Error when "Run 2D Inference" is clicked #35
Description
🐛 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