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Error Converting max_pool2d
🐞Describing the bug
import coremltools as ct
import torch
import torch.nn.functional as F
class Model(torch.nn.Module):
def __init__(self, window_size=3):
super().__init__()
self.window_size = window_size
def forward(self, image):
_, ixs = F.max_pool2d(
image,
kernel_size=self.window_size,
stride=1,
padding=self.window_size // 2,
return_indices=True,
)
return ixs
pytorch_model = Model()
example = torch.rand(1, 3, 224, 224)
pytorch_model.eval()
traced = torch.jit.trace(pytorch_model, example)
model = ct.convert(traced, inputs=[ct.TensorType(shape=example.shape)])
Stack Trace
----> 1 model = ct.convert(traced, inputs=[ct.TensorType(shape=example.shape)])
File ~/miniconda3/envs/prod/lib/python3.10/site-packages/coremltools/converters/_converters_entry.py:635, in convert(model, source, inputs, outputs, classifier_config, minimum_deployment_target, convert_to, compute_precision, skip_model_load, compute_units, package_dir, debug, pass_pipeline, states)
632 if len(states) > 0 and exact_source != "pytorch":
633 raise ValueError("'states' can only be passed with pytorch source model.")
--> 635 mlmodel = mil_convert(
636 model,
637 convert_from=exact_source,
638 convert_to=exact_target,
639 inputs=inputs,
640 outputs=outputs_as_tensor_or_image_types, # None or list[ct.ImageType/ct.TensorType]
641 classifier_config=classifier_config,
642 skip_model_load=skip_model_load,
643 compute_units=compute_units,
644 package_dir=package_dir,
645 debug=debug,
646 specification_version=specification_version,
647 main_pipeline=pass_pipeline,
648 use_default_fp16_io=use_default_fp16_io,
649 states=states,
650 )
652 if exact_target == "mlprogram" and mlmodel._input_has_infinite_upper_bound():
653 raise ValueError(
654 "For mlprogram, inputs with infinite upper_bound is not allowed. Please set upper_bound"
655 ' to a positive value in "RangeDim()" for the "inputs" param in ct.convert().'
656 )
File ~/miniconda3/envs/prod/lib/python3.10/site-packages/coremltools/converters/mil/converter.py:186, in mil_convert(model, convert_from, convert_to, compute_units, **kwargs)
147 @_profile
148 def mil_convert(
149 model,
(...)
153 **kwargs
154 ):
155 """
156 Convert model from a specified frontend `convert_from` to a specified
157 converter backend `convert_to`.
(...)
184 See `coremltools.converters.convert`
185 """
--> 186 return _mil_convert(
187 model,
188 convert_from,
189 convert_to,
190 ConverterRegistry,
191 ct.models.MLModel,
192 compute_units,
193 **kwargs,
194 )
File ~/miniconda3/envs/prod/lib/python3.10/site-packages/coremltools/converters/mil/converter.py:218, in _mil_convert(model, convert_from, convert_to, registry, modelClass, compute_units, **kwargs)
215 weights_dir = _tempfile.TemporaryDirectory()
216 kwargs["weights_dir"] = weights_dir.name
--> 218 proto, mil_program = mil_convert_to_proto(
219 model,
220 convert_from,
221 convert_to,
222 registry,
223 **kwargs
224 )
226 _reset_conversion_state()
228 if convert_to == 'milinternal':
File ~/miniconda3/envs/prod/lib/python3.10/site-packages/coremltools/converters/mil/converter.py:294, in mil_convert_to_proto(model, convert_from, convert_to, converter_registry, main_pipeline, **kwargs)
289 frontend_pipeline, backend_pipeline = _construct_other_pipelines(
290 main_pipeline, convert_from, convert_to
291 )
293 frontend_converter = frontend_converter_type()
--> 294 prog = frontend_converter(model, **kwargs)
295 PassPipelineManager.apply_pipeline(prog, frontend_pipeline)
297 PassPipelineManager.apply_pipeline(prog, main_pipeline)
File ~/miniconda3/envs/prod/lib/python3.10/site-packages/coremltools/converters/mil/converter.py:106, in TorchFrontend.__call__(self, *args, **kwargs)
103 def __call__(self, *args, **kwargs):
104 from .frontend.torch.load import load
--> 106 return load(*args, **kwargs)
File ~/miniconda3/envs/prod/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/load.py:88, in load(spec, inputs, specification_version, debug, outputs, cut_at_symbols, use_default_fp16_io, states, **kwargs)
76 model = _torchscript_from_spec(spec)
78 converter = TorchConverter(
79 model,
80 inputs,
(...)
85 states,
86 )
---> 88 return _perform_torch_convert(converter, debug)
File ~/miniconda3/envs/prod/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/load.py:151, in _perform_torch_convert(converter, debug)
149 def _perform_torch_convert(converter: TorchConverter, debug: bool) -> Program:
150 try:
--> 151 prog = converter.convert()
152 except RuntimeError as e:
153 if debug and "convert function" in str(e):
File ~/miniconda3/envs/prod/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/converter.py:1402, in TorchConverter.convert(self)
1397 cast_value = mb.cast(
1398 x=output_var, dtype=builtin_to_string(buffer_var.dtype)
1399 )
1400 mb.coreml_update_state(state=buffer_var, value=cast_value)
-> 1402 graph_outputs = [self.context[name] for name in self.graph.outputs]
1404 # An output can be None when it's a None constant, which happens
1405 # in Fairseq MT.
1406 for g in graph_outputs:
File ~/miniconda3/envs/prod/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/converter.py:1402, in <listcomp>(.0)
1397 cast_value = mb.cast(
1398 x=output_var, dtype=builtin_to_string(buffer_var.dtype)
1399 )
1400 mb.coreml_update_state(state=buffer_var, value=cast_value)
-> 1402 graph_outputs = [self.context[name] for name in self.graph.outputs]
1404 # An output can be None when it's a None constant, which happens
1405 # in Fairseq MT.
1406 for g in graph_outputs:
File ~/miniconda3/envs/prod/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/converter.py:485, in TranscriptionContext.__getitem__(self, torch_name)
483 if torch_name in current_graph:
484 return self._current_graph[idx][torch_name]
--> 485 raise ValueError(f"Torch var {torch_name} not found in context {self.name}")
ValueError: Torch var 16 not found in context
