def aux_logits():
return mo.siso_sequential([
relu(),
avg_pool2d(D([5]), D([3]), D(['VALID'])),
conv2d(D([128]), D([1])),
batch_normalization(),
relu(),
global_convolution(D([768])),
batch_normalization(),
relu(),
flatten(),
fc_layer(D([10]))
])
def intermediate_node_fn(num_inputs, filters):
return mo.siso_sequential([
add(num_inputs),
conv2d(D([filters]), D([3])),
batch_normalization(),
relu()
])
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 conv_op(filters, filter_size, stride, dilation_rate, spatial_separable):
if spatial_separable:
return mo.siso_sequential([
conv2d(D([filters]), D([[1, filter_size]]), D([[1, stride]])),
batch_normalization(),
relu(),
conv2d(D([filters]), D([[filter_size, 1]]), D([[stride, 1]])),
])
else:
return conv2d(D([filters]), D([filter_size]), D([stride]),
D([dilation_rate]))
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 generate_stage(stage_num, num_nodes, filters, filter_size):
h_connections = [
Bool(name='%d_in_%d_%d' % (stage_num, in_id, out_id))
for (in_id,
out_id) in itertools.combinations(range(1, num_nodes + 1), 2)
]
return genetic_stage(
lambda: mo.siso_sequential([
conv2d(D([filters]), D([filter_size])),
batch_normalization(),
relu()
]), lambda num_inputs: intermediate_node_fn(num_inputs, filters), lambda
num_inputs: intermediate_node_fn(num_inputs, filters), h_connections,
num_nodes)
def wrap_relu_batch_norm(op):
return mo.siso_sequential([relu(), op, batch_normalization()])
def stem(filters):
return mo.siso_sequential(
[conv2d(D([filters]), D([3])),
batch_normalization()])
def stem():
return mo.siso_sequential([
conv2d(D([128]), D([3])),
batch_normalization(),
relu(),
])