def generate(filters):
return cell(
lambda channels: mo.siso_sequential(
[conv2d(D([channels]), D([1])),
batch_normalization(),
relu()]),
lambda num_inputs, node_id, channels: intermediate_node_fn(
num_inputs, node_id, channels, cell_ops), concat,
h_connections, 5, filters)
def intermediate_node_fn(num_inputs, node_id, filters, cell_ops):
return mo.siso_sequential([
add(num_inputs),
mo.siso_or(
{
'conv1': lambda: conv2d(D([filters]), D([1])),
'conv3': lambda: conv2d(D([filters]), D([3])),
'max3': lambda: max_pool2d(D([3]))
}, cell_ops[node_id]),
batch_normalization(),
relu()
])
def stem():
return mo.siso_sequential([
conv2d(D([128]), D([3])),
batch_normalization(),
relu(),
])