def test_activation_elu_infer(self):
graph = build_graph(
self.nodes_attributes, [('node_1', 'activation_node'),
('activation_node', 'node_3')], {
'node_1': {
'value': np.array([6, -4, -2, -1])
},
'activation_node': {
'operation': 'elu',
'alpha': 1.0,
},
'node_3': {
'value': None
}
})
graph.graph['layout'] = 'NCHW'
activation_node = Node(graph, 'activation_node')
Elu.infer(activation_node)
exp_shape = np.array([4])
res_shape = graph.node['node_3']['shape']
res_value = graph.node['node_3']['value']
exp_value = np.array([6., -0.98168436, -0.86466472, -0.63212056])
for i, value in enumerate(exp_shape):
self.assertEqual(res_shape[i], value)
for i, value in enumerate(exp_value):
self.assertAlmostEqual(res_value[i], value)
def extract(cls, node):
attrs = get_mxnet_layer_attrs(node.symbol_dict)
act_type = attrs.str('act_type', 'leaky')
if act_type == 'prelu':
prelu_attrs = {'channel_shared': 1,
'filler_type': 'constant',
'filler_value': 0,
'min': 0,
'max': 1,
'mean': 0,
'std': 0,
'sparse': -1,
'variance_norm': "caffe.FillerParameter.FAN_IN"}
PReLU.update_node_stat(node, prelu_attrs)
elif act_type == 'elu':
alpha = attrs.float('slope', 0.25)
Elu.update_node_stat(node, {'alpha': alpha})
elif act_type == 'leaky':
negative_slope = attrs.float('slope', 0.25)
if negative_slope == 0:
ReLU.update_node_stat(node)
else:
LeakyReLU.update_node_stat(node, {'negative_slope': negative_slope})
elif act_type == 'gelu':
GeLUOP.update_node_stat(node, {'approximation_mode': 'erf'})
else:
raise Error(
"Operation '{}' not supported. Please register it as custom op. " +
refer_to_faq_msg(86),
act_type)
return LeakyReLUFrontExtractor.enabled
def extract(cls, node):
Elu.update_node_stat(node)
return cls.enabled
def extract(cls, node):
param = node.pb.elu_param
attrs = collect_attributes(param)
Elu.update_node_stat(node, attrs)
return cls.enabled