def test_FLOPsEstimator():
x = nn.Variable((1, 3, 12, 12))
y = PF.depthwise_convolution(x, kernel=(5, 5), with_bias=True)
t = PF.fused_batch_normalization(y)
z = F.relu6(F.sigmoid(PF.affine(t, (3, 3), base_axis=2) + 3))
z = F.global_average_pooling(z)
est = FLOPsEstimator()
assert est.predict(z) == 17644
def ref_activation(x, nonlinearity, nonlinearity_args):
if nonlinearity == 'identity' or not nonlinearity:
return x
elif nonlinearity == 'relu':
return F.relu(x)
elif nonlinearity == 'sigmoid':
return F.sigmoid(x)
elif nonlinearity == 'tanh':
return F.tanh(x)
elif nonlinearity == 'leaky_relu':
return F.leaky_relu(x, nonlinearity_args[0])
elif nonlinearity == 'elu':
return F.elu(x, nonlinearity_args[0])
elif nonlinearity == 'relu6':
return F.relu6(x)
raise ValueError("unknown nonlinearity type {}".format(nonlinearity))
def call(self, input):
return F.relu6(input)
def hswish(self, x):
return x * F.relu6(x + 3.0) / 6.0