Hello,
I’m new to pytorch3d rasterization. I wanted to plot facial landmarks with shape while training my model So I came across a GitHub which is doing similar to that and using the same face model.
  
      github.com
  
  
    HavenFeng/photometric_optimization/blob/master/renderer.py 5
"""
Author: Yao Feng
Copyright (c) 2020, Yao Feng
All rights reserved.
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from skimage.io import imread
from pytorch3d.structures import Meshes
from pytorch3d.io import load_obj
from pytorch3d.renderer.mesh import rasterize_meshes
import util


class Pytorch3dRasterizer(nn.Module):
    """
    This class implements methods for rasterizing a batch of heterogenous
    Meshes.


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But now I am getting below issue. I tried many things but the issue still persists.  Any suggestion where should I look in my code.
File “/home/kak/Documents/Thesis/Reconstruction/train_copy.py”, line 125, in train_epoch
**    shape_images = self.render.render_shape(pred_vert, trans_vertices, input_image)**
**  File “/home/kak/Documents/Thesis/Reconstruction/renderer.py”, line 274, in render_shape**
**    rendering = self.rasterizer(transformed_vertices, self.faces.expand(batch_size, -1, -1), attributes)**
**  File “/home/kak/anaconda3/envs/gt/lib/python3.6/site-packages/torch/nn/modules/module.py”, line 722, in _call_impl**
**    result = self.forward(input, kwargs)
**  File “/home/kak/Documents/Thesis/Reconstruction/renderer.py”, line 67, in forward*
**    perspective_correct=raster_settings.perspective_correct,**
**  File “/home/kak/anaconda3/envs/gt/lib/python3.6/site-packages/pytorch3d/renderer/mesh/rasterize_meshes.py”, line 150, in rasterize_meshes**
**    cull_backfaces,**
**  File “/home/kak/anaconda3/envs/gt/lib/python3.6/site-packages/pytorch3d/renderer/mesh/rasterize_meshes.py”, line 205, in forward**
**    cull_backfaces,**
TypeError: rasterize_meshes(): incompatible function arguments. The following argument types are supported:
**    1. (arg0: at::Tensor, arg1: at::Tensor, arg2: at::Tensor, arg3: int, arg4: float, arg5: int, arg6: int, arg7: int, arg8: bool, arg9: bool, arg10: bool) → Tuple[at::Tensor, at::Tensor, at::Tensor, at::Tensor]**
Invoked with: tensor([[[-2.9129e-01,  3.8580e-01,  9.9854e+00],
**         [-3.1403e-01,  3.8133e-01,  9.9865e+00],**
**         [-2.8892e-01,  3.7910e-01,  9.9895e+00]],**
**        [[-3.2832e-01,  4.5427e-01,  1.0014e+01],**
**         [-3.4195e-01,  4.3908e-01,  1.0008e+01],**
**         [-3.3878e-01,  4.2805e-01,  1.0044e+01]],**
**        [[-2.9508e-01,  2.3414e-02,  1.0420e+01],**
**         [-2.4708e-01, -2.5840e-04,  1.0480e+01],**
**         [-2.3614e-01,  6.3354e-02,  1.0425e+01]],**
**        …,**
**        [[ 8.7310e-02,  2.2173e-01,  9.9789e+00],**
**         [ 9.3325e-02,  2.2365e-01,  9.9810e+00],**
**         [ 9.5288e-02,  2.1816e-01,  9.9787e+00]],**
    [[ 8.8948e-02,  2.1716e-01,  9.9770e+00],
     [ 9.5288e-02,  2.1816e-01,  9.9787e+00],
     [ 9.6179e-02,  2.1224e-01,  9.9768e+00]],

    [[ 9.6179e-02,  2.1224e-01,  9.9768e+00],
     [ 9.5954e-02,  2.0613e-01,  9.9754e+00],
     [ 8.9500e-02,  2.0716e-01,  9.9743e+00]]], device='cuda:0',
   grad_fn=<IndexBackward>), tensor([     0,   9976,  19952,  29928,  39904,  49880,  59856,  69832,  79808,
     89784,  99760, 109736, 119712, 129688, 139664, 149640, 159616, 169592,
    179568, 189544], device='cuda:0'), tensor([9976, 9976, 9976, 9976, 9976, 9976, 9976, 9976, 9976, 9976, 9976, 9976,
    9976, 9976, 9976, 9976, 9976, 9976, 9976, 9976], device='cuda:0'), 224.0, 0.0, 1, 16, 10000, False, False, False