Fix tanh activiation in pytorch parser (#1055)

* fix tanh activiation in pytorch parser

* simplify fix but making the activation attribute lower case

hls4ml/converters/pytorch/core.py

@@ -43,14 +43,12 @@ def parse_activation_layer(operation, layer_name, input_names, input_shapes, nod
     layer = {}
 
     layer['class_name'] = operation
-    layer['activation'] = layer['class_name']
+    layer['activation'] = layer['class_name'].lower()
     layer['name'] = layer_name
     layer['inputs'] = input_names
 
-    # if layer['class_name'] != 'Activation':
-    #    layer['activation'] = layer['class_name']
     if node.op == 'call_module':
-        if layer['class_name'] == 'ReLU' or layer['class_name'] == 'Sigmoid':
+        if layer['class_name'] in ['ReLU', 'Sigmoid', 'Tanh']:
             layer['class_name'] = 'Activation'
         if layer['class_name'] == 'LeakyReLU':
             layer['activ_param'] = class_object.negative_slope
@@ -68,7 +66,7 @@ def parse_activation_layer(operation, layer_name, input_names, input_shapes, nod
         if hasattr(node, 'dim'):
             layer['axis'] = class_object.dim
     else:
-        if layer['class_name'] == 'ReLU' or layer['class_name'] == 'Sigmoid':
+        if layer['class_name'] in ['ReLU', 'Sigmoid', 'Tanh']:
             layer['class_name'] = 'Activation'
         if layer['class_name'] == 'LeakyReLU':
             layer['activ_param'] = node.kwargs['negative_slope']

hls4ml/converters/pytorch_to_hls.py

@@ -84,6 +84,7 @@ def decorator(function):
 # map names of operations between toch.nn and torch.nn.functionals
 layer_name_map = {
     'relu': 'ReLU',
+    'tanh': 'Tanh',
     'leaky_relu': 'LeakyReLU',
     'elu': 'ELU',
     'prelu': 'PReLU',

test/pytest/test_pytorch_api.py

@@ -64,6 +64,7 @@ def test_linear(backend, io_type):
     "activation_function",
     [
         nn.ReLU(),
+        nn.Tanh(),
         nn.LeakyReLU(negative_slope=1.0),
         nn.ELU(alpha=1.0),
         nn.PReLU(init=0.25),
@@ -102,7 +103,7 @@ def test_activations(activation_function, backend, io_type):
 
     assert nNodes - 1 == len(hls_model.get_layers())
 
-    if activation_function.__class__.__name__ == 'ReLU' or activation_function.__class__.__name__ == 'Sigmoid':
+    if activation_function.__class__.__name__ in ['ReLU', 'Sigmoid', 'Tanh']:
         assert list(hls_model.get_layers())[2].attributes['class_name'] == 'Activation'
     elif activation_function.__class__.__name__ == 'Threshold':
         assert list(hls_model.get_layers())[2].attributes['class_name'] == 'ThresholdedReLU'
@@ -118,6 +119,14 @@ def forward(self, x):
         return nn.functional.relu(x)
 
 
+class TanHModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return nn.functional.tanh(x)
+
+
 class LeakyReLuModel(nn.Module):
     def __init__(self):
         super().__init__()
@@ -154,6 +163,7 @@ def forward(self, x):
     "activation_function",
     [
         ReLuModel(),
+        TanHModel(),
         LeakyReLuModel(),
         EluModel(),
         SigmoidModel(),
@@ -172,7 +182,7 @@ def test_activation_functionals(activation_function, backend, io_type):
 
     config = config_from_pytorch_model(model, (1,))
     fn_name = activation_function.__class__.__name__
-    output_dir = str(test_root_path / f'hls4mlprj_pytorch_api_activations_functional_relu_{backend}_{io_type}_{fn_name}')
+    output_dir = str(test_root_path / f'hls4mlprj_pytorch_api_activations_functional_{fn_name}_{backend}_{io_type}')
     hls_model = convert_from_pytorch_model(model, hls_config=config, output_dir=output_dir, backend=backend, io_type=io_type)
     hls_model.compile()
 
@@ -268,7 +278,7 @@ def test_conv1d(padds, backend, io_type):
         act_index = 2
     assert list(hls_model.get_layers())[conv_index].attributes['name'] == convNode.name
     assert list(hls_model.get_layers())[conv_index].attributes['class_name'] == 'Conv1D'
-    assert list(hls_model.get_layers())[act_index].attributes['activation'] == class_object_relu.__class__.__name__
+    assert list(hls_model.get_layers())[act_index].attributes['activation'] == class_object_relu.__class__.__name__.lower()
     if io_type == "io_stream" and (backend == "Vivado" or backend == "Vitis") and padds == 1:
         assert list(hls_model.get_layers())[conv_index].attributes["in_width"] == size_in + 2
     else:
@@ -412,7 +422,9 @@ def test_conv2d(padds, backend, io_type):
             act_index = 2
         assert list(hls_model.get_layers())[conv_index].attributes['name'] == convNode.name
         assert list(hls_model.get_layers())[conv_index].attributes['class_name'] == 'Conv2D'
-        assert list(hls_model.get_layers())[act_index].attributes['activation'] == class_object_relu.__class__.__name__
+        assert (
+            list(hls_model.get_layers())[act_index].attributes['activation'] == class_object_relu.__class__.__name__.lower()
+        )
         assert list(hls_model.get_layers())[conv_index].attributes["in_width"] == size_in_width
         assert list(hls_model.get_layers())[conv_index].attributes["in_height"] == size_in_height
         assert list(hls_model.get_layers())[conv_index].attributes['filt_width'] == class_object_conv.kernel_size[1]