fix: move predict_image input tensor to the model's device

[giswqs]

Summary

Fixes #1390.

deepforest.predict._predict_image_ builds the input tensor on CPU and never moves it to the model's device, so any caller that has placed the model on CUDA (model.model.to('cuda'), or a Lightning trainer that has trained on GPU) gets:

RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor)
should be the same or input should be a MKLDNN tensor and weight is a dense tensor

This PR moves the input tensor to next(model.parameters()).device before the forward pass. Guarded with try/except StopIteration to handle the (unrealistic) case of a parameter-less model gracefully.

Repro

from deepforest import main
m = main.deepforest()
m.load_model(model_name='weecology/deepforest-tree', revision='main')
m.model.to('cuda')
m.predict_image(path='some_rgb.tif')
# before this PR: RuntimeError as above
# after this PR: returns a DataFrame of detections

Changes

• src/deepforest/predict.py: move the input tensor to the model's device inside _predict_image_.
• tests/test_main.py: add test_predict_image_on_cuda regression test, skipped when CUDA is unavailable.

Test plan

• ruff format --check and ruff check on the changed source files (clean, using the version pinned in .pre-commit-config.yaml).
• CI: existing CPU predict_image tests should still pass; new CUDA test is auto-skipped on CPU-only runners.
• Verified locally that the workaround applied downstream in opengeos/geoai#746 becomes unnecessary once this lands.

[codecov]

Codecov Report

❌ Patch coverage is 50.00000% with 2 lines in your changes missing coverage. Please review.
✅ Project coverage is 86.84%. Comparing base (f3bd776) to head (9006f50).
⚠️ Report is 3 commits behind head on main.

Files with missing lines	Patch %	Lines
src/deepforest/predict.py	50.00%	2 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1391      +/-   ##
==========================================
- Coverage   86.96%   86.84%   -0.12%     
==========================================
  Files          26       26              
  Lines        3712     3717       +5     
==========================================
  Hits         3228     3228              
- Misses        484      489       +5     

Flag	Coverage Δ
unittests	86.84% <50.00%> (-0.12%)	⬇️

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[bw4sz]

Thanks @giswqs! @jveitchmichaelis will have a look at this.

[jveitchmichaelis]

Thanks, this looks pretty sensible to me. I'll double check I can reproduce too.