update TF

lib/tf_models/cell_searchs/__init__.py

@@ -2,5 +2,7 @@
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
 ##################################################
 from .search_model_gdas     import TinyNetworkGDAS
+from .search_model_darts    import TinyNetworkDARTS
 
-nas_super_nets = {'GDAS': TinyNetworkGDAS}
+nas_super_nets = {'GDAS' : TinyNetworkGDAS,
+                  'DARTS': TinyNetworkDARTS}

lib/tf_models/cell_searchs/search_model_darts.py

@@ -0,0 +1,83 @@
+import tensorflow as tf
+import numpy as np
+from copy import deepcopy
+from ..cell_operations import ResNetBasicblock
+from .search_cells     import NAS201SearchCell as SearchCell
+
+
+class TinyNetworkDARTS(tf.keras.Model):
+
+  def __init__(self, C, N, max_nodes, num_classes, search_space, affine):
+    super(TinyNetworkDARTS, self).__init__()
+    self._C        = C
+    self._layerN   = N
+    self.max_nodes = max_nodes
+    self.stem = tf.keras.Sequential([
+                    tf.keras.layers.Conv2D(16, 3, 1, padding='same', use_bias=False),
+                    tf.keras.layers.BatchNormalization()], name='stem')
+  
+    layer_channels   = [C    ] * N + [C*2 ] + [C*2  ] * N + [C*4 ] + [C*4  ] * N    
+    layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
+
+    C_prev, num_edge, edge2index = C, None, None
+    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
+      cell_prefix = 'cell-{:03d}'.format(index)
+      #with tf.name_scope(cell_prefix) as scope:
+      if reduction:
+        cell = ResNetBasicblock(C_prev, C_curr, 2)
+      else:
+        cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space, affine)
+        if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index
+        else: assert num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges)
+      C_prev = cell.out_dim
+      setattr(self, cell_prefix, cell)
+    self.num_layers = len(layer_reductions)
+    self.op_names   = deepcopy( search_space )
+    self.edge2index = edge2index
+    self.num_edge   = num_edge
+    self.lastact    = tf.keras.Sequential([
+                        tf.keras.layers.BatchNormalization(),
+                        tf.keras.layers.ReLU(),
+                        tf.keras.layers.GlobalAvgPool2D(),
+                        tf.keras.layers.Flatten(),
+                        tf.keras.layers.Dense(num_classes, activation='softmax')], name='lastact')
+    #self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
+    arch_init = tf.random_normal_initializer(mean=0, stddev=0.001)
+    self.arch_parameters = tf.Variable(initial_value=arch_init(shape=(num_edge, len(search_space)), dtype='float32'), trainable=True, name='arch-encoding')
+
+  def get_alphas(self):
+    xlist = self.trainable_variables
+    return [x for x in xlist if 'arch-encoding' in x.name]
+
+  def get_weights(self):
+    xlist = self.trainable_variables
+    return [x for x in xlist if 'arch-encoding' not in x.name]
+
+  def get_np_alphas(self):
+    arch_nps = self.arch_parameters.numpy()
+    arch_ops = np.exp(arch_nps) / np.sum(np.exp(arch_nps), axis=-1, keepdims=True)
+    return arch_ops
+
+  def genotype(self):
+    genotypes, arch_nps = [], self.arch_parameters.numpy()
+    for i in range(1, self.max_nodes):
+      xlist = []
+      for j in range(i):
+        node_str = '{:}<-{:}'.format(i, j)
+        weights = arch_nps[ self.edge2index[node_str] ]
+        op_name = self.op_names[ weights.argmax().item() ]
+        xlist.append((op_name, j))
+      genotypes.append( tuple(xlist) )
+    return genotypes
+
+  def call(self, inputs, training):
+    weightss = tf.nn.softmax(self.arch_parameters, axis=1)
+    feature = self.stem(inputs, training)
+    for idx in range(self.num_layers):
+      cell = getattr(self, 'cell-{:03d}'.format(idx))
+      if isinstance(cell, SearchCell):
+        feature = cell.call(feature, weightss, training)
+      else:
+        feature = cell(feature, training)
+    logits = self.lastact(feature, training)
+    return logits