update GDAS (TO-FINISH)

README.md

@@ -48,7 +48,7 @@ CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-cifar.sh cifar10 ResNet56
 args: `cifar10` indicates the dataset name, `ResNet56` indicates the basemodel name, `CIFARX` indicates the searching hyper-parameters, `0.47/0.57` indicates the expected FLOP ratio, `-1` indicates the random seed.
 
 
-## One-Shot Neural Architecture Search via Self-Evaluated Template Network
+## [One-Shot Neural Architecture Search via Self-Evaluated Template Network](https://arxiv.org/abs/1910.05733)
 
 <img align="right" src="https://d-x-y.github.com/resources/paper-icon/ICCV-2019-SETN.png" width="450">
 
@@ -67,7 +67,7 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k SETN
 Searching codes come soon!
 
 
-## [Searching for A Robust Neural Architecture in Four GPU Hours](http://openaccess.thecvf.com/content_CVPR_2019/papers/Dong_Searching_for_a_Robust_Neural_Architecture_in_Four_GPU_Hours_CVPR_2019_paper.pdf)
+## [Searching for A Robust Neural Architecture in Four GPU Hours](https://arxiv.org/abs/1910.04465)
 
 
 <img align="right" src="https://d-x-y.github.com/resources/paper-icon/CVPR-2019-GDAS.png" width="300">

lib/models/cell_searchs/cells.py

@@ -0,0 +1,196 @@
+import math, torch
+import torch.nn as nn
+import torch.nn.functional as F
+from copy import deepcopy
+from .operations import OPS, ReLUConvBN
+
+
+class SearchCell(nn.Module):
+
+  def __init__(self, C_in, C_out, stride, max_nodes, op_names):
+    super(SearchCell, self).__init__()
+
+    self.op_names  = deepcopy(op_names)
+    self.edges     = nn.ModuleDict()
+    self.max_nodes = max_nodes
+    self.in_dim    = C_in
+    self.out_dim   = C_out
+    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]
+        self.edges[ node_str ] = nn.ModuleList( xlists )
+    self.edge_keys  = sorted(list(self.edges.keys()))
+    self.edge2index = {key:i for i, key in enumerate(self.edge_keys)}
+    self.num_edges  = len(self.edges)
+
+  def extra_repr(self):
+    string = 'info :: {max_nodes} nodes, inC={in_dim}, outC={out_dim}'.format(**self.__dict__)
+    return string
+
+  def forward(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( sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) ) )
+      nodes.append( sum(inter_nodes) )
+    return nodes[-1]
+
+  # GDAS
+  def forward_acc(self, inputs, weightss, indexess):
+    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] ]
+        indexes  = indexess[ self.edge2index[node_str] ].item()
+        import pdb; pdb.set_trace() # to-do
+        #inter_nodes.append( self.edges[node_str][indexes](nodes[j]) * weights[indexes] )
+      nodes.append( sum(inter_nodes) )
+    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()
+        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]
+
+  # select the argmax
+  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]
+
+
+class InferCell(nn.Module):
+
+  def __init__(self, genotype, C_in, C_out, stride):
+    super(InferCell, self).__init__()
+
+    self.layers  = nn.ModuleList()
+    self.node_IN = []
+    self.node_IX = []
+    self.genotype = deepcopy(genotype)
+    for i in range(1, len(genotype)):
+      node_info = genotype[i-1]
+      cur_index = []
+      cur_innod = []
+      for (op_name, op_in) in node_info:
+        if op_in == 0:
+          layer = OPS[op_name](C_in , C_out, stride)
+        else:
+          layer = OPS[op_name](C_out, C_out,      1)
+        cur_index.append( len(self.layers) )
+        cur_innod.append( op_in )
+        self.layers.append( layer )
+      self.node_IX.append( cur_index )
+      self.node_IN.append( cur_innod )
+    self.nodes   = len(genotype)
+    self.in_dim  = C_in
+    self.out_dim = C_out
+
+  def extra_repr(self):
+    string = 'info :: nodes={nodes}, inC={in_dim}, outC={out_dim}'.format(**self.__dict__)
+    laystr = []
+    for i, (node_layers, node_innods) in enumerate(zip(self.node_IX,self.node_IN)):
+      y = ['I{:}-L{:}'.format(_ii, _il) for _il, _ii in zip(node_layers, node_innods)]
+      x = '{:}<-({:})'.format(i+1, ','.join(y))
+      laystr.append( x )
+    return string + ', [{:}]'.format( ' | '.join(laystr) ) + ', {:}'.format(self.genotype.tostr())
+
+  def forward(self, inputs):
+    nodes = [inputs]
+    for i, (node_layers, node_innods) in enumerate(zip(self.node_IX,self.node_IN)):
+      node_feature = sum( self.layers[_il](nodes[_ii]) for _il, _ii in zip(node_layers, node_innods) )
+      nodes.append( node_feature )
+    return nodes[-1]
+
+
+
+class ResNetBasicblock(nn.Module):
+
+  def __init__(self, inplanes, planes, stride):
+    super(ResNetBasicblock, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    self.conv_a = ReLUConvBN(inplanes, planes, 3, stride, 1, 1)
+    self.conv_b = ReLUConvBN(  planes, planes, 3,      1, 1, 1)
+    if stride == 2:
+      self.downsample = nn.Sequential(
+                           nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
+                           nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, padding=0, bias=False))
+    elif inplanes != planes:
+      self.downsample = ReLUConvBN(inplanes, planes, 1, 1, 0, 1)
+    else:
+      self.downsample = None
+    self.in_dim  = inplanes
+    self.out_dim = planes
+    self.stride  = stride
+    self.num_conv = 2
+
+  def extra_repr(self):
+    string = '{name}(inC={in_dim}, outC={out_dim}, stride={stride})'.format(name=self.__class__.__name__, **self.__dict__)
+    return string
+
+  def forward(self, inputs):
+
+    basicblock = self.conv_a(inputs)
+    basicblock = self.conv_b(basicblock)
+
+    if self.downsample is not None:
+      residual = self.downsample(inputs)
+    else:
+      residual = inputs
+    return residual + basicblock

lib/models/cell_searchs/operations.py

@@ -0,0 +1,113 @@
+import torch
+import torch.nn as nn
+
+__all__ = ['OPS', 'ReLUConvBN', 'SearchSpaceNames']
+
+OPS = {
+  'none'         : lambda C_in, C_out, stride: Zero(C_in, C_out, stride),
+  'avg_pool_3x3' : lambda C_in, C_out, stride: POOLING(C_in, C_out, stride, 'avg'),
+  'max_pool_3x3' : lambda C_in, C_out, stride: POOLING(C_in, C_out, stride, 'max'),
+  'nor_conv_7x7' : lambda C_in, C_out, stride: ReLUConvBN(C_in, C_out, (7,7), (stride,stride), (3,3), (1,1)),
+  'nor_conv_3x3' : lambda C_in, C_out, stride: ReLUConvBN(C_in, C_out, (3,3), (stride,stride), (1,1), (1,1)),
+  'nor_conv_1x1' : lambda C_in, C_out, stride: ReLUConvBN(C_in, C_out, (1,1), (stride,stride), (0,0), (1,1)),
+  'skip_connect' : lambda C_in, C_out, stride: Identity() if stride == 1 and C_in == C_out else FactorizedReduce(C_in, C_out, stride),
+}
+
+CONNECT_NAS_BENCHMARK  = ['none', 'skip_connect', 'nor_conv_3x3']
+
+SearchSpaceNames = {'connect-nas' : CONNECT_NAS_BENCHMARK}
+
+
+class POOLING(nn.Module):
+
+  def __init__(self, C_in, C_out, stride, mode):
+    super(POOLING, self).__init__()
+    if C_in == C_out:
+      self.preprocess = None
+    else:
+      self.preprocess = ReLUConvBN(C_in, C_out, 1, 1, 0)
+    if mode == 'avg'  : self.op = nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False)
+    elif mode == 'max': self.op = nn.MaxPool2d(3, stride=stride, padding=1)
+    else              : raise ValueError('Invalid mode={:} in POOLING'.format(mode))
+
+  def forward(self, inputs):
+    if self.preprocess: x = self.preprocess(inputs)
+    else              : x = inputs
+    return self.op(x)
+
+
+class ReLUConvBN(nn.Module):
+
+  def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation):
+    super(ReLUConvBN, self).__init__()
+    self.op = nn.Sequential(
+      nn.ReLU(inplace=False),
+      nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=False),
+      nn.BatchNorm2d(C_out)
+    )
+
+  def forward(self, x):
+    return self.op(x)
+
+
+class Identity(nn.Module):
+
+  def __init__(self):
+    super(Identity, self).__init__()
+
+  def forward(self, x):
+    return x
+
+
+class Zero(nn.Module):
+
+  def __init__(self, C_in, C_out, stride):
+    super(Zero, self).__init__()
+    self.C_in   = C_in
+    self.C_out  = C_out
+    self.stride = stride
+    self.is_zero = True
+
+  def forward(self, x):
+    if self.C_in == self.C_out:
+      if self.stride == 1: return x.mul(0.)
+      else               : return x[:,:,::self.stride,::self.stride].mul(0.)
+    else:
+      shape = list(x.shape)
+      shape[1] = self.C_out
+      zeros = x.new_zeros(shape, dtype=x.dtype, device=x.device)
+      return zeros
+
+  def extra_repr(self):
+    return 'C_in={C_in}, C_out={C_out}, stride={stride}'.format(**self.__dict__)
+
+
+class FactorizedReduce(nn.Module):
+
+  def __init__(self, C_in, C_out, stride):
+    super(FactorizedReduce, self).__init__()
+    self.stride = stride
+    self.C_in   = C_in  
+    self.C_out  = C_out  
+    self.relu   = nn.ReLU(inplace=False)
+    if stride == 2:
+      #assert C_out % 2 == 0, 'C_out : {:}'.format(C_out)
+      C_outs = [C_out // 2, C_out - C_out // 2]
+      self.convs = nn.ModuleList()
+      for i in range(2):
+        self.convs.append( nn.Conv2d(C_in, C_outs[i], 1, stride=stride, padding=0, bias=False) )
+      self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
+    else:
+      raise ValueError('Invalid stride : {:}'.format(stride))
+    
+    self.bn = nn.BatchNorm2d(C_out)
+
+  def forward(self, x):
+    x = self.relu(x)
+    y = self.pad(x)
+    out = torch.cat([self.convs[0](x), self.convs[1](y[:,:,1:,1:])], dim=1)
+    out = self.bn(out)
+    return out
+
+  def extra_repr(self):
+    return 'C_in={C_in}, C_out={C_out}, stride={stride}'.format(**self.__dict__)

lib/models/cell_searchs/search_model_gdas.py

@@ -0,0 +1,117 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+###########################################################################
+# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
+###########################################################################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from .infer_cells  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
+    self.nan_count  = 0
+
+  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, _nan_count=0):
+    self.tau = tau
+    self.nan_count = _nan_count
+
+  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):
+    def gumbel_softmax(_logits, _tau):
+      while True: # a trick to avoid the gumbels bug
+        gumbels    = -torch.empty_like(_logits).exponential_().log()
+        new_logits = (_logits.log_softmax(dim=1) + gumbels) / _tau
+        probs      = nn.functional.softmax(new_logits, dim=1)
+        index      = probs.max(-1, keepdim=True)[1]
+        if index[0].item() == self.op_names.index('none') and index[3].item() == self.op_names.index('none') and index[5].item() == self.op_names.index('none'): continue
+        if index[1].item() == self.op_names.index('none') and index[2].item() == self.op_names.index('none') and index[3].item() == self.op_names.index('none') and index[4].item() == self.op_names.index('none'): continue
+        if index[3].item() == self.op_names.index('none') and index[4].item() == self.op_names.index('none') and index[5].item() == self.op_names.index('none'): continue
+        if index[3].item() == self.op_names.index('none') and index[0].item() == self.op_names.index('none') and index[1].item() == self.op_names.index('none'): continue
+        one_h      = torch.zeros_like(_logits).scatter_(-1, index, 1.0)
+        xres       = one_h - probs.detach() + probs
+        if (not torch.isinf(gumbels).any()) and (not torch.isinf(probs).any()) and (not torch.isnan(probs).any()): break
+        self.nan_count += 1
+      return xres, index
+
+    feature = self.stem(inputs)
+    for i, cell in enumerate(self.cells):
+      if isinstance(cell, SearchCell):
+        alphas, IDX  = gumbel_softmax(self.arch_parameters, self.tau)
+        feature = cell.forward_gdas(feature, alphas, IDX.cpu())
+      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/searchs/SoftSelect.py

@@ -9,8 +9,7 @@ def select2withP(logits, tau, just_prob=False, num=2, eps=1e-7):
   else       :
     while True: # a trick to avoid the gumbels bug
       gumbels = -torch.empty_like(logits).exponential_().log()
-      new_logits = (logits + gumbels) / tau
-      #new_logits = (logits.log_softmax(dim=1) + gumbels) / tau
+      new_logits = (logits.log_softmax(dim=1) + gumbels) / tau
       probs = nn.functional.softmax(new_logits, dim=1)
       if (not torch.isinf(gumbels).any()) and (not torch.isinf(probs).any()) and (not torch.isnan(probs).any()): break