51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import math, random
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import tensorflow as tf
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from copy import deepcopy
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from ..cell_operations import OPS
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class NAS201SearchCell(tf.keras.layers.Layer):
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def __init__(self, C_in, C_out, stride, max_nodes, op_names, affine=False):
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super(NAS201SearchCell, self).__init__()
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self.op_names = deepcopy(op_names)
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self.max_nodes = max_nodes
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self.in_dim = C_in
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self.out_dim = C_out
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self.edge_keys = []
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for i in range(1, max_nodes):
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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if j == 0:
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xlists = [OPS[op_name](C_in , C_out, stride, affine) for op_name in op_names]
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else:
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xlists = [OPS[op_name](C_in , C_out, 1, affine) for op_name in op_names]
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for k, op in enumerate(xlists):
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setattr(self, '{:}.{:}'.format(node_str, k), op)
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self.edge_keys.append( node_str )
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self.edge_keys = sorted(self.edge_keys)
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self.edge2index = {key:i for i, key in enumerate(self.edge_keys)}
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self.num_edges = len(self.edge_keys)
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def call(self, inputs, weightss, training):
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w_lst = tf.split(weightss, self.num_edges, 0)
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nodes = [inputs]
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for i in range(1, self.max_nodes):
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inter_nodes = []
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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edge_idx = self.edge2index[node_str]
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op_outps = []
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for k, op_name in enumerate(self.op_names):
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op = getattr(self, '{:}.{:}'.format(node_str, k))
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op_outps.append( op(nodes[j], training) )
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stack_op_outs = tf.stack(op_outps, axis=-1)
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weighted_sums = tf.math.multiply(stack_op_outs, w_lst[edge_idx])
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inter_nodes.append( tf.math.reduce_sum(weighted_sums, axis=-1) )
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nodes.append( tf.math.add_n(inter_nodes) )
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return nodes[-1]
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