223, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "C:\Users\AT.conda\envs\tryon\lib\multiprocessing\context.py", line 322, in _Popen
return Popen(process_obj)
File "C:\Users\AT.conda\envs\tryon\lib\multiprocessing\popen_spawn_win32.py", line 33, in init
prep_data = spawn.get_preparation_data(process_obj._name)
File "C:\Users\AT.conda\envs\tryon\lib\multiprocessing\spawn.py", line 143, in get_preparation_data
_check_not_importing_main()
File "C:\Users\AT.conda\envs\tryon\lib\multiprocessing\spawn.py", line 136, in _check_not_importing_main
is not going to be frozen to produce an executable.''')
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.

    This probably means that you are not using fork to start your
    child processes and you have forgotten to use the proper idiom
    in the main module:

        if __name__ == '__main__':
            freeze_support()
            ...

    The "freeze_support()" line can be omitted if the program
    is not going to be frozen to produce an executable.

Traceback (most recent call last):
File "C:\Users\AT.conda\envs\tryon\lib\site-packages\torch\utils\data\dataloader.py", line 511, in _try_get_batch
data = self.data_queue.get(timeout=timeout)
File "C:\Users\AT.conda\envs\tryon\lib\multiprocessing\queues.py", line 105, in get
raise Empty
queue.Empty

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "test.py", line 40, in
for i, data in enumerate(dataset, start=epoch_iter):
File "C:\Users\AT.conda\envs\tryon\lib\site-packages\torch\utils\data\dataloader.py", line 576, in next
idx, batch = self._get_batch()
File "C:\Users\AT.conda\envs\tryon\lib\site-packages\torch\utils\data\dataloader.py", line 553, in _get_batch
success, data = self._try_get_batch()
File "C:\Users\AT.conda\envs\tryon\lib\site-packages\torch\utils\data\dataloader.py", line 519, in _try_get_batch
raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str))
RuntimeError: DataLoader worker (pid(s) 10200) exited unexpectedly

#I tried to change worker =0 but no work

pls help. Thank much