KeyError: 'min'

KeyError                                  Traceback (most recent call last)
<ipython-input-22-6be9743dbc2b> in <module>
      1 from onnx2keras import onnx_to_keras
      2 model=onnx.load("optimized_mobile_pydnet.onnx")
----> 3 k_model = onnx_to_keras(onnx_model=model, input_names=['input'])

~/anaconda3/envs/e2r/lib/python3.7/site-packages/onnx2keras/converter.py in onnx_to_keras(onnx_model, input_names, input_shapes, name_policy, verbose, change_ordering)
    179             lambda_funcs,
    180             node_name,
--> 181             keras_names
    182         )
    183         if isinstance(keras_names, list):

~/anaconda3/envs/e2r/lib/python3.7/site-packages/onnx2keras/operation_layers.py in convert_clip(node, params, layers, lambda_func, node_name, keras_name)
     29     input_0 = ensure_tf_type(layers[node.input[0]], name="%s_const" % keras_name)
     30 
---> 31     if params['min'] == 0:
     32         logger.debug("Using ReLU({0}) instead of clip".format(params['max']))
     33         layer = keras.layers.ReLU(max_value=params['max'], name=keras_name)

KeyError: 'min'```

[dtlam26]

I have come here to rescue you guys. This bug is because of conflict among version of onnx and the onnx torch use to export. By inspecting the file in onnx, you guys con fine the key here is not match with the newest onnx ops converter. The correct dict now is here:

AVAILABLE_CONVERTERS = {
    'Conv': convert_conv,
    'ConvTranspose': convert_convtranspose,
    'Relu': convert_relu,
    'Elu': convert_elu,
    'LeakyRelu': convert_lrelu,
    'Sigmoid': convert_sigmoid,
    'Tanh': convert_tanh,
    'Selu': convert_selu,
    'Clip': convert_clip,
    'Exp': convert_exp,
    'Log': convert_log,
    'Softmax': convert_softmax,
    'PRelu': convert_prelu,
    'ReduceMax': convert_reduce_max,
    'ReduceSum': convert_reduce_sum,
    'ReduceMean': convert_reduce_mean,
    'Pow': convert_pow,
    'Slice': convert_slice,
    'Squeeze': convert_squeeze,
    'Expand': convert_expand,
    'Sqrt': convert_sqrt,
    'Split': convert_split,
    'Cast': convert_cast,
    'Floor': convert_floor,
    'Identity': convert_identity,
    'ArgMax': convert_argmax,
    'ReduceL2': convert_reduce_l2,
    'Max': convert_max,
    'Min': convert_min,
    'Mean': convert_mean,
    'Div': convert_elementwise_div,
    'Add': convert_elementwise_add,
    'Sum': convert_elementwise_add,
    'Mul': convert_elementwise_mul,
    'Sub': convert_elementwise_sub,
    'Gemm': convert_gemm,
    'MatMul': convert_gemm,
    'Transpose': convert_transpose,
    'Constant': convert_constant,
    'BatchNormalization': convert_batchnorm,
    'InstanceNormalization': convert_instancenorm,
    'Dropout': convert_dropout,
    'LRN': convert_lrn,
    'MaxPool': convert_maxpool,
    'AveragePool': convert_avgpool,
    'GlobalAveragePool': convert_global_avg_pool,
    'Shape': convert_shape,
    'Gather': convert_gather,
    'Unsqueeze': convert_unsqueeze,
    'Concat': convert_concat,
    'Reshape': convert_reshape,
    'Pad': convert_padding,
    'Flatten': convert_flatten,
    'Upsample': convert_upsample,
}

Therefore, you can edit the line in here as the correct node_type in the dict (ie when it returns to min/Resize as Min/Upsample). This can easily be done by editing the source file of onnx2keras. It may also ask you to change the node_params as the Upsample ask for size param as scales. For detail, pls look in here

[e2r-htz]

Also KeyError: 'Resize'