update TF models (beta version)

lib/tf_models/__init__.py

@@ -0,0 +1,32 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import torch
+from os import path as osp
+
+__all__ = ['get_cell_based_tiny_net', 'get_search_spaces']
+
+
+# the cell-based NAS models
+def get_cell_based_tiny_net(config):
+  group_names = ['GDAS']
+  if config.name in group_names:
+    from .cell_searchs import nas_super_nets
+    from .cell_operations import SearchSpaceNames
+    if isinstance(config.space, str): search_space = SearchSpaceNames[config.space]
+    else: search_space = config.space
+    return nas_super_nets[config.name](
+                  config.C, config.N, config.max_nodes,
+                  config.num_classes, search_space, config.affine)
+  else:
+    raise ValueError('invalid network name : {:}'.format(config.name))
+
+
+# obtain the search space, i.e., a dict mapping the operation name into a python-function for this op
+def get_search_spaces(xtype, name):
+  if xtype == 'cell':
+    from .cell_operations import SearchSpaceNames
+    assert name in SearchSpaceNames, 'invalid name [{:}] in {:}'.format(name, SearchSpaceNames.keys())
+    return SearchSpaceNames[name]
+  else:
+    raise ValueError('invalid search-space type is {:}'.format(xtype))

lib/tf_models/cell_operations.py

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+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import tensorflow as tf
+
+__all__ = ['OPS', 'ResNetBasicblock', 'SearchSpaceNames']
+
+OPS = {
+  'none'        : lambda C_in, C_out, stride, affine: Zero(C_in, C_out, stride),
+  'avg_pool_3x3': lambda C_in, C_out, stride, affine: POOLING(C_in, C_out, stride, 'avg', affine),
+  'nor_conv_1x1': lambda C_in, C_out, stride, affine: ReLUConvBN(C_in, C_out, 1, stride, affine),
+  'nor_conv_3x3': lambda C_in, C_out, stride, affine: ReLUConvBN(C_in, C_out, 3, stride, affine),
+  'nor_conv_5x5': lambda C_in, C_out, stride, affine: ReLUConvBN(C_in, C_out, 5, stride, affine),
+  'skip_connect': lambda C_in, C_out, stride, affine: Identity(C_in, C_out, stride)
+}
+
+NAS_BENCH_102         = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']
+
+SearchSpaceNames = {
+                    'nas-bench-102': NAS_BENCH_102,
+                   }
+
+
+class POOLING(tf.keras.layers.Layer):
+
+  def __init__(self, C_in, C_out, stride, mode, affine):
+    super(POOLING, self).__init__()
+    if C_in == C_out:
+      self.preprocess = None
+    else:
+      self.preprocess = ReLUConvBN(C_in, C_out, 1, 1, affine)
+    if mode == 'avg'  : self.op = tf.keras.layers.AvgPool2D((3,3), strides=stride, padding='same')
+    elif mode == 'max': self.op = tf.keras.layers.MaxPool2D((3,3), strides=stride, padding='same')
+    else              : raise ValueError('Invalid mode={:} in POOLING'.format(mode))
+
+  def call(self, inputs, training):
+    if self.preprocess: x = self.preprocess(inputs)
+    else              : x = inputs
+    return self.op(x)
+
+
+class Identity(tf.keras.layers.Layer):
+  def __init__(self, C_in, C_out, stride):
+    super(Identity, self).__init__()
+    if C_in != C_out or stride != 1:
+      self.layer = tf.keras.layers.Conv2D(C_out, 3, stride, padding='same', use_bias=False)
+    else:
+      self.layer = None
+  
+  def call(self, inputs, training):
+    x = inputs
+    if self.layer is not None:
+      x = self.layer(x)
+    return x
+
+
+
+class Zero(tf.keras.layers.Layer):
+  def __init__(self, C_in, C_out, stride):
+    super(Zero, self).__init__()
+    if C_in != C_out:
+      self.layer = tf.keras.layers.Conv2D(C_out, 1, stride, padding='same', use_bias=False)
+    elif stride != 1:
+      self.layer = tf.keras.layers.AvgPool2D((stride,stride), None, padding="same")
+    else:
+      self.layer = None
+  
+  def call(self, inputs, training):
+    x = tf.zeros_like(inputs)
+    if self.layer is not None:
+      x = self.layer(x)
+    return x
+
+
+class ReLUConvBN(tf.keras.layers.Layer):
+  def __init__(self, C_in, C_out, kernel_size, strides, affine):
+    super(ReLUConvBN, self).__init__()
+    self.C_in = C_in
+    self.relu = tf.keras.activations.relu
+    self.conv = tf.keras.layers.Conv2D(C_out, kernel_size, strides, padding='same', use_bias=False)
+    self.bn   = tf.keras.layers.BatchNormalization(center=affine, scale=affine)
+  
+  def call(self, inputs, training):
+    x = self.relu(inputs)
+    x = self.conv(x)
+    x = self.bn(x, training)
+    return x
+
+
+class ResNetBasicblock(tf.keras.layers.Layer):
+
+  def __init__(self, inplanes, planes, stride, affine=True):
+    super(ResNetBasicblock, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    self.conv_a = ReLUConvBN(inplanes, planes, 3, stride, affine)
+    self.conv_b = ReLUConvBN(  planes, planes, 3,      1, affine)
+    if stride == 2:
+      self.downsample = tf.keras.Sequential([
+                                tf.keras.layers.AvgPool2D((stride,stride), None, padding="same"),
+                                tf.keras.layers.Conv2D(planes, 1, 1, padding='same', use_bias=False)])
+    elif inplanes != planes:
+      self.downsample = ReLUConvBN(inplanes, planes, 1, stride, affine)
+    else:
+      self.downsample = None
+    self.addition = tf.keras.layers.Add()
+    self.in_dim  = inplanes
+    self.out_dim = planes
+    self.stride  = stride
+    self.num_conv = 2
+
+  def call(self, inputs, training):
+
+    basicblock = self.conv_a(inputs, training)
+    basicblock = self.conv_b(basicblock, training)
+
+    if self.downsample is not None:
+      residual = self.downsample(inputs)
+    else:
+      residual = inputs
+    return self.addition([residual, basicblock])

lib/tf_models/cell_searchs/__init__.py

@@ -0,0 +1,6 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+from .search_model_gdas     import TinyNetworkGDAS
+
+nas_super_nets = {'GDAS': TinyNetworkGDAS}

lib/tf_models/cell_searchs/search_cells.py

@@ -0,0 +1,50 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import math, random
+import tensorflow as tf
+from copy import deepcopy
+from ..cell_operations import OPS
+
+
+class SearchCell(tf.keras.layers.Layer):
+
+  def __init__(self, C_in, C_out, stride, max_nodes, op_names, affine=False):
+    super(SearchCell, self).__init__()
+
+    self.op_names  = deepcopy(op_names)
+    self.max_nodes = max_nodes
+    self.in_dim    = C_in
+    self.out_dim   = C_out
+    self.edge_keys = []
+    for i in range(1, max_nodes):
+      for j in range(i):
+        node_str = '{:}<-{:}'.format(i, j)
+        if j == 0:
+          xlists = [OPS[op_name](C_in , C_out, stride, affine) for op_name in op_names]
+        else:
+          xlists = [OPS[op_name](C_in , C_out,      1, affine) for op_name in op_names]
+        for k, op in enumerate(xlists):
+          setattr(self, '{:}.{:}'.format(node_str, k), op)
+        self.edge_keys.append( node_str )
+    self.edge_keys  = sorted(self.edge_keys)
+    self.edge2index = {key:i for i, key in enumerate(self.edge_keys)}
+    self.num_edges  = len(self.edge_keys)
+
+  def call(self, inputs, weightss, training):
+    w_lst = tf.split(weightss, self.num_edges, 0)
+    nodes = [inputs]
+    for i in range(1, self.max_nodes):
+      inter_nodes = []
+      for j in range(i):
+        node_str = '{:}<-{:}'.format(i, j)
+        edge_idx = self.edge2index[node_str]
+        op_outps = []
+        for k, op_name in enumerate(self.op_names):
+          op = getattr(self, '{:}.{:}'.format(node_str, k))
+          op_outps.append( op(nodes[j], training) )
+        stack_op_outs = tf.stack(op_outps, axis=-1)
+        weighted_sums = tf.math.multiply(stack_op_outs, w_lst[edge_idx])
+        inter_nodes.append( tf.math.reduce_sum(weighted_sums, axis=-1) )
+      nodes.append( tf.math.add_n(inter_nodes) )
+    return nodes[-1]

lib/tf_models/cell_searchs/search_model_gdas.py

@@ -0,0 +1,99 @@
+###########################################################################
+# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
+###########################################################################
+import tensorflow as tf
+import numpy as np
+from copy import deepcopy
+from ..cell_operations import ResNetBasicblock
+from .search_cells     import SearchCell
+
+
+def sample_gumbel(shape, eps=1e-20):
+  U = tf.random.uniform(shape, minval=0, maxval=1)
+  return -tf.math.log(-tf.math.log(U + eps) + eps)
+
+
+def gumbel_softmax(logits, temperature):
+  gumbel_softmax_sample = logits + sample_gumbel(tf.shape(logits))
+  y = tf.nn.softmax(gumbel_softmax_sample / temperature)
+  return y
+
+
+class TinyNetworkGDAS(tf.keras.Model):
+
+  def __init__(self, C, N, max_nodes, num_classes, search_space, affine):
+    super(TinyNetworkGDAS, 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, tau, training):
+    weightss = tf.cond(tau < 0, lambda: tf.nn.softmax(self.arch_parameters, axis=1),
+                                lambda: gumbel_softmax(tf.math.log_softmax(self.arch_parameters, axis=1), tau))
+    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