Getting Warnings When Instantiating Object Detection Models Due to Meta Tensor Initialization
Author: HichTala
Pull request: no
Labels: name: bug

### System Info

- `transformers` version: 4.51.3
- Platform: Linux-6.11.0-21-generic-x86_64-with-glibc2.39
- Python version: 3.12.4
- Huggingface_hub version: 0.30.2
- Safetensors version: 0.5.3
- Accelerate version: 1.6.0
- Accelerate config: 	- compute_environment: LOCAL_MACHINE
	- distributed_type: NO
	- mixed_precision: no
	- use_cpu: False
	- debug: False
	- num_processes: 1
	- machine_rank: 0
	- num_machines: 1
	- gpu_ids: 1
	- rdzv_backend: static
	- same_network: True
	- main_training_function: main
	- enable_cpu_affinity: False
	- downcast_bf16: no
	- tpu_use_cluster: False
	- tpu_use_sudo: False
	- tpu_env: []
- DeepSpeed version: not installed
- PyTorch version (GPU?): 2.6.0+cu124 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: NVIDIA RTX 3500 Ada Generation Laptop GPU

### Who can help?

@amyeroberts, @qubvel

### Information

- [x] The official example scripts
- [ ] My own modified scripts

### Tasks

- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

When instantiating object detection models (e.g., `facebook/detr-resnet-50`) using `AutoModelForObjectDetection`, PyTorch emits flooding warnings.

Minimal Reproducible Example

```python
from transformers import AutoConfig, AutoModelForObjectDetection

config = AutoConfig.from_pretrained(
    "facebook/detr-resnet-50",
)

model = AutoModelForObjectDetection.from_pretrained(
    "facebook/detr-resnet-50",
    config=config,
)
```

The warning may be print when instantiating more models using auto mapping...

### Expected behavior

When instantiating object detection models (e.g., `facebook/detr-resnet-50`) using `AutoModelForObjectDetection`, PyTorch emits flooding warnings like:

```shell
UserWarning: for module.name.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
```

From my understanding, the model is instantiated inside this `with` clause:
https://github.com/huggingface/transformers/blob/a1b82563f11d9101d54b06fd61aef8c90f63c9d2/src/transformers/modeling_utils.py#L4411-L4413

And here one of the `model_init_context` element is this function:
https://github.com/huggingface/transformers/blob/a1b82563f11d9101d54b06fd61aef8c90f63c9d2/src/transformers/integrations/accelerate.py#L36-L67

That put the model in `meta` device, which means (from my understanding) that weights are placeholder tensors (they have shape/dtype but no real data). Later in the code, real weights are loaded using `state_dict`, with pytorch's `load_state_dict` function but without passing the flag `assign=True`.

The thing is pytorch's `load_state_dict` function is called in `timm`'s `_builder` function:
https://github.com/huggingface/pytorch-image-models/blob/3ff38990264d7464ffd43ac2ddc26da04ff6355c/timm/models/_builder.py#L270

I am wondering if I should open an issue in `timm` repo instead of this one?

In previous version of `transformer`, `model_init_context` list did not contains except when a variable, `low_cpu_mem_usage`, was `True`.

Comments:
cc @qubvel @nielsrogge

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.

Hey @HichTala, thanks for opening a detailed issue and sorry for the delayed response! Indeed, any model with a `timm` backbone generates many warnings due to the reasons you described above. I see the following solutions:

1. Disable weight loading for the backbone while loading the entire checkpoint.
2. Check if we can use `assign=True` in `timm`.

I'm not sure if there are any side effects for the second solution (cc @rwightman), but the first one seems to be logical, although it might be harder to implement. Let me know if have bandwidth to explore it 🤗 

Hi @qubvel, sure I'll try to take care of that ! 🫡

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.

Just ran into this issue today with a model that has historically had no issue:
```py
from transformers import pipeline

model_id = "facebook/detr-resnet-50-panoptic"
pipe = pipeline("image-segmentation", model=model_id)

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
output = pipe(url)
```

<details>

<summary>See full warnings log</summary>

```
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.0.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.0.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.0.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.0.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.0.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.0.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.0.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.0.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.0.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.0.downsample.0.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.0.downsample.1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.0.downsample.1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.1.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.1.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.1.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.1.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.1.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.1.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.1.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.1.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.1.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.2.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.2.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.2.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.2.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.2.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.2.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.2.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.2.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer1.2.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.0.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.0.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.0.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.0.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.0.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.0.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.0.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.0.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.0.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.0.downsample.0.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.0.downsample.1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.0.downsample.1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.1.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.1.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.1.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.1.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.1.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.1.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.1.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.1.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.1.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.2.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.2.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.2.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.2.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.2.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.2.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.2.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.2.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.2.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.3.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.3.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.3.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.3.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.3.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.3.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.3.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.3.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer2.3.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.0.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.0.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.0.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.0.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.0.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.0.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.0.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.0.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.0.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.0.downsample.0.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.0.downsample.1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.0.downsample.1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.1.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.1.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.1.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.1.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.1.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.1.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.1.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.1.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.1.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.2.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.2.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.2.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.2.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.2.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.2.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.2.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.2.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.2.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.3.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.3.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.3.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.3.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.3.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.3.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.3.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.3.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.3.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.4.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.4.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.4.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.4.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.4.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.4.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.4.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.4.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.4.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.5.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.5.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.5.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.5.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.5.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.5.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.5.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.5.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer3.5.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.0.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.0.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.0.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.0.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.0.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.0.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.0.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.0.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.0.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.0.downsample.0.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.0.downsample.1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.0.downsample.1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.1.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.1.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.1.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.1.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.1.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.1.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.1.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.1.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.1.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.2.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.2.bn1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.2.bn1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.2.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.2.bn2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.2.bn2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.2.conv3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.2.bn3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py:2441: UserWarning: for layer4.2.bn3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
  warnings.warn(
Some weights of the model checkpoint at facebook/detr-resnet-50-panoptic were not used when initializing DetrForSegmentation: ['detr.model.backbone.conv_encoder.model.layer1.0.downsample.1.num_batches_tracked', 'detr.model.backbone.conv_encoder.model.layer2.0.downsample.1.num_batches_tracked', 'detr.model.backbone.conv_encoder.model.layer3.0.downsample.1.num_batches_tracked', 'detr.model.backbone.conv_encoder.model.layer4.0.downsample.1.num_batches_tracked']
- This IS expected if you are initializing DetrForSegmentation from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing DetrForSegmentation from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Device set to use cpu
`label_ids_to_fuse` unset. No instance will be fused.
```

</details>

I don't believe this is a timm-specific model, so perhaps the issue is larger than that?

I'm able to reproduce this.

I started working on this a while ago, but it got lost in the shuffle and I forgot about it. I'll try to publish a PR later this week.