Project-MONAI / Project-MONAI/MONAI

[BUG]einops.layer.torch.Rearrange got an unexpected keyword argument

Open
#8,462 2 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

Dominant language
Python
Stars
8.7k
Forks
1.6k
Avg merge
5d 1h
Merged PRs (30d)
20

Description

Describe the bug
When I am building ViT from monai

self.patch_embedding = PatchEmbeddingBlock(
            in_channels=in_channels,
            img_size=img_size,
            patch_size=patch_size,
            hidden_size=hidden_size,
            num_heads=num_heads,
            proj_type=proj_type,
            dropout_rate=dropout_rate,
            spatial_dims=spatial_dims,
        )

the issue happened here in monai.networks.blocks.patchembedding.py

elif self.proj_type == "perceptron":
        # for 3d: "b c (h p1) (w p2) (d p3)-> b (h w d) (p1 p2 p3 c)"
        chars = (("h", "p1"), ("w", "p2"), ("d", "p3"))[:spatial_dims]
        from_chars = "b c " + " ".join(f"({k} {v})" for k, v in chars)
        to_chars = f"b ({' '.join([c[0] for c in chars])}) ({' '.join([c[1] for c in chars])} c)"
        axes_len = {f"p{i+1}": p for i, p in enumerate(patch_size)}
        self.patch_embeddings = nn.Sequential(
            Rearrange(f"{from_chars} -> {to_chars}", **axes_len), nn.Linear(self.patch_dim, hidden_size)
        )

the error appers as
TypeError: Rearrange.init() got an unexpected keyword argument 'p1'

Image

Reproduction steps

Expected behavior

Your platform
Version of einops, python and DL package that you used
accelerate==1.3.0
aiofiles==23.2.1
aiohttp==3.11.12
alembic==1.14.1
anyio==4.8.0
beautifulsoup4==4.13.3
bert-score==0.3.13
cachetools==5.5.1
databricks-sdk==0.44.0
deepspeed==0.16.3
docker==7.1.0
einops==0.8.1
evaluate==0.4.3
fastapi==0.115.11
Flask==3.1.0
fonttools==4.56.0
fsspec==2024.12.0
gradio==5.20.1
grpcio==1.70.0
gunicorn==23.0.0
huggingface-hub==0.28.1
imageio==2.37.0
Jinja2==3.1.5
joblib==1.4.2
lmdb==1.6.2
matplotlib==3.10.0
mlflow==2.20.1
monai==1.4.0
nibabel==5.3.2
nltk==3.9.1
numpy==1.26.4
opencv-python-headless==4.9.0.80
pandas==2.2.3
peft==0.14.0
Pillow==11.1.0
protobuf==5.29.3
psutil==6.1.1
pyarrow==18.1.0
pydantic==2.10.6
pydicom==3.0.1
python-multipart==0.0.20
scikit-learn==1.6.1
scipy==1.15.1
SimpleITK==2.4.1
tensorboard==2.19.0
timm==1.0.14
torch==2.6.0
torchvision==0.21.0
tqdm==4.67.1
transformers==4.48.3
triton==3.2.0
Unidecode==1.3.8
uvicorn==0.34.0
wandb==0.19.6

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with monai/networks/blocks/patchembedding.py and the perceptron branch of PatchEmbeddingBlock; reproduce the TypeError using the listed MONAI, einops, Python, and torch versions. Done means the ViT patch embedding can be constructed without the reported unexpected keyword argument.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, pytorch
Domain
machine-learning
Issue type
Bug
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
45/100

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.