update GDAS

lib/models/cell_searchs/search_model_gdas.py

@@ -88,7 +88,9 @@ class TinyNetworkGDAS(nn.Module):
       index   = probs.max(-1, keepdim=True)[1]
       one_h   = torch.zeros_like(logits).scatter_(-1, index, 1.0)
       hardwts = one_h - probs.detach() + probs
-      if (torch.isinf(gumbels).any()) or (torch.isinf(probs).any()) or (torch.isnan(probs).any()): continue
+      if (torch.isinf(gumbels).any()) or (torch.isinf(probs).any()) or (torch.isnan(probs).any()):
+        continue
+      else: break
 
     feature = self.stem(inputs)
     for i, cell in enumerate(self.cells):

lib/models/l2s_cell_searchs/__init__.py

@@ -1,17 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##################################################
-from .search_model_darts_v1 import TinyNetworkDartsV1
-from .search_model_darts_v2 import TinyNetworkDartsV2
-from .search_model_gdas     import TinyNetworkGDAS
-from .search_model_setn     import TinyNetworkSETN
-from .search_model_enas     import TinyNetworkENAS
-from .search_model_random   import TinyNetworkRANDOM
-from .genotypes             import Structure as CellStructure, architectures as CellArchitectures
-
-nas_super_nets = {'DARTS-V1': TinyNetworkDartsV1,
-                  'DARTS-V2': TinyNetworkDartsV2,
-                  'GDAS'    : TinyNetworkGDAS,
-                  'SETN'    : TinyNetworkSETN,
-                  'ENAS'    : TinyNetworkENAS,
-                  'RANDOM'  : TinyNetworkRANDOM}

lib/models/l2s_cell_searchs/_test_module.py

@@ -1,12 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##################################################
-import torch
-from search_model_enas_utils import Controller
-
-def main():
-  controller = Controller(6, 4)
-  predictions = controller()
-
-if __name__ == '__main__':
-  main()

lib/models/l2s_cell_searchs/genotypes.py

@@ -1,197 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##################################################
-from copy import deepcopy
-
-
-
-def get_combination(space, num):
-  combs = []
-  for i in range(num):
-    if i == 0:
-      for func in space:
-        combs.append( [(func, i)] )
-    else:
-      new_combs = []
-      for string in combs:
-        for func in space:
-          xstring = string + [(func, i)]
-          new_combs.append( xstring )
-      combs = new_combs
-  return combs
-  
-
-
-class Structure:
-
-  def __init__(self, genotype):
-    assert isinstance(genotype, list) or isinstance(genotype, tuple), 'invalid class of genotype : {:}'.format(type(genotype))
-    self.node_num = len(genotype) + 1
-    self.nodes    = []
-    self.node_N   = []
-    for idx, node_info in enumerate(genotype):
-      assert isinstance(node_info, list) or isinstance(node_info, tuple), 'invalid class of node_info : {:}'.format(type(node_info))
-      assert len(node_info) >= 1, 'invalid length : {:}'.format(len(node_info))
-      for node_in in node_info:
-        assert isinstance(node_in, list) or isinstance(node_in, tuple), 'invalid class of in-node : {:}'.format(type(node_in))
-        assert len(node_in) == 2 and node_in[1] <= idx, 'invalid in-node : {:}'.format(node_in)
-      self.node_N.append( len(node_info) )
-      self.nodes.append( tuple(deepcopy(node_info)) )
-
-  def tolist(self, remove_str):
-    # convert this class to the list, if remove_str is 'none', then remove the 'none' operation.
-    # note that we re-order the input node in this function
-    # return the-genotype-list and success [if unsuccess, it is not a connectivity]
-    genotypes = []
-    for node_info in self.nodes:
-      node_info = list( node_info )
-      node_info = sorted(node_info, key=lambda x: (x[1], x[0]))
-      node_info = tuple(filter(lambda x: x[0] != remove_str, node_info))
-      if len(node_info) == 0: return None, False
-      genotypes.append( node_info )
-    return genotypes, True
-
-  def node(self, index):
-    assert index > 0 and index <= len(self), 'invalid index={:} < {:}'.format(index, len(self))
-    return self.nodes[index]
-
-  def tostr(self):
-    strings = []
-    for node_info in self.nodes:
-      string = '|'.join([x[0]+'~{:}'.format(x[1]) for x in node_info])
-      string = '|{:}|'.format(string)
-      strings.append( string )
-    return '+'.join(strings)
-
-  def check_valid(self):
-    nodes = {0: True}
-    for i, node_info in enumerate(self.nodes):
-      sums = []
-      for op, xin in node_info:
-        if op == 'none' or nodes[xin] == False: x = False
-        else: x = True
-        sums.append( x )
-      nodes[i+1] = sum(sums) > 0
-    return nodes[len(self.nodes)]
-
-  def to_unique_str(self, consider_zero=False):
-    # this is used to identify the isomorphic cell, which rerquires the prior knowledge of operation
-    # two operations are special, i.e., none and skip_connect
-    nodes = {0: '0'}
-    for i_node, node_info in enumerate(self.nodes):
-      cur_node = []
-      for op, xin in node_info:
-        if consider_zero:
-          if op == 'none' or nodes[xin] == '#': x = '#' # zero
-          elif op == 'skip_connect': x = nodes[xin]
-          else: x = '('+nodes[xin]+')' + '@{:}'.format(op)
-        else:
-          if op == 'skip_connect': x = nodes[xin]
-          else: x = '('+nodes[xin]+')' + '@{:}'.format(op)
-        cur_node.append(x)
-      nodes[i_node+1] = '+'.join( sorted(cur_node) )
-    return nodes[ len(self.nodes) ]
-
-  def check_valid_op(self, op_names):
-    for node_info in self.nodes:
-      for inode_edge in node_info:
-        #assert inode_edge[0] in op_names, 'invalid op-name : {:}'.format(inode_edge[0])
-        if inode_edge[0] not in op_names: return False
-    return True
-
-  def __repr__(self):
-    return ('{name}({node_num} nodes with {node_info})'.format(name=self.__class__.__name__, node_info=self.tostr(), **self.__dict__))
-
-  def __len__(self):
-    return len(self.nodes) + 1
-
-  def __getitem__(self, index):
-    return self.nodes[index]
-
-  @staticmethod
-  def str2structure(xstr):
-    assert isinstance(xstr, str), 'must take string (not {:}) as input'.format(type(xstr))
-    nodestrs = xstr.split('+')
-    genotypes = []
-    for i, node_str in enumerate(nodestrs):
-      inputs = list(filter(lambda x: x != '', node_str.split('|')))
-      for xinput in inputs: assert len(xinput.split('~')) == 2, 'invalid input length : {:}'.format(xinput)
-      inputs = ( xi.split('~') for xi in inputs )
-      input_infos = tuple( (op, int(IDX)) for (op, IDX) in inputs)
-      genotypes.append( input_infos )
-    return Structure( genotypes )
-
-  @staticmethod
-  def str2fullstructure(xstr, default_name='none'):
-    assert isinstance(xstr, str), 'must take string (not {:}) as input'.format(type(xstr))
-    nodestrs = xstr.split('+')
-    genotypes = []
-    for i, node_str in enumerate(nodestrs):
-      inputs = list(filter(lambda x: x != '', node_str.split('|')))
-      for xinput in inputs: assert len(xinput.split('~')) == 2, 'invalid input length : {:}'.format(xinput)
-      inputs = ( xi.split('~') for xi in inputs )
-      input_infos = list( (op, int(IDX)) for (op, IDX) in inputs)
-      all_in_nodes= list(x[1] for x in input_infos)
-      for j in range(i):
-        if j not in all_in_nodes: input_infos.append((default_name, j))
-      node_info = sorted(input_infos, key=lambda x: (x[1], x[0]))
-      genotypes.append( tuple(node_info) )
-    return Structure( genotypes )
-
-  @staticmethod
-  def gen_all(search_space, num, return_ori):
-    assert isinstance(search_space, list) or isinstance(search_space, tuple), 'invalid class of search-space : {:}'.format(type(search_space))
-    assert num >= 2, 'There should be at least two nodes in a neural cell instead of {:}'.format(num)
-    all_archs = get_combination(search_space, 1)
-    for i, arch in enumerate(all_archs):
-      all_archs[i] = [ tuple(arch) ]
-  
-    for inode in range(2, num):
-      cur_nodes = get_combination(search_space, inode)
-      new_all_archs = []
-      for previous_arch in all_archs:
-        for cur_node in cur_nodes:
-          new_all_archs.append( previous_arch + [tuple(cur_node)] )
-      all_archs = new_all_archs
-    if return_ori:
-      return all_archs
-    else:
-      return [Structure(x) for x in all_archs]
-
-
-
-ResNet_CODE = Structure(
-  [(('nor_conv_3x3', 0), ), # node-1 
-   (('nor_conv_3x3', 1), ), # node-2
-   (('skip_connect', 0), ('skip_connect', 2))] # node-3
-  )
-
-AllConv3x3_CODE = Structure(
-  [(('nor_conv_3x3', 0), ), # node-1 
-   (('nor_conv_3x3', 0), ('nor_conv_3x3', 1)), # node-2
-   (('nor_conv_3x3', 0), ('nor_conv_3x3', 1), ('nor_conv_3x3', 2))] # node-3
-  )
-
-AllFull_CODE = Structure(
-  [(('skip_connect', 0), ('nor_conv_1x1', 0), ('nor_conv_3x3', 0), ('avg_pool_3x3', 0)), # node-1 
-   (('skip_connect', 0), ('nor_conv_1x1', 0), ('nor_conv_3x3', 0), ('avg_pool_3x3', 0), ('skip_connect', 1), ('nor_conv_1x1', 1), ('nor_conv_3x3', 1), ('avg_pool_3x3', 1)), # node-2
-   (('skip_connect', 0), ('nor_conv_1x1', 0), ('nor_conv_3x3', 0), ('avg_pool_3x3', 0), ('skip_connect', 1), ('nor_conv_1x1', 1), ('nor_conv_3x3', 1), ('avg_pool_3x3', 1), ('skip_connect', 2), ('nor_conv_1x1', 2), ('nor_conv_3x3', 2), ('avg_pool_3x3', 2))] # node-3
-  )
-
-AllConv1x1_CODE = Structure(
-  [(('nor_conv_1x1', 0), ), # node-1 
-   (('nor_conv_1x1', 0), ('nor_conv_1x1', 1)), # node-2
-   (('nor_conv_1x1', 0), ('nor_conv_1x1', 1), ('nor_conv_1x1', 2))] # node-3
-  )
-
-AllIdentity_CODE = Structure(
-  [(('skip_connect', 0), ), # node-1 
-   (('skip_connect', 0), ('skip_connect', 1)), # node-2
-   (('skip_connect', 0), ('skip_connect', 1), ('skip_connect', 2))] # node-3
-  )
-
-architectures = {'resnet'  : ResNet_CODE,
-                 'all_c3x3': AllConv3x3_CODE,
-                 'all_c1x1': AllConv1x1_CODE,
-                 'all_idnt': AllIdentity_CODE,
-                 'all_full': AllFull_CODE}

lib/models/l2s_cell_searchs/search_cells.py

@@ -1,148 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##################################################
-import math, random, torch
-import warnings
-import torch.nn as nn
-import torch.nn.functional as F
-from copy import deepcopy
-from ..cell_operations import OPS
-
-
-class SearchCell(nn.Module):
-
-  def __init__(self, C_in, C_out, stride, max_nodes, op_names, n_piece):
-    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.n_piece   = n_piece
-    self.multi_edges = nn.ModuleList()
-    for i_piece in range(n_piece):
-      edges          = nn.ModuleDict()
-      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) for op_name in op_names]
-          else     : xlists = [OPS[op_name](C_in , C_out,      1) for op_name in op_names]
-          edges[ node_str ] = nn.ModuleList( xlists )
-      self.multi_edges.append( edges )
-      
-    self.edge_keys  = sorted(list(edges.keys()))
-    self.edge2index = {key:i for i, key in enumerate(self.edge_keys)}
-    self.num_edges  = len(edges)
-
-  def extra_repr(self):
-    string = 'info :: {max_nodes} nodes, inC={in_dim}, outC={out_dim}, nP={n_piece}'.format(**self.__dict__)
-    return string
-
-  def forward(self, inputs, weightss):
-    nodes = [inputs]
-    with torch.no_grad():
-      xmod, xid, argmax = 1, 0, weightss.argmax(dim=1).cpu().tolist()
-      for i, x in enumerate(argmax):
-        xid += x * (xmod % self.n_piece)
-        xmod = (xmod * len(self.op_names)) % self.n_piece
-      xid = xid % self.n_piece
-    edges = self.multi_edges[xid]
-    for i in range(1, self.max_nodes):
-      inter_nodes = []
-      for j in range(i):
-        node_str = '{:}<-{:}'.format(i, j)
-        weights  = weightss[ self.edge2index[node_str] ]
-        inter_nodes.append( sum( layer(nodes[j]) * w for layer, w in zip(edges[node_str], weights) ) )
-      nodes.append( sum(inter_nodes) )
-    return nodes[-1]
-
-  # GDAS
-  def forward_gdas(self, inputs, alphas, _tau):
-    avoid_zero = 0
-    while True:
-      gumbels = -torch.empty_like(alphas).exponential_().log()
-      logits  = (alphas.log_softmax(dim=1) + gumbels) / _tau
-      probs   = nn.functional.softmax(logits, dim=1)
-      index   = probs.max(-1, keepdim=True)[1]
-      one_h   = torch.zeros_like(logits).scatter_(-1, index, 1.0)
-      hardwts = one_h - probs.detach() + probs
-      if (torch.isinf(gumbels).any()) or (torch.isinf(probs).any()) or (torch.isnan(probs).any()):
-        continue # avoid the numerical error
-      nodes   = [inputs]
-      for i in range(1, self.max_nodes):
-        inter_nodes = []
-        for j in range(i):
-          node_str = '{:}<-{:}'.format(i, j)
-          weights  = hardwts[ self.edge2index[node_str] ]
-          argmaxs  = index[ self.edge2index[node_str] ].item()
-          weigsum  = sum( weights[_ie] * edge(nodes[j]) if _ie == argmaxs else weights[_ie] for _ie, edge in enumerate(self.edges[node_str]) )
-          inter_nodes.append( weigsum )
-        nodes.append( sum(inter_nodes) )
-      avoid_zero += 1
-      if nodes[-1].sum().item() == 0:
-        if avoid_zero < 10: continue
-        else:
-          warnings.warn('get zero outputs with avoid_zero={:}'.format(avoid_zero))
-          break
-      else:
-        break
-    return nodes[-1]
-
-  # joint
-  def forward_joint(self, inputs, weightss):
-    nodes = [inputs]
-    for i in range(1, self.max_nodes):
-      inter_nodes = []
-      for j in range(i):
-        node_str = '{:}<-{:}'.format(i, j)
-        weights  = weightss[ self.edge2index[node_str] ]
-        #aggregation = sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) ) / weights.numel()
-        aggregation = sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) )
-        inter_nodes.append( aggregation )
-      nodes.append( sum(inter_nodes) )
-    return nodes[-1]
-
-  # uniform random sampling per iteration
-  def forward_urs(self, inputs):
-    nodes = [inputs]
-    for i in range(1, self.max_nodes):
-      while True: # to avoid select zero for all ops
-        sops, has_non_zero = [], False
-        for j in range(i):
-          node_str   = '{:}<-{:}'.format(i, j)
-          candidates = self.edges[node_str]
-          select_op  = random.choice(candidates)
-          sops.append( select_op )
-          if not hasattr(select_op, 'is_zero') or select_op.is_zero == False: has_non_zero=True
-        if has_non_zero: break
-      inter_nodes = []
-      for j, select_op in enumerate(sops):
-        inter_nodes.append( select_op(nodes[j]) )
-      nodes.append( sum(inter_nodes) )
-    return nodes[-1]
-
-  # select the argmax
-  def forward_select(self, inputs, weightss):
-    nodes = [inputs]
-    for i in range(1, self.max_nodes):
-      inter_nodes = []
-      for j in range(i):
-        node_str = '{:}<-{:}'.format(i, j)
-        weights  = weightss[ self.edge2index[node_str] ]
-        inter_nodes.append( self.edges[node_str][ weights.argmax().item() ]( nodes[j] ) )
-        #inter_nodes.append( sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) ) )
-      nodes.append( sum(inter_nodes) )
-    return nodes[-1]
-
-  # forward with a specific structure
-  def forward_dynamic(self, inputs, structure):
-    nodes = [inputs]
-    for i in range(1, self.max_nodes):
-      cur_op_node = structure.nodes[i-1]
-      inter_nodes = []
-      for op_name, j in cur_op_node:
-        node_str = '{:}<-{:}'.format(i, j)
-        op_index = self.op_names.index( op_name )
-        inter_nodes.append( self.edges[node_str][op_index]( nodes[j] ) )
-      nodes.append( sum(inter_nodes) )
-    return nodes[-1]

lib/models/l2s_cell_searchs/search_model_darts_v1.py

@@ -1,93 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-########################################################
-# DARTS: Differentiable Architecture Search, ICLR 2019 #
-########################################################
-import torch
-import torch.nn as nn
-from copy import deepcopy
-from ..cell_operations import ResNetBasicblock
-from .search_cells     import SearchCell
-from .genotypes        import Structure
-
-
-class TinyNetworkDartsV1(nn.Module):
-
-  def __init__(self, C, N, max_nodes, num_classes, search_space, n_piece):
-    super(TinyNetworkDartsV1, self).__init__()
-    self._C        = C
-    self._layerN   = N
-    self.max_nodes = max_nodes
-    self.stem = nn.Sequential(
-                    nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False),
-                    nn.BatchNorm2d(C))
-  
-    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
-    self.cells = nn.ModuleList()
-    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
-      if reduction:
-        cell = ResNetBasicblock(C_prev, C_curr, 2)
-      else:
-        cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space, n_piece)
-        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)
-      self.cells.append( cell )
-      C_prev = cell.out_dim
-    self.op_names   = deepcopy( search_space )
-    self._Layer     = len(self.cells)
-    self.edge2index = edge2index
-    self.lastact    = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
-    self.global_pooling = nn.AdaptiveAvgPool2d(1)
-    self.classifier = nn.Linear(C_prev, num_classes)
-    self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
-
-  def get_weights(self):
-    xlist = list( self.stem.parameters() ) + list( self.cells.parameters() )
-    xlist+= list( self.lastact.parameters() ) + list( self.global_pooling.parameters() )
-    xlist+= list( self.classifier.parameters() )
-    return xlist
-
-  def get_alphas(self):
-    return [self.arch_parameters]
-
-  def get_message(self):
-    string = self.extra_repr()
-    for i, cell in enumerate(self.cells):
-      string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self.cells), cell.extra_repr())
-    return string
-
-  def extra_repr(self):
-    return ('{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
-
-  def genotype(self):
-    genotypes = []
-    for i in range(1, self.max_nodes):
-      xlist = []
-      for j in range(i):
-        node_str = '{:}<-{:}'.format(i, j)
-        with torch.no_grad():
-          weights = self.arch_parameters[ self.edge2index[node_str] ]
-          op_name = self.op_names[ weights.argmax().item() ]
-        xlist.append((op_name, j))
-      genotypes.append( tuple(xlist) )
-    return Structure( genotypes )
-
-  def forward(self, inputs):
-    alphas  = nn.functional.softmax(self.arch_parameters, dim=-1)
-
-    feature = self.stem(inputs)
-    for i, cell in enumerate(self.cells):
-      if isinstance(cell, SearchCell):
-        feature = cell(feature, alphas)
-      else:
-        feature = cell(feature)
-
-    out = self.lastact(feature)
-    out = self.global_pooling( out )
-    out = out.view(out.size(0), -1)
-    logits = self.classifier(out)
-
-    return out, logits

lib/models/l2s_cell_searchs/search_model_darts_v2.py

@@ -1,93 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-########################################################
-# DARTS: Differentiable Architecture Search, ICLR 2019 #
-########################################################
-import torch
-import torch.nn as nn
-from copy import deepcopy
-from ..cell_operations import ResNetBasicblock
-from .search_cells     import SearchCell
-from .genotypes        import Structure
-
-
-class TinyNetworkDartsV2(nn.Module):
-
-  def __init__(self, C, N, max_nodes, num_classes, search_space):
-    super(TinyNetworkDartsV2, self).__init__()
-    self._C        = C
-    self._layerN   = N
-    self.max_nodes = max_nodes
-    self.stem = nn.Sequential(
-                    nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False),
-                    nn.BatchNorm2d(C))
-  
-    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
-    self.cells = nn.ModuleList()
-    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
-      if reduction:
-        cell = ResNetBasicblock(C_prev, C_curr, 2)
-      else:
-        cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
-        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)
-      self.cells.append( cell )
-      C_prev = cell.out_dim
-    self.op_names   = deepcopy( search_space )
-    self._Layer     = len(self.cells)
-    self.edge2index = edge2index
-    self.lastact    = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
-    self.global_pooling = nn.AdaptiveAvgPool2d(1)
-    self.classifier = nn.Linear(C_prev, num_classes)
-    self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
-
-  def get_weights(self):
-    xlist = list( self.stem.parameters() ) + list( self.cells.parameters() )
-    xlist+= list( self.lastact.parameters() ) + list( self.global_pooling.parameters() )
-    xlist+= list( self.classifier.parameters() )
-    return xlist
-
-  def get_alphas(self):
-    return [self.arch_parameters]
-
-  def get_message(self):
-    string = self.extra_repr()
-    for i, cell in enumerate(self.cells):
-      string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self.cells), cell.extra_repr())
-    return string
-
-  def extra_repr(self):
-    return ('{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
-
-  def genotype(self):
-    genotypes = []
-    for i in range(1, self.max_nodes):
-      xlist = []
-      for j in range(i):
-        node_str = '{:}<-{:}'.format(i, j)
-        with torch.no_grad():
-          weights = self.arch_parameters[ self.edge2index[node_str] ]
-          op_name = self.op_names[ weights.argmax().item() ]
-        xlist.append((op_name, j))
-      genotypes.append( tuple(xlist) )
-    return Structure( genotypes )
-
-  def forward(self, inputs):
-    alphas  = nn.functional.softmax(self.arch_parameters, dim=-1)
-
-    feature = self.stem(inputs)
-    for i, cell in enumerate(self.cells):
-      if isinstance(cell, SearchCell):
-        feature = cell(feature, alphas)
-      else:
-        feature = cell(feature)
-
-    out = self.lastact(feature)
-    out = self.global_pooling( out )
-    out = out.view(out.size(0), -1)
-    logits = self.classifier(out)
-
-    return out, logits

lib/models/l2s_cell_searchs/search_model_enas.py

@@ -1,94 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##########################################################################
-# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 #
-##########################################################################
-import torch
-import torch.nn as nn
-from copy import deepcopy
-from ..cell_operations import ResNetBasicblock
-from .search_cells     import SearchCell
-from .genotypes        import Structure
-from .search_model_enas_utils import Controller
-
-
-class TinyNetworkENAS(nn.Module):
-
-  def __init__(self, C, N, max_nodes, num_classes, search_space):
-    super(TinyNetworkENAS, self).__init__()
-    self._C        = C
-    self._layerN   = N
-    self.max_nodes = max_nodes
-    self.stem = nn.Sequential(
-                    nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False),
-                    nn.BatchNorm2d(C))
-  
-    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
-    self.cells = nn.ModuleList()
-    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
-      if reduction:
-        cell = ResNetBasicblock(C_prev, C_curr, 2)
-      else:
-        cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
-        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)
-      self.cells.append( cell )
-      C_prev = cell.out_dim
-    self.op_names   = deepcopy( search_space )
-    self._Layer     = len(self.cells)
-    self.edge2index = edge2index
-    self.lastact    = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
-    self.global_pooling = nn.AdaptiveAvgPool2d(1)
-    self.classifier = nn.Linear(C_prev, num_classes)
-    # to maintain the sampled architecture
-    self.sampled_arch = None
-
-  def update_arch(self, _arch):
-    if _arch is None:
-      self.sampled_arch = None
-    elif isinstance(_arch, Structure):
-      self.sampled_arch = _arch
-    elif isinstance(_arch, (list, tuple)):
-      genotypes = []
-      for i in range(1, self.max_nodes):
-        xlist = []
-        for j in range(i):
-          node_str = '{:}<-{:}'.format(i, j)
-          op_index = _arch[ self.edge2index[node_str] ]
-          op_name  = self.op_names[ op_index ]
-          xlist.append((op_name, j))
-        genotypes.append( tuple(xlist) )
-      self.sampled_arch = Structure(genotypes)
-    else:
-      raise ValueError('invalid type of input architecture : {:}'.format(_arch))
-    return self.sampled_arch
-    
-  def create_controller(self):
-    return Controller(len(self.edge2index), len(self.op_names))
-
-  def get_message(self):
-    string = self.extra_repr()
-    for i, cell in enumerate(self.cells):
-      string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self.cells), cell.extra_repr())
-    return string
-
-  def extra_repr(self):
-    return ('{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
-
-  def forward(self, inputs):
-
-    feature = self.stem(inputs)
-    for i, cell in enumerate(self.cells):
-      if isinstance(cell, SearchCell):
-        feature = cell.forward_dynamic(feature, self.sampled_arch)
-      else: feature = cell(feature)
-
-    out = self.lastact(feature)
-    out = self.global_pooling( out )
-    out = out.view(out.size(0), -1)
-    logits = self.classifier(out)
-
-    return out, logits

lib/models/l2s_cell_searchs/search_model_enas_utils.py

@@ -1,55 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##########################################################################
-# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 #
-##########################################################################
-import torch
-import torch.nn as nn
-from torch.distributions.categorical import Categorical
-
-class Controller(nn.Module):
-  # we refer to https://github.com/TDeVries/enas_pytorch/blob/master/models/controller.py
-  def __init__(self, num_edge, num_ops, lstm_size=32, lstm_num_layers=2, tanh_constant=2.5, temperature=5.0):
-    super(Controller, self).__init__()
-    # assign the attributes
-    self.num_edge  = num_edge
-    self.num_ops   = num_ops
-    self.lstm_size = lstm_size
-    self.lstm_N    = lstm_num_layers
-    self.tanh_constant = tanh_constant
-    self.temperature   = temperature
-    # create parameters
-    self.register_parameter('input_vars', nn.Parameter(torch.Tensor(1, 1, lstm_size)))
-    self.w_lstm = nn.LSTM(input_size=self.lstm_size, hidden_size=self.lstm_size, num_layers=self.lstm_N)
-    self.w_embd = nn.Embedding(self.num_ops, self.lstm_size)
-    self.w_pred = nn.Linear(self.lstm_size, self.num_ops)
-
-    nn.init.uniform_(self.input_vars         , -0.1, 0.1)
-    nn.init.uniform_(self.w_lstm.weight_hh_l0, -0.1, 0.1)
-    nn.init.uniform_(self.w_lstm.weight_ih_l0, -0.1, 0.1)
-    nn.init.uniform_(self.w_embd.weight      , -0.1, 0.1)
-    nn.init.uniform_(self.w_pred.weight      , -0.1, 0.1)
-
-  def forward(self):
-
-    inputs, h0 = self.input_vars, None
-    log_probs, entropys, sampled_arch = [], [], []
-    for iedge in range(self.num_edge):
-      outputs, h0 = self.w_lstm(inputs, h0)
-      
-      logits = self.w_pred(outputs)
-      logits = logits / self.temperature
-      logits = self.tanh_constant * torch.tanh(logits)
-      # distribution
-      op_distribution = Categorical(logits=logits)
-      op_index    = op_distribution.sample()
-      sampled_arch.append( op_index.item() )
-
-      op_log_prob = op_distribution.log_prob(op_index)
-      log_probs.append( op_log_prob.view(-1) )
-      op_entropy  = op_distribution.entropy()
-      entropys.append( op_entropy.view(-1) )
-      
-      # obtain the input embedding for the next step
-      inputs = self.w_embd(op_index)
-    return torch.sum(torch.cat(log_probs)), torch.sum(torch.cat(entropys)), sampled_arch

lib/models/l2s_cell_searchs/search_model_gdas.py

@@ -1,96 +0,0 @@
-###########################################################################
-# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
-###########################################################################
-import torch
-import torch.nn as nn
-from copy import deepcopy
-from ..cell_operations import ResNetBasicblock
-from .search_cells     import SearchCell
-from .genotypes        import Structure
-
-
-class TinyNetworkGDAS(nn.Module):
-
-  def __init__(self, C, N, max_nodes, num_classes, search_space):
-    super(TinyNetworkGDAS, self).__init__()
-    self._C        = C
-    self._layerN   = N
-    self.max_nodes = max_nodes
-    self.stem = nn.Sequential(
-                    nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False),
-                    nn.BatchNorm2d(C))
-  
-    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
-    self.cells = nn.ModuleList()
-    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
-      if reduction:
-        cell = ResNetBasicblock(C_prev, C_curr, 2)
-      else:
-        cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
-        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)
-      self.cells.append( cell )
-      C_prev = cell.out_dim
-    self.op_names   = deepcopy( search_space )
-    self._Layer     = len(self.cells)
-    self.edge2index = edge2index
-    self.lastact    = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
-    self.global_pooling = nn.AdaptiveAvgPool2d(1)
-    self.classifier = nn.Linear(C_prev, num_classes)
-    self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
-    self.tau        = 10
-
-  def get_weights(self):
-    xlist = list( self.stem.parameters() ) + list( self.cells.parameters() )
-    xlist+= list( self.lastact.parameters() ) + list( self.global_pooling.parameters() )
-    xlist+= list( self.classifier.parameters() )
-    return xlist
-
-  def set_tau(self, tau):
-    self.tau = tau
-
-  def get_tau(self):
-    return self.tau
-
-  def get_alphas(self):
-    return [self.arch_parameters]
-
-  def get_message(self):
-    string = self.extra_repr()
-    for i, cell in enumerate(self.cells):
-      string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self.cells), cell.extra_repr())
-    return string
-
-  def extra_repr(self):
-    return ('{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
-
-  def genotype(self):
-    genotypes = []
-    for i in range(1, self.max_nodes):
-      xlist = []
-      for j in range(i):
-        node_str = '{:}<-{:}'.format(i, j)
-        with torch.no_grad():
-          weights = self.arch_parameters[ self.edge2index[node_str] ]
-          op_name = self.op_names[ weights.argmax().item() ]
-        xlist.append((op_name, j))
-      genotypes.append( tuple(xlist) )
-    return Structure( genotypes )
-
-  def forward(self, inputs):
-    feature = self.stem(inputs)
-    for i, cell in enumerate(self.cells):
-      if isinstance(cell, SearchCell):
-        feature = cell.forward_gdas(feature, self.arch_parameters, self.tau)
-      else:
-        feature = cell(feature)
-
-    out = self.lastact(feature)
-    out = self.global_pooling( out )
-    out = out.view(out.size(0), -1)
-    logits = self.classifier(out)
-
-    return out, logits

lib/models/l2s_cell_searchs/search_model_random.py

@@ -1,81 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##############################################################################
-# Random Search and Reproducibility for Neural Architecture Search, UAI 2019 # 
-##############################################################################
-import torch, random
-import torch.nn as nn
-from copy import deepcopy
-from ..cell_operations import ResNetBasicblock
-from .search_cells     import SearchCell
-from .genotypes        import Structure
-
-
-class TinyNetworkRANDOM(nn.Module):
-
-  def __init__(self, C, N, max_nodes, num_classes, search_space):
-    super(TinyNetworkRANDOM, self).__init__()
-    self._C        = C
-    self._layerN   = N
-    self.max_nodes = max_nodes
-    self.stem = nn.Sequential(
-                    nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False),
-                    nn.BatchNorm2d(C))
-  
-    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
-    self.cells = nn.ModuleList()
-    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
-      if reduction:
-        cell = ResNetBasicblock(C_prev, C_curr, 2)
-      else:
-        cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
-        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)
-      self.cells.append( cell )
-      C_prev = cell.out_dim
-    self.op_names   = deepcopy( search_space )
-    self._Layer     = len(self.cells)
-    self.edge2index = edge2index
-    self.lastact    = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
-    self.global_pooling = nn.AdaptiveAvgPool2d(1)
-    self.classifier = nn.Linear(C_prev, num_classes)
-    self.arch_cache = None
-    
-  def get_message(self):
-    string = self.extra_repr()
-    for i, cell in enumerate(self.cells):
-      string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self.cells), cell.extra_repr())
-    return string
-
-  def extra_repr(self):
-    return ('{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
-
-  def random_genotype(self, set_cache):
-    genotypes = []
-    for i in range(1, self.max_nodes):
-      xlist = []
-      for j in range(i):
-        node_str = '{:}<-{:}'.format(i, j)
-        op_name  = random.choice( self.op_names )
-        xlist.append((op_name, j))
-      genotypes.append( tuple(xlist) )
-    arch = Structure( genotypes )
-    if set_cache: self.arch_cache = arch
-    return arch
-
-  def forward(self, inputs):
-
-    feature = self.stem(inputs)
-    for i, cell in enumerate(self.cells):
-      if isinstance(cell, SearchCell):
-        feature = cell.forward_dynamic(feature, self.arch_cache)
-      else: feature = cell(feature)
-
-    out = self.lastact(feature)
-    out = self.global_pooling( out )
-    out = out.view(out.size(0), -1)
-    logits = self.classifier(out)
-    return out, logits

lib/models/l2s_cell_searchs/search_model_setn.py

@@ -1,152 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-######################################################################################
-# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
-######################################################################################
-import torch, random
-import torch.nn as nn
-from copy import deepcopy
-from ..cell_operations import ResNetBasicblock
-from .search_cells     import SearchCell
-from .genotypes        import Structure
-
-
-class TinyNetworkSETN(nn.Module):
-
-  def __init__(self, C, N, max_nodes, num_classes, search_space):
-    super(TinyNetworkSETN, self).__init__()
-    self._C        = C
-    self._layerN   = N
-    self.max_nodes = max_nodes
-    self.stem = nn.Sequential(
-                    nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False),
-                    nn.BatchNorm2d(C))
-  
-    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
-    self.cells = nn.ModuleList()
-    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
-      if reduction:
-        cell = ResNetBasicblock(C_prev, C_curr, 2)
-      else:
-        cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
-        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)
-      self.cells.append( cell )
-      C_prev = cell.out_dim
-    self.op_names   = deepcopy( search_space )
-    self._Layer     = len(self.cells)
-    self.edge2index = edge2index
-    self.lastact    = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
-    self.global_pooling = nn.AdaptiveAvgPool2d(1)
-    self.classifier = nn.Linear(C_prev, num_classes)
-    self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
-    self.mode       = 'urs'
-    self.dynamic_cell = None
-    
-  def set_cal_mode(self, mode, dynamic_cell=None):
-    assert mode in ['urs', 'joint', 'select', 'dynamic']
-    self.mode = mode
-    if mode == 'dynamic': self.dynamic_cell = deepcopy( dynamic_cell )
-    else                : self.dynamic_cell = None
-
-  def get_cal_mode(self):
-    return self.mode
-
-  def get_weights(self):
-    xlist = list( self.stem.parameters() ) + list( self.cells.parameters() )
-    xlist+= list( self.lastact.parameters() ) + list( self.global_pooling.parameters() )
-    xlist+= list( self.classifier.parameters() )
-    return xlist
-
-  def get_alphas(self):
-    return [self.arch_parameters]
-
-  def get_message(self):
-    string = self.extra_repr()
-    for i, cell in enumerate(self.cells):
-      string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self.cells), cell.extra_repr())
-    return string
-
-  def extra_repr(self):
-    return ('{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
-
-  def genotype(self):
-    genotypes = []
-    for i in range(1, self.max_nodes):
-      xlist = []
-      for j in range(i):
-        node_str = '{:}<-{:}'.format(i, j)
-        with torch.no_grad():
-          weights = self.arch_parameters[ self.edge2index[node_str] ]
-          op_name = self.op_names[ weights.argmax().item() ]
-        xlist.append((op_name, j))
-      genotypes.append( tuple(xlist) )
-    return Structure( genotypes )
-
-  def dync_genotype(self, use_random=False):
-    genotypes = []
-    with torch.no_grad():
-      alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1)
-    for i in range(1, self.max_nodes):
-      xlist = []
-      for j in range(i):
-        node_str = '{:}<-{:}'.format(i, j)
-        if use_random:
-          op_name  = random.choice(self.op_names)
-        else:
-          weights  = alphas_cpu[ self.edge2index[node_str] ]
-          op_index = torch.multinomial(weights, 1).item()
-          op_name  = self.op_names[ op_index ]
-        xlist.append((op_name, j))
-      genotypes.append( tuple(xlist) )
-    return Structure( genotypes )
-
-  def get_log_prob(self, arch):
-    with torch.no_grad():
-      logits = nn.functional.log_softmax(self.arch_parameters, dim=-1)
-    select_logits = []
-    for i, node_info in enumerate(arch.nodes):
-      for op, xin in node_info:
-        node_str = '{:}<-{:}'.format(i+1, xin)
-        op_index = self.op_names.index(op)
-        select_logits.append( logits[self.edge2index[node_str], op_index] )
-    return sum(select_logits).item()
-
-
-  def return_topK(self, K):
-    archs = Structure.gen_all(self.op_names, self.max_nodes, False)
-    pairs = [(self.get_log_prob(arch), arch) for arch in archs]
-    if K < 0 or K >= len(archs): K = len(archs)
-    sorted_pairs = sorted(pairs, key=lambda x: -x[0])
-    return_pairs = [sorted_pairs[_][1] for _ in range(K)]
-    return return_pairs
-
-
-  def forward(self, inputs):
-    alphas  = nn.functional.softmax(self.arch_parameters, dim=-1)
-    with torch.no_grad():
-      alphas_cpu = alphas.detach().cpu()
-
-    feature = self.stem(inputs)
-    for i, cell in enumerate(self.cells):
-      if isinstance(cell, SearchCell):
-        if self.mode == 'urs':
-          feature = cell.forward_urs(feature)
-        elif self.mode == 'select':
-          feature = cell.forward_select(feature, alphas_cpu)
-        elif self.mode == 'joint':
-          feature = cell.forward_joint(feature, alphas)
-        elif self.mode == 'dynamic':
-          feature = cell.forward_dynamic(feature, self.dynamic_cell)
-        else: raise ValueError('invalid mode={:}'.format(self.mode))
-      else: feature = cell(feature)
-
-    out = self.lastact(feature)
-    out = self.global_pooling( out )
-    out = out.view(out.size(0), -1)
-    logits = self.classifier(out)
-
-    return out, logits