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Hi, when I convert my own model to IR, it occurs such errors:
[ 2018-05-04 14:22:09,623 ] [ ERROR ] [ infer:85 ] Shape is not defined for output 0 of "ExpandDims". [ 2018-05-04 14:22:09,623 ] [ ERROR ] [ infer:111 ] Cannot infer shapes or values for node "ExpandDims". [ 2018-05-04 14:22:09,623 ] [ ERROR ] [ infer:112 ] Not all output shapes were inferred or fully defined for node "ExpandDims". For more information please refer to Model Optimizer FAQ, question #40. [ 2018-05-04 14:22:09,623 ] [ ERROR ] [ infer:113 ] [ 2018-05-04 14:22:09,623 ] [ ERROR ] [ infer:114 ] It can happen due to bug in custom shape infer function <function tf_expand_dims_infer at 0x7f1812ccc158>. [ 2018-05-04 14:22:09,623 ] [ ERROR ] [ infer:115 ] Or because the node inputs have incorrect values/shapes. [ 2018-05-04 14:22:09,623 ] [ ERROR ] [ infer:116 ] Or because input shapes are incorrect (embedded to the model or passed via --input_shape). [ 2018-05-04 14:22:09,623 ] [ DEBUG ] [ infer:120 ] Node "ExpandDims" attributes: {'pb': name: "ExpandDims" op: "ExpandDims" input: "x_in" input: "ExpandDims/dim" attr { key: "T" value { type: DT_FLOAT } } attr { key: "Tdim" value { type: DT_INT32 } } , 'precision': 'FP32', 'op': 'ExpandDims', 'shape_attrs': ['shape', 'window', 'stride', 'output_shape', 'pad'], 'is_const_producer': False, 'infer': <function tf_expand_dims_infer at 0x7f1812ccc158>, 'IE': [('layer', [('id', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc0f28>), 'name', 'precision', 'type'], [('data', ['epsilon', 'min', 'max', ('axis', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc1048>), 'tiles', ('dim', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc10d0>), 'num_axes', ('pool-method', 'pool_method'), 'group', 'rounding_type', ('exclude-pad', 'exclude_pad'), 'operation', 'out-size', 'power', 'shift', 'alpha', 'beta', 'coords', 'classes', 'num', ('local-size', 'local_size'), 'region', 'knorm', 'num_classes', 'keep_top_k', 'variance_encoded_in_target', 'code_type', 'share_location', 'nms_threshold', 'confidence_threshold', 'background_label_id', 'top_k', 'eta', 'visualize', 'visualize_threshold', 'save_file', 'output_directory', 'output_name_prefix', 'output_format', 'label_map_file', 'name_size_file', 'num_test_image', 'prob', 'resize_mode', 'height', 'width', 'height_scale', 'width_scale', 'pad_mode', 'pad_value', 'interp_mode', 'img_size', 'img_h', 'img_w', 'step', 'step_h', 'step_w', ('offset', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc1158>), 'variance', 'flip', 'clip', ('min_size', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc11e0>), ('max_size', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc1268>), ('aspect_ratio', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc12f0>), 'decrease_label_id', 'normalized', ('type', 'norm_type'), 'eps', 'across_spatial', 'value', 'mean', 'std', 'sparse', 'variance_norm', 'channel_shared', 'negative_slope', 'engine', 'num_filter', ('type', 'sample_type'), ('order', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc1378>), 'pooled_h', 'pooled_w', 'spatial_scale', 'cls_threshold', 'max_num_proposals', 'iou_threshold', 'min_bbox_size', 'feat_stride', 'pre_nms_topn', 'post_nms_topn', ('type', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc1400>), ('value', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc1488>), ('output', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc1510>), ('input_nodes_names', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc1598>), ('output_tensors_names', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc1620>), ('real_input_dims', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc16a8>), ('protobuf', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc1730>), {'custom_attributes': None}, ('stride-x', <function spatial_getter.<locals>.<lambda> at 0x7f1811dc17b8>), ('stride-y', <function spatial_getter.<locals>.<lambda> at 0x7f1811dc1840>), ('kernel-x', <function spatial_getter.<locals>.<lambda> at 0x7f1811dc18c8>), ('kernel-y', <function spatial_getter.<locals>.<lambda> at 0x7f1811dc1950>), ('kernel-x', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc19d8>), ('kernel-y', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc1a60>), ('dilation-x', <function spatial_getter.<locals>.<lambda> at 0x7f1811dc1ae8>), ('dilation-y', <function spatial_getter.<locals>.<lambda> at 0x7f1811dc1b70>), ('pad-x', <function spatial_getter.<locals>.<lambda> at 0x7f1811dc1c80>), ('pad-y', <function spatial_getter.<locals>.<lambda> at 0x7f1811dc1d90>), ('scale', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc1e18>), ('stride', <function update_ie_fields.<locals>.<lambda> at 0x7f1811dc1ea0>), 'crop_width', 'crop_height', 'write_augmented', 'max_multiplier', 'augment_during_test', 'recompute_mean', 'write_mean', 'mean_per_pixel', 'mode', 'bottomwidth', 'bottomheight', 'chromatic_eigvec', 'kernel_size', 'max_displacement', 'stride_1', 'stride_2', 'single_direction', 'do_abs', 'correlation_type', 'antialias', 'resample_type', 'factor', 'coeff'], []), '@ports', '@consts'])], 'is_output_reachable': True, 'name': 'ExpandDims', 'kind': 'op', 'is_undead': False, 'is_partial_inferred': False, 'dim_attrs': ['channel_dims', 'batch_dims', 'spatial_dims', 'axis']} [ 2018-05-04 14:22:09,623 ] [ ERROR ] [ main:227 ] Stopped shape/value propagation at "ExpandDims" node. For more information please refer to Model Optimizer FAQ, question #38. [ 2018-05-04 14:22:09,624 ] [ DEBUG ] [ main:228 ] Traceback (most recent call last): File "/opt/intel/computer_vision_sdk_2018.0.234/deployment_tools/model_optimizer/mo/middle/passes/infer.py", line 100, in partial_infer 'For more information please refer to Model Optimizer FAQ, question #40.', node_name) mo.utils.error.Error: Not all output shapes were inferred or fully defined for node "ExpandDims". For more information please refer to Model Optimizer FAQ, question #40. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/opt/intel/computer_vision_sdk_2018.0.234/deployment_tools/model_optimizer/mo/main.py", line 222, in main return driver(argv) File "/opt/intel/computer_vision_sdk_2018.0.234/deployment_tools/model_optimizer/mo/main.py", line 190, in driver mean_scale_values=mean_scale) File "/opt/intel/computer_vision_sdk_2018.0.234/deployment_tools/model_optimizer/mo/pipeline/tf.py", line 143, in tf2nx partial_infer(graph) File "/opt/intel/computer_vision_sdk_2018.0.234/deployment_tools/model_optimizer/mo/middle/passes/infer.py", line 123, in partial_infer 'For more information please refer to Model Optimizer FAQ, question #38.') from err mo.utils.error.Error: Stopped shape/value propagation at "ExpandDims" node. For more information please refer to Model Optimizer FAQ, question #38.
How can I solve the problem? Thanks so much!
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Hi Pu Hui,
What is the base model of your model and can you share your model and ckcp for better investigation?
If you don't want to upload here, you can upload files in any cloud storage and send information to my company email.
Regards,
Peter.
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Hi Peter,
Thanks for your reply, the base of my model is inception V1.
At first I have converted inception V1 successfully, that means the CVSDK environment is correct. Then I try to convert my model which based on inception V1, but it occurs above errors.
Could you give me some advices? Thanks so much!
Pu Hui.
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Hi Pu Hui,
If the shape is not defined for specific node 'ExpandDims', please try to use --input-shape to override model input shapes. Thanks.
Best regards,
Fiona
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Fiona Z. (Intel) wrote:
Hi Pu Hui,
If the shape is not defined for specific node 'ExpandDims', please try to use --input-shape to override model input shapes. Thanks.
Best regards,
Fiona
Hi Fiona,
I have encountered the same problem as the poster did. However using input_shape wouldn't help.
I am wondering what the reason is to set the flag "input_shape", as the input is usually a placeholder with predefined shape (e.g. [32, 32, 3] for a typical image). I can see that the 'ExpandDims' op is trying to expand the dimension of input (e.g. from [32, 32, 3] to [1, 32, 32, 3]) after preprocessing the image so as to be able to feed into the neural network. But I just have no idea why the shape of 'ExpandDims' cannot be inferred from previous ops. Any advice would be highly appreciated.
Best regards,
Junwei
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