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 enas_op(h_op_name, out_filters, name, weight_sharer):
return mo.siso_or(
{
'conv3':
lambda: enas_conv(out_filters, 3, False, weight_sharer, name),
'conv5':
lambda: enas_conv(out_filters, 5, False, weight_sharer, name),
'dsep_conv3':
lambda: enas_conv(out_filters, 3, True, weight_sharer, name),
'dsep_conv5':
lambda: enas_conv(out_filters, 5, True, weight_sharer, name),
'avg_pool':
lambda: avg_pool(D([3]), D([1])),
'max_pool':
lambda: max_pool(D([3]), D([1]))
}, h_op_name)
def intermediate_node_fn(reduction, input_id, node_id, op_num, filters,
cell_ratio, cell_ops):
stride = 2 if reduction and input_id < 2 else 1
h_is_not_none = co.DependentHyperparameter(
lambda op: op != 'none', {'op': cell_ops[node_id * 2 + op_num]})
op_in, op_out = mo.siso_or(
{
'none':
lambda: check_filters(filters, stride),
'conv1':
lambda: wrap_relu_batch_norm(
conv2d(D([filters]), D([1]), h_stride=D([stride]))),
'conv3':
lambda: full_conv_op(filters, 3, stride, 1, False),
'depth_sep3':
lambda: separable_conv_op(filters, 3, stride),
'depth_sep5':
lambda: separable_conv_op(filters, 5, stride),
'depth_sep7':
lambda: separable_conv_op(filters, 7, stride),
'dilated_3x3_rate_2':
lambda: full_conv_op(filters, 3, stride, 2, False),
'dilated_3x3_rate_4':
lambda: full_conv_op(filters, 3, stride, 4, False),
'dilated_3x3_rate_6':
lambda: full_conv_op(filters, 3, stride, 6, False),
'1x3_3x1':
lambda: full_conv_op(filters, 3, stride, 1, True),
'1x7_7x1':
lambda: full_conv_op(filters, 7, stride, 1, True),
'avg2':
lambda: pool_op(filters, 2, stride, 'avg'),
'avg3':
lambda: pool_op(filters, 3, stride, 'avg'),
'max2':
lambda: pool_op(filters, 2, stride, 'max'),
'max3':
lambda: pool_op(filters, 3, stride, 'max'),
'min2':
lambda: pool_op(filters, 2, stride, 'min')
}, cell_ops[node_id * 2 + op_num])
drop_in, drop_out = miso_optional(lambda: drop_path(cell_ratio),
h_is_not_none)
drop_in['In0'].connect(op_out['Out'])
drop_in['In1'].connect(global_vars['progress'])
return op_in, drop_out