I have trained a model in Pytorch with your code. But I wonder how to transform the Pytorch model to tensorflow one. Cause your code don't have forward code.
I hope you can help me. Thanks a lot.

stat_loss = SSIM(mask_path=FLAGS["mask_path"], gauss=FLAGS["gauss_kernel"])
raise FileNotFoundError("Mask File Not Found! Please Check your Settings!")
FileNotFoundError: Mask File Not Found! Please Check your Settings!
I did not find that "mask_path" is assigned or default. I would like to ask your how to solve the problem.

after training by all 300WLP datasets. when i evaluate the model. the NME is much lower than the paper. what is the problem?

where to download the pytorch1.1.0 version .

batch，epoch，lr衰减等等怎么设置，我发现batch越大越难收敛，但怎么都收敛不到原论文的精度，请问下你是怎么复现的

Solution

Original Code

Fixed version

if img_id is not None:
    ...

or simply just remove if img_id:

why

The TypeError: cannot unpack non-iterable NoneType object occurs when the function get_img_path in PRNetDataset tries to load img_id == 0.

When the generate_posmap_300WLP.py is executed according to README.md, each folder is generated sequentially numbered from folder 0 as described.

However, in python, 0 would be same with False as below:

img_id = 0
if img_id: 
    print("true")
else:
    print("false")

>>> false

Therefore, the function does not return a value, causing the data loader to bug out.

生成uv_pos_map时相应的jpg original图像文件是这样的,是不是错了??

how can I get depth image, I get only black image

when i run the inference code it shows errors like below
RuntimeError: Error(s) in loading state_dict for ResFCN256:
Missing key(s) in state_dict: "block0.weight", "block1.shortcut_conv.weight"
hope you replay! thanks

Hello,

I am trying to run the landmark detector on a webcam video stream and plot the landmarks but the landmarks are very unstable and almost always far off from what is correct. This is the piece of code that i am running for plotting the landmarks. How can i fix this?

transform_img = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
prn = PRN("results/latest.pth")
for frame from cam:  #psuedo code
    frame_temp = cv2.resize(frame[int(bbox[1]*0.7):int(bbox[3]*1.3),int(bbox[0]*0.7):int(bbox[2]*1.3)],     (256, 256)) #sending only the face portion of the video stream
    image_t = transform_img(frame_temp)
    image_t = image_t.unsqueeze(0)
    pos = prn.net_forward(image_t.cuda())


    out = pos.cpu().detach().numpy()
    pos = np.squeeze(out)
    cropped_pos = pos * 255
    pos = cropped_pos.transpose(1, 2, 0)
    kpt = prn.get_landmarks(pos)
    cv2.imshow('sparse alignment', plot_kpt(frame_temp, kpt))

cv2.waitKey(10)

I am running this repeatedly over each frame from the web cam.

Hi, I am on reproducing your training code, however I find the model is pretty small(2 MB). I wonder if it's my training data fault or what.
My email : [email protected]

The following error is output in train.py.
TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>
origin, gt_uv_map = transform_img(origin), transform_img(gt_uv_map)
I keep getting errors in this section, how do I fix it?

I can not get the .obj file, this code have some error I can not solve it

Using the inference.py, got some weird results on my images.