Why am I getting this error : ´AttributeError: 'NoneType' object has no attribute 'shape'´ ? #476

[Anouar Nechi]

Hello, I trained a ResNet model on Keras, then I followed the getting started a notebook to use hls4ml for research purposes. First, please check the config output using the following code:

config = hls4ml.utils.config_from_keras_model(model, granularity='model')
print_dict(config)

Model
  Precision:         ap_fixed<16,6>
  ReuseFactor:       1
  Strategy:          Latency
-----------------------------------
Interpreting Model
Topology:
Layer name: input_2, layer type: InputLayer, input shapes: [[None, 1024, 2]], output shape: [None, 1024, 2]
Layer name: reshape_1, layer type: Reshape, input shapes: [[None, 1024, 2]], output shape: [None, 1, 1024, 2]
Layer name: ReStk1_conv1, layer type: Conv2D, input shapes: [[None, 1, 1024, 2]], output shape: [None, 32, 1024, 2]
Layer name: ReStk1_conv2, layer type: Conv2D, input shapes: [[None, 32, 1024, 2]], output shape: [None, 32, 1024, 2]
Layer name: ReStk1_conv3, layer type: Conv2D, input shapes: [[None, 32, 1024, 2]], output shape: [None, 32, 1024, 2]
Layer name: add_12, layer type: Merge, input shapes: [[None, 32, 1024, 2], [None, 32, 1024, 2]], output shape: [None, 32, 1024, 2]
Layer name: activation_13, layer type: Activation, input shapes: [[None, 32, 1024, 2]], output shape: [None, 32, 1024, 2]
Layer name: ReStk1_conv4, layer type: Conv2D, input shapes: [[None, 32, 1024, 2]], output shape: [None, 32, 1024, 2]
Layer name: ReStk1_conv5, layer type: Conv2D, input shapes: [[None, 32, 1024, 2]], output shape: [None, 32, 1024, 2]
Layer name: add_13, layer type: Merge, input shapes: [[None, 32, 1024, 2], [None, 32, 1024, 2]], output shape: [None, 32, 1024, 2]
Layer name: activation_14, layer type: Activation, input shapes: [[None, 32, 1024, 2]], output shape: [None, 32, 1024, 2]
Layer name: max_pooling2d_6, layer type: MaxPooling2D, input shapes: [[None, 32, 1024, 2]], output shape: [None, 32, 512, 1]
Layer name: ReStk2_conv1, layer type: Conv2D, input shapes: [[None, 32, 512, 1]], output shape: [None, 32, 512, 1]
Layer name: ReStk2_conv2, layer type: Conv2D, input shapes: [[None, 32, 512, 1]], output shape: [None, 32, 512, 1]
Layer name: ReStk2_conv3, layer type: Conv2D, input shapes: [[None, 32, 512, 1]], output shape: [None, 32, 512, 1]
Layer name: add_14, layer type: Merge, input shapes: [[None, 32, 512, 1], [None, 32, 512, 1]], output shape: [None, 32, 512, 1]
Layer name: activation_15, layer type: Activation, input shapes: [[None, 32, 512, 1]], output shape: [None, 32, 512, 1]
Layer name: ReStk2_conv4, layer type: Conv2D, input shapes: [[None, 32, 512, 1]], output shape: [None, 32, 512, 1]
Layer name: ReStk2_conv5, layer type: Conv2D, input shapes: [[None, 32, 512, 1]], output shape: [None, 32, 512, 1]
Layer name: add_15, layer type: Merge, input shapes: [[None, 32, 512, 1], [None, 32, 512, 1]], output shape: [None, 32, 512, 1]
Layer name: activation_16, layer type: Activation, input shapes: [[None, 32, 512, 1]], output shape: [None, 32, 512, 1]
Layer name: max_pooling2d_7, layer type: MaxPooling2D, input shapes: [[None, 32, 512, 1]], output shape: [None, 32, 256, 1]
Layer name: ReStk3_conv1, layer type: Conv2D, input shapes: [[None, 32, 256, 1]], output shape: [None, 32, 256, 1]
Layer name: ReStk3_conv2, layer type: Conv2D, input shapes: [[None, 32, 256, 1]], output shape: [None, 32, 256, 1]
Layer name: ReStk3_conv3, layer type: Conv2D, input shapes: [[None, 32, 256, 1]], output shape: [None, 32, 256, 1]
Layer name: add_16, layer type: Merge, input shapes: [[None, 32, 256, 1], [None, 32, 256, 1]], output shape: [None, 32, 256, 1]
Layer name: activation_17, layer type: Activation, input shapes: [[None, 32, 256, 1]], output shape: [None, 32, 256, 1]
Layer name: ReStk3_conv4, layer type: Conv2D, input shapes: [[None, 32, 256, 1]], output shape: [None, 32, 256, 1]
Layer name: ReStk3_conv5, layer type: Conv2D, input shapes: [[None, 32, 256, 1]], output shape: [None, 32, 256, 1]
Layer name: add_17, layer type: Merge, input shapes: [[None, 32, 256, 1], [None, 32, 256, 1]], output shape: [None, 32, 256, 1]
Layer name: activation_18, layer type: Activation, input shapes: [[None, 32, 256, 1]], output shape: [None, 32, 256, 1]
Layer name: max_pooling2d_8, layer type: MaxPooling2D, input shapes: [[None, 32, 256, 1]], output shape: [None, 32, 128, 1]
Layer name: ReStk4_conv1, layer type: Conv2D, input shapes: [[None, 32, 128, 1]], output shape: [None, 32, 128, 1]
Layer name: ReStk4_conv2, layer type: Conv2D, input shapes: [[None, 32, 128, 1]], output shape: [None, 32, 128, 1]
Layer name: ReStk4_conv3, layer type: Conv2D, input shapes: [[None, 32, 128, 1]], output shape: [None, 32, 128, 1]
Layer name: add_18, layer type: Merge, input shapes: [[None, 32, 128, 1], [None, 32, 128, 1]], output shape: [None, 32, 128, 1]
Layer name: activation_19, layer type: Activation, input shapes: [[None, 32, 128, 1]], output shape: [None, 32, 128, 1]
Layer name: ReStk4_conv4, layer type: Conv2D, input shapes: [[None, 32, 128, 1]], output shape: [None, 32, 128, 1]
Layer name: ReStk4_conv5, layer type: Conv2D, input shapes: [[None, 32, 128, 1]], output shape: [None, 32, 128, 1]
Layer name: add_19, layer type: Merge, input shapes: [[None, 32, 128, 1], [None, 32, 128, 1]], output shape: [None, 32, 128, 1]
Layer name: activation_20, layer type: Activation, input shapes: [[None, 32, 128, 1]], output shape: [None, 32, 128, 1]
Layer name: max_pooling2d_9, layer type: MaxPooling2D, input shapes: [[None, 32, 128, 1]], output shape: [None, 32, 64, 1]
Layer name: ReStk5_conv1, layer type: Conv2D, input shapes: [[None, 32, 64, 1]], output shape: [None, 32, 64, 1]
Layer name: ReStk5_conv2, layer type: Conv2D, input shapes: [[None, 32, 64, 1]], output shape: [None, 32, 64, 1]
Layer name: ReStk5_conv3, layer type: Conv2D, input shapes: [[None, 32, 64, 1]], output shape: [None, 32, 64, 1]
Layer name: add_20, layer type: Merge, input shapes: [[None, 32, 64, 1], [None, 32, 64, 1]], output shape: [None, 32, 64, 1]
Layer name: activation_21, layer type: Activation, input shapes: [[None, 32, 64, 1]], output shape: [None, 32, 64, 1]
Layer name: ReStk5_conv4, layer type: Conv2D, input shapes: [[None, 32, 64, 1]], output shape: [None, 32, 64, 1]
Layer name: ReStk5_conv5, layer type: Conv2D, input shapes: [[None, 32, 64, 1]], output shape: [None, 32, 64, 1]
Layer name: add_21, layer type: Merge, input shapes: [[None, 32, 64, 1], [None, 32, 64, 1]], output shape: [None, 32, 64, 1]
Layer name: activation_22, layer type: Activation, input shapes: [[None, 32, 64, 1]], output shape: [None, 32, 64, 1]
Layer name: max_pooling2d_10, layer type: MaxPooling2D, input shapes: [[None, 32, 64, 1]], output shape: [None, 32, 32, 1]
Layer name: ReStk6_conv1, layer type: Conv2D, input shapes: [[None, 32, 32, 1]], output shape: [None, 32, 32, 1]
Layer name: ReStk6_conv2, layer type: Conv2D, input shapes: [[None, 32, 32, 1]], output shape: [None, 32, 32, 1]
Layer name: ReStk6_conv3, layer type: Conv2D, input shapes: [[None, 32, 32, 1]], output shape: [None, 32, 32, 1]
Layer name: add_22, layer type: Merge, input shapes: [[None, 32, 32, 1], [None, 32, 32, 1]], output shape: [None, 32, 32, 1]
Layer name: activation_23, layer type: Activation, input shapes: [[None, 32, 32, 1]], output shape: [None, 32, 32, 1]
Layer name: ReStk6_conv4, layer type: Conv2D, input shapes: [[None, 32, 32, 1]], output shape: [None, 32, 32, 1]
Layer name: ReStk6_conv5, layer type: Conv2D, input shapes: [[None, 32, 32, 1]], output shape: [None, 32, 32, 1]
Layer name: add_23, layer type: Merge, input shapes: [[None, 32, 32, 1], [None, 32, 32, 1]], output shape: [None, 32, 32, 1]
Layer name: activation_24, layer type: Activation, input shapes: [[None, 32, 32, 1]], output shape: [None, 32, 32, 1]
Layer name: max_pooling2d_11, layer type: MaxPooling2D, input shapes: [[None, 32, 32, 1]], output shape: [None, 32, 16, 1]
Layer name: flatten_1, layer type: Reshape, input shapes: [[None, 32, 16, 1]], output shape: [None, 512]
Layer name: dense1, layer type: Dense, input shapes: [[None, 512]], output shape: [None, 128]
Layer name: dense2, layer type: Dense, input shapes: [[None, 128]], output shape: [None, 128]
Layer name: dense3, layer type: Dense, input shapes: [[None, 128]], output shape: [None, 7]
Layer name: activation_25, layer type: Softmax, input shapes: [[None, 7]], output shape: [None, 7]

However, When I use the convert_from_keras_model() as follows:

hls_model = hls4ml.converters.convert_from_keras_model(model,
                                                       hls_config=config,
                                                       output_dir='model_1/hls4ml_prj',
                                                       part='xczu7ev-ffvc1156-2-e')

I get the following errors:

Creating HLS model

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/tmp/ipykernel_764/2595051309.py in <module>
     17 print_dict(config)
     18 print("-----------------------------------")
---> 19 hls_model = hls4ml.converters.convert_from_keras_model(model,
     20                                                        hls_config=config,
     21                                                        output_dir='model_1/hls4ml_prj',

/usr/local/lib/python3.8/dist-packages/hls4ml/converters/__init__.py in convert_from_keras_model(model, output_dir, project_name, input_data_tb, output_data_tb, backend, board, part, clock_period, io_type, hls_config, **kwargs)
    218     _check_hls_config(config, hls_config)
    219 
--> 220     return keras_to_hls(config)
    221 
    222 

/usr/local/lib/python3.8/dist-packages/hls4ml/converters/keras_to_hls.py in keras_to_hls(config)
    337 
    338     print('Creating HLS model')
--> 339     hls_model = HLSModel(config, reader, layer_list, input_layers, output_layers)
    340     return hls_model

/usr/local/lib/python3.8/dist-packages/hls4ml/model/hls_model.py in __init__(self, config, data_reader, layer_list, inputs, outputs)
    316         self._make_graph(layer_list)
    317 
--> 318         self._optimize_model(self.config.optimizers)
    319 
    320     def _make_graph(self, layer_list):

/usr/local/lib/python3.8/dist-packages/hls4ml/model/hls_model.py in _optimize_model(self, optimizers)
    334 
    335     def _optimize_model(self, optimizers):
--> 336         optimize_model(self, optimizers)
    337 
    338     def make_node(self, kind, name, attributes, inputs, outputs=None):

/usr/local/lib/python3.8/dist-packages/hls4ml/model/optimizer/optimizer.py in optimize_model(model, passes)
     36         for opt in optimizers:
     37             for node in model.graph.values():
---> 38                 if opt.match(node):
     39                     res = opt.transform(model, node)
     40                     if res:

/usr/local/lib/python3.8/dist-packages/hls4ml/model/optimizer/passes/repack_stream.py in match(self, node)
     95             inp1 = node.get_input_variable(node.inputs[0])
     96             inp2 = node.get_input_variable(node.inputs[1])
---> 97             return inp1.shape != inp2.shape
     98         else:
     99             return False

AttributeError: 'NoneType' object has no attribute 'shape'

Could someone explain to me what is the problem here and how to fix it? Thank you

[Javier Duarte]

Hi Anouar,

Can you share a script to reproduce it?

We can also follow up on your open issue: https://github.com/fastmachinelearning/hls4ml/issues/476

Thanks!

Javier

--
You received this message because you are subscribed to the Google Groups "hls4ml help" group.
To unsubscribe from this group and stop receiving emails from it, send an email to hls4mlhelp+...@googlegroups.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/hls4mlhelp/f01a0909-fcf7-4709-464f-91f8566d8af3%40iti.uni-luebeck.de.