Disable some unsupported layers

fastmachinelearning · Nov 11, 2021 · 9f5c249 · 9f5c249
1 parent 36cd38c
commit 9f5c249
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Showing 2 changed files with 11 additions and 3 deletions.
diff --git a/hls4ml/converters/onnx/core.py b/hls4ml/converters/onnx/core.py
@@ -23,7 +23,10 @@ def parse_gemm_layer(reader, node, inputs_map, input_shapes, graph, config):
     return layer, output_shape
 
 #------------------Global paras for activations
-activation_layers = ['Relu', 'Tanh', 'Sigmoid', 'LeakyRelu', 'ThresholdedRelu', 'HardSigmoid', 'Elu', 'Selu', 'PRelu', 'Softmax', 'Softsign', 'Softplus', 'Clip']
+# TODO: repair HardSigmoid support
+# https://github.com/fastmachinelearning/hls4ml/issues/409 
+#activation_layers = ['Relu', 'Tanh', 'Sigmoid', 'LeakyRelu', 'ThresholdedRelu', 'HardSigmoid', 'Elu', 'Selu', 'PRelu', 'Softmax', 'Softsign', 'Softplus', 'Clip']
+activation_layers = ['Relu', 'Tanh', 'Sigmoid', 'LeakyRelu', 'ThresholdedRelu', 'Elu', 'Selu', 'PRelu', 'Softmax', 'Softsign', 'Softplus', 'Clip']
 
 activation_map = {'Relu':'ReLU', 'Tanh':'Activation',
                 'Sigmoid':'Activation', 'LeakyRelu':'LeakyReLU',

diff --git a/hls4ml/converters/pytorch/core.py b/hls4ml/converters/pytorch/core.py
@@ -2,6 +2,8 @@
 
 from hls4ml.converters.pytorch_to_hls import pytorch_handler
 
+# TODO: propagate use_bias info properly
+# https://github.com/fastmachinelearning/hls4ml/issues/409 
 @pytorch_handler('Linear')
 def parse_linear_layer(pytorch_layer, layer_name, input_shapes, data_reader, config):
     assert('Linear' in pytorch_layer.__class__.__name__)
@@ -15,6 +17,7 @@ def parse_linear_layer(pytorch_layer, layer_name, input_shapes, data_reader, con
     layer['n_out'] = pytorch_layer.out_features
 
     #Handling whether bias is used or not
+    assert not pytorch_layer.bias is None, "PyTorch Linear with bias=False not yet supported"
     if pytorch_layer.bias is None:    
         layer['use_bias'] = False
     else:
@@ -24,8 +27,10 @@ def parse_linear_layer(pytorch_layer, layer_name, input_shapes, data_reader, con
 
     return layer, output_shape
 
-
-activation_layers = ['LeakyReLU', 'ThresholdedReLU', 'ELU', 'PReLU', 'Softmax', 'ReLU']
+# TODO: propagate parametrized activation parameters
+# https://github.com/fastmachinelearning/hls4ml/issues/409 
+# activation_layers = ['LeakyReLU', 'ThresholdedReLU', 'ELU', 'PReLU', 'Softmax', 'ReLU']
+activation_layers = ['Softmax', 'ReLU']
 @pytorch_handler(*activation_layers)
 def parse_activation_layer(pytorch_layer, layer_name, input_shapes, data_reader, config):