pytorch model is transformed into coreml model expect a tensor of type integer, but actually receive a tensor of type floating point.

When I was converting pytorch model into coreml model, an error occurred, indicating that the coreml model required int32 parameters but input fp32 parameters. I checked the previous error report and tried to modify it, but it seemed to be different from my error feeling. How should I improve my code to match the result?
[Prediction Mismatch between Pytorch and CoreML #1428](https://github.com/apple/coremltools/issues/1428)
[Wrong output from PyTorch converted model #1486](https://github.com/apple/coremltools/issues/1486)
[Model converts fine to neuralnetwork but produces all nan values for mlprogram #1809](https://github.com/apple/coremltools/issues/1809)

> python: 3.10.14
> pytorch: 2.2.2
> numpy: 1.26.4
> coremltools: 7.1
> Ubuntu 18.04.6

my code

> import torch
> import time
> import torch
> import coremltools as ct
> from torchvision import transforms
> from models.yowo.yowo import YOWO
> from config.yowo_v2_config import yowo_v2_config
> from coremltools.converters.mil.mil import types
> import numpy as np
> yowo_v2_config["yowo_v2_tiny"]
> path_to_ckpt = '/data1/home/pengyuling/yowov2/weights/yowo_v2_tiny_epoch_986.pth'
> device = "cpu"
> transform = transforms.Compose([
> transforms.Resize([224,224]),
> transforms.ToTensor(),
> ])
> model = YOWO(
> cfg = yowo_v2_config["yowo_v2_tiny"],
> device = device,
> num_classes = 8,
> conf_thresh = 0.,
> nms_thresh = 0.2,
> topk = 3,
> trainable = False,
> multi_hot = True )
> device = torch.device(device)
> checkpoint = torch.load(path_to_ckpt, map_location='cpu')
> checkpoint_state_dict = checkpoint.pop("model")
> model_state_dict = model.state_dict()
> for k in list(checkpoint_state_dict.keys()):
> if k in model_state_dict:
> shape_model = tuple(model_state_dict[k].shape)
> shape_checkpoint = tuple(checkpoint_state_dict[k].shape)
> if shape_model != shape_checkpoint:
> checkpoint_state_dict.pop(k)
> else:
> checkpoint_state_dict.pop(k)
> print(k)
> model.load_state_dict(checkpoint_state_dict)
> model.eval()
> x = torch.rand(1, 3, 16, 224, 224)
> traced_model = torch.jit.trace(model, x)
> scripted_model = torch.jit.script(traced_model)
> mlmodel = ct.convert(
> scripted_model, source="pytorch",
> convert_to="mlprogram",
> inputs=[ct.TensorType(shape=x.shape)]
> )
> mlmodel.save('yowo_v2_tiny_epoch_986.mlpackage')
>
>

my error

> Tuple detected at graph output. This will be flattened in the converted model.
> Converting PyTorch Frontend ==> MIL Ops: 66%|███████████████████████████████████▍ | 1717/2613 [00:03<00:01, 473.30 ops/s]Saving value type of int64 into a builtin type of int32, might lose precision!
> Saving value type of int64 into a builtin type of int32, might lose precision!
> Converting PyTorch Frontend ==> MIL Ops: 76%|█████████████████████████████████████████▏ | 1992/2613 [00:03<00:01, 592.53 ops/s]Saving value type of int64 into a builtin type of int32, might lose precision!
> Saving value type of int64 into a builtin type of int32, might lose precision!
> Converting PyTorch Frontend ==> MIL Ops: 87%|███████████████████████████████████████████████▏ | 2281/2613 [00:04<00:00, 585.92 ops/s]Saving value type of int64 into a builtin type of int32, might lose precision!
> Saving value type of int64 into a builtin type of int32, might lose precision!
> Converting PyTorch Frontend ==> MIL Ops: 93%|██████████████████████████████████████████████████▍ | 2439/2613 [00:04<00:00, 582.65 ops/s]
> Traceback (most recent call last):
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/runpy.py", line 196, in _run_module_as_main
> return _run_code(code, main_globals, None,
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/runpy.py", line 86, in _run_code
> exec(code, run_globals)
> File "/data1/home/pengyuling/.vscode-server/extensions/ms-python.debugpy-2024.0.0/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in
> cli.main()
> File "/data1/home/pengyuling/.vscode-server/extensions/ms-python.debugpy-2024.0.0/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main
> run()
> File "/data1/home/pengyuling/.vscode-server/extensions/ms-python.debugpy-2024.0.0/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file
> runpy.run_path(target, run_name="__main__")
> File "/data1/home/pengyuling/.vscode-server/extensions/ms-python.debugpy-2024.0.0/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path
> return _run_module_code(code, init_globals, run_name,
> File "/data1/home/pengyuling/.vscode-server/extensions/ms-python.debugpy-2024.0.0/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code
> _run_code(code, mod_globals, init_globals,
> File "/data1/home/pengyuling/.vscode-server/extensions/ms-python.debugpy-2024.0.0/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code
> exec(code, run_globals)
> File "/data1/home/pengyuling/yowov2/export_coreml.py", line 53, in
> mlmodel = ct.convert(
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/_converters_entry.py", line 574, in convert
> mlmodel = mil_convert(
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/mil/converter.py", line 188, in mil_convert
> return _mil_convert(model, convert_from, convert_to, ConverterRegistry, MLModel, compute_units, **kwargs)
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/mil/converter.py", line 212, in _mil_convert
> proto, mil_program = mil_convert_to_proto(
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/mil/converter.py", line 286, in mil_convert_to_proto
> prog = frontend_converter(model, **kwargs)
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/mil/converter.py", line 108, in __call__
> return load(*args, **kwargs)
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 80, in load
> return _perform_torch_convert(converter, debug)
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 99, in _perform_torch_convert
> prog = converter.convert()
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/converter.py", line 519, in convert
> convert_nodes(self.context, self.graph)
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/ops.py", line 88, in convert_nodes
> add_op(context, node)
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/ops.py", line 3789, in index
> x = mb.gather(x=x, indices=indices, axis=axis, name=node.name)
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/mil/mil/ops/registry.py", line 182, in add_op
> return cls._add_op(op_cls_to_add, **kwargs)
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/mil/mil/builder.py", line 168, in _add_op
> new_op = op_cls(**kwargs)
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/mil/mil/operation.py", line 190, in __init__
> self._validate_and_set_inputs(input_kv)
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/mil/mil/operation.py", line 503, in _validate_and_set_inputs
> self.input_spec.validate_inputs(self.name, self.op_type, input_kvs)
> File "/data1/home/pengyuling/.conda/envs/yowo2/lib/python3.10/site-packages/coremltools/converters/mil/mil/input_type.py", line 163, in validate_inputs
> raise ValueError(msg.format(name, var.name, input_type.type_str,
> ValueError: Op "pred_reg.1" (op_type: gather) Input indices="anchor_idxs.1" expects tensor or scalar of dtype from type domain ['int32'] but got tensor[is1,fp32]

Reproduce the conversion from export_coreml.py, then inspect the PyTorch index conversion in coremltools/converters/mil/frontend/torch/ops.py, especially the index entry reaching gather. Read the validation path in coremltools/converters/mil/mil/input_type.py and operation.py. Done means the supplied model conversion no longer fails because anchor_idxs.1 reaches gather as fp32.

Written by the indexing model from the issue text.