Coremltools: Unable to Convert tf FasterRCNN Resnet50 to CoreML!

[HussainHaris]

🐞Unable to Convert tf FasterRCNN Resnet50 trained on COCO 2018_01_28 to CoreML!

Hi,

After loading FasterRCNN Resnet50 .pb frozen weights directly from Tensorflow, I specify the input tensor as 1 300x300 RGB image since thats the input the Resnet50 feature extractor will ultimately use in the graph! Using the Tensorflow convert tfcoreml, I cannot convert this model. I'm aware FasterRCNN is a cyclic graph, so I specified iOS minimum deployment model 13. If someone could point out how I am incorrectly converting this model I would deeply appreciate it!

• Convertor: tfcoreml

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59 assert nodes deleted
Fixing frame name: Preprocessor/map/while/while_context
Fixing frame name: map_1/while/while_context
Fixing frame name: BatchMultiClassNonMaxSuppression/map/while/while_context
Fixing frame name: map/while/while_context
Fixing frame name: SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/while_context
[Constant Propagation] Skip "dead" tensor: BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond/cond/switch_t:0
[Constant Propagation] Skip "dead" tensor: SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond/cond/switch_f:0
[Constant Propagation] Skip "dead" tensor: BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond/cond/switch_f:0
[Constant Propagation] Skip "dead" tensor: SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond_3/cond/Switch:1
[Constant Propagation] Skip "dead" tensor: SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond_1/cond/Switch:1
[Constant Propagation] Skip "dead" tensor: SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond/cond/switch_t:0
[Constant Propagation] Skip "dead" tensor: SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond/cond/Switch:0
[Constant Propagation] Skip "dead" tensor: BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond/cond/Switch:0
[None, True]
BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond/cond/switch_t:0
None
BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond/cond/switch_f:0
None
[None, True]
SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond/cond/switch_t:0
None
SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond/cond/switch_f:0
None
[False, None]
[False, None]
5 nodes deleted
Fixing cond at merge location: Preprocessor/map/while/ResizeToRange/cond/Merge
Fixing cond at merge location: BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond/cond/Merge
Fixing cond at merge location: BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond/Merge
Fixing cond at merge location: SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond/cond/Merge
Fixing cond at merge location: SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond_1/cond/Merge
Fixing cond at merge location: SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond_3/cond/Merge
Fixing cond at merge location: SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond/Merge
Fixing cond at merge location: SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond_1/Merge
Fixing cond at merge location: SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond_3/Merge
ERROR:root:[TypeInference] Unable to infer type of node Preprocessor/map/TensorArray (TensorArrayV3)
ERROR:root:[TypeInference] Unable to infer type of node Preprocessor/map/TensorArray_1 (TensorArrayV3)
ERROR:root:[TypeInference] Unable to infer type of node Preprocessor/map/TensorArray_2 (TensorArrayV3)
ERROR:root:[TypeInference] Unable to infer type of node Preprocessor/map/TensorArray (TensorArrayV3)
ERROR:root:[TypeInference] Unable to infer type of node Preprocessor/map/TensorArray_1 (TensorArrayV3)
ERROR:root:[TypeInference] Unable to infer type of node Preprocessor/map/TensorArray_2 (TensorArrayV3)
ERROR:root:[TypeInference] Unable to infer type of node Preprocessor/map/TensorArray_1 (TensorArrayV3)
ERROR:root:[TypeInference] Unable to infer type of node Preprocessor/map/TensorArray_2 (TensorArrayV3)
WARNING:root:make_tuple at make_input_0 has an unknown type [None, None, <class 'coremltools.converters.nnssa.commons.builtins.type_int.make_int.<locals>.int'>, <class 'coremltools.converters.nnssa.commons.builtins.type_list.list.<locals>.list'>, None, None, <class 'coremltools.converters.nnssa.commons.builtins.type_int.make_int.<locals>.int'>, <class 'coremltools.converters.nnssa.commons.builtins.type_list.list.<locals>.list'>]
ERROR:root:[TypeInference] Failed to infer type of GridAnchorGenerator/Reshape_3:Reshape
Traceback (most recent call last):
  File "/Users/hxh85ki/Desktop/Projects/thdEnv/lib/python3.6/site-packages/coremltools/converters/nnssa/frontend/graph_pass/type_inference.py", line 171, in visit
    ret = visitor(node)
  File "/Users/hxh85ki/Desktop/Projects/thdEnv/lib/python3.6/site-packages/coremltools/converters/nnssa/frontend/graph_pass/type_inference.py", line 1487, in visit_Reshape
    self.gdict[node.inputs[0]].attr['symbolic_value'].val, shape)
  File "/Users/hxh85ki/Desktop/Projects/thdEnv/lib/python3.6/site-packages/coremltools/converters/nnssa/frontend/graph_pass/type_inference.py", line 109, in reshape_with_symbol
    shape = [int(s) for s in shape]
  File "/Users/hxh85ki/Desktop/Projects/thdEnv/lib/python3.6/site-packages/coremltools/converters/nnssa/frontend/graph_pass/type_inference.py", line 109, in <listcomp>
    shape = [int(s) for s in shape]
  File "/Users/hxh85ki/Desktop/Projects/thdEnv/lib/python3.6/site-packages/sympy/core/expr.py", line 293, in __int__
    raise TypeError("can't convert symbols to int")
TypeError: can't convert symbols to int
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-14-d9f3448ad806> in <module>
     11         mlmodel_path=coreml_model_file,
     12         input_name_shape_dict=input_tensor_shapes,
---> 13         output_feature_names=output_tensor_names)

~/Desktop/Projects/thdEnv/lib/python3.6/site-packages/tfcoreml/_tf_coreml_converter.py in convert(tf_model_path, mlmodel_path, output_feature_names, input_name_shape_dict, image_input_names, is_bgr, red_bias, green_bias, blue_bias, gray_bias, image_scale, class_labels, predicted_feature_name, predicted_probabilities_output, add_custom_layers, custom_conversion_functions, custom_shape_functions, minimum_ios_deployment_target)
    689                   add_custom_layers=add_custom_layers,
    690                   custom_conversion_functions=custom_conversion_functions,
--> 691                   custom_shape_functions=custom_shape_functions)
    692     if mlmodel_path is not None:
    693       mlmodel.save(mlmodel_path)

~/Desktop/Projects/thdEnv/lib/python3.6/site-packages/coremltools/converters/tensorflow/_tf_converter.py in convert(filename, inputs, outputs, image_input_names, is_bgr, red_bias, green_bias, blue_bias, gray_bias, image_scale, class_labels, predicted_feature_name, predicted_probabilities_output, add_custom_layers, custom_conversion_functions, custom_shape_functions, **kwargs)
    170     # convert from TensorFlow to SSA IR
    171     from ..nnssa.frontend.tensorflow import load as frontend_load
--> 172     ssa = frontend_load(filename, resume_on_errors=False, inputs=inputs, outputs=outputs, **kwargs)
    173 
    174     # convert from SSA IR to Core ML

~/Desktop/Projects/thdEnv/lib/python3.6/site-packages/coremltools/converters/nnssa/frontend/tensorflow/load.py in load(tfgraph, resume_on_errors, **kwargs)
     74                 print("Ignoring and continuing to next pass")
     75 
---> 76     common_pass(ssa, resume_on_errors)
     77 
     78     for f in ssa.functions.values():

~/Desktop/Projects/thdEnv/lib/python3.6/site-packages/coremltools/converters/nnssa/frontend/common_pass.py in common_pass(ssa, resume_on_errors, **kwargs)
     30     if resume_on_errors is False:
     31         for p in passes:
---> 32             p(ssa)
     33     else:
     34         for p in passes:

~/Desktop/Projects/thdEnv/lib/python3.6/site-packages/coremltools/converters/nnssa/frontend/graph_pass/type_inference.py in type_inference_pass(nnssa)
   2779         graph_make_symbolic_values(nnssa.functions[i].graph)
   2780     for i in range(len(nnssa.functions)):
-> 2781         type_inference_pass_impl(nnssa)
   2782     for i in nnssa.functions:
   2783         graph_replace_symbolic_values(nnssa.functions[i].graph)

~/Desktop/Projects/thdEnv/lib/python3.6/site-packages/coremltools/converters/nnssa/frontend/graph_pass/type_inference.py in type_inference_pass_impl(nnssa)
   2722     # run it for real
   2723     for k in function_names:
-> 2724         TypeInferenceVisitor(nnssa.functions[k].graph, nnssa).visit_all()
   2725 
   2726 

~/Desktop/Projects/thdEnv/lib/python3.6/site-packages/coremltools/converters/nnssa/frontend/graph_pass/type_inference.py in visit_all(self)
    183     def visit_all(self):
    184         for i in self.gdict:
--> 185             self.visit(self.gdict[i])
    186 
    187     def _get_type_from_attr(self, node):

~/Desktop/Projects/thdEnv/lib/python3.6/site-packages/coremltools/converters/nnssa/frontend/graph_pass/type_inference.py in visit(self, node)
    169         ret = None
    170         try:
--> 171             ret = visitor(node)
    172         except Exception as e:  # pylint: disable=broad-except
    173             logging.exception("[TypeInference] Failed to infer type of %s:%s", node.name, node.op)

~/Desktop/Projects/thdEnv/lib/python3.6/site-packages/coremltools/converters/nnssa/frontend/graph_pass/type_inference.py in visit_Reshape(self, node)
   1485                 node.attr['symbolic_value'] = r()
   1486                 node.attr['symbolic_value'].val = reshape_with_symbol(
-> 1487                     self.gdict[node.inputs[0]].attr['symbolic_value'].val, shape)
   1488             return r
   1489 

~/Desktop/Projects/thdEnv/lib/python3.6/site-packages/coremltools/converters/nnssa/frontend/graph_pass/type_inference.py in reshape_with_symbol(v, shape)
    107     if is_symbolic_or_unknown(v):
    108         return np.array(v).reshape(shape)
--> 109     shape = [int(s) for s in shape]
    110     return v.reshape(shape)
    111 

~/Desktop/Projects/thdEnv/lib/python3.6/site-packages/coremltools/converters/nnssa/frontend/graph_pass/type_inference.py in <listcomp>(.0)
    107     if is_symbolic_or_unknown(v):
    108         return np.array(v).reshape(shape)
--> 109     shape = [int(s) for s in shape]
    110     return v.reshape(shape)
    111 

~/Desktop/Projects/thdEnv/lib/python3.6/site-packages/sympy/core/expr.py in __int__(self)
    291         from sympy import Dummy
    292         if not self.is_number:
--> 293             raise TypeError("can't convert symbols to int")
    294         r = self.round(2)
    295         if not r.is_Number:

TypeError: can't convert symbols to int

## To Reproduce

`FROZEN_GRAPH_PATH = "faster_rcnn_resnet50_coco_2018_01_28/frozen_inference_graph.pb"`
`input_tensor_shapes = {"image_tensor":[1,300,300,3]} # batch size is 1
coreml_model_file ='faster_RCNN.mlmodel'
output_tensor_names = ['detection_classes', 'detection_boxes','num_detections', 'detection_scores' ]`

`tfcoreml.convert(
        minimum_ios_deployment_target='13',
        tf_model_path=FROZEN_GRAPH_PATH,
        mlmodel_path=coreml_model_file,
        input_name_shape_dict=input_tensor_shapes,
        output_feature_names=output_tensor_names)`

- If applicable, please attach the source model.
[Download model here
](http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_coco_2018_01_28.tar.gz)

System environment (please complete the following information):

• coremltools 3.3
• tfcoreml 1.1
• Tensorflow 1.14.0
• Python 3.6.6
• MacOS 10.14

Additional context

I also tried extracting the subgraph between Preprocess/sub and the 4 output tensors and then ran the same conversion script, producing a different issue regarding Nonetype(), I don't know if it was necessary for me to extract the subgraph of the feature extractor/resnet though so I just reran the convertor on the entire frozen model and attained the above error. Overall I just would love/appreciate help converting a Faster RCNN into iOS .mlmodel !

[srikris]

Thanks for the report. This looks like something that should be runnable. We will take a look.

[HussainHaris]

Hey @srikris , any updates on where I could go from here?

[srikris]

Sorry, no update yet. Marking this as a P1.

[HussainHaris]

Appreciated!

[fotiDim]

Having the same issue.