Train on a new model: mesh_vertex_embeddings are all zeros in my model

[Haozong-Zeng]

I am using DensePose CSE for capturing poses from videos and guiding a mesh model. My goal is to use my own mesh and the corresponding dataset to train a DensePose CSE model.

I formated the dataset & annotation correctly, and successfully registered the mesh by providing the vertices, geometric distances, symmetry, etc. information. The only thing I missed is the INIT_FILE which contains 'eigenvalues', 'features', & 'baricel_area'. My trained model seems to work well on providing the

E (tensor [1, D, H, W]): D-dimensional embedding vectors for every point of the default-sized box

&

S (tensor [1, 2, H, W]): 2-dimensional segmentation mask for every point of the default-sized box

However, when I tried to find the closest vertices, I noticed that the

mesh_vertex_embeddings (tensor [N, D]): vertex embeddings for a chosen mesh N is the number of vertices in the mesh, D is feature dimensionality

is a all-zeros tensor and thus I could not calculate the squared_euclidean_distance_matrix.

When training the model, I received the following warning:

Some model parameters or buffers are not found in the checkpoint:
roi_heads.embedder.embedder_my_mesh.{embeddings, features}

1. Full changes you made:
   _BASE_: "Base-DensePose-RCNN-FPN.yaml" MODEL: WEIGHTS: "https://dl.fbaipublicfiles.com/densepose/cse/a_checkpoint.pkl" RESNETS: DEPTH: 50 ROI_HEADS: NUM_CLASSES: 1 ROI_DENSEPOSE_HEAD: NAME: "DensePoseV1ConvXHead" COARSE_SEGM_TRAINED_BY_MASKS: True CSE: EMBED_LOSS_NAME: "SoftEmbeddingLoss" EMBEDDING_DIST_GAUSS_SIGMA: 0.1 GEODESIC_DIST_GAUSS_SIGMA: 0.1 EMBEDDERS: "my_mesh": TYPE: vertex_feature NUM_VERTICES: n FEATURE_DIM: 256 FEATURES_TRAINABLE: True (also try False) IS_TRAINABLE: True INIT_FILE: ""

2. What exact command you run:
   The train_net.py script in the tutorial

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