Error when "Run 2D Inference" is clicked

[ngordon1QO]

🐛 Bug Report

--- Reported issue to napari, they said it's an empanada error. See issue here ---

Using MacBook Air apple M3, conda environment with Python3.9, clicking the "Run 2D Inference" on the empanada plugin widget raises the following error:

RuntimeError                              Traceback (most recent call last)
File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/psygnal/_signal.py:1196, in SignalInstance._run_emit_loop(self=<SignalInstance 'changed' on PushButton(value=False, annotation=None, name='call_button')>, args=(False,))
   1195     with Signal._emitting(self):
-> 1196         self._run_emit_loop_inner()
        self = <SignalInstance 'changed' on PushButton(value=False, annotation=None, name='call_button')>
        self._run_emit_loop_inner = <bound method SignalInstance._run_emit_loop_immediate of <SignalInstance 'changed' on PushButton(value=False, annotation=None, name='call_button')>>
   1197 except RecursionError as e:

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/psygnal/_signal.py:1225, in SignalInstance._run_emit_loop_immediate(self=<SignalInstance 'changed' on PushButton(value=False, annotation=None, name='call_button')>)
   1224 for caller in self._slots:
-> 1225     caller.cb(args)
        args = (False,)
        caller = <StrongFunction on magicgui.widgets._function_gui.FunctionGui.__init__.<locals>._disable_button_and_call>

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/psygnal/_weak_callback.py:357, in StrongFunction.cb(self=<StrongFunction on magicgui.widgets._function_gu...onGui.__init__.<locals>._disable_button_and_call>, args=())
    356     args = args[: self._max_args]
--> 357 self._f(*self._args, *args, **self._kwargs)
        args = ()
        self._f = <function FunctionGui.__init__.<locals>._disable_button_and_call at 0x336dff3a0>
        self = <StrongFunction on magicgui.widgets._function_gui.FunctionGui.__init__.<locals>._disable_button_and_call>
        self._args = ()
        self._kwargs = {}

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/magicgui/widgets/_function_gui.py:228, in FunctionGui.__init__.<locals>._disable_button_and_call()
    227 try:
--> 228     self.__call__()
        self = <FunctionGui widget(viewer: napari.viewer.Viewer = Viewer(camera=Camera(center=(0.0, np.float64(2047.5), np.float64(2047.5)), zoom=np.float64(0.12820129394531252), angles=(0.0, 0.0, 90.0), perspective=0.0, mouse_pan=True, mouse_zoom=True), cursor=Cursor(position=(np.float64(3814.2529273454825), np.float64(4898.485378934922)), scaled=True, style=<CursorStyle.STANDARD: 'standard'>, size=1.0), dims=Dims(ndim=2, ndisplay=2, order=(0, 1), axis_labels=('0', '1'), rollable=(True, True), range=(RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0)), RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0))), margin_left=(0.0, 0.0), margin_right=(0.0, 0.0), point=(np.float64(2047.0), np.float64(2047.0)), last_used=0), grid=GridCanvas(stride=1, shape=(-1, -1), enabled=False), layers=[<Image layer 'VG_0141' at 0x336f27220>], help='use <2> for transform', status='Ready', tooltip=Tooltip(visible=False, text=''), theme='dark', title='napari', mouse_over_canvas=True, mouse_move_callbacks=[], mouse_drag_callbacks=[], mouse_double_click_callbacks=[], mouse_wheel_callbacks=[<function dims_scroll at 0x12f080310>], _persisted_mouse_event={}, _mouse_drag_gen={}, _mouse_wheel_gen={}, _keymap={}), label_head='', image_layer: napari.layers.image.image.Image = <Image layer 'VG_0141' at 0x336f27220>, model_config='MitoNet_v1', downsampling=1, confidence_thr=0.5, center_confidence_thr=0.1, min_distance_object_centers=3, fine_boundaries=False, semantic_only=False, maximum_objects_per_class='10000', tile_size=0, batch_mode=False, use_gpu=False, use_quantized=True, viewport=False, output_to_layer=False, output_layer: napari.layers.labels.labels.Labels = None, pbar: magicgui.widgets.ProgressBar = ProgressBar(value=<function match_type.<locals>.<lambda> at 0x336dcdc10>, annotation=<class 'magicgui.widgets.ProgressBar'>, name='pbar'))>
    229 finally:

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/magicgui/widgets/_function_gui.py:346, in FunctionGui.__call__(self=<FunctionGui widget(viewer: napari.viewer.Viewer...s 'magicgui.widgets.ProgressBar'>, name='pbar'))>, *args=(), **kwargs={})
    345 with _function_name_pointing_to_widget(self):
--> 346     value = self._function(*bound.args, **bound.kwargs)
        self = <FunctionGui widget(viewer: napari.viewer.Viewer = Viewer(camera=Camera(center=(0.0, np.float64(2047.5), np.float64(2047.5)), zoom=np.float64(0.12820129394531252), angles=(0.0, 0.0, 90.0), perspective=0.0, mouse_pan=True, mouse_zoom=True), cursor=Cursor(position=(np.float64(3814.2529273454825), np.float64(4898.485378934922)), scaled=True, style=<CursorStyle.STANDARD: 'standard'>, size=1.0), dims=Dims(ndim=2, ndisplay=2, order=(0, 1), axis_labels=('0', '1'), rollable=(True, True), range=(RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0)), RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0))), margin_left=(0.0, 0.0), margin_right=(0.0, 0.0), point=(np.float64(2047.0), np.float64(2047.0)), last_used=0), grid=GridCanvas(stride=1, shape=(-1, -1), enabled=False), layers=[<Image layer 'VG_0141' at 0x336f27220>], help='use <2> for transform', status='Ready', tooltip=Tooltip(visible=False, text=''), theme='dark', title='napari', mouse_over_canvas=True, mouse_move_callbacks=[], mouse_drag_callbacks=[], mouse_double_click_callbacks=[], mouse_wheel_callbacks=[<function dims_scroll at 0x12f080310>], _persisted_mouse_event={}, _mouse_drag_gen={}, _mouse_wheel_gen={}, _keymap={}), label_head='', image_layer: napari.layers.image.image.Image = <Image layer 'VG_0141' at 0x336f27220>, model_config='MitoNet_v1', downsampling=1, confidence_thr=0.5, center_confidence_thr=0.1, min_distance_object_centers=3, fine_boundaries=False, semantic_only=False, maximum_objects_per_class='10000', tile_size=0, batch_mode=False, use_gpu=False, use_quantized=True, viewport=False, output_to_layer=False, output_layer: napari.layers.labels.labels.Labels = None, pbar: magicgui.widgets.ProgressBar = ProgressBar(value=<function match_type.<locals>.<lambda> at 0x336dcdc10>, annotation=<class 'magicgui.widgets.ProgressBar'>, name='pbar'))>
        bound = <BoundArguments (viewer=Viewer(camera=Camera(center=(0.0, np.float64(2047.5), np.float64(2047.5)), zoom=np.float64(0.12820129394531252), angles=(0.0, 0.0, 90.0), perspective=0.0, mouse_pan=True, mouse_zoom=True), cursor=Cursor(position=(np.float64(3814.2529273454825), np.float64(4898.485378934922)), scaled=True, style=<CursorStyle.STANDARD: 'standard'>, size=1.0), dims=Dims(ndim=2, ndisplay=2, order=(0, 1), axis_labels=('0', '1'), rollable=(True, True), range=(RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0)), RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0))), margin_left=(0.0, 0.0), margin_right=(0.0, 0.0), point=(np.float64(2047.0), np.float64(2047.0)), last_used=0), grid=GridCanvas(stride=1, shape=(-1, -1), enabled=False), layers=[<Image layer 'VG_0141' at 0x336f27220>], help='use <2> for transform', status='Ready', tooltip=Tooltip(visible=False, text=''), theme='dark', title='napari', mouse_over_canvas=True, mouse_move_callbacks=[], mouse_drag_callbacks=[], mouse_double_click_callbacks=[], mouse_wheel_callbacks=[<function dims_scroll at 0x12f080310>], _persisted_mouse_event={}, _mouse_drag_gen={}, _mouse_wheel_gen={}, _keymap={}), label_head='', image_layer=<Image layer 'VG_0141' at 0x336f27220>, model_config='MitoNet_v1', downsampling=1, confidence_thr=0.5, center_confidence_thr=0.1, min_distance_object_centers=3, fine_boundaries=False, semantic_only=False, maximum_objects_per_class='10000', tile_size=0, batch_mode=False, use_gpu=False, use_quantized=True, viewport=False, output_to_layer=False, output_layer=None, pbar=ProgressBar(value=<function match_type.<locals>.<lambda> at 0x336dcdc10>, annotation=<class 'magicgui.widgets.ProgressBar'>, name='pbar'))>
        self._function = <function test_widget.<locals>.widget at 0x336d5fd30>
    348 self._call_count += 1

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/empanada_napari/_slice_inference.py:166, in test_widget.<locals>.widget(viewer=Viewer(camera=Camera(center=(0.0, np.float64(204...use_drag_gen={}, _mouse_wheel_gen={}, _keymap={}), label_head='', image_layer=<Image layer 'VG_0141'>, model_config={'FINETUNE': {'criterion': 'PanopticLoss', 'criterion_params': {'ce_weight': 1, 'l1_weight': 0.01, 'mse_weight': 200, 'pr_weight': 1, 'top_k_percent': 0.2}, 'dataset_class': 'SingleClassInstanceDataset', 'dataset_params': {'weight_gamma': 0.7}, 'engine': 'PanopticDeepLabEngine', 'engine_params': {'confidence_thr': 0.5, 'label_divisor': 1000, 'nms_kernel': 7, 'nms_threshold': 0.1, 'stuff_area': 64, 'thing_list': [1], 'void_label': 0}}, 'class_names': {1: 'mito'}, 'description': 'MitoNet_v1 was trained on the large CEM-MitoLab ...ww.biorxiv.org/content/10.1101/2022.03.17.484806\n', 'labels': [1], 'model': 'https://zenodo.org/record/6861565/files/MitoNet_v1.pth?download=1', 'model_quantized': 'https://zenodo.org/record/6861565/files/MitoNet_v1_quantized.pth?download=1', 'norms': {'mean': 0.57571, 'std': 0.12765}, 'padding_factor': 16, 'thing_list': [1]}, downsampling=1, confidence_thr=0.5, center_confidence_thr=0.1, min_distance_object_centers=3, fine_boundaries=False, semantic_only=False, maximum_objects_per_class=10000, tile_size=0, batch_mode=False, use_gpu=False, use_quantized=True, viewport=False, output_to_layer=False, output_layer=None, pbar=ProgressBar(value=<function match_type.<locals>....ass 'magicgui.widgets.ProgressBar'>, name='pbar'))
    165 if not hasattr(widget, 'engine') or widget.last_config != model_config_name or use_gpu != widget.using_gpu or use_quantized != widget.using_quantized:
--> 166     widget.engine = Engine2d(
        widget = <FunctionGui widget(viewer: napari.viewer.Viewer = Viewer(camera=Camera(center=(0.0, np.float64(2047.5), np.float64(2047.5)), zoom=np.float64(0.12820129394531252), angles=(0.0, 0.0, 90.0), perspective=0.0, mouse_pan=True, mouse_zoom=True), cursor=Cursor(position=(np.float64(3814.2529273454825), np.float64(4898.485378934922)), scaled=True, style=<CursorStyle.STANDARD: 'standard'>, size=1.0), dims=Dims(ndim=2, ndisplay=2, order=(0, 1), axis_labels=('0', '1'), rollable=(True, True), range=(RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0)), RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0))), margin_left=(0.0, 0.0), margin_right=(0.0, 0.0), point=(np.float64(2047.0), np.float64(2047.0)), last_used=0), grid=GridCanvas(stride=1, shape=(-1, -1), enabled=False), layers=[<Image layer 'VG_0141' at 0x336f27220>], help='use <2> for transform', status='Ready', tooltip=Tooltip(visible=False, text=''), theme='dark', title='napari', mouse_over_canvas=True, mouse_move_callbacks=[], mouse_drag_callbacks=[], mouse_double_click_callbacks=[], mouse_wheel_callbacks=[<function dims_scroll at 0x12f080310>], _persisted_mouse_event={}, _mouse_drag_gen={}, _mouse_wheel_gen={}, _keymap={}), label_head='', image_layer: napari.layers.image.image.Image = <Image layer 'VG_0141' at 0x336f27220>, model_config='MitoNet_v1', downsampling=1, confidence_thr=0.5, center_confidence_thr=0.1, min_distance_object_centers=3, fine_boundaries=False, semantic_only=False, maximum_objects_per_class='10000', tile_size=0, batch_mode=False, use_gpu=False, use_quantized=True, viewport=False, output_to_layer=False, output_layer: napari.layers.labels.labels.Labels = None, pbar: magicgui.widgets.ProgressBar = ProgressBar(value=<function match_type.<locals>.<lambda> at 0x336dcdc10>, annotation=<class 'magicgui.widgets.ProgressBar'>, name='pbar'))>
        model_config = {'class_names': {1: 'mito'}, 'labels': [1], 'thing_list': [1], 'model': 'https://zenodo.org/record/6861565/files/MitoNet_v1.pth?download=1', 'model_quantized': 'https://zenodo.org/record/6861565/files/MitoNet_v1_quantized.pth?download=1', 'padding_factor': 16, 'norms': {'mean': 0.57571, 'std': 0.12765}, 'description': 'MitoNet_v1 was trained on the large CEM-MitoLab dataset and is a generalist for mitochondrial segmentation. The underlying architecture is PanopticDeeplab. This model is fairly large but powerful. If GPU memory is a limitation, try using MitoNet_v1_mini instead. Read the preprint: https://www.biorxiv.org/content/10.1101/2022.03.17.484806\n', 'FINETUNE': {'criterion': 'PanopticLoss', 'criterion_params': {'ce_weight': 1, 'l1_weight': 0.01, 'mse_weight': 200, 'pr_weight': 1, 'top_k_percent': 0.2}, 'dataset_class': 'SingleClassInstanceDataset', 'dataset_params': {'weight_gamma': 0.7}, 'engine': 'PanopticDeepLabEngine', 'engine_params': {'confidence_thr': 0.5, 'label_divisor': 1000, 'nms_kernel': 7, 'nms_threshold': 0.1, 'stuff_area': 64, 'thing_list': [1], 'void_label': 0}}}
        downsampling = 1
        min_distance_object_centers = 3
        center_confidence_thr = 0.1
        confidence_thr = 0.5
        maximum_objects_per_class = 10000
        semantic_only = False
        fine_boundaries = False
        tile_size = 0
        use_gpu = False
        use_quantized = True
    167         model_config,
    168         inference_scale=downsampling,
    169         nms_kernel=min_distance_object_centers,
    170         nms_threshold=center_confidence_thr,
    171         confidence_thr=confidence_thr,
    172         label_divisor=maximum_objects_per_class,
    173         semantic_only=semantic_only,
    174         fine_boundaries=fine_boundaries,
    175         tile_size=tile_size,
    176         use_gpu=use_gpu,
    177         use_quantized=use_quantized
    178     )
    179     widget.last_config = model_config_name

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/empanada_napari/inference.py:193, in Engine2d.__init__(self=<empanada_napari.inference.Engine2d object>, model_config={'FINETUNE': {'criterion': 'PanopticLoss', 'criterion_params': {'ce_weight': 1, 'l1_weight': 0.01, 'mse_weight': 200, 'pr_weight': 1, 'top_k_percent': 0.2}, 'dataset_class': 'SingleClassInstanceDataset', 'dataset_params': {'weight_gamma': 0.7}, 'engine': 'PanopticDeepLabEngine', 'engine_params': {'confidence_thr': 0.5, 'label_divisor': 1000, 'nms_kernel': 7, 'nms_threshold': 0.1, 'stuff_area': 64, 'thing_list': [1], 'void_label': 0}}, 'class_names': {1: 'mito'}, 'description': 'MitoNet_v1 was trained on the large CEM-MitoLab ...ww.biorxiv.org/content/10.1101/2022.03.17.484806\n', 'labels': [1], 'model': 'https://zenodo.org/record/6861565/files/MitoNet_v1.pth?download=1', 'model_quantized': 'https://zenodo.org/record/6861565/files/MitoNet_v1_quantized.pth?download=1', 'norms': {'mean': 0.57571, 'std': 0.12765}, 'padding_factor': 16, 'thing_list': [1]}, inference_scale=1, label_divisor=10000, nms_threshold=0.1, nms_kernel=3, confidence_thr=0.5, semantic_only=False, fine_boundaries=False, tile_size=0, use_gpu=False, use_quantized=True)
    191     model_url = model_config['model']
--> 193 model = load_model_to_device(model_url, device)
        device = device(type='cpu')
        model_url = 'https://zenodo.org/record/6861565/files/MitoNet_v1_quantized.pth?download=1'
    194 model = model.to(device)

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/empanada_napari/utils.py:66, in load_model_to_device(fpath_or_url='https://zenodo.org/record/6861565/files/MitoNet_v1_quantized.pth?download=1', device=device(type='cpu'))
     64         torch.hub.download_url_to_file(fpath_or_url, cached_file, hash_prefix, progress=True)
---> 66     model = torch.jit.load(cached_file, map_location=device)
        cached_file = '/Users/natangordon/.empanada/MitoNet_v1_quantized.pth'
        torch.jit = <module 'torch.jit' from '/Users/natangordon/miniforge3/envs/napari-cell/lib/python3.9/site-packages/torch/jit/__init__.py'>
        device = device(type='cpu')
     68 return model

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/torch/jit/_serialization.py:163, in load(f='/Users/natangordon/.empanada/MitoNet_v1_quantized.pth', map_location=device(type='cpu'), _extra_files={}, _restore_shapes=False)
    162 if isinstance(f, (str, os.PathLike)):
--> 163     cpp_module = torch._C.import_ir_module(cu, os.fspath(f), map_location, _extra_files, _restore_shapes)  # type: ignore[call-arg]
        map_location = device(type='cpu')
        cu = <torch.jit.CompilationUnit object at 0x16f422f30>
        f = '/Users/natangordon/.empanada/MitoNet_v1_quantized.pth'
        _extra_files = {}
        torch._C = <module 'torch._C' from '/Users/natangordon/miniforge3/envs/napari-cell/lib/python3.9/site-packages/torch/_C.cpython-39-darwin.so'>
        _restore_shapes = False
    164 else:

RuntimeError: The following operation failed in the TorchScript interpreter.
Traceback of TorchScript, serialized code (most recent call last):
  File "code/__torch__/torch/nn/intrinsic/quantized/modules/conv_relu.py", line 72, in __setstate__
    self.groups = (state)[8]
    self.padding_mode = (state)[9]
    _7 = (self).set_weight_bias((state)[10], (state)[11], )
          ~~~~~~~~~~~~~~~~~~~~~ <--- HERE
    self.scale = (state)[12]
    self.zero_point = (state)[13]
  File "code/__torch__/torch/nn/intrinsic/quantized/modules/conv_relu.py", line 94, in set_weight_bias
      _13 = [_11, _12]
      _14, _15, = dilation
      _16 = ops.quantized.conv2d_prepack(w, b, _10, _13, [_14, _15], groups)
            ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
      self._packed_params = _16
    else:

Traceback of TorchScript, original code (most recent call last):
  File "/data/IASEM/iasemconda/lib/python3.9/site-packages/torch/nn/quantized/modules/conv.py", line 176, in __setstate__
        self.groups = state[8]
        self.padding_mode = state[9]
        self.set_weight_bias(state[10], state[11])
        ~~~~~~~~~~~~~~~~~~~~ <--- HERE
        self.scale = state[12]
        self.zero_point = state[13]
  File "/data/IASEM/iasemconda/lib/python3.9/site-packages/torch/nn/quantized/modules/conv.py", line 401, in set_weight_bias
    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
        if self.padding_mode == 'zeros':
            self._packed_params = torch.ops.quantized.conv2d_prepack(
                                  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
                w, b, self.stride, self.padding, self.dilation, self.groups)
        else:
RuntimeError: Didn't find engine for operation quantized::conv2d_prepack NoQEngine


The above exception was the direct cause of the following exception:

EmitLoopError                             Traceback (most recent call last)
File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/magicgui/widgets/bases/_value_widget.py:71, in ValueWidget._on_value_change(self=PushButton(value=False, annotation=None, name='call_button'), value=False)
     69 if value is self.null_value and not self._nullable:
     70     return
---> 71 self.changed.emit(value)
        value = False
        self.changed = <SignalInstance 'changed' on PushButton(value=False, annotation=None, name='call_button')>
        self = PushButton(value=False, annotation=None, name='call_button')

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/psygnal/_signal.py:1176, in SignalInstance.emit(self=<SignalInstance 'changed' on PushButton(value=False, annotation=None, name='call_button')>, check_nargs=False, check_types=False, *args=(False,))
   1172     from ._group import EmissionInfo
   1174     SignalInstance._debug_hook(EmissionInfo(self, args))
-> 1176 self._run_emit_loop(args)
        self = <SignalInstance 'changed' on PushButton(value=False, annotation=None, name='call_button')>
        args = (False,)

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/psygnal/_signal.py:1213, in SignalInstance._run_emit_loop(self=<SignalInstance 'changed' on PushButton(value=False, annotation=None, name='call_button')>, args=(False,))
   1205     loop_err = EmitLoopError(
   1206         exc=cb_err,
   1207         signal=self,
   (...)
   1210         emit_queue=self._emit_queue,
   1211     ).with_traceback(cb_err.__traceback__)
   1212     # this comment will show up in the traceback
-> 1213     raise loop_err from cb_err  # emit() call ABOVE || callback error BELOW
        loop_err = EmitLoopError("\n\nWhile emitting signal 'magicgui.widgets.PushButton.changed', a RuntimeError occurred in a callback:\n\n  Signal emitted at: /Users/natangordon/miniforge3/envs/napari-cell/lib/python3.9/site-packages/magicgui/widgets/bases/_value_widget.py:71, in _on_value_change\n    >  self.changed.emit(value)\n\n  Callback error at: /Users/natangordon/miniforge3/envs/napari-cell/lib/python3.9/site-packages/torch/jit/_serialization.py:163, in load\n    >  cpp_module = torch._C.import_ir_module(cu, os.fspath(f), map_location, _extra_files, _restore_shapes)  # type: ignore[call-arg]\n\n    Local variables:\n       f = '/Users/natangordon/.empanada/MitoNet_v1_quantized.pth'\n       map_location = device(type='cpu')\n       _extra_files = {}\n       _restore_shapes = False\n       cu = <torch.jit.CompilationUnit object at 0x16f422f30>\n\nSee RuntimeError above for original traceback.")
   1214 finally:
   1215     self._recursion_depth -= 1

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/psygnal/_signal.py:1196, in SignalInstance._run_emit_loop(self=<SignalInstance 'changed' on PushButton(value=False, annotation=None, name='call_button')>, args=(False,))
   1192     self._max_recursion_depth = max(
   1193         self._max_recursion_depth, self._recursion_depth
   1194     )
   1195     with Signal._emitting(self):
-> 1196         self._run_emit_loop_inner()
        self = <SignalInstance 'changed' on PushButton(value=False, annotation=None, name='call_button')>
        self._run_emit_loop_inner = <bound method SignalInstance._run_emit_loop_immediate of <SignalInstance 'changed' on PushButton(value=False, annotation=None, name='call_button')>>
   1197 except RecursionError as e:
   1198     raise RecursionError(
   1199         f"RecursionError when "
   1200         f"emitting signal {self.name!r} with args {args}"
   1201     ) from e

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/psygnal/_signal.py:1225, in SignalInstance._run_emit_loop_immediate(self=<SignalInstance 'changed' on PushButton(value=False, annotation=None, name='call_button')>)
   1223 args = self._emit_queue.popleft()
   1224 for caller in self._slots:
-> 1225     caller.cb(args)
        args = (False,)
        caller = <StrongFunction on magicgui.widgets._function_gui.FunctionGui.__init__.<locals>._disable_button_and_call>

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/psygnal/_weak_callback.py:357, in StrongFunction.cb(self=<StrongFunction on magicgui.widgets._function_gu...onGui.__init__.<locals>._disable_button_and_call>, args=())
    355 if self._max_args is not None:
    356     args = args[: self._max_args]
--> 357 self._f(*self._args, *args, **self._kwargs)
        args = ()
        self._f = <function FunctionGui.__init__.<locals>._disable_button_and_call at 0x336dff3a0>
        self = <StrongFunction on magicgui.widgets._function_gui.FunctionGui.__init__.<locals>._disable_button_and_call>
        self._args = ()
        self._kwargs = {}

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/magicgui/widgets/_function_gui.py:228, in FunctionGui.__init__.<locals>._disable_button_and_call()
    226 self._call_button.enabled = False
    227 try:
--> 228     self.__call__()
        self = <FunctionGui widget(viewer: napari.viewer.Viewer = Viewer(camera=Camera(center=(0.0, np.float64(2047.5), np.float64(2047.5)), zoom=np.float64(0.12820129394531252), angles=(0.0, 0.0, 90.0), perspective=0.0, mouse_pan=True, mouse_zoom=True), cursor=Cursor(position=(np.float64(3814.2529273454825), np.float64(4898.485378934922)), scaled=True, style=<CursorStyle.STANDARD: 'standard'>, size=1.0), dims=Dims(ndim=2, ndisplay=2, order=(0, 1), axis_labels=('0', '1'), rollable=(True, True), range=(RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0)), RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0))), margin_left=(0.0, 0.0), margin_right=(0.0, 0.0), point=(np.float64(2047.0), np.float64(2047.0)), last_used=0), grid=GridCanvas(stride=1, shape=(-1, -1), enabled=False), layers=[<Image layer 'VG_0141' at 0x336f27220>], help='use <2> for transform', status='Ready', tooltip=Tooltip(visible=False, text=''), theme='dark', title='napari', mouse_over_canvas=True, mouse_move_callbacks=[], mouse_drag_callbacks=[], mouse_double_click_callbacks=[], mouse_wheel_callbacks=[<function dims_scroll at 0x12f080310>], _persisted_mouse_event={}, _mouse_drag_gen={}, _mouse_wheel_gen={}, _keymap={}), label_head='', image_layer: napari.layers.image.image.Image = <Image layer 'VG_0141' at 0x336f27220>, model_config='MitoNet_v1', downsampling=1, confidence_thr=0.5, center_confidence_thr=0.1, min_distance_object_centers=3, fine_boundaries=False, semantic_only=False, maximum_objects_per_class='10000', tile_size=0, batch_mode=False, use_gpu=False, use_quantized=True, viewport=False, output_to_layer=False, output_layer: napari.layers.labels.labels.Labels = None, pbar: magicgui.widgets.ProgressBar = ProgressBar(value=<function match_type.<locals>.<lambda> at 0x336dcdc10>, annotation=<class 'magicgui.widgets.ProgressBar'>, name='pbar'))>
    229 finally:
    230     self._call_button.enabled = True

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/magicgui/widgets/_function_gui.py:346, in FunctionGui.__call__(self=<FunctionGui widget(viewer: napari.viewer.Viewer...s 'magicgui.widgets.ProgressBar'>, name='pbar'))>, *args=(), **kwargs={})
    344 self._tqdm_depth = 0  # reset the tqdm stack count
    345 with _function_name_pointing_to_widget(self):
--> 346     value = self._function(*bound.args, **bound.kwargs)
        self = <FunctionGui widget(viewer: napari.viewer.Viewer = Viewer(camera=Camera(center=(0.0, np.float64(2047.5), np.float64(2047.5)), zoom=np.float64(0.12820129394531252), angles=(0.0, 0.0, 90.0), perspective=0.0, mouse_pan=True, mouse_zoom=True), cursor=Cursor(position=(np.float64(3814.2529273454825), np.float64(4898.485378934922)), scaled=True, style=<CursorStyle.STANDARD: 'standard'>, size=1.0), dims=Dims(ndim=2, ndisplay=2, order=(0, 1), axis_labels=('0', '1'), rollable=(True, True), range=(RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0)), RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0))), margin_left=(0.0, 0.0), margin_right=(0.0, 0.0), point=(np.float64(2047.0), np.float64(2047.0)), last_used=0), grid=GridCanvas(stride=1, shape=(-1, -1), enabled=False), layers=[<Image layer 'VG_0141' at 0x336f27220>], help='use <2> for transform', status='Ready', tooltip=Tooltip(visible=False, text=''), theme='dark', title='napari', mouse_over_canvas=True, mouse_move_callbacks=[], mouse_drag_callbacks=[], mouse_double_click_callbacks=[], mouse_wheel_callbacks=[<function dims_scroll at 0x12f080310>], _persisted_mouse_event={}, _mouse_drag_gen={}, _mouse_wheel_gen={}, _keymap={}), label_head='', image_layer: napari.layers.image.image.Image = <Image layer 'VG_0141' at 0x336f27220>, model_config='MitoNet_v1', downsampling=1, confidence_thr=0.5, center_confidence_thr=0.1, min_distance_object_centers=3, fine_boundaries=False, semantic_only=False, maximum_objects_per_class='10000', tile_size=0, batch_mode=False, use_gpu=False, use_quantized=True, viewport=False, output_to_layer=False, output_layer: napari.layers.labels.labels.Labels = None, pbar: magicgui.widgets.ProgressBar = ProgressBar(value=<function match_type.<locals>.<lambda> at 0x336dcdc10>, annotation=<class 'magicgui.widgets.ProgressBar'>, name='pbar'))>
        bound = <BoundArguments (viewer=Viewer(camera=Camera(center=(0.0, np.float64(2047.5), np.float64(2047.5)), zoom=np.float64(0.12820129394531252), angles=(0.0, 0.0, 90.0), perspective=0.0, mouse_pan=True, mouse_zoom=True), cursor=Cursor(position=(np.float64(3814.2529273454825), np.float64(4898.485378934922)), scaled=True, style=<CursorStyle.STANDARD: 'standard'>, size=1.0), dims=Dims(ndim=2, ndisplay=2, order=(0, 1), axis_labels=('0', '1'), rollable=(True, True), range=(RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0)), RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0))), margin_left=(0.0, 0.0), margin_right=(0.0, 0.0), point=(np.float64(2047.0), np.float64(2047.0)), last_used=0), grid=GridCanvas(stride=1, shape=(-1, -1), enabled=False), layers=[<Image layer 'VG_0141' at 0x336f27220>], help='use <2> for transform', status='Ready', tooltip=Tooltip(visible=False, text=''), theme='dark', title='napari', mouse_over_canvas=True, mouse_move_callbacks=[], mouse_drag_callbacks=[], mouse_double_click_callbacks=[], mouse_wheel_callbacks=[<function dims_scroll at 0x12f080310>], _persisted_mouse_event={}, _mouse_drag_gen={}, _mouse_wheel_gen={}, _keymap={}), label_head='', image_layer=<Image layer 'VG_0141' at 0x336f27220>, model_config='MitoNet_v1', downsampling=1, confidence_thr=0.5, center_confidence_thr=0.1, min_distance_object_centers=3, fine_boundaries=False, semantic_only=False, maximum_objects_per_class='10000', tile_size=0, batch_mode=False, use_gpu=False, use_quantized=True, viewport=False, output_to_layer=False, output_layer=None, pbar=ProgressBar(value=<function match_type.<locals>.<lambda> at 0x336dcdc10>, annotation=<class 'magicgui.widgets.ProgressBar'>, name='pbar'))>
        self._function = <function test_widget.<locals>.widget at 0x336d5fd30>
    348 self._call_count += 1
    349 if self._result_widget is not None:

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/empanada_napari/_slice_inference.py:166, in test_widget.<locals>.widget(viewer=Viewer(camera=Camera(center=(0.0, np.float64(204...use_drag_gen={}, _mouse_wheel_gen={}, _keymap={}), label_head='', image_layer=<Image layer 'VG_0141'>, model_config={'FINETUNE': {'criterion': 'PanopticLoss', 'criterion_params': {'ce_weight': 1, 'l1_weight': 0.01, 'mse_weight': 200, 'pr_weight': 1, 'top_k_percent': 0.2}, 'dataset_class': 'SingleClassInstanceDataset', 'dataset_params': {'weight_gamma': 0.7}, 'engine': 'PanopticDeepLabEngine', 'engine_params': {'confidence_thr': 0.5, 'label_divisor': 1000, 'nms_kernel': 7, 'nms_threshold': 0.1, 'stuff_area': 64, 'thing_list': [1], 'void_label': 0}}, 'class_names': {1: 'mito'}, 'description': 'MitoNet_v1 was trained on the large CEM-MitoLab ...ww.biorxiv.org/content/10.1101/2022.03.17.484806\n', 'labels': [1], 'model': 'https://zenodo.org/record/6861565/files/MitoNet_v1.pth?download=1', 'model_quantized': 'https://zenodo.org/record/6861565/files/MitoNet_v1_quantized.pth?download=1', 'norms': {'mean': 0.57571, 'std': 0.12765}, 'padding_factor': 16, 'thing_list': [1]}, downsampling=1, confidence_thr=0.5, center_confidence_thr=0.1, min_distance_object_centers=3, fine_boundaries=False, semantic_only=False, maximum_objects_per_class=10000, tile_size=0, batch_mode=False, use_gpu=False, use_quantized=True, viewport=False, output_to_layer=False, output_layer=None, pbar=ProgressBar(value=<function match_type.<locals>....ass 'magicgui.widgets.ProgressBar'>, name='pbar'))
    163     widget.using_quantized = use_quantized
    165 if not hasattr(widget, 'engine') or widget.last_config != model_config_name or use_gpu != widget.using_gpu or use_quantized != widget.using_quantized:
--> 166     widget.engine = Engine2d(
        widget = <FunctionGui widget(viewer: napari.viewer.Viewer = Viewer(camera=Camera(center=(0.0, np.float64(2047.5), np.float64(2047.5)), zoom=np.float64(0.12820129394531252), angles=(0.0, 0.0, 90.0), perspective=0.0, mouse_pan=True, mouse_zoom=True), cursor=Cursor(position=(np.float64(3814.2529273454825), np.float64(4898.485378934922)), scaled=True, style=<CursorStyle.STANDARD: 'standard'>, size=1.0), dims=Dims(ndim=2, ndisplay=2, order=(0, 1), axis_labels=('0', '1'), rollable=(True, True), range=(RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0)), RangeTuple(start=np.float64(0.0), stop=np.float64(4095.0), step=np.float64(1.0))), margin_left=(0.0, 0.0), margin_right=(0.0, 0.0), point=(np.float64(2047.0), np.float64(2047.0)), last_used=0), grid=GridCanvas(stride=1, shape=(-1, -1), enabled=False), layers=[<Image layer 'VG_0141' at 0x336f27220>], help='use <2> for transform', status='Ready', tooltip=Tooltip(visible=False, text=''), theme='dark', title='napari', mouse_over_canvas=True, mouse_move_callbacks=[], mouse_drag_callbacks=[], mouse_double_click_callbacks=[], mouse_wheel_callbacks=[<function dims_scroll at 0x12f080310>], _persisted_mouse_event={}, _mouse_drag_gen={}, _mouse_wheel_gen={}, _keymap={}), label_head='', image_layer: napari.layers.image.image.Image = <Image layer 'VG_0141' at 0x336f27220>, model_config='MitoNet_v1', downsampling=1, confidence_thr=0.5, center_confidence_thr=0.1, min_distance_object_centers=3, fine_boundaries=False, semantic_only=False, maximum_objects_per_class='10000', tile_size=0, batch_mode=False, use_gpu=False, use_quantized=True, viewport=False, output_to_layer=False, output_layer: napari.layers.labels.labels.Labels = None, pbar: magicgui.widgets.ProgressBar = ProgressBar(value=<function match_type.<locals>.<lambda> at 0x336dcdc10>, annotation=<class 'magicgui.widgets.ProgressBar'>, name='pbar'))>
        model_config = {'class_names': {1: 'mito'}, 'labels': [1], 'thing_list': [1], 'model': 'https://zenodo.org/record/6861565/files/MitoNet_v1.pth?download=1', 'model_quantized': 'https://zenodo.org/record/6861565/files/MitoNet_v1_quantized.pth?download=1', 'padding_factor': 16, 'norms': {'mean': 0.57571, 'std': 0.12765}, 'description': 'MitoNet_v1 was trained on the large CEM-MitoLab dataset and is a generalist for mitochondrial segmentation. The underlying architecture is PanopticDeeplab. This model is fairly large but powerful. If GPU memory is a limitation, try using MitoNet_v1_mini instead. Read the preprint: https://www.biorxiv.org/content/10.1101/2022.03.17.484806\n', 'FINETUNE': {'criterion': 'PanopticLoss', 'criterion_params': {'ce_weight': 1, 'l1_weight': 0.01, 'mse_weight': 200, 'pr_weight': 1, 'top_k_percent': 0.2}, 'dataset_class': 'SingleClassInstanceDataset', 'dataset_params': {'weight_gamma': 0.7}, 'engine': 'PanopticDeepLabEngine', 'engine_params': {'confidence_thr': 0.5, 'label_divisor': 1000, 'nms_kernel': 7, 'nms_threshold': 0.1, 'stuff_area': 64, 'thing_list': [1], 'void_label': 0}}}
        downsampling = 1
        min_distance_object_centers = 3
        center_confidence_thr = 0.1
        confidence_thr = 0.5
        maximum_objects_per_class = 10000
        semantic_only = False
        fine_boundaries = False
        tile_size = 0
        use_gpu = False
        use_quantized = True
    167         model_config,
    168         inference_scale=downsampling,
    169         nms_kernel=min_distance_object_centers,
    170         nms_threshold=center_confidence_thr,
    171         confidence_thr=confidence_thr,
    172         label_divisor=maximum_objects_per_class,
    173         semantic_only=semantic_only,
    174         fine_boundaries=fine_boundaries,
    175         tile_size=tile_size,
    176         use_gpu=use_gpu,
    177         use_quantized=use_quantized
    178     )
    179     widget.last_config = model_config_name
    180     widget.using_gpu = use_gpu

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/empanada_napari/inference.py:193, in Engine2d.__init__(self=<empanada_napari.inference.Engine2d object>, model_config={'FINETUNE': {'criterion': 'PanopticLoss', 'criterion_params': {'ce_weight': 1, 'l1_weight': 0.01, 'mse_weight': 200, 'pr_weight': 1, 'top_k_percent': 0.2}, 'dataset_class': 'SingleClassInstanceDataset', 'dataset_params': {'weight_gamma': 0.7}, 'engine': 'PanopticDeepLabEngine', 'engine_params': {'confidence_thr': 0.5, 'label_divisor': 1000, 'nms_kernel': 7, 'nms_threshold': 0.1, 'stuff_area': 64, 'thing_list': [1], 'void_label': 0}}, 'class_names': {1: 'mito'}, 'description': 'MitoNet_v1 was trained on the large CEM-MitoLab ...ww.biorxiv.org/content/10.1101/2022.03.17.484806\n', 'labels': [1], 'model': 'https://zenodo.org/record/6861565/files/MitoNet_v1.pth?download=1', 'model_quantized': 'https://zenodo.org/record/6861565/files/MitoNet_v1_quantized.pth?download=1', 'norms': {'mean': 0.57571, 'std': 0.12765}, 'padding_factor': 16, 'thing_list': [1]}, inference_scale=1, label_divisor=10000, nms_threshold=0.1, nms_kernel=3, confidence_thr=0.5, semantic_only=False, fine_boundaries=False, tile_size=0, use_gpu=False, use_quantized=True)
    190 else:
    191     model_url = model_config['model']
--> 193 model = load_model_to_device(model_url, device)
        device = device(type='cpu')
        model_url = 'https://zenodo.org/record/6861565/files/MitoNet_v1_quantized.pth?download=1'
    194 model = model.to(device)
    196 self.thing_list = model_config['thing_list']

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/empanada_napari/utils.py:66, in load_model_to_device(fpath_or_url='https://zenodo.org/record/6861565/files/MitoNet_v1_quantized.pth?download=1', device=device(type='cpu'))
     63         hash_prefix = None
     64         torch.hub.download_url_to_file(fpath_or_url, cached_file, hash_prefix, progress=True)
---> 66     model = torch.jit.load(cached_file, map_location=device)
        cached_file = '/Users/natangordon/.empanada/MitoNet_v1_quantized.pth'
        torch.jit = <module 'torch.jit' from '/Users/natangordon/miniforge3/envs/napari-cell/lib/python3.9/site-packages/torch/jit/__init__.py'>
        device = device(type='cpu')
     68 return model

File ~/miniforge3/envs/napari-cell/lib/python3.9/site-packages/torch/jit/_serialization.py:163, in load(f='/Users/natangordon/.empanada/MitoNet_v1_quantized.pth', map_location=device(type='cpu'), _extra_files={}, _restore_shapes=False)
    161 cu = torch._C.CompilationUnit()
    162 if isinstance(f, (str, os.PathLike)):
--> 163     cpp_module = torch._C.import_ir_module(cu, os.fspath(f), map_location, _extra_files, _restore_shapes)  # type: ignore[call-arg]
        map_location = device(type='cpu')
        cu = <torch.jit.CompilationUnit object at 0x16f422f30>
        f = '/Users/natangordon/.empanada/MitoNet_v1_quantized.pth'
        _extra_files = {}
        torch._C = <module 'torch._C' from '/Users/natangordon/miniforge3/envs/napari-cell/lib/python3.9/site-packages/torch/_C.cpython-39-darwin.so'>
        _restore_shapes = False
    164 else:
    165     cpp_module = torch._C.import_ir_module_from_buffer(
    166         cu, f.read(), map_location, _extra_files, _restore_shapes
    167     )  # type: ignore[call-arg]

EmitLoopError: 

While emitting signal 'magicgui.widgets.PushButton.changed', a RuntimeError occurred in a callback:

  Signal emitted at: /Users/natangordon/miniforge3/envs/napari-cell/lib/python3.9/site-packages/magicgui/widgets/bases/_value_widget.py:71, in _on_value_change
    >  self.changed.emit(value)

  Callback error at: /Users/natangordon/miniforge3/envs/napari-cell/lib/python3.9/site-packages/torch/jit/_serialization.py:163, in load
    >  cpp_module = torch._C.import_ir_module(cu, os.fspath(f), map_location, _extra_files, _restore_shapes)  # type: ignore[call-arg]

    Local variables:
       f = '/Users/natangordon/.empanada/MitoNet_v1_quantized.pth'
       map_location = device(type='cpu')
       _extra_files = {}
       _restore_shapes = False
       cu = <torch.jit.CompilationUnit object at 0x16f422f30>

See RuntimeError above for original traceback.

💡 Steps to Reproduce

Click 'Run 2D Inference"

💡 Expected Behavior

No response

🌎 Environment

napari: 0.5.3
Platform: macOS-14.6.1-arm64-arm-64bit
System: MacOS 14.6.1
Python: 3.9.20 | packaged by conda-forge | (main, Sep 22 2024, 14:06:09) [Clang 17.0.6 ]
Qt: 5.15.8
PyQt5: 5.15.9
NumPy: 1.22.0
SciPy: 1.11.4
Dask: 2024.8.0
VisPy: 0.14.3
magicgui: 0.8.3
superqt: 0.6.7
in-n-out: 0.2.1
app-model: 0.2.8
npe2: 0.7.7

OpenGL:

• GL version: 2.1 Metal - 88.1
• MAX_TEXTURE_SIZE: 16384
• GL_MAX_3D_TEXTURE_SIZE: 2048

Screens:

• screen 1: resolution 1470x956, scale 2.0

Optional:

• numba: 0.60.0
• triangle not installed
• napari-plugin-manager: 0.1.3

Plugins:

• empanada-napari: 1.1.1 (38 contributions)
• napari: 0.5.3 (81 contributions)
• napari-console: 0.0.9 (0 contributions)
• napari-svg: 0.2.0 (2 contributions)

💡 Additional Context

No response