100 lines
4.2 KiB
Python
100 lines
4.2 KiB
Python
###########################################################################
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# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
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###########################################################################
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import tensorflow as tf
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import numpy as np
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from copy import deepcopy
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from ..cell_operations import ResNetBasicblock
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from .search_cells import SearchCell
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def sample_gumbel(shape, eps=1e-20):
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U = tf.random.uniform(shape, minval=0, maxval=1)
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return -tf.math.log(-tf.math.log(U + eps) + eps)
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def gumbel_softmax(logits, temperature):
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gumbel_softmax_sample = logits + sample_gumbel(tf.shape(logits))
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y = tf.nn.softmax(gumbel_softmax_sample / temperature)
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return y
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class TinyNetworkGDAS(tf.keras.Model):
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def __init__(self, C, N, max_nodes, num_classes, search_space, affine):
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super(TinyNetworkGDAS, self).__init__()
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self._C = C
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self._layerN = N
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self.max_nodes = max_nodes
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self.stem = tf.keras.Sequential([
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tf.keras.layers.Conv2D(16, 3, 1, padding='same', use_bias=False),
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tf.keras.layers.BatchNormalization()], name='stem')
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layer_channels = [C ] * N + [C*2 ] + [C*2 ] * N + [C*4 ] + [C*4 ] * N
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layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
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C_prev, num_edge, edge2index = C, None, None
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for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
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cell_prefix = 'cell-{:03d}'.format(index)
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#with tf.name_scope(cell_prefix) as scope:
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if reduction:
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cell = ResNetBasicblock(C_prev, C_curr, 2)
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else:
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cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space, affine)
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if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index
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else: assert num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges)
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C_prev = cell.out_dim
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setattr(self, cell_prefix, cell)
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self.num_layers = len(layer_reductions)
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self.op_names = deepcopy( search_space )
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self.edge2index = edge2index
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self.num_edge = num_edge
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self.lastact = tf.keras.Sequential([
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tf.keras.layers.BatchNormalization(),
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tf.keras.layers.ReLU(),
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tf.keras.layers.GlobalAvgPool2D(),
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tf.keras.layers.Flatten(),
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tf.keras.layers.Dense(num_classes, activation='softmax')], name='lastact')
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#self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
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arch_init = tf.random_normal_initializer(mean=0, stddev=0.001)
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self.arch_parameters = tf.Variable(initial_value=arch_init(shape=(num_edge, len(search_space)), dtype='float32'), trainable=True, name='arch-encoding')
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def get_alphas(self):
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xlist = self.trainable_variables
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return [x for x in xlist if 'arch-encoding' in x.name]
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def get_weights(self):
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xlist = self.trainable_variables
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return [x for x in xlist if 'arch-encoding' not in x.name]
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def get_np_alphas(self):
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arch_nps = self.arch_parameters.numpy()
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arch_ops = np.exp(arch_nps) / np.sum(np.exp(arch_nps), axis=-1, keepdims=True)
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return arch_ops
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def genotype(self):
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genotypes, arch_nps = [], self.arch_parameters.numpy()
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for i in range(1, self.max_nodes):
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xlist = []
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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weights = arch_nps[ self.edge2index[node_str] ]
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op_name = self.op_names[ weights.argmax().item() ]
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xlist.append((op_name, j))
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genotypes.append( tuple(xlist) )
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return genotypes
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#
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def call(self, inputs, tau, training):
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weightss = tf.cond(tau < 0, lambda: tf.nn.softmax(self.arch_parameters, axis=1),
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lambda: gumbel_softmax(tf.math.log_softmax(self.arch_parameters, axis=1), tau))
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feature = self.stem(inputs, training)
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for idx in range(self.num_layers):
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cell = getattr(self, 'cell-{:03d}'.format(idx))
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if isinstance(cell, SearchCell):
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feature = cell.call(feature, weightss, training)
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else:
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feature = cell(feature, training)
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logits = self.lastact(feature, training)
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return logits
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