Community
cancel
Showing results for 
Search instead for 
Did you mean: 
Khan__Saad
Beginner
196 Views

Unable to convert caffe model to ir - openvino

I am unable to convert caffe model to ir with the following command:

python3 mo_caffe.py --input_model <PATH_TO_DIRECTORY>/model.caffemodel --input_proto <PATH_TO_DIRECTORY>/model.prototxt --mean_values [104,117,123] --scale 255 --model result --output_dir ~/Documents --data_type FP16 --reverse_input_channel --log_level=DEBUG 

It is giving me following error:

[ ERROR ]  Shape is not defined for output 0 of "detection_out".
[ ERROR ]  Cannot infer shapes or values for node "detection_out".
[ ERROR ]  Not all output shapes were inferred or fully defined for node "detection_out". 
 For more information please refer to Model Optimizer FAQ (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #40. 
[ ERROR ]  
[ ERROR ]  It can happen due to bug in custom shape infer function <function multi_box_detection_infer at 0x7fa839ae0598>.
[ ERROR ]  Or because the node inputs have incorrect values/shapes.
[ ERROR ]  Or because input shapes are incorrect (embedded to the model or passed via --input_shape).
[ ERROR ]  Run Model Optimizer with --log_level=DEBUG for more information.
[ ERROR ]  Stopped shape/value propagation at "detection_out" node. 
 

 

 

in my code i am loading the network as follows:

net = caffe.Net('<PATH_TO_DIRECTORY>/model.prototxt', '<PATH_TO_DIRECTORY>/model.caffemodel', caffe.TEST)

 

net.blobs['data'].reshape(1, 3, process_frame.shape[0], process_frame.shape[1])
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2, 0, 1))
transformer.set_mean('data', np.array([104, 117, 123])) # mean pixel
transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]
transformer.set_channel_swap('data', (2, 1, 0))  # the reference model has channels in BGR order instead of RGB
transformed_image = transformer.preprocess('data', process_frame)
net.blobs['data'].data[...] = transformed_image

t1 = time.time()
detections = net.forward()['detection_out']
print "Time to process a frame: ",time.time()-t1

I am attaching the model files here. Kindly let me know whats the issue. Thank you!

 

 

0 Kudos
2 Replies
Shubha_R_Intel
Employee
196 Views

Dear Saad:

MO_FAQ question #40 says:

40. What does the message "Not all output shapes were inferred or fully defined for node" mean?

Most likely, the shape is not defined (partially or fully) for the specified node. You can use --input_shape with positive integers to override model input shapes.

I don't see --input_shape as a parameter in your mo command.  Please supply it with 4 numbers separated by comma in brackets, i.e. [1,216,216,3] or something like that and this should fix your problem. 

About –input_shape:

--input_shape INPUT_SHAPE

                        Input shape(s) that should be fed to an input node(s)

                        of the model. Shape is defined as a comma-separated

                        list of integer numbers enclosed in parentheses or

                        square brackets, for example [1,3,227,227] or

                        (1,227,227,3), where the order of dimensions depends

                        on the framework input layout of the model. For

                        example, [N,C,H,W] is used for Caffe* models and

                        [N,H,W,C] for TensorFlow* models. Model Optimizer

                        performs necessary transformations to convert the

                        shape to the layout required by Inference Engine

                        (N,C,H,W). The shape should not contain undefined

                        dimensions (? or -1) and should fit the dimensions

                        defined in the input operation of the graph. If there

                        are multiple inputs in the model, --input_shape should

                        contain definition of shape for each input separated

                        by a comma, for example: [1,3,227,227],[2,4] for a

                        model with two inputs with 4D and 2D shapes.

LI__LI
Beginner
196 Views

Dear Shubha:

I had a same probleme above when convert a R-FCN caffe model using model optimizer, I specifized  everything i could and the error message is same as Saad "Shape is not defined for output 0 of "detection_out"."

Here is my error messages:

D:\programme_file\OpenVino\openvino_2019.1.133\deployment_tools\model_optimizer>python mo.py --framework caffe --input_model D:\myRFCN_model\my_RFCN_model.caffemodel --input_proto D:\myRFCN_model\my_RFCN_model.prototxt --input data,im_info --input_shape (1,3,300,300),(1,3) --scale 1.0 --mean_values (102.9801,115.9465,122.7717)
Model Optimizer arguments:
Common parameters:
        - Path to the Input Model:      D:\myRFCN_model\my_RFCN_model.caffemodel
        - Path for generated IR:        D:\programme_file\OpenVino\openvino_2019.1.133\deployment_tools\model_optimizer\.
        - IR output name:       my_RFCN_model
        - Log level:    ERROR
        - Batch:        Not specified, inherited from the model
        - Input layers:         data,im_info
        - Output layers:        Not specified, inherited from the model
        - Input shapes:         (1,3,300,300),(1,3)
        - Mean values:  (102.9801,115.9465,122.7717)
        - Scale values:         Not specified
        - Scale factor:         1.0
        - Precision of IR:      FP32
        - Enable fusing:        True
        - Enable grouped convolutions fusing:   True
        - Move mean values to preprocess section:       False
        - Reverse input channels:       False
Caffe specific parameters:
        - Enable resnet optimization:   True
        - Path to the Input prototxt:   D:\myRFCN_model\my_RFCN_model.prototxt
        - Path to CustomLayersMapping.xml:      extensions\front\caffe\CustomLayersMapping.xml
        - Path to a mean file:  Not specified
        - Offsets for a mean file:      Not specified
Model Optimizer version:        2019.1.0-341-gc9b66a2
[ ERROR ]  Shape is not defined for output 0 of "detection_out".
[ ERROR ]  Cannot infer shapes or values for node "detection_out".
[ ERROR ]  Not all output shapes were inferred or fully defined for node "detection_out".
 For more information please refer to Model Optimizer FAQ (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #40.
[ ERROR ]
[ ERROR ]  It can happen due to bug in custom shape infer function <function multi_box_detection_infer at 0x000001E27D451840>.
[ ERROR ]  Or because the node inputs have incorrect values/shapes.
[ ERROR ]  Or because input shapes are incorrect (embedded to the model or passed via --input_shape).
[ ERROR ]  Run Model Optimizer with --log_level=DEBUG for more information.
[ ERROR ]  Exception occurred during running replacer "REPLACEMENT_ID" (<class 'extensions.middle.PartialInfer.PartialInfer'>): Stopped shape/value propagation at "detection_out" node.
 For more information please refer to Model Optimizer FAQ (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #38.

 

Any ideas? thanks for your time!!

 

HL

Reply